From 4a4bd1b9d8a8888bce055d866bb264e72a3fd40f Mon Sep 17 00:00:00 2001
From: jakobrunge <jakobrunge@gmail.com>
Date: Fri, 23 Jun 2023 01:16:26 +0200
Subject: [PATCH 1/5] re-wrote pcmciplus in more modular form

---
 tigramite/pcmci.py      | 305 ++++++++++++++++++++++++++++++++--------
 tigramite/pcmci_base.py |   8 +-
 2 files changed, 253 insertions(+), 60 deletions(-)

diff --git a/tigramite/pcmci.py b/tigramite/pcmci.py
index 85eda661..75aeaaab 100644
--- a/tigramite/pcmci.py
+++ b/tigramite/pcmci.py
@@ -12,7 +12,7 @@
 import numpy as np
 import scipy.stats
 
-from .pcmci_base import PCMCIbase
+from pcmci_base import PCMCIbase
 
 def _create_nested_dictionary(depth=0, lowest_type=dict):
     """Create a series of nested dictionaries to a maximum depth.  The first
@@ -2155,17 +2155,12 @@ def run_pcmciplus(self,
                         max_conds_px_lagged=max_conds_px_lagged,
                         fdr_method=fdr_method)
 
-        # else:
-        #     raise ValueError("pc_alpha=None not supported in PCMCIplus, choose"
-        #                      " 0 < pc_alpha < 1 (e.g., 0.01)")
-
-        if pc_alpha < 0. or pc_alpha > 1:
+        elif pc_alpha < 0. or pc_alpha > 1:
             raise ValueError("Choose 0 <= pc_alpha <= 1")
 
         # Check the limits on tau
         self._check_tau_limits(tau_min, tau_max)
-        # Set the selected links
-        # _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)
+        # Set the link assumption
         _int_link_assumptions = self._set_link_assumptions(link_assumptions, tau_min, tau_max)
 
         # Step 1: Get a superset of lagged parents from run_pc_stable
@@ -2200,6 +2195,147 @@ def run_pcmciplus(self,
                   + "\nfdr_method = %s" % fdr_method
                   )
 
+        skeleton_results = self._pcmciplus_mci_skeleton_phase(
+                            lagged_parents, _int_link_assumptions, pc_alpha,
+                            tau_min, tau_max, max_conds_dim, max_combinations, 
+                            max_conds_py, max_conds_px, max_conds_px_lagged, 
+                            reset_lagged_links, fdr_method,
+                            p_matrix, val_matrix
+                            )
+
+        colliders_step_results = self._pcmciplus_collider_phase(
+            skeleton_results['graph'], skeleton_results['sepset'], 
+            lagged_parents, pc_alpha, 
+            tau_min, tau_max, max_conds_py, max_conds_px, max_conds_px_lagged,
+            conflict_resolution, contemp_collider_rule)
+        
+        final_graph = self._pcmciplus_rule_orientation_phase(colliders_step_results['graph'],
+         colliders_step_results['ambiguous_triples'], conflict_resolution)
+
+        # Store the parents in the pcmci member
+        self.all_lagged_parents = lagged_parents
+
+        return_dict = {
+            'graph': final_graph,
+            'p_matrix': skeleton_results['p_matrix'],
+            'val_matrix': skeleton_results['val_matrix'],
+            'sepset': colliders_step_results['sepset'],
+            'ambiguous_triples': colliders_step_results['ambiguous_triples'],
+        }
+
+        # No confidence interval estimation here
+        return_dict['conf_matrix'] = None
+
+        # Print the results
+        if self.verbosity > 0:
+            self.print_results(return_dict, alpha_level=pc_alpha)
+        
+        # Return the dictionary
+        self.results = return_dict
+        
+        return return_dict
+
+    
+        # # Set the maximum condition dimension for Y and X
+        # max_conds_py = self._set_max_condition_dim(max_conds_py,
+        #                                            tau_min, tau_max)
+        # max_conds_px = self._set_max_condition_dim(max_conds_px,
+        #                                            tau_min, tau_max)
+
+        # if reset_lagged_links:
+        #     # Run PCalg on full graph, ignoring that some lagged links
+        #     # were determined as non-significant in PC1 step
+        #     links_for_pc = deepcopy(_int_link_assumptions)
+        # else:
+        #     # Run PCalg only on lagged parents found with PC1 
+        #     # plus all contemporaneous links
+        #     links_for_pc = {}  #deepcopy(lagged_parents)
+        #     for j in range(self.N):
+        #         links_for_pc[j] = {}
+        #         for parent in lagged_parents[j]:
+        #             if _int_link_assumptions[j][parent] in ['-?>', '-->']:
+        #                 links_for_pc[j][parent] = _int_link_assumptions[j][parent]
+
+        #         # Add contemporaneous links
+        #         for link in _int_link_assumptions[j]:
+        #             i, tau = link
+        #             link_type = _int_link_assumptions[j][link]
+        #             if abs(tau) == 0:
+        #                 links_for_pc[j][(i, 0)] = link_type
+
+        # results = self.run_pcalg(
+        #     link_assumptions=links_for_pc,
+        #     pc_alpha=pc_alpha,
+        #     tau_min=tau_min,
+        #     tau_max=tau_max,
+        #     max_conds_dim=max_conds_dim,
+        #     max_combinations=max_combinations,
+        #     lagged_parents=lagged_parents,
+        #     max_conds_py=max_conds_py,
+        #     max_conds_px=max_conds_px,
+        #     max_conds_px_lagged=max_conds_px_lagged,
+        #     mode='contemp_conds',
+        #     contemp_collider_rule=contemp_collider_rule,
+        #     conflict_resolution=conflict_resolution)
+
+        # graph = results['graph']
+
+        # # Update p_matrix and val_matrix with values from links_for_pc
+        # for j in range(self.N):
+        #     for link in links_for_pc[j]:
+        #         i, tau = link
+        #         if links_for_pc[j][link] not in ['<--', '<?-']:
+        #             p_matrix[i, j, abs(tau)] = results['p_matrix'][i, j, abs(tau)]
+        #             val_matrix[i, j, abs(tau)] = results['val_matrix'][i, j, 
+        #                                                                abs(tau)]
+
+        # # Update p_matrix and val_matrix for indices of symmetrical links
+        # p_matrix[:, :, 0] = results['p_matrix'][:, :, 0]
+        # val_matrix[:, :, 0] = results['val_matrix'][:, :, 0]
+
+        # ambiguous = results['ambiguous_triples']
+
+        # conf_matrix = None
+        # TODO: implement confidence estimation, but how?
+        # if self.cond_ind_test.confidence is not False:
+        #     conf_matrix = results['conf_matrix']
+
+        # # Correct the p_matrix if there is a fdr_method
+        # if fdr_method != 'none':
+        #     p_matrix = self.get_corrected_pvalues(p_matrix=p_matrix, tau_min=tau_min, 
+        #                                           tau_max=tau_max, 
+        #                                           link_assumptions=_int_link_assumptions,
+        #                                           fdr_method=fdr_method)
+
+        # # Store the parents in the pcmci member
+        # self.all_lagged_parents = lagged_parents
+
+        # # p_matrix=results['p_matrix']
+        # # val_matrix=results['val_matrix']
+
+        # Cache the resulting values in the return dictionary
+        # return_dict = {'graph': graph,
+        #                'val_matrix': val_matrix,
+        #                'p_matrix': p_matrix,
+        #                'ambiguous_triples': ambiguous,
+        #                'conf_matrix': conf_matrix}
+
+        # # Print the results
+        # if self.verbosity > 0:
+        #     self.print_results(return_dict, alpha_level=pc_alpha)
+        # # Return the dictionary
+        # self.results = return_dict
+        # return return_dict
+
+    def _pcmciplus_mci_skeleton_phase(self,
+        lagged_parents, _int_link_assumptions, pc_alpha,
+        tau_min, tau_max, max_conds_dim, max_combinations, 
+        max_conds_py, max_conds_px, max_conds_px_lagged, reset_lagged_links,
+        fdr_method,
+        p_matrix, val_matrix,
+        ):
+        """MCI Skeleton phase."""
+
         # Set the maximum condition dimension for Y and X
         max_conds_py = self._set_max_condition_dim(max_conds_py,
                                                    tau_min, tau_max)
@@ -2227,65 +2363,112 @@ def run_pcmciplus(self,
                     if abs(tau) == 0:
                         links_for_pc[j][(i, 0)] = link_type
 
-        results = self.run_pcalg(
-            link_assumptions=links_for_pc,
+
+        if max_conds_dim is None:
+            # if mode == 'standard':
+            #     max_conds_dim = self._set_max_condition_dim(max_conds_dim,
+            #                                                 tau_min, tau_max)
+            # elif mode == 'contemp_conds':
+            max_conds_dim = self.N
+
+        if max_combinations is None:
+            max_combinations = np.inf
+
+        initial_graph = self._dict_to_graph(links_for_pc, tau_max=tau_max)
+
+        skeleton_results = self._pcalg_skeleton(
+            initial_graph=initial_graph,
+            lagged_parents=lagged_parents,
+            mode='contemp_conds',
             pc_alpha=pc_alpha,
             tau_min=tau_min,
             tau_max=tau_max,
             max_conds_dim=max_conds_dim,
             max_combinations=max_combinations,
-            lagged_parents=lagged_parents,
             max_conds_py=max_conds_py,
             max_conds_px=max_conds_px,
             max_conds_px_lagged=max_conds_px_lagged,
-            mode='contemp_conds',
-            contemp_collider_rule=contemp_collider_rule,
-            conflict_resolution=conflict_resolution)
+        )
 
-        graph = results['graph']
+        # Symmetrize p_matrix and val_matrix coming from skeleton
+        symmetrized_results = self.symmetrize_p_and_val_matrix(
+                            p_matrix=skeleton_results['p_matrix'], 
+                            val_matrix=skeleton_results['val_matrix'], 
+                            link_assumptions=links_for_pc,
+                            conf_matrix=None)
+
+        # Update p_matrix and val_matrix with values from skeleton phase
+        # Contemporaneous entries (not filled in run_pc_stable lagged phase)
+        p_matrix[:, :, 0] = symmetrized_results['p_matrix'][:, :, 0]
+        val_matrix[:, :, 0] = symmetrized_results['val_matrix'][:, :, 0]
 
-        # Update p_matrix and val_matrix with values from links_for_pc
+        # Update all entries that are in links_for_pc
         for j in range(self.N):
             for link in links_for_pc[j]:
                 i, tau = link
                 if links_for_pc[j][link] not in ['<--', '<?-']:
-                    p_matrix[i, j, abs(tau)] = results['p_matrix'][i, j, abs(tau)]
-                    val_matrix[i, j, abs(tau)] = results['val_matrix'][i, j, 
-                                                                       abs(tau)]
-
-        # Update p_matrix and val_matrix for indices of symmetrical links
-        p_matrix[:, :, 0] = results['p_matrix'][:, :, 0]
-        val_matrix[:, :, 0] = results['val_matrix'][:, :, 0]
-
-        ambiguous = results['ambiguous_triples']
-
-        conf_matrix = None
-        # TODO: implement confidence estimation, but how?
-        # if self.cond_ind_test.confidence is not False:
-        #     conf_matrix = results['conf_matrix']
+                    p_matrix[i, j, abs(tau)] = symmetrized_results['p_matrix'][i, j, abs(tau)]
+                    val_matrix[i, j, abs(tau)] = symmetrized_results['val_matrix'][i, j, 
+                                                                 abs(tau)]
 
         # Correct the p_matrix if there is a fdr_method
         if fdr_method != 'none':
             p_matrix = self.get_corrected_pvalues(p_matrix=p_matrix, tau_min=tau_min, 
                                                   tau_max=tau_max, 
-                                                  link_assumptions=_int_link_assumptions,
+                                                  link_assumptions=links_for_pc,
                                                   fdr_method=fdr_method)
 
-        # Store the parents in the pcmci member
-        self.all_lagged_parents = lagged_parents
+        # Update matrices
+        skeleton_results['p_matrix'] = p_matrix
+        skeleton_results['val_matrix'] = val_matrix
+
+        return skeleton_results
+
+
+    def _pcmciplus_collider_phase(self, skeleton_graph, sepset, lagged_parents,
+        pc_alpha, tau_min, tau_max, max_conds_py, max_conds_px, max_conds_px_lagged,
+        conflict_resolution, contemp_collider_rule):
+        """MCI collider phase."""    
+
+        # Set the maximum condition dimension for Y and X
+        max_conds_py = self._set_max_condition_dim(max_conds_py,
+                                                   tau_min, tau_max)
+        max_conds_px = self._set_max_condition_dim(max_conds_px,
+                                                   tau_min, tau_max)
+
+        # Now change assumed links marks
+        skeleton_graph[skeleton_graph=='o?o'] = 'o-o'
+        skeleton_graph[skeleton_graph=='-?>'] = '-->'
+        skeleton_graph[skeleton_graph=='<?-'] = '<--'
+
+        colliders_step_results = self._pcalg_colliders(
+            graph=skeleton_graph,
+            sepset=sepset,
+            lagged_parents=lagged_parents,
+            mode='contemp_conds',
+            pc_alpha=pc_alpha,
+            tau_max=tau_max,
+            max_conds_py=max_conds_py,
+            max_conds_px=max_conds_px,
+            max_conds_px_lagged=max_conds_px_lagged,
+            conflict_resolution=conflict_resolution,
+            contemp_collider_rule=contemp_collider_rule,
+            )
+
+        return colliders_step_results
+
+    def _pcmciplus_rule_orientation_phase(self, collider_graph,
+         ambiguous_triples, conflict_resolution):
+        """MCI rule orientation phase."""  
+
+        final_graph = self._pcalg_rules_timeseries(
+            graph=collider_graph,
+            ambiguous_triples=ambiguous_triples,
+            conflict_resolution=conflict_resolution,
+        )
+
+        return final_graph
 
-        # Cache the resulting values in the return dictionary
-        return_dict = {'graph': graph,
-                       'val_matrix': val_matrix,
-                       'p_matrix': p_matrix,
-                       'ambiguous_triples': ambiguous,
-                       'conf_matrix': conf_matrix}
-        # Print the results
-        if self.verbosity > 0:
-            self.print_results(return_dict, alpha_level=pc_alpha)
-        # Return the dictionary
-        self.results = return_dict
-        return return_dict
 
     def run_pcalg(self, 
                     selected_links=None, 
@@ -2759,14 +2942,16 @@ def _pcalg_skeleton(self,
             adjt = self._get_adj_time_series(graph)
 
         val_matrix = np.zeros((N, N, tau_max + 1))
+        
         val_min = dict()
         for j in range(self.N):
             val_min[j] = {(p[0], -p[1]): np.inf
                           for p in zip(*np.where(graph[:, j, :] != ""))}
 
         # Initialize p-values. Set to 1 if there's no link in the initial graph
-        pvalues = np.zeros((N, N, tau_max + 1))
-        pvalues[graph == ""] = 1.
+        p_matrix = np.zeros((N, N, tau_max + 1))
+        p_matrix[graph == ""] = 1.
+
         pval_max = dict()
         for j in range(self.N):
             pval_max[j] = {(p[0], -p[1]): 0.
@@ -2840,8 +3025,8 @@ def _pcalg_skeleton(self,
                                                             (i, -abstau)))
 
                         # Store max. p-value and corresponding value to return
-                        if pval >= pvalues[i, j, abstau]:
-                            pvalues[i, j, abstau] = pval
+                        if pval >= p_matrix[i, j, abstau]:
+                            p_matrix[i, j, abstau] = pval
                             val_matrix[i, j, abstau] = val
 
                         if self.verbosity > 1:
@@ -2856,6 +3041,8 @@ def _pcalg_skeleton(self,
                                 graph[i, j, 0] = graph[j, i, 0] = ""
                                 sepset[((i, 0), j)] = sepset[
                                     ((j, 0), i)] = list(S)
+                                # Also store p-value in other contemp. entry
+                                p_matrix[j, i, 0] = p_matrix[i, j, 0]
                             else:
                                 graph[i, j, abstau] = ""
                                 sepset[((i, -abstau), j)] = list(S)
@@ -2898,7 +3085,7 @@ def _pcalg_skeleton(self,
 
         return {'graph': graph,
                 'sepset': sepset,
-                'p_matrix': pvalues,
+                'p_matrix': p_matrix,
                 'val_matrix': val_matrix,
                 }
 
@@ -3702,8 +3889,8 @@ def nonlin_f(x): return (x + 5. * x ** 2 * np.exp(-x ** 2 / 20.))
     c = 0.8
     for t in range(1, T):
         data[t, 0] += 0.4*data[t-1, 0] + 0.4*data[t-1, 1] + c*data[t-1,3]
-        data[t, 1] += 0.5*data[t-1, 1] + c*data[t-1,3]
-        data[t, 2] += 0.6*data[t-1, 2] + 0.3*data[t-2, 1] + c*data[t-1,3]
+        data[t, 1] += 0.5*data[t-1, 1] + c*data[t,3]
+        data[t, 2] += 0.6*data[t-1, 2] + 0.3*data[t-2, 1] #+ c*data[t-1,3]
     dataframe = pp.DataFrame(data, var_names=[r'$X^0$', r'$X^1$', r'$X^2$', 'Sun'])
     # tp.plot_timeseries(dataframe); plt.show()
 
@@ -3731,16 +3918,22 @@ def nonlin_f(x): return (x + 5. * x ** 2 * np.exp(-x ** 2 / 20.))
         else:
             link_assumptions[j] = {}
 
-    print(link_assumptions)
+    for j in link_assumptions:
+        print(link_assumptions[j])
     pcmci_parcorr = PCMCI(
         dataframe=dataframe, 
         cond_ind_test=parcorr,
-        verbosity=2)
+        verbosity=0)
     results = pcmci_parcorr.run_pcmciplus(tau_max=tau_max, pc_alpha=0.01, 
-                                      # link_assumptions=link_assumptions
+        reset_lagged_links=True,
+                                      link_assumptions=link_assumptions
                                       ) #, alpha_level = 0.01)
     print(results['graph'].shape)
-    print(results['graph'][:,3,:])
+    # print(results['graph'][:,3,:])
+    print(np.round(results['p_matrix'][:,:,0], 2))
+    print(np.round(results['val_matrix'][:,:,0], 2))
+    print(results['graph'][:,:,0])
+
     # Plot time series graph
     # tp.plot_time_series_graph(
     #     val_matrix=results['val_matrix'],
diff --git a/tigramite/pcmci_base.py b/tigramite/pcmci_base.py
index 4d58e4df..a0ee1a13 100644
--- a/tigramite/pcmci_base.py
+++ b/tigramite/pcmci_base.py
@@ -634,8 +634,8 @@ def symmetrize_p_and_val_matrix(self, p_matrix, val_matrix, link_assumptions, co
         # Symmetrize p_matrix and val_matrix and conf_matrix
         for i in range(self.N):
             for j in range(self.N):
-                # If both the links are present in selected_links, symmetrize using maximum p-value
-                # if ((i, 0) in selected_links[j] and (j, 0) in selected_links[i]):
+                # If both the links are present in link_assumptions, symmetrize using maximum p-value
+                # if ((i, 0) in link_assumptions[j] and (j, 0) in link_assumptions[i]):
                 if (i, 0) in link_assumptions[j]:
                     if link_assumptions[j][(i, 0)] in ["o-o", 'o?o']:
                         if (p_matrix[i, j, 0]
@@ -645,8 +645,8 @@ def symmetrize_p_and_val_matrix(self, p_matrix, val_matrix, link_assumptions, co
                             if conf_matrix is not None:
                                 conf_matrix[j, i, 0] = conf_matrix[i, j, 0]
 
-                    # If only one of the links is present in selected_links, symmetrize using the p-value of the link present
-                    # elif ((i, 0) in selected_links[j] and (j, 0) not in selected_links[i]):
+                    # If only one of the links is present in link_assumptions, symmetrize using the p-value of the link present
+                    # elif ((i, 0) in link_assumptions[j] and (j, 0) not in link_assumptions[i]):
                     elif link_assumptions[j][(i, 0)] in ["-->", '-?>']:
                         p_matrix[j, i, 0] = p_matrix[i, j, 0]
                         val_matrix[j, i, 0] = val_matrix[i, j, 0]

From 3f187d104aa3db556fb8a70d0a9d78be5cdd9e44 Mon Sep 17 00:00:00 2001
From: jakobrunge <jakobrunge@gmail.com>
Date: Fri, 23 Jun 2023 12:04:50 +0200
Subject: [PATCH 2/5] re-wrote pcmciplus in more modular form

---
 tigramite/pcmci.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tigramite/pcmci.py b/tigramite/pcmci.py
index 75aeaaab..a24b9777 100644
--- a/tigramite/pcmci.py
+++ b/tigramite/pcmci.py
@@ -12,7 +12,7 @@
 import numpy as np
 import scipy.stats
 
-from pcmci_base import PCMCIbase
+from .pcmci_base import PCMCIbase
 
 def _create_nested_dictionary(depth=0, lowest_type=dict):
     """Create a series of nested dictionaries to a maximum depth.  The first

From c52f5845dcbd279cc5e8035de391909f4d0315e5 Mon Sep 17 00:00:00 2001
From: jakobrunge <jakobrunge@gmail.com>
Date: Fri, 23 Jun 2023 14:37:41 +0200
Subject: [PATCH 3/5] re-wrote pcmciplus in more modular form

---
 setup.py                                      |   2 +-
 tigramite/pcmci.py                            | 227 ++++++++++--------
 .../tigramite_tutorial_pcmciplus.ipynb        | 110 ++++-----
 3 files changed, 187 insertions(+), 152 deletions(-)

diff --git a/setup.py b/setup.py
index 0d09a452..7f973b97 100644
--- a/setup.py
+++ b/setup.py
@@ -62,7 +62,7 @@ def run(self):
 # Run the setup
 setup(
     name="tigramite",
-    version="5.2.1.21",
+    version="5.2.1.22",
     packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
     license="GNU General Public License v3.0",
     description="Tigramite causal inference for time series",
diff --git a/tigramite/pcmci.py b/tigramite/pcmci.py
index a24b9777..27b51796 100644
--- a/tigramite/pcmci.py
+++ b/tigramite/pcmci.py
@@ -2127,7 +2127,7 @@ def run_pcmciplus(self,
             Estimated matrix of test statistic values regarding adjacencies.
         p_matrix : array of shape [N, N, tau_max+1]
             Estimated matrix of p-values regarding adjacencies.
-        sepset : dictionary
+        sepsets : dictionary
             Separating sets. See paper for details.
         ambiguous_triples : list
             List of ambiguous triples, only relevant for 'majority' and
@@ -2163,18 +2163,23 @@ def run_pcmciplus(self,
         # Set the link assumption
         _int_link_assumptions = self._set_link_assumptions(link_assumptions, tau_min, tau_max)
 
-        # Step 1: Get a superset of lagged parents from run_pc_stable
-        lagged_parents = self.run_pc_stable(link_assumptions=link_assumptions,
-                                            tau_min=tau_min,
-                                            tau_max=tau_max,
-                                            pc_alpha=pc_alpha,
-                                            max_conds_dim=max_conds_dim,
-                                            max_combinations=max_combinations)
 
+        #
+        # Phase 1: Get a superset of lagged parents from run_pc_stable
+        #
+        lagged_parents = self.run_pc_stable(link_assumptions=link_assumptions,
+                            tau_min=tau_min,
+                            tau_max=tau_max,
+                            pc_alpha=pc_alpha,
+                            max_conds_dim=max_conds_dim,
+                            max_combinations=max_combinations)
+        # Extract p- and val-matrix
         p_matrix = self.p_matrix
         val_matrix = self.val_matrix
 
-        # Step 2+3+4: PC algorithm with contemp. conditions and MCI tests
+        #
+        # Phase 2: PC algorithm with contemp. conditions and MCI tests
+        #
         if self.verbosity > 0:
             print("\n##\n## Step 2: PC algorithm with contemp. conditions "
                   "and MCI tests\n##"
@@ -2196,21 +2201,45 @@ def run_pcmciplus(self,
                   )
 
         skeleton_results = self._pcmciplus_mci_skeleton_phase(
-                            lagged_parents, _int_link_assumptions, pc_alpha,
-                            tau_min, tau_max, max_conds_dim, max_combinations, 
-                            max_conds_py, max_conds_px, max_conds_px_lagged, 
-                            reset_lagged_links, fdr_method,
-                            p_matrix, val_matrix
+                            lagged_parents=lagged_parents, 
+                            link_assumptions=_int_link_assumptions, 
+                            pc_alpha=pc_alpha,
+                            tau_min=tau_min, 
+                            tau_max=tau_max, 
+                            max_conds_dim=max_conds_dim, 
+                            max_combinations=max_combinations, 
+                            max_conds_py=max_conds_py,
+                            max_conds_px=max_conds_px, 
+                            max_conds_px_lagged=max_conds_px_lagged, 
+                            reset_lagged_links=reset_lagged_links, 
+                            fdr_method=fdr_method,
+                            p_matrix=p_matrix, 
+                            val_matrix=val_matrix,
                             )
 
+        #
+        # Phase 3: Collider orientations (with MCI tests for default majority collider rule)
+        #
         colliders_step_results = self._pcmciplus_collider_phase(
-            skeleton_results['graph'], skeleton_results['sepset'], 
-            lagged_parents, pc_alpha, 
-            tau_min, tau_max, max_conds_py, max_conds_px, max_conds_px_lagged,
-            conflict_resolution, contemp_collider_rule)
+                            skeleton_graph=skeleton_results['graph'], 
+                            sepsets=skeleton_results['sepsets'], 
+                            lagged_parents=lagged_parents, 
+                            pc_alpha=pc_alpha, 
+                            tau_min=tau_min, 
+                            tau_max=tau_max, 
+                            max_conds_py=max_conds_py, 
+                            max_conds_px=max_conds_px, 
+                            max_conds_px_lagged=max_conds_px_lagged,
+                            conflict_resolution=conflict_resolution, 
+                            contemp_collider_rule=contemp_collider_rule)
         
-        final_graph = self._pcmciplus_rule_orientation_phase(colliders_step_results['graph'],
-         colliders_step_results['ambiguous_triples'], conflict_resolution)
+        #
+        # Phase 4: Meek rule orientations
+        #
+        final_graph = self._pcmciplus_rule_orientation_phase(
+                            collider_graph=colliders_step_results['graph'],
+                            ambiguous_triples=colliders_step_results['ambiguous_triples'], 
+                            conflict_resolution=conflict_resolution)
 
         # Store the parents in the pcmci member
         self.all_lagged_parents = lagged_parents
@@ -2219,9 +2248,9 @@ def run_pcmciplus(self,
             'graph': final_graph,
             'p_matrix': skeleton_results['p_matrix'],
             'val_matrix': skeleton_results['val_matrix'],
-            'sepset': colliders_step_results['sepset'],
+            'sepsets': colliders_step_results['sepsets'],
             'ambiguous_triples': colliders_step_results['ambiguous_triples'],
-        }
+            }
 
         # No confidence interval estimation here
         return_dict['conf_matrix'] = None
@@ -2328,12 +2357,21 @@ def run_pcmciplus(self,
         # return return_dict
 
     def _pcmciplus_mci_skeleton_phase(self,
-        lagged_parents, _int_link_assumptions, pc_alpha,
-        tau_min, tau_max, max_conds_dim, max_combinations, 
-        max_conds_py, max_conds_px, max_conds_px_lagged, reset_lagged_links,
-        fdr_method,
-        p_matrix, val_matrix,
-        ):
+            lagged_parents, 
+            link_assumptions, 
+            pc_alpha,
+            tau_min, 
+            tau_max, 
+            max_conds_dim, 
+            max_combinations, 
+            max_conds_py, 
+            max_conds_px, 
+            max_conds_px_lagged, 
+            reset_lagged_links,
+            fdr_method,
+            p_matrix, 
+            val_matrix,
+            ):
         """MCI Skeleton phase."""
 
         # Set the maximum condition dimension for Y and X
@@ -2345,7 +2383,7 @@ def _pcmciplus_mci_skeleton_phase(self,
         if reset_lagged_links:
             # Run PCalg on full graph, ignoring that some lagged links
             # were determined as non-significant in PC1 step
-            links_for_pc = deepcopy(_int_link_assumptions)
+            links_for_pc = deepcopy(link_assumptions)
         else:
             # Run PCalg only on lagged parents found with PC1 
             # plus all contemporaneous links
@@ -2353,22 +2391,18 @@ def _pcmciplus_mci_skeleton_phase(self,
             for j in range(self.N):
                 links_for_pc[j] = {}
                 for parent in lagged_parents[j]:
-                    if _int_link_assumptions[j][parent] in ['-?>', '-->']:
-                        links_for_pc[j][parent] = _int_link_assumptions[j][parent]
+                    if link_assumptions[j][parent] in ['-?>', '-->']:
+                        links_for_pc[j][parent] = link_assumptions[j][parent]
 
                 # Add contemporaneous links
-                for link in _int_link_assumptions[j]:
+                for link in link_assumptions[j]:
                     i, tau = link
-                    link_type = _int_link_assumptions[j][link]
+                    link_type = link_assumptions[j][link]
                     if abs(tau) == 0:
                         links_for_pc[j][(i, 0)] = link_type
 
 
         if max_conds_dim is None:
-            # if mode == 'standard':
-            #     max_conds_dim = self._set_max_condition_dim(max_conds_dim,
-            #                                                 tau_min, tau_max)
-            # elif mode == 'contemp_conds':
             max_conds_dim = self.N
 
         if max_combinations is None:
@@ -2388,7 +2422,7 @@ def _pcmciplus_mci_skeleton_phase(self,
             max_conds_py=max_conds_py,
             max_conds_px=max_conds_px,
             max_conds_px_lagged=max_conds_px_lagged,
-        )
+            )
 
         # Symmetrize p_matrix and val_matrix coming from skeleton
         symmetrized_results = self.symmetrize_p_and_val_matrix(
@@ -2402,7 +2436,9 @@ def _pcmciplus_mci_skeleton_phase(self,
         p_matrix[:, :, 0] = symmetrized_results['p_matrix'][:, :, 0]
         val_matrix[:, :, 0] = symmetrized_results['val_matrix'][:, :, 0]
 
-        # Update all entries that are in links_for_pc
+        # Update all entries computed in the MCI step 
+        # (these are in links_for_pc); values for entries
+        # that were removed in the lagged-condition phase are kept
         for j in range(self.N):
             for link in links_for_pc[j]:
                 i, tau = link
@@ -2411,11 +2447,11 @@ def _pcmciplus_mci_skeleton_phase(self,
                     val_matrix[i, j, abs(tau)] = symmetrized_results['val_matrix'][i, j, 
                                                                  abs(tau)]
 
-        # Correct the p_matrix if there is a fdr_method
+        # Optionally correct the p_matrix
         if fdr_method != 'none':
             p_matrix = self.get_corrected_pvalues(p_matrix=p_matrix, tau_min=tau_min, 
                                                   tau_max=tau_max, 
-                                                  link_assumptions=links_for_pc,
+                                                  link_assumptions=link_assumptions,
                                                   fdr_method=fdr_method)
 
         # Update matrices
@@ -2425,7 +2461,7 @@ def _pcmciplus_mci_skeleton_phase(self,
         return skeleton_results
 
 
-    def _pcmciplus_collider_phase(self, skeleton_graph, sepset, lagged_parents,
+    def _pcmciplus_collider_phase(self, skeleton_graph, sepsets, lagged_parents,
         pc_alpha, tau_min, tau_max, max_conds_py, max_conds_px, max_conds_px_lagged,
         conflict_resolution, contemp_collider_rule):
         """MCI collider phase."""    
@@ -2443,7 +2479,7 @@ def _pcmciplus_collider_phase(self, skeleton_graph, sepset, lagged_parents,
 
         colliders_step_results = self._pcalg_colliders(
             graph=skeleton_graph,
-            sepset=sepset,
+            sepsets=sepsets,
             lagged_parents=lagged_parents,
             mode='contemp_conds',
             pc_alpha=pc_alpha,
@@ -2465,7 +2501,7 @@ def _pcmciplus_rule_orientation_phase(self, collider_graph,
             graph=collider_graph,
             ambiguous_triples=ambiguous_triples,
             conflict_resolution=conflict_resolution,
-        )
+            )
 
         return final_graph
 
@@ -2556,7 +2592,7 @@ def run_pcalg(self,
             Estimated matrix of test statistic values regarding adjacencies.
         p_matrix : array of shape [N, N, tau_max+1]
             Estimated matrix of p-values regarding adjacencies.
-        sepset : dictionary
+        sepsets : dictionary
             Separating sets. See paper for details.
         ambiguous_triples : list
             List of ambiguous triples, only relevant for 'majority' and
@@ -2609,7 +2645,7 @@ def run_pcalg(self,
         )
 
         skeleton_graph = skeleton_results['graph']
-        sepset = skeleton_results['sepset']
+        sepsets = skeleton_results['sepsets']
 
         # Now change assumed links marks
         skeleton_graph[skeleton_graph=='o?o'] = 'o-o'
@@ -2618,7 +2654,7 @@ def run_pcalg(self,
 
         colliders_step_results = self._pcalg_colliders(
             graph=skeleton_graph,
-            sepset=sepset,
+            sepsets=sepsets,
             lagged_parents=lagged_parents,
             mode=mode,
             pc_alpha=pc_alpha,
@@ -2653,7 +2689,7 @@ def run_pcalg(self,
             'graph': graph_str,
             'p_matrix': symmetrized_results['p_matrix'],
             'val_matrix': symmetrized_results['val_matrix'],
-            'sepset': colliders_step_results['sepset'],
+            'sepsets': colliders_step_results['sepsets'],
             'ambiguous_triples': colliders_step_results['ambiguous_triples'],
         }
 
@@ -2701,7 +2737,7 @@ def run_pcalg_non_timeseries_data(self, pc_alpha=0.01,
             Estimated matrix of test statistic values regarding adjacencies.
         p_matrix : array of shape [N, N, 1]
             Estimated matrix of p-values regarding adjacencies.
-        sepset : dictionary
+        sepsets : dictionary
             Separating sets. See paper for details.
         ambiguous_triples : list
             List of ambiguous triples, only relevant for 'majority' and
@@ -2714,16 +2750,13 @@ def run_pcalg_non_timeseries_data(self, pc_alpha=0.01,
                   conflict_resolution=conflict_resolution)
 
         # Remove tau-dimension
-        # results['graph'] = results['graph'].squeeze()
-        # results['val_matrix'] = results['val_matrix'].squeeze()
-        # results['p_matrix'] = results['p_matrix'].squeeze()
-        old_sepsets = results['sepset'].copy()
-        results['sepset'] = {}
-        for old_sepset in old_sepsets:
-           new_sepset = (old_sepset[0][0], old_sepset[1])
-           conds = [cond[0] for cond in old_sepsets[old_sepset]]
+        old_sepsets = results['sepsets'].copy()
+        results['sepsets'] = {}
+        for old_sepsets in old_sepsets:
+           new_sepsets = (old_sepsets[0][0], old_sepsets[1])
+           conds = [cond[0] for cond in old_sepsets[old_sepsets]]
 
-           results['sepset'][new_sepset] = conds
+           results['sepsets'][new_sepsets] = conds
 
         ambiguous_triples = results['ambiguous_triples'].copy()
         results['ambiguous_triples'] = []
@@ -2919,7 +2952,7 @@ def _pcalg_skeleton(self,
             Estimated matrix of test statistic values regarding adjacencies.
         p_matrix : array of shape [N, N, tau_max+1]
             Estimated matrix of p-values regarding adjacencies.
-        sepset : dictionary
+        sepsets : dictionary
             Separating sets. See paper for details.
         """
         N = self.N
@@ -2957,10 +2990,10 @@ def _pcalg_skeleton(self,
             pval_max[j] = {(p[0], -p[1]): 0.
                            for p in zip(*np.where(graph[:, j, :] != ""))}
 
-        # TODO: Remove sepset alltogether?
+        # TODO: Remove sepsets alltogether?
         # Intialize sepsets that store the conditions that make i and j
         # independent
-        sepset = self._get_sepset(tau_min, tau_max)
+        sepsets = self._get_sepsets(tau_min, tau_max)
 
         if self.verbosity > 1:
             print("\n--------------------------")
@@ -3034,18 +3067,18 @@ def _pcalg_skeleton(self,
                                                   val=val)
 
                         # If conditional independence is found, remove link
-                        # from graph and store sepset
+                        # from graph and store sepsets
                         if pval > pc_alpha:
                             nonsig = True
                             if abstau == 0:
                                 graph[i, j, 0] = graph[j, i, 0] = ""
-                                sepset[((i, 0), j)] = sepset[
+                                sepsets[((i, 0), j)] = sepsets[
                                     ((j, 0), i)] = list(S)
                                 # Also store p-value in other contemp. entry
                                 p_matrix[j, i, 0] = p_matrix[i, j, 0]
                             else:
                                 graph[i, j, abstau] = ""
-                                sepset[((i, -abstau), j)] = list(S)
+                                sepsets[((i, -abstau), j)] = list(S)
                             break
 
                     # Print the results if needed
@@ -3084,13 +3117,13 @@ def _pcalg_skeleton(self,
                     " reached." % max_conds_dim)
 
         return {'graph': graph,
-                'sepset': sepset,
+                'sepsets': sepsets,
                 'p_matrix': p_matrix,
                 'val_matrix': val_matrix,
                 }
 
-    def _get_sepset(self, tau_min, tau_max):
-        """Returns initial sepset.
+    def _get_sepsets(self, tau_min, tau_max):
+        """Returns initial sepsets.
 
         Parameters
         ----------
@@ -3101,15 +3134,15 @@ def _get_sepset(self, tau_min, tau_max):
 
         Returns
         -------
-        sepset : dict
-            Initialized sepset.
+        sepsets : dict
+            Initialized sepsets.
         """
-        sepset = dict([(((i, -tau), j), [])
+        sepsets = dict([(((i, -tau), j), [])
                        for tau in range(tau_min, tau_max + 1)
                        for i in range(self.N)
                        for j in range(self.N)])
 
-        return sepset
+        return sepsets
 
     def _find_unshielded_triples(self, graph):
         """Find unshielded triples i_tau o-(>) k_t o-o j_t with i_tau -/- j_t.
@@ -3153,7 +3186,7 @@ def _find_unshielded_triples(self, graph):
 
     def _pcalg_colliders(self,
                         graph,
-                        sepset,
+                        sepsets,
                         lagged_parents,
                         mode,
                         pc_alpha,
@@ -3171,7 +3204,7 @@ def _pcalg_colliders(self,
         ----------
         graph : array of shape (N, N, tau_max+1)
             Current graph.
-        sepset : dictionary
+        sepsets : dictionary
             Separating sets. See paper for details.
         lagged_parents : dictionary
             Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
@@ -3207,7 +3240,7 @@ def _pcalg_colliders(self,
         -------
         graph : array of shape [N, N, tau_max+1]
             Resulting causal graph, see description above for interpretation.
-        sepset : dictionary
+        sepsets : dictionary
             Separating sets. See paper for details.
         ambiguous_triples : list
             List of ambiguous triples, only relevant for 'majority' and
@@ -3234,11 +3267,11 @@ def _pcalg_colliders(self,
 
         if contemp_collider_rule is None or contemp_collider_rule == 'none':
             # Standard collider orientation rule of PC algorithm
-            # If k_t not in sepset(i_tau, j_t), then orient
+            # If k_t not in sepsets(i_tau, j_t), then orient
             # as i_tau --> k_t <-- j_t
             for itaukj in triples:
                 (i, tau), k, j = itaukj
-                if (k, 0) not in sepset[((i, tau), j)]:
+                if (k, 0) not in sepsets[((i, tau), j)]:
                     v_structures.append(itaukj)
         else:
             # Apply 'majority' or 'conservative' rule to orient colliders          
@@ -3333,12 +3366,12 @@ def subsets(s):
                                     "    Fraction of separating subsets "
                                     "containing (%s 0) is = 0 --> collider "
                                     "found" % self.var_names[k])
-                            # Also delete (k, 0) from sepset (if present)
-                            if (k, 0) in sepset[((i, tau), j)]:
-                                sepset[((i, tau), j)].remove((k, 0))
+                            # Also delete (k, 0) from sepsets (if present)
+                            if (k, 0) in sepsets[((i, tau), j)]:
+                                sepsets[((i, tau), j)].remove((k, 0))
                             if tau == 0:
-                                if (k, 0) in sepset[((j, tau), i)]:
-                                    sepset[((j, tau), i)].remove((k, 0))
+                                if (k, 0) in sepsets[((j, tau), i)]:
+                                    sepsets[((j, tau), i)].remove((k, 0))
                         elif fraction == 1:
                             # If (k, 0) is in all of the neighbor_sepsets,
                             # leave unoriented
@@ -3347,12 +3380,12 @@ def subsets(s):
                                     "    Fraction of separating subsets "
                                     "containing (%s 0) is = 1 --> "
                                     "non-collider found" % self.var_names[k])
-                            # Also add (k, 0) to sepset (if not present)
-                            if (k, 0) not in sepset[((i, tau), j)]:
-                                sepset[((i, tau), j)].append((k, 0))
+                            # Also add (k, 0) to sepsets (if not present)
+                            if (k, 0) not in sepsets[((i, tau), j)]:
+                                sepsets[((i, tau), j)].append((k, 0))
                             if tau == 0:
-                                if (k, 0) not in sepset[((j, tau), i)]:
-                                    sepset[((j, tau), i)].append((k, 0))
+                                if (k, 0) not in sepsets[((j, tau), i)]:
+                                    sepsets[((j, tau), i)].append((k, 0))
                         else:
                             if self.verbosity > 1:
                                 print(
@@ -3385,12 +3418,12 @@ def subsets(s):
                                     "    Fraction of separating subsets "
                                     "containing (%s 0) is < 0.5 "
                                     "--> collider found" % self.var_names[k])
-                            # Also delete (k, 0) from sepset (if present)
-                            if (k, 0) in sepset[((i, tau), j)]:
-                                sepset[((i, tau), j)].remove((k, 0))
+                            # Also delete (k, 0) from sepsets (if present)
+                            if (k, 0) in sepsets[((i, tau), j)]:
+                                sepsets[((i, tau), j)].remove((k, 0))
                             if tau == 0:
-                                if (k, 0) in sepset[((j, tau), i)]:
-                                    sepset[((j, tau), i)].remove((k, 0))
+                                if (k, 0) in sepsets[((j, tau), i)]:
+                                    sepsets[((j, tau), i)].remove((k, 0))
                         elif fraction > 0.5:
                             if self.verbosity > 1:
                                 print(
@@ -3398,12 +3431,12 @@ def subsets(s):
                                     "containing (%s 0) is > 0.5 "
                                     "--> non-collider found" %
                                     self.var_names[k])
-                            # Also add (k, 0) to sepset (if not present)
-                            if (k, 0) not in sepset[((i, tau), j)]:
-                                sepset[((i, tau), j)].append((k, 0))
+                            # Also add (k, 0) to sepsets (if not present)
+                            if (k, 0) not in sepsets[((i, tau), j)]:
+                                sepsets[((i, tau), j)].append((k, 0))
                             if tau == 0:
-                                if (k, 0) not in sepset[((j, tau), i)]:
-                                    sepset[((j, tau), i)].append((k, 0))
+                                if (k, 0) not in sepsets[((j, tau), i)]:
+                                    sepsets[((j, tau), i)].append((k, 0))
 
         if self.verbosity > 1 and len(v_structures) > 0:
             print("\nOrienting links among colliders:")
@@ -3474,7 +3507,7 @@ def subsets(s):
             self._print_parents(all_parents=adjt, val_min=None, pval_max=None)
 
         return {'graph': graph,
-                'sepset': sepset,
+                'sepsets': sepsets,
                 'ambiguous_triples': ambiguous_triples,
                 }
 
diff --git a/tutorials/causal_discovery/tigramite_tutorial_pcmciplus.ipynb b/tutorials/causal_discovery/tigramite_tutorial_pcmciplus.ipynb
index a42f2c54..a3f5e5c2 100644
--- a/tutorials/causal_discovery/tigramite_tutorial_pcmciplus.ipynb
+++ b/tutorials/causal_discovery/tigramite_tutorial_pcmciplus.ipynb
@@ -150,7 +150,7 @@
     "\n",
     "* **Skeleton discovery phase**: Starting from a completely connected graph first a skeleton of adjacencies $X^i_{t-\\tau} - X^j_t$ is estimated by identifying which pairs of nodes are conditionally independent for certain subsets of the other nodes. See the paper for the particular way that conditions are chosen, which is different from the original PC algorithm. The adjacency between conditionally independent pairs is removed. The lagged adjacencies in that skeleton are then automatically oriented by time-order. For example, an undirected link $X^i_{t-2} - X^j_t$ can only be oriented as $X^i_{t-2} \\to X^j_t$ since causal effects cannot go back in time. \n",
     "\n",
-    "* **Collider orientation phase**: The contemporaneous adjacencies $X^i_{t} - X^j_t$ are then oriented based on the following collider rule. For an unshielded triple $X^k_{t-\\tau} - X^i_t - X^j_t$ with $\\tau\\geq 0$ (for $\\tau>0$ we always have $X^k_{t-\\tau} \\rightarrow X^i_t$) with no adjacency between $X^k_{t-\\tau}$ and $X^j_t$: If $X^i_t$ is *not* part of the conditioning set that makes $X^k_{t-\\tau}$ and $X^j_t$ independent, then orient  $X^k_{t-\\tau} - X^i_t - X^j_t$ as  $X^k_{t-\\tau} \\rightarrow X^i_t \\leftarrow X^j_t$. This rule is applied to all unshielded triples. There are three options (``contemp_collider_rule={'none', 'majority', 'conservative'}``) to decide whether a middle node $X^i_t$ is *not* part of the separating conditioning set: ``'none'``: In the original PC algorithm the conditions that lead to conditional independence in the skeleton discovery phase are stored (``sepset`` in Tigramite) and then used in the collider phase. Alternatively, all separating conditioning sets are *re-computed* based on the neighbors of $X^k_{t-\\tau}$ and $X^j_t$ and collider motifs are oriented based on the ``'majority'`` or ``'conservative'`` rule as discussed in the paper.\n",
+    "* **Collider orientation phase**: The contemporaneous adjacencies $X^i_{t} - X^j_t$ are then oriented based on the following collider rule. For an unshielded triple $X^k_{t-\\tau} - X^i_t - X^j_t$ with $\\tau\\geq 0$ (for $\\tau>0$ we always have $X^k_{t-\\tau} \\rightarrow X^i_t$) with no adjacency between $X^k_{t-\\tau}$ and $X^j_t$: If $X^i_t$ is *not* part of the conditioning set that makes $X^k_{t-\\tau}$ and $X^j_t$ independent, then orient  $X^k_{t-\\tau} - X^i_t - X^j_t$ as  $X^k_{t-\\tau} \\rightarrow X^i_t \\leftarrow X^j_t$. This rule is applied to all unshielded triples. There are three options (``contemp_collider_rule={'none', 'majority', 'conservative'}``) to decide whether a middle node $X^i_t$ is *not* part of the separating conditioning set: ``'none'``: In the original PC algorithm the conditions that lead to conditional independence in the skeleton discovery phase are stored (``sepsets`` in Tigramite) and then used in the collider phase. Alternatively, all separating conditioning sets are *re-computed* based on the neighbors of $X^k_{t-\\tau}$ and $X^j_t$ and collider motifs are oriented based on the ``'majority'`` or ``'conservative'`` rule as discussed in the paper.\n",
     "\n",
     "* **Rule orientation phase**: Orient further adjacencies such that the graph does not contain cycles (rules R1-R3 in paper).\n",
     "\n",
@@ -216,7 +216,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 9 Axes>"
       ]
@@ -281,7 +281,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 1500x1000 with 81 Axes>"
       ]
@@ -304,7 +304,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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YRuarr77C22+/jQULFuCJJ57o8D5cpTq0SU10dgQnNRmG6QxtugfAMEz/88EHH2Dfvn2w2Wy46667YLPZ8MYbb2DmzJnYsWMH7rzzTqxevTrdw8w4IpEIvvvd72LLli0AjnmW1dfXY82aNVi7dm2aR8gwJ46iKFAUBYlEQlSs0AZDpVIl3ZiTx+PxYMKECRgzZgw0Gg3uuece3HzzzTCbzXj88ccxYsSIdA+R6WfomvLkk08CAMaPH4+lS5dCpVLhmWeeQXl5OUaPHo05c+Zg7NixmDdvXhpHyzDMQKa8vBz3338/Zs2ahXPOOQdLly5FXl5e0n1Wr16Nu+66K00jZNINJTobGhpQXl7e4X0MBgMMBkP/DoxhmAEBi8wMMwT561//CgC46qqrYLPZAAAlJSVYu3YtLr74Ytx///24+OKLMWvWrHQOM+Mgz7LHH38cwDHPMqfTeVzPMrkynKtBmExCURTEYrEkkZluQFsFCwnMarU66XsWnXsGHVVetWoVAGDp0qWcrGIAAMuXLxdfpzb3u/fee/t5NAzDDEZmz54Nk8kEtVqN2bNno7Kysp3IvHLlSqxYsUJ87/F42A5hCLFq1aqkRCfDMMyJwHYZDDPECIfDePPNNwEkb2gB4KKLLsL3v/99JBIJ3HjjjUmeXAx7ljGDC1lgjsViiMfjiEQiIjESCoUQCAQQDAbFz8LhMKLRKKLRqHhMZ7HPdEzqUeXuNNgB+PgywzAMc/LccccdePfdd5FIJLB161aMGjWq3X3Yemdo8uSTT6K8vByzZ8/G5s2bsWnTJowePTrdw2IYZoDBIjPDDDHWr18Pv9+PoqIinHLKKe1+/8ADD8Bms+Gzzz7DU089lYYRDhzYs2xoU1VVhcWLF2POnDlYs2ZNhx7dt912G+bOnYsf//jHaR5teyhJQkJxJBJBNBpFKBRCMBhEKBQSQrPP54Pf7xeicyQSEfdnwfnEoKPK7733Hp566qluJasATlgxTKajKAri8ThisVjS3MjzIpNJrFixAmvWrMH8+fPxne98Bzk5OekeEsMwDDOIYJGZYYYYr7/+OgBgyZIlHR53LywsFMe477jjDm4CeBxSmzN1BFeDDF4efPBB3Hnnnfj000/x7rvv4vbbb8dbb72Fhx9+GGvWrMGRI0dw4MABbNy4ETabDZs2bUr3kJOQrTFisRgSiQTC4bD4t7W1FQ0NDWhoaEBTUxNaWlrg8XiE4CyLzXKFM4vNx2f27Nk444wzoNfrMXv27G4lqwBOWDFMpkE2Q/F4vF3CTbYfisfj6R4qwwiKi4vx4YcfYuPGjbj++uvTPRyGYTKU1GIawuPxoLi4GIsWLcJZZ52VxhEymQp7MjPMEOPDDz8EAJx77rmd3uenP/0p/vCHP6C6uhqPPfYYfvKTn/TX8AYU7Fk2tPnVr34Fh8MBoK0aWKPRJHl0b9u2DXPnzgUAzJ8/H5s3b8YZZ5zR4XOlw7tb9l4moRlo+7+0trYCaKt2DgQCiEaj0Gq1MJlM0Ov1MBqNMJvN0Ov10Gq10Gq10Gg00Gg04m+h0WiEhzNzjDvuuAPnnHMOvvGNb2Dr1q0wGAxdNtgBuMlOf0PiIAmEOp3uuEkAZnAiJ+Pk+TL193LjVEKj0UClUnHSjWEYhhlwUDHNwoULcdZZZ+GGG26AzWZr11ukM7gv0dCFRWaGGUK0tLRg165dANpEr84wGo341a9+hRtvvBH33XcffvCDH0Cv1/fXMDOeJ598EkDbsffU5kzM0MHpdAJoa6Q5c+ZMfPHFF+J3iqLA6/XCarUCACwWC3w+X6fPle5O7rKAEggEALSNeffu3fB6vUn3NZvNyMrKQlZWFsxmMywWSzuxWavVIpFIQK1Ws9icwooVK3DFFVfg7rvvxlVXXYVp06ZxsipDkIXl1OpTRVFgNBrTNDKmr5DnvtSfdSYOy79PJBLipigKVCoVtNq27VU8Hue5j2EYJo0oiiJOmcjJQGpondrYmpKDTPtiGrq2UW+Rd999F9dccw2uueaaDh+f7r0Nkz64JINhhhAbN24EAIwdOxb5+fnHve8PfvADFBUVoaamBi+++GJ/DI9h2kGiTzQaTWpKR/YM3bVmKC8vx/jx4zF9+nRMnz4d//znP3tlfH/729+wbt06rFmzpp3tgdVqhd/vBwD4/f7jWqWkwwpBpVKJMctfk7jW3NwMr9cLnU4Hl8uFffv2Ye/evdizZw92796NvXv3oqqqCo2NjWhtbU3ycg6Hw8KzmfxJM72RaEVFBebMmYOxY8fitNNOw+7du/vkdVKPKnODnfSTSCQQiURE/AaDQVHZT6jV6n6LEaZvka8rZG8h32SrC7LDiMViYi6Tv6Z5jh739ddfY/HixZg2bRoWLFiA3bt3cwU8045zzjkHU6dOxfTp0zF//nxs37493UNiMgy+3pwciqIgFArB5XLB5/MhFAoJize60fXe7/fD6/XC4/GgtbVVPCYSiWTsSRSXyyX2NNOnT8fYsWOh1WrR0tLSa6/hdDqh0WhEMY3JZALQvrdIY2Njh49nm7f08/bbb+PUU0/F1KlTMXv2bOzYsaNfXpcrmRlmCPHJJ58AOH4VM2EwGPCjH/0Id9xxB/70pz/h+9//fl8Pj2GSPCxTjx/L1QcAhCCgUqm6dZT9xRdfxOTJk3ttrP/5z3/w1FNP4dVXX4Verxce3WR7MGPGDDz33HO48cYbsWHDBlxwwQWdPlc6rBBSRXGNRoNYLCb+jlTBHI/HcejQISiKIhbdRqMRDocDBQUFGDZsGIqLixEOh2GxWGCxWBCLxaDX66HRaKAoCjQaTdLrZGKVyPXXX48f/vCHWL58OV588UVcd911GeejzfQuiURCeOmSYChXMNvtdlHdpNPpOEYGOHRt6Ug0oDkptUK5s+eg6xPdR6fTAWg7qXD11Vdj+fLlWLduHa6//no+8cS04/nnn0dWVhYAYN26dbj22muxbdu29A6KySj4enNyBAIBYdVAvUYikYhIIFMFM53A0+l0Yn1KiedIJAKNRgOLxSKqeDOFrKyspOTUAw88gPXr14tTlr0FFdOsW7dO/Gz27NkwmUxQq9WYPXs2KisrkZeX1+6xbPOWXlpbW3HFFVdgw4YNmDBhAtavX4/LL79cnGrvSzi1zjBDiM8//xwAOvWFTeW//uu/oNPpsGnTJmzdurUvh8YMYWijHo1GRZXy/PnzMWvWLDidTkyePBmnnXYa7rjjDlFBBhxrkkZH4fq72uCee+5BfX09zjvvPCxatAi/+tWvsHTpUlx33XVYuXIlysvLMXr0aMyZMwcNDQ2YN29ev46vK6h6OdXSgioVSBiuqakBAOj1ehw+fBh1dXWorKzErl27sH37duzcuRN79uzBkSNH4Ha70dLSglgsllQtIjcFpMrBk3m/FEXBnDlzMH36dJjNZowZMwbTp0/H7bff3qPna2howLZt23DFFVcAAL797W+jsrIShw4d6vEYmfRyvBihOcPn88Hj8cDlcsHtdrdrdEsxajAY0NjYyDEyQCFhOBaLtTsqvXDhQsyaNQt2ux0TJkzAqaeeilWrViUJzCRAk/BAVczBYFDcEokEGhoasGPHDlx22WUA2mLk0KFDvRoj3Iip/+nt6w0AITADgNvt5mr3AQ6vSTIL2kvQtb61tRU+nw9erxderzepupau/y6XCy0tLfD7/VCpVNDr9VCpVIjH4/B4PO3WBydKX8wjMk888QSuu+66XnkugoppXnrppSTbzDvuuAPvvvsuEokEtm7dilGjRvXq6w5VejtGDhw4gPz8fEyYMAEAsHDhQhw+fLhfEpqZlZJhGKbPUBRFTCozZ87s1mMKCgpwySWX4B//+AceffRRnHLKKX05RGYI0VHFslxB9u677wIARo8ejVdffRWlpaVJj73ssstw8OBBqNXqJLFSpVLhtddeS7o/cfnllyORSOD000/H6tWrO8y6nwivvfZau5+lVqzde++9J/UafQkJJ2q1GolEAlqtFnq9XnjPZmVlobGxUSwsDx48CABwOBxQFAUejwf19fVoaWmBy+WC1+uF3+/H8OHDEYvF4HA4YDQaEQqFYDQaxftE1c3k2dwTz1KVSoVPP/0UQNs89f7776OsrCzpPt/5znewf//+Dh+fGiNVVVUoKioSlSoqlQplZWU4cuTIcZvxMZlLZzESj8fh8/kQCASwfPlyUaVPUHLlueeeQ3Z2NgwGA9RqNcfIAIXsLohUz81PP/0UiUQCRUVFeP3115PmhUQigcsvv1zMffLPgbZNfVFREQAgGAyiqqoKBQUFMBgMwqO+t2OEGzH1P719vSGuuuoq0Yz77bff7uVRM/0Jr0kyB0VRRG+RWCwGj8cjbDFSG7XSGhSAEJbj8ThCoRC0Wi1yc3MBANFoNEl87gl9NY8AwKZNm9Dc3IylS5f2aGydIRfTAMDixYtxySWXtOstkpOT06uvm04oPmQP79R9puzj3dN9TEf0doyMGTMGjY2N2Lx5M2bPno2XX34ZPp8Phw4d6rYW1FNYZGaYIcLhw4fhcrmg0+kwadKkbj/ummuuwT/+8Q88//zzePDBB7kBINMjOmqSJP+uo6PJ9fX1iMViGDVqFLRabZJNBjVIMxqNUKvViEajANoqDju60H/88ccoKytDNBrF7bffjquvvhpvvvlmX/13BwS0SJIb9Gm1WsTjcdjtdkSjURQWFiIcDqOhoUEceTObzfjjH/+ITz/9FE888QRaW1tx8OBBBAIBBAIBRKNRDB8+HIqiwOFwwGQyCaGZIAsNet9J9DnRRVpdXR2i0Wi7RRiAE/aST33tTPXhY04MipHS0lIEg0FRzRQKhbBmzZp2m0yKWbPZ3G4+4RgZOHR0XaETGzKxWAyHDx8WMUK2QZFIBMCxRr8ajQbxeByBQEC873q9XtxPtgQigTk1CdobcCOmrkm11uotevN6AwBPP/00AOCpp57CL37xiyG/JhkM8Jok/VAFcyKREAIzzduRSAQej0fsGYC2eZws7xRFQTgchtFohNFoRH19Pex2OywWCyKRCHw+H+x2+0lZZ/T2PAIAjz/+OK666qpet/ToqJiGoATZYEFuEHm8z5q8n5WhBHZvCM+9FSMOhwMvvfQS/ud//gderxfz5s3DxIkThb1XX8IiM8MMEb744gsAwKRJk05IKF68eDEKCwtRV1eHt99+GxdeeGFfDZEZRHTHWzk1Q5zaaGnbtm2YNGkS3G43DAYD7Ha7EEYvvfRSHDhwQAiT8vO/9tpr7S7M9L1Op8PNN9+MsWPH9vWfYEAgi8xUzUzvS3Z2NhKJBIYNGyaqQFpbW1FXV4d77rkH99xzD+bPn48//elPeP/991FfX5/UBIuqbVQqFYxGI8LhsBDt6AgiicvkrX2ifs1ffvklZsyY0eHvTiTbX1paiurqasRiMWi1WiiKgqqqqg4XeMzA4ssvv8T06dPhcrng8Xjg9/sRCoUQCoVw2223oaamRgiPsnVM6jzCMTJwSPVe7mzDR6LDrl27MG3aNJhMJgSDQSEc6/V6XHXVVThw4EBSRTRdhx577DEUFhaKBN3w4cNRW1srXrMvYoT8NjtrxDRr1iycc845WLp0aYendVauXIkVK1aI7z0eT6fVcZlG6joi9Za61gCOif69UWnWW9ebVK6++mr86Ec/QnNz86CqCByK8Jok/dBJjVAoJBKDiURCnLajeZ+uEZFIBDqdDj6fD2azWcyp1H+EhGe73S6ex263i8TiidLb84jf78c///lPfPbZZz0az1CHxGU58aAoitib0E1OXtIaIPXrjq4/Pal47s0YWbBgAT766CMAbZ+NgoICYZ/Rl7DIzDBDBLLK6GzS6gyNRoPvfe97+L//+z8888wzLDIzx4U24nRBThWR5X/lx9DRJPl3dXV14ihyOBxGLBYT2ddnn30W0WgUer0eBoNBeGUSkUhECEZUXUseiM8+++wJfw4GKyTskuALICkJRYJGPB5HJBJBOBxGZWUltm/fjttuuw133XUXbr31VowdOxZ/+ctfkrpay5XJTqcTer0esVhMvCY9r06nE3ESi8U6rDbsjJqaGpSUlHT4uxPJ9ufn52PGjBn429/+huXLl+Oll15CeXk5H0sd4CiKgsrKSuTm5uLo0aOiERDNM/feey80Gg2sViscDoeoYu6oGohjZGCQKjAfbz6hSqS6ujoh3pBAYbFYoNfr8dJLLyEYDKK5uRkAYLPZoNPp0NraKo5bGwwG6HQ6FBUVYfr06Xj22Wf7NEYGYyMmufK8s7VDT6C1gUqlEg2+eio299b1xuPxwOfzifXNyy+/jJycnF5v2MX0P7wmSS/U8yMejyMYDIoKZp/PB7/fj2g0Co/Hg2AwmPQ4nU4Ho9GIWCyGQCAAi8UCs9ks5o94PI5oNAqr1QqdTgePxwObzdajyuHeihHihRdewNSpUzF+/PgTfuxQhvYc8t4ROLbfofVB6nWJ9jayuAwc20/JPW46ayBMgjPdP/Wa1JsxUldXh8LCQgDAb37zG5x55pkYPXr0CT1HT+AuAwwzRKBK5p548Fx++eUAgDfeeEP4XDEMQZt68nqkxki02JMv0PQvNWGix1DjPmo853A48M1vfhOff/45fvKTnwBAklBAF23ZV5iadNDv6flramqwePFiTJ06FVOmTMH69evFMVUGYjEkV3HKxwdzcnJQUlKC0aNHY8KECSgvL4darcaOHTtw0003ob6+HhdffDF+8YtfQKVSoaWlBVVVVTh06BCqqqrg8/ngcrlEsyy5IpBiQV6oU8x0h7lz52Ljxo2iOc7JsHbtWqxduxZjx47FmjVr8Nhjj530czLpQW72M2bMGGzatAk333wzQqFQkmClVqtht9uRm5uL3NxcWK3W424a+ypGUpu5bdq0CaeffjoWL16MyspKAMBtt92GuXPn4sc//nGvvOZgRRYnu0pY0f3mzJnTbh6hOUhRFLhcLgBtwrOiKGhpaRGWP0ajETqdTtz6eh4ZTI2YaJMfDocRCoXE2iG1V4N8/1TbrVRBWr6WkW0JPZaaC/e08WxvXW/cbje+9a1vYcqUKZg2bRoefvhhvP76671u78H0P7wmSS+UJJQbtIZCIfh8PoRCITQ0NCAYDMJkMqGgoEAUn0QiEXi9Xhw9ehQtLS1obW1FY2MjWltbEQgE4PF44PF4RANBagaYuqboDr0ZIwDw2GOP9XrDv8EOVafLAnMikRAn3eha4ff74ff7EQgERJNfsgYMBAIieREMBsXzhcNhBINBUUmfKkbTa9G1jwqhZHozRlatWoXx48dj9OjROHz4cL/NIyqFDX4YZkhQXFyM2tpafPLJJ5g7d+4JPVZRFIwYMQKHDx/Gyy+/jG9961t9M8ghgMfjgcPhgNvtht1uT/dwTgq5OYK8KexoA0j37wwSNEkoVhRFXNiBNh9gq9Uq7h8MBhGLxQC0b8IgV8nSa+p0uh4fbUsH/R0ncvUficDyEbJQKCSsMioqKvDVV1/h0KFDiMVisFqtuP3223HKKafgX//6F/785z8DAEaMGIHhw4dj+PDhGDFiBKxWK5xOJ7RaLQwGg6gSASCOJ3a3ApEZXHNJb5JIJBAMBuF2u0XFYGqlCtA252RnZ8PpdMJms51UhePJcsstt+CCCy4QzdyAtuqk+vp6PPjgg7jtttvwy1/+Es899xx++ctf4qKLLsIZZ5zR5fMOxRiRk1Sdzfl0zaAqJrL0UalUYnMIAHa7HYqioL6+HsAxeyEAolGqLDD3VvOf43HBBRfg0KFDwlaBGjE5HA5cccUViEQiuOqqq3D99dd36/nSFSNUIZjaUIlsRuh97Exw7i5kl0GxIFednWxl81BiKM4lzImR7hhJJBJwuVziX+q90NTUhGAwiMbGRiQSCTidTtTV1YnTKUDbeoD2GGTrptfrYbFYYDKZYDQaYbVaYTAYYDKZYDKZYLFYRJLRYrEMqD1Gukh3jABt+51QKJRUrBQMBkUFPNn+0f6WGgCmkrrvpN5B9LVOpxPXFo1GI/p8yPtkec9D64nBAttlMMwQ4OjRo6itrYVKpcK0adNO+PEqlQoXXXQR/vCHP7DIzHQoLtPFsquGCQRdhMnuQhYTo9EovF6vEJFpMSdDF2rZlkNeBFAlrnx0jheAnUMLo1gsJoQSsrKQG1nQe6bX62E0GnHgwAH4fD6sXLkSV1xxBS6//HJUVFTgvffeQ3V1NYxGI8xmMzQaDUaOHAmXy4Xs7GyRwadFGTX/I+sOem970gyQGZpQUsTr9cLtdosKk4488sxmM3Jzc5GVlQWTyZT2GEtt5qbRaOB0OuF0OlFRUYFt27aJ5PD8+fOxefPmDkVmOhlCeDye/vkPZBCyEEwnJjpCFh7Jl5PmNfKVj8Vi0Ov14jkTiYQQH6hKtj8FZmBwNGJKJBJJcUrXgWg0ikAgkPS+pfotp1Yuy/cjUo8zy9cSet+pYXA0Gk36WU9IHZ88hv6KC4YZypAlBgBxKoKqkyORCFpbW8V+ZdeuXQDaik8cDgc8Hg8ikYiwe9NoNOJaEAqFhEDo9XphNpths9lgNpsRCoVgsVhgtVoRi8WEGM2f98ylI4HZ5/MlVTZT8lneU8qCsCwu016FKpFpTUDPYTAYxDqDPMFtNpuwrJKLeSKRyKAqrmGRmWGGAGSVMXbs2KRq0BOBRObXXnsN0Wh0UGXbmK6RG/mRkCx7Kcs/o6yv3ISHBEqDwSC8zlKho0rklaZSqWCz2WA0Gtvdl4QieXMnC92RSERsXJnuQRtw8k0mr+RoNAqj0Siy9TqdTiQHzGYzKioq0NraimeeeQZffvklbr75ZuzcuRNHjx5FY2MjdDodTCYTqqqqMHz4cHi9XthstqQKABKZZbEbgPg5wxwPmjtcLpfwXSQvRoLEQYfDIaqXM2WOSG3mRtdsoG3u9Xq94tptsVjEZjqV1atX46677ur7AWcwNI+leigSFBPUUEun0wlRORgMik0hCRIqlQp5eXnw+/1JSTidTieEZhYSuw8J+sAxm6tIJJKUEJLXEt1NXKfeJ/X9oCQmvaeU6Far1eJ1jueRmfpaHVWidQbFC8dJ/9DdxERqMoLfn4FJPB4X87NscUA2GS0tLQiFQkmN/yZNmgSr1Qqv14thw4ZBrVYjGAyirq5O7ENoL0InLY1GI4xGI2w2G2w2GxwOB0KhEMLhMKxWq0ieWSwW3iNnICQky+IxWZ+QRQYJ0HSdopNxqXYWQNv8YTabYTQaxT6FriW0V6LG6larVRTUuN1umM1mWCwWcW2g9QYJ0x3NQ6lzWKYX4bDIzDBDANqwnkyzs7lz5yI3NxdNTU3YsGEDzjzzzN4aHpOBpDbh6ehfyt7KmdjOvHSpmjgcDsPj8cDpdCI7O1u8Fi0G5WND1NDP5/OJ48ypgqPcRI6eixaRJIrT/ZiukRsBkueoVqsVCyz5WBgtvElorqurw44dO3DzzTdj8eLFWLduHZqbm5GdnY1Dhw5Bp9OhtrYWxcXFSV2ZySKFBL/UMTBMZ9BGQPZKDIVCYvEfjUZFbOn1etjtdmRnZ2dE9XIqcjO3c889V/ycNig1NTUA2jrJd3bMdOXKlVixYoX43uPxdNiNfiDRUXVoV6ReF1Kfj5JYtBmkKlq6lqUiJ8AACIG5O4Ikk4xcDabX65PEHLIwSW3KRe+/3Fi4IzsNGbpOyUeX6Sg0zQfxeDypnwOteWjeSBUd5TVQKrJgScgiJ1WpyUeomZNHXo/K/wLJvuqE/D519D7IXt78PmU2sVhMVC3L+wiv14twOCy8lf1+P1paWhCJRISI+P7773f6vA6HQ6wfotGoEK+pZ0wgEIDX60UgEIDD4RCFLZFIBDabDYlEAnq9HmazedBUpQ50SGCWT8lQYoJiiPy1ZZ9kWi+QrZY8b+j1epHQANpsHbOzs5OuUVSU4/V6heUKPb9KpRJCs16vFx7O4XBYrCtS7SdToQKuTJyvWGRmmCFAb4jMGo0GF154IR5//HG8/PLLLDIPQkiUTfVATPVVlquLUo8VkbhjMBiSRGGqMPZ6vQgGg/B6vcI2wePxiE2/vOGjn9Hr0tG0440/HA4nVa7RuHvSAXqoQkfD6X2n94TEZlr80MKG3muz2Yyqqiq43W6sW7cOQNvfv6mpCQaDAUeOHBHHEEmwkf0x6bg6cPxj7gwDHKtebm5uhsvlSmpKS597OrpqMpmQlZWVUdXLMtTM7dVXXxXJm6amJjQ0NKC8vBwzZszAc889hxtvvBEbNmzABRdc0OHzGAwGcQxzMCA3CQWOJblOBvnEBgBhpaDX60WVkyxqJxIJeL1ecY2TLYQyMZYyHXo/NRoNotGoEJQ1Gk27v3M8HkcgEOjQ9kZGTooDEH0d5NjRaDSi7wOtYUgAoCPxcvPg1MenkurH2VkcyGsmijWucjw56P2mEwi0vpS/Tl27plYsA+0FZbIJo2QEWeGc6Gc81cKN1qRdCdxM94jFYggEAknJwkAgIKqVQ6EQmpub0dLSArfbjZaWFgSDQdGoj/YJVI1MiefGxkY0NDTA7XbD7XYnvSb55dKJF4vFIoTJcDgMp9MpPudWq1UklthCI/1Qhbm8r6HqZGrYR8kEn88nbDN8Ph8CgUCStZOMSqVCdnY2HA4HVCqVaAZoNptRUFAghGqyUiEROTs7G8FgEH6/XzQPputPJBIRc1Bn0PWN/m/kIZ1pxRO862aYIcC2bdsAADNnzjyp57nooovw+OOP45VXXsFDDz2UUZMZ03M6at6XetwwNZNK2dbUSmG/34+GhgYoigKtVovCwkIUFxdDr9eLY99AmxhCnXkJ8r6UGyXQ4l8+wtTZMaJQKCQWnQaDIWkzy7F6YpCPpbyxV6vVMBgM4ogxvVe0OCKBq7a2VnjbAUBrayvy8vLg8XhQV1eXtMmWRWb6V36v2CqDSYWqTT0eDxoaGuD1etstyE0mk6hWpKOtJpMpY6uK7rnnHtTX1+O8884DANx7771YunQpVCoVnnnmGZSXl2P06NGYM2cOxo4di3nz5qV5xH2PXJFI9NY8LltqdNRUTk56ysInXaO4IrXnyKIfVYBptVq43W6xbtBoNDh69GhSoln27Cf/SvIgl+8nQ/0cKMns8/mgVqtht9uFyB2Px0ViJhgMQq1Ww2g0isekVsHK1z658plIFZ1lqwzy++TkRM8g4Z/+jrKXqeyjnlrdLtugAMc+/3Qigd5Xs9kshEQSCUls7sgWR14bd1VxKCO/fqZekzKVUCgkEspUWOL3+8W/oVAILS0tQmBuamoSfRqoyIXwer2ora0V38+aNQs/+9nPxDq2trYWR44cQWVlpYgFssYAICpdKd5ycnLE+x+Px2GxWISIaTQaO7VBYPoOqkIHjs0fJCKHQiHRRD4YDIom0S0tLWJvSvOLfCpGtsagWNPpdBg+fDjUajUCgQAOHjyY9H0gEIDFYkE0GoXL5RJWKx6PByqVCgaDQSSw5VPB8j6L5gqKIUrCUyU/xVmmwCIzwwxy3G43Dhw4AODkKpkB4KyzzhLVil988cVJi9ZMeiGxJvWYYWfHT+WKHNrU0SLb5XK1E3pisRiqqqpgNpvhdDrh8XgQDAbFposu4qkbdqouoguqPI6O/DVpEUH3o0aCNB6uYu45HXmaUuYdQLvOylT5Y7VaUVtbK+Jk3LhxqKysRENDg8i2pwrNhNyEiRfkjEwikRDd4hsbGzusMJE7wNtsNuGPmMmx1FEzt82bNyd9f++99/bXcNJOZwJzb76HJCzJooCcWJWTmwALzL2FfCqK/uZUqWwwGBAOh1FfXw/g2FqAEkqdicnAMeEOOBY/shVHbm5u0ibfYrHAYrGI+1GlYiKRECIWVbnKDSK72+RYp9PBbDaLMdE1jZL6XM18YpA4RKfnqIKUhD868i6vYwEkiTOUEKeKdjqZRclzn88HjUYDq9UqvNplOzgA7dafx0OuWk49GUjPSTHO80nX0MkGoO3z5Xa7EQgEEAwGhWBIFhkejwfNzc3CSqu1tVXM5ePHj8eMGTPgcDjgcrmwc+dOfPXVV9iyZQs+//xzfPe738XVV18t3pNIJIIdO3bgjTfewKFDh+B2u2EymRCJROB2u8WpFro+UDFNIpEQgjQJjdQ4m68hfY8sMNNpSTk5GQwGEY/Hxd7U5XKJBpFutxuhUEgkQjsiKytLCMlerxf79++H3W7H6NGj4Xa7cfjwYRQXFyMrK0uctqMKeJ/PB7PZLGLIbDYLe5WOTqN1tBeT91/0fyEP6EyAd94MM8jZsWMHAKC0tBQ5OTkn9Vwmkwnf/OY38fLLL+OVV15hkXkAQ9lP2eNQPmKYKjTTxoigzZbb7RY/N5lMGDZsGLKzs6HT6XDgwAE0NzfD5/PBZrOhubkZQJtvFT2GMrckOspVsoTs0RsMBkXlCY1broyiCiRaWHDFUM+Q/2bxeDxp8y6/d6mPoQ35gQMHYDab0dLSgkmTJuH666/Hv//9b3zwwQeoqqqC0WhEVVUVRowYAZfLleTPLcNCM0PQUcajR4+ipaUlyboFaIsV6vRuMplgtVrZE3EAItseyPT2iRRZUJaFKRKnZLsEEgTk0zXMyUHXEfLDpsrgpqYmAEB2djZqa2vR2toqHkPCbapfJQl25LFst9tFc8+jR4+ivr4eTU1N8Hg8GDlypGgAFo1GkZ+fLwTKaDQqnl9uPHg8ZO9mOaai0SjcbrdIegEQz9tZ7wqmPfS3pCPtJChSs0iqYvV4PKKSNbXggYRl8toNBoPCHxVoEwB1Op14n9xuN7RaLSwWi7BXkeeFVEjIlm/Hs0+hanz6F8CgsjnqK+hvRadQyPKAmra5XC74fD7hwRwIBOB2u4XAXFJSghUrVmDixIntnrumpgZ/+9vf8OGHH+K5555DVlYWvvWtbwFoi59Zs2Zh5syZWLt2LT777DPk5uaiubkZwWBQNBC0WCxwu93IyckRvWTIHkGtVidVxwPHkmicZOh9yCIDaPt8knUFCcyBQECIycFgEA0NDfD7/fB6vWhtbU1KMFGVsV6vh9FohE6nQ2NjI1wuF1wuFwwGA0499VR4vV54PB58+eWXmDVrFhoaGlBTU4MRI0bA4XCI1zKZTOLUjMlkEuMJBAKdJjXleYcKeWgPJl9/MqmPDYvMDDPI6S2rDGLZsmVCZB7qXewHIqn+gOTnJB+76apaiHyU5cVxWVkZcnJykhZK9Dx6vR5NTU1QFCVpMaXX65O8LeWGTKmbebLXoPGmjkmuBpEvtJlgtxAOh3HppZeiqakJy5cvx3/913+le0hdIgv7tCmi476yZQpwbOMvex5qtVocOHAAWq0WTU1NWL9+PZYsWYLa2lrs3bsX1dXVGDVqFKqqqjBy5Ei0trbC6XS2G4eiKFzxMcShjZnL5UJdXR0aGxvh8XiEp6LNZoNarRZfW61WIQ5w3AwsOvPB7W1hN1VYJkGxI/GPjrRTVSsnLXoHep/ldQQ1t8zOzsa+ffsQi8WgUqlgs9ng9XpRU1Nz3MqyVIxGI2bPno0FCxZg+/bt8Hg8+PrrrzF58mR4PB5EIhHU1taitLRUHIsmn0y73S6sE2SvZ9l6Qa6SJSi2gsGgqFijGErtFcEcH9kGLRQKCauEUCgkKg0bGxvhdrsRDoeFHy8JRFS5TMlHt9sNu92O3NxcAG3WC1lZWTAajSJRQf0nFEURvrwkMpEAlGqbciLI/Sxk8ZytM7pGtj+hamGaP2hPQw35otEovF4vfD4fEokEysvL8cADD4jEQirFxcW49dZbMXbsWKxduxYvvvgili1blvT+UpwBbXPLqFGjsH//fphMJlE1S3YHZAdktVrR0tIimsKRcCknx4LBIMxms+hJwpw8FBcajSbJXicSiYi9JIm+dXV1CAaDIhFJAnVjY2OS9Z/MlClTMHv2bNTV1aG6uhobN27E5MmTUVRUhNraWmzZsgXz588XdiszZsxAJBIRiRGdTge/349EIoGsrCwxz3UnsRmNRtHa2ioKugAMPZH59ddfx89//nMkEgnceuut+MEPftCXL8cwTAf0RtM/mfPPPx9qtRo7duxAZWUlRowY0SvPy/Q9sj0GbajJZoIWugQ1dKMqI8q0yh7KOp0OWVlZUKvVaG5uRiQSQUFBgcjY030NBoO4UJNgSAIzbdLkamRZ1CSRWKPRwGKxJFWUyJs9eSEobwgzQWR6/vnnce655+L666/HueeeiyuvvDKjfLM6g94XufJKFu/lY+WyEEzvi1qtxpEjR9Dc3Iy33noLe/fuxfnnn4+amhqR8Ver1aivr0dRURFaW1uTTluQwEwLskx4L5n+haqVmpubUVVVhaampqR5KpFIQKVSwWq1isZ+8hF1ZmDRkch7IgKzbMWQ6qUr3ye1gplEKeBY41N6bRaY+wb6G9OGOhAIQFEUmEwmVFRUIBaLwWw2o7m5GQcPHkx6rNVqFZ91s9ks/E61Wq2oTjt06BA8Hg8++ugjjB07FmeeeSZ27NiB+vp67Ny5E6eeeio8Hg+8Xi8OHz6MoqIiOJ1OuN1uxONxtLa2CrHZYrF0O2FNIiKJmlStyn6sJwatS8kvlTx36Vi73+9HTU0NQqEQGhoaRCM4EnFoXWI2m+Hz+USFKdDmxVtWVgaj0QiXywW1Wg2HwyGuG5FIJOl0HY1FRq5epnXoiVpf0PqG1lg8vxwf2QuXoK/9fr8omCGhjvYuAHDjjTd2KjDLLFmyBGvXrhWCo8ViwaZNm/DJJ5/g66+/FsmLb3zjG3jnnXcAAMOGDUNNTU2StztZ81DlKlWqWq1WIQ5SVW0ikYDP54PRaMy45m0DFdkuh2yvyFaH9qfhcBhHjx4VySqv14tYLIampiZh2WQ0GjFu3Djk5uYiFouhuroaBw8exM6dO7Fz506cd955+Na3voXXXnsNu3btwimnnILi4mLU1NRg+/btmDJlCmpqarB7927MmjULBw8eRDgcFgkFmtuysrKQlZXVroGtvJ+i0zterxeRSAQejwdOp/OELHz6kz5bhcdiMaxYsQIffvgh7HY7Zs6ciYsvvrjDSiWGYfqO3haZc3NzMX/+fKxfvx6vvvoq/vu//7tXnpfpW44nMJOXEwBYLBYAgMfjQWNjY5KgA0BszmOxGOrr67F///6k32dnZ2PRokVJnshyMz75gqlWq0WlEHDMOkPuGC5v7NRqdbcy/XLDhExg69atuO6666BWqzFlyhTs2bOn3eeRFpuEx+Pp72F2CAnNJMzIAozRaEyqBKT3l8R9uYqouroalZWVePjhh8UiKBwOQ61Wiypmk8kEl8sFp9OZ9LegjR8LzUMHmmMCgQDq6upw5MgR1NbWIhKJwGazieoNEptycnJgt9szurkfc+J0V2BOTYZ1575yBTNwTByUK6DoRA0LzL2PbM0lNwUmsUin0+HgwYPCIqu4uFhUBpMPq8lkQnZ2NgoKCsSN3qdEIoEvv/xSiEMajQZnnnkmvvjiC9TV1eGLL77A3LlzYTQa0djYiNraWuTl5aG4uBg+nw8ej0eIza2trTCZTDCZTEkVp6lN/mTk00CpSVqma6LRqPBfjkajojrV5XIhFAqhuroaoVBIXBdcLpc44SKj1+uRl5cHRVFQU1ODsrIy6HQ6HD58GFarFcOGDYNOpxO2LOTXbDAYxCkaAKLfBK1fZWsdGY1GA6fT2e1EJ1e3dx+67icSiSSrgHg8LgpUCPrM0b/Dhg3r1mtQ1TpVi/7xj39M2ueMGzcOl1xyCQ4dOoTa2lpotVqUlJQIkZk85idNmoT6+nqxls3Ozhbzl8/ng8lkQk5ODrKyshAIBIQAqlKpYDKZTv6PNcShWEitdKfkQyAQEF7MXq8XXq8XKpUKjY2NOHr0KOx2O6699lqcc8457YqCGhsb8fzzz+Pll1/GW2+9BavViuuuuw6PPvootm7diiuvvFJ4g0ejUWGPUVNTg/Lychw4cABerxcFBQWiulpuIJjaOFbeS+l0OtjtdrS0tCAej4tqedqbxePxjPH77zOR+bPPPsOkSZNQXFwMoC0z9M477+B73/teX70kwzAphEIh7N69G0Dv2WUAbZYZ69evxyuvvMIi8wCABBvZw5hsD8i/jryhamtrkwQ+qlSNRqPw+/3tFvFarRZOpxMGgwFHjx5Fa2srPvvsM5x66qkAjh1Voq/pMVTNLFeMyVXLsif0iQiL8nHrTBEFvF6vqKCwWCxJ1eDE6tWrM9J+JtU2Q17IUPMiORFgtVrbdXSnxVFtba04fgq0Nc2YPHkyvvzySzQ1NWH48OEizqxWK0KhEIDkqkQWmgc/NC/5/X5UVVVh//79QlAAIE5MUOVHfn4+bDYbjEYjx8YAR56zu3MUXRaX5Qrl7jxGbtxGVYgUY7K4zAJz75EqAsmV51RN3tDQAEVRcPToUQSDQXGU+KOPPury+a1WK6ZPn4758+cjPz8f06dPh8PhwOuvv449e/agtLQUM2fOxObNm9Hc3IwvvvgCCxYsED0CGhsboVarUVJSApvNJjw6qSKRmgjK6PV6DBs2rEPbDNn+i/7PQOasTTIVWqOSPRqtV2ntRB67ZNsWi8XE2mLcuHEYPnw4YrEY9u3bh5qaGtTU1GD8+PEIhUI4cuQIcnNz4XQ6kwQ/p9OZVF0IQCQWqEkgVUjb7XZkZWUBQJKXezgcRjweh8/nE78/kf8zc3xUKpVoDir3ciGbE7kIhfYZWq0WsVgMH3zwAb773e92+Rput1sIk6+++ir2798Ps9mM888/H6eddho8Hg/ee+89HDp0CABw3nnnic+1xWKBw+HAkSNHUFNTg8mTJ2Pfvn2iYragoEA0PQ8Gg6iurobD4UBeXp7wDaakWqYIhZ3Z/Q0UG0DaD8r9ZOja7/P5kmxxcnJy8MUXXyA3NxcPPfQQCgoKOnzOvLw83HDDDZg8eTLuuusuvPTSS7j00kuxcOFCfPjhh9i0aRMWLlyI9evXY+fOnfjmN7+J7du348iRIygvLxeWGkePHsWYMWOEBSWdKE4t7iKam5thsViQk5MDm80mGl9qtdok73g6/ZluTvgql0gkMH78eNx6661JP3/nnXeg1+vxwgsvAABqa2uFwAxAZHkYhuk/du7ciXg8jtzc3KTP48mybNkyAMDHH3/cqV8RkznQAlhuSEOLePnYWWVlJcLhMHQ6HRwOh8jqHj58WAiEiUQCer0eTqcT5eXlmDlzJubOnYu5c+fizDPPhEqlQk1NTVKzntQLJgkHsjiZ2jGXOJGFt1x1QseMMgGr1Sp8JP1+P+x2e7v7rFy5Em63W9yqqqr6e5idQkIzfU1/144WQ+SfaTabYbPZkJeXh/LycpSXl2PkyJEYP348CgsLMXnyZJx33nmYOHEiVCoV3G43CgsLAUAs/AAIf0X5+CNvxgYvsVgMfr8fra2t2LNnDzZv3owtW7agqqoqqaLMbDYjLy8PhYWFcDgcLDAPEmiuOV6TP1kkTp0b5Eol+SbfhyojaR4xmUwwGo1CYKbkGAvMvY88d9P7KP9cri53u93Q6XSoqanBnj17oFarMXnyZCxbtgxXXnklrrzySlx88cVYtGgRxowZA4PBAJ/Ph08++QRr1qzBunXrEIvFMGLECMyePRsAsGnTJgBtRRc6nQ4ejwdHjx5Ffn4+hg8fDgA4evQofD6f8HkvLCxMmmcoIU5xQT6bqdAxeBLCMjEBnqnQXE/JbbKUoKIIl8slqsyBNqsVAPjJT36CH/7wh3j33Xfx6quv4o477sC1114LlUqFvXv3Ij8/H2azGU1NTfj6669F88dAIICamhpUVlaiurpaNAULBoNoaWlBc3OzsJAjUaq2tlbYuNntdmRnZwthWe5d0RWyBUQmrW3C4TCWLVuGuXPn4pFHHkn3cARU5asoimiqSU2/NRoNjEajaN5pNBrF6cxnnnkGu3bt6vL5N2/eDAAoKCjAtm3boFarccstt2Dy5Ml44YUX8Oijj+LQoUPQ6XRYtmwZ5syZI0TKrKwsjBo1CkBbI0G3243JkyfD4XBAURTU1dWhqakJw4YNE/sAt9uNo0ePwmAwCLsNsg7KBMjub8OGDXjhhRfEdbKzn2cKqScE5BMlkUgE8XhcNBANh8Mwm81Cp7zmmms6FZhlFi5ciJEjRyKRSKCiogJz5swBABw8eBCjRo0S16R4PA6bzYZ4PI66ujrk5eXBbreLmLDb7SgpKcHw4cNRVFSEYcOGIT8/H/n5+cjLy0N2draIe7/fj+rqasRiMfEzSraRsNxVb6X+4oSvcmq1GitXrsSf//xnMbnv2LEDl1xyCe677z5ccsklADoWBngDwDD9CzX9mzFjRq9+/kaOHInJkycjHo/jzTff7LXnHWhk6iJMRq7YkisuaLNNC3jyn6LuyF9++SUOHTqU1JG3ubkZFRUV2LZtGz7++GO8/vrrePzxx7F69Wq88cYbsFqtKC0tBdCWaCRoY5Uag6mCZerx5Y4e0xlURZKJFa8zZ87Exx9/DEVRsH37dowbN67dfQwGA+x2e9Itk6CKEABJG2yKjVT/U5vNBpPJBIvFgtzcXBQUFGD06NHIzc3FsGHDoNVq8cEHH+DFF18Uj3U4HMjOzgaApCNfJDTLt0xZgDO9AyWIyB5j69ateP/997Fjxw7RMd7r9UKn02HYsGEoKSlBaWkpHA4He50OMlKvVXIzHFkwln9H17NwOCz+pa/lx8td2vV6PRwOB9RqtUgCkv8yebFmqhjY2doj09ckqSIzfZ/qz0wVq1TZ7HA4sGLFCixcuBB79+7FP//5Tzz99NN499130dzcjNmzZ+Ouu+7CD3/4Q0ycOBGKomD9+vV47LHHEI/HMWPGDOj1eni9XjQ1NcFoNIrCCyqUyM3NFZ69TU1NYpx06iorKwvDhg1DcXEx8vLykuwQUm284vG4ED7JYzXTGhJnKrQmBSCs3Wg+AJB04o2g98JsNiMUCmHUqFEwmUyIx+O44IIL8NOf/hQqlQqfffYZQqFQ0jqVbA/kxnGHDx/G4cOHYTAYRFPAqqoqJBIJYbcRjUZRU1OTFNPkx0xCeGcViTK0ViURPVPWNpkqItKpS+CYxR7Z2JjNZjgcDuj1epjNZtjtdpjNZhgMBsRiMdx+++1CRO4In8+Hv/3tb+J1AOCss85CdXU11q5di6qqKuh0OsyePRsrVqwQyStKihgMBmRlZWHkyJEAgC1btqC5uRnjxo3DyJEjodVqEQgEsG/fPpjNZlFY4fV64ff7xVwhV92mm61bt2LevHlJdn/H+3kq4XAYHo8n6dYfHG9NKFtr0ftstVrF57U73t3AsQQBkLx/oT4PRUVFANrsNejr5uZmqFQqcf0hH2igbR4zmUzCBs5ms8Fut8PpdKKoqAglJSUwGAxIJBKor68XNoU0lkQiIebC1D5L6aBHq6fLL79clJJXV1fj/PPPx5VXXolbbrlF3IdMr4nq6mrxYWIYpn/YsmULAGDWrFm9/txUzbxu3bpef+6BQqYuwgi5ajm1IkRu+EdHD2OxGHbv3o3GxkYAbRfKuro67Nu3DxUVFairq0MkEoFWq0Vubi5KSkrgcDgQi8WwadMmvPzyyyL763K5ACRf6MmrV84wy56bJBTIR0y7Eo/oOCVdTGlDmEniwHe/+128++67OO200/Dtb397QDT96whZaJabMlJFgFyJI1c001HUnJwclJeXY8SIEbDb7VCr1eK9njx5MkwmE2w2G4C2RXtubq7YvNPzy0Jzd/xXmcyHmvt5PB5UVlZi48aNeP/991FdXZ30HlssFpSVlWHkyJEoKyuD3W7PiCOBTO9AGzMSe2QbjFRBUq5YJi/LYDAojhuTyBwKhYTfJc0bJD7rdDpROQ8MHIEZGLjVZfL1Qf5erjBLJBJwu92IxWI4cOAAAOD73/8+Xn31Vfzwhz/E888/jy1btgi/5b/97W+45ZZbcNVVV+Hrr7/Gddddhx/84AfQ6/XYu3cvPvzwQ2i1WiEgk8jRUfKbxKvOBB65KRQ1h8vLy0uah6gxEzUIo+u9XMXMSbHOIcGO7EZkSzUAQlRRFEVUDlPi/rHHHkNRURGuu+46/O///q/QHRYvXoxf/OIXMJvNOHjwIDZs2ACfzycEyUAggMrKSuHTbLfbEQ6HsX37duj1epSXlwMAjhw5gmAwiLKyMqjVaoTD4SQLMLVaLQoEQqEQmpqa0NDQII7Cd1aAJ8dIaoPBdNEdETFdAqLRaBSfI7vdLpp+Ul8GKtQwGAzIzs5GdnY29Ho9QqEQ7rzzTjzyyCMizohIJIL77rsPjY2NsNvtovq0oKAAb731FhRFwYwZM/CLX/wCy5YtS7JDoWrphoYGAMCUKVNQUFCAeDyOzZs3o6qqCnl5eZgyZQpsNhsSiQS+/vpr6PV6IUxSA0pa86aOL110ZvfXHRtAoM0K0OFwiBslePoa2dMYSJ536aSSTFNTE8aOHQsAePbZZ7u8dlZVVeEXv/gF/H4/RowYgXHjxgndkxIeFBehUEj8rejUC/WwATq/3qRiMBhQXFws5pimpiZhIQNA2FnKDUzTKTT3aAWl1Wpx66234qGHHsKSJUswc+ZMPPTQQ0n3Oe2007Br1y7RRf7NN9/EN7/5zV4ZNMMw3ePzzz8HAOGP25uQyPz2229n3Eamv8jkRRhw7KihbJMhV0uEQiF4PB7EYjG4XC5RlaFSqXDw4EHs3r0bLS0tSCQSwpfX7Xajvr4eNTU1sNls+NGPfoQrrrgCKpUKO3bsENU7qT7MJBjL1gvkb6fX65MEZWoI2FnjFBLNyauPNiHk/5tp4oDRaMTLL7+MLVu24Cc/+Um6h3NSkNBMVej0NVWiBoNBhEIhIfKo1WrRMdtmsyE3NxfZ2dkoKyvD+PHjMWbMGEybNg1OpxOHDh0SizSz2Qy1Wo2cnByxgSe/w8EoNGd6BWJfQCKCz+dDc3Mz9uzZg3feeQdvvPEG1q9fjy1btohklcPhwMSJEzFhwgQMHz4cVqu1242VmMyG5nO5iZbs1UvXJIoXuqWKyDT/UCNbWSimBmKUxIxGo3C73R0KzHJjt0zlZKrL0rkmIVLFNprH5ZMqNL+XlZVh8+bNwo5x4cKFuOWWW3DHHXfgxhtvxDe+8Q3Y7XY0NjbiwQcfxKpVqzB27Fh85zvfAQCsX79eJDCAtj1sOBwWp63y8vIAtMUIiUSplWyJRAIulwu1tbUiZiwWC4qKimA2m8X94vG4aBhIidZUQT3TYyudyO+TPAcAx6rFNRoNsrKyoFKpMH78eABtf/fS0lK0trbisccew/jx4zFhwgR8+eWXuO+++3DTTTfhX//6FyZNmoTRo0dDo9GgqakJO3bswIEDB5CXl4eysjLE43HRLP2UU04B0NZjyuFwoKSkBABENWtubi6AtspEOZ7NZjNyc3OThGO/34+WlhYhOKeKSnIygpJs6aa7vUTSJSCSgKdWq2GxWGCxWERs2Gw2WK1WOJ1OmM1mZGdni68B4KWXXsKPfvQjbNq0SRTb3Hvvvdi2bRsMBoOoKD7vvPPwwQcfAADOOeccXHrppaIQQmbMmDEAgN27d6OpqQlqtRqnn346SktLoSgKtm7dirq6Ouj1eowfPx5msxmxWAy1tbVCMAwGg0mVqJkQA0Dndn/dsQEE0mcFKDdnpX9p70nXeKvVKj7LiUQCdrsdFosFe/fuxY033oiNGzcmJX18Ph82b96M++67D9deey3279+PrKws3H777YhEIvjXv/4FoK1oRqPRiGuryWRq588fCoXEde9EiiVUKhVyc3PhcDgAtFVJG43GJKFZfk6yBkkHPV6hX3755bj55puhKAqeffbZdkd/tFotfve732Hx4sVIJBL45S9/KbLIDMP0PcFgUPhP9UUl8ymnnCJOLHzwwQdYsmRJr79GppPJDd1oQ5569JiEulAoJI7pNDc3IxQKQaPRoLm5GdXV1QDaFvUejwd79uzp8CK1c+dOvPfee3jwwQcxcuRIHDhwIMkmAzh2oZMb32i1WiESk1/h8S6yJDRQZVuqsEgCAW/e+ge5mYqcNJCTGjIk/tN9tVotIpEI/H6/6Awue7ubzWYMGzZMxK3D4UBra6uIGYPBkJSdHwiiUFdQBeL111+Pc889F1deeeWArXjvDrSxI4H566+/xvr167F169akWEgkErDZbDjllFMwefJklJSUwGKxsMA8SJBP2QAQR4U72xSR+EinXsiWgIRlGaPRKASlcDgsxGm5oSjQXmAeCFWmJ1NdlolNZum9oPeVvgaA0tJS/PWvfwUA3HLLLR2uNSORCF5//XX89a9/xaZNm/D0009j+fLleOmll+Dz+VBfXy8qTu12O/bv3494PA673Y6cnBwkEgkcOHAAkUgEer1eCM+KoiAQCIjrDwBRHUmberofXdMoKWK325P2xpkkMmdq0y56z9Vqtaj4SxWZFUVBQUEBXC4XioqKoNFoUFdXh6uuugr33nsvNm/ejHA4jD/96U/C+qAjiouLkZubC5fLhS1btmD48OG45JJL8K9//Qtbt27FKaecgvLychw6dAgVFRWYPHkyqqur4Xa7RRV1c3MzotEoWlpakjQOnU6H7OxscWqQEmKJRAJ+vx9+vx8WiyUpCUEe8JQES7elSnd7iaxYsUJ87/F4+k1o1ul0ogmgyWRCOByGxWKBoijCA5n2QPSZpErhQCCA2tpa3HXXXSguLhanNvV6Pb7xjW/g888/x9ixY4UFweTJk7Fo0SJ4vV5s3boVR48eFa9TVFSE0tJSjBgxQpzEWrZsGdRqNU455RRoNBocOnQIO3bsEA1Cy8rKsHfvXjQ1NYmqeEqw0PyQKQUUZPc3adIkbN++Hffee+9xf56K7DXdn9DfUa5elpvPA8eukZR82Lt3L5YtW4Y333wTBw8exKpVq6BWq5GdnZ3UXJQ4/fTTcdNNN6G5uRm///3v0djYCJPJhCVLlqCxsVEI6nJfOvoc0Ynh1OtEd1CpVMjJyRF+8i0tLSgoKBAnJjweD5xOp/gM0Gekv9c2PV6l33jjjQDaSrU7++NceOGFuPDCC3v6EgzDnATbt29HPB4XHnK9jVqtxre+9S08/PDDeO6554akyJzJizDKmtIGXq7gikaj8Hg8SCQSaG1tRSgUgsFgQGVlJVpaWqDVamE2m7F+/XqxsRo1ahRmzZqFkSNHwmq1orGxEX//+99RV1eHV155RVQV0KaWRGO5+kT2JSRPOxK9O2rcRRYeHS22aMEo+wMz/Ueq0Awkv8d0H+DYYpner1gsJoRnsjuJxWJQq9WiiqS5ubnDI2QkPKtUKuEpTvE0kI8hb926Fdddd11SBeKMGTOS7kObVSIdFYi9QSKRQCAQEE23tm3bho8++gh79uxJSh5kZWWhvLwcp59+OqZMmYKioiKYTCYWmAcJssBMt1ShWP48k2BDR0B9Ph8CgUBS9WNWVhZ0Oh38fj9CoRCqq6sxevRocb2hjR0lu+RTMwNFYAZOrrosncIQkVqlKieSKQZIZKypqUEsFsO0adM6XWfq9XpcfPHFyMnJwV133YWXX34ZV199NbKzs1FfX4/a2lpEo1FotVoYjUYcOnQIADBhwgQAbY2afD4fNBoNRo0aJa5TlIAH2mImOzsbZrO5XVz6fL6kSmmr1dpub5wpXrtAZiY1ZS9m+vsDx4RnWlPKlhSxWAx5eXmor68Xp+gURcGqVavw7rvvAgCWLl2KmTNnIhgMorq6Gl999RW++uor1NTUoKamBqeeeipisRgOHz6MXbt2Ye7cudiwYQO2b9+OSZMm4dChQ3C5XMJKheYbSkbU19ejqakJJpMpqaqdxmoymWAymURi1e/3i4RELBZDdnZ20im+TKE7ImK6BETCbDaLdWB2drbYSwDHGr0lEgk4nU60tLSIQgfybKZTmUDbaanbb78d69evB9B2AnjHjh0AgNmzZ8Pn8+G5555LOrnb2NiI/fv3J41JbnquUqkwdepUVFdXIxgMwufzCSsPKrAIhULCyoOEcgDiBGq6r0nf/e538b3vfQ9PPPEErrnmGvzxj3/E+eef3+7n6Z4/UqHPEu0PyL5IFpgVRYHNZoPX68Xw4cNx+PBh7N69GwsXLkQ8HsemTZvQ3NyM5uZm8byFhYU49dRTMXXqVASDQTz22GOiMMLhcOCaa65BJBLB22+/DUVRMGrUKKjVatTV1QEAioqKEAgExHPm5eVBURR4PB54vV4x3+n1enEKlOxhZFQqFZxOJwKBgJj77HY7WltbhQ0YNRukuTW1d0Bf06OV+qpVq/DGG29g8+bNOPvss/HYY4/hhhtu6O2xMQxzEpBVxqxZs/rsInX55Zfj4Ycfxr/+9S/8+c9/FhfHoUKmLsJosy7bZNACV1EUsQkNh8MIBAIwGAxobm5GS0sLHA4HtFot3nnnHQBtmdorr7wSo0ePbvc6oVAIf/7zn1FZWYn8/HwAx454yf55JAoajUax0KKGS1RhFggEkjZvZIsgo1arxU32dmbSAwnNtJBP9UAj6P2i+1BFAfmqajSapA247EWXetQYgGjoI4tLwDHhaCDGxUCtQDxRqLFSS0sLqqqqsHHjRnzyySdJPTwAwOl0YtasWZg9ezYmTZqEwsJC4bvIDHxkG6fUamYSfmneIGsn+ebxeBAMBtHc3Ay32y3mgezsbJxzzjlQFAV79uyB1+sFcMxLN5FIiGSWRqMZkAIzcHLVZekWhjqioyaAlFA7cuQIgDabjK6YP3++qFRsaGgQz0EJuWHDhokGbtnZ2cjLy0NtbS3cbjdUKhVGjx4Ns9kMv9+fZIPgcDiEMCQTjUaF/zIAISimxpL8/8uEOMvEpCa973Kimk69AcmViXICm8b12WefiZ+9++670Gq1uOeeezq06nS5XHjxxRfxyCOP4PPPP8fFF18skp0///nPAbTZYMhCVUfvm8PhgM/ng8/nQ1VVFXJzc0VCS06cW61WYR1mNBoRCoXQ2toq/sYk0Ml/g3ST6SIi0PZ3os8rCYZUvUkCG61PHQ6HsERwu90IhUK46KKLMGrUKESjUcyaNQtWq1XYpdTX1ydZte3YsQOhUAg5OTk47bTTxMnPqqoq1NXVIR6PQ6PRtLOnJH954FiihHx0g8EgotGoiIlAICCq29MlDKZCdn8d0dnPMwH5M6TX6xGNRqHT6aDX6xGLxWA0GhEIBJCdnS3muTFjxghvdqAtuUAnFGhOcrlcqKurS7L9MBqNOOOMMzB16lTRwwgAcnJyMH36dGzbtk2cwLBYLOL3WVlZMBgMqK6ubufBTWsdl8sl4sVgMIi1EZ2KAI6dFFapVLBarXC5XAiHw7DZbNDpdKKQJ+NF5kcffRS/+93v8MEHH2DatGm4+eabcf/99+OHP/whN2BhmAyiL/2YidmzZ2P06NHYv38/Xn75ZVxxxRV99lqZSKYuwmihKnflJn9D8qZUqVQik1pUVIStW7dCq9VixowZeOCBB6BSqXDjjTdiyZIlnS546SJrsViESJRqoWAymYRgZrFYROWyy+USVUHkRUZWCLJ4KPv+ZsLCm0mG3mu1Wp20kZZFYdqg0f0o6UCicGqDL3peuTKZfkfdl1UqFXw+X1IyQt6YDrRYGSgViD2FNkxUvbxv3z6sX78en3/+eTtBPT8/H6effjpOP/10jB49Gvn5+WJzMNDe194kU4+49wT5My8LzHq9PimRoChKUkM/n88Hj8eDhoaGJBGQxKDW1lZ8/fXXolEXVUQ2NjYmJTvpaPpAFJiBgVtd1lkiUr6+p/6OKprJg/J4BAIBcT2Ix+OiqpDmmKKiIrFuGTFiBILBIOrr6wEA5eXlsFqtIgkGtAnycl8AGbL7Adri7Hg+8XKFfibEWqYnNeXrPiWkCdlKgoQ5lUqF//znP/D7/WhsbIRarcb999+PxYsXd/j8WVlZ+MEPfoCCggKsWrUKn376KaxWK0KhkDgSr1arxd9F9lQFkkWdoqIi1NTUiNfuCI1Gg6KiIlGIYzQaYbFYxIkLo9EoxFEAGZFMPZ64mElQFXA8HhfrKLPZDI/HA5vNhnA4jKysLLGv8Pl8cDqdaGhowIcffogJEyZg3rx54vlmzpyJDz74AJ9++inOOussHD16FLt27RKV7FOnThVFNyUlJRg3bpy4jlksFtFQ1ufzoaamRjQvpZNYQHICh9Y2LpcLPp8Pubm5MBgMopntQLw+ZQK0xyDxn6wZqXk90DYPtLa2YtiwYaivr0c0GsX48eMRjUbx9ddfi9MOHWG1WjF69GiUlJRAp9OhqqoKb7zxhvj9mDFjUFBQgO3btwNou36NGDECFRUVwpbJ4XCguroaiqIIWw6z2ZzU4yYQCIiK9876X+Xk5CSdKAWO7Yfk7/u7Mv6EZrG33noLN9xwA5599lnMnj0bAPDTn/4UDzzwAJ555hlce+21fTJIhmFOnC1btgDoW5FZpVLhiiuuwJ133omnnnpqyInMmboIS60QA9o3kTCbzeJoGW3Epk2bhs2bNwNoszs6//zzO32N/fv3i2pnrVYLRVGQn58PlUoljrTrdDohMssX0dbWVtFBnrwNQ6GQyLTKAjMvsAYOJAx3hHwknqo0Uo/KU9x2BFVBJxIJeL1e0UjQ5/MJGxhZ4B5ocTNQKxC7A1WitrS0oLq6Gtu3b8dHH32Effv2tZuXysrKcOqpp+KUU07BiBEjkJOTA71eD4PBMKDez74gE4+49wT5lA3NAVSpI88f5IdLArPL5UJrayuqqqpEIy2VSoUpU6bg3HPPxSeffIKDBw+KhkpA28ZusAnMwMCtLkuF3m8SAfR6vRB0DQYDAoGAEEO3bNnSqWhIvPTSS1AUBeXl5Th48CAAiN4hQNsJiaamJmi1WhQUFAjbDGoMFgwGhcBss9mSrAwISnyQ+K3X62G1WjuNIzkRmikJ0ExMaspVw/R1PB4XcUDXinA4LCryqIqZrLvoPbn22mu7jBXg2P6osbFRWF3Q+09CJH1NR9KpUbU87oKCAiEmElT57vf7EY1GUVdXl3QikIQuKgqh/9/x1lFMe2heJ/sR6uFhNpsRCASE5zqd5IzH4wgGgygqKkJtbS0effRRZGVlYezYsQCASZMmiTmD3ptdu3bhtNNOAwDx+G3bton46Ir8/HzMmDEDgUAATU1NaGhoQCKRgMViEcIzVTPX1dWhsLBQFOT4fL7jzi9M5+h0OtHjwWQyIRAICJu+eDwOnU4Hu90u5jay3VGpVDjrrLOg0+kQCoVEYRatRSkBdfToUezdu1e8nkajwfDhw4XXO61DCgoK4HQ6xTVJp9PBZrOJucZkMiE/Pz8puWQwGERlfjQaFeMgC0naH9tsNjEfUuNZ4Ni1Jp02Td0Wmbdu3YpLLrkE999/Py6++GLxc7vdjp/+9KdYs2YNrr766rQb1TMM01alQBNfX4rMAHDVVVfhrrvuwnvvvYd9+/Zh3Lhxffp6TNfIYl3qv3KTEfo5HdOxWCziokdduztiy5YtuP/++xGNRjF27Fg0NDSIRjdA23VBpVIhPz9fLJzp2kC+hi0tLaKqWr5uyGMfqALAUEP20ezMI1uuVKOjXrQAovf8eBsr2a+TxKdIJAKLxSKq11I9nAdS/GR6BWJPofeqoaEBBw4cwKeffoqNGzeK6kFCrVZj7NixmDFjBiZPnozi4mJkZ2fDYrG0Ex+HKpl4xP1EkRu3yp6THQnMwWAQfr9fNLYhb926urokj8T6+nqUlZWJI650DFa25SEhgpr7kVXGQJkfBgvye6zRaJL8+oE2AY9OqpC4SLz99tsYO3Ysli1b1mH/hnXr1uHpp58GAHz729/Ge++9BwAYPnw46urqYLfbxVqHGsu6XC4AbT6biqKI9Y/Vam0nMCcSCVFNRmsUo9HYzqNZhprV0v8xEypUgcxMasp9HqLRqBAJaX3o8/mEEEfrDYqPWCwmBDkAuPLKK7v1mhs3bgQA5ObmCu9eOspeWlqKnTt3AmhLVFBspAry4XA46eg84fF4xEktoL3HbqoNiFzFzPPSiUEWOSTculwu6PV6BINBaLVaWCwW8Vm02WyiN01+fj4aGhrw29/+Ft/73vewcOFCaDQanHHGGXjxxRfR3NyMrKwsYT8AtCVlNm3aJCreqcgmNWGu1Wphs9mQk5MDo9GIr776KskSwWKxYMyYMeK9JiufUCiEmpoa5OTkiP4DdPKA10EnhrwGIKtFKmTS6/XCqoYsG8vKyhAIBHDkyBGRgASO7Vk6wmQyoaSkBHl5eYjH40kezjabDXl5eQiFQuJnlOgm4To7O1tYudBeJtU+jPrZmM1m8T1Bthly01x6bXl/T4nc/qTbV7tTTjmlw+M0AHD33Xfj7rvv7rVBMQxzcmzZsgWKoqC0tBTDhg3r09caMWIEli5ditdeew0PP/wwHnrooT59PaZrOspckk0BZTz9fj90Op04SgYAX331FcaMGYPdu3fj6aefhtPpxOTJk6HRaOD1erFjxw68+eab2LZtG4C2RRFtsIqKimA0GmG1WmEwGJKaogAQ2XoAYoMfiUSSRGW1Wp1xFT9M18iLL1ocHa8Jn2ydIXs5Ex3FL4nMFMfk5+z1esURVFloHmiJikw9FXEyUNV5bW0tdu3ahY8++ghffPGFqKAjDAYDJk6ciEmTJmHMmDEYNmwYcnNzRYVGpggz6SbTj7h3hZyMou+BNoGA7HaAtvkhFArB5/PB7/ejqakJR44cQW1tLY4cOSKuEdSoBwB27NiBeDyO3NxcBIPBpKQnCcwUSywwpxdKLpLfruyNbTab4fP5hJ8qNUIaN24c9u3bh4ceegjvvfcezjzzTJSWliIej+PgwYN4//33UVlZCQA477zzsHfvXvh8PuTn54uTWlOmTBEVzbJoSNWEgUBA+OjKAjOdxJAFb7VaDbPZfFwRlsRSun8m2f1kalKT/FITiYTwrKXKXrIjkI+My9YolMhWFAVHjx7tsDpb5uOPP8YDDzwgXhdoK66ora0VwlMikUBeXh7sdjv27NkDAKL/CNBm11FVVSXW1sOGDUM0GoXb7UYwGBTrEZVKhYKCgiQvabkyWhYpuVjvxKGkFFV72mw2eDwe4dfscDhExX40GoXdbkdLSwvUajWGDRuGo0eP4umnn8arr76KUaNGieuKxWKB2WyGy+USYh31A9DpdDj99NNFX4DU03p0kxuzqVQqOBwO5OXltUti6fV6FBcXo7a2FpFIRCTGyH7D4/Ec15KH6RhKNiiKIhLQZMtIySmyUwmFQrBYLFi8eDFqa2tRXV0Nv9+flAzNyspCbm4uCgoKUFBQgGg0iiNHjuDo0aPiNZ1Opzj9IPsmy+tZs9kMu92ORCKBlpaWTkXs1ORFV+h0OhEnkUhEPD4dlsYcqQwzCNmwYQMAJPlM9SU//elP8dprr+HJJ5/EPffc0+XijulbUqtG4/E4tFqt8GU2GAwIh8NwOp04evSoWIw1NzejvLwc+fn5qKurw6233ioujHQMkZ6zuLgYeXl5YpGWnZ0No9GI7Oxs4T9XV1cHRVGg1+ths9nE4+WMq1qtbtfwQPZ0ZjKf1CNZlDjoKnMui83y4zp6DAnXJEqr1WqxCZSPE9JxMhkWlPof8l2vrKzE559/jo8//hh79+5tt2B2OByYOHEiRo8eLeae3NxcOBwOYbvDtJGJR9y7iyyiqFSqpCo+EvHkhJPH44HP50NTUxMOHjyIqqoqcfQ0NzcXl156KXbt2oXDhw/DarUiHA5Dp9MJb0K5Al6uYGYLpvRCcz41fKUjyPF4HGazGbFYDFarFYlEAsFgUCSqDQYDxowZg0OHDmH37t3YvXt3u+c2mUxYuHAhXC6XaCQ8bNgw4cFqNBoRjUZhsViQl5cnTvs5nU4Ax6piUysGZWsMrVYrEhadxRB569J1KBOTGpma1KRGedQQmsR/EpxJKKH1Bq0do9Eo1Gq1SL796Ec/wvXXX49FixYhLy9PrFEaGhqwbds2vPbaa9i0aROANl9WtVqN3NxcYX9w+umnixibNWsWDhw4gEgkAqPRiLy8PABtcUFNJI1GI0pLS8W6NSsrK6mKneYiqix0u91IJBLQaDSikXGqhypzYhiNRmExYTQa4fV6k5qk5ebmoqmpSVw3yboPOJZ0crlc2Lp1K4C2tcmcOXPwz3/+E8CxSnNKchQXF+Pw4cNJyQH5My4LzdR/hIpnWlpa4Pf7YbPZ4HA4xOOMRiPKysrQ2Ngo+g9QJbZWq4XH4xHXNqZ7UIKPPos6nU7sS+mkXTwex7Bhw9Dc3Ayfz4cjR47A4XBg8eLF4npAjafJi7+5uRk7d+4Uz0vFVoWFhWhpaRFNh+UkKtlJGo3GpMZ9BNlFyUVWcm8l+loWpKmymby9ac9Pwjo9bzrmFV69M8wghETmBQsW9MvrnX322ZgwYQL27NmDBx98EKtWreqX12U6Rhbj5A0ddb22WCyIRqMwGAziSGJxcTEOHDiAQ4cOYeTIkRg2bBgqKipE8wmg7Zig1WpFTk6OqFYuLCyEzWaD2WwW/qnZ2dliw2Y0GkUFh6IoCIfD4uKb6r9MZNqGjDk+HR0lkyuPu3ovUxtWdERqU0CqXCIvZqp+U6lUYoMqC99arZaPGvYT0WgUzc3N2LdvHzZt2oRNmzYlHT0kioqKMG7cOBQWFopTNwUFBcjJyYHFYuHNdgqZeMS9O8gNreiaJB8hl5OMkUgEoVAIXq9XWKxUVlaipaUFkUgE06ZNw/Lly7Fnzx58/PHHACCSnUVFRdDr9eJEDQnMVD0kN5Fl0gdVMMsbfRIPqQFaNBoVlhYFBQVobGyESqXCmDFjhHgXCASgVqths9lE5XNdXR2ANgEpKysLPp8PJpMJU6dOFX6YEyZMENWIKpUKTqdT2LMAyaeuotGo+LnZbIbRaDxu/NDYSXiShSWme1AFnqIowjIl9dodj8dht9vhdruRm5uLxsbGJGG3paUFq1evxurVq2E0GkUFozzXqFQqIfI5HA5YrVbEYjGMHDkSTU1NUBQFY8aMgUajEQmucePGQa1Ww+12o76+XlRHygKz/PwU68FgENFoFOFwOKkqMjs7W6yLAV77ngxykiEej8PhcMDlconTETqdTjRVo/dApVKJ0xIOhwM5OTmwWq2wWCywWq145plnEI1GkZ2dLax1IpGIqC6moh2j0XhC6xVKrIZCIXg8HhQVFYmEularRWFhIfx+PxoaGhCLxeB2u2EymWAymYT39PFsephk6HNIPszU/4e8moPBIEKhEHJycuB0OlFdXQ232y0sUY6HXq9HaWkpSkpK4Pf7hZc3VdfL4jIJ3PJ6iNZs3TnpktpQPbWnDc2bBCVx01WswSIzwwwyotGoyNDPnz+/X15TpVLhjjvuwPe+9z088MADuOGGG0R1CNP/yB5UtJGLRCIwGAziAkQLdKfTKTZE5eXlOHr0KPx+PwwGAyZNmiSqn8nHEmgTMpxOJ7Kzs6HT6ZCVlSWOnFJFqVqthtPpFFUDJFbLdhg0Pro/LcpZDBxYyF2cZWgh3xtioVwVQkkTACJxoiiKqAwBICpaZHgD17fQZrm+vh67d+/Gpk2b8Pnnn4sNOqHVajFq1CgMHz4cOTk5KC0tRWFhofC1I985JplMPeLeFXICiqq9ZKEROCZEB4NBeDweHD16FAcOHMD+/fvR2tqKuro6eDweHDlyBHPnzsU//vEPAG1H1+12O/Ly8pCVlSU6tms0mnYCc6qXIZMeaOMbDodF8ps2yRaLBYlEQohvhYWFaGhogE6nQ2trq2gibDQakyr5adNuNpthsViEhZLT6RQVhwAwatQo2Gw27N+/HwBEsyW5spSSNHRNAdrWPLL43BFUpUrxThVpzIlB84O8VqSTUSQeut1uFBUVCQsBt9sNm80mxB2fzwefzyeSVrLFBvl+W61W6HQ6FBQUiCRHSUmJWC/n5+dj+PDhIlZGjBgBh8OB2tpa4XVvsVhQXFwMoK2ymTx0U5Pcqf8/k8kEs9ksGtLRz8m2g+kZer0+ycvbarXC5/OJn1GMAMeKG3Q6nbC0iMfj7cRFh8Mh5gSHwyGaxVERhclkgkajgd1uT1rrdrTWpBM1ZKPR0tKCcDiM2tpalJSUiIKNyspKtLa2iqQpJcGi0ag4uZNIJLghYDchMZcShkajEcFgUPxtZaFZrVZjwoQJCIfDaG5uhtfrTTodqdFoYLFYhG2G0+kU1c9UXEXiMlUY02ed5gRKTNDnnaw7UoVjubjmRKCEeroTnHz1Y5hBxrZt2xAIBOB0OjFhwoR+e91LL70U9913H3bu3InVq1fjt7/9bb+9NpMMXWDkCyMtnOlIGdB2pM/j8ST5FpaXl8Pr9cLn84nFMomItDCnY+xU2azX65OOJ5PgrCgKXC6X2CzIYyFRmZ6bNha8KRuYUMIgtaJZtrfozddKJBJiI0pCczweRzgcFo2j5AZBBAvNfQMJMjU1Ndi5cye2bNmC/fv3txOYs7OzMWrUKBQWFsJut6OkpATDhw9HSUkJ8vPzu6wUHMpk6hH34yHPCfJxYqoyps9sNBpFIBAQFYIVFRU4ePAgjh49itraWvE5DoVCeOaZZxCJRGC321FQUACHw4Fhw4aJhKcsMFNyNJ0d1pn2yEeYqTkaVZuSXYbT6RQNj1pbW0XT4EAggHA4nPSekhhD1WAWiwUOhwN6vR4+nw9qtRpjxoyB0WjEvn37ALQJhIWFhfB4PKJKkY6uK4oCr9crTsyYzebj/n9Sq8jSdTx5sKDRaMQ13WAwIBaLiapmi8UiRMCioiLU1taiqKgIjY2NYl4JBoPtmmjRGlOlUol1K8WMRqNBWVmZELIp6Uke3sOHD4fdbsfBgwfFXEQnbuSmcKnQ+la+0VpJfgyNm699Jw/9HalYRj4B5/f7hT0KefW73W4hSFOSgOKFriE0p5BtBSV4KSGeWlQlX+tkqyhqOKhWq0XFdHV1NcLhMBobGzFs2DC0traiqakJwDGrHrLMANqspKiYwuv1ihN8zPGRfbvp8xYKhcS/ZrNZnIJsamqCSqVCbm6umBdkG0c6MeHxeFBXV5eUuDCZTCK+zGZzUvNPqkanYhnZ0qKn/ye6yQ0CMyUeeDfPMIMM2Y+5P6t21Go11qxZg/PPPx+///3vcfnll2P69On99vpMMrSYjkajwl6AFuvyxTI7Oxter1ccO/V4PCIrnwoJzXRslKoGaJFOWXoASR7OwDHhW7ZPoIsiLea4ymxgQ4ubVGFXbspzMpBgJYtWVAFJjSTD4XBS1ZksNA+0ZoADhXg8Dp/PJ/xSv/jiC7hcrqTGdAaDAWVlZSgqKhLVymVlZRg+fDhKS0uRk5PD78sgRN5ApXZEp6Y04XAYwWAQra2tqKmpEQJzVVWVSH4S+fn5IpFUVlYGi8WCkpISaLVaZGVlJXnmarXapKZaLDRnFrKFl16vF0JzMBgUaxE6LUXrE7/fD5PJ1K6PAyUWqGqMxGaNRiOOwHu9XmHVlZ2djfz8fDQ0NIjnItGHTl3RdcZmsx13bZIqMFOynek5tD4lywy/3y/WF4qiCH9dh8MBRVFQV1eHgoIChEIhtLS0IBQKwel0JonM9LwUT0CbIOl0OpNOPIwYMQIGg0EkJ8jTnvya9Xo97Ha7qHolaL6hcdJYU+ed1JMdlAjja1/vYTAYoNFo4PP5hJUFxRA1AjQYDPB6veJUBVWiy1XoWq0WOp1OFNHo9fp21xmHw5H0uiQyytA1iNao1BTZbDajoKAANTU1ojcOoVKpUF5ejrq6OoRCIbjdbvFaXq9X7NHkniTM8aEeQ5FIRPgsA23ibygUEuIvzRsej0ecWjgeVLEsn5qipACdwqDrSKqlBYAkoVgWjlP3TZ19namwyMwwg4z3338fQP/5McssWbIE3/nOd/Diiy/iuuuuw6effppx/pBDCVq4UiWxfCGj34dCITgcDuFxSJtyeXFOF1BqXkEeUlR5LC+SqVqEKjZooS03dZKrl+k5B8IFM5UdO3bghhtuQDQaxY033ogrr7wS69atw3333Yfc3Fz87W9/Q3Z2Nq677jpUVFTg7LPPxq9//et0D7tPodhKFZo7a+h3IsgCM8UWfU8+jtQgg3xfOztuxoJm7xCNRuF2u3Ho0CFUVFRg7969oiqwsLAQarUaZrMZ+fn5yMnJER21hw8fjrKyMpSUlIjqU2ZwIVcxy/MCXVvoqLjf70drayuOHDmCiooK7N+/H0eOHEE4HIZKpcLIkSNx4MABqFQqcSS5rKxMCM1arRbZ2dlJG3+yPZDnCP68Zx5U5RWPx8Xag0QAEprp9JTb7RY9H8hfE0DSWgM4Zocgb/rJLoHmH0VR0NzcDACislWn08HlconrBVUbUuK8IzqyO2CB+eShdWQsFhON3EKhkGgEqNVqhW2Gw+GAzWbDoUOHoNFoYDabhWAkzzcAktabcvWwSqXCsGHDYLPZhD2H1WqFzWZLalJNlatyLxGDwdCuIWFH6w7Zsic1Zpneh2KECl4cDocQmik+KKlFsZX6vqXOKeSDTM3brFarWLtQlWpHyCc1ZX9walBKTQiPHj2KwsJCkXzzeDyYNGkSjhw5gsbGRng8HuTm5iIejwt7uGg0KsbPdI3sz0zrhHA4DLPZLHzT5etSqnWFvI9N/SxTtbS8nqW4oCp2eRx0umGwzgMsMjPMICIYDOKjjz4CAJx77rlpGcMf//hHvP/++9i2bRtuuOEGPPLII4N2Ah0I0LHUeDyOWCyWVMERiUTExY82SJTFpWw+XUhJTKaLKi2Y6Hv5JgvL8gZfztSScD2QN2S/+c1v8Pe//x0lJSWYN28errzySvzhD3/AJ598gvfeew9r167FvHnzkJeXh8cffxyXXXYZampqhIffYEUWgOWjit2hMx8y+We0UQuFQknVyfKmNBwOC7ETOFaRIi8SWWjuOeQ/2traiqqqKnFL9b4sLy9HVlYWbDYbdDod8vLyUFZWhtLSUhQUFMBisQzoOYDpHPnzD7SdKqCfRSIRRCIR+P1+NDU1obKyEhUVFaioqEBtbS0URUF2djZ+/vOf4+GHHwbQ5q9rNBpRUlIiKuFJdNTpdEJgpmuLfB0CwHGWoeh0OrHhJ4GPRMHs7Gy43W6oVCrRP4LWMqnJAznhLSMnHqgRMj2Ofk4xCRyzLjiefQGdoElt4MTXk96D1qNU4U7WKiQKkW1bc3MzNBoNRo8ejXg8jqNHjyZd+ztDpVKJaxPZIgBt8WKxWMT39N7Kjbno1J5cSEGkCk+yAMX0L2RZoNfr4ff7YbVaYTQahZUGWSeQZy6tH+W9j7xPIbFZq9UKyxXg+AJzKlTlSmtYEprpBEV9fT1KSkpw6NAhNDc3IxAIoLCwEF6vV4wXgEieaLVaBINBPkHRTWTbDPLhJ09mOtGQmpw63nPRZz1VMJYbTcrWOLIv92CHRWaGGUSsX78eoVAIpaWlmDhxYlrGUFBQgGeffRZLlizBY489hsLCQtx9991DYkLNVCiLTkd16CJKi3iVSiUy53IjHnpsqpAsL6JTqzE6uiinisupthkDlT//+c/Iy8sTdhAulws5OTnQ6/WYP38+1q5dC5PJhHnz5gEA5s6di88++wwXXXRRu+cKh8NJC5HuHNHKZGRLlu4id13uDIo98uSUq8dogU0VCDK0WU1trDNUFnu9CXVGb21tRW1tLerr6+H3+xGPx8Ux4kQiAZPJhLy8PBiNRhgMBmRnZ6OgoEDYY7AoM3iRE4y0ySLxkCqv/H4/GhoacODAAezbtw8VFRXCHmPmzJlYuXIlnn/+edTV1UGn04lq+KysLBQXF8NkMolGTHS6Rj6lk9rFnWMtM6FrhbwmkUWArKwsRKNReL1esZbp6IQcrWPkk1Spp6Tk01OpHuGycAgg6VqUWqGaKirKAiTTO1CVMDXxJcsTeu/D4TD0ej0KCgrgdrsRDAahVqtRWFiI0tJSMddQ82p6zs6aTMuWCLRW1ev17b6n2CLk03uDuSpxIEM9ZKh62G63IxqNwmAwCIsM+fQmrS3l+YISWLSeoTiyWCwn3LCRqqEBiMaUDodDnIxwu92i6WkwGMTBgwfFY8mbnn6XnZ2NWCwmfIWZrqEqdppb6H0kT266HsinITuqYk79rMune1P3M2SnM5TmBxaZGWYQ8fbbbwNoq2JO50T2zW9+E7/73e/ws5/9DPfccw+qqqrw+9//HtnZ2eI+X3/9Nd544w28+eab2LNnD0wmE+bPn4///u//xrRp09I29sGM3GxHtreg6h76fUePk8Vm2StKrtLo6KigfN/BtgjPy8sDANx+++247LLL4PV6YbVaAbRtWHw+X4c/64jVq1fjrrvu6p+BZyiyV2FHopBcNUQislyJRgs6WiDS1w6HAy6XC4lEAqFQKEmgoA3iYInJviaRSCAYDMLtdqOhoUE04SIvwlAohKKiInHUmEQbh8OBgoICFBQUwG63H/cIOjOwkT2Q6WuqEiOB2ePxoKGhAV9//TX27t2L/fv3IxgMQqVS4eqrr8b3v/99bN26FS+++CKANusVq9WKoqIi5OfnIysrS1SlpTb4o+axqR3h+TOeuVDiG0BSQyYSAXQ6nbDJkCvkyc6rM/uB41kUyEKznKBMbV7bGbK4yLHVN5BYQz6mVqsVfr8fRqMxyQbF4XDAbrfD7/fD5/OJz/7xmknLAiG9j8Ax31ZKQMuJC1lkkk/z8fuf+cgV6STs0ilP6g8g2/CkFtjQ+y3bIlAc9nQ8ZL8RDAYRjUaFpYff70coFEJWVhYURRG/t1qtKCwsFHNUNBqF0WiEz+dDJBJhkfkEoPdQtrGgpBKdlknt5dDRiVx5j0vzgHwNkU8BDzVYZGaYQcRbb70FIH1WGTI333wzDAYDbrjhBjz11FN46aWXsGDBAuj1emzfvh2HDh1q95j9+/fjqaeewqpVq/DrX/+aF259AAnJZJEhXzSB9gIxPSb1Ipr63nR0IabHDhb/uYceegjPP/+8+P62227DF198gYaGBqxevRqtra3w+/0AAL/fD7vdLjZF9LOysrIOn3vlypVYsWKF+N7j8YiGM0MFOe462xgCSDomTRtDaqZClc0ARHVcKBRCfn4+WlpahOdaKiw0d00ikUAgEIDX64XH4xFHN2nuKCkpAQBRoUxNm5xOJ/Ly8oQn81BcbA8VKNkj2yVRRSFZZJD3JAnMBw4cQDQahcViwapVq3DqqaeisrIS99xzDxRFgdPphNPpRHl5uWjYZjKZYLFYkjb/cnOv1Aoi/mxnNlR1TCerqILVaDQKWwo6Gt4RtIbpKCFOyEnujtYjna1vUr/u7PFM30DVf3TdNpvN4mutVotwOIxIJAKVSgWbzQaLxSKSEXTrqCgidV6Qk1UUg6nVzrLPN7//AxOqIiYLlnA43O1TdzqdLqm6vTcwmUyiSSE1aNfr9fB4PEmJN2pW2NLSInymKUECHLOn4rjsPrRfoJ4usne73HxUvn9XyEnNoZ6AZJGZYQYJu3btwtdffw29Xo+zzjor3cMBAPz4xz/GpEmTcMMNN2DXrl148803xe90Oh0WLlyI888/H3PnzkVrayv++te/4qWXXsJdd92FyspKPPbYY8cVm5ieQUKzXPnZkQ9uR0eD5BvdP9V7M3XDN1i46aabcNNNN4nvX3rpJezatQt/+9vfALT5RzY2NiIUCmHDhg2YOXMmZs6ciTfeeANLly7FJ598gt/+9rcdPjcd9x7KqNVqUTlAHp0ARCPK1AW0/DUJz7L9RXZ2NpqamkSFZG5uLlpaWoQ1SWq8sxjVOVTB7Pf7xbFSaq6Yn58vklby31+n08FutyM3N1ccRea/7+CGPockLlNFUCgUQjQaRUtLC+rr67F//34hMMdiMRQUFGDNmjUoKSnBwYMHceutt8Lv98NsNqOoqAjl5eVwOp0oKSmBwWCA3W4Xx+nlzy0dcQWSG8wOJjpqOEt8+eWX+Na3voWysjIUFxfj73//expHemJQsyXyyTQajUnzCiUuOjol1VEFs1x1mHofeY3CnrmZD1UCUuUyNdujRKdsewB0z3+d4ooSVRQHqXOKLERxjAwe6L02GAxJfszyHEPzQ1+fWNDr9XA4HAgEAmLOczgc4tpJYrhcIKFWq4VdH3NykA8/JcnlOOjsRGXqnpjmHJ4jjsHqDcMMEv75z38CaKtidjgcaR7NMRYsWIAvv/wSn332Gb744gvE43FMmDABs2bNgs1mS7rvOeecg8cffxw//OEP8fTTT0Or1eLRRx/lSbsPkI8Bys2R5N93toFL5US9dwcLd955J4xGI84880wAwPvvv48VK1Zg4cKFMJlMeOmll+B0OvHUU0/hjDPOwOLFizutZGaOJTvkI8tqtTqpwZKMvAjU6XSiogmA8M3LysqCy+USi/fs7Gx4vV4Eg0FR/STDG8n2kM0IVXrQxstkMgnxRxZrEomEEAKtVqtolMMMbuTKQRKZUwXmhoYGHDp0CPv370dlZSVisRiGDx+OBx54ANnZ2di0aRNWr16NQCAAo9GI4cOHY+TIkaJhJG3GSSCgiiEg+To0GJOcREcNZ4ndu3fjrrvuSvrZQIHEPJrvSTykeOpsnSKLxbLtRSqpvSSYgYVGo4HZbBa+qfQ92fDIp/Fkkaijk3mp8wOJTPIcwuLy0KEzq8D+RKPRwGq1IhqNIhgMCpsg2RuY5jVqQKgoirDg6yu/38Ga1OwI2kuQnVtnhVdM91ApqX9BhmEGHIqiYOzYsdi/fz/+8Y9/4Hvf+166h3RSvPzyy/jOd76DRCKBW2+9FWvWrEn3kHoNj8cDh8MBt9sNu92e7uEwGcpQjZNUkRlo2wB0ZHFB0HH5SCQijhHS48gyw+/3iwWj3OBDpWprAEK+abLvXqbTHzFCTf7IF5DeB9kXWxaXqbkidXTnjufppb/mEdkmg5psUvVVLBZDc3MzXC4XDhw4gK+//hqHDh1Cc3MzcnNz8fDDD0Ov1+Oxxx7D66+/DqDtSHx5eTnGjBmDYcOGYfjw4aJ5pEajEZWMVDkvf2ZTG3MNNhobG0UF2/z58/Hpp5+K361atQqffvopotEoVq5cifPOO6/d4ztqMltaWppR1xq5svBk4GPLvUcmrUnIgkdOPlNiS15DpJ6yI6g6NdVqh5A9mZnuk0kxMpCh9RUVTnQl1Wk0Gthstj657n3nO9/B7373O5HU3LRpk/jdc889h2g0ekJJTY6RoQOXljDMIGDr1q3Yv38/TCYTLrjggnQP56S56KKL8Mgjj+C6667D//7v/yI/Pz/Jr5ZhmMGJfMSdBIZEIiGqOWR/RdpAUuWbXOlEQheJ1dTggwRnghbzqcfehmp1voyiKMJWRKVSwW63w+v1JlUuA0j6+5PwRw0ZeZM+NEi1TpJ9dL1eLyKRCKqrq4WdkMvlAgAsWbIE69atw6uvvio+lzk5OSgrK8PIkSNRUFCA4uJicSohVWCWvXcHc/WyTGrDWZmpU6fi+9//PoqLi3H22WfjnHPOaTeXDYQms1RZSJWosmDYkbUXkOyXPFRiYahCiQNKRhzPJoOuX6kn81IrnYFjnrscN0w6ocSpVqsV1fp0kxMntObqSyuyP//5zyKpmfoaX331FT799FM88sgjJ5TUZIYGLDIzzCDgkUceAQBceOGFsFqtaR5N73DttdeiqakJt956K37+85+jtLQUl1xySbqHxTBMP0ACgdzdmaqP6HckPkSjUVHFBBzrDk+Po+O0BoNBVEDJldJk+UBHtFM91oYqskhPvthGo7Hd/ehoobwx4k360EI+zks3Oi0QCATQ3NyMaDQqNph0wuDpp58Wz2EwGFBYWIiysjKUlJSgpKQE2dnZsFgsokqLBGaKs6EgKHbVcFbmm9/8pqgOGzlyJJqbm5Gfn590n4HUZPZ4czEfZWYoGUHXe7LJ6MhTtbPkhNw8lGOIyTRSLRz6m6GQ1GT6BhaZGWaA09raKhqP/fjHP07zaHqXX/ziF6iqqsKf/vQnXHnllSgqKsLcuXPTPSyGYfoBEi1lAVkWPkmAIJGYGo0BSDpmTRtRqoJWq9XieXQ6HYxGo7DZoNdKbWLHtCGLPXI1DVkU8N9r6JFaXUrVhQBEo65AIADgWPJo0qRJ2LNnD2KxGEwmE7KyspCfn4+SkhIUFBSgqKgIZrMZdrsdBoMhqYKZmjDJVcyDma4azspcc801uPfeezFixAgcOXIEubm57e4zWJrMDvb3nTkxaL0gIzeLlBPLtHYYCvMHw5wIQzmpyfQuQ7tMh2EGAU8++SQCgQAmT56MBQsWpHs4vYpKpcIf/vAHLFu2DOFwGBdeeCH27duX7mExDNOPdNbdO/UYtXy/VMGTNpRUEUIezACSfJxTn3soQ5Wjer0eRqMRJpMJRqNR3EwmE0wmE1tjnCQ7duzAvHnzcPrpp+OZZ54BAKxbtw6nnXYalixZgpaWFiiKgmuvvRbz58/PuKogEpdlawNK8kSjUQDHBMFx48aJn48ePRrjx4/HxIkTMXXqVEyZMgUTJ07EiBEjkJOTg9zcXNHA02w2C5F5qFce3nnnnaioqMCZZ56JRYsWIR6P4+c//zlaWlpw55134rrrrsPixYvxP//zP0P+NAYztKHrvlarFRXP5Lc8VOcPhjkeN910Ez755BNxCwQC2LVrlzgxLXPNNddg7969CIfDx01q2u32pBszNOBKZoYZwITDYfz+978HANx4442DcsGk0Wjwj3/8A4sXL8Znn32G8847D5s2bcKwYcPSPTSGYfoJ+di0XDkpH3+VvRfln6V+nUgkhJ+jXBlNFbl030QiMaSrc2mDzvQtv/nNb/D3v/9dNNa58sor8Yc//AGffPIJ3nvvPaxduxbz5s1DXl4eHn/8cVx22WWoqalBcXFxu+fKFP/D1GPpxcXFcLvdiMViGDt2LILBoPBQt9lsyM3NRXZ2NsxmMywWi0gCydYYXDHfxs6dO9v97He/+x0AwOl0YuPGjf09JIZhGGYQcuedd8JoNOLMM88EALz//vv45S9/idtuu00kNRVF4aQm0w4WmRlmALN27VpUVVWhuLgYV111VbqH02eYzWa89tprOOOMM3Dw4EFccMEF+PDDD2GxWNI9NIZh+plUIVkmtapSFqLpMbJQ3Vn3ednHcSgLWkzfk9pYx+VyIScnB3q9HvPnz8fatWthMpkwb948AMDcuXPx2Wef4aKLLmr3XJnmf0iNODUaDYYPH47m5mYYjUbk5eUJOwyLxQKz2Qyz2SzE5VRhmW78WWQYhmGY/oGTmkxP4ZQDwwxQvF4v7r33XgDAqlWrYDKZ0jyiviU/Px9vvfUWcnJysGXLFixfvrxDcagviUajmD17tvg+9UgzwzDpRa54pmOysmjV2U1u+EfVkgzTH6Q21vF6vaKBr8Vigc/n6/BnHbFy5Uq43W5xq6qq6vPxk/Ar/0veqAaDQaxN7HY7RowYgTFjxmDkyJEYPnw4CgsLkZeXh6ysLFgsFlgsFmHLIh9v56PtDMMwDMMwAwPeRTHMAOVXv/oVGhoaMHr0aFx77bXpHk6/MHbsWLz66qvQ6XR48cUX8ac//anfXrulpQXnnnsuDh48KH5GR5pvvPFGrF27tt/GwjDMiUECckcidKoQTQLz8SqmGaanPPTQQ5g3b564vfXWW7jvvvvQ0NCAm266CVarFX6/HwDg9/tht9s7/FlHpMP/sKPPluyhbrfbkZOTg6ysLGRlZSEnJwf5+fnIycmBw+GAzWYT4jJ5gLO4zDAM039wEQ3DML0Ji8wMMwD59NNP8fDDDwNoO2pLDayGAnPmzMEDDzwAAPj5z3+Ozz//vF9e1+/34/e//z0mTpwIAO2ONG/evLnDx4XDYXg8nqQbwzCZgSw4d1Tx3BcC15dffomRI0di0aJFuPzyywHwhm4o0VVjnezsbDQ2NiIUCmHDhg2YOXMmZs6ciY8//hgA8Mknn2DGjBnp/C+0g04CkK2FVquFwWAQzfqMRiOsVitsNpu4Wa1WWCwW0dxPbsrF1hgMwzD9AxfRMAzT27DIzDADjObmZnzve9+Doii4+uqrcfbZZ6d7SP3OT3/6U1x88cWIRqO49NJL4XK5+vw1S0tLMXXqVPF9d48vr169Gg6HQ9xKS0v7fKwMw/QMuSKzr9i9ezfuuusufPTRR/j73/8OgDd0Q5k777wTFRUVOPPMM7Fo0SLE43GsWLECCxcuxP3334+f/OQnWLBgAZqamnDGGWdg1KhRKCsrS/ewk1Cr1eJGCRqDwQCj0Sia+dHNbDaLqmUSlynBM9Sb+jEMw/Q3XETDMExvw43/GGYAEY1G8f3vfx9HjhzB6NGj8eCDD6Z7SGlBpVLhsccewxdffIHKykosX74c//rXv3rVR/Whhx7C888/L76/7bbbcN5554nvu3t8eeXKlVixYoX43uPxsNDMMEOYr776Cp9++ikeeeQRrFy5EmeccUa7Rm8dEQ6HEQ6Hxfe8oRscdNRYZ9myZVi2bFnSzx5//PH+GlKPkAVilUrVrmeCbKchW9IwDMMw6aO0tDRpX3IiRTSZ1GiWYZjMgSuZGWaAkEgk8IMf/AD//ve/YTKZ8NJLL8HhcKR7WGkjKysLzz//PPR6PV555RXcfffdvfr8qUeaZYEZ6PhIc0ekwyOTYZjMZerUqfjTn/6E119/Hb/+9a/hdrv5VAQz4JH9mHU6XdKNqpVlKwwWmBmGYfqfjvoCyJxIEU1/N5plGGZgwCIzwwwAotEorrnmGjz99NPQaDR44YUXkqwbhiqnnnoq/vKXvwAA7rrrrqTK4/4g9UgzMzRZvnw5PvroIwDtvXUVRcG1116L+fPnc8XHECV1Q6fX6zFhwgTY7XaMHDkSRqORN3TMoCG1YpkqnFlUZhiGST9cRMMwTF/DdhkMk+EcPXoUV1xxBd577z1oNBo89dRTOP/889M9rIzhmmuuwc6dO/H73/9eNNG69NJL++z1SEwEOj7SzAwtdu/ejXXr1mH58uUAjnnrvvfee1i7di3mzZuHvLw8PP7447jssstQU1OD4uLids/DVgiDl5tuugk33XST+P7b3/42xo0bhxEjRuDIkSPIy8vr9obOYDD017AZhmEYhhmCUBENnZxlGIY5EVhkZpgMJRKJ4Mknn8Ttt9+OxsZGmEwmPPfcc7jwwgvTPbSM4/7778fRo0fxj3/8A5dddhn+85//4O6774bFYkn30JhBzm9/+1tcddVVANo3S1m7di1MJhPmzZsHAJg7dy4+++wzXHTRRe2eh73thg533nknrrvuOiiKgv/5n/+BWq3mDR3DMAzDMGmDi2gYhuktWGRmmDSiKApCoRCCwSD8fj9qa2tRUVGBjRs34qWXXkJjYyMAYPLkyfjnP/8pOv8yyWi1Wjz99NPIycnBH//4R/zf//0fnnjiCVxxxRVYtGgRRo8ejaKiIlgsFhiNRj62y/QKW7ZswYgRI5BIJAB03Cyluw1UuEHk0GHKlCnYuHFj0s94Q8cwDMMwDMMwzECHRWaGSRNffvklzjzzTDQ3N3d6n8LCQtx666348Y9/DL1e34+jG3hoNBo89NBDOOecc/Df//3fOHjwIP74xz/ij3/8Y9L9TjnlFPznP/+BRqNJ00iZgcpDDz2U5PtdUVGBiooK/N///R+AjpulpP6srKysw+dmKwSGYRiGYRiGYRhmIMMiM8OkialTp6KpqSndwxh0LF26FEuXLk33MJhBiOyt6/P5cNppp+HCCy/EoUOHsG7dOnzyySftvHVnzpyJN954A0uXLsUnn3yC3/72t2n+XzAMwzAMwzAMwzBM76NSFEVJ9yAYhmGGCoqiwOv1wmazsW3HIOHOO+/EokWLsGjRIrzyyiu47777hLeu0+nEddddhz179mDx4sW47777uvWcHCdMV3CMMF3BMcJ0BccI0x04Tpiu4BhhuoJjZOjAIjPDMAzDMAzDMAzDMAzDMAzTY9TpHgDDMAzDMAzDMAzDMAzDMAwzcGGRmWEYhmEYhmEYhmEYhmEYhukxLDIzDMMwDMMwDMMwDMMwDMMwPUab7gEwzFCBzO6ZzCQTmhBwjGQ2mRAjAMdJJsMxwnSHTIgTjpHMhmOE6YpMiBGA4yST4RhhukMmxAnHSGZzojHCIjPD9BNerxcOhyPdw2A6we12w263p3UMHCOZTSbECMBxkslwjDDdIRPihGMks+EYYboiE2IE4DjJZDhGmO6QCXHCMZLZnGiMqBRFUfpwPAzD/P90laHzeDwoLS1FVVVV2if6zhjMY8zkLO5A+LsTA2WsPRlnJsQIMPDjZKCMEzjxsXKM9A4DZZzAwJ1LBnqMAANnrIMtRoDB/bdPF3y9SQ8DZZwAx0g6GShj5etN+hjM4+RKZobJUFQqVbc+yHa7PaMnJoDH2Fd0FSMD6f80UMY6UMYpM1jiZKCMExhYYwU4RtLBQBorMHhiBBg4Yx0o4yS6s24dKP+ngTJOYGCNFRg8c8lAGScwsMYKDJ4YAQbOWAfKOAm+3vQ/fTlObvzHMAzDMAzDMAzDMAzDMAzD9BgWmRmGYRiGYRiGYRiGYRiGYZgewyIzw2QIBoMBv/71r2EwGNI9lE7hMaaHgfR/GihjHSjjPBEGyv9poIwTGFhj7Q4D5f8zUMYJDKyxdoeB9P8ZKGMdKOM8EQbK/2mgjBMYWGPtDgPl/zNQxgkMrLF2h4H0/xkoYx0o4zwRBsr/icd5DG78xzAMwzAMwzAMwzAMwzAMw/QYrmRmGIZhGIZhGIZhGIZhGIZhegyLzAzDMAzDMAzDMAzDMAzDMEyPYZGZYRiGYRiGYRiGYRiGYRiG6TEsMjNMmvnyyy8xcuRILFq0CJdffjkAYN26dTjttNOwZMkStLS0pHmEbYTDYSxbtgxz587FI488ku7hJOHxeFBcXIxFixbhrLPOwqZNm3D66adj8eLFqKysTPfwToqBEh9AZscIMHjjhGOk9+AYyQw4TtLDQIoTjpH0wDHSe3CMpJ9MjxGA4yQTyPQ44RhJPxwjKSgMw6SVZ599Vnn66aeTfrZw4UIlHA4rb7zxhnLfffelaWTJPP3008r/+3//T4nH48o3vvENJRgMpntIgk2bNil33323+P78889Xmpubla+++kr54Q9/mMaRnTwDJT4UJbNjRFEGb5xwjPQeHCOZAcdJehhIccIxkh44RnoPjpH0k+kxoigcJ5lApscJx0j64RhJhiuZGSbNfPXVV3jyySexYMECvPXWW3C5XMjJyYFer8f8+fOxefPmdA8RALB161bMmzcParUaU6ZMwZ49e9I9JMFXX32Ft99+GwsWLMATTzyBYDAIp9OJiRMnoqKiIt3DOykGSnwAmR0jwOCNE46R3oNjJDPgOEkPAylOOEbSA8dI78Exkn4yPUYAjpNMINPjhGMk/XCMJKPt9WdkGOaEmDp1Kr7//e+juLgYZ599Nl544QVYrVYAgMVigc/nS/MI2/B6vRk5LgAoLy/H/fffj1mzZuGcc86BSqUSv1MUJY0jO3kGSnwAmR0jwOCNE46R3oNjJDPgOEkPAylOOEbSA8dI78Exkn4yPUYAjpNMINPjhGMk/XCMJMOVzAzTzzz00EOYN2+euOn1ekyYMAF2ux0jR46E0WiE3+8HAPj9ftjt9jSPuA2r1ZqR4wKA2bNn44wzzoBer8fs2bOTJk61emBNcwM1PoDMjhFg8MQJx0jfwTGSGXCc9A8DOU44RvoHjpG+g2Mk/WR6jAAcJ5lApscJx0j64RhJZuBEHcMMEm666SZ88skn4vb0009j7969CIfDOHLkCPLy8tDY2IhQKIQNGzZg5syZ6R4yAGDmzJn4+OOPoSgKtm/fjnHjxqV7SII77rgD7777LhKJBLZu3QqDwYCmpibs3r0b5eXl6R7eCTFQ4wPI7BgBBk+ccIz0HRwjmQHHSf8wkOOEY6R/4BjpOzhG0k+mxwjAcZIJZHqccIykH46RFHrd5ZlhmBPiyy+/VObMmaOcccYZyiuvvKIoiqKsW7dOOe2005SFCxcqTU1NaR5hG8FgUPnWt76lnHrqqcrDDz+c7uEkUV1drSxatEiZM2eO8pe//EXZtGmTcvrppyuzZ89WKioq0j28k2KgxIeiZHaMKMrgjROOkd6DYyQz4DhJDwMpTjhG0gPHSO/BMZJ+Mj1GFIXjJBPI9DjhGEk/HCPJqBRlABu1MAzDMAzDMAzDMAzDMAzDMGmF7TIYphdRFAUej2dAm+wzDJN+eC5huoJjhOkKjhGmKzhGmO7AccJ0BccI0xUcI0MHFpkZphfxer1wOBzwer3pHgrDMAMYnkuYruAYGXzceeedePLJJ7Fp0yacfvrpWLx4MSorKwEAt912G+bOnYsf//jH3X4+jhGmKzhGmO7AccJ0BccI0xUcI0MHFpkZhmEYhmEYJo00NTXhkUceAQDce++9eOutt/Dwww9jzZo1OHLkCA4cOICNGzfCZrNh06ZNHT5HOByGx+NJujEMwzBMd/j2t7+NQ4cOdZjoZBiG6S4sMjMMwzA9QlEUPProo7juuuvw9ttvp3s4DMMwA5b7778fV111FQAgGAzC6XRi4sSJqKiowLZt2zB37lwAwPz587F58+YOn2P16tVwOBziVlpa2m/jZxiGYQYmkUgEF110Ef7zn/8AaJ/o7AhOajIM0xksMjMMwzA94p577sF//dd/4fHHH8d5552H5557Lt1DYhiGGXBUVVXB6/Vi3LhxAIBEIiF+pygKvF4vrFYrAMBiscDn83X4PCtXroTb7Ra3qqqqvh88wzADCo/Hg+LiYixatAhnnXVWuofDZADhcBg/+9nPcPbZZwNon+jsCE5qMgzTGSwyMwzDMCdMVVUVfvOb3wAATjnlFADAjTfeCJfLlcZRMQzDDDxWr16NW2+9VXyvUqnE12q1GlarFX6/HwDg9/tht9s7fB6DwQC73Z50YxiGkdm9ezd+9KMf4aOPPsL777/f4X24SnVoYbPZsGDBAvF9aqKzIzipyTBMZ7DIzDBon9VnLyqGOT5/+ctfEI1GsWjRImzevBkTJ05Ec3Mz/vd//zfdQ2MYhhlQbNmyBcuXL8eaNWuwZs0amEwmNDU1Yffu3SgvL8eMGTOwYcMGAMCGDRswc+bMNI+YYZiByldffYW3334bCxYswBNPPNHhfbhKdWiTmujsCE5qMgzTGdp0D4BhMgHK6q9atQoAsHTpUrz11luor6/HmjVrsHbt2jSPkGEyB0VR8MILLwAArr/+emi1WqxevRrLli3DX/7yF9x+++2wWCxpHiXTXyiKgkQiAUVRxE1GpVK1u9HPhyIejwcTJkzAmDFjoNFocM899+Dmm2+G2WzG448/jhEjRqR7iEw/s2XLFgDAk08+CQAYP348li5dCpVKhWeeeQbl5eUYPXo05syZg7Fjx2LevHlpHC3DMAOZ8vJy3H///Zg1axbOOeccLF26FHl5eUn3WblyJVasWCG+93g8LDQPISjR2dDQgPLy8nQPh2GYAQaLzAyDY1n9d999F9dcc43wonI6nZ16UQFtx8nC4bD4no+TMUOB3bt3o6KiAnq9HkuWLAHQlpgZOXIkDh78/9g77/i2y2v/f7S3LM94xbGzCVmENiQhZJCE1UAICRcou7wuHVDaci8tKSuhjBToooVCaRkX2tKUUHYYYSaEkJAJmc5wbMdT1pa1pd8f/p0nj2Q507Zk+7xfL71sS/JXj62jZ5zxOQfw97//HTfffHOGR8n0JIlEArFYLMm5nM7RLDuUT+TWn+GgJtMVN9xwg/g+tbnfgw8+2MujYRimPzJlyhQYDAYolUpMmTIFBw8e7ORk1ul00Ol0GRohk2nuueeepEAnwzDMicByGQyDI1H91atX44UXXjguLSqAy8mYgckbb7wBAJg7d64oj1MqlfjRj34EAF2WXzJ9G3Ish8NhhMNhRKNRRCIRhEIhBINBBAIBBAIBtLe3o729HX6/Hz6fD+3t7eKxQCCAYDCIUCiEcDiMSCSSdAuHw+KxaDSKWCx21Dm4L5Jaqnw8DXYA1shkGIZhTp17770XH3zwAeLxODZt2oRhw4ZlekhMlvD888+jsrISU6ZMwfr16/HFF19g+PDhmR4WwzB9DHYyMww6ovpTp06FVqvFlClTjkuLCuCmB8zA5LPPPgMAnH/++Un3X3311VAqlVi/fj327duXiaH1KnV1dZg9ezamTZuG5cuXp9Vyv+uuu3D22Wfjhz/8YYZHe2rE43FEIhHh+O3Kuez1euH1euHz+eD3++H3++H1euHxeOD1euH3+9M6nelGzmX5Rs5oypTu63BQk2GyF7kyg279Yd5hGOL222/H8uXLcc4552Dx4sXIz8/P9JAYhslCUs85RGovK4ZJheUyGAYdUf3zzjsP8+bNw6ZNm6DT6Y5Li4rLyU4eOsjJmYpKpRIqlarfl8v3ZWKxGNatWwcAOOecc5IeKy4uxrx58/Dee+/h73//O+67775MDLHX+MMf/oClS5di5syZmDNnDj744IMk2YO77roL+/fvx+eff46f//zn+OKLLzB16tRMD/uEicViiEajSCQSwsmc6gSOx+OIRqPCUUxSGsCRz7VarYZWq4VWqxX3yTeFQgGlUilu8s8qlQrxeBwKhQJqtfqowb9sJ7VUecOGDeKxYwU1WSOze0iVeJEhuxsI0i39jXTSPTLpZHlkiZ90vyOTOj8xTF+krKwMH3/8caaHwTB9gnQycANlf5B6zrnllltgsVg6yb4xTCrsZGYYdET1r7nmGtx///247rrrMGHCBNai6iHoAEhOK5l4PI5YLAatVjsgFu++yNdffw2PxwOLxYLx48d3evzqq68WTuZ77723X7+Pv/zlL5GTkwOgwxGrUqmStNw3b96Ms88+G0CHQ379+vVdOpmzVd+dHMr0mSWJjFgshmAwiHg8jnA4DJ/PJ7KQfT4fQqEQIpEIEokEVCoVNBoNdDodjEajcDbrdDro9XqoVCoAEI5olUqV5GSmn+nxSCSS5Jjua3BQs3c5WmaqvAal2pJSqeS1qI8g7yuO9bzjuRZ9TW1SSvZD98lzFcMwDNM/oKQK2u+mQ6lUQq1Wi8SH/rgOpJ5z1OoO12FqL6sbb7wx7e9n69mG6XnYycwwSB/VT226w5w6VHIvH/QoKwiAcDyHw2FoNJoBEynuS3z++ecAgGnTpgnnoMzChQthNBpRXV2NjRs3YvLkyb09xF4jLy8PAPCXv/wFkyZNwpYtW8RjiUQCXq8XZrMZAGAymeDz+bq81sMPP4xly5b17IBPENpk01facEciEQSDQfE3tre3IxKJwG63w+v1IpFICN3lWCwGhUIBrVYLg8EAlUoFg8EAq9UKi8UCr9crnM16vV5s5mnzrtFokjKY4/E4VCqVcCrRPNGX4KBmzyJXychOwa6aU8pQMAPoWK9CoRB0Ol2fs7GBRLqgtXzgp/1FV9nK6YIQqcjBLvn3otGomKvYRhiGYfo2iURC9AdJVwlD64VCoRBJFuFwWOxz++Ke9GiknnMMBgOAI7Jv3/72t3Heeedh/vz5nZqHAtl5tmF6h75bb8owTJ8h3aKtUqmg0+mg1WpFJFir1YrnUwMwclalOqeJ2267DZWVlVAoFPjmm2969e8aiJAj9dvf/nbax81mMxYsWAAAeOmll3ptXJnipZdewmuvvYbly5d30nI3m83w+/0AAL/fL5okpiMb9d3J4UvOOtJkJgez2+1Ge3s7fD4famtr4fF44HA4cPDgQdTU1KCxsREtLS1obm5GXV0d9u7di0OHDqG5uRlNTU2oqamBw+FAIBCA2+2G3W5HIBAQrxcOh+H3+0XGNM0h5PBOF7Q6WYLBIC699FKMHDkSEydOxAUXXICamppTvm46KKj5+eef4/vf/z432OkG5KaU8pqRGhyJxWIiO5++lzObZZuiG2XH9qaNMMcH6cPLZcwUHJAbkAYCAfE+yoGE1EamZA9ygIIc0KRDHw6HxXWAzsHz8847D+PHj8fEiRNxzjnnYOvWrb38X2H6CsuWLeO9K9MlPJf0LvF4HIFAAKFQSDiSlUolIpEIfD4fnE4nnE4nXC6X2P+SPFwikUAoFILf70/roD4VjnbOra6uxrRp0zBy5EhMnjwZO3fu7LbXJeRzDpHay4r60KSSjWebgUYoFMKtt96KESNG4PTTT8c111zTK6/LTmaGYXoUchaRw4pKTOlwl5p9pNPpOumSkgMhFAp1yjJavHgx1q5diyFDhvT8H8Ng27ZtAICJEyd2+Zyrr74aALBixYpjli/3Zb788ku88MILWLlypcjUtdvt2LlzJyorK3HGGWdgzZo1AIA1a9Zg0qRJXV5Lp9PBarUm3TINfdbI6ULBHwBJAaDGxkZEIhHhUA4EAvB4PGhsbER1dTX27t2LmpoaNDU1wel04vDhw6ipqRGO5bq6OgSDQcRiMbjdbrS1tSGRSAhpiEgkgvb29iTnsyzj0V2O5ptvvhl79uzB1q1bMX/+fNx8882nfE2mZyEnMDn/5MaUcqNKeg41qJSbTVIDykQiIQKd0WhUBI3kLFm2keyB3leCKiioSah8P+0fyOlMwSuaOygbmuyIAlp0I9kfuh49j0qH6TWAjnVv+/bt2Lp1K/7nf/4H3/ve97r17+ZGTP2DzZs3Y/369aioqMj0UJgspafnEuYI8Xhc7DMBQK1WIxAIoK2tDT6fr9M+k4KZfr8fHo8Hfr9f7BVor9FdjuajnXO///3v4+abb8bevXvx85//HDfddFO3vCaRes4h7r33XnzwwQeIx+PYtGkThg0blvb3s/FsM9C48847oVQqsXfvXuzYsQOPPvpor7wuO5kZhukR0mUvKxQKzJgxA5MmTUJeXh7GjBmDCRMm4K677hK/RyVHpD+aWn6U6pieMWMGysvLe/ePG6BEo1F8/fXXAIAJEyZ0+bx58+YhLy8Pzc3N+OSTT3ppdL3PAw88gKamJlx44YWYNWsWfvnLX2L+/Pm46aabsGTJElRWVmL48OGYNm0aWlpaMH369EwP+YSQszkpo4+czR6PB4lEAi6XS2yqHQ4HlEolnE4nampq0NraikAggGAwCI/Hg5aWFuzduxctLS1ob29HbW0t3G43gsEgamtrEYlEYDKZEI/H0dzcDIfDgcLCQiGZEQgExDjC4TDmzJmDyZMnw2aziezSu++++6T+Vr1ej4suukjMM1OmTMGBAwe689/JdCOyc5kylGX9RLLZefPmYfr06SgrK8NZZ52FOXPm4Ne//rWQOJAdh5SVT8GNVAcm20j2IOsvK5XKpACYWq2GwWCA2WyG2WyGyWSCXq9PahhKwW6VSoW5c+di+vTpKC8vx5QpU3Duuefi17/+dZIDmZwJKpVKHLQpUE7PI8e2zWYT43S73d3epJQaMa1btw4ffPABvF4vAIhGTJ988gk+/PDDbn3NgUwikcC0adMwceJEGI1GjBgx4pTWGqAjSHvLLbfgySef7Fel9QOZnrCTnp5LmA7IwSxnL7e1tSEQCACAkHijHiK0f5D7ZITDYXi9XgQCAcydOxdTpkyByWTqFjvo6pzb0tKCzZs3i8zURYsWiUrC7iL1nLNs2TLs3LkTt99+O5YvX45zzjkHixcvRn5+fre9Zl/geJsFn8x1u3Me8fv9eO655/DQQw+JtaakpKTbxns0WJOZYZhuJzW7UKFQiOyy999/HwAwcuRIvPHGGygvL4dOpxNOgcsvvxz79u1Luh5pM7/yyisoLy9HNBqFRqPp9b9roLNnzx6EQiGYzWYMHTq0y+dptVpcfvnlePrpp/GPf/wDc+fO7cVR9h5vvvlmp/tStdwffPDB3hpOj0Kfz9Tv3W43EokE6uvrAXRkLRw+fBhmsxlXXXUVxo8fD4VCgYMHD+Ljjz/G5s2b0dTUhFgshqKiIjQ0NIimIgcPHsSoUaMwcuRI7N+/H16vF3V1dRg9ejSampoQDAYRDofFpv7dd9+FSqXCyJEj8c4772D48OFJB/bFixd3mkuIN998E4MHD+7y73388cdx8cUXn/o/jul2ZIkEWXuZoIaQfr8fr732GoCOyos33ngDw4cPT6qWIRuRZTFI8/vll1/GsGHDujxEsI1kBvm9IocAOQP0en1SthUhO4eJaDQKl8uFt956CwAwbtw4vP/++xg2bJiYRxKJBBYtWoTq6mphA6T3TDYyevRo8VxyUlx33XWiz8e7777brX8/N2LqXRQKBdatWwcAKC4uxocfftgp+/hE15p7770X11xzDaqqqnpm0Eyv0xN2AqBH5xLmiEQGzd2JRAIOhwMAoNFooNVqYbfbhfRdKiqVCvn5+dDr9aJK6vXXX4fJZMLw4cPx5ptvYtSoUd22N5Wpq6tDaWmpWAMUCgUqKipQW1t71CbSJ0K6cw6R2suqP0N7Taqg7KqnB+1J6CvQuak0kdpYmL7vznlk//79yM/PxwMPPIDVq1fDYDBg6dKlvVLtxE5mhmG6FXIAEEqlMknmQqPRwOl0IhqNYsSIEUL/ig49zz33XNrr6nQ6aDSaTqWwTO9BenATJkw4ZkbFd7/7XTz99NN49dVX8eSTT0Kv1/fCCJnuhDbc9D1thOjzJ0vgRKNRmM1modl9zz33JGW7Dx8+HPPmzcNXX32FX//612htbUVBQQFUKhV27dqFBQsWoLq6Gnv27EFlZSXGjRuH7du3w+VywePxoLi4WGQ7m81mIYPQ2tqKSCSCioqKJIcLALzyyisn9Xc/9NBDqK6uxlNPPXVSv8/0DJStmk7GBehYW3Q6HWKxGOx2O4AO2/T5fIjFYhg3bhyADuei3++HQqHAihUrxFzmdrvh9/thNBqTnIzpYBvJHPIBT6PRJGU0H2/wmTTlqZEolTqnaqIrFAq8+uqrSCQSCAQC8Pl8UCgUKCgogM/nS5LJkPm///s/AMALL7yAO+64A++8886p/MlJcCOmDijYkKqrTmsVZarLTSBPBZKFSidvcSJrzRdffIGNGzcmSZ0w/YfushOiJ+eSgU4sFkuStYhEIqIyRKvVwufzobGxUTyfqmJkneZoNIqWlhbodDqUl5fD6/UiHA6jpaUFkUgEZWVlCAaD0Ov1Yh462b1pOlLntu7MqmWOVGXLfo2uSD0bHS/kkJYbDDc1NXXLPBKJRHDgwAGMGTMGy5cvx7Zt2zB37lzs3Lkz7f6gO2EnM8Mw3YK84QeObPQpw0ihUMBoNEKtVmPNmjU444wzoNVqha4Vcf3114sGAuTkoon7rbfewqBBg5Iyhpjeg/SYjyaVQVD5cX19PVatWoWFCxf29PCYboY+w7T5IWdcaoCBytRJ3xZAlxrp3/rWt/DAAw/g9ttvx65du3DJJZegpqYGu3btwtixY7Fr1y7s2bMHs2fPRmFhIZqbm+F2u5GbmwuDwZCUTRKPx/HNN99g/Pjx4meZk8kWeeyxx/Dqq69i9erVMBqNx/NvYnoBkiYgKLABdNijwWAQAQa32w2g45CYl5eH7du344wzzgDQkbXZ3NwsDmI/+clP0NDQIKpt4vG4sO+XX34ZVVVVSRkpANtIpqHPOWWsyw34fD6f+Fl2OqrVamg0GvFYKBRCLBaDUqlEbm4uvvrqK2EjqcjzCNmcWq1GPB7Hyy+/jBEjRnQ51uuvvx4/+MEP0NbW1q3lxNSIiTL1gQ75FoPBAKVSKRoxpTtELlmyBLfffrv42ePxHHfmXDZAwaau+j3IEk/kGKDm0qficJbnkVROZK359NNPsXv3bpHFXF9fj/PPPx9//etfceGFF57U2JjsobvsJJWemksGIuQ4pLNnPB4XPUZo/mhqakI8HodCoYDNZoPBYBA/G41GWCwWKBQKuFwu1NfXIxQK4dChQ6iqqoLL5cK2bduSAtuRSERU03RXJvPgwYNRX1+PaDQKtVqNRCKBuro61nnvBuRm0rLjnvaeqYFNcg7LEmz0+LEc//RalKSnUCiwadOmbplHhgwZAqVSKXolTZgwAVVVVdixYwdmzZp1fP+Mk4SdzAzDnDK0YMuHPwBob28H0LHBl7PDDh8+LPSl9Hq9KH8HgFdffVVkF9HzSf/VaDQmObGZ3oUymY/W9I9QKpW48sor8dhjj+Ef//gHO5n7IEqlUjjdVCqVkKmJRCIwGAwIBAIwmUyiSV8oFEJRURFaWlqwfv16XHDBBWmvO2rUKMycORMfffSRcBTs27cP559/Pnbt2iWyUOUmbMCRjZrs8GtoaEBpaal4XA4+nWi2yG9/+1v885//xOrVq5O0EJnMQgc04Mhmvqu1BUi2D4VCIdabUCiEpqYmAB22FYvF8Ic//AH5+fnIzc1FS0sL4vG4cNSRQ0pei3rKRurq6nDdddchFArhkksuwcyZM/HTn/4URqMRzz77LKqqqnDXXXfhk08+wfjx4/HnP/+52147W0g9yB0vKpUKer0ewWCwy8xiavxHgW56jlarhVKpTNqTpELzSCAQgNfrhUKhQF5engh4ySWvXq8XPp9PzEn/+c9/kJ+fL7KPuwNqxPTGG290asR03nnnYd68edi0aRPuuOOOtL8v64j2NUiHnZAP9bJsDgVI6edoNJokh0MOgRNxOh+PjRwPd955J+68807xc2VlJd566y2MHTv2uK/BZC/dZScej6fH55KBCDWIpSZ91FiamkpTQ1iaLyKRCNra2jpdR61Wo7i4GEOGDMHo0aOxb98+BAIBHDp0CIMHD0ZTUxOKiorE80OhkNBy7q5M5qKiIpxxxhl46aWXcMMNN2DlypWorKzsNqmMgYrcYFy+jxqUE7KTWYaqqrRabacAt0xq3xtyNFOwoLi4GOFwOKkvFXBi80hBQQHmzJmD9957DxdddBEOHTokpAl7GlaQZxjmlEgtYdZoNNBoNEkHAbVajVAoJDRVp02bhs8//1w0KwAgGoc1NzejtbUVdrtdRAppIqfXkCfrW265RWTMzp07t1O5K9M9JBKJE3IyAx2SGUBHBjrrPvY95MxlOsyTU4MyOCmbgzRCqXP0iy++KMoO0zFmzBgAgNPphF6vFxs4AMKhSHMIabbT4/K4pk6divXr1+OGG24AcPKlgvX19fif//kfuFwuzJ49GxMnTsRZZ511Utdiug9Zfonsj+xCpVJ1cjADgNlshkKhQDgcRiAQwNlnn43PP/8cV111FYCOktchQ4YkSfh4PB4RUKHrkfOKJBh60kZSm7ndfffdWLVqFZ544gksX74ctbW12L9/Pz7//HNYLBZ88cUX3fK62YCcMUQ3uadDKvT+yAcyrVYLi8UCo9EIg8GQdKPyZqAj8B2LxcR7S3sXshF5TyKPjxzMQMfcJ9ukHPxyu9249NJLMW7cOEyYMAFPPPEE3nrrrW4Nig/ERky0z6TPvkKhgE6nQzweh8vlQmtrKxwOB1wuF1wuF5xOJxwOB/x+v0iAoPeJbC0YDKK9vR1+vx/t7e1ob29HIBBAIBAQP9Pjfr8fZ5xxBtauXYsrr7xS7GVluR6GAXDUueRE6I25ZKARj8eFNFIsFoPP54PP50MwGITb7Ybb7YbH44HL5UJbWxtaWlpgt9vh8/nQ3t4uslqpSqK+vh7r1q3D/v37UVFRAZ1Oh3A4jMOHD2P69On46quvcP3114vXDwQCXVZgHI2jnXOffvppPP300xg5ciSWL1+Ov/3tb93yvxqIUNAhEAgIZzI1haa1hJoAk73QzefzIRAIiCqbUCgEr9cLp9OJlpYWtLS0wOFwwOPxCFuizHiVSgWNRgO9Xg+j0QidTodp06Zh/fr1uO6660TT85PlqaeewiOPPIJx48ZhwYIF+Mtf/tIrzf8UCV4dGabb8Hg8yMnJgdvtFs6W/gxt/GkaoawgAPB6vV1OihqNRjipotEo3G53Uik0kZOTIzSdKQKsUCiSXofpHRoaGlBWVgalUgmfzyd0II9GIpHAaaedhj179uCFF17Adddd1wsj7R9ky1xCjS7I2RcKhcRh3+PxwO/3o76+Hj6fDzU1NQgGg6ivr4fL5cKZZ56JZcuWJekkE2+88QaefPJJTJs2DSqVCm63G9dddx02btwIo9GIK664Alu3boXf78fo0aNhNBrR0NAApVKJvLw8hEIhaDQaqFQqqNVq4TTSarUD5hCWLTbSU8hSSrQJJ0kEoKPjOx34EomEyB5RqVTwer3wer3QarUoKCiAy+VCS0sLFAoFhgwZglgshrq6OgDAoEGDRFaqTqcT9kRObbKtnsThcCAnJwcqlQqzZs2CSqXChx9+CAA499xzcdttt6Gurg4//vGP8eabb2Lfvn342c9+1uk66Zq6DR48OKttRJbZkpE/1zJ0EEw9vqQ2z5HlfoAjB3wqd3Y6nSKwoNfrk7KF4vE44vE4otFoUhBdp9PBbDYLe6ESagBdZitlO9k+j6TKY5BdOJ3OpH2jSqUSgYdYLJZUwkxotVoxR3Tne9UdchzZTrbbCZN5stlGZP3lcDiM9vZ2RKNR4WQOBALw+/1JQU66pftcq1QqcQ6ifWh5eblYZ7RaLXJzc4Usk9VqFdeg9WYgkq02QvtNuRqGgpH0GMmpUGWM/Nx0ZxHKZD4W9HuUpEfJPKlyHbQ/7Sv0nZEyDJNVHM3BDHQcvmhCpoMebf6pTKm9vT0pw9VkMsFoNMLhcIgMETowy91a++JBrq9DesyjRo06Lgcz0LHwfve738V9992HF198kZ3MfRD58yZvuqnRH0lkBINBFBUVob6+HoWFhfD7/di0aRMeffRR3HHHHZ02Rvv37wcAlJWV4fDhw+L6QIezhrIHgY7MQcpiNhgMwrFA4+oNJyDTu9D6AhwpPUw95JF9yIRCIZhMJnGAo87xra2tADpKB9VqtbA5k8kkHIYajUY4imSHdW+Q2syNGmgCHf8Lr9cLs9ksxkyO9lT6WlM3KhEFjsgY0D6BZLNSUSgU0Gg04nmE7HROdUCr1Wro9XpRFt3e3g6LxSKaQpL8SlcoFArR9InsTh6b3BiV6T6ogoUO8xREtNvtwpGs0Wjg8Xjg9XrTZhWTA1itViMajSbJ35DTmUqa5ceIdI215CAEcESOg8ZDCRFMdkLvn1wpKQel+PPcv4hGo2LeDoVCIpOUsko9Ho/QZKZs1NRApk6nQ05ODsxms0i6oHWY5peDBw+KCppQKAS73S6q/Dwej3A0k+yCTqdjO8sCYrGYOGMAHe+n1+sVUm2U2Uw2QnOHXF1N76O81tAeloLTFJSmwDrtX2j9kCt1DAaDqMySHd3xeDztfjgbYSczwzAnTGomUbrMYppo5d+hw7xKpYLL5RKTularRU5ODtRqtTj0pTZ2okl8oEZ/M82JSmUQ1157LZYuXYrVq1ejurr6qE2SmOyDmmaFw2GoVCqxwaENVk5ODmKxGAoKCpBIJJCXlweHw4Fhw4ahuroan376KTweD+68806x2fb5fPj8888BAGeccYZwONN8kdrwU6PRiFJ1ariVqsGbKnXA9F3kACZVrsgSFnq9PskBTe87bdzlbF6VSoWGhgYkEgmYTCbYbDbRtVt2KMm6eZRR0tsZI3IzN1nPXKlUwmw2C8e43+/vMgOorzV1kx3M9P/uSudQhuYlanaUrsGOrM8cjUZFg0gqPQ2FQkKbOLWRjywRRJlFCoVCNAwEOrLRZJ1fnnu6F/osy1lcCoUCra2tIjswGo2iubkZAMShnuYBykCUM8Qog5myzEh7k5DtiuaAdNUMcraaXO1Dc1ckEoFOp+MAaJYgZx9SksuxSOdslucaeg7ZGAcWshPZwUwZzFSJ5/V64Xa7EQgEhKM5EokkaTZTwJmcgC0tLQCA4uJiWCwWkRFNj5Oj2mg0Qq/XiyoljUYjsneVSqXQfTYYDDxPZJDUZDaNRgOn0ykCCSSvQl+pkjO1ISDNAbS2BINBse7QaygUClgsFlitViH1JiffRSIRUTlFck1GoxFms1nYC8k/9YUABTuZGYY5YeQykeMtEaXDGWUokqahxWKByWQSpSnkTKLDH236ycGc7ZNqf+VkncxVVVW44IILsGrVKvzlL3/Bo48+2v2DY3oU+uxFIhEhWUCZgeS8oyBSYWEhwuEwfD4fJk2ahO3bt2PLli24+eabsXjxYgwbNgyvvPIKfD4fKioqMHbsWLz88ssAOjI0AYjuyuQ8pp8BpM06ksfJ9G1SNf7TyZ901biMsk2i0ahoAufxeBCLxaDValFSUgKXy9VpjSFZjFSHVG+S2szNYDDAbrejpaUFlZWVOOOMM/Dyyy/j1ltvxZo1a3DxxRenvU5fa+qW2swzNTvoeDha1mFqZnQikRDVEHKzYpLjokNiuteWM41o7iO4uqp7kTPLFAoF9Hp9JwdzMBiEy+USz6WquEgkIpIgZL1Leo91Ol2SPAo1n6aSZfp9uVKCgltGozFpH5oa7CAnAGVg6/V6diBlEAoiUCCBAk+ppe5yg1f6ejwaqPJzKCA6kCS7sh1yGNP3JIdBDman0wm/3w+n0ynmEzmjNZXCwkLk5OQgHA6jqakJTU1NYm9B0o7kgCSHoclkgtvthsVigVarhdvths1mg1KpFBW9bDeZQ05o0Ol0cLvdYs9AjmWv1ys0vCnwGQqFkpLtaO9AzmStVivWmWAwCIPBALVaDY/HA4/HI9YektQgJ3VOTo6wCzkokpubC6VSKXoBBAKBrA9kspOZYZjjhjbRlMnTlWZiKhT5ow0fZSLm5uaKA3EsFoPT6RTXpcWWNvR9Ve+wv3CyTmYA+OEPf4hVq1bhueeew69+9aukhltM34AccOSwAyCyms1mM8LhMAYNGoRgMIhBgwaJz/yCBQvw5Zdf4tChQ0kNSbRaLX72s58llbvbbDYAR5pzGY1G+Hw+eDweIdESDAZFwydZvgNgJ3NfhwIKsoP5eOd8WTuXHMyhUAjRaBRqtRplZWUIhUJCNoOawSmVSuh0OuGAykQGM5DczA0AHnzwQcyfPx8KhQIvvvgiKisrMXz4cEybNg0jR47E9OnTe32MPYnclA3oPvkJcgJS5hjtYSg7mbKDqHyeHFJ0+JPHR05HshEKlHMGY/ciN3lVqVQiY8vtdouAYyQSgcvlQiQSET09fD4f/H6/+N3UdUGn08FgMAg5HbVaLcqX6XdMJpMoa6aKPdrzUgNAWvMMBkMnHXC6LiVVBIPBtM1JmZ5F/izT+0jNGmW5EwBJEhkE7XdSs5nl4DYFLCijXnY8cXZq5pEbhVIvB3IU+v1+uFwu+P1+OBwOeL1eOBwO8Vw5AG00GqFSqWC329Ha2orW1lYUFRVhzJgxsNvt8Hq9OHToEICO7GaSdyNt5ng8DrPZDK/XKzKaXS4X8vLykmQVYrFYpzmF6VloPwB0rA/Ud4bWe2oUGYvF4PF4xDzi9XqTel/IaDQasdYEg0GRkCNXU1FVDTWaTUWtVsNmsyEnJ0c4uNva2pCbmwu9Xi/2uHT9bA1QsJOZYZjjgjZqtDFLlcPoinA4nFSemkgkoFKpxAJL13a5XKLkXXYs01fesGUOv9+P6upqAMCECRNO+PcvuugiDB48GHV1dVixYgVrM/dRSC6DDljk6I1Go7BYLIhEIsjPz0draytKS0tRU1OD3bt347rrroPH48Fnn30Gu92OiooKXHXVVRg+fDhcLheAI5IAtIFyOBzIy8uDz+dDc3MzxowZA6BjPqENFcnqpCuVZ/oOcikzcSIN96iUkZxF9DUWi0GlUomGpY2NjUnXpkwSOYs1Uw7DN998s9N969evT/r5wQcf7K3h9BrUq4GcNLJ8Rne+DySRQId+eh3KNqL76LBPpa40Brm/hF6vTwqOcfC7eyHnPQWAqMpN1j9tbm5GNBqFy+VCKBSCw+EQWWaUAZYqxaLX62EwGOByuWA0GpGTkyPmDqvVCp1OJxzJAGA2m5GXl4f8/HyR2UxVeE6nE263GyaTCSaTqZM+t06nE03GIpFIUsCC6TmoEkbONmxvbxfOI3ICU1UlkJzJTI5l+pp6Sxf80ul0yM/Ph0qlEsk0fr9fOJOY3keuOlEqlXC5XKLaTnYSOp1O+Hw+OBwOJBIJqNVq7N27t1MjeqVSienTp+O0007D5s2b0dLSgpaWFsybNw/nnnsuduzYgf3796OpqQlKpRKnn346HA6HyJ4GAKvVCrfbjcLCQiQSCTgcDhQVFYk9L/UG4KBU7yFXIiiVyqTeDLLzn+aQ9vZ2EYygvQPpNFP2ukajEeuVWq2G0WiEyWRCe3u72DvQ/ob6PFBwkiSiotEo7HY7XC4XiouLxbzldDphs9lgNBpFIJP2zlSBl022w05mhmGOCkX9aeMPHJ+DmTZ4NBFSBoFarUZeXl7SppxKmGT9Tco+oo0fkzm+/vprJBIJFBcXY9CgQSf8+yqVCt///vdx991345FHHsE111zDB/M+CB3EqHmS3CAtkUhAr9fDZrOJjdqIESNQXV2NN954A//1X/+Fhx9+uNM13W43gI4MMqVSicLCQtTV1aGlpQUjR45EXV0dvF4vfD4frFar0M2jzAAaD23QWBu175DOuUz2dDzzAwUuSYeVDvjkaFYqlSgvL4dOp0NTU5OwF7lMntYa+X6m91CpVGKPITtuU0vZ6ftTgQLW8n6GHICyI4mcAoTchJKqcORqLraZ7oWcf/JcTlIYOp0OjY2NQgonHA6jtbUV7e3tcLlcIiuM9DTp802Hd4/HA5PJJDQvc3NzYbFY4HK5oFKpUFRUBJ1Oh1AoBJ/PB5/PB5VKhYKCAlEqT47oeDwOr9cLr9cLo9EIq9Uq9qpka7Kzm+lZSCaJzht0rvD7/QgEAmhvbxfZzHKZO+1r5ECjvCbIa5FSqYRerxeZ7ORgamhoQElJCUwmk5BtokxW+RqplVfcR6L7kSshqJ8HBXvocx0IBERlBFXQKpVK0eC8qqoK48aNAwDs2bMHe/bswWeffYYdO3Zg6dKl2LJlCzZs2IAPPvgAGo0GF110EVwuFz777DPU1NTgm2++wezZs7Fnzx4hmxEKhaBSqeB0OlFcXIxgMIi2tjaUlJTAaDSKZCx2NPceqdJctM5TkIrmC5o/KBhhs9mwd+/eLqVVZHmlSCQCr9cLk8kEi8WCQCAAg8EAnU6HRCKR1MTZYrGgsLBQyKVFIhEcPnwY5eXlItjqdDpFRjPNdbSniUajInEiG2AnM8MwXUJdUOUD3/E4AOLxuNhgyR1WafPW1tYGm80mmqbIGpmyY5kOCExmORWpDOKWW27Bo48+ih07dmDlypW4/PLLu2dwTK9Cn30580er1QrtuWAwiLy8PPj9fqhUKpx22mnYtWsX/vWvf2HPnj2YNWsWSkpKRIftzz77DABQUVEBAMLJ7HA4oNVqUVxcjMbGRtTW1mLMmDHw+XxJkXtyJNB4YrEYzxlZTjrnsuzsPdrhStY9pcoacjLL2UskkaHT6UT3eOCIxjM5FimYma3lhv0d2QmY6mRO1UQlp8ypBpLo/ZZ1WqkyggJksvNYzqylscrjYXoWueyd9pXkMHQ4HKLknZyHlAUmU1xcjNzcXOE4jsfjsFgscDqdCIfDKC0tRSQSQWNjI9RqNcrLy0XmYTgcRnNzM1paWpCXl4eioiLhLPD5fCLDLRAIIC8vTwQiqGEuVfzw/NIzUEILOfR9Ph+CwaB4r30+H7xer8hipUxBek9kJzNlA2q1WhF0JMkWyoSnhlwmkwmjRo2C0+lEe3s7WlpaUFFRIbIMyR7lBJ1UlEoljEYjzyPdiPzekiQByR+Ew2HR1I903OPxOAwGAzZu3AiFQoGf/OQnuOCCC5I+r3v27MFjjz2G2tpaLF++HE899RTKysrwn//8B++//z6mT58Om82Giy++GO+++y6qq6vR2NiIMWPGYMeOHQiFQtBoNDAajWL/QlIZbrcbubm5wrYoUE5BcKbnkHtCyNUN1ACSEhni8Tg8Ho+wq+3btwMAcnNzUVBQkDS/UJIDSSzpdDpYLBYRQLBYLMIerVYrcnNzhZ+EgpZmsxmjRo1CS0sL/H4/GhoaUFlZKap1nE6naB4oO5vpugaDISvmFD6JMQzTiVTnMk28qZqZcpkxHdLkxie04afn+v1+seGKRqMoLS0VWUNyFhllg3CWUHbQHU5mm82Gn/70p1i2bBmWLVuGhQsXDhhnYCgUwn/913/BbrfjhhtuwH//939nekgnTWo3ZcrEofeSmveVlpaioaEBKpUKU6ZMwZdffomtW7di69at4rBGTdkUCgVmzpwJ4EimIDmAysvL0dzcLLKZCwoK0NLSgmAwKDZRdA3aKHI2c/ZCGYZyNtexnMvyWkK/K5cJytkcAGAwGFBSUiKacZEOM8lkkEOB7JcdzJlHdihTdrOc9QccadpFjd9OZX8gO7RJ45eyYClbkaA9DJW+95SkB9NBapNXeY/Y0NCASCQCj8cjnIdOpxOhUAjBYFA4lxUKhShLb21tFU26zj//fFEdQ5nLfr8fBw8exPjx4xEKheD3+1FTUwOVSoWKigqUl5eLBmFtbW1oa2tDTk4OiouLUVRUhFAoJLIi29raUFBQIGQ+SM4pHo9zRV4PQI4bcszJWaperxcul0s02iLnMAUlCVqDZC1VCj7q9XpYrVYRgMrLy8OoUaPQ2NgonD9VVVWoq6sT+uCUZUhBBtl5BRwJ1Ms68NynpPuQJR1pH0ma/HSTNW2BIxV1CxcuFH0RZEaNGoXHHnsMP/zhD3H48GGsXr0aF154Id59910EAgE4HA6UlJQgkUgk7UuHDx+OnTt3ChugecBut2P48OFoa2uD1+uFxWIRGvEUSAuFQmwXvQit63J/CJpXvF6vcASTJMrYsWOxbt06fPLJJ52updPpMHz4cFEVEwqFYLPZYLFYRGNBqsz0eDwYPnw4xo4dC7vdjsOHD8Pn82H//v0YPXo0Dh06hFAohObmZpSVlcHtdosACUkV0n6WHNzZMqf0qJt74cKFyM3NxeLFi3vyZRiG6SbkZhmyw5juk7XM6MBHm3La3NHzKFpMjmkSzSdok0eLPB306eDGWULZw5YtWwCcnB6zzE9/+lPk5uZix44d+NOf/tQdQ+sTrFixAhdccAHWrFmDf//730ftXp3tpJaxyw06gSNOZpPJhIKCApFZdNFFF2Hs2LFQq9UIBoNwOp2IxWIoKCjAjTfeiMrKSkSjURw8eBBAR9kY0DEvFBUVAQBaWlpgNpthNBoBICmYRYEx+X4me6D3ibItAIjAJDXJkR1/JFHg9/vh8Xjg9/tFtiCVq9N9tLFWKBSw2WwoKysTQQ/SXpQ1/qliRs5kZjIHZXoCEJ3W5WxCWSeV5hvaq5yoFEFq82KlUinsDzjS6Ekub6d5hTLPAM5i7i3k7HLKQPb7/UKPmT7fPp9POJjPPfdc/O53v8Ojjz6K3/zmN/j973+PGTNmAADef/99fOc730F+fr4ooy8vL0c0GsXmzZsRDAYxZswY4Vg8ePAgNm/ejEQigaqqKlitVgAdTqk9e/agtrYWGo0GhYWF4lDvdDo7NR1MzcpnTh25KRetE263Gw6HAy0tLWhqasLhw4fR0NCAw4cPo7m5GY2Njaivr0ddXR0OHTqEuro6NDQ0oLm5GU6nE06nEw0NDbDb7ULPuaWlBUVFRVCr1XA4HHC73Rg5ciQACJvLzc0F0PHe0/6O9FgtFou4Wa1WmM1mmM1modPNe5XuRf7MpfbrIEevfDYFgJqaGgBI62AmcnJyMH/+fAAdZyKv1yvWjZycHDQ3N4uKPQA4/fTTodFoxLxA8jpUwev3+5PmDABCOx6A2C8xPUfqXhQ4MleTvArNM+ToBTqSaF588UXs378fGo0G48aNw8yZM/Gtb30Lubm5CIVC2LFjB/bs2YPhw4dDpVIJOSe1Wi0CpCQ/uW/fPhw8eBDl5eUYO3YsFAoFXC4X2tvbUVpaCqBj/Wtvb0dOTg7MZjOAjibpVAlOe1vgiIM80/RoGtltt92G733ve3jhhRd68mUYhjlFaNElB3K6CYoO5LJGMkX26HepRETOOJIdyzk5OTCZTGhoaEh6noyczcxZQpknEokInbIzzzzzlK5ls9nw61//GjfffDPuueceLFq0CIMHD+6OYWY1mzZtwk033QSlUolx48Zh165dOOOMM5KeQ9Fugsr7s4nUxlzk2JMdQKSr3NraioKCAmg0GjQ2NuLAgQPQ6XSYO3euaHJBm6VwOIwNGzagpqZGyGyMGjVKvG5ubi6amprg9/tFhlptba0oOVSr1UI2gzKZOXMseyAHszzXy5UywJEMZ8o+ltcHymKmdYYek38mp6PdbsehQ4dw+umnQ6/XCydAajNZWssGSjVFtiI7cWkOoQCDXDVFpGYwkx0dK6tZdi7QzwBERhv1iyDJHQqgydIYQHKWHO9Pegb54E9ZzIlEQmRwBQIBOJ1OBAIBUeFCDWTnzJmDzZs34y9/+QtisRimTJmC//3f/8VNN92EcDiM9evXY/Xq1bjmmmvw5JNP4sCBA5gwYQJKS0uxceNG7N+/H3V1dZg8eTKGDRuG/fv3w+PxiPtPP/10lJaWCqdkW1sbgsEghg8fjtzcXDQ3N4vAmcFgYBvpIRKJBNrb20U2KjlbfD4fWlpahLO5ra0Nfr8fLpdLOP9JLzkejwsnYHt7O5xOJwoKCmCxWERgY+zYsWhqasKBAwcwe/ZsbNy4ES0tLaIRMWWcms1m0S/i8OHDMBgMopSdstpTSdWDZboHtVotegHRHjCdw02WSiBnrs1mO+q1y8rKAHQ4helcNGTIEOzbtw+ffvopEokEdDodzj33XAwePBgOh0MEHXQ6HWKxGEwmk5DgGT58uJjTSIuZ+gaQTBCteUzPQnsD2lfSZ5vmCp/Ph0Qigfz8fLz99tsAOpra33jjjSL4CHTY1fbt2/G3v/0Ne/bswdtvv42lS5fi9ddfh91uR1VVlchEdjqdmDhxIrZu3Yp9+/Zh+PDhMJlMyMnJgcvlgt/vx6BBg5Cbmwun04nW1lYMGTIEZrMZKpUKbrdbNBSUe0tQlUSm55YeffXZs2eLbCSGYbIPObtM1rmkQ7usJUe6Z7RhczgcojMvdd2mG2WXkYPZaDSioqJCdNIFIF6PSN1wsYMoO9i9ezdCoRAsFguGDRt2yte76aabMHXqVPh8Plx99dVJZYv9FdLYAjoyfOVGD8TDDz+MnJwccctG53tqFp/8maVGagBE9+N4PI7c3FyMHz9e6BQeOnQI+/btw+7du/Hll1/iww8/xJo1a7Bjxw74/X4YjUbMnTsXNpsN8XgcLpcLhw8fBnAkW1qtVovMIcqMpflKdkByhlDmSedgJmkLyrwwGo3iECVXz7S3tyfpalKmmtPphMPhEBnO1JyFiEajaGxshNPpRDQaTWomKEtzcKO/zCPPKdTEk3QHaf8h3+Rqq9T7KBhBgYfUSiyaEyhwQXZIP1M2pNPpFPqLsgNcnu8yfXjrr8iBTIVCIdZKv9+PRCIhmriRYzkcDotS97KyMvzpT3/C2rVrhe7u2rVrceONN6KhoQELFiwAAGzfvh0WiwVz5swBALz33nsYM2YMFi9ejMLCQoTDYaxduxZr167FaaedhkmTJgnH0JYtW1BfX4/KykqRoeb3+2G325NkVjgDsWeRP7s0X5BsATmMSa+7tbUVDodDNHnbtWsXDhw4gJqaGlRXV6O+vh4FBQVQKpUiKFlcXCx0l4EOpyKtUZFIBEqlUjiWDhw4gHA4jJKSErHPCwQCaGtrw+HDh1FTU4Pm5uakJAIg+53MoVAICxYswNlnn41nnnkm08M5bijpIR1yUgTtA2ifCgDV1dVHvTZ9rjUajdDlLSsrwyeffIJEIoGhQ4fi7LPPRktLC9566y189NFHSCQSKCwsFBV5Xq8XOp0OkUgETU1Nwk/W1tYm1huq6AGOBEIzRVd20FftQ0YOaFIFJO0VKNO9vb0dkUhErEW7du0CAFx88cX4yU9+kuRgpmtNmDABv/nNb3D66acjGAxi27Zt+O53vwugIwt+zpw5UCgUOHDgAKxWq2hG63A40o4zPz9f9LGhAJjBYIDBYAAAkVGfbZzwzBaPxzF69Gj84he/SLr/vffeg1arxb///e9uGxzDMD2H3DBJdi5TYwS6yeXJqY5k+TE66JOThzZhgwcPFg2YACRlQtNkKUtkUJYZH/6zg82bNwMAzjjjjG7ZDCuVSrzwwguwWCxYs2YNfvnLX57yNbMds9ksMrL8fn+nTQkALFmyBG63W9zq6up6e5hHhZw5wJEsvtQsP9rwtLe347TTToPBYBANViorKzFmzBhUVlaKyLzVakVOTg4KCgowZMgQTJw4EVOmTEEgEMDXX3+NL7/8Ejt27IDH44FSqRTNAYGOqgjSYCa7JCcSjXMgBDCyHVmagLpjAx2HNIvFAr1eLxriaLVa4eyjckJyKpMmKjkf6Ro5OTkoLS3FyJEjMWnSJJSUlADosFfasKeTyTieBrZMzyI396OGepSdCByRTKEqj1AolBQElw+D9LlP1eqWb1R5Rc5ouracMU0He7mRGJDsZOYAeM8h63CHQiHh6Jf1dKmMmBzM8Xgcer0e77//PuLxOC688EL861//wooVK0RjtmXLlqG0tBRFRUWIxWLYuXMnpk2bhkGDBqG9vR3vvPMOCgsLsWjRIsyYMQM6nQ6tra1YuXIlYrEYpk+fjqFDhwLocES1tbXBYrGI+YYc3WQbqWX6vJ/tPijhBUDSVypvJ/1UOrN4vV4olUoEAgHU1dUhHo9j8ODBOP3002EymeB0OvHRRx/hsssug8lkwsGDB8Veo6mpSbwuBblJFrCiokKUtW/atAk7d+5EOByGzWZDbm6ukN2hLMj6+nq0tbV1solsDYb3ZZk3kiKJxWJi3dfpdCLITPKMtGel9/vll18+qsxAQ0MDgA6nH0m7kYTCiBEjEI1GsXv3bjQ2NiIYDEKhUKC8vBxTpkwRiSMtLS3iPExNI7VaLRKJBOx2u0jsosZ/6Sp+e5Ou7KAv2wchy//RPoCCzrLTmf62/Px8NDY2wmAw4Hvf+95Rr63RaITEZFtbG0aMGAGbzSb2HuXl5QCAQ4cOIScnB8CRClaa18hOqPJTfg5wxM7lCq1sCl6d8AiUSiWWLFmCP//5z0JDZtu2bbj88svx0EMP4fLLL+/2QTIM073QJEdOZdm5LHfmpU7L5MyhTGba8Hu9XpH543A4YLfb0draipaWFjQ2NmLfvn345ptvsH///qRJUC5VBY5MhuS84kNc9kBO5kmTJnXbNUeMGCEi348++ij+/Oc/d9u1s5FJkybhs88+QyKRwNatW5OkIAidTger1Zp0yybkzZhSqUz6HNNhicq1yKkzYcIElJWViQzFaDQKrVaLnJwcFBYWori4GIMGDUJeXh50Oh3a29tRX1+PpqYm0fVbrVZj0KBBmDhxYtL/RKlUiqwhuTEplcTL2c1MZiDHHtDhfJE3zkajUdhNOBwWDbU8Ho9o2OR0OuFyuWC329HY2Ii6ujocPHgQhw4dgsPhgNlsxujRozFkyBDk5+dDo9GITDSyT5LEINtMlXxiMgNluAPoFHSm8mVyMgJH5hg5C1l2FtPz6ft0P9PvpDqWNRoNrFYrCgsLxVwEICkDn14/W7SY+2t2mTxfUGCW7IASG6gRE30fj8dFc+If/vCHeOCBBzB8+HAMGzYMjz32GHQ6HTZt2oSPPvpINC/euHEjVCoVLr30UigUCmzZsgV79uwRDZ2uuuoqlJWVIRqN4t1330UkEsHo0aOFY4CcTalOZQqiUfPKbDrw9xdo3lAqlZ2czMCRBBrgSNBo1KhROHjwIKxWK15//XV8/vnnWLVqFbZs2YLrr78eAPDPf/4T3/rWtwAc0VqWGwyTXns4HMZ7772H2tpalJeXi0xUj8eDuro67NmzB3v27IHT6YTJZErKcKYqULoegIw6EI/Gpk2bMH369CSZt1RCoZBoXka3bIB0/IEjjjpZ55/2A+RkBjr2r9u3b8czzzzTpeN/9+7dADqcjYlEAiaTCdFoFCaTCe3t7YhGozCbzSguLsbQoUMxa9YsTJkyRezvq6qqAHRoQJM0GDmpad2j/6F8Fs6kjXRlB8djH0D22oh8hpHnDgBJwWnZyU/2MnjwYNEbpisOHjyIN954A0CHPresAa5UKsVa0tTUJBzIXWUyA0jSYSZSA1XyeScbApsntepdffXVKCgowOOPP476+np85zvfwbXXXov//d//7e7xMQzTjdDBjg5ndNAi5zIAkVWmVCrh8/lE6ahcrmy324VDgDJK5AwjOYsjGAyisbFRLM7UvIXKlACI5kuUYZYNkyPTQU84mQHgiiuuwNKlSwEAt956K15//fVuvX42ccUVV+CDDz7A5MmTsWjRoqzo+nuiyJuZ1A24fIAmR7Ddbkc8HkdlZSUmT56M0047DWVlZcjPzxfa7EajEQaDAXq9XmgYWq1W5OXloaSkBFVVVRg5ciSKi4uFY1uGDg/RaFR8T3OK3CG6L9DXnUOpUAYIAKFzS9/L7xXpzJGeJmUu2+121NfX49ChQ7Db7SKLmZyMDocDGzduxFdffSVes6mpCZFIRGgaAh0HR3I0U8UMZTExmUP+XKpUKlHuSY34SE+QAlJ5eXmwWq1C55YOfuRMDgaD4mvqjZzNqY2LKaONbrIjGUguZ6f5JFuCE/01u4zsgjKY5aZLlNEcDAaTGgLS+zp79mzcdNNNSdcrLS3FtddeCwB44oknMHnyZAAdTma3242KigpMmzYNAPD222+L99xoNGL+/PmiiRNlLdJBn/bL5BQwmUwigAEAer0+yaHATubuIXVdkZNX6HMr/7/pPSBbqaqqSuqHodfrcfvttwPoKIPPy8sDcCQzvaGhQWSrv/XWW6ioqIDBYEAgEMDOnTvxxRdfYO/evQgEAsJpSc5Dl8uFffv2Yc+ePbBYLCgsLATQ4WgOBoNJElHZmM3cl2XeaJ0HkpOYSNJGqVTCaDTCZDJBr9cjFosJu3j11Vdx3333JWWxAx1BhK+//hpAh5wKAOFotFqtSCQSsNlsUKlUCAaDcDgc2LJli5B7A4ChQ4cKR3NLS4tYixoaGsS5wOPxJM2D9Pdkiq7s4HjsA8heG5EdsjSPkP8i1ZdB6z69R/X19V3+vfF4HO+88w5+9rOfwefzYfTo0ZgxYwb+85//wO/3w2QyobS0VDT9a25uFvbU3NyMQCAg7IqSeQEkBb9Tncpynywge3pGnJSSuFqtxi9+8Qv88pe/xMqVKzFp0iQ8/vjj3T02hskooVAI//Vf/wW73Y4bbrgB//3f/53pIZ0StGlPLSOlTTEd/t1ut7gvHo+L0jDKeJazSchZTWVHRUVFGDVqlNhMJRIJeL1e7NmzBy6XC9FoVGQI0CRIhzvqKM+b8ewhHo9jy5YtAE696V867r33Xhw+fBjPPPMMrrzySnz88ceYMmVKt79OptHr9fjPf/6T6WGcEvKGRT7QAUc28VQq6vP5EI1GUVtbC41Gk9RIlKojjkUikRDSPDI6nQ46nQ4ajSZJbkev1yc1e4lGo8LZmA0NMI4FOYe+//3v44ILLsC1117bJ4MRBDkCqCKGfqb5nhzF4XBYrCd+vx9er1fc397eLhpB5ubmYuLEiaiqqkI8Hsfhw4exefNm7Nu3D9/+9rcRjUZRX18PAKLMlBzMqTIZ2bD5HsjImUFqtVqUqAJHqhHoPQ8EAmhpaUnSM6UML3L+nsj72VVjY5VKhbKyMmi12qQDfmplVbbMI101k+3LTWbTSR3JwUJaE8jJHAgEEI1GsX//fgAdzebT2cK1116LV155BYcOHcI333wjGvq99dZbuPrqqzFnzhxs2rQJbW1tOHTokHACqVQq5OXlwel0iv9Xa2srgA6nElVdAB2ZjeSY1Ov1UKvVYn3ipl3dh/zZlZ3K8r5Crlyh5zQ2NgLo0OOuqalBZWWluGZubi6GDBmCQ4cOiWt//fXXOP300+F0OnHo0CHo9Xo4HA588MEH0Ov1KCgoEHNFLBYTlThAR0+K0aNHQ6FQoLGxUTikJ06cCLPZLBJ25HNSLBbLOjs5Xpk3ctIDHXNJtjgR6f9JUlmJRAIajUbsIc1mM9rb22Gz2dDS0gK/34/Zs2dj7dq1+PLLL/HVV1/hrLPOwplnnom8vDysWrUKkUgEw4cPR35+PoDkbHRyYpPGs1arRXNzM/bv3y8aBnq9XiHXUl9fD5fLJQIbLS0tQk6hvb0dZrM5KTifKbqyg+OxDyB7bURu/Cj3hyLktZ/W/ZaWFgwePBh1dXV44IEHsGTJEpGFHI/H8dVXX+HFF1/E3r17AQATJkzAJZdcgscff1wEJBcsWAC1Wi0+/z6fDxqNBvn5+Whra8Pu3btRWVmJtrY2NDc3o7S0VOxhU5ErNlJ7SGQDJ71buvrqq4V22j//+c+0f9D555+Pyy+/HO+88w7Ky8uxcePGUxosw/Qmx5MRkq1lIKnQ5CM7l+WSMjp402GOnMl2ux1utxstLS2oq6vDvn370NDQILKY6feplLGmpgbvvfceampqoNPpoNfrxaaNNO7IKU0OAJVKBY1GIzLNmOxh165dohlbOomHU0WhUODJJ5/E/PnzEQwGcfHFF2Pfvn3d/jrMqSOXicdiMREYApCUNRSPx1FaWiocpKl6p3LmoFarhU6n63QflTTSTY7K05zb1tYmHNBWq1VkrchNwoi+kM3cl0tTU5EdAeRUljNyQqEQWltbRQMvt9sNh8OB+vp6NDQ04PDhw9i7dy8OHjwopJd27tyJf/3rX3C5XLDZbKLUkKitrUUsFhO2Q44HWSbjaA2BmN5DlqCQD3iypIrFYoHP50NjY6O4T66mkPWVUyUxjnZLlcmgzENyFMkHtdQsMsqEzwZOJbssWzPLZLuQM8voMdrDyk2j9Xq9aLgla/bLmM1mkeH817/+FZdccgkA4KOPPoLD4YBOpxNNjUkGA+g4/B86dAgAUFRUBKfTiba2NigUChQXF4u+CYWFhYjH48JOc3JyxPjkdTKb6IuVM7IWM+n3Ax2fY3Lsm81mUYlJlVIkcTF27FgkEgk8/fTTna49d+5cAMDrr7+OYcOGCf1mlUqFhoYG1NTUiMzGQCCA+vp6HDhwAIcOHUI4HEZZWZmQBnO5XFi/fj2USiXOOOMM6HQ6IQlF0hqk10u2kY2yXn1d5k12EtI+RK/XCyezUqmExWKBTqcTTuPW1lacf/75mDhxImKxGNatW4c//vGPWLZsGTZs2ACNRoMf/OAHoiEkzVk+n0/YBgXLhgwZAuBIc2q/348PP/wQ27Ztw9dffy1e0+l0iqZutL5QNQ+RyeBmV3ZwPPYBZK+NyPsJuYopVSpUr9fDaDRCr9fD4/Fg8uTJ0Ol0Qmrnvvvuw69+9Stce+21uOeee7B3714YDAYsWLAARUVFeOONN+BwOGCxWHDDDTcIySYKeAAdTvoxY8YA6NjLqlQqWK1WxONx1NbWinHKUBU6AJGNT39DtuxzT9pqb731VgAdJbFd/THvvfceWltbhc7it7/97ZN9OYbpdY7n0J+tm/VUUhvgkGxGIpEQmUQul0scsNra2uBwONDU1IQDBw6Ig39tbS327t2LHTt2YPfu3di5cydCoRDOO+88XHDBBSIDZMOGDeI1ampqAHRsvOnAQ+VK5BBQq9VZuREf6Kxbtw4AcNZZZ/XYoqVWq/Hyyy/jzDPPhN1ux0UXXSQyQpjsgmyAsg0pc0iWq6BsrrKyMlRUVKC0tBSlpaUoLCyEwWAQG3wKQul0OtElmaQzUm8GgwEmk0nczGYzzGYzrFYrysrKRAk9jU8epzzebKYvO4dkZK1dcuymat+1tbWJzGWXy4XW1lbU1taivr4ee/fuRUtLCyKRCIxGIyZOnIh58+ahsrISsVgMGzZsAACx8S4sLITf70dLSwuAI1nMOp1OyGRQRlu2lBAOZOQsZnLaykEgahoMQLynpGVZWVkJm80mDuSk3e1wOEQmIe1d3G43/H4/IpGIOLAVFBSguLgYgwcPxtChQzFkyBCUlZWhqKgIAIQjmg6d5ASXnczZwqlkl2Vrk1k5syy1XDx1Dqf7aQ0ip09XLFq0CHl5eWhubobL5cKIESMQiUTw8ccfA0BSUBTo2DOvXr0a0WgUgwYNwqBBg7Bjxw4AHWub1+tFOBwWPQNo3bNarVCpVOI62SrN0xdlVej8AnT8XylTnAJFAJCXlwetVguz2QyTyQSFQiFsgzIHX3nllU4B2u9///uwWq3Yvn072trakJ+fj+bmZtTW1iInJwfxeBz19fWoq6tDJBJBZWUlSktLoVAo0NzcjPXr1yMWi2H+/PnCufjNN98IvXegw7ZSNVRprsvG/Ul/kHmjOZvsg9Yc2nuSZIbRaMSQIUOgVCpRU1MDm82GX/7yl7jmmmvwrW99C6NHj8bs2bPx29/+FuPGjUNBQQGADukTlUqF9vZ2UV0Rj8fhdrvFXsVqtYokLnmts9vtMJvN4hwOHJl/SCpIHnOmSLWDP/7xj9i5c2eftw/5s5h6dpATXMxmMxQKhQgQ7du3D4sWLcLQoUMRCASwfv16rF27Fna7HSaTCRdddBHOP/981NTUoKWlBQaDAdOmTcO8efPQ0NCAN998EytXrsTzzz+PUCgElUoFi8UiZAKBDs1umkfsdruoGqcxKhSKpEoZtVrd6fFs4KS8Ovfccw/efvttrF+/HnPnzsXf/vY33HLLLd09NobJKMdz6M/WMhAZyvyQM3TofjpIuVwuxGIx+P1++P1+OBwOOBwOBAIB2O124YBOJZFIYO/evfjHP/6BO+64AxaLBQcPHhQaeXv27EEkEklafOQSZpocuYQ5OyEnM2kW9hQmkwlvvfUWpkyZgurqalx//fV488032SayDMoEpTmFqg9isVjSY06nE1qtVmQIkuNGvobsvEl9n2VNd8oKoeCYPBaj0SgymOk+Gos8RnJsZUupezr6emkqIevYpWpOUjAzEAigvb0dHo8Hzc3NcDqdqKurQyAQgNfrFVVypaWlOPPMMzFy5EjodDrU1NSIJmBUIl9VVSWyDQ0GQ1JVDK0rqQeIvkp/k/Ciz6P8eQc67MblcgHoeE8LCgrQ0NCAurq6tFUJNE/QfqYrKChFzieCrqlUKpO0oeXqDXm+ygYoi+z000/H1q1b8eCDDx71fhkK8mUbcuZ46n5Tfm/l94Wq6aqrq+HxeLrMktPpdJg7dy5WrFiBLVu24Nxzz0V1dTW+/vprLFq0SDiJTSYTEokEPv30UzQ0NECj0eDcc8/Fvn374PF4oNFoMHToUDH/lJWVIRQKCYkXucQ9mzLKUumLsipy5rJCoRDrpclkEvq55AgymUzwer0wGAzi3GO32zF06FAcOHAAn376KS6++GJx7eLiYjz11FO48cYb8dlnn+G8887DmDFjsHPnTmzevBkXXXQRcnJysHnzZjQ1NaGpqQnnn38+LrnkEnz11Vf46quvsHHjRowcORKjR4/GoUOH0N7eLtY8oEO/l+YXed+SrfQHmbdUqSMKPNMcGAqFYDabEY1GEQqF8O1vfxu7d++G3W7HG2+8gdNPPx0//OEPO1VOFRYWQq1WIxKJoKKiQmi2A0AgEEBhYSECgQB0Oh1Gjx4N4IieO2EwGJL2JlRlQ4H5bGkaejQ76Mv2QRJq1GCc7qNeDcFgUDR2pPeOJC127NiB8ePH4/LLLxeSSTk5OXC5XNiwYQOi0ShUKhVGjx6NRCKBw4cPJ2lzE1qtFjNmzBDrcUlJCRobG+F2u2GxWKDX6xEMBsVcAnSsZdSrADiyZtG6czxyhL3FCVvuX//6V/zmN7/Bm2++iQkTJuCnP/0pHnnkkbR6JgzTlzmeQ3+2loEQclYZbbSogy3QcZhzOp2IRCJwuVxwu92ora1Fc3Mz6urqUF1dDbvdDrvdLrKZnU4nJk6ciN/85jdYsmQJVCoVGhsb4XQ6k5oi1NXVweVyQalUikggOZQpy0x2BDDZR285mYEOm3njjTeg0+nw9ttv4w9/+EOPvyZz4tCmmDbBciMukroAIDR1ySkoVy7Q557kDI52o+fQoYCi9PF4HD6fT0j6UGdvIH3Ti2zPZu7rpalEavYNNX8lff9AIAC/3w+3243Dhw8LzUKn04m9e/di165dOHToEGpra7F+/XrccccdCAaDorohNzcXzc3N8Hq90Gg0yM3NhcfjEdnLwJFAJt36SxZzX8w+PB5SG+0pFApxcLPZbNi9ezdqamoQi8VECStJaRw4cADV1dXYt28fqqurUVtbC7fbDZ1Oh/LycgwaNEg00fF4PMK+ZGifR2XtwBH7lZtGZRP9NbuMoP+7nIlIn2GSXaLsrfLycgSDQdx3331H/UzQ+hCLxUQmosfjQTAYFDZRVlaGTZs2Yffu3VAoFJg3bx4CgYBwKo8dOxZOpxPxeBxGoxG5ubkio4z2uXJANVvnnL5YOSPLblHgWqlUQqfTdXLe0rnCaDRCrVaLCggqSd++fXun60+fPh1/+9vfoNFo8P777+Pcc8/FFVdcAQB45513MGHCBCxbtgyzZs0C0FGt7fP5MHPmzCS5Feo7Y7FY0NzcjEgkAq1WK3S8gSMOx1R9aaZ7kR20VFVgNBpFHw/aKwwZMgQKhQJNTU24+OKLcfbZZ0OpVGLHjh343e9+hz/+8Y/47LPPUF9fD7/fj6amJvGe0V7NbrfDarWKeWbGjBmYPn262KcVFBRg7NixsFqtKC0tRVFREdxud1IATdaRzsZmkP0N+n/HYjFxlpHldrRaLYxGI3JycqDX62E2m0WA4eDBg3j33Xfx1VdfYevWrXjnnXewbt06RKNRDBkyBGPGjIHP54Pf74dWq8XQoUNxxhlnYMqUKZg5cyYuuugifPe738WQIUPgdrtRU1OD3bt3A+iYO2RZKKVSKYJVFECjDPhsbjJ7QrumVatW4ZZbbsE///lP0Zzpxz/+MR577DG8+OKL+N73vtcjg2SYTHA8GSHZDk08qVFR0sx0uVzCwez1ekUEra6uDuFwGE1NTWhtbU3KZgA6skaMRiPOO+88ET1zOp1igqysrBRRu7y8PJG5SA27ZCdTNk2IzBHsdrtoXjB16tReec3x48fjd7/7HX70ox9hyZIluPjii8XmnckO6JBP+pgqlQpGo1Fkn1KHdZpz5MMTOW1SI+2p5V30u3LpPDmJafNN2c2khUlzFDmzqQu93AAwm7OZr7jiClx11VV47rnncOONN/ZZ51AqpPEfDAZFRobH48Hhw4fR2tqK+vp6tLW14eDBg6J5zty5czF58mT88Y9/hNPpRHV1tWjqV1JSIhw+Q4YMQXNzM4COjTetK5SNIh/esjWj8EToi9mHR0NuliTreJMDSa1Wo7GxEQ6HQ2gj7tmzp1MzUBmys+bmZphMJsybNw8jRoxAKBRCY2Mj6uvrUV9fj9LSUqHrSg42ymqigxrZUDaVnxL9MbvsaOXLZCdms1lI7ZhMJrjdbiFt8Nlnn+GKK67A1VdfjalTp6K0tBTxeBz79+/HG2+8gVdffRVAhzNx7dq1AIDy8nKsXr0akUgEhYWFsNvtosz9nHPOgV6vF82Pq6qqRNADAEpLSxGLxcQ+22AwiPHLf0820pcrZ1LPM9RIlh4DjjiP6KvZbIbL5RLBBXoPU5kxYwbmzZuHd955B19//TXuuOMO7N27F1u2bMHu3bsxduxYXHLJJdi2bRucTiccDgcGDRqU5OQm+ZnS0lKxbpWXl8Pj8YhAGckkZENjt/6M/BnU6XQIBoMiAEEJT+FwGH6/H2eeeSa++uorrFu3DosXL8bZZ5+N1atXY+vWraipqRHSjzKDBg3C8OHDYTKZ4Pf7MXToUGzbtk00fJQz1hUKBUaOHImCggLU1NSIwFZJSYmwH4PBAL/fn3VBzf4KnWXi8XiSzjtJ7NBZJhaLiSxm4EjAorm5Wey5FAoFqqqqUFpaisOHD8Pj8UCr1WLUqFFQKBRwOp2icezRMBqNKC8vx86dOxGLxURT81AoJPZBFIS3Wq1JVerZViFx3Fa8adMmXH755XjkkUdw2WWXifutVit+/OMfY/ny5bj++ut5omT6DX390C9n7skbL3IGhUIhhMNhOJ1OeDweNDY2ioxlv9+PmpoasQktLS3FnDlzMGbMGKxbtw5vvvkmtmzZIhbCyspKsTEfOnQonE4ngI6SItoEkg4zOZm5CVN288UXXwAAxowZg9zc3F573R/84AdYuXIlPvzwQ/zgBz/A+++/n9WHtYEIfaZpc6PRaGAymRAKhYTWOz1PJlWfUtbLTYfsTCbHD5UoksNa7u5N2Qd0gCAnM228aE7MRkdzfyhNBZI1JmOxmFhnQqGQkMNobGxEW1sb6uvr0dTUJA5b48ePx89//nPRif25556D0+lEKBQSDbmKi4uFjmpZWZkIZspOBTlzWdbW7escb/bhsmXLentox026IFWqbAZlEyuVSjQ1NYmKLHIMmUwmUY5Mmob5+fkYMWKEOPjt2rULPp8Pq1evxmWXXQadTofKykq0tLQIe9RqtaLjO5WgAkdsKDWblulZ5MayFIhMfQ+oUaxOp4PZbEY4HEYgEMCoUaNEAOHXv/51l69x5ZVXYseOHfjss88AdMwntNcZNmyYcD5PmjQJRqMRmzdvBtDhCBo5ciSqq6sBdGTYWywWIbNBVTZA33Ay90VZFVmqi9Z0yvajQFVqVTXZD1UzkP76+vXrsX///qQkhgMHDuCZZ57BO++8AwCYOHEi/vGPf4ggw+jRo9HW1oaXX34ZTqcTBoNBBDppfSoqKsLOnTsBdGQjulwuaLVa5OfnC+dzXl6eaG5JtsJzTM9A8zg5EeX7SFaL7Co3NxeVlZWoqanBa6+9hrPOOgtz5szBjBkzsHv3btH4vr29HRqNBsOHD8cFF1yAb775RlQzFBYWIi8vD21tbXC73cjJyUEikYDdbkdzczPsdntSVXF5ebk4ZxcWFiY1NE3V72a6H4VCIaQx4vE4LBYLnE4n1Gq1cDDL///8/Hy43W6x9yotLYXFYhFZ0Ha7XXzOy8rKkJeXJ/whAITNxePxTp9/vV4Pk8kErVYrErxUKhUKCgpEdUReXp54baPRCI1GI6p3yJazieN2Mp955plpN7QAcP/99+P+++/vtkExTDbQ1w/9sgi83MwkEokgGAwiHA7D5XKJstPW1lYhe3HgwAFRUnjLLbdg7ty5YvL66KOPAHRkBtCGPCcnB4FAADabTXQ5pQxmKqMnBzNlBvWH8uX+zKeffgqgd6QyZBQKBZ566imMGzcOq1evxquvvopFixb16hiYo0OOItKQkzOYdTpd0v20oaeNPnE8VQw0V9ABkrJ+DAYDIpEIwuGwODyk6t3R75GzUz6gskRPzyFLH5CESSgUEo3aWltb4XK5UFdXB7vdLhzMCxcuxC233CLWGaqkATo0DmOxGHJyctDc3CwOhJTNYTabxZoiry1yw7/+QF/OPpSRg1TUGwI4oudNEjt0sJIbCFdWVmLPnj3YuHFjp+uqVCosXLgQU6ZMwciRI/Gf//xH7G8GDx4s9j0AhNYh2RA5IGjeSA1WMD2PvB6Qkzkej8NsNsPn88FisSCRSIj5IBqNwmazAeiYI6ipI1VK0B7YYDBg+PDhqKysRHV1NYLBIBQKBYYNGyaa+ZWXlwvnwPjx46FQKERQY/DgwTjttNNw6NAhBAIBqFQqlJeXIxqNJuliAsmNLbPZbvpiEo0cOKbGwcFgEC6XC2azGW63W3y+ZZ114Ig8hcPhwFlnnYUvv/wSF1xwASZOnAitVouampqkBpgXXnghVqxYgcbGRgDAeeedh23btuHZZ58VQZDrrrsODodDOKVHjRolgg55eXkiO7WwsFAk92i1WmErVHHC+5Gehf63ct8QChAZDAYh7bZ//37MnTsXb7/9NhobG0UgSoakEpRKJUKhkKiOADref2o8C3TYnMPhwJ49e8Q8AXS83yUlJcjJyUFDQwMSiQTMZjMKCgpEpqvRaExqXM30HLRnJKevxWKB1+sVZxkguVEnJc0Fg0H4fD54vV6xjwA6ApC0DjmdTiiVSgwZMgR6vR4ej0fMUfJ15YACZUWTXAcFMHJycpIyls1ms+gHQPdlG/1j580wTCdkrS86vAUCAdGllBowNTQ0wOVyobGxER6PB/v370c8HsfUqVNx3333JR1k9+7diw8//BAA4Ha7EY/HRUmiVqsV5YMmkwl5eXnwer1JWnoUacs23SCmM6tXrwYAzJkzp9dfe/jw4bjjjjvwq1/9CkuWLMEll1ySVc0MmGRHETU9AiCyiOWmfXJ5Kz1+IocqmjvoWolEQlRGkOOIpBJkKEMhHA7DZDKJ5oHyeJnuhQ73lOUeDAYRCATEZpwczFQtAwCLFy/Gj370oySb2LhxIyKRCAoKCrBnzx4AHZlklCU2ZMgQocVMTmR6/1OdzP3lve6L2YfpoPeEylRTG3zG43FEIhGh6V5bWwug44D/3nvviaqFoUOHJnVjr6urw8qVK1FeXo7y8nIUFBSgtrZWHPDJYZSTkwO1Wi2CGJSFRLYkNyblfUrvQXZB87TcmMvn88FgMMDn88FmsyEUCokssby8PLS3t4s9qdlsxsiRI5OkCOLxOA4cOACgw/ljNBpF+bHNZhMSHSNGjIDP5xMVL6NHj0ZxcTEOHDgAv98vSqLVajXsdrtwHOr1+qQACZB9Ot4yfTGJRnYGBYNB5ObminNLTk4O3G63cOySI0eu5lQoFNiwYQMWLFiARCKBDRs24MsvvxTXp8xSo9EoKmQMBgMqKiqwf/9+IdNUUVGBUaNGYcOGDUm9AmbOnIk1a9aI55DD2WazCSeUxWIR8h5kn9k+X/d1aE6Jx+MwmUxCxoAeo4SHYDCInTt3YuHChdixYwf27dsHt9stKvQIcvQRBoMBOTk58Pv9ovKhsLAQNTU1wuGsVquFDnNOTo6QCgM6FAEqKyuFLZH0lxx8pUQJpmfQaDSiuookS+LxOHJzc9Ha2iqymoEj+0vSaKYG43SLRCIie5kczq2trSJRt6vgtVydSYEMGhtVaAId5xpaA2l+o/NQtpG9KyDDMCeNXN5BkxA5l8PhsIimNTY2ilLkWCyG+vp6xONxzJo1C/fff3/SJtnv92PZsmWIx+OoqqpCOBwWi6tCoUBlZSVisRi0Wi0qKytFFhJNppzF3Hdobm7Gtm3bAGTGyQwAd9xxB5566ilUV1fjmWeewY9+9KOMjINJT2oZGQCRXZYOOSv5ZD778uvJm614PI5wOIxIJNLJyazRaMShPxKJiANqNBrN6qZMfRXK4qP/dzgcRjgcFjq5ra2tsNvtCAaDqK2tRSwWw7e+9S384Ac/SHovYrEYXnzxRQDAhAkTUF1dnVRKX1BQILI7qIkPOQepaoYOaf0pS6wvZh92hZy5IzuaSW4nEAggkUigra1NBJWoMeZpp52GhQsXIi8vT1wvkUjgxRdfxPbt27Fp0yaUl5cLG9Hr9YhGo8KpXFpaCq/XKzKGaJ8jazDLPzO9h1qtFlUr5AimJnvt7e3Iz8+H3W5HUVERmpuboVQq4fF4RCUNOQq8Xm9SE0e9Xg+VSiWCMAqFAgaDAVarVTSGs1gsovQ4Pz8fI0eOhN/vx+7du0W1TlVVlchQDIfDUCgUQk6MSq4BiNdguhe9Xg+fz4dYLAadTieyDim7OBwOw2g0IpFIiF4MGo0GHo9HaO6+/vrrUCgUOOuss+D1euHz+UR5PH32qXSdGrlRMIKckxT4VKlUGDlyJEaMGIH169fD5/NBr9cLOyUNVToPkRYz2RmtV0zPQY0io9EozGazaJpG39P6QIFvr9eLiooKTJo0CU6nE42NjSITPXXfKydMUWBKq9UiHA7D4XBAoVCgrKwMw4YNE8kXhw8fRktLC4COZoCDBw9O6qWQk5Mjrk171lAoJCRfmO5HrraktYecwjTHUICI9gyxWKyTfnY0GoVCoUBOTg6KiooQiUTEvsNoNMJkMonndAVdn27yWcVsNossarJDWtuyEXYyM0w/h0rXI5GIWMiCwSAcDgdCoRDa2tqErpnP50N5eTnuvffepIkzGAxiyZIlOHjwIEwmE2w2G5RKJUpKSqBWqzF48GAxMQ4dOjRJHJ8mSYoYkzOAyV4oW33ixIkoLCzMyBgsFgvuu+8+3HrrrVi6dCmuu+66TpIITGaQG00ARxpmyffJWrin6tCl+YuuKzscKRsoHQqFQjR7CYVCIrhFztBszjTri5CzkN4X0r5tb28X643dbofH44Hb7YZOp8Mdd9yRtB4kEgk88cQT2LNnT9JGf8iQIfD7/dBoNMjJyRHVM+TMIU1UWm/kW3+hL2YfdoV8UCfHIH3OSWeXtA+j0Si2b9+ORCKBqVOnYt68efjmm28QCAQwduxYDBo0CAqFAqNHj8b27dvR0tKCaDQqssjy8vKEzIrRaERubq7IjrZYLACQlEEkl1czvYusa0z7x2g0KrKV1Wq1yFotLS1FU1MTVCoVTCaTCBwAEI5Ggt5TtVoNvV4v9CxNJpM4uCsUClitVpSUlCAWiyU1+jKbzaioqIBGo4HD4UhyRiuVShEUAToczLy29AzktKU13WazCVkm+pwbDAa0t7fDbDYjEokgPz8fTU1NcDgcmDhxIurq6tDW1oZAICACDBSgJNuQ1xWj0SiCmQDEffQ++3w+odus0WjwrW99K6n6grKYDQYD1Gq1sBW6PtOzyA7EaDQKq9UKt9st5hcKTiUSCbS3t6OtrU2cYQk636buKeS+D3IShdFoREFBgciMTyQS8Pv9OHz4cJKeL61dVG1jNpuT5g6tVivWR8qYZXoGqnihbGafz4dIJAKbzYZgMIhIJCLWCrfbDbVaDa1Wm9S0WNZFdrlcADrsr7CwULyP9Lmn91JuUJ3urEQBUYPBIBqd0/1yL4BshK2VYfox5AyiLOZYLCZKAam0kA5i5Ky58sorkzKkGhsbcffdd2PXrl3QaDQYMmQINBoNSktLYTKZUFxcDIPBAI1Gg7KyMlEmolarRRYZTah0iGOymw8++AAAMG/evIyO4+abb8bvf/977Nu3D3/4wx9w1113ZXQ8TAdyU1H6jJ+KHMbRkLOj5aZh4XA4ycHc1WGNsptoc6bT6ZKajnG2WfdBWczkYCZnYSQSgcfjgcPhEJnnQIfOJTViAoD29nb8/ve/F/NPZWUlEokEbDYbDAYDlEoliouLhR2YTCaRwUhZivImP7XRJJM9yHqTdLALh8PiMB6NRkUAvKGhAdFoFKNGjYLL5cJ3v/tdcdBSKpW4+eabsWjRIjEHUBZZPB6HXq+HxWIRpe4lJSUIBAIiO5FKqVOzmOWDH9N70PpBcgLUiCkSiaCwsBCtra0wGo0i85D2nOQ0oqawVLEiN3MkpxLNHbIz0Wq1wmq1IhaLwePxiPFYLBYUFRXBYrEgGo3CbreLgGd+fj40Go2QaKASag5O9CxU2k77gMLCQtEglBx6VN5OwYDi4mK0tLTA6/UiJydHlLnLMn70vU6ng8FgEBmCarVaZMCnCyAoFAqRfGO1WlFbWyuCoEVFRSJrNScnB5FIRNgPyfQwPQudQWkvotfrRbIVaTObzWaxr6CsZ+CIRAvNH6lyj/L7JydWKBQKBINBEaiS5VGUSiUqKiqSKnFoPTMYDEljp+QsShLj83PPQVIXVJFtMpng9/vh8/nE/EGNhinRgT7P6ZJdqDktXZfuS32PASQl48hNzimwIe+baax9YX/L1sow/Rg6wJGjmZzNwWBQHLJIQ5kWVYfDgUgkgsbGRrzzzjtYsWIFgsEgtFothg0bBrPZjLKyMuTm5qKgoAA5OTnQ6XSi+R/QsQmkMjDalPWVSXGgE4/H8f777wPIvJNZo9Fg2bJluPrqq/Hoo4/iRz/6kShNzQa2bduGW265BZFIBLfeeiuuvfZavPbaa3jooYdQUFCAl156Cbm5ubjppptQXV2NuXPn4r777sv0sE8ZWesydbPdXZ2w5YxY4EiEv729PakRCmWmdXWwpywAclyRA5yzmbsX+f2iwAA1mfV4PMJ5CBwJCAwbNgxAh77/hx9+iH/+85+ixLiyshJmsxkmkwklJSXQaDQYNGiQWE8sFgtUKpXIDiOHADkLeK3JXijzh+Qx6JBGQYlQKITW1lZEo1GhZWgymbBx40YcPHgQQEejNrPZjN27d+Opp57C8OHDk+YgCnbn5+cnNWgrKCgQWqlms7lTk7bUBqVM7yMf9nU6nSg/p8xjv98vSsr9fj9sNhvy8vKSdC9l6SRaAzQajTjkk0PJYrFAo9GIYAPQUTGQm5uLvLw8EaR0uVxCfkWpVIrHaM9LGbY85/Q85Mwn3VSNRgOr1QqPxwOTyYRAIACr1SoynWlPMmjQICiVSrS3t4vmjwBEFrNcDUMOZb1enyT5R9WYlDSTKgFGAQqz2YzS0lLRyM1kMkGn0wkbooAH0zvQ+ZOC32azWczzVqsVfr8fSqVSSK/Je1yyD9pbyNmj8n6X9kAU3KJACEHSOiUlJUlJEXJlYLrmbRQ8k5tpM92PHHQOhUIwmUzCHrxeL2w2G6LRqJBikvvAyH1n6Fp0zqDAZ2qjZjnImU6KJbVilK5LttgX4BmOYfohcpaQ3OyKJixa4KgzdygUwvTp07F582Y8++yzePbZZ5OuZ7PZMGTIEBgMBpSXl8Nms2HQoEHIycmBwWAQUWCaACn6JjuYWaOub7Bx40Y0NDTAYrFgxowZ1RswSQABAABJREFUmR4OrrzySixfvhxff/01Hn30UTz00EOZHpLgV7/6Ff7+97+jvLwc06dPx7XXXovf//73WLt2LVavXo2nn34a06dPR2FhIZ599llceeWVOHz4MMrKyjI99FNC7rRMyJ/tU9kI00aLyheBjo0+lTISarVaZCenOp7pd6gUUqvVigNGMBiEwWAQzlDOZu4e5HWGHIeUeUGOGHr/xowZg927d+Pxxx/HCy+8IByCQMdhfPDgwSKzsKysDAaDQTiYdTodTCaTcCxTQDPVwdxXNuEDDfmzTYfwWCwmsoupU7vX60VbWxucTiei0Sh27twJl8sFm82Gn/zkJzj77LOhUCjw29/+FqtWrcK//vUvXHXVVeI15PJjsj+dTieyIAGIDDHgyFzWXUEy5uSRtUipXJmCDjabDfF4HIFAAGazGVqtFk6nE4lEAvn5+WJPS3tfpVIpGnsBEFnM5DiiuV+j0SA3NxcWiwVKpRLRaBRut1tohBN6vR42m01kKtJ42cHcu1AFS3t7OyKRCCwWi8hUtdlscDqdyM3NhcPhEFIrVL5usViETA5dixzKcvYy7Rto3pCd0rJzWZZLoOdTtSgAodFM8xBlSzO9CzlwqQqO5gEAIkmKtHZTob2GPJd0BTURlftEaDSaLpMhaJ/UVaPZ7tpbM8dGq9UiEAiIzOHc3Fx4vV4R/AY6nMMkXSE3K5ZvAMTaQ0EJWke0Wq0IcnTVw0ZG7mXV13rJsJOZYfohcrd2mgSBIwcpk8kEoCMLpKSkBI2NjTh8+DAuu+wyvP/++/D5fFCpVMjNzYXNZkNubi70ej0GDx4Mi8WCkpISmEwmWCwWsXCSc5ki/iSXwRnMfQvS/LzooouyYiOsVCrxq1/9Cpdeein+8Ic/4LbbbkNxcXGmhwUA+POf/4zCwkKhs+VyuZCfnw+tVotzzjkHTz/9NAwGA6ZPnw4AOPvss7FhwwYsXLiw07Xkxh8Akkp2sw058i7fR1mA1AClK1Kbfcn3pzp5KNuHHJR0KKCMta4gJzJJZFgsFuEwkBtvxONxdkh2A/JGm7J56PBEX8kpMHLkSEyaNAmbN28WDuacnBzYbDYUFhZCrVZj0KBByMvLg9lsRlFRkXAOUWYjbd5Tm6Owgzl7oawe4IgMDlVWxWIxuN1uhMNhtLa2wu/3o7W1FeFwGIcPH4bH48GQIUPw0EMPJUmsXH755Vi1ahW2bNmCyy+/HACSDm9arbZTw1A54zk1c5mey87mzELBAFon8vPz0dbWhkgkAqvVKpq56fV6FBYWIhwOw+fzddpnkjOSKutkZ6FWqxV7WIVCITLWUiEnsslkEs1mqfESZdXy/rb3oeSVUCiEcDiM/Px8NDc3Q6PRCCdyfn4+nE6naP5IWYW0B5ETYPR6vQhkkp1oNBoh1STr8h4NmnuoWSBlV9N9RqOR7SUDUBayUqkUDaEpUz0QCIiKB7mCLvU9p7mAqh+AI70oKChG8mxykD1VroeuR72QgK7lU+T1ie2mZ5E13yn4TdI5wWBQBChkmZ3UhuPpoLXCbDZDpVKJfRC9JiVIpJNf6cvSXexkZph+CC169D0hR3JtNhtcLhcGDx4Mp9OJuro6AMBpp52GaDQqIvUqlQqFhYXIz8+H1WrFoEGDRFduiuzShowcy7SZpwW1r06QA41EIiGczOkcoZnikksuweTJk7FhwwY8/PDD+MMf/pDpIQGAaIp4991348orr4TX6xXNCU0mE3w+X9r70vHwww9j2bJlvTPwU4RkdihTLLXUvCsHTaoExrFeQ61WJ2Ul0pxEjgDKDkl38CMHs5yBQFp8pMtHDlF2Sp4aqQFNuZIGgMgOOv3007Fx40Z89NFHUKvVmDhxopAwoTXEZrOhqKgIBoMB+fn5MJvNSdnL5ACQ9ZdJw+54soyYzCFrdssSXpFIBH6/H+FwGM3NzQiHw2hqakIsFkNLSws8Hg8qKirw2GOPiUxVory8XHR/t9vtADo7iOkQSEEm2geFQiEYjUaEQiERGDtWgIzpHSi7lOZrjUYjHIYko1FQUCDWAqpaSW3ClJr5RU3cNBqNWIfkAz8FquS9rdwwVq7QYAdz5iGnP0kcFBQUoLW1NUn3tKioCD6fTzSOldFoNEI3lZJhDAYDcnJyoFAohENJfj6tPXL1A93kjGYKlFDGO8k7sb1kFvpMk3SGWq0W0kkkKSlLU1AWKe0vUqXhyAlIme/ytUjfOVU6IxVqQJlKIpEQ+1c+S/cOFJCiQAStD/TZl5P4UhNm5PuAIw5kuYmtLMVCwc/+CjuZGaYfImcEpjazMRgMCAQCKCoqgt/vRyAQwNChQ+Hz+YSwPWkF5eTkCEmMwsJCWCwWmM1mGAwGcdiXy4JoQu1rukFMB7t27cLevXuh1Wpx4YUXZno4AoVCgQcffBDz5s3DU089hf/5n/9BRUVFr4/j8ccfx4oVK8TPd911F7Zs2YKWlhY8/PDDcDqdwiHq9/thtVphNpuT7utq3EuWLMHtt98ufvZ4PBg8eHAP/jUnD+mW0QaMNr/H0jKVGzGlRujlr3Sjsmja7BsMBtGo1Gg0wmQydbnplktXXS6XyGhOzcKWD4fMyUG2IDubgSPBiEGDBqG2thbBYBCjRo1CfX09/H4/tFqtyBKxWCxJ+v65ubnQaDQwGo3i8EbSGLTuUPYH/czvYfZCzmU544syhSiby+PxoL29HW1tbQiFQkIyw2q14qGHHkp7CKfsIK/XK+ZZuXKKmv+RwygcDgsHlKzdSs7l1KxnJnNQkJF0MZVKJQoKCoTDkHQu5coJyiIEjuhXUvWDXAovZypS9irtWWWnIb2+PK/RtSkrkskc5OiX9UsLCgrgcDhgNBqhUqng9/uhUqlgtVqTMhFTk2Byc3PFc2QnI+01KAOZHIdHq3ZIDaaTHfK8kh3Q5570jqm67Xh1suUAFs0T8j5IlmChvaxc5SVLwVEQPXUuIQcz2VE6vWamZ6CAkNxAlt5jgs4px7sGyA3MB4ome///CxlmACI7TVQqleh0HA6HRZO/SCSCwYMHw263w+PxCIeyjMFggM1mg8VigdFoFBp4cqYHLbS06HL2ct/ltddeAwDMmTOnU5OCTDNnzhzMnj0bH3/8Me6//3789a9/7fUx3HbbbbjtttvEzytXrsQ333yDl156CUDHIaW1tRXBYBBr1qzBpEmTMGnSJLz99tuYP38+1q5di0cffTTttemg2xcgxx5lBsqZYADSBpfk6D4FpQCIjI/UDOjURhpGoxHAkezYozmYZciRGQgEEA6HxTxIGcz0OhwQO3lS3ze5HD0SiYisQ7vdDo1Gg6qqKvF+0/pBkibUiEsOZNKGnJxAlLlOpYq81mQ/dMCiTB6SHAA6pG/i8TgcDgei0SgcDgdisRgaGxsBALfeeisGDRqU9rqhUEhkMFOGqc1m69ScVKvVCkkiqiyR55xUhxHbVHZAmafk+AuFQjAYDDAajWhvbxdl7qTRDqDLQAHZBD2XqmDI+UNZ9UdDrthjG8kOKAAdDAaFHEZeXp6oGqPPfiQSEWsScCRjkQKZqVnH1BdA7v+Qah+pWa2pj8nrFpN9yBnIcqVdqnQbVUvRLd1nX+4nQuucfB25wjddlYV8HQqa0X6XtMKZ3oPOORqNJimBIjVJJfVrV8EnOSFvoKwd7GRmmH4KTWKUvaHRaMSCZbPZRJlhcXGxaHpA5e/kmNFqtUladHJGiJy1TLfj0Stjspd///vfALJLKoOgbOZp06bh+eefx89//nOMHDkyo2NaunQp9Ho9zj33XADAhx9+iNtvvx0zZ86EwWDAypUrkZeXhxdeeAFTp07F7NmzM5KB3RPQPCFnJwPocg6QM5hps0yN+I4GHQTlxhkAhMzC8UAZA7IzmfQYj7YpZI6PVG1uuQs7lYzabDahyUwOR3ISGwwGEZzU6/VJWcv0HNqcy05DXmv6BqllpHQAp2A3Ne+MRCIIBoOIx+MwGAwIhUIoLS3FrFmzurz2+++/j0gkgqKiItTU1AAAKisr0dLSAgCwWq2dKrtkuqq+yGbb2rZtG2655RZEIhHceuutuPbaa8Vj27dvx6WXXoqKigqUlZXh73//ewZH2j1QpQJlnMsNrKkBE2Uxp65HNB9RqTNlH5P9pVt/5IZudA3KWOtrjZcGCuRoVqlUCAaDosKB1hzKeAbSByFkm6IKNKpsSO2XITuOU53MqWsh20rfQc5uP9nfT+e0lgOrqXrP8pxC62NqtiwFw5jMIWswH4vUuSC1WnMgwVbLMP0Q0hYkDSDK3iMtsVAoBKvVCr1ej0AgAJVKJTIFUzfkFMmjJkupnZXln5m+yzfffIOtW7dCo9Hgsssuy/Rw0jJ16lTMnz8fb731FpYsWYKVK1dmdDxff/11p/sWLFiABQsWJN337LPP9taQehWK9KcerLp6LpAsTyFrzaWrfkidV8hxGQqF4Ha7YbPZjrn5jsViomkgZSQdbZzMiSO//7Tm6HQ6BINBmM1m4QCiElLgyAGL3vt0DmVZD5WCF1wp0/eQNbrlbK/Ux4Ajmau5ubkAgJKSki7f6+3bt+Mvf/kLAGDcuHFoaWmBXq+H1WrF7t27oVQqRc8Jsj+j0SgyHNVqtZiD5MZO8tds5Fe/+hX+/ve/o7y8HNOnT09yMu/cuRPLli1Lui+VvtRklqAGfpRxLDcaBY44/uh9kw/2qdIXqUFFSpg4WnYh0zegJBjKOlar1bBarSI4ITv8gCPZpTqdDkajUQSeqadDaoMucmSnMpAdSUx6Up3WqTZ4tB4ltLeWpZ8yxUALap4qPBccgZ3MDNMPIYkMOpTToZ90p6i7qXzoJ+TDv9z4pCvnMk+k/QPaHFx00UXIz8/P8Gi65qGHHsKqVavw6quv4u2338Z3vvOdTA9pwHM8cwBleZCDSXZOn4g+mdlsFvIapLuY7uBHes7t7e1CloGCagCSMqN5Djs16L2l94C0A41GIyKRiNAdTPd75OAhx3Gq3rJcLcPZy30TObOHnDup2T70vpLtkG7ytm3bsG7dOkydOlV8Tu12O1599VW8+uqriMViGDJkCFpbW6FQKPCtb30LGzduBABMmDABwWAQe/bsAdBRtRWLxYS8Bmm5ynYoZ+JnK3/+859RWFjYqSkZAOzYsQPr1q3DM888gyVLlqTtrdCXmsymQnMDrSOykyZVN/lo0B6XEyT6H+QMpkoachRThimR2heCGgWnQsFtlkhhTgXaw8hZznLgVc5szqZg10AMajLdgyLBdaIM022QtrHb7c6opq28AQcgSnBoQZMzQdI1CUx346zl/ks8HkdlZSXq6urw73//G4sXL870kI7KHXfcgcceewxDhgzBjh07YDKZMj2kbidb5pLuhLrAAxDdvUlDkUoC5Y21nOmR2jCQyusJOcOVyvDlx0wmk8hio2Y+lE1J2vJ9jWyxEWqoJh+c6L2WdexSocOU7EhOV5rOjf1OnmywEVkegz7zwWBQyBvY7XaEw2HU1NRAqVTim2++Ec2Stm/fDqDDQTxo0CC4XC7U1tYKeyopKUFBQQGUSiWqqqqEc7qqqgolJSXCoWy1WlFVVQW73S7kw6gpE9kXBTH6ijNpyZIlKCkpSeoT8O9//xtjx45FWVkZ5s6diy+++KJTAC7doX/w4MEZn0dOlnR6melKlVny4tTIhrnkZEiV6aEs0qMha/7z2nP89FUbYbqmtbVVBDXPOeccrFu3Tjx2zz33YN26dYhEIl0GNZcuXZo2qMk20v/hTGaG6YfQwUmpVCIajYqNNTmbU0vX5d+jDZUcUeUywv7NmjVrUFdXB6vVivnz52d6OMdk6dKlWLFiBQ4dOoRly5bhkUceyfSQmOOA5hJyOGk0GuGUDAQC4nHgSGDsaNei6gy6HjmNCXIcka4i/Z5erxfPpTmOOXnk9YXWj9Su2101RurqBnQto8L0Xei9JNugzzs5dCKRCEpLS9HQ0IBEIoFRo0Zh//79aGpqQlNTk7iOzWZDbm4urFYrNBoNioqKYDAYhESGWq0WDubCwkKYTCY0NzcD6LArnU4nypnlBpLZaG+PP/44VqxYIX6+6667sGXLFrS0tODhhx9Oeu75558vDu5Dhw5FW1sbioqKkp7Tl5rMHg8nopfJDDzS2YesEy8HJGjdyrY5gGEyRWFhIQDg7rvvxpVXXpn02Pjx4/Hd735XBDXPO++8TvPwkiVLcPvtt4ufKajJ9H/Yycww/RhyDlOGGQDxM5C+SUW2luwwPcfzzz8PAFi8eLFojpLNmEwmPPHEE7j44ovx29/+FldccQXOPPPMTA+LOQ6o1JmcyFqtVuhsdlXuTI4gILkqA4CQ80lt9kRf5QMkZSXJzmtuqHLqULMbqpCRS9qJdLrdqWuLLFswkNaedJqHr732Gh566CEUFBTgpZdeQm5uLm666SZUV1dj7ty5uO+++zI97OMmNZuUPosU+LFYLPB6vRg0aBAaGhqQk5ODaDSK1tZW6PV6jB49WjSMVKlUMBgMIohus9lQUFAgtFfz8vJESbzZbIbJZIJCoRAN3qiBsayXKet9Z2PA6bbbbkvKVl65ciW++eYbvPTSS52ee+ONN+LBBx9EVVUVamtrUVBQ0JtDZZg+gSzvxDDMETioyXQXLJfBMN1ItpcKpZYSUum4nD02UA72TAcOhwNlZWUIBoNC+7KvcMUVV2DFihUYN24cvvrqqyS9vb5Ots8lp0K6hicU/IrH40kav7KWc6qeK13jaBnPXWnHk6OrL8932WgjcoYY/ZxK6noz0NeexYsX4ze/+Y3QPPziiy8wa9YsvP/++1i9ejW2bduG6dOn46233sKvf/1rXHnllfjNb36DsrKyY147G2xEbrgWiUSEnE00GhWa6R6PB4FAAMFgEM3NzQiFQgiHw3C5XMLBnEgkhL632WyG2WyGWq2G2WxGbm4u9Hq90GM1GAxJASRqokRzATmp6ascyMp2xo0bB71eL2SiPvzwQ/z85z/HXXfdhcOHD+MHP/gBEokE7rzzTlxyySXHvF422AiT/bCdMMeCbaT/sXLlSqxcuRIvvfRSpyDsokWLRFBz9uzZWLt27TEDtWwjAwdO4WGYAYR8oGcYAHjhhRcQDAYxYcIETJkyJdPDOSH+9Kc/4aOPPsLXX3+NBx98sM82MxpoUBYhyfnIDmR5fkqVv+jqOuQcOt5s2YGWKdubcIbYiZPayM3lciE/Px9arRbnnHMOnn76aRgMBkyfPh0AcPbZZ2PDhg1YuHBhp2tlY5Md+pxGo1Go1eqkz6nRaEQgEBCyF0qlEhUVFfB6vfB4PNDpdJ0CFSqVCnq9XjiayfFMzb5S5VYomCTPO6nNJrMxg7krvv766073/eY3vwEA5OXl4fPPP+/tITEMwzD9kKVLl0Kv1+Pcc88FkBzUXLp0KW666SYR1OxL6yjT87CTmWEYZoASi8Xw5z//GQDwwx/+sM853QoLC/HEE0/giiuuwEMPPYSFCxdi4sSJmR4Wc5xQJqHcMI5IdSylVlzQc9I5llNhxzKTzaRqHnq9XpjNZgAd0kA+ny/tfel4+OGHszLYJmufy05f+myGQiHRADQYDEKr1cJqtYrsZ6pwkJsRa7Va6PV60bCPMpIp8CTLf8nNJeXvs1GDmWEYhmGyAQ5qMicLO5kZhmEGKP/+979RXV2N3NxcXH311Zkezklx+eWXY8WKFVi5ciVuuOEGbNy4ERqNJtPDYo4T2RHUlVa8/DUd8u91pTPPMNnCsTQPnU4n/H4/AMDv98NqtcJsNifdV1FRkfba2dpkh7KISdqGsocVCgVisRiUSqXQ9Var1TAajYjH46KxJ0l7yU5mOSOZnMVyL4muvpczmhmGYRiGYZjuhXdYDMMwA5BYLIb7778fAHD77beLLLm+hkKhwBNPPIH8/Hxs27atU2MKpu/QVePRYzmJUzMWZScUO5iZbOO2227D2rVrxa29vR3ffPMNnnnmGQBAbm4uWltbEQwGsWbNGkyaNAmTJk3CZ599BgBYu3YtzjjjjLTX1ul0sFqtSbdsgZy71IBTrVaLpkA6nQ56vR5Go1HIYJhMJlgsFuTk5MBms8Fms8FqtcJisYib2WyG0WgUv6/X68X3Op0OWq1WNAdVq9XQarUi85lhGIbpIBKJJEnmvfbaa5g8eTIuuugiOByODI6MYZi+CO+yGIZhBiD/93//h127diE3Nxc//vGPMz2cU2LQoEH44x//CAB44IEHsH379gyPiGG6Zvv27Rg6dChmzZolKgj4QDdwWbp0Kaqrq3Huuedi1qxZiMViuP322zFz5kw88sgj+NGPfoQZM2bAbrdj6tSpGDZsWJeZzH0BksnRarVJzl/ZUWwwGGA0GmEymZKcziaTCUajUTT3MxgMSc5ljUaT5FSm16HX4qATwzBMMg6HAxdccAEOHDgg7vv973+PtWvX4tZbb8XTTz+dwdExDNMXYbkMhkHHof/SSy9FRUUFysrK8Pe//x2vvfYaHnroIRQUFOCll15CXl5epofJMN1CY2Mj/vd//xdAR3l1Tk5Ohkd06lx55ZVYsWIFXnvtNdx4441Yv349y2YwWcnOnTuxbNkyXHvtteI+OtCtXr0aTz/9NJYsWZLBETK9STrNwwULFmDBggVJ9z377LO9NaReQW7cSc0/4/F4UnPAdNrs9JWqF2RZHHYiMwzDnBh+vx+/+93vcNtttwFA2uaz6cjGRrMMw2QHnMnMMDhy6P/kk0/w97//HQBHcZn+icfjwaWXXgqHw4GJEyfiZz/7WaaH1C0oFAr8+c9/Rm5uLjZv3oxHH30000NimLTs2LEDzz//PGbMmIFVq1Z1OtCtX78+7e+FQiF4PJ6kG8P0dchZLMtZpLuR/AXJa1B2MkvjMAzDnDyDBw/G+PHjxc8n0mg2JydH3LJB/59hmOyAncwMAz70M/2baDQKu92O1157DWeddRY2bNiAvLw8/Pvf/4Za3X8KWoqLi/H4448DAO677z689957vfK6N9xwAz755BMAnWUPEokEvve97+Gcc87BsmXLemU8THYzfvx4/OlPf8Jbb72F++67D263mw90DPP/kXXZUzXW2ZnMMAxzajz++OOYPn26uK1atSrp8dRGs11p+y9ZsgRut1vc6urqenzsDMP0DdjJzDDgQz/Tfzh48CAeeughnH/++Rg+fDgsFgs0Gg0KCwuxcOFC7N69GyUlJXj//fcxfPjwTA+327n66qvx3e9+F9FoFJdddhlWr17do6+3c+dOvPbaa+Ln1AqItWvXorCwEGvWrMGuXbtw+PDhHh0Pk32kHui0Wi1OO+00WK1WDB06FHq9ng90DMMwDMP0OKnNZy+88MKkx9M1n01HNjeaZRgms/SfFDaGOQEef/xxrFixQvx8xx134LTTTgOAEz7033777eJnj8fDjmam10kkEvj444/x+OOP44033uikY0lUVFTgiiuuwC9+8Qvk5+f38ih7B4VCgeeeew5tbW147733cP755+Oee+7Bz3/+cxiNxm5/vUcffRTXXXcdgPQ6dgaDAdOnTwcAnH322diwYQMWLlzY6Tqsbdd/ue2224TWIQAsWrQIo0aNQlVVFWpra1FYWHjcBzqdTtdbw2YYhmEYZgBCzWcNBgNWrlyZ6eEwDNPHYCczMyDhQz/TH6itrcUrr7yCZ555Brt37xb3z507FwsWLMCECRNQXFyM3Nxc2Gy2fiWNcTS0Wi1ee+01/OhHP8Jzzz2HZcuW4amnnsL111+Piy++GN/+9re75XO7ceNGVFVVIR6PA0ivY3ci2nYspzEwWLp0KW666SYkEgnceeedUCqVfKBjGIZhGCZjkOwbkL75LMMwzPEyMDwODHMM+NDPZCvvv/8+vvrqKwSDQQQCAfj9ftTW1mLXrl04cOCAeJ7JZMINN9yAW2+9FaNHj87giLMDvV6Pv/3tbzj//PNx5513oqamBo888ggeeeQR6HQ6DBs2DMOGDcOgQYNgtVphsVig1WqhUqlQXFyMq666ClqtNumaqRUQ1dXVqK6uxm9/+1sA6XXsUu+rqKhIO16uihg4jBs3Dp9//nnSfXygYxiGYRiGYRimr6NIdFVXzTDMCePxeJCTkwO3283aVMwp4/f7kZOTg1gslvZxlUqFs846C9dddx2uuuoqtrkuCIfDeOutt/Dyyy/jk08+QWtr6zF/580338T8+fO7fNzn82Hy5MkoKipCTU0NbDYb1q5di+985zt477338NFHH2HTpk2YMWMG3n77bTzyyCO44oor8Oijj3bpaJbhuYQ5FmwjzLFgG2GOBdsIczywnTDHgm2EORZsIwMHzmRmGIbJUkwmE6LRaKaH0efRarW47LLLcNlll3XbNc1mM3bu3AmgoxJi1qxZMJvNnSog8vLy8MILL2Dq1KmYPXv2cTmYGYZhGIZhGIZhGKavwZnMDNONJBIJeL1eWCwWKBSKTA+HYZg+Cs8lzLFgG2GOBdsIcyzYRpjjge2EORZsI8yxYBsZOLCTmWEYhmEYhmEYhmEYhmEYhjlplJkeAMMwDMMwDMMwDMMwDMMwDNN3YSczwzAMwzAMwzAMwzAMwzAMc9Kwk5lhGIZhGIZhGIZhGIZhGIY5adSZHgDDDBRI7J7JTrKhCQHbSHaTDTYCsJ1kM2wjzPGQDXbCNpLdsI0wxyIbbARgO8lm2EaY4yEb7IRtJLs5URthJzPD9BJerxc5OTmZHgbTBW63G1arNaNjYBvJbrLBRgC2k2yGbYQ5HrLBTthGshu2EeZYZIONAGwn2QzbCHM8ZIOdsI1kNydqI4pEIpHowfEwDPP/OZEIncfjweDBg1FXV5fxST8d/XF8fTWKm63vRbaOCzj5sWWDjQA8l/QWfXUeAdhGepO+aidsI70H20jm6Y/jywYbAdhOepMTHR/bSPfTH8eXDXbCNtJ79IaNcCYzw/QSCoXihCcaq9WalZMTwePrXk7GRohs/VuzdVxAdo/taPBc0rtk89i6gm2k98n28aXCNtL7ZPv4UmEb6X2yfXzpYDvpfbJ9fKmwjfQ+2T6+VNhGep+eHB83/mMYhmEYhmEYhmEYhmEYhmFOGnYyMwzDMAzDMAzDMAzDMAzDMCcNO5kZJgvR6XS47777oNPpMj2UtPD4sods/VuzdVxAdo+tu8n2vzWbx5fNY+tOsv3v5PFlnmz/G3l8mSfb/0YeX3aQ7X8njy/zZPvfyOPLPNn+N/L4uPEfwzAMwzAMwzAMwzAMwzAMcwpwJjPDMAzDMAzDMAzDMAzDMAxz0rCTmWEYhmEYhmEYhmEYhmEYhjlp2MnMMAzDMAzDMAzDMAzDMAzDnDTsZGaYLCMSiWDKlCni59deew2TJ0/GRRddBIfDkcGRdRAKhbBgwQKcffbZeOaZZzI9nE4sWrQINTU1+OKLL3DWWWdh9uzZOHjwYKaH1SNkq61ks42wfWTePohsthNgYNgK28ipMRBsBMhuO2EbyQ7YRk6NgWAn2WwjQPbbCdsI28ixGAg2AmS3nbCN/H8SDMNkDW1tbYlzzz03UVhYKO6bOXNmIhQKJd5+++3EQw89lMHRdfB///d/iSeffDIRi8US8+bNSwQCgUwPKZFIJBKhUChx6aWXJsrKyhIHDx5MfOc730m0tbUlduzYkbj55pszPbxuJ5ttJRtthO0je+yDyEY7SSQGjq2wjZw8A8VGEonstxO2kczDNnLyDBQ7yXYbSSSy107YRthGjsVAsZFEIvvthG2kA85kZpgswu/343e/+x3GjBkDAHC5XMjPz4dWq8U555yD9evXZ3iEwKZNmzB9+nQolUqMGzcOu3btyvSQAHREDn/2s59h7ty5AIBAIIC8vDyMGTMG1dXVGR5d95PNtpKNNsL2kT32QWSjnQADx1bYRk6egWIjQPbbCdtI5mEbOXkGip1ku40A2WsnbCNsI8dioNgIkP12wjbSATuZGSaLGDx4MMaPHy9+9nq9MJvNAACTyQSfz5epoQmycUwAYLFYMGPGDPFzPB4X3ycSiUwMqUfJZlvJprEQbB/Z955k45iAgWMrbCMnz0CxESD77STbxkOwjWTPe5Jt45EZKHaS7TYCZOeYALYRIHvej2wcEzBwbATIfjvJtvEQvW0j7GRmmAzy+OOPY/r06eK2atWqpMfNZjP8fj+Ajsid1WrNxDCTyMYxpUOhUIjvlcq+P9X1JVvJprF0BdtH5t+TbBxTOvqLrbCN9Bz9xUaAvmcn2TaermAbyRzZNp6j0V/spK/ZCJCdY0oH20jmyMYxpaO/2AjQ9+wk28bTFT1tI+puvyLDMMfNbbfdhttuu63Lx3Nzc9Ha2opgMIg1a9Zg0qRJvTi69EyaNAmfffYZTj/9dGzduhUPPvhgpoeUFoPBALvdjpaWFlRWVmZ6OKdMX7KVvmAjbB88lxwv/cVW2EZ6jv5iI0DfsxO2kd6HbaTn6C920tdsBOg7dsI2kjnYRnqfvmYnbCMdsJOZYbKc22+/HTNnzoTBYMDKlSszPRxcccUVuOqqq/Dcc8/hxhtvhF6vz/SQ0nLPPfdg/vz5UCgUePHFFzM9nF4hW2ylL9gI2wfPJcfLQLIVtpGTYyDZCJBddsI2kp2wjZwcA8lOsslGgL5jJ2wjmYNtJDvJJjthG+lAkehvQi0Mk0ESiQS8Xi8sFktSGQLDEGwjDMN0BzyXMMeCbYQ5FmwjzPHAdsIcC7YR5liwjQwc+rZIC8NkGV6vFzk5OfB6vZkeCpOlsI0wDNMd8FzS/1i6dCmef/55fPHFFzjrrLMwe/ZsHDx4EABw11134eyzz8YPf/jD474e2whzLNhGmOOB7YQ5FmwjzLFgGxk4sJOZYRiGYRiGYTKI3W7HM888AwB48MEHsWrVKjzxxBNYvnw5amtrsX//fnz++eewWCz44osvMjxahmEYpr+xaNEi1NTUpA10MgzDHC/sZGYYhmEYpl8SCoXgdrszPQyGOSaPPPIIrrvuOgBAIBBAXl4exowZg+rqamzevBlnn302AOCcc87B+vXr014jFArB4/Ek3RiGYRjmaITDYSxcuBBffvklgM6BToZhmBOBncwMwzAMw/Q7PvroI5SWliI3Nxe/+MUvwC0omGylrq4OXq8Xo0aNAgDE43HxGGkYms1mAIDJZILP50t7nYcffhg5OTniNnjw4J4fPMMwfQqPx4OysjLMmjULc+bMyfRwmCwgFArhZz/7GebOnQugc6Czq9/hoCbDMOlgJzPDMAzDMP0Ku92Oyy+/HA6HA4lEAo888gjefPPNTA+LYdLy8MMP4xe/+IX4WW6Io1QqYTab4ff7AQB+vx9WqzXtdZYsWQK32y1udXV1PTtwhmH6HDt37sQPfvADfPLJJ/jwww8zPRwmC7BYLJgxY4b4OTXQmQ4OajIM0xXsZGYYdI7qsxYVczRYs4xhspvHHnsMDocD48aNw09+8hMAwAMPPJDhUTFMejZu3IgbbrgBy5cvx/Lly2EwGGC327Fz505UVlbijDPOwJo1awAAa9aswaRJk9JeR6fTwWq1Jt0YhmFkduzYgXfffRczZszAc889l/Y5nKU6sEkNdKaDg5oMw3SFOtMDYJhsgKL699xzDwBg/vz5WLVqFZqamrB8+XI8/fTTGR4hkw2Ew2FcccUV2LhxI4AjmmVsJwyTPYTDYfztb38DANx///2YNm0annzySWzcuBE7d+7EmDFjMjo+j8eD0047DSNGjIBKpcIDDzyAn/70pzAajXj22WdRVVWV0fExvQ+tKc8//zwAYPTo0Zg/fz4UCgVefPFFVFZWYvjw4Zg2bRpGjhyJ6dOnZ3C0DMP0ZSorK/HII4/g29/+Ns477zzMnz8fhYWFSc95+OGHsWzZsgyNkMk0FOhsaWlBZWVl2ufodDrodLreHRjDMH0CdjIzDI5E9T/44APceOONQosqLy+vSy0qoCPSHwqFxM8c6e/fkGbZs88+CwDHZSdsIwzTu7z++uuw2+0oKSnB/PnzoVarce655+K9997DO++8k3EnMwc1ma644YYbxPepzf0efPDBXh4NwzD9kSlTpsBgMECpVGLKlCk4ePBgJyfzkiVLcPvtt4ufPR4PyyEMIO65556kQCfDMMyJwHIZDIMjUf3Vq1fjhRdeOC4tKqB39KhisRgikYi4RaNRxGIxbmKVAVizjGGyn3/9618AgOuvvx5qdUcs/cILLwQAvPvuuxkbF5Faqnw8DXYALl9mGIZhTp17770XH3zwAeLxODZt2oRhw4Z1eg5L7wxMnn/+eVRWVmLKlClYv349vvjiCwwfPjzTw2IYpo/BTmaGQUdUf+rUqdBqtZgyZcpxaVEBPa9HFY/HhSMzkUgkOZzD4TAikQji8Tg7nDMEa5YxTHYRCoXw3nvvAQAuu+wycT85mT/77DPRQC1TZHNQk2EYhunf3H777Vi+fDnOOeccLF68GPn5+ZkeEsMwDNOPYLkMhkFHVP+8887DvHnzsGnTJuh0umNqUQG9p0eVSCQQjUbFz7JTQqVSQalUiq+y45PpWVizjGGyi08//RQ+nw/FxcU488wzxf0jRoxAeXk56uvr8dVXX2HmzJkZG2NqqfKGDRvEY8cKavZ0+XIikUAikRABznROb1pn6CuvOQOLRCKBYDCIeDwOjUYDjUbDNsAwfYiysjJ8/PHHmR4GwzBZTl1dHa677jqEQiFccskluPPOOwF07i3y4YcfZnikTLbBTmaGQUdU/5prrsH999+P6667DhMmTMgKLSo6uKUe9MPhsPheqVQKJ7N840Nfz8OaZQyTXbz55psAOnSOZYetQqHAWWedhfr6emzYsCGjTuZsDWomEglEIhGx3pDDmW4EOZbpRmsQBzkHDhTojkQiCIVCMBqNUKlUGR4VwzAMwzDdxR/+8AcsXboUM2fOxJw5c3DLLbfAYrF06i3CMKmwk5lhkD6qn9p0JxPIB3kiEokkPYcyzqLRqHAwq9Vqdjb3IM8//zyAjrL3bLAThmE6nKJvvPEGAODiiy/u9PjkyZOxcuXKpMzhTJCtQc1oNJqUyRyLxQCgk6M51cksZzVzVU3/R6FQQK/XIxgMCidze3s7zGYzjEYjv+8MwzAMcxSoQpn6LHVVPSbvq9RqNTQazVEr3rqbX/7yl8jJyQHQ0SOK+pxQb5EPPvgAN954I2688ca0vx8KhRAKhcTP3Etk4MCazAyT5ZCjOHVRUavVMBgMCAaDaG9vRzAYFJM53cLhsHAc9Feqq6sxbdo0jBw5EpMnT8bOnTszPaSsg4IQ4XC4k43IdkIbnf5mL2wjA4Ovv/4atbW10Ov1mDt3bqfHJ0+eDAAZdzJTUPPzzz/H97///axpsEPZqQqFQjSXjUQiCAaDSetLMBhEIBAQ6w71B5Bv4XBYzCuyvFN/YdmyZVAoFPjmm28yPZSMoFKpYDQak/YlPp8PDoejX77fJwKvNwwA3HbbbaisrEw7T7CNMMcD20n/gvZUPp8PTqcTXq8XgUAgaa8ky5XJSWShUAh+vx8ulws+n08kAVRWVmL06NGYOHEiJk6cKBpfdxd5eXlQqVT4y1/+gkmTJsFgMIjXlXuLtLa2pv197iWSeYLBIC699FKMHDkSEydOxAUXXICampoef112MjNMlqNQKJIyw6gkNRqNIhAIiANdPB5HOBxGIBBIcgjQYZ+cBv2N73//+7j55puxd+9e/PznP8dNN92U6SFlDbShiUQind5/OTORmkpGo9GkppLypqcvwzYyMCCpjDlz5sBoNHZ6nDSaa2tru9wQD2QoA1XW/5flM+hnWltkhzKtO6nzhzyn9JeA5+bNm7F+/XpUVFRkeigZRaFQwGKxiM9aNBqF3W7H/v37B3S2Eq83DAAsXrwYa9euxZAhQzo9xjbCHA9sJ/2HSCQCj8cDr9ebJHlJ57T29nZ4PB44HI6km8vlgt/vT6piDofD8Hg84jqvvPIKtm7diq1bt+KKK67o9rG/9NJLeO2117B8+XJx35QpUzB16lRotVpMmTIFBw8eTPu7S5YsEY3v3W436urqun18zLG5+eabsWfPHmzduhXz58/HzTff3OOvyU5mhukDUEkyOZupXEXOGJId0JR9RllnlMGaztnYmyQSCUybNg0TJ06E0WjEiBEjMHHiRNx9990ndb2WlhZs3rwZ11xzDQBg0aJFOHjwYK9E6LIdCjrI2YlUniVnHnaVxUzl8umyEnvSfthGmJOFnMyXXHJJ2sctFguqqqoAdJT6McmkaurSocZgMMBoNMLj8SAQCAgncyAQgM/ng9/vRygUQiwWS1pzyMEsO5xp7umNbNfunkuAjtLPW265BU8++STLQuCIozk3Nzcp4N3Y2IjGxkaRbZWt9MX1pq6uDrNnz8a0adOSDv0ejwdlZWWYNWsW5syZ022vNxDoiblixowZKC8v73Q/70n6J31xLmF6nng8Dp/PB6/Xi2g0igsuuAAzZ85EeXk5Jk2ahOnTp2Pp0qXw+XxiP+X3+8X39LtutxttbW2IRCJQKpVIJBLw+Xw9Pv4vv/wSL7zwAlauXAmtVivuv/fee/HBBx8gHo9j06ZNGDZsWNrf1+l0sFqtSTfm6HQ1l9x1113ibH4i1cd6vR4XXXSR2LNOmTIFBw4c6PG/gzWZGaYPQHIZ8XhcOJhpUtFoNCLbTK/XQ6PRwOv1IhaLIRwOi8VIpVIhHo8nNQfsbd1MhUKBdevWAQCKi4vx4YcfdsoGW7x4Mfbt25f29998882kUpu6ujqUlpaK/4lCoUBFRQVqa2uP2kCrvxOPx4WDiN5fWRMr3fNlyDbIdoAj2c7kNJAbfqXqhp8KbCPMydDY2Igvv/wSQEfTv644/fTTcfDgQezYsQOzZs3qpdH1DUj6gNYa+kyHw2EEg0EAHeuN0WgUQSxyLtN6o9PpxJoUjUahVquhVqvF/EHzRiwWEwHT7pw/ZLp7LgE6DlbXXHONCFYwHWi1WpSWluLQoUNi7fF4PPD7/Rg8eHCPNqw8FfrienOqjZhYI7MzPTFXdAXvSfonfXEuYXqWSCQCv98vzlgajQZvvvkmPB4Ppk2bhueeew4FBQUiMxkA7rzzzqRsX9obKRQKPPHEE6ioqIBer0dOTo5wMl599dWIx+M466yz8PDDD6OwsLDb/oYHHngATU1NuPDCCwEAs2fPxuWXX96pt0h+fn63veZAh+aSRCKBkpISrFq1CmVlZYjFYmhvbwcAXHPNNUmOYtlOjrUePf7442n71nQ37GRmmD4GOQJSm/rRYqbVapGfn49gMAiv14t4PI5gMAiNRtPJyZwpZ3NjYyMikUjacuNXXnnlhK6VOu7+UI59KlC2MtDZwUzNIygCmkgkRAa8SqUS98nOZGoiSQ5n2iylPo8czt1lS2wjzInw+uuvAwDOOusslJaWdvm8sWPH4q233hqwWrpHQ6FQQK1WIxqNCgcwzRUyeXl54uAbi8VElg05oyngSWWg0WgUWq1WrF00b5Azm16rp5rZdNdc8sUXX2Djxo1J2aPMEVQqFaqqqtDW1oa2tjZxINq7dy9KSkqQn5+ftdnffWm9OdVGTA8//DCWLVvWrWPqL3SnHRwN3pP0X/rSXML0DIlEQkiIAR3vYzweR2trK+LxOJqamhCJRGA2m4XsJcla3nnnnZ2up9FooNVqodFohJORqojeeustDB06FDqdDnfffTeuv/56vPPOO932t1CFYDo+/vjjbnud/ojcNFtO2CLkzzdVGwMQWtyHDx9GOBxGcXFx0j5coVDgH//4R9J1CaVSCa1WK873qTz00EOorq7GU0891a1/azrYycwwfYTUyQjoKGP2+/1iAWpvb0c4HEZLSwvMZjPy8/Ph9XqFVEY8HodWqxXlFmq1GrFYrNedzdu3b8cZZ5yR9rETifQPHjwY9fX1ImMukUigrq5uQGtlynIoCoVCbHLove4qa0mhUMBkMkGr1QqZDLn7MZDsSE6FFkW6FtnSydoU2whzIvznP/8BACxcuPCozzv99NMBsFxGV8ja/4lEAlqtFqFQSKwPkUgEdXV1KC4uhsFggEqlgs1mQ05ODjweD1pbW8XzbTYb2tvbEY/HEQqFoFQqodFoxGtR0IoaDcoBre6ku+aSTz/9FLt37xZZzPX19Tj//PPx17/+VWT5DHQUCgUKCgpgNBpx4MABIZdSV1cHr9eLioqKTrIs/4+98w6Pskrf/z29z2TSSYHQi4KCICCggF1Rv8q6uosNXde2srZ1Rd0V1wKWdde27trr2rCsimURFwUkKlVqSEI6KTOZZHqf9/dHfs/hzGQCIaRMkvO5rlyQycz7vjNz3lPucz/Pkwr0p/EmPT0dADosxDRt2jScccYZWLBgQVJH29KlS3Hbbbex310ulyjGhLb+aNu2bTj++OPj8tITl1xySYft4KOPPkJhYSGLyjjUvEfMSQY2/akvEXQvfH5lvogf5VSmNAdbt27FmDFjWApCr9fLjvHAAw+gvr6eiY68iLh8+fK4HO8ajQYFBQUIhULQarW45ZZbMGbMmF59z4KDkLZCDvOjTQu3Y8cOHHfccUyj4Y9/+eWXMydz4ljz3nvvYdiwYdBoNHHz6ccffxwffvghvv7666R1a7obITILBP0E6kRod4oGH71eD4/HA0mSkJWVhdbWVoRCIZbfiVwuAOLcq7RzSh0X7yjrqfBloq6uLmmuOuDIdvqzs7MxefJkvPnmm7jqqqvwwQcfoKioaNCGktEABLS5yvx+P4A2gZmKQNLvNPhQHtVoNAqPxwO9Xg+j0cg2I8iJyFc85qG2wrcZfkeWfj/S1BqijQg6S2trK7755hsAwP/93/8d8rnHHnssAGDnzp0d7vQPZsjNHA6H41IzhUIh6PV6+Hw+RCIR1NbWwmg0wmq1QqPRQCaTwWKxQKPRoLa2Fj6fD5mZmcjMzITP54PH42FiMw+NOdR3KRQKlmKju76b7upL7rrrrjiXUVFRET777DPWpgQH0ev1GD9+PEpKSliBIo/Hg3379mHYsGG9ssA5EvrbeEOFmD7++GP22IwZM6DT6SCXy1khpmQis0ajSdn0Jb0FzZ95QUCSJFRWVmLIkCFJ04u9/vrrhzwmX8wLiK+Tws+HxJxkYNPf+hLB0UPist/vZ/0JXwuJL4gsSRLq6+uZCYxIS0tDVlYW/vGPf6C5uRk+nw9KpRIWiwVqtZoVqyZBWqFQoL6+HmlpaTCZTAgEAnj77bc73OAQ9Bz0fSfbnATai8DJ5rb8eoTWyk1NTcjPz2d1Tng39HPPPRd3LBpvKGowGo3C7/dDq9VCoVDgiSeewNtvv42vv/4aaWlp3fXWD4lMErEXAkG34XK5YLFY4HQ6eyS5PYl95DCVyWTQaDQsLYZer2fOM4/Hk3SibLFYoNVq4ff74zrEjtJo9AQlJSVYsGABpk+fjjfffPOoj3XVVVehubkZZrMZr732GnMrpiI92UYotF0ulzMHMg025Gg2mUzQarVxgxyFdlERCavVGuc4BA4K2Py/hxo+EsVnXlzuTGqNwdxGBEfGyy+/jGuuuQbjx4/H7t27D/ncQCAAg8GAWCyGAwcOYMiQIb10ld1PT/UlfE532qyinMuUfomvdK5UKqHT6ZhY7HQ6AQAFBQXQ6XTw+/2dyvtK4jKNPyqVql1aqK7QnX0JT3eKzDU1NbjiiisQDAZx/vnn45RTTsEtt9wCvV6Pl19+GcOHD8c999yDtWvXYtKkSWyBcTh6ek5yOCRJQmNjI2pra6FQKKDT6SCTyZCRkZFS6TP603jzww8/4N5778Unn3zCXMwAcPvtt+OMM87A6aefjjPPPBPvvPNOp/Jk9nUb6S14UbmjAtj79u3DxRdfjGnTpuHll18+7PGShULTXOe2227DqlWr0NjYiIyMDJhMJpSWlkImk/XLOclgaSdHS3/qS7qbwdZGaOOcjDrhcJgVP+aFZVqb0b/V1dX485//jHHjxuGRRx6B2WxGdXU1Kisrk/ZLMpkMw4cPZyYitVoNvV4Pp9OJW2+9lfVBo0ePxpNPPpnSGxEDrY1EIhG2vgYOpqWkdTjfBhLpyIAVi8UQiURQWlqKK664ApMnT8aTTz7JjF4dmb34+bNGo2GbnHa7HcOHD8eIESNgMpkAtG02Ux2bnkKIzAJBN9LTnScJfHzeXZVKxXasZDIZjEYjc6BFIhHmYJUkCUqlEnq9nnU8kUiECQgE5cbk/02VheBAoCfbCH3PvItZpVIxgYechh3hdDoRDAah0+nYQHQoeHczDaL84JfM4ZwoLtOA3NPuecHAZfbs2diwYQOWL1+eNJ9dImPHjsW+ffvw3//+F6effnovXGHP0Bsis0ajYRuS1K/QQokcOsmmkTqdDkOGDIHP52M5BNVqNWQyWdzmJ18UkKC0GiQ608R5IPcPd9xxB8477zxWzA0A3n//fTQ0NODJJ5/EPffcgzvvvBPvvPMO7rzzTlx44YWYOXPmYY+bKgu6QCCA6urquLZiNBoxZMiQHtvMHqicd955qKysZAIyFWKyWCy47LLLEAqFcMUVV+C6667r1PH6so3wm9aJcweaKxztvc/PTxLh5yMk1NAGerIfus7EPPXJoKgMeg/0vvjFf38iVfoSQeoyWNoICYvkXg0Gg/D5fExwDgaDzOjD17Gh/kMmk0Gv10OSJDQ0NDCDDwBkZWWhsLAQJpMJoVAI1dXVqKurA9DmdjYajVAoFDAYDOwnPT2drf/VanWffCadZSC1kVgsxua35CKmuketra1Jx5zE3MzJ8q4njjc0JiVLxUFrZ368ofmzWq1m824qyt2biHQZAkE/ghfrVCoVW+RrNBpWNMDtdrOOhh/gKO8lheeQU0yv1zORmndIk1gZi8XY8QbyIn8gkCj2yGQyNshptdrDhsiSC76jkJ9EaCHYkWs+8Xn8go7+zxcTPJTDuSPHEB1fiNSDk59//hkbNmyAQqHAlVde2anXHHPMMdi3bx927drVr0XmnoK/j2hBBLSNGenp6fD5fPB6vcwtwRcApbFJqVTCbrez4+h0Omg0GrbZGQgEmLNHq9XCZDLB7/cjGAwydxCl7KDjqVSqATsOJRZzo886PT0dpaWl2LJlC2bNmgUAmDNnDoqLi5OKzLTAJTrjIO8NtFotRo0ahbq6OrYo8/v9qK+vR3Z2dq8vfvoz/b0QE1/r4VA5K3khl49s6Cx8MS2C5h50DXwIO399ya6Z/j3UD52DX/STw4yuPxAIsCJeAoGgf0B9ht/vZy5l2kSnIn+UIoPMXZFIhNVDOlRfp1KpMHLkSIwaNQqtra2oqalBRUUFFAoFcnJyMHLkSPz4449obW1FNBpFWloa/H4/lEoltFotwuEw1Go1+1fQO5AZQy6XswjhSCSC5uZmtkal6C1qI51dX9PamHfBA/Ebs3QO3j1N6TDJwBGLxaDVahEMBtlY1FsIkVkg6EfQ5JU6FoVCwcKYqUptsrxAfF44GuhIGAgEAiyMlUKhKb8ULXZjsVjcLpkgNUksFNFRFduOoHbCT4b4nM3U5vgwdl7U4B1H/KSK34Gla+EHxWSCM3/NnQ244Y87EIUoQXuWLVsGAFi4cGGnU1+MGzcOQFsoqqA9dB/RBDfRMWEwGKDT6RAIBOLEYB6616nStUwmi8tZSgsicgRFIhFYLBaYTCZ4vV54PB7WZ4TDYahUKlYAaSCKzYnF3LZu3cr+JkkS3G43jEYjAMBgMMQ5n3iWL1+O+++/v+cvuAvI5XIUFBTA6XTCbrezftrhcMBkMrHFmGDgQhtIyQSXxMLB1Afx8wc+52RH0PyVT+lDc41AIMDmuMlyMicW2jocvOOMoOsnhyP1WZFIhC3+KepMCEICQeoTiUTg8/lYOgyPx8NqTQQCAfj9fni9XrYGp/6Hnz9Rn6bRaGAwGGA2m5GWlgaDwYBQKISysjJ89dVXSaMk5HI5Jk+ezIxiMpkMVquVidqBQIAZz0StkeQk9vcEX6z1SNOE0nFUKhX7zN1uNzNTpKWlwe12o6Wlpd1rab1O0en8GMRHrfPj2eE2ZklgJvMhvw7XarUIBALQ6/W9puMIkVkg6GfwIrNarWYTZirMRLua5ESmzo93dSQL/fP5fJDL5TAYDGxyzP+d8m3y7gxBakH5l/nikDSY0OByJE4gahcE70BSKBTQarVsQUbOO75d0GQsMWRMoVDEhchTODzvck50JnfkGgLincz0GXRlwiBITcLhMMrLy2EymZCfn88e//jjj/HRRx9BJpPhvvvu6/TxSGTeu3dvt1/rQIEXQug+DIVCbKNRLpdDr9dDr9fHpV1KdPPx9x+9DjjokFYoFCx6wuFwwGKxQCaTseI25IxOFJvJ3TyQxGa+mNtZZ53FHpfL5TAajSxk1uv1dhhmunTpUtx2223sd5fLhcLCwp698CNAJpOxhXVLSwtrL263G5FIBCaTacB8n4J4+NoQANgmdWLBPB61Ws02H8jRRQ4+2rxKPEcwGIyblyiVSuY2pHkHzWOA9ik7kkVL0fUS/Py5Iyg3plqtZn0WCcu0yUb9myC14DcOjiRqjtoO35Y6crsDBwt88fNfQeogSRJLgxEKheB2u+H1euF2u+H3++F2u9n/SXj2+/1xfQp9v501v1itVuTm5rI1d0tLCw4cOIDNmzcjOzsbOp0OLpeLOWf5SGZa6/fHdDw9BR+10pkNRHL70rr0UMflzRTAwRSlQFtqE4/HwwRmvV4Pk8nEUml4PJ64jVD+uHyKJ/45NP4la19UG4XW58nSOpGjObEuU08hRGaBoJ9CEx+NRoNAIMA6JZVKxcKS+Z27RBdpothHHZbX64VarWbVainUJxQKsYFSpNBITXiRWaVSsUJdSqWSDWxpaWkdDpy0AKQwG/qdwuIBsB3VaDTK2gqlYvH7/dDr9QDA3GodwRcoAMDStPCTsY5SZvCTdyBe0OIn7bFYTOQV78fEYjH8/e9/x4MPPsgmaiNHjsRpp50Go9HIip/dcsstmDBhQqePK5zMh4dPycRH0Ph8Puh0urg+hEQiflOH3wwF0G6sIJciHZv6qtbW1rjroA1UErgpzJz/GQhj0Q8//IDXXnsNn3zyCRPW7HY7mpqaUFRUhMmTJ+Odd97B7373O6xbtw7nnXde0uNoNJrDpkVKBVQqFbKysuByuRAIBCCXyxEMBqFSqXptASToPXiBWSaTIRwOs98TxV1+PKd7X6vVMgMEX0CLoqr4DXA6hlqthiRJaGlpYQtuWrhTv0LHORqor6S+j85D8+pQKMSuhRel1Wo1QqFQXM5mQd+QWFfkcGLUoean/O+8Sz6ZaJ0oMpO4xbcnQd8QiURYRBVFV7lcLvZ/h8MBn8+HlpYWeDwe+P1++Hw+th5PRK1WIz09HRaLhaXNociw7OxsmEwmNDU1Yffu3di+fXvca4cPH45YLIampiZYrVakpaWhtbUVWq2W9ZEU6RUOh0V/8v+RJCkuBSTda4lRv/RculdpncvnN0683/lj8mYuAKy/dzgcANoi1YxGI6qrq9ulMKM+gIRwihzmzxMIBODz+dhr+U1O/j0plUrmkHe5XDAYDHHjKj2PNj17GiEyCwT9DD6FADlVabLKT7KTQR0mdTJAvKOMJlh0LKPRyAo/8WI1726mzlcsCvse/jvgQ6f0ej2bLDkcDuj1elZ8hs8zRjuwOp2OOdllMhm0Wi0bRMmZQzlVQ6EQ2+jweDys8AENrnq9ng24tMtL7ZTaIO3CkrjND978ezqUa4j/DMgBQJN2EtrFxKv/IEkSbrrpJvzzn/8E0NYmQ6EQysvLUV5ezp531lln4ZFHHjmiY48dOxYAUF9fD5fL1e+Lj/QUdP/Q4oXEGZ/PF1dQhCaxOp2OPa8j4SZRaOadYeTGoXuX7nedTscKDpJLQ6PRxDkEqf/or2Lzgw8+iIaGBpx99tkAgIceeggLFiyATCbDG2+8gaKiIowaNQonnXQSxowZg9mzZ/fxFR89MpkMZrOZzTHoO/f5fCytgKD/Qwt9AOw+J7dxoruZh6IlaMM7EonAarWyOSoJxYlQ/xAKhdimVWLdkcSUHVqtlm2eUf9FYiD/w5s3SBQIBAJwOp2sf1SpVCylEO/Wjkaj0Ol0rL+k+TgVW+6P/VZvkCjW8t/L0aZHI+d7R+umRPc6/3/e8c6HuCf7ScyjSu+Bj+KjdstvoPbXIpH9nWAwCK/Xi1gsxoRlp9MJr9eLlpYWuFwutLS0oKWlhf2NIjcpZzM5inU6XVyRv4aGBhQUFGDBggXIz89HSUkJvvvuO1RXV7PzS5IErVbL+q2KigrI5XJkZWWhpaUFcrkcFosFXq+XRZ2RQzUcDkOr1fbhp5c68AVfaW3Ku815+EJ5fJ9DKSH5+SVfHJs0Fd6YpdVqmTFGp9PBYDCgrKyMbSxYrVbo9Xq0tLTAZrMlrWkUi8XgdDrhcDiY8O31ehEOh+Hz+eL6LIVCwSILw+EwPB4PsrOz2d/5KGFqL71hvhIzOIGgn5EouvFu0GTPTXR40mSIjkMTHHqMOk8aXE0mE0wmExs8yQXNhxeKFBqpQeLnzxfyo9xQNFBROHoiBoMhbgDVaDTMqcw7PMhpQc+j/OBOp5MtBAEgMzOz3Y4p5Tfzer3w+XxsIk4DObXpRMEoMeyQL3xAPwqFgk0sKDyVF8iEcNE/+Mc//oF//vOfkMlkePrpp3H99dfD5/Nh7dq1WL9+PTweD0466SRceumlR7wIs1gsyM3NRUNDA0pKSjBt2rQeehf9H1rAU95l3gVIE1V+sp4YQpgoJPNQzmf+b3Tv6nQ6+Hw+FnEzdOhQtLS0wO12szQ+tMGlUqmYw5mvHdCfxqNkxdyKi4vjfn/ooYd663J6DdrEVCqVcQstEiFFKoH+D58nlARnWjSTSEwiL+UypvynHo+HpVChaCyr1crCgvlUXHy9CBJ+gfYCs9/vB9A2t8nOzoZGo0FtbS3279/f4bzocCRGVpHjTC6XIzs7G0qlkvVber2etXUSF3vLWdZf4EPcO+M0J/Gks8Uh+eKyBI0dNJclcSmxyFayY/FzUhrDyITBu9o7unZquxSJotFo4gq6aTQa4WzuBUgoJBHS7XbHOZhtNhu8Xi8aGxvh9XrR3NwMn8+H1tZWOJ3Odg74SCTCUmosXLgQWVlZ+Pbbb1FbW8sMFIRKpUJeXh72798fJzgDbesojUaDxsZGZGZmQiZrKyhHtRr4jazEKLLBCr8JSevY5ubmuPkq6Rl8JAxtchsMBgAHx69kG1F03wKIq4elUChY3Qyr1YqqqioEAgEolUoMHToUDQ0N2L9/f9yGI6WKs9lsqKurYxuplOubdzHT2EnfsVqtZvm6dTodMjMzEYlEWJ0a3shFkUG9MeaI1bZA0M8gIY3Px5M4sPHpDYB4JwCRLD8mpVsgUUGS2goOmUwmlsyeroHEBT5fs0hN0Lck5ikmlwxNONLS0hAMBpkrMNFRo1Kp2gnMJNomtjE6Jjmm6bW0kNJoNOxciQMZtTez2YyMjAyWz4zcSvxgzu8cdyafFjn7aTClc5O4TPePIHWpqKjAnXfeCQB4/PHHcdNNNwEATCYTzjvvvA5TBRwJY8eORUNDA/bu3StE5sNA9xQJzXx4e2IYIs/hxgLqdyi8M/G5er2ehabqdDrk5uYiIyMDzc3NLIcvTZTpX/qhsUgUAe0fUFshJxAtoijqRtA/4fsIpVLJnMXkaJbL5cjJyWFptvjXOZ1Odq/n5OQw4dnn88FgMLB7PfF1Pp8Pbreb/Z5MYB4yZAiys7Oxf/9+lJSUtHM1U3HBREMFzZvJgU0Fv2iznN6bTqeDTqeDSqVCQ0MD1Go1MjMzAQA+n4/1l7Tgp75V9FXxeUcJPiSd/qXvhsRd2vikaLtknycJNLx7noplyeVylv6gI2dzonuZdzDTnJrC20ngIqGRcvvS86gNqNVq6PV6FjlK6ZJ0Oh20Wi2re3CkNVUERwYJeiTueTweNv/w+XwsPYbdbme/+/1+OBwOJgAee+yxOOusszBhwgSkpaXB6XTi3//+N1avXo3i4mK8//77WLhwIf773/+iuLgYHo8H+fn5mDJlCnbs2IGPPvoIQNsafvTo0VAoFCgpKYHdbodSqURWVhacTie0Wi1cLle7KGY+H+9gbyt0D1Of7XA42OdCG32U2oQ3J1BxR4owoYLLiUWw+fklaSVA27yVNjh1Oh2am5vh8XjYWLdr1y7W/1itVmRnZ8Nut6OkpISNj7QJ1trayuoi0SaC3W5n5+IZMmQIE8br6+uZkaawsBBer5cJ4rRxRev2nkSIzAJBP4QmvzS54V3KQPu0AnxBJt5dRpBrlAYo+ht1qonuDj5nERVlIrGZnGf9zUk2EOAHQPqXXOo0kGq1Wlawjx6nyQm/SKKd30SnB++GTyY0h0IhlreVRGZKRxAMBlFeXt4u76pOp4PFYoHJZGKFwPjCkzw0KSfnIr2/UCgEr9fLFn/kCuGvn8QofsEiSD3uvvtu+Hw+zJ07F7fcckuPnGPcuHH49ttvRV7mTkL3OX//JG5S0WYQ/5rOHJfyCPJCdTQahUKhgNVqZWGpFosFKpUKubm5SE9Ph91uZ+GDVE2bT6HBb3zym6qC1IRCiyncWJIktLa2QqPRMJePoH/Bbw7TPFQul7M5ZWZmZjuBGThYIJKKbfn9fphMJuYoJNdz4rncbjdbwJNoRGKk3++HTCbD8OHDYTAYsGnTJjQ2NgJoy5k5fPhwZGVlxS28SbS22WxobGxEfX09Ghoa2hVsIpGR+hy+aLLRaERaWhoaGhrY8SkPOTmcKVpssEdaUfoRgkwOia5juVzO3Ia8OYIvDklmGz4tHOX+p2NQFEUgEEBra2ucuEzjHV+HpKOUbby4TNcZDofR0tKC1tZWxGIxllKOz7lKArfRaGSCclZWFhPMKaqUcqsaDIZBLx52N+R4pY0NuudpA4mcpCQ2kyHG5/OxiF+FQoF7770XZ5xxBhunHA4HfvjhB2zcuBEAMGbMGNTV1aGkpAQKhQLXX389hg8fDgB45ZVXmMD8q1/9CpdffjnS0tIAADabDffffz+2bNkCh8MBtVoNt9sNlUqFjIwMBAIBGAyGOLe8EJnB7kOlUsnWhtRvNDU1dWhaMhqNrN/x+/0IBAIwGo3Q6/UdRjJQuiSaG5OLWaFQsDHGbDZj9+7diMVi0Gq1GDlyJOrq6rB69Wq2IUW6SV1dXVyNJJ/Ph7KyMnZOpVKJvLw8GI1GuN1u1NbWor6+HgAwatQoAEBTUxPy8vLQ3NyMnJwc1p/Qep/Wzj05L+6x0aympgaXX345mpqaoFQq8ac//QkXX3xxT51OIBh08GEesViMTYgo/ywPdaZ8YaZEeIc0dT7kBqVOkQq7qVQqluOXT01AkzBKd9CR2JzouBV0D3yYOoWX8qGkPBQWluhuTyYWJXOo06SaPz4tIPl0LDR4hsNh7Ny5k4WJqtVqJnD7/X7mMAIO5rCiHFp0PgphdLlccXn0VCoVjEYjMjMzoVQq4XA4mEhBLiHaRKGQf7Va3Q2fuKC72bJlC9555x0AwN/+9rcemwBRXua9e/f2yPEHIiQ0033L9+O82w/AEX1v/BjBjz3kuAAQ5/6iXKvRaJRtJJHzg0+hwUdnkMgsNkBTG8rDS7mZKQTU6XQiLy9PpBToZ/DzAz6HJc1VyHnVEfR90xyU30gmcZpScLjd7rjNbxKYqS2RwKzX61FcXMxym06cOBGFhYUsf3JZWRkOHDgAu92O5ubmpDmjyX3q8XjQ2NjYzllGIjml7AoEAsjKyoLdbmeOZnLrUxgzzU8GK7zDmFKIUF7TRGKxGJs3arVapKens76eBFpe6OchEwU9v7m5mc0/SSTii2zx8Gsu+p3GHpqP0mPV1dVsfutwOODxeNh10/xUqVRCq9VCpVJBp9MhJycHfr8fOTk57FiUTspoNMLv98NgMIjxq5PwubCB+LVnogMdaPv+qV2RuEwiIr0GaBMMbTYbJk+ejKqqKkiShI0bN2LXrl1wuVyoqKhAWVkZO252djZcLheL0AOA9957D7feeiuGDh2KV199FQDwxz/+ERdccEHce8jKysIjjzyCRYsWoampCV6vF3q9nqVjoTU6v6HXmajPgUyiQYn6AYVCwQrSazQaZmyiIo+UV5vSV1D7cLvdbHOT6kXQfU+5u4E2gdpmswFoGyNI+NVoNKioqAAAZGRkwGq1Ys2aNUyMptoUO3bsYBuwVqsVJpMJ33zzDVtHn3jiiTjttNMwefLkuAgvh8OB9957D59//jnKysowfvx4Nk9WKBRIT09Pmrqn34rMSqUSf//733H88cejqakJU6ZMwTnnnHPYCYVAIOgcJArzg8vhwoMPNzHhHbCJi37+OUajEQaDgU3EaGLHC858ISa6JnLBJuYmo4ldsrydgs7B5wSkdkET9sRK1bRTz+dXJnGZT41BLsNkgxD9jYTmxA0KajP0b0VFBStMMX78eOh0OgBgTiWn0wmn08kmeLzofDhCoRAcDgccDgcMBgOKiorgcDiYC4VCgxI3ZoSzMfX485//DAD49a9/jeOPP77HzjNu3DgAEE7mLkCbUSQ2A+2jZ47URcOnauKhyTWJzpWVlUmFBwpxJ1can3eOz9VJG2C0CSrGm9RDJpPBZDJBrVajqamJLaL379+PrKwsWK1W8b31E/i5BMGPuzRn6Oi1tAinIqM6nY45CfkiR9RP0GY3CYQkNgNAUVERDAYDNm7ciNbWVqhUKpx44oksKmLz5s0oLy9POj9NS0tDdnY2srKy0NzcjM2bN2Pfvn1xz7NYLNBqtSxdB/VTFosFFosFNpsNubm5cLvdsFqtrAYFH0U2WHOp8kWzyOlN35tarYbRaGQCPIlCVNcjEAigqakJWVlZLFqP6pEkup8pfz8AlkuX5oQymYyZZwj6fqiwWqKDnYfEnNraWlRWVgJoGxdrampYRGhra2vSYxgMBmRkZCAYDCI/Px8NDQ1sDOXXU7RZI0wSHUNtKbG456Gg9QxFSlBqHlrXBoNBlusdAAoKClBeXo7KykoMHToU1dXV+O9//9vuuCRk6vV6OBwOqFQqjBkzBrFYDHv27MGHH36IY445BuFwGMceeyzOP//8pNdnMBiwYMECvPzyy+1SsfA5wYXI3AbdY3K5PG4DkDYLLBYLS8dGmkVmZibbdAqFQmhpaYHRaITJZGJCckf1jBQKBQwGA+x2O1sPt7S0MONVQ0MDgLaUFoFAAF9++SWAtu91/PjxKC4uRlVVFYA2EfrUU0/Fzz//jA8//BAAMHHiRPzmN79hzvdE0tPTcf3110Ov12PlypWw2+3Iyspi62gq1k1jDW2sdPb+6Co9JjIPGTKEJZzOzs5Geno6EwAEAkH3wIf98zkyEwv9dZZkz6WBi6+aSvnyaEJGz6F0CdRpkxOV343v6LwkMIiw5q7Bp8ogtx8NcIk5LfnK6Xq9ngk8/OS3M8Wz+I0OOh7fHgmv18t2j8eMGQOdTodwOIwDBw6wSTcf/kiv5x0I9L74AgYajYaFMLW2tqK+vh5erxclJSUYOXIkWltb2eSQjknishCZU489e/Zg1apVkMlkWLZsWY+ei5zMpaWlLDWDoPPQvcinbuLzTB6pUEJ9UCK8+3HXrl1scUXhi6FQCE6nE4FAgBVWIZHB5/Mx0YHf/KQxib53ITanJhqNBnl5eaipqUEkEkEwGERlZSXq6+sxbtw4cc/2M/i5gVarRSAQgNvtRnp6etLn02JfLpezlFtarRYejwfhcBjNzc1x6Q3IAU9zDa1Wi6amJgDA0KFDYbFYsGnTJiYwz5w5E0ajERs2bMDWrVvZ/DctLQ2FhYXIyclBRkYG0tPTEY1G8cMPP+DTTz9lQoVGo8Fxxx2H4cOHs0JOTqcTJpMJEydOhFKpxPfffw+n04loNMryyVNaGJ1Ox+Y51BcN1pQZ/HyV3xhIS0uDUqlEQ0MDcyPK5XJYrVZkZWUxYScSiaC5uRlZWVlsHko5jRPXH9FoFC0tLWxNQ7ng+Y0KSoPQkahMxgyas+p0OtjtdmzZsoW55i0WC3744Qc21thsNrZOopRwWq2WiVeSJCErKwtNTU0YOnQompubYTAYWF0TjUbD5ulCZE4OFTZPVn+IH+OpPVCf5Pf7YbPZ4tzBFHVJ66ecnJy4/NqUyksulyM3N5eJ2rSZQY5XrVYLq9WKIUOG4Pjjj8esWbMQCoVwww03oLKykp1n3rx5h5yHZGdnA0DcuoiHb+PdOZ8JBoP45S9/CbvdjquuugrXXnvtIR/va/i6QmQ0oihYoG2caGlpQU1NTbvXms1m5Ofnsw0hj8eDSCSCnJwclj6DIueAtn6A5pv19fUs5ShpIzJZW7FBAMjPz0dzczN27twJoG0NUlhYiDfeeIOJwGeccQZmzpwZJzBfeeWVuOiiizr1nS5cuBAffPABbDYbrFYr+zyofwXau/l7kiMeyWKxGCZMmIALLrgAjzzyCHv8q6++wnnnnYe33nqrXVqMTZs2IRaLobCw8OivWCAQxMEv9vkiGHzKgq4eN7HIBe32lpSUJN3NI6GQJvt8MSYSB2lg59Mg8DuxfMoNsfDvOrT4IgcQDx/STiIRXyThSArQ8JO3xIk8/Z0WehkZGTAajdi3bx82bdp0SFfIkWA0GjFs2DCMHTsW1dXV8Hg8qKioQF5eHpsUkPuFF8UEqcXf/vY3AMAFF1yA0aNH9+i5hg0bxopTVlVVYcSIET16voFK4mKtqySmzCDHFvUjVJBHrVazSAh+sdXU1MRypXo8HpaPkxbmtClFxU94sZnSc4hNp9RDoVBg2LBhsNlsqK2tZekzfvzxR4wbN44tpASpCd3TFGkHtAlBZrOZ5cE1mUzt0qBQZBMA5lAFwIo2+Xw+NmfRarVsc7q6uhpAmzh54MABAGBicUNDAxoaGiCXyzF9+nSYzWZ8+eWXLNflyJEjMXXqVCbmAEBLSwu+/PJLFBcXM1eY2WzG3LlzMWbMGLz99tt49dVXk85l0tLSsHjxYmzcuBEejyeuuGUgEGCb7Xw/NljnJdTPq1Qq5jyklGn79++P+1zIbej1ejFs2DAmzIZCIbhcLlgsFvbcRHNLOByG3W6Py4lMjkMaGxobG+P+np6eDrPZzK6HwtCdTidaW1tRXl4Ou93O1ktU0Pqrr74C0NYOtm3bBplMhoyMDPzwww9x791isWD48OFs84TSdcjlcgQCgbgIUPrpDcd7qoqIHREKhVjkA20yUMpHypVN91piwUaC0mXwa6Lc3FxUVlaitbUVJ510Eg4cOIDGxkaWtoQKxPFGG4qeojmHTCaD3W7H119/ja1bt+LGG2+E0WiEx+NBbW0tAHToUiXq6uoAgK2lAcRFDCdGIHcX7733Hs466yxcd911OOuss3D55ZdDq9V2+HhfQ2IyAJY3m8953NjYyO5Vo9HI7mmPxwOXywWfz4fRo0dDq9WisbERgUAALS0tyMzMZO+Pj9CJxWKoq6tjAjO538n8BLQZbz0eDxOYZ86ciby8PDzzzDPw+XzIz8/HokWL2IbrunXrAABnnXUWFi5c2On3Tuv9jlztve1wP2KRWS6XY+nSpbj55ptx1113wWq1Yvv27bj44ovx8MMPtxOYm5ubccUVV+DFF1/stosWCATtIbGZT0fBdyidEW2T/T0xDy9N7uRyOTIyMlh4ssvlYgU3/H4/1Go1m5TxhZhoIkfHpkGZF8dpgD9UDulUIJUmYfxi7nADSUeiM4AuVTinQS2xrQBtYwYtFjMzM1FaWsoKYZhMJuTk5LD8zLT4oirc5Irnr4+chzSxohxau3btwt69e3Hccccxl1RraytLlZE4+PIbJ4K+x2az4fXXXwcA3HbbbT1+PoVCgTFjxmDHjh3Yu3evEJlTANooJScZvxlJIg4Jx+vWrUN1dTUkSYJer0dubi6Kioogk8lQX1/PCpAGAgFoNJo4NzO/AUp9CY1NXen/BD2LTCZDdnY2NBoN9u3bxwpnff/998wdlsrzhJ6gv7jLePcypcii/9M43dLSEifshkIh5kZOT0+H0WiMOyYJA7FYLK5uA4mFJOzQPZ6bmwsAzLk2fPhwWK1WlJaWoqysDHK5HGeeeSYrmAS0bWp9/vnn+PHHH9n8IzMzE/PmzcPUqVPx008/4cYbb2SC6OjRozF16lRkZmbC4XBg7dq1qKurw5NPPolrr70WxcXFcLlcMJlM8Hg8rNgxLxzSZzMY4U0JJBIZDAY0Njay9pKfn8/qwRw4cACBQAB1dXUoLCyE1WqFw+GA2+2GTqdL6vSNRCKw2WxsU1Gj0bCcqSaTiaW2oHMXFhYiPT2dhbtv3boVtbW1HaZyS0tLw7Bhw7B//34WDp+fn48NGzYAAAoLC/HNN99ALpdj0aJFGDNmDF577TWUlZWxNDCRSCQu8pCEzsSaB70xd01VETEZlFMXaNuoMBgMcDqdaGlpSRohlQgVXaypqYEkSTCZTHA6nZDJZBg3bhx27doFt9uNjRs3YtiwYfB4PHC73dBoNOw7SiYwm0wmZGVloaCgAIFAABs2bEBzczO+/fZbmM1mlgcYQLuIU55YLIb//e9/AMDaN9XIoD6QfoAjT1d2KDZv3oxrrrmG5a/fs2cPJk+e3OHjiZBrn0iW67y74DdhyL1L/0qSxERfcizzn3kgEEBlZSUCgQBqa2sxcuRI5OTkoKGhAW63GxkZGUmd4hSNS8aGlpYWtpkYi8VgtVphNpuZcHzSSSfhmGOOwcsvvwyfz4eCggJce+21cddCm6ZlZWVwOp1xG2eH4vvvv0csFoPZbGbtkNokXXeio78n6VJMzqJFi3D//ffjqaeewjXXXINzzz0Xl19+Oe6444645wWDQVx44YVYunQpTjrppG65YIFA0DHUgfBiGv9YZ1/PT2ASj0kTrIyMDBQWFmLr1q0oKSlhOTANBgM0Gg3MZjMramI0GlmhDbVazfLwkYjM78Ly6Rf4auSpuPBPpUkY//mQc49CvnixJhm8INyVz/lwojafamXLli0AgGOPPRZTpkyBTCZDZWUlduzYgfr6erhcLvj9/sPmvwPa3nNWVhaGDBkClUoFn8+HLVu2YOjQodBoNGhtbWWLVz4nMwlYQmROHZ577jkEg0FMmzYNs2fP7pVzjh07Fjt27EBJSQnOOeecXjmnoGP4lBl83melUske93g8KC8vZ/nrgDa3yv79+7F//36YTCaMGzcOeXl5aGhoQCAQYBXCSWwOh8PQaDRMWCYXGwAWziz6hdTDYrFg8uTJ2L59O+rr6xGNRlFRUYHa2lrMnz+/nRg5kOkv7jJ+85vmdyQEpKen48CBA+2Ks5Fwq9frkZaWlvSYyQQZcqsZDAY2d6B0YMDB4k9UeI8KMU2ePDlOYPb5fHjqqaeY0D1y5EicfPLJmDBhAuRyOdasWYMVK1ZAkiSMGjUKN910EyZOnBh3LYsWLcJjjz2G//3vf1i5ciWmTp2KhoYGeDweVgyQUnnx5orBmks1MWoSiM+nOmTIECYcm0wmDBs2DBUVFcx9aLFYmMGFNi0S+/CWlhbEYjGWf5W+/7S0NJjNZmzbtg1Amxg8dOhQ5lL97rvvmEMeAEuFQa9TKBSs2NvKlSvZd5mXl4eNGzciFoshMzMT33zzDQDgvvvuY7l309PTcdNNN2Hfvn2wWq3wer0wGAydEkZ7ms6IiL0pIHaEJEksspbSF9TW1rLrIlczbSqT4YrWRWq1GnK5HHv37mXr2JEjR+LAgQOIxWIoLy/HrFmzsHPnTuZY1Wq1ne5XKU/vggULYLVa8dZbb2HPnj0s2oIKmFLe3mSsXr0atbW1LJKD0u1Q9DBtkPP1j7oLt9vNxlaDwcDE/I4eT2T58uW4//77u+16DgWNAQBYjSi6H+n6MjMzkZ+f3+4z0mq1KCoqwt69e1kRWRpnOjIl8fn/MzIy2LyUz8E9YcIEfPHFFwDa0kUec8wxcDqd2LdvH2QyGS699NJ249mCBQuwZs0alJWV4cYbb8TChQtxxhlnHHKOU1FRgeeffx5AWx8mSRJ0Oh1kMhlz9ZPmwt8DPUmXRGalUok//vGPuPvuu/HBBx9gypQpeOqpp+KeI0kSrrrqKsyfPx+XX355t1ysQNCbpJoj5Eg4mkGGHCD0eloU0OM0AXQ4HFAoFNixYwd7LTlRAaC+vh5msxlWqxXhcBgqlSpObOZDlhOvlXcw86kdUm3hn0qTMEplQon9Sbynyur8Z83nnks8xpFCGwFAxzm96Tukgj1qtZp9Th999BGKi4sP+94AsPfG51NtbGxk6TiKioqgVqtRXV2NnJwcmEwmtLS0IC0tjbmZ+fy7g3VBl2oEAgE8++yzANpczL11n1Pxv7179/bK+Q5Ffx5vuhuZTBa3GUluIerfGhsbAbTlKJw7dy6am5tRXV2NyspKuN1u/PTTT9DpdJgwYQITm8nVTGIz5V7UarWIRqNMdCYBKLFYqiA1UCqVmDJlCkpKSrB3717m/vv3v/+NGTNm4Nhjjx0U39vRuMv6UhjqTN/OFzBOtqj3+XwIhUJQqVRxIjKN63RfA2Ch7zKZDFqtFi6XC263G9nZ2ew8iTmQf/jhB9jtdlitVvz617+Oi3I5cOAAHn/8cUiShLPPPhtLlixpl+oDaAtbvuOOO7Bnzx40NDSw90HuNgqp5tPFiToR8YYH2hSgzUEerVaLzMxM2Gw2NDU1wWw2Iy0tjaVFoHzHBOV0B9rEIOr/ZTIZcnNzmYickZGBYcOGAWgTqT7//HM4HA7I5XKMGjUKI0eOREZGBsrKyrBz5058//33cYXFJEmC1WpFa2srcxXKZDKWIuPGG2+MK+5GURjNzc0wmUwIBAJxc+a+nKN2RkTsTQGxIwKBAMt/q9VqUVdXx0wlZrMZbrcbDQ0NzHGsUqlgsViQm5vLoivIrSqXyzF69GhoNBqMGDECZWVlrG0oFAoUFBSwtBsdGblkMhnMZjOGDBkCr9eLXbt2weFwYOvWrWwzymazsfQYVqsVFRUV2LBhA84+++x278/lcuGZZ54B0ObAVavV0Ov10Ov1LF0GRXbybubuwmg0MhHf6/Wy/PgdPZ7I0qVL46ITXS5Xj6TPpfQnAFjfykeIkEM9mcBMJD5OUbi0EZHsnLxGQe2OzHgFBQUIhUJoamqCTCbDtGnTAID9XaVSJTVT5eTkYMWKFXjsscdQVVWFV199Ff/+979x4oknYurUqRg7dizbSDtw4AC+/fZb/Oc//0EoFEJBQQFb55rNZmi1Wqa78EIzXXNP0uXqAosWLcItt9wCSZLw9ttvt2vUGzZswLvvvotJkybh448/BgC88cYb7XZ7BYJUJdUcIb0F7zbh/1WpVIhGo0wgpsJtQFtHdd5558HlcmHNmjUs0X0gEIDT6WQTQhKbDQYDtFptXNVk3s1MThdeKKXzpJLQnGqTMD5dCgkmfDXZxNxjtDjqykSWzxFH8ANusrAi2oAwGAyQy+UoKSlBcXExZDIZpkyZgpEjRyItLY2Fg+3atQvffvsttm/f3uEuuclkQl5eHhQKBSorK6HT6ZCbm8vCLNPS0uD3+2EwGOJyMtNEXhSP6nveeustNDU1obCw8Ijyjx0tVPyvpKSk187ZEYN1vOHhXcw0GaaweBKUXC5XXB5ml8uFtLQ0rFu3Dg0NDaxvkSQJmzdvhlarxdixYzFixAjYbDa43e64oks0ppGwDBwUnYTQnJpQCHNOTg7++9//orm5GcFgEP/973+xdetWXHzxxcwdNlA5GndZXwlD/KJfoVCgpaUFANp9VxTq7na7kZaWFpf6wOfzsfdEKXFMJhO0Wi0Lcfd4PDAajSw/psPhQEZGBnJyctDU1ISKigoUFRWhoKAA+/btw86dO3HccccxNxnNkzIyMtrlSf3qq68QDodx/PHH49Zbbz1k/6DRaHDxxRfj6aefxt69e2GxWJjIHAwG2XkSncyDOd8uP9cnkbmjezkjIwMOhwPhcBhOpxNpaWmszXs8nrjxkz+uUqlkgjO5+Uj04cPSf/jhBzgcDuh0Opx55plIT0/Hpk2b8MorrzDxCWhry0OGDIEkSaiursbWrVsBtAldLpeLFYe77bbbsGjRorj3oNfrMXLkSJSWlsLv90OpVHZYMyRRyOxpOiMi9paA2BGSdLAovV6vh81mQzgchlKphMFgQGVlZbv1DeXlttvtGDFiBDIzM+PS81A/MHz4cOj1etTV1bHNzMSaNfy/9N1REcmGhgbMmjUL6enpWL16NaqqqjBjxgzodDr4/X5kZWUBAGuna9euxe7duzFhwgR2rdFoFA888ACam5uh0+lYbnC5XM7W0TRPohQa3b2mmTJlCr777jscc8wx2LZtGx566KFDPp4IpTjrafi0JaFQKK42FfUlQ4YM6fDekSSJucmNRiNLuQigw9oP/LHIvKXRaFj/QLUH6HHa1MjOzkZOTg4aGxvx5JNPwmKxsJpFRqMRZrMZ6enpuPvuu7Fz50588sknqKqqwvr167F+/foOP4Nx48ax9G9Wq5VFlZNxg9+MAFJYZP7d734HoC0MIFmDnj179qDNLSUYGKS6I6Qn4TseCiWmXHeBQABmsxkOh4OFYfj9fvzwww9wOp34+eef2Wvr6uqg1+uRkZEBr9cLrVaLjIwMWK1WlrNZp9PFhfqQsMynNuD7mFQSmlNxEkafFe208zlHqU+mTYPEInidGXDo+TSg06KIXO78IhI4WJiCJmjAQXcRhRZNmTIFv/zlL9k5IpEIHnjggTiHs1KpRG5uLsxmMwujPHDgANxuN0pKSqBQKDBy5EhIkoSamhoUFhbCZrNBo9EwhwO5nwZ7aGoqIUkSK/jXkSusp0glJ/NgHm8AtOufaFLMu4uj0ShbkJnNZrhcLmzYsAEWi4WFOhNGoxHp6emIxWLYvn075HI5CgsLkZWVxSrQ8wW4IpEIKyZIrg9Jkjp0sAj6HqvVioULF+Krr77Cjh07EIlEUFlZiWeeeQYXXnghRo4cOWC/u6Nxl/XmnISfW/BRT9FolF1nYkoMnU7HivvZ7Xbk5eWxv5FYQDnWI5EIXC4XgsEgzGYzTCYT3G43bDYbsrKy0NDQgAMHDsBsNqOwsJCJefv27cPYsWOxefNmOJ1OfPXVVzj33HOhUCgwefJkrF69GmVlZfjkk09w/vnnszknRVGccMIJnWpbc+fOxTPPPIPKykpMmzaNOZgplJs+o94uRpyqm5p8Qa3D5aqlujBNTU2w2WywWCxMZCZHc+LGIbkd6fHE4m/0vMbGRrb5PG/ePOj1erzyyitsrmA0GnH88cdDqVRi165d2LhxY9wxtFottm/fDkmSkJubiwcffBAnnHBC0vdxwgknoLS0lM2dyRiS+Hn0Np0REXtLQOyIYDDIUipGIhHWp5hMJuzfvx9A21whLy+P5bn1er2or6+H0+lEbW0tMjMzodPpWH9jt9vjzE0AmDh4OBQKBSZNmoQdO3bA7/ejqamJFXOja0tPT0ddXR1L5dfQ0ICTTz4Z3333He666y489thjGDt2LILBIB577DFs2LABSqWSrZtJhFQoFExk5h3M3T3mXXLJJfjVr36FV155BYsXL8bTTz+Nc889t93jfd1/dNR/qtVqdh919D2SwEwbnxkZGayvNxqNLGc6tTfSL2gtzffntM4E2sarnJwcVrtq27ZtLHrh6quvxqeffordu3fHFbpNRKPR4Pjjj8esWbNgt9tRWVmJysrKOFF7/PjxGDFiBOtH0tPT2aYbFdbl2wlpLSmZk/lPf/oTVq1aheLiYpx22ml46aWXcNNNN3X3tQkEfUoqO0J6A97xKUkS26WlCUVaWhpaW1uRl5eHiooKNDQ0wGazsdcXFBSwfHs+nw8HDhxAeno6vF4vbDYb0tPT2a4xhf7w4T587iCaRPD5M1NBaE7FSRjtqNOki3fG8I5xEoZpMtuZFCuSJLEwJPqdr9YcDofZzjFfLZzC3WlCFAgEYLfbYTKZALSFoPJpLNasWYPi4mKo1WosWLAAp5xyCkaNGtUurDUUCmH79u346KOPsHnzZuzbtw8jR46EyWRCQ0MDhg4dCrvdjoKCAuZ4Ive1yMucGqxZswa7du2CwWDAb37zm14995gxYwC0LShbW1uT5v/sLQb7eMOLUbRI4iuo03NIEMjIyIDP54Pb7Y6bnE+YMAGVlZXMzabRaJCdnQ2tVouqqipUVVVBLpcjMzMTGo0GSqUyLge8TqdjgrdGo0EoFBJCcwqjVCpxzjnnID8/H6tWrUIsFoPX68W///1vTJgwAeedd16fL357gqNxl/XmnIQXyfhwderfqDh0IpmZmaiurmZ5dvn7EmgTkWQyGbxeL7xeL4LBIBwOR1zKBNqUCgQCqKmpwfDhw3Hsscdi06ZNKCsrQ05ODs4880x8+OGHqKqqwueff46zzjoLmZmZuPjii/HOO+/gu+++gyRJOP/88yGXyzFkyBAAwO7duzv1/i0WC/Ly8lBXV8c2ukmQSNyo5+dVPT0nSbVNTZqTEom5mTsiPT2duZlbWlqQnp7OvnOn08mKdZEDlT53jUbDhMRIJMLchiRIkcFh9OjRSEtLw/PPP4+6ujoolUqcccYZMJlM+Pe//x2Xq3no0KGYPn06Vq1axTY9L7jgAtx+++1srpsMygfOh/YfLl1Gb8xZU01ETIR3MWs0GuZGNpvNqK2tBdAmFo4YMSLu8zKbzYjFYnA6nezzLSoqglKpRHNzc7ti47SGIaGO3MIajYalgPT7/aiqqoLP54PL5WJCciAQYO54OmZOTg7q6urg8/kwYcIE7N69G8OGDcOIESOwf/9+XHPNNZgwYQIaGhqY4D127Fj4fD5kZGQw5zLQttlGBi3e2NOdaLVafPTRR0n/1tHjfUGiyEzfOR8RTfnbeYLBIOrq6lj/lpWVhdbWVkiSxApC2my2uHuR+oz09HTo9Xo4nU6EQiG2mWowGNDa2oqqqipkZmZi6tSpKC4uxqZNm1BVVYVx48ahoKAAixYtQigUQn19PVpbW9m81el0wm63o6GhAcFgEKWlpezcer0ep59+OiwWC9RqNSKRCMrLy9kGWGFhIVvrp6enM5MVLzTz8+ye5IjP8OKLL+Kvf/0rPv30Uxx33HG45ZZb8Oijjx62QJNA0N/orCOEdqCcTierTDxQoI6IOiZKlUFFD6xWKwwGA4YNGwa5XA6LxcIGuHA4jBtuuAHnn38+MjMzEY1GYbPZsHv3bpSVlWH//v0oKSlBdXU1y6vmdDpZqBEt/BMn5SQ0pIIL9ZJLLsHq1atx4oknYuHChSk1CaPCWXxoF4nC9FnyAvPhil1RHkH+cydRmcI/6Xgmkyku1JHCHX0+H8t399NPP+GYY45hFb5XrlzJFl0UojR69Gj85je/YSFAiajVakybNg0PPfQQfv/730Mmk6G8vJwVqGxtbUUgEIDX62XuaVp80oRERNz0LX//+98BAIsXL+51kZccLkDfp8wQ4008sViMhTBTjkONRsOcGWq1GkOGDGGbZiTQNzU14frrr8f555+P9PR0BINB1NTUoKysDC0tLQiFQohEImhqakJNTQ2qqqpgt9sRCASYYO12u1mxwHA4zEIv+5pgMIgLLrgAs2bNwgsvvNDXl5MyyGQyHH/88bjppptgNpvZBurOnTvx1FNPoaqqKiW+v+4kce7x9NNPY/fu3Sk9J+E3kki87CgVgkqlYsIc5bzlU3Hx9316ejpb3DudTmRlZTEnLI0pTqcTLS0tGDJkCPLz8wEAW7ZsgdVqxTnnnMPSba1cuRJOpxPTpk3DL37xCwDAunXr8OGHHyIWi2Hu3LkAgI0bN8bVIzkUiQ5DPl0X/zvRG3Pbzm5qWiwW9tOTEXiJOUIjkQj73A6lL9CGIdCW5zYWizERiYRm+qz5+S4v5FKBbGpP5eXlsNvtUKlUOOGEE/Dmm2+irq4OBoMB119/PSorK7FixQpUV1dDp9Ph7LPPxmOPPYbHH38ca9aswc8//wyj0Yi//vWvWLZs2SEFZgDt5qLJDB983t/eMkWQuPjTTz/hxhtv7JVzHgm8GEyCs0wmY2tItVqNoqKipJ8XpXSkNAgKhQLDhg3D5MmTMXnyZEycOBFjx45FUVER0tPTmTOV1k/BYBAulwsHDhxAa2srTCYTcnJyALStczr6LiVJQlFREQBgz549uPDCCwG0pZi97rrrMH/+fMRiMezcuRN2ux2ZmZlYtGgRfD4fS3egUCjY5hxFqtJaj97LYCYxlUkgEGDfc0VFBaqqqtDU1ISGhgaUl5djz549cLlckMlksFqtzK2s0WigVqvh8XiY0Y5yv1PB1paWFtaPBgIBNt4Eg0HodDqEQiFs3rwZubm5mDlzJpRKJWw2G9atW4e3334br7/+Oj755BNs374d1dXVaG5uht/vh0ajwbBhw3DyySfj1FNPxcyZMzF27Fi2UVlRUYFt27bhxx9/xJYtW+B0OmEwGDBnzhy2gTZ8+HBmpKA2wm9G9EY7OaLtji+++AI33XQT3n77bcyYMQMAcPPNN+Pxxx/HG2+8gauvvrpHLlIg6AtSzRHSHfAT285MWGjHlpzE/M4gX52XfqqrqzFy5Ejs378fjY2NeP7553HuuefirrvuwsqVK/HZZ5+xMMnW1laWPiM9PR0WiwWZmZlsZ1iv18cVfOLTHPCDaV+6UA+1w5sK0PdH3yGf+4/+ThsJh/ocadHOv4YEIABMfKZCA263m4UUaTQaWK1W2Gw2NDY2YsKECaipqYHNZsOGDRuwYMECfPjhh9iyZQsOHDiA8847D7Nnz8Ybb7yBXbt2Yfny5bj11ltZqFtH7/Occ85BXV0dVq5cyfKtejwe5ng0Go0IhULss0h0Dwl6n5KSEqxatQoymQxLlizpk2sYO3YsDhw4gL1792L69Ol9cg3AwBxvjgR+IwxA3KYVTZQpeoaeL5PJkJeXh/r6egwZMoQJxi+99BKuvPJKnHjiidi1axe+//57VFVVob6+HkCb0ycnJ4cdt7W1lS0UMzIy2KZmYs75vnY0p2qIe6qQlpaGW265BR988AF27tyJWCwGj8eDV155BSeeeCJOPfXUAXP/9Bd3GQ9/j9P/DyX+GwwGlkOdXscfh1CpVEhPT0draysikQg8Hg8rCufz+VjO1draWphMJkycOBGtra3wer3YsmULpk+fjgsvvBCrVq2C3W7Hu+++i/nz52PmzJlQKBR47733sHHjRkiShIULF+Kss87Cl19+iQceeABPPPEECgoKOnwPZKQg8QtAu7yYvSkcEqmWVoVPp0KuQxL2vF4vy1+bDKvViubmZpab2Wq1wmq1oqWlBR6PJy7ajtYSZIJQKpUsEoZMMrR5MGnSJOzZswelpaVQqVS49tprsX79eqxatQoAcPbZZ+OXv/wlE5l27dqFH3/8EQqFAi+++CKr+XAoYrEYvvjiC/YZkOGDrwsAHGwjqZQusK/h06lQmgOtVsvG+aFDhyYV0kgcBNqczgSZU1wuF7xeb7u+iQxWNA8goZmERjpXLBZjGyMqlYq5jiVJQjAYxIQJE/Dpp5+ivr4e4XAYp512Gr7++mu8+OKLuPzyy3Hddddh3759bPPn9ddfB9AWHRyJRJCWlsZyMvNmIhKcB2vUFfUbfLQun6rRYrGwzUb6/gm9Xs/qTgFt7Yiib2QyGUwmE3Q6Hbv3VCoVHA4H0zMoTRNFwJHITJsfO3fuhMlkwrx58+BwOFBdXQ273Y5QKMTSXnSGCRMmsCge6i+1Wi3S0tIQCoWwe/duSJKEYcOGxaU55bWa3ir6BxyByLx582ZcfPHFePTRR3HRRRexx81mM26++WasWLECV1555aDfQREMHFI9VKgr8Lv3BAm1h6q2Sn/j8/0mprWgcKKGhgYolUpUV1fD7Xbj448/xubNm/Htt9+yiZ3NZoNSqYTRaITNZoPJZEJ6ejqysrJgtVqRlZXFQkGMRiMTOSlHJ+8EOZwDV9AGLzgfKbxzXKVSscWSQqFgu75UsZnyJQNti36ZTIaMjAwYDAZ4vV6Ul5dj8uTJ2Lp1K5qammC32zFjxgxs27YNDQ0NeOGFFzBy5EhcfvnleP3117Fu3Trs2bMHV199NebOnXvI6580aRJWrlwJn88HrVYLr9fL3MvAQecDtR0hMvctTz31FABgwYIFGD16dJ9cw7hx4/C///2vz53MA3G8ORJ4AYrP7a7T6eI2Fa1WK5xOZ7som/r6egwdOhTV1dVwOp345z//iYsvvhgTJ05EWloaXn/9dTQ3NyMUCrHCMLQYsVqtbEHg9XqRkZHBHEs0/tD3QW6QviDVQtxTEYVCgV/+8pc47rjj8M4777DNguLiYpSVleEXv/jFIYv/CLoXWshGo1EmtoRCIWi1Wvh8PrS2tsJoNCaNVEocmxOLCvNQ30AL/3A4zBb+fNqM6upqjBgxAieccALWr18Pm82GHTt2YOLEibjkkkvw5ZdfoqGhAV988QUmTJiAOXPmQC6X45133kFxcTGCwSCuvfZalJSUoKKiAjfddBN+85vf4Mwzz4xL+xGNRrFu3To8/fTTAIBjjjkGgUCAhdmTWEWfER++3BuL/1Tb1FQoFCwyjtIP8HlNyZmaDJlMhrS0NNhsNni9XhZhKUkSWltb4wwRVqs1TvDX6XRx81Wv18vqzYwfPx6vvvoqAODUU09FZmYm/vOf/wAAbrjhBsyfPz/uOijfazQaxeuvv46rr74aI0eO7PA9RyIR/PWvf8XWrVuZQcNoNLL1lFwuZ+MfbwgZrCIiTzQaZeKcJEnw+Xzs+5YkCRaLpcNibU6nk60daXz3eDxwOBxxgp9CoYBer2e1gzrqo9xuN1wuV9ymEc1fyEVKNSQqKysxbtw4zJkzB9988w0++OADXHLJJXA6nfjpp5/wyiuvwGKxIDc3F01NTUwMHTp0KCtoSKklyc2s1WpZ7ZmBsonaFfh0OHq9Hl6vF0qlEtFoFMFgEHK5nNXk4ItM82mK5HJ5nJmACuclzvkUCgXS0tJYm6Eo73A4zL4DMjup1Wq43W72I5PJUFRUhIkTJ8aNZfwmEq1bfT4fnE4nmpqa4HA42uVuTjRnAG3pd8iQQddFOklvFv0DjkBkPuGEE5KG0wDAX/7yF/zlL3/ptosSCFKBVHepdoVkk3O+UFtnHBXUaVMIczAYZAMpTZ7NZjN0Oh0rulJTU8MWFUQkEmEOMrlcDrPZzATm3NxcZGZmIicnB6FQCDqdDiaTiYUtU4dKnaUQmnsOXvChyS79TqFEQFuKDD4cFDgYMqRUKjF+/Hjs2bMHXq8XLS0tKCoqgt1uR0tLC5xOJwoLC1lBjvLycgDA1KlTsW/fPtjtdjz66KN46623WI5m3oEAtIXJv/nmmwDaKggHg0E22B9qMBUic9/Q0tLCFnC33HJLn10HuY36uvjfQBxvjgQae6i/of8nborK5XKkpaXB5/OxjTNy8dhsNigUCtTU1MDn8+Gtt97CvHnzsHr16nY5VOVyOau+TYs3Ck+NRqPw+XzIzs5OmpqJJuu9zWDP230kjB07FnfeeSf++c9/wuFwQJIk2Gw2vPrqq5g9ezZmzJjRoWgl6D4S7xNaiPP/r6mpYcIgFSkmsQ84KN6RMNhRcVi6pymvKkUqRCIRmEwm5jy02+3IysrC5MmTsXnzZlRVVUGSJBx77LG46KKLUFxcjC1btmD37t2orq7GKaecgl//+td4++23sXXrVtjtdvzhD3/As88+i127duHJJ5/ECy+8gAkTJsBqtcLj8WDv3r1MIOILF5JblopJAYjLqdpbruZU29TkDSwkMofDYWZOsNlsLM1JMviIE8JgMCAYDDKRWa/Xs3ueX/fw81vaoCMHKvWxubm5LHUSgHbzTwAYMWIEbrjhBjz33HP4/PPP8fnnn2Py5Mk47bTTMH36dAwbNgxKpRJutxvFxcV45ZVXsGfPHnY8ErRI2CTHqlqtZm5Vyi092OGNLtS/RyIR+Hw+KBQK5Ofno66uDn6/n6WySCzeSZvJDQ0NrI3I5XKYTCaWkouMWTQP4NMPAgejLQKBABubKaoTOJjqZdy4cfjxxx+xceNGqFQqzJs3D3V1dSgpKcGbb76JKVOmIC8vD2vWrIkTEjUaDfLz8xEOh5lhR6VSIS0tDSqVCjqdjq27qa0MVsjwFovF4grOa7VaFp1G93eiOEv3FfXB1A4o5zFF6/JzTvoeWlpa4sTlcDjMUpt4vV62gURO9mAwmNRNzV8XvR8aK6iuiN/vh91uR2NjI1paWuKuPzc3F+PHj4fX64XdbodMJoNOp2Ptgq/J1FsREaKnEggGEbRrlyxE8VBiMy34eecnPYd26gKBAJRKJdRqNfR6PUwmE6xWK9LS0lBXV8fSJzQ2NrJjjB49Gq2trbDZbExwPnDgAOrq6pCXlweHw4GcnBzk5eUhFAqxkBV6PT+gCqG58/A56ngBJTHvGy8o88IKhTaSo51EGQrjsVgscbuuNGHPz89nizwq3qXX6+FyueB2u2GxWKDX6+FwOFg+5YKCAhiNRjQ2NqKurg7/+te/8K9//QtDhw5FQUEB1Go1bDYb9uzZg1gsxir+AmBFX6id0ASxL8JTBfG8+OKL8Pl8mDRpEubNm9dn1zFu3DgAfZ+TebBDk3dynqpUKjYhT4zAkMlkMBgM0Gg0cf0LLboUCgUaGhrgcDjwv//9L875MXv2bJSUlMBms7G8jA0NDbBarSyHMxW0DYVCyMvLi9vYJPoidUaqhbinOjqdDr///e/xzTff4NtvvwUA+P1+bNiwAdXV1TjjjDOQnZ3dx1c5sKHw/3A4jHA4DKPRyHKjU3sOhUJobm5m9yOPWq2G1WqF1+uNc592BDnMWlpaEAwGYbFY0NzcjGAwiPT0dDQ3N6O2thZqtRp5eXmIRCIsH2ZLSwvGjx+Pk046CUVFRfj666/hcrmwatUqFBQU4KKLLsLnn3+OmpoavPDCCzjppJNwwgkn4IsvvoDNZsOmTZvirsVgMKCwsBAqlQpyuRxGoxEmkwmZmZnQ6XRMIOoLATHVNjX5vpTma+FwmLmLnU4nFAoFsrKy4saCUCgEu93O0igl5mPm8zn7fD5Eo1FWHBAAPB4PdDodWltb0dzcjDFjxkCpVCIUCmH//v0YPnw4Ghsb8fnnn+Oaa67BzJkzsXHjRjz44IMYOXIkjj32WBQWFiIjIwNpaWlYtGgRZs2ahZdeegnffvsttm7diq1bt7L3SAI6QaleKJpTrVazHOMWi4XNX/kUGoNdZObFQsqnHQqFmJtZq9WiuLg4bp1bWFiI8ePHw+Vysegei8WC6upqtqlttVphNpvZZgL/PRGUt5s2ukhYpMgooG0tbTabmRhYUFCAiRMn4sCBA6itrcU333yDrKwszJgxAxaLheXVBdoKENLcx+fzMWeuQqFARkYGjEYjDAYDjEYj24ygtBl8OofBCDm5/X4/K77n8/kQDoeZm5dMDCQY82taWhcaDAaoVCpEIpGkbYBc9KR3UKomXtz2+/1QKBQwGo2svogkSazfp81U2gRLVuiTvvtAIMD6N6CtzxgzZgxb65I72ePxoK6ujqX40Ov1LHJGJpMxYZzm2r2BTBI2LoGg26DKpU6nM+kCMFXgi2AkgzohPm8eLzbSbn7ioEadqc/nQzAYhMfjQUtLCxMI6+rq2CSfwpYvvfRSnHDCCVizZg2++eYbNnlQq9XIzc3F0KFDUVhYiLy8PGRmZrLCT9TB0w4dvwuZyvR1G6EwnCPt+unzBdom/7yTgNweFF6mVqtZ4T0+dJu/BhKQKN1GOBxm4UShUAjRaBQtLS1obW2N+93j8cQNuDyFhYWwWq2Qy+XQ6/UoKCiASqXC0KFDodVqkZmZyYQo2hyhgpaCg0iShHfffRcvvPACFAoFFi5ciGuvvbbbRLVgMIiRI0eirq4OL730Up/Wc6iqqmITe5/P168WcH3dl3Q3iX0TpbchR0bixJicSzTp9/l8cLvdaG1tRWNjIxobG1FfX49AIICysjJEo1HMmTMHd9xxBxoaGvDjjz/iu+++i6vcTRE0Go0GBQUFMJlMyM/PZ84yo9HIwlN7W2h+7bXX4PV6ccMNN+C0007DqlWrDutAHGhtpKs0NTXhxRdfZEV1KAfjjBkzMHny5H5133c3Pd1GKNcpOQFjsRgTemjORoWeCQpl12g0CAQCrCYECSyHw+v1xs1LqAAccLCQYEFBATIzM9HU1IRt27axea3BYEB+fj4yMzNRUlKCrVu3MhGAcj1XVVWxc1E6ML5YKEXtkXiRlpYGs9mMzMxMFrGnUqmYSESCgV6vT9k5bE+3ExLV1Go1fD4ffD4fE+htNhuAg0KSXC5nGxdEZmYmLBYLc5bS8+laXS4XC4kn80s4HGZ5fKkoW1NTE0thMW3aNHz88cdwOp1Qq9WYOXMmKisrsW7dug7XUDqdDrm5ubBarWhtbUVVVRUqKyvj2rfFYmH9EAlVJpMJ2dnZsFqt7F+dTseERZPJBJVK1WGxzFSgN/oSl8vFhEJKV0A5lMPhMBMGyZF84MAByGQyzJs3D7t372aF4Mg8pVarkZ2djUgk0q7IJJ9eIBKJsD7EYrGw4m8UeZmXl4ddu3ZBJpNh3LhxKC4uhiRJSEtLQ15eHsxmM/bt24eff/45zj1PJpr6+vqkkVs091CpVKzwnMFgYAU5acPdYDCkbN/B09NtJBwOs3uNTFCUOrEjaM1Ikdr8c3ltIRaLxRWcBA6mUEusH8CndKFc0RShwZ9Xq9XGrUFJbyEXPZ2TBOnDrd2pFhKJyrTWJQMgCdC9MXcVIrNA0I30pwUdv6OXDNr5oufSJF8ul7PQEdqZTxzYotEoPB4PQqEQAoEA3G43mpub2U5uTU0N6urqmFg4evRo3HXXXUhPT8dnn32G999/ny1CTCYThg4dipEjRyIvLw+FhYVsgFWpVHFOEF4ITVX6uo2QUxA4GF5EdFQQL9FJSMUMqE2QaJxsYAbAcmDRufnJGi84k5hMYa2UT5E2K2jRSOISpU9RKpVsogW01QrIzs5mk0eLxQKTyQS9Xs8m9Xy7SfU205tIkoQlS5bgmWeeiXv83HPPxQcffNAtOd9eeOEF/Pa3v0VeXh7279/fp3nkYrEYjEYj/H4/SktLMWrUqD67liOlr/uSnoAvmgPEV5Cne5bGG+ozgIMCdSAQgMfjYXns6uvrUVNTg5aWFlRWVkKSJMyZMwe33XYbm9jv378f77//PtatWwegTSQoLCyEXq9HYWEhLBYLCgoK2GKP+hEaf3prYRcIBPCrX/0KtbW1WLx4MW688cbDvmYgtpGuEolE8Omnn2L//v1x4152djbOOuuspCHwg4HeaCO8+1Cj0SAajcYJvzTf5I0NNL+gv1OKCT6tDkUZ8Pkm6e8ulwuBQIDNZ8jpSOk4gLYUCrm5udBqtSgrK0NVVVWceKjVamEymWCz2VBdXR3nOAsEArDZbB0WLyTRmATFzMxM5mQmgVmv18c5mlM53L03xCESCLVaLex2O2KxGAsbdzqdSYtk6fV65hjk/24wGOLyqYbDYTgcDja+aDQaJl5LkhSX3qS8vBzV1dUA2tKvlZWVoba2lh07IyODiUYejwderxdutzup85GOzxc2pGsiIVmj0SAnJwcWiwVmsxm5ublQKpVMRNTr9dBqtUwIS1V6YyOC7mmn04lgMMjWCbThTOn5cnNzEQgE2Lg+evRollKLcl/r9XrmXib4yKjE9ZHb7WYbIbTZVVFRgUgkgry8PJSVlSEYDCIrKwtarRa7du2K6x8osoPSAdFmCADmbOUFaNrIpogOnU7H2rXJZGL57M1mc78QmIHeGW/4gnpUHJE2pXgtgz5jIH4TgSIIEtOjECQg82OFSqVixYbp8USxmTY1aCOSH+M6A5++MrFPobZK18yLzSQw01r+UNFA3YkQmQWCbqQ/Luho0p1soszvrtGEHoh3MycmyqfnkuhIiwufzweXy4WmpibU1taiqqoKVVVVOHDgAAuPXrx4MS666CIEg0G8++67ePfdd9mkobCwEKNGjUJhYSGKiopgMpmQlpbGHGW80JzKhTH6uo1QcRWauHeWxFQpQPxATgMe304OBZ/rjH54oSgYDCISicDr9cLlcrEB2e12w+v1titwRc4ncnxQaCXlB6f8WokuRLVa3W8mZ73BM888g5tvvhkymQx33XUXTCYTHnjgAfj9fixatAhvvPHGUX1ekUgE48aNQ3l5OZ544gnceuut3Xj1XeP444/H9u3b8emnn2LBggV9fTmdpq/7kp6CF5p5MQk4ON5Qvj0qpqXValnoLI03TqcTdrudjTeNjY2orq6GJEk4/vjjsXTpUhgMBnbeXbt24YknnkBjYyPUajWKiopgMBgwfPhwmM1m5Ofnw2QysbyZOp0urqhKKjJQ28jRUF5ejs8//zxugSdJEqZPn44ZM2YMuk3H3mojNKYDB1NeeL3eQ7qz6N4mBxa/WZ0MEnFobkORUDTPpXPFYjEmOgNtC3NKx+bxeGC325nISVBBQb5AWCwWixOKqL+iDSiFQgGDwQCr1Qqj0Yi0tDT2mFarZeHT/SHcvbcc7xTurVKp0NraGmdmUCgU7dpK4u+Uro/C4wnafGhtbWWbDDKZjG120PwSaDO3+Hw+7Nmzhx1Dq9XCZrMx13NH74HGBBINPR4PPB4PAoEAS5tisVjY+5HJZCzajiJoaD5LoiKJianuVu3JNhKJRJgbnfoNl8vF1gmULm/y5MnM7V1SUoKqqqq44qKU7oIK+vHpF5NFTCW7BuBgEcn6+np4PB6Wb72srAzAwbkKRThQP0RIksSMWNQ38qIh1TkiYwz1FyQu04YDXw+nP9Bb4w1pD/y6lYxV5EpOjOo+nLic7BxkdqLX09yU2iTQXmzmnwsgaUo23vBF18kXLewIEpXJQMWbqnrTxQwIkVkg6Fb684Iu0cGamBg+0fnMu5sBsIk9TeRot5meS+Kh1+tFa2sr6uvrUVFRgfLyclRVVTGX6qhRo3DLLbdg7NixqKiowMMPP4zKykrIZDIMGzYMRUVFKCoqwogRI2A2m2GxWNjkjMJWOjtA9AV93Ub4BVriTn0iHeVuBuIr4YZCobiFQGJbOtSEPHEQpYGUXCkURksLAJqs0+spnxqfU4uKSFJoIU0wdTpd3IYEtRdBG5WVlTjmmGPg8/niBODVq1fj7LPPRjQaxZtvvolFixZ1+Rxvv/02fv3rXyMzMxOVlZVxIl9fcckll+C9997D448/jttvv72vL6fT9HVf0pOQk5HvIxJdHzTWUEgpvY4WbSQ0OxwO1NXVoaKiAnV1daiqqkIsFkNeXh7uvvtuFBUVsWO6XC4sW7YM+/btg06nw4gRI2AymTBs2DBkZ2cjLS2Nhb4n5lRNRQZyGzka/H4/Pv/8c+ZWpNzA6enpWLx4McvtOhjorTZC80JaeGu1WiYa0uKZ0uOQ+4yv/5Ds/icHV2L/wJsfeEcz9Q/0f3K3JdsYp8JfJGaRS5XeB81PeJGZIGGcNqIyMjKg1WrZIp+KdZG7jD6LVKY32gk5znk3J7n+DiWu8AYC4GAETGJEHgkwPp+POZdpw4HaA4lD5J6vq6tDfX09O040GmXtkkLY6edIJRWLxcLyTCsUChQVFbFrJNMEpXejsPdUpqfaCJ8mIxwOw+l0wuPxIBwOM+eo2WzGCSecwMxRra2t+OmnnyBJElsDkFBLznDqQygPb2euw+l0MkFbo9HA5/Ohrq4OwMFignV1de1c9zSPoX6O/zdZuyHHO21Y0YYUieRqtTrlNx2S0ZtzEhLyE9OgJELjTVe0Axp7+E0EEpCpP6HHqU9K3HA4EnhhPHHTgi/uR++HUmVQqqHeTA+ZmrNigUDQ6xyuGBrtAtLuH3WivAM1HA4zNzHl0gMQJ+zRbjG5OjIyMpCZmYnS0lI0NDSgrKwMN998M+bPn4/LL78cTz31FB5++GEUFxejuro6LoxpxIgRbGeS/6HwSUF76Duk74zPv02iDXD4vN182A5N3mmwPdxOK9/WeBGJJl20QKAdWFoY6nQ6WK1WRKNRtsij9kc7tuREINcHLd6o2AL9jQ8vErTxwAMPwOfz4eSTT8bvf/979vjpp5+OP//5z7jvvvtw0003Ye7cuYes9N4RsVgMy5cvBwDccsstKSEwAweL/+3du7ePr0RA0KSfBCaaNPObnbwDiYdEGxpvNBoNE3WoP6AomltvvRWLFy/Gueeey6qAL1u2DLfeeisaGxtht9uhUCjgcrmgUqlgMpkQCASYS5Em9P2hHoDgIDqdDhdeeCFKSkrw3XffsYVgc3MzHn/8cZxzzjmYPn26GCO6ESoUTUIzRSHwP8DBzW3eIcYfgx/D+XuOwpBJNKbjGwwGqNVquN1uJvJS2DSJ0bxDmhcCADCRmPoicqTqdDpYLBb2WrpWmgPT+6UiogCY6Mz3RYnpyAYz9P1QbmtyNVN6A/pu+BQpNOcn0bcj6PsLBoNQq9XIyspCS0sLIpEI9Ho9ey318ZSKwWKxICcnB263G01NTcztTN8zzWPoGvi2QPNZfiODBHESEKnmQH5+PmsL5F6ltRSNf4MVSiNBKSvI2EKPa7VaTJ48mX1GXq8X27ZtY+kJ+Py0JNrTZ0qF8zoDX1wuEAiwNAQkmlLxUhKxEwVk3njDt+XE5/H9At9fkDGG0nWJOcehoe+L+nh+7sinWEocS470HKRv8GIzCdtms5lF3RFarTZuEywx4oL/l+DbSkftlfpDGh+pn0mM8OkthMgsEAg6DR9uwguMvOBIFVH5DpImATQp4wd8k8mEjIwMZGRkoLy8HJWVlXA6nawQ4IwZMzBr1izs3LkTHo8HDocDGo0GFosFdXV1TFzkRWYSMcUA3B4abHinx+HyQtFkh0RoPsVKomuZr2Cb7Nz8a4jEXV5a6JHIFIlEmCuZFgm0AOErBdOEjIQlCn3iJ5m0oysmaPGUlZXhtddeAwCsWLGi3STm7rvvxmeffYaffvoJv//977Fy5cojPse7776LHTt2wGw246abbuqW6+4Oxo4dC6AttFKQOtBGplwuZ30U9eskyiTLKw8cFKlpTKB7n9xAWq0WlZWV8Hg8eP7557FmzRpcc801mDhxIsxmM2644QYsW7YMDoeDiREWiwVerxdKpRKhUIgtFEhIGMwiQH9ELpdj/PjxyMvLw+rVq7F9+3YAbQLFSy+9hHXr1uHmm2/utfyFgwESXkkkooX/4V7TkbCc+Dwa6/n5AS3KLRYLi6ijOQAfQUWbWMkch9FolM2DyGVKRUoDgQD8fj97H1RYLjMzEwBYEUASiWiDihb8ot+Ih0RXtVrNotj4NQY9BziYdo1/nD5bfmyg75E2Lkj0N5vNzIVMgkwgEGAiHqWE8/v9UCqVyM/PZ/NSSs9Ez6HfjyTPqkajYfl7qX2RU5U2RkkoG6zzVbrHgIMOdUqDQN/juHHjmIvd4XBg+/btCIfDzGBCm000/lM/1JUUNWSgooJyBoOBfYdU7JzaIa2beCEzmcGGfgcORmjwIjMdgzbMB2tb6Co0l+xJkTVRbKaNJTJJ0DyCNJIjzcdMJI5TtP7lf/h83vSavkgNKURmgUBwxFAnxu/AkgCZTEQkQYAKBfKOUlr4WywWZGZmIicnBxUVFSzP1caNG7Fx40Z2rLS0NEhSW1VhrVYLr9fLBmJ+ISLczB2T6ApMDCukgYoX7ZP9DWifUiPxu6fXdBQaxE+66F/auCCnPC0AScyhXelDQYMqHwZF7iIhMLfnkUceQTQaxdlnn42ZM2e2+7tSqcSLL76IKVOm4IMPPsCqVatw7rnndvr4oVAI99xzDwDgzjvvRFpaWndd+lEjnMypDd3DdP/TZlWye5gXnPkoF36TiSJpTCYTqqurUV9fj/Lyctx9990YNWoU5s6di+HDhzOxKhQKwefzsdBHk8nENr8ikQiUSmVcATJB/8JiseDCCy/EsGHD8Pnnn8Pv98Pn82Hjxo3YuXMnbrvtNhx33HHiu+0m+LBdfqOY/zvdt3yUVWeheSYVeyJhivoMrVYL4GCdEd55mriBzoe104Y8/ZCopdVqYbFYWD+jUChYigd6PyQwkwjOh+oLt3xyyLFJ31GyuSpvLjmUI5EX7MgBy28OKpVK+P1+trYhoVCtVjN3IqV0IWGTro/y/xJ0XN7IwV83L4TzgiG5YslQwacAHMwbEfQ5ymQy+P3+uChLum91Oh2am5tRV1eHhoYGAGDCslKpZPmtKbc7Xyj8SKH0GlTcPhwOszzcWVlZce31aOYE1KZpnSyiHfoHvNhMKXh4QZnWosnMVonr4Y4ifqkv5GuV8OMPvR5Ar6fI4BEis0Ag6DLJxGYSeOn/vOuMfw11gIkFDjIyMpCTk4O6ujrU1tbC4XDA6/UiHA6zggeRSIRV9nW5XDAajfD7/XH5mIWYeHjouzjaY9AEnl+g8e6SjhYFhwoHisVirG1Q8UfeVZS4GKRr4Y9Nk3M6DrUJsaiLx+l04t///jeANsdyR0yaNAm33norHn/8cZY2o7MpL/75z3+ioqICubm5uOWWW7rjsruNMWPGAABsNhvsdjtzoAlSh8QoGj5NE39/U59GC1NaqGk0mrixRqfTsZz+VqsV9fX1cDgcKCsrY4V7gLbFa2ZmJsv/SAIDL17x0R1iIdg/USgUmDZtGoqKivDOO+/ghx9+gEzWVhTs/vvvx2mnnYbrrrsuZXNv90coVUVPHp9CpXlXM8076LtUq9Vx9zC/oOc30Pnc0PR4ophI6TqAg/Mr6ntIiKLf6dxijnpourOdJAtt552GFClHbScUCrFxg9pDYr+fzFXY2WumdkHmGz69EwnQqV4QsqchgY42fWm+T2mxAoFAnBEJOFjEj/IWk8BMhRePVnRTq9UwmUwsf3di/mWCN87QT6KjOfG5Hf1d0L9IXBvzRe5p0+Rw33FH5q/OtN9EN3NfIGZLAoHgqOHFZt4xAhx0hlBHxw+6Wq2WTeio46T8VtnZ2RgyZAhsNhtaWlpY0ZVIJAKtVovCwkI4nU7W2QaDQRgMhnaOWjFQ9x6JgyqQfJA81OtpEOUXeHTcZK5p3oHEhwslOqH4PFWCeN588034fD4cc8wxmDVr1iGfu2zZMrz33nuoqqrC/fffj0cfffSwx7fb7fjLX/7CXp8quZgJo9GI0aNHo7S0FFu2bMEZZ5zR15ck6AAaaxQKRdyEPRQKxeVap7/zVb/5aBeVSsUiaLKyspCVlQWbzQaHw8FyPup0OowaNYoVpaUwaQBxG1xCZB44ZGVl4brrrsPo0aPx5ptvwu12IxqN4quvvsK2bdtw3333oaCgoK8vU3AE0L1P0VuJTmX+Hk42P0h0NPNh0Idyq5JYRIIjLfpJ3OZdZ4LehW8TJCxTVAqNHbSBmZhrmdY6hyJxXEh0LSbWkeHbBjkPZTIZE0YHMzSG8ymy6Dsym82Qy+UsnQb/OVJhXqPRCK1WC7lcDpPJ1G33nEqlgsViabfRlJju4mjy/QoGBonpOpJtTtHjPPzjyfqRxHNQm+tKAcOeQIjMAoGg2+DdZrybNVneNOCgQ4HyWya6mtPT09Ha2srczDSR0Ov1cDqdAIDCwsK4hPp8/jxe3Bb0LokpMLry+sRdYN7d3FFqDnot/yMmeh0jSRL+9a9/AQCuu+66w35GBoMBzz77LM477zw88cQTuOyyyzBp0qRDvuaOO+5Ac3Mzjj32WFx99dXddu3dydSpU1FaWopNmzYJkbkfQJN2ytdMoey8U43/G72GnI18oVCj0YiMjAw0NTXBZrPB6XSyMYUcSsOGDWOboAAOuTAQ9G/UajXOPPNMjBkzBk8++SQqKioAAI2NjViyZAmuuOIKnH/++X02t9i+fTtuuukmhMNh/O53v8Pll1/O/vbzzz/j//7v/zB06FDk5+fjrbfe6pNrTFX4eUUi/P3ML+pJUKbXaDQa5ozm8wUnFqXj5x5UhClReBb0PXxaDopW4V3oJDiTozaxffAkhr/zz6f/JzNCUHvg6whQLuHBDm9Y0uv18Pl8MBqNcLlcCIVCrO4KQYUVNRoNS5dBjubuXgPwmxUCQWc5Gqd6VwxcfYUQmQUCQbfDT66TTcbo/+QKo9x5tPinnTiDwcAKAwYCAXg8HpaTS61WIz09nYkBfHGeZOFrgv4Ln9ajox3gxOfzTiJBxxQXF2PHjh3Q6XRxYsWhWLBgAS666CJ8+OGHuO6667Bhw4YOBZfPP/8cr732GmQyGV544YWUnYxPnToVb7/9NjZt2tTXlyI4AhLzNUuSFOdq5kPh6flAm3ud0umQ28lqtSInJwculwsulwuBQACxWCyuqjvlEk+WikmIzQMHmUyGESNGYPny5XjjjTfw5ZdfMlHx5Zdfxo8//oh77723XT7W3uCBBx7AW2+9hYKCAsyePTuu3969ezfuv//+TvflgoMkqw1B8GIz5fFNrGuR6DTjBW1+LkKil5ibpBaJ6VVog5EfY5LlRD2c2JM4T+U3IKhtUMQdAFYUXbSPNmjuHwwGkZ6ejmAwCKCtaCMVcuTT49HnR6kCKL+1oGuITc3U4mgNXL2JEJkFAkGPkZj+AGgf/kFQzl1+h58mDEajEYFAAGazOS4nF8FPIhKFRSEyDyxSdce2v0Iu5ksuueSIivE9+eST+O9//4vi4mIsX76cFfXjKS8vx6JFiwAAS5YswYwZM7rlmnuCqVOnAgB++umnPr4SwZFCC0yZTNbO1cw7CXlo8UnuMXI9mc1m+Hw+VvyNcnbSpieFPNLr6PyCgYler8e1116LqVOn4oknnoDb7QYA7Ny5E9dddx3uuusuHHPMMb16Tc899xyysrLi8gsTu3btwvfff48XXngBS5cuxdlnn93u9cFgkAk1QFtdC8Gh4UVBEhxJcO7s/U+bXsKdmtokptKgPNs0hvDfH603EtcZhwt/TwYVhhTtIx4+h7bf72emo0AgwNIC8MV96TGtVsuiBwRdR2xqCrqKTBLWC4Gg23C5XLBYLHA6nTCbzX19OSlPsrAzvnAcX+iNry7NF3DhQx9pkkETDcqvlkqTDNFGBKlCS0sL8vLyEAgE8P3332PmzJlH9PoXXngBv/3tbwEAr7zyCq666ir2t5qaGpx66qkoLS3FjBkz8O2336Z0eLDH44HZbIYkSaivr0dubm5fX9JhEX1Je3inGc/hCo6GQiEEg0GEw2EEg0H2eygUYsI1iQ8UnksOZxKcSYBKJUQb6T5aW1vxxBNPYNu2bewxrVaLhQsX4he/+EWvi0NLly7FkCFDsGTJEvbY+++/j2OPPRb5+fk47bTTsHHjxnbXtWzZMtx///3tjifaSNfg8zV3lHs31eahXWEw9yV8XuZk40tXoHZBa5WBsFnZU21EkiR4vd4OC+wR5Ebni2sKjg6bzcY2NefMmYPvv/+e/e1Pf/oTvv/+e4TD4SPa1KSaSoOtHxlsCCezQCDoMxKdztFoNC5nHRVSisVizFXAC9B8CDRN4hOF5YEwcRMIeoI33ngDgUAAEydO7JLL+Nprr8W+ffvw+OOPY/HixVi3bh0uvPBC7Nu3Dw899BAcDgeGDRuG999/P6UFZqAtfcL48eOxe/dubN68Geeee25fX5KgCyQrDAjE1wVITKdD4jFFz6jVaiYo8EV96LV8pXh6HR1PMHBJS0vDn//8Z3z99dd45ZVXEAwGEY1G8dFHH6G8vBxLliyB0Wjs9vM+9dRTeO+999jv99xzD7Zu3YqmpiYsX7487rlnnnkmW7iPGDECzc3NyM7OjnvO0qVLcdttt7HfadEv6BrJIiUEA4uOCnfx6xDezcyvOxL/35/C3VMFmUwGo9GISCSCcDjM1oJ8kT3a6BV0L1lZWQCAe++9F5deemnc3yZNmoRf//rXbFPzjDPOaPcdLF++POmmpmDgI0RmgUCQEtAkjq/+TeIzPZZY8C0xzxkt9pPlORMIBAc50oJ/HfHII48AAB5//HG8/PLLePnll9nfpkyZgo8++ggFBQVHf8G9wNSpU7F7925s3LhRiMz9HF5s5scUfuzg0zURfB5nPpomMcd/okNRiMyDA6VSiTPPPBNTpkzBY489hsrKSphMJtTW1uJvf/sbFi1ahOHDh3frvGPJkiVxbuUPPvgAO3fuxJtvvtnuuYsXL8ZDDz2E4cOHo7q6GpmZme2eQ04/gUDQNfiNSkHvwQv9gp5BbGoKugvROwoEgpSCRGI+3QX/O6XD4MOWKVyZnjNQwhMFgp7iu+++w+7du6HX63HZZZd1+ThyuRyPPfYY1q9fj0svvRQTJ07Eqaeein/+85/YuHEjhg4d2o1X3bOcfPLJAIBvv/22j69E0F3w+RpprKBxJVEIpOfROKNWq6HRaKDT6Vh+R/q/RqNhzil6fleFxe3bt2P27NmYPn063njjDQDAxx9/jBNPPBHnnHMOHA4HJEnC1VdfjTlz5ghXUB8jk8mQnZ2N5cuX47LLLoPZbEZ6ejoikQjeeecdFBcXJ93A6C6WLVuG0tJSzJ8/H3PnzkU0GsXtt98Oh8OBZcuW4ZprrsG8efNw1113iTmQQCAQCDrNkiVLsH79evbj8/mwc+dOvPDCC+2eu3jxYuzduxfBYPCQm5pmsznuRzA4ENtBAoEgJUms8M2HpCUrqsGHoInicALBofnHP/4BALjssstgsViO+nizZs3CrFmzjvo4fcm8efMAAD/88AN8Ph/0en0fX5Ggu6FNzCOlo0JO3THWJCus8/e//x3r16/H119/jX/961+YPXs2srKy8PLLL+PSSy9FXV0d8vPz2x1LFHXrPZRKJc4//3yceOKJeP311xEMBqFWq7Fv3z4AbZERPZGje8eOHe0e++tf/woASE9Px4YNG7r9nAKBQCAYfCxbtgxarRbz588HAKxZswZ33nkn7rnnHrapKUmS2NQUtEOIzAKBoF8g8pgJBN1DfX09PvzwQwDAjTfe2MdXkzoMHz4chYWFqKmpwYYNG3D66af39SUJUoSeHH+ee+45VlhHJpOhtbUVGRkZUKvVmDNnDv71r39Bp9Nh9uzZANo2dH788UdceOGF7Y4l8h/2LjKZDEOGDMFtt92G1atXo6mpCVarFV6vFzt27MDYsWNhMBj6+jIFAoFAIDhixKamoKuILQeBQCDoJOFwOK5AWmJIs0DQH3jhhRcQiUQwa9YsHHfccX19OSmDTCbDaaedBgBYtWpVH1+NYLCQWFjH7XazAnIGgwEejyfpY8lYunQpnE4n+6mpqemdNzHIUalUOPvss3H22WfDbDbDZDIBAA4cOACn09nOAS8QCAQCgUAwUBEis0AgEHQCh8OBs846C/v372ePUUjz7373O1ZETSBIZXw+H5555hkAwsWcjAsuuABA2waSEIYEPcFTTz2F2bNns58vvvgCDz/8MJqamrBkyRIYjUZ4vV4AgNfrhdlsTvpYMkT+w75DJpMhJycHc+bMQXp6OnQ6HVQqFQKBAHw+X4/maRYIBIKjQZhoBAJBdyJEZoFAIOgEXq8Xf/vb3zBhwgQAaBfSXFxcnPR1wWAQLpcr7kcg6Cv+9a9/wWazYfjw4bj44ov7+nJSjtNPPx06nQ5VVVXYunVrX1+OYAByuMI6VqsVNpsNgUAA69atw5QpUzBlyhR89913AID169dj8uTJffkWBIdAoVBg9OjRKCgogFarhVKphCRJCAaDYuNKIBCkHMJEIxAIuhshMgsEAkEnKCwsxKRJk9jvnQ1fXr58OSwWC/spLCzslesVCBLx+Xx47LHHALSF1fdEUar+jl6vxznnnAMAeO2113rkHD///DNGjBiBuXPnYtGiRQCEa2gws2zZMpSWlmL+/PmYO3cuotEobrvtNpxyyil49NFHceONN+Lkk0+G3W7HzJkzMXLkSAwdOrSvL1twGEwmE3JycqDX6yGXy0VRJIFAkJIIE41AIOhuxIxHIIBY9Avakyykmaez4csiR6YgVXj44YdRX1+PoUOH4sorr+zry0lZrr32WgDA66+/Dr/f3+3H3717N+6//36sXbsWb731FoDOuYbEgm5gsmPHDvz0009Yu3Yt1q5dC4VCgQsuuAA//PAD1q5di4yMDMhkMrz88svYuHEjHn744b6+ZEEnkclk0Ol0zNEsChcLBIJUQ5hoBAJBdyNEZoEAXV/0CwYuiSHNZ599dtzfk4U0J0PkyBSkAnv37sWjjz4KAPjb3/4GtVrdx1eUupx++ukYPnw4Wltb8fzzz3f78Xft2oVXX30VJ598Mr744otOu4bEgk4g6J/I5XIoFIq+vgyBQCAQJhqBQNDjCJFZIEDXF/3CWTa4SQxpFghSEb/fj1/96lcIh8M455xzcOGFF/b1JaU0crkcS5cuBQA8+OCDaGlp6dbjT5o0Cc888ww+++wz3HfffXA6nZ1yDYkFnUAgEAgEgqNBmGgEAkFPo+zrCxAIUoFJkybh17/+NfLz83Haaafh/fff73So0P3339+blyroY9auXcv+f8EFF+CCCy7ou4sRCA5DMBjEJZdcgm3btiErKwv//Oc/Rch2J1i8eDGeeOIJ7N27F7/97W/x3nvvdflze+qpp/Dee++x3//whz9g/PjxAIARI0ZAq9V2yjWk0Wig0Wi6dA0CgUAgEAgEnYFMNDqdDh988EFfX45AIOhnCCezYFCSGCqkVqsxfvx4mM3mI1r0C2eZQCBIVUpKSjB37lx8+umn0Gq1eP/990WKhU6iVCrx+uuvQ6lUYuXKlbj22msRCAS6dKxE19Drr7+OvXv3IhgMorq6GllZWZ1yDQkEAoFAIBD0BIkmGr4ugEAgEBwJwsksGJQsWbIES5YsYb8vXLgQY8eOxfDhw49o0S+cZQKBIBUIh8Ow2+2oq6vD5s2bsWrVKqxatQqxWAxpaWlYuXIlTjnllL6+zH7FtGnT8NJLL+Gqq67CSy+9hG+++QbXXHMN5s6di7FjxyIzM7NLx122bBmuueYaSJKEu+66C3K5XLiGBAKBQCAQCAQCQb9HiMwCAcSiXyAQ9F/uvvtuLF++POnfFixYgKeeegrDhw/v5asaGFxxxRXIzMzENddcg4qKCtx7773sb08++WTcZmVnmThxIjZs2BD3mEi9IxAIBAKBQCAQCPo7MkmSpL6+CIFgoOByuWCxWOB0OkUBBEFSRBsRCATdgehLBIdDtBHB4RBtRNAZRDsRHA7RRgSHQ7SRwYMQmQWCbkSSJLjdbphMJlFcS5AU0UYEAkF3IPoSweEQbURwOEQbEXQG0U4Eh0O0EcHhEG1k8CBEZoFAIBAIBAKBQCAQCAQCgUAgEHQZeV9fgEAgEAgEAoFAIBAIBAKBQCAQCPovQmQWCAQCgUAgEAgEAoFAIBAIBAJBlxEis0AgEAgEAoFAIBAIBAKBQCAQCLqMsq8vQCAYLFCye0FqkgpFCEQbSW1SoY0Aop2kMqKNCDpDKrQT0UZSG9FGBIcjFdoIINpJKiPaiKAzpEI7EW0ktTnSNiJEZoGgl3C73bBYLH19GYIOcDqdMJvNfXoNoo2kNqnQRgDRTlIZ0UYEnSEV2oloI6mNaCOCw5EKbQQQ7SSVEW1E0BlSoZ2INpLaHGkbkUmSJPXg9QgEgv9P4g6dy+VCYWEhampqerxjF+c6PKm4i9tfP8tUOld3ni8V2ggwONpJf31Poo2Ic3WGVGgnoo2k9rlEGxmY5+rO86VCGwHi20l//SwH6rlSsY0A/fOzHMjnSoV2ItpIap9POJkFghRFJpMlvbnNZnOv7R6Kc6U2oo0MrPP1FIOpnQzE99QbDKY2MpDP1ZOINjIwztWTiDYysM7XUyRrJwP5sxyo5+pJRF8yMM7Vk4g2MrDOJwr/CQQCgUAgEAgEAoFAIBAIBAKBoMsIkVkgEAgEAoFAIBAIBAKBQCAQCARdRojMAkEfodFocN9990Gj0Yhz9YNz9QUD9bPs7e9NtJP+d66B+J76koH6eQ7Uc/UFA/WzHKjn6gsG6mcp5iTdx0D+LAfqufqCgfpZDtRz9QUD9bMcyH0kjyj8JxAIBAKBQCAQCAQCgUAgEAgEgi4jnMwCgUAgEAgEAoFAIBAIBAKBQCDoMkJkFggEAoFAIBAIBAKBQCAQCAQCQZcRIrNAIBAIBAKBQCAQCAQCgUAgEAi6jBCZBYJe5qqrrsLatWsBAB9//DFOPPFEnHPOOXA4HJAkCVdffTXmzJmD+++/v8vn2L59O2bPno3p06fjjTfe6NFz8QSDQVxwwQWYNWsWXnjhhW45JgDU1NRg3rx5OOmkk7BixQps3LgR06dPx7x581BRUQEAuOeeezBr1izccMMN3XbevkK0ka4h2oloJ4disLUPQLSRrjDY2oloI11jMLWTgdxGANGXdAe90UaAgdeXDKY2AgzsvkS0ke5BtJGukXLtRBIIBL3Grl27JIvFIv3vf/+TJEmSTjnlFCkYDEqrVq2SHn74Yem7776T7rzzTkmSJOmSSy6Ramtru3SehQsXSpWVlVIkEpFmzJjRo+fief3116V//OMfUjQalU4//XTJ7/cf9TElSZJuv/12ae3atZIkSdL8+fOl+fPnS83NzdKuXbuk3/72t1JVVZV0ySWXSJIkSX/4wx+k77//vlvO2xeINtJ1RDsR7eRQDKb2IUmijXSVwdRORBvpOoOlnQz0NiJJoi85WnqrjUjSwOtLBksbkaSB35eINnL0iDbSdVKtnQgns0DQizz22GO44oorAACtra3IyMiAWq3GnDlzUFxcjM2bN2P27NkAgFmzZuHHH3/s0nmee+45DBs2DAAgk8l69Fw8dEy5XI6JEydiz549R31MALj77rvZtUajUQBAeno6JkyYgNLSUmzZsgWzZs0CAPb++iuijXQd0U5EOzkUg6l9AKKNdJXB1E5EG+k6g6WdDPQ2Aoi+5GjprTYCDLy+ZLC0EWDg9yWijRw9oo10nVRrJ0JkFgh6iZ9++gnDhw9Heno6AMDtdsNoNAIADAYDPB5P0se6QlZWFgDg3nvvxaWXXtqj5+LpiWMCbZ2kQqHA888/jylTpiAWi7G/SZLUY+ftbUQbOTpEOxHt5FAMlvYBiDZyNAyWdiLayNExGNrJYGgjgOhLjobebCPAwOtLBkMbAQZHXyLayNEh2sjRkWrtRNmjRxcIBjFPPfUU3nvvPfZ7aWkpSktL8cQTTwAAjEYjvF4vAMDr9cJsNrd7bOjQoV061z333IOtW7eiqakJy5cvR0tLS7ed61Ake0/dxZtvvomPP/4YH3/8Mc466yz2uFwuh9FoRF1dXY+ctycRbaT7vyvRTkQ7ORQDsX0Aoo2I8ebwiDYixpvDMRjbSEfvq7sQbeTovrdUaSeijRwZg7EvEW3kyBBtZGDPSYSTWSDoIZYsWYL169dj/fr1+PLLL5GRkYHzzz8fr776Km655RaoVCrYbDYEAgGsW7cOU6ZMwZQpU/Ddd98BANavX4/Jkycf8bnWr18Pn8+HnTt3sqTyVqu12851KOiYkiRh27ZtGDt27FEfEwB++OEHvPbaa/jggw+gVquh0+lgt9uxe/duFBUVYfLkyVi3bh0AsPfXHxBtpPvaCCDaiWgnh2agtg9AtBEx3hwe0UbEeHM4BmMbAURfciT0ZhtJPN9A7EsGYhsBBmdfItrIkSHayACfk/RoxmeBQNCO++67jyW0//jjj6UTTzxROuWUUyS73S7FYjFp8eLF0owZM6SlS5d2+RzHHnusNHXqVOmUU06RTjnlFCkSifTYuXj8fr/0f//3f9LUqVOlZ599tluOKUmStGDBAunYY49l72f9+vXS9OnTpRkzZkilpaWSJEnS3XffLc2cOVO68sorpVgs1m3n7gtEG+kaop2IdnIoBlv7kCTRRrrCYGsnoo10jcHUTgZyG5Ek0Zd0B73RRiRp4PUlg6mNSNLA7ktEG+keRBvpGqnWTmSSJEk9K2MLBAKBQCAQCAQCgUAgEAgEAoFgoCLSZQgE3YgkSXC5XBB7N4KOEG1E0BlEOxEIBEeL6EcEh0O0EUFnEO1EcDhEGxEcDtFGBg9CZBYIuhG32w2LxQK3293XlyJIUUQbEXQG0U4EgsHHsmXL8Oqrr2Ljxo2YPn065s2bh4qKCgBthWpmzZqFG264odPHE/2I4HCINiLoDKKdCA6HaCOCwyHayOBBiMwCgUAgEAgEAkEfYrfbWRGahx56CF988QWeffZZrFixAtXV1SgvL8eGDRtgMpmwcePGPr5agUAgEAw0Fi5ciMrKyqQbnQKBQNBZlH19AQKBQCAQCASCgU1rayueffZZ2Gw2LFq0CNOmTevrS0opHn30UVxxxRUAAL/fj/T0dKSnp6O0tBRbtmzBrFmzAABz5sxBcXExZs6c2e4YwWAQwWCQ/e5yuXrn4gUCgUDQbwmFQrjkkkvw008/ATi40dnQ0IAVK1bgX//6V7vXiPFGIBB0hHAyCwQCgUAgEAh6jObmZsyYMQP33nsvnnzyScyYMQPvvvtuX19WylBTUwO3242xY8cCAGKxGPubJElwu90wGo0AAIPBAI/Hk/Q4y5cvh8ViYT+FhYU9f/ECgaBf4XK5kJ+fj7lz5+LUU0/t68sRpADBYBC33norTjvtNAAHNzonTJiA0tLSpK8R441AIOgIITILBAKBQCAQCHqMO++8EyUlJSgoKMC5556LWCyGK664Alu3bu3rS0sJli9fjj/+8Y/sd5lMxv4vl8thNBrh9XoBAF6vF2azOelxli5dCqfTyX5qamp69sIFAkG/Y/fu3bj++uuxdu1arFmzJulzgsEgXC5X3I9g4GIymXDyySez3xM3OpMhxhuBQNARQmQWCNB+V1/kohIcCpGzTDDQkCSJ/QgE3cnOnTvxyiuvAADee+89/Oc//8GCBQsQCoXw29/+FtFotI+vsO/56aefcNVVV2HFihVYsWIFdDod7HY7du/ejaKiIkyePBnr1q0DAKxbtw5TpkxJehyNRgOz2Rz3IxAIBDy7du3Cl19+iZNPPpn1zYkIl+rgJnGjMxlivBEIBB0hcjILBDi4q/+nP/0JALBgwYLD5qISDD66krNMIEhlJElCJBJBLBaLE5hlMhnkcnncv/yiQyDoLE899RQkScJFF13E8gg///zzGDduHDZt2oS3334bl112WR9fZd9CY8qrr74KABg3bhwWLFgAmUyGN954A0VFRRg1ahROOukkjBkzBrNnz+7DqxUIBP2ZoqIiPProo5g2bRrOOOMMLFiwAFlZWXHPWbp0KW677Tb2u8vlEkLzIII2OpuamlBUVNTXlyMQCPoZQmQWCHBwV3/16tVYvHhxu6I7HSGKHgwuKGfZyy+/DKB9caaOXiPaiCBViUQiiEaj7Id3M5PArFAo2P+VSiUTnQWCw+H1evHOO+8AAG6++Wb2+JAhQ3DXXXfh7rvvxl/+8hdceumlUCrFlPSqq65i/y8uLo7720MPPdTLVyMQCAYiM2bMgE6ng1wux4wZM1BRUdFOZNZoNNBoNH10hYK+5k9/+lPcRqdAIBAcCSJdhkCAg7v6X3/9NV577bVO5aICRDjZYKMrOctEGxGkKpIkIRaLMYE5GAwiEAjA7/fD6/XC4/GwH5/PB7/fz/4NhUJMlBYIOuKTTz6B2+3GyJEjccopp8T97Xe/+x0yMjJQWlqKt956q4+uUCDoOtSHRiIRhEIhhEIhhMPhuLmBQJBq/PnPf8bq1asRi8WwefNmjBw5sq8vSZAivPrqqygqKsKMGTNQXFyMjRs3YtSoUX19WQKBoJ8hRGaBAG27+jNnzoRarcaMGTM6lYsKEEUPBjudaSeijQxcampqMG/ePJx00klYsWJF0hzd99xzD2bNmoUbbrihj6+2PSQQx2IxJoz4fD643W60traiubkZdrsdNpsNLS0tcDqdcLvd8Hq98Hq98Pv9CAQCQmwWdMhnn30GAPjFL37Rzv1uMpnwhz/8AUDbZpwQ5gT9BUozRBtzwWAQ4XCYbdiFQiHRngUpy2233YYVK1Zgzpw5+MUvfoGMjIy+viSBQJCCJK5ziMRaVgJBIkJkFgjQfldfo9HEFd3pCFH0YHCTWJwpGaKNDFyefPJJLFu2DN9//z1Wr16Ne++9F1988QWeffZZrFixAtXV1SgvL8eGDRtgMpmwcePGvr7kOHjRj4TmcDgMl8vFHMyUk6+hoQGNjY1oampCU1MTWltb4XQ6meAcCAQQiUSE2CxgRKNRfPnllwCAc889N+lzbrjhBlgsFpSUlOCTTz7pzcsTCI4Y6idJWCZxmdIO8S7mSCTSx1crECQnPz8f//vf/7BhwwZcd911fX05AoEgRUlc57jdbgAHa1mtXbsWa9as6eOrFKQiIgGeQIC2Xf3LLrsMf/nLX3DFFVfguOOOE7moUhzKHduXBclEzrLBzd133w2LxQKgTVBTKBRxObq3bNmCWbNmAQDmzJmD4uJiVvgskb7M3U33EAkolDrjwIEDiEajANruN6VSCZ1OB4PBAIPBAJ1OB61WC4PBAK1WC41GA7VaDZVKJfI2C/Djjz/C4XAgLS2tw3ZvNptx4403Yvny5XjkkUdwwQUXiHYjSAlojkE/fGohPpc9oVAooFQqEYlEoFKpWDFV0Z4FAoGg/8BH+QEH58iDsS9PXOdQ7YzEWlaLFy9O+npRl2jwIkRmgQAHd/V5EovuCFIDyn9ILiGZTAatVturg/+rr74KoC2Xt2gnPQs/2eMX/MDBiZ9cLj9kWpueIj09HQDw/PPPY8qUKdi6dWvcdbvdbhiNRgCAwWCAx+Pp8FjLly/H/fff37MXnAD/2SkUCigUCva31tZWRKNR+Hw+1NXVIRwOM5HZaDTCbDbDYrHAbDbDZDJBr9fDaDRCr9dDrVZDo9EwsVkwOPn8888BAGeeeeYhi/otWbIETzzxBIqLi7Fu3bq4vPcCQW/Ci8mJNRdIXI5EIklTYUSjUdaXCnFZIBAI+gfU5yfbPORRKBRQqVRQq9WDpn9PXOfodDoAB2tZTZs2DWeccQYWLFjQrngo0DdrG0FqIFZ/AoGgXyBJEst/yIehlpaWYtasWRgzZgxOPPFE7N69uw+vUtAd0EI/HA63C0mmBT4/KQyFQggGgx3mwPzyyy8xdepUTJo0CTNmzMD27du77VrffPNNfPzxx1ixYkW7HN1GoxFerxcA4PV6D5kqpa9yd5PQTC48lUoF4KCoX1NTg3A4jFAohAMHDqCsrAwlJSXYs2cP9uzZg9LSUlRUVKC+vh6NjY2w2Wws3Ybf70c4HO43KTRKS0tx0kknib6km1i9ejUA4Oyzzz7k83Jzc3HllVcCAB599NEevy6BIBESkRML9/GPB4NBVtzP6/Wyfk6j0cBgMABoE5rLy8sxf/58TJw4EdOnTxf9iCApS5YsQVFREWQyGXbu3NnXlyNIQcScpGegfj0QCMDtdsPtdsPn87GC1jz8vJ5/TSgU6u3LTkowGMTvfvc7jB49Gscccwwuu+yybj8Hv84hEmtZUR2aRERdotTgjDPOwKRJk3D88cdjzpw52LZtW4+fU4jMAoEgpZEkCeFwGD6fj/3wIvOSJUvw29/+Fvv27cOdd96Ja665pg+vVnA0UDElWtBT6gYSOUkAoH/5yaAkSUkniC0tLbjsssvwxhtv4Oeff8YjjzyCRYsWdcv1/vDDD3jttdfwwQcfQK1Wt8vRPXnyZKxbtw4AsG7dOkyZMqXDY/VF7m5ybMhkMqjVaqjVauj1emi1WubAJrHZbrejvr4eDQ0NqKiowK5du7Bnzx7s27cPJSUlKC8vR1VVFZqammCz2dDa2sqKBPaX4oDXXXed6Eu6iUAggC1btgBoSxVzOO644w7IZDKsWrVKCC6CXiUWi7HxhTYy6Yfy1NOYQ7nqSYiORCJobGyM2+D8/e9/j6uvvhq7d+/ukX5EFGIaGPziF7/A+vXrMWzYsL6+FEGKIuYk3UssFkMwGGT9OJlTeGOL3++Hx+OB0+lES0sLWlpaWJ+vUCggl8shSRL8fj/8fn+fz2vvuusuyOVy7Nu3D7t27cJjjz3WrcdPXOcQibWsRo4cmfT1oi5RavDee+/h559/xrZt23D77bfj6quv7vFzyqS+vjsEggGEy+WCxWKB0+kUHWkSJEnCrFmz4PP5sG/fPuTn58NgMGDBggV48MEH2z2fJgSBQACBQCAuTYLFYkFTUxOOP/542O12KJVKSJKEIUOGoLi4+JAFG/sS0Ubaw4cix2IxzJ8/Hz6fD2VlZRgyZAj0ej3OOOMM3HPPPcx5y7sLNBoNyykMAGq1mqVp2LRpE6644oo4B4jJZMK33357SNG3M5x33nmorKxkldkfeugh3H777SxH96hRo3DPPffgf//7H8aMGYNXXnml0yF2Pd1OSJQH2u4npVLJJs1utxt2ux3V1dXYv38/WlpasGPHDkiShKFDh8Ln88Fut7NjpaenIzc3FxkZGezfzMxMllaDhGvK19wdYYZH2pccjqamJowZM6Zf9SWpzMaNG3HSSSchOzsbDQ0NnfrOL774YqxcuRKXX345Xn/99V64yoGPGG86hjY158yZEzfeGAwGnHnmmbj33nvZuEQb3TQHsVgsMJlMqKurgyRJSEtLA9C2qTl9+nTU1tYyd3N39yN33HEHzjvvPJxyyik49dRT8fHHH8NkMqG4uBirV6/Gn/70pyM6nmgjh6e7xxueoqIifPbZZzj22GO76Wp7BtFODo2Yk6RuG6G1ZKL7WC6XIxQKtTMvHQqDwQCj0YhwOAwArB5JZ+juNuL1epGfn4/a2lpmDOluEtc58+bNw8UXXwyLxYLLLrsMoVAIV1xxRacLiKZqG0lGokTaWylSenK8AYDXXnsNTz/9NDZt2tQNV9sxIiezQCDoNWQyGb7//nsAbSHSa9aswdChQ+Oe84tf/AJlZWUs9y45IGUyGV599VXk5+czAbGxsRF5eXks36dMJsPQoUNRXV2dspMwwUF4cZnPhfnVV18hGo1i3LhxeP/991FQUAAAbBJ4zTXXoKqqKu5YNPivXLkSQ4cOZTnTRo8eDZvNhuLiYsyYMQMfffQRPB4PKisrj1pk/vTTT9s9lpij+6GHHjqqc/QU/OSJhF+dTsdSkZCj3OfzwePxwGKxoLW1FZIk4ZlnnsH+/fvx0Ucf4aeffoLD4YDT6URBQQG8Xi/8fj98Ph9ycnKYC9BkMjHntEqlgkKhOKoJ25H0Jcn49NNPUVhYyH6vqakRfUk3snHjRgBtIZWd/Z7/+Mc/YuXKlXj77bfx4IMPtvs+BYLugiKkotEoVq9eDUmSMHr0aHz66acYMWIE5HI5/H4/IpEIFi9ejP379wNo6xeov4zFYnj66aeRl5fH5igNDQ0YMmRIXJ2I7u5HRCGm3qe7xxvBwEPMSVIPSZIQCATixGW5XA6VSgWfz4eWlhY2F+bdzHykpEqlYkJyJBKB1+tFKBRCeno6wuEwAoEASzd3OLq7jZSXlyMjIwMPPvggvv76a+h0OixbtqxbI1mSrXOIxFpW/R3aeKa1SjKRObGWTU8UOe+p8eaKK65g39mXX37ZrdecDCEyCwSCXqe+vh7hcDipiPD+++8jFAqxcCXewUy7njqdDjKZLKlQJYIzUp+OxGV+gG9sbEQkEkFBQUFcUbpIJIKXXnoJAGA0GpkTQSaTwWAwIBQKMQFBpVLBYrHggw8+wF133QW3243Zs2djwoQJLPfwYIW/b0ggkcvl0Ov1bIOHvheKJvB4PKipqcG9996Lv/zlL7jvvvuwZ88ePPfcc9i/fz+qqqqYI4Qm9jk5OSz03GQyQafTMaG5OwoDHqovWblyZZc/E/pcBF2DNltmzpzZ6ddMnToV8+fPxzfffIMnnngCf//733vo6gSDGRISaAwCgIaGBkQiEYwfPx4ymQxerxfBYBCRSAR/+9vfALRFwOTk5LAxp7q6GrFYjG1oKpVKtuDki6h2dz8yGAsxJRb95T9T6rdJAOAf6266c7wRDEzEnKTvoUi9YDDIPjOFQgGNRgO/34+mpiYmIiuVSoTDYWaioNdTZCTNgYG2MUClUiEcDsPhcCAtLY0VyKb1SGforjYSDoexf/9+TJgwAStWrMD27dtx2mmnYffu3Un7fkFy+FSNye4xvl3wUbMEXzz9aA00PN093lCE4GuvvYY//OEPrDh3TyFEZoFA0Ov8/PPPmDx5crvHJUnCRRddhH379iWt7vv6669j5MiRUCqV0Gg0GDp0KGpraxGJRFg4WU1NjXDApRiJoiW/YIxGo3FF/Ig9e/Zg4sSJUCgU0Gq1LPRYkiRceOGFbAdXqVSyAV8mk+E///kPcnNzmZCgUqlw8sknY+3atQDaXFy5ubkYP358734IKQYJIfS5kztPoVCwz5omSvRdRSIRlJWVoaysDLfddhvuu+8+jB8/Hk888QTefPNNrFy5Ejabjd279L3wgk40Go1zTB+tq7mjvgQ4sp3+wsJC0Zd0I7yT+Uj44x//iG+++QYvvPAC/vSnP7EQTYGgO6D8y9SfAW0LxLKyMkyePBkymYy51cgFt2TJEtTU1LCNSdrElCQJzz//PMaMGcMWmUOHDkVdXR1zGPdUP0KFmD7++GP22IwZM6DT6SCXy1khpmRCw9KlS3Hbbbex310uV8q5bPn5As0RjgTeadadi/7uGm8EAxcxJ+lbSPSlPoOcy4FAAC0tLazfp3VkY2MjczrL5XJmquAhQ4TL5YJSqWRzZKfTCaPRCEmS4PP5YDAYOtXXdFcbGTZsGORyOaszc9xxx2H48OHYtWsX5s6de9jrEBx0u9O6hQw3icXmCd7JTD+8C56eQy5nGoe6Mgb11Hhz5ZVX4vrrr0dzc3OPzrGFyCwQCHqduro6lgKBiEaj8Hg8+Otf/4rW1lY2yBuNRigUCuh0OhgMBha2JJfLkZ2djcmTJ+PNN9/EVVddhQ8++ABFRUUilCwF4AVkGqB5sZkvtkGOZqBtcI5Go6isrERWVhar/AyATeA+/PBDlhM4MzMT0WgUfr8fQNtkUKlUsolBOByG3W5HXl4eAOCBBx7A/PnzMWrUqN7+SFIOEpnpeyD3nUKhYK4MfpJETr2ysjI0NDTglltuwZIlS3DKKafgqquuQmFhIZ588kk4HI44d59MJmPfL33ffBtQKBRddjUn60uII9npF31J91FbW4va2lrI5XJMmzbtiF57+umnY/Lkydi6dSueffZZ/PnPf+6hq0wtampqcMUVVyAYDOL888/HKaecgltuuQV6vR4vv/wyhg8fjnvuuQdr167FpEmT8Nxzz/X1Jfc7SGAGELeJLZfLUVVVhSFDhiAWi8Hn87G/RSIRPPPMMxg+fDjkcjncbjeampoQi8Ugl8tZP6lSqaBWq5Gfn9/j/QgVYvrkk0/aFWI644wzcPrpp2Pz5s34wx/+kPT1Go2m0zlEexsS/0nETySZUzmZu5lP+wS0CUq807yrdNd4Ixi4iDlJ35FMIKYUbgQVuiZHMwC2gehwONjzqG+loq/0f5VKhUgkAp1Ox+bOJDp7vV7o9frDzmW7q41kZmbi1FNPxVdffYVzzjkHVVVVqKiowNixYzt9jMFMNBqNi5ZWKBRxbYhfx/LjC782oggapVIZJywn5vjmhefOptnornbicrng8XjYOvijjz5CRkYGi4rqKUThP4GgG+lPCe37kpKSEixYsADTp0/HG2+8gXA4DKfTCbvdDpfLFbcANBqN0Gg0MJlMMJvN0Ol0caGoJSUluOqqq9Dc3Ayz2YzXXnsNxxxzTF+8rU4xkNsIDciJbmUaoBMf43Ne0SDtdrsRi8VQXl6O3/zmNzjuuOPw97//Henp6XGfl91uRywWQ1paGtRqdVyeSblcDo1Gw9rRjTfeiO+//x6RSAQzZ87E008/zQo1pSq91U7IXQyACcME7fD7fD643W40NjZi//792LNnD0pKSuD1egEAc+fOxfXXXw+j0Ygvv/wSzzzzDABg1KhRMBqNGDZsGPR6PTIyMmC1WqHRaGA0Gtm9rVQq49JnHIkIwPclb7755lF9Fv2tL0lVVq5ciYsvvhjHH388tm7desSvf+edd/CrX/0KmZmZqKqqgl6v74GrTC0Si7kBbamjGhoa8OSTT+Kee+7BnXfeiXfeeQd33nknLrzwwk6lIhnI482RIElS3PhADiWitLQUv/zlLzFt2jQ888wzTER2OBzQaDQwm81wuVzsGEqlEnq9HgqFghU0pX5s3759PdqPDNRCTFSci3eNyeVyJhYnOpoTc2PyC3w6XmJeTZpnkOB8pHQ03iTWEKFzkYMt2Zh200034T//+Q8aGhpYodyOnGmpQKq0k1RnMM9J+qqNJOZeJjdzOBxmZhPqD/j8zJIkwWg0wuv1MjNLYWEh8vLy0NjYiLq6OuaKplQJFH2nVquh0WhgMBig0Wig0+mYAUqn0zGDRTK6s43s378fV199NZqbm6FQKHDffffhwgsvPKpj9iSp0I/QZiZfm4A2mPl0jhT1xK9hk+Vl5k041O93JkozcexKHCu6q53U1NRg4cKF8Pv9kMvlyMrKwuOPP47jjz++y8fsDEJkFgi6kVToPPsTNDFoaWmB3W6Hx+OJ+7tarYbBYIDZbIbZbGaLuv7MQGsjvGDMD8S80EyuosTdYIJynPEOssS/DxkyJG5R6HQ6EQwG2YROq9UiFouxXWkqZEdCglqtPur8v71Jb7UTPvSbL2rFQ+HjbrcbNpsN1dXVKCkpQUlJCRobGwEAVqsVN954I2bOnIknnngC33zzDSwWC4YNGwaLxYK8vDzI5XIMGTIEBoMBarWaCc0k0pBQc6jJuSD1ueOOO/DXv/4VN9xwA/7xj38c8esjkQjGjBmDiooKPPPMM7jpppt64CpTC4fDAYvFAoVCgblz50KhUGDNmjUAgPnz57OUDTfffDM+/fRTlJWV4dZbb213nGRF3QoLCwfMeNNVKGUPudj4tBm0KKSxgz4/g8GAurq6uOPIZDLmBJbL5VCr1czBpNFo+mW/lQpzksQFP4Wt+/3+OKcyn5c5WS5mgsYyGlNIrOaRy+VHPd4cznlN16JWq7s1bUdfkArtRJDa9EUbkSQJfr+f3YOBQID9HggEWG79ZGsQvk9QKpWYMGECTCYTNm7cyKIlk0GpNuhHr9czsVmv17MxIdEUJej7fiQxPQb/OwnLVPyR5g2JOZmBg5uIJDDz/6ffaWzSarVsDXqotTAdlx8neFGbF7f7w1gi0mUIBII+IRaLwev1wuFwwOFwxIUzAW3Col6vh9lshsVi6VQIkqB34IVkfqDm02Pw/0/mZqbXHCrXokwmY99/4ndvMBiYO8Hr9bIwNb1eD7/fz9xP/KAu2k97yNlFE3TK/8dPYJRKJUwmE3Pq8U7k0tJSVFVVoaWlBQ899BCmTp2KX/3qV9i0aROcTid8Ph9kMhmGDBkCAGhuboZWq2XFPclJkjjhEkJz/4WK/h1pPmZCqVTi9ttvx+9+9zs8/vjjuO666zpVub0/k1jMjXeAS5IEt9sNo9EIoK3vS9yQJfpjUbfegB9zeHGZ+hhauPFRVEqlErm5uSy6hnfKUooMekz0V10nHA7H5USNRqOsCBfNMfi5RkfwC28qzsgvyGn8ou+KNhpCoVCcQNCZ77EjcTnRSU3znmAw2K83IgSCVIQXmGktEAwG4fF4EAgEEAgE4sRCvn4L3asajQZZWVkYNWoUZDIZE5iVSiXGjh0LnU6H8vJyNDU1IRAIsFoiZI7QarVM2Kbi41qtFnq9nhW51mg0QmxOAWKxGNu4pPGFvtNgMMjGg1AoxPr3jsYePo0gPw+gMYb+plKp4Pf74ff7oVKpYDQaodVq48a3ZGklDwe14UQndCoxsGftAoEgJYlGo3C5XHA4HHC73UkFZoPBAKvVirS0NGi12pTrPAcDyRxE/GBIf+MHysRF4eEWhomQA5kcYpFIBA6Hg52TBmmVSoX09HSWP4uc0BTGRjvTQlg+PDQ5IrdHMqFZLpezjR6lUgmtVgudTgeLxQKr1YrKykrYbDZs2rQJW7duZd+7x+OBwWBAWVkZjjnmGFbxmybjhMFgEELzACAUCmHTpk0A0Kl0Dh2xePFiLFu2DJWVlXj//ffxq1/9qrsuMWXhi7mdddZZ7HHK/UuuWq/X26EDqD8UdesLFApF3CIuEX5DkvpCp9MJ4GC+ToLSCvG5FYWA0DXIOQaAuckpPJ3EocR0GYmL8ERHc7LCTIkhzST4UuQOzVV4wTkx1QUftZVYR4IXr3koUojEimAwKITmXqKjdG1EokOwo7QmgtSF5v6xWIwJy263Gz6fj/1QGg2a39J3zqdpa21tRWlpKTuuUqnEnDlzEI1G8dlnn7XL88ynTdLpdP+PvfeOj7pK2/+v6b2mVwKBQOigNAEFC5bFZ+29oa592f3is66uDSxr3V0fdVdd93EV8YFVUVx7XwUFCwJKABMghPRkkum9/f7I7z6cmUwgQJKZJOf9es0LMpnMnGTu+ZxzrnPf1w2NRsOypgOBAPR6PYLBILRaLbsGkCAt9iTpge+3QAd/dG2mGKF4CoVCCXtX3t+fDixpL0x+3XTwzAvMVMFCcw0A2O12qNVqGI1GFn80plTXqVS9jFIlevHVO5lyHRMis0AgGDAoi8jlcsFutyMUCnUTmKmM3mq1wmw2Z9QFc6iRPKmlyjQ+2M/yGcu0CSSvy+SfTbZDoE2c2+1m3r5GoxFyuTzhRDkZ8u/OyspiVhkajQYejwc+nw+hUAgajYY9ns9cEPQMiSR8w8Tkzx6Vh+l0OuY5Roslo9GI+vp6NDU1MW87AGhpacH8+fNRXV2N+vp65OXloampCdOmTYPH40EoFErYxCcjhObBxbZt2xAMBmG1Wo+quaZWq8XSpUtxzz334JFHHsFFF100pOMguZmbRqOBzWZDW1sbysrKMG3aNKxZswa33HIL1q9fjzPPPDPl82RyU7d0QmIwPx9QPJFIQV/z10KCrDEoa40exzc3FRweJLoCXdd5v98Pn8/H7qcsM9rEpyJ5zujpfeCzyuj/dLBA3qok/iQfjvc0P/HiMnCgQRhvy6JSqZjQQAff4XA4oWGj4Ojh16NHmuAAIOGAIVOzAwVd0B4hHo+zDGan08ms3TweD9xud0IiCl3naf1K4h9d2yl5Zdq0aTAajXjttdcQCASQlZWF8ePHQyaTYffu3WhtbYXb7WZNAL1eL/R6PRMrQ6EQy2QmsRkA++zz1xtB/0MZ70QgEEAkEmEHA36/n4nMZI9Bnt7JjSOBrmu/TqdjCU8kMJNdGc0r4XCYNVjn+zhQPFosFjZ/HM4hF185zPdBotijXjfpvnYJkVkgEAwIdJF3Op1wuVwpSw01Gg2MRiMsFgsTHAV9R2/8kelxyaTK5qGSURKYk0VhKkklDAZDggDs8XiYwKzX6xOaMSV7KsrlcsTjcTgcDua9TM9F4wAONHWi/xNiQXdoSDShzyV10ub/drTx0uv1bNNOpYEkNnd2dqK9vR2xWAwLFy7Eeeedh6effhp2ux0qlQoWiwU//fQTZs2ahdbW1gQvzmRIxBEMDjZu3AigyyrjaBe4N998Mx555BFs27YNH330EU499dS+GGJG8sADD6ClpQWnn346AODBBx/E4sWLIZFI8PLLL6OsrAyjR4/Gcccdh4qKCsybNy/NIx58kLiQjFwuZ4dd1B+A99SkzZpEImGbzeRSVcHhwXtfy2QyRCIRJjCTAEC2VwBYeTEJ0SToJttt0XvMN/ejdQQJPpRhRvMb2WbQASrd11PmO58RTbZP5OedjFQqhclkYk3BKHvuSBsPCg5Ah+F8ljv9nxIekte6qRpF8v6pqQ4Ykm1WDkVyAkZyBiI9L41F9KE4fMj2ADggNlPCisvlgsvlgtPpZFnNANieE0DC35x6/6hUKphMJpjNZshkMtTW1rKs0zPPPBNKpRIvvvgi6urqEsai1+uh1WrhcrkSDpLo9QwGA6LRKOsdQ+MVYvPAwWsN5NFNdip0QEFNZyl+aD9Ln2f6vNLn2OPxJNiW6fV6GAwGdmhBArZSqUQ0GoVSqUQ8HodKpYJarUY0GkVnZyesVuth73HouhSPx1kmNMUbWX9Eo9G0V82InZtAIOh3otEoO112u91sIajT6RAIBCCRSKDVamEymWC1WqHT6cTE24eQCMsvnvkyQvqa//dQz8cv4kOhENuQ0cI/lWdoa2srxo4dy6wSOjo6AIA1YgIOiJiUQUJjpgwkjUbDPJipkQJlxNJz0eaVFgQiG6X38CfwVObHlw7zggplNFNWiEajgU6nw/79+5l/LB0IXHrppXj++efR0tICiUQCs9mMLVu24JhjjkFzczOCwSATclJljAkhZ3BAfsxHY5VBWK1W/OpXv8ITTzyBRx55ZEiLzG+//Xa3++hvSTz44IMDNZxhBc03tJHUaDTw+/3sWkRrEV6k4LOYBYcPVTvR39ButzPhmQTneDzOrEnq6+sTMst7gj/45uFttuj1ZTIZ1Go1IpEIEwZcLhez7OKzwWj9QOsbt9vdTVimuVMmk7FmUrFYjP1uGo2GxVkkEhHZzEcArTd5r1Qqb+dL3fmKOt5/nReN6UafZbVaDbVaDZVKlZBIQSX1JEjSwTufeMEL272NU1rz0gEXZb2LteqhofW+RCKBz+dj2cokGpIdI9no0f6Th9asRqORHTiFw2F88803+Oabb9jjxo8fD7VajcbGRtTV1UEqlWL8+PHo7OxEU1MTPB4PgsEgzGYzAMDhcABI9GTnM5kNBgM7oCKxma4bgr6HEmbo//R5DgaD8Hq9CVYZNpuNXbfpeuJwOLrZpVC1tV6vh0wmYz7gtO81mUwwmUwsu5muRfxhk1arZfOD1Wrt1ftP4/f7/UwUB8DmMsqep2sgzUnpQqyOBAJBv0EXdxIFaTKmk+VgMMgWdeTtqtFoxCKrD6FFOJCYyZzK8ylVw4Hk9yJ58U0Lajo9pfcW6Cp5p42dw+GA1+uFzWaDVquFzWZDPB5PyATjF+90o9ejcjQSAGKxGBOpCb1ezyZdOuUFIBZvhwll9NEhAr3ftOGmTR5l4ej1evZ/pVIJrVaLhoYGNDU1oaGhAU899RTOOOMMXHDBBVi9ejWam5vZ+7x161ZMmTIFLS0t7MAp1Xgo80eQ2fCZzH3BsmXL8PTTT+Pzzz/Hd999hxkzZvTJ8woEPLQZ5MUf2miSBRNlzvGN/8Ra5fChtQPQ9Xd3u93svmg0yhozqVQq2O125o2t0+mQn58PmUwGr9fL/Fb5jGapVMr6BWg0GoTDYbS3tyMcDsNut0MqlSI7O5uVMNNhNYnT1AuC/FyJVIefdD8dklPZPAmWVquVPY/D4WCiJokOQmTuPZSFzmfqkahITdf4LGbegxcA69XBHxrwYjMJy7QG0el0rOE0iVJ0CJKcxNBTYkay5Qa/fknl702xrNPpxHXlENC+g/7+vLcuiX30dUtLC4Cu9ys3NxcAYLPZ2M+4XC5WQRsOh5Gbm8ves6ysLEyZMgVA1/6C9iR5eXk455xz0N7ejn//+99obGxEMBiEwWCA3+9nh1XJnu56vR4OhwNGo5FdN0hspvWzyGrvW/hDH9o7UiM++tt7vV60trYiGo3C5/Ohvb29W4Ul9XVIlcWsUChQXFwMk8mEQCAAp9PJbB3NZjN7XjpsoMSZ5IzmnvaqdH3wer0pD7FoLvP7/bBaraxqhuIqXXsnITILBIJ+gcoeyRKBSgr5CyR5FJnNZpjNZnGK38fwWRW0iUpuhkICYm+hzRQvLkciEXg8HvY8WVlZyMvL6+aNTNYYbrebZW9QNjLva0X30YLO6/WyUlmNRgOz2cwsV4ADjbGUSiWb+Kk8lr4vODxIaOYz1imrmRZffDMMpVLJygxp06bRaNDY2Aifz4e33noLo0aNwqxZs7Bp0yY0NjYy79iffvoJkyZNQktLC8sgTEW6S78EB6elpQX79u2DRCLBzJkz++Q5S0pKcMkll2DlypV45JFH8Prrr/fJ8woEPDQP8hmJfOUPZb7S5pDmKMHhk1xBRQIhZZNR88X29nZ4vV4oFAqMHTsW7e3t+Oabb+ByuXr9WtnZ2Zg0aRKsViv27dsHu92OtrY2aLVa5OfnM6EhEolApVKxxAi+QRcvMNP7T1VWfr+/W4YkAPh8PtjtduTn57MNv8fjYdmOfIatoGd4YZe3S/H7/azMnaxVaK9BlXX8+pDeM2oqbjKZoFar2WE5xZ5Wq2XrSI/HA4PBwB5LMcqvfYlUNi3Jmc4kVNP1g/8+ZSfSvolfOwt6ht4DPqudt9qx2WwAgAULFuCCCy6AwWAA0JVRvHPnTnz++ef4/vvvmT1CVlYW2traMG7cOJx66qmwWCys+bjBYMD8+fPx5Zdf4tNPP8XmzZsxfvx4VFZWorGxES6XC2eddRY+++wzJl5SzAFgMUHWGmQdx++pKPuUb0oqOHL4A03gQCUEVSXRIXJbWxui0SgcDgfa2toAdB0qRKNRNDU1sSQYqVQKi8WCvLw85OTkQK/Xswrt2tpaAIDFYkFlZSXsdjs6Ojrg9XpRUlLCGg/yFdxklxKNRtHR0QGj0cj2OTR2OkjjrzdkvUKxRXYx0WgUdrud9SxKd9WMEJkFAkGfQot0WnyTJ1ayXy+VL1qtVnaqK+g7aKNM8NnM/AY6VddaIlUWM+9TSKVo9FxGoxFFRUUsQ4SHBGa1Ws3KyWgRRQIzX05Ki3GpVAqtVstOcCn73Wq1MmGANhC8xyNN3JnQMTwYDOKCCy6AzWbDVVddhV/96ldpHc/hkOzTTJsyvoEWxYxcLofBYGAZPOQ91tbWhra2Nuzdu5ctxACgsbERlZWVcDqd2LVrFyorK9Hc3JyQQcZv7ulQIt3vpyA1VGI6YcIEGI3GPnve2267DStXrsSbb76Juro6jBgxos+eWzC84e0V6HawRnOUbSYE5iOH1iVyuZwdIPN/e8oU83q9UKvVKC0txaeffsrWEDKZDLm5ubBYLMxLlSwqyKrLZrOhtbUVNpsNn3/+OUwmExYsWIBRo0Zh+/bt8Pl8qK2tRVlZGROnSOxWKpUJjZ540TB5XQWA2WtoNBrm50z+rC0tLSgqKmKZt/R4WneJKqueoeZbFBsej4ftK+i9IlsEvlydvscjl8sTrBEcDgeys7OZ6O/3+1mWtFwuZ1ZgtIehqjw6fEj2eU5eZ5LQ6ff7mU8vj1QqhdVqhcFgSFjX0F5JrVaLdc5BoPU9QZ8pek8o8z0UCiE7OxtLlixJuGYrFApMnjwZkydPxp49e/D3v/8dTU1NsNlsyM/Px65du1BYWIhp06Zh3bp18Pl8kEqlyM/Px7HHHouqqio4HA58/fXX7DlNJhNGjBiByspKbN++nSVRBAIBKBQKlkBBQnMwGERraytMJhOMRiOzeqFMW2oeKOaaI4d80QGwgyo6AKD9ot1uZ5nGbW1tkMlkyMvLw0cffdRtLUAVtHwVbVZWFk444QTk5+fjxx9/hN1ux9dff41p06axapyGhgaUlpayQySqpHA6ncjPz2ceyg6Hg1U9pLpmaLVadmhJ8yEdnGVlZcFms7HDT4VCkWBlmQ76TWR2u9048cQTWfnT0qVLB9XGWiAQHD5kpu92u5k9QrK4DBzIeszKyoLRaBQL7T6GP73lxVepVMpKUfkmCIczCdEmi7IueL+7+vp6bN++nZW6VlZWYtSoUQDAhENaoPOZHyQwU/kgQRs+KiuiU2c6AU6OG/pZ3iojExZor776Kk477TRcf/31OO2003D55ZdDrVane1i9hvdppq+Ty1BprqdSZcpKp4xmg8GAtra2hCy0YDCISZMm4aeffoLNZkNjYyMKCwvR0tICn8+XsMniDz+E0JyZkFVGX/gx80yYMAEnnXQSPv30UzzzzDN4+OGH+/T5BcMPPmuZRMZk2wX+II23AxLrlSOH/u4AWCNF+vvz6xS73Q6ZTAar1YoPP/wQ8XgcZrMZM2fOhMFgQEtLC1wuFzt8tlqtKCgogNVqZXNDIBDAzp078f3338PpdOKtt97ChAkTMHfuXOzcuRPNzc2ora1FTk4O8vLymIdrOByGWq1mZeupypNlMhm0Wi10Oh0TJHlMJhPq6+sRCATYXMb/7oKDkywwU7KKy+ViIjIdSLvdbmZ/QKXvJK7Q51an0zEv7ezsbJhMJthsNrjdbowfP549p8fjgVKpRCQSgVwuh1qtTrBnAZDg5UwCMx0aULZzqveZ1rpUGWaz2Zj4Td/nf3+xxukZmUzGEh7oa956j6xMACRUJaSivLwcy5cvx2OPPYaamhr4/X7odDqsX78ewIG9SywWQ1NTEwDAbDYjPz+fzQ3Z2dmYM2cO5HI5rFYrALDDSDpkkEgk7KApFovBZDIhFovB4XDA4/EgOzsbBoOBZe6TBYLoUXRk8F7MANj1gfawlCXsdrsRDofR0NAAABg7dixeeeUVAMDUqVNxxhlnoKKiAiaTCaFQCO3t7di7dy9+/PFHfPvtt+jo6MAbb7yBnJwc3HXXXWhsbMQnn3yCLVu2YO7cubBYLLDb7fD5fFCpVAiHw0wApoqdkpISZvuTPEeSV3soFILdbk84ACXICkav17N50WKxAMDQFJm1Wi2++OILaLVa+Hw+TJw4Eeeccw6ysrL66yUFAkGaII80j8eDjo4OdpHjO+gSKpUK2dnZyMrKEqe0/QS/OeY3zdSoRiaTJWR7kMjLb6Ap25kmZspOp0mbDhSofDGZYDCIrVu3QqFQoLCwkL0WLwTzQjOfGcJ7X/GNMfjSSeoIzsMLy4djAdLfbN68Gddccw2kUikmTZqEnTt3Ytq0aQmP4b3+ABxWSfBAwPs0A13vH2+bkpx5k52dzTZ4dNKuVqtZGXE0GsXIkSNRUVEBs9mMzz77DHv37kVZWRlyc3PR1tbG/C2B7g0phdCceVCjur7yY+b59a9/jU8//RTPP/887r33XlFOLDhsqBKHv/HzHKHRaJgHIoAE31YhMB8d/DzPryVI7I/H42hvbwfQNYd89dVXiMfjGDduHPR6PdatW8e+nwqz2Yxx48Zh2rRpKC8vx7Rp0zBhwgRs3LgRW7duRVVVFZqamvCLX/wC2dnZqKqqYrYcU6ZMgdPphN1uZ+89VefodDpotdpuzeKArixYykDLyclh3zMYDOxgnPfvzaS1SaZCJe3xeJzZYlCWciwWYwKz3W5He3s7YrEYnE4nq5JLxuVyobi4GEqlkgnS5eXl8Hg82Lp1K6ZOnQqlUgmHw5HgkRsIBNhak973cDjcK99cpVLJMtypLB7oWsvYbDaWrU9zmVjP9B6FQsE+o9SMkaz2+P8DXRVz7e3tyMnJ6fH5NBoNfvOb3+B3v/sd2traUFlZiWAwiLq6OgDA/PnzYbVaUVVVhdbWVtb4k4hGo/jiiy8gl8vR2toKAMjJyUF2dja2bduGQCAAg8HA9k0A0NHRgezsbFbJ0dLSAoPBgKysLCgUCni93gSLBREfhwcd5tA+kv7OdJAIIKG6BAAqKyvx/vvvAwAuvfRSXH311d3+7jk5ORg/fjwWL16McDiML7/8Ev/85z/R1NSEu+++G//85z9RUFCAlStX4uuvv8Y111yD9evXo76+HtOnT4fL5WLXEDp8aG5uRnFxMYsRajgcj8fhcDhgs9kSqogVCgW7LlGPnJaWFuTm5rJ9fypbn4Gm30RmOuUFwE5xxMQqEAwt6OJNRvk2mw1Op5MtBAGwEiOgq3ELlTkKf9X+g/cipo0cb3VBFhcymQwWiwUKhYKVAPHvXTJUUkriMiGVSpGXl4fCwkLk5ORApVJhx44dqKmpwc6dO9nhIk2I9DOUBUIiM4BuflTkoUWnuXQKTc2YeJKbGQKZsXB3u93Q6/UAuj4DfMMI4qGHHsKKFSsGemiHBb1X/EEBCc0+n49lMANdmwBqsEQCjU6ng81mY1nc4XAYH3zwAUaMGAGTyQSHw4Hdu3dj3rx5zKPM5/OxuOF9PEn0zoT3V9C1oP/uu+8A9H0mMwAsXrwYZWVl2LdvH1avXo2rr766z19DMLTgraD4+Y/+z/vNA13zE5Ut897+QmDuO2jukMlkrI8DzefAAZ9/rVaLrVu3Ih6Po7y8HDU1Ndi7dy+ArnmntLSUNUoKBAKw2WxoaWmBw+HApk2bsGnTJlgsFsydOxfHHXccTjjhBJSXl+PDDz+E3W7Ha6+9hrPOOgvz5s3D999/D5/Ph++//x5z5sxBUVER2tra0NnZiUgkArvdzpoGGgwGWCwWWK1WxONx1NXVJZRO+3w+Vr1F8M0keUSCRWoomQA40FfE7/ez+6jBNH8gQWKMRCLBMcccA71eD6fTiZKSEng8Hnz55ZdoaGjAmDFjYDQa0dnZiYaGBkyfPh179+7Fli1bkJOTg7FjxyIYDLKeH8m+zuS5TBYXKpWKVWuR8MP3FUkFNYak5nSZSibbvFECQyQSgVqtRjAYZO+HRqNhdhQ6nQ5erxdr167FDTfccNDnNJlMmD17Nj7//HO2rnS5XJDL5fD7/fjmm28Qi8VYQg5VgdKeqLm5mT1XdnY2pkyZAoVCgd27dzNveYqpQCAApVKJ9vZ2GI1GGI1GlpUfDoeRl5fH9gp0CJouS8me4iCT44PPYqbmnbwlE32u6TG0n83Pz0dnZye0Wi2uvPLKQ+4vFAoFTjrpJMyZMwdLly7F3r178fnnn+Occ87BhAkTUFVVhfr6eowYMQJ1dXXs807vp9/vZ9nr9fX1yM7OZtnOZNWTnDyV3AeArHwCgQDa2tpgtVrZnEprlnRVRhz2DBeLxTBu3Dj8/ve/T7j/ww8/hFKpxGuvvcbuczgcmDJlCoqLi3HbbbchOzv76EcsEAjSDl3AXS4XmpqaUFVVhaqqKlRXV6O9vT2hVIwWWxaLBcXFxcjOzhZ+YwME71NGflSUNSSXy5GXlweHw4Ht27ejubmZnZzz0Ia7tbUVTU1NsNvtrJEj3RwOB6qrq1FVVQWXywWZTIZx48ZBIpEkdOHlxWReZOY9fSkukkVEOvGlhVZyOSK/keMzuDMhzvR6PfPP8nq9Kf1q77jjDtaR2Ol0or6+fqCH2StIaKbSTyrxpAaN5KkJdAkJ1F05KysL2dnZKC4uRmlpKWvAYrfbsXXrVpaBRNYro0aNgsFgSDiRJw9waoTBv5Ygvfz000/w+XwwmUwYO3Zsnz+/TCbDTTfdBAB46qmnxPs+zKHrPWXAJgvH1MiLfDmTb3Q/HZC5XC54PB5WMg8c8F8WAnPfwc//fLYq/UvzAFVOmc1m7Ny5E3v37oVKpcIvf/lL3HfffbjllltwySWX4MILL8SVV16JW2+9FQ8++CCuu+46zJ49GxqNBna7He+88w4eeOABbNq0CUVFRbj44otRUFCAYDCIf//731AoFJg/fz4sFgvC4TC+//57KJVKlJaWYvLkyRgzZgxycnKY17LT6cS+fftQVVXFGjvx8OsSEkV5GwR+PZIJa5NMhOZ1qVTKbAN4wZfWUrQWHDNmDPbv3w+5XI4HHngAOTk5ePbZZ7F69Wo8+uij0Ov1eOihh6DRaFBTU4PRo0fDbDajra0NO3bswKxZs6BQKNDe3o4NGzZg+/btrKSdqjCp8TEJPD6fL8Gqo76+nsXCoewZ+LHT70dQTKQz+5Agm7f169fjtddeS8jczQSo7wutRdVqNdRqNbRaLTQaDeRyOUwmEwDgq6++wrfffnvI5ySdiv7+JEI2NjYiFoshKysLpaWlsFgsTNyjytxRo0ZhxowZOPPMM3HOOecwq5Xp06cD6GqMTAI1HZyEw2G4XC60t7fDYrEwSw2bzcZsAoEDfsLpoKc4yOT44K0c+QNMvsEvcEBcpmsLJcfS/rS3qNVqtqeh16PkOrfbzfqIdHZ2Jmih/F4pFAqhqakJtbW1rJEkWU8qFAq2NyShmtYk5CVN1w6qLOYrY9N1PTlskVkqleKOO+7AM888A7vdDgDYtm0bzj//fPzxj3/E+eefzx5rNpuxbds21NbW4v/+7/9YCYFAIBicUFap1+tlC7Rvv/0WW7Zswa5du9Dc3IzGxkZ24VapVMjJyUF+fj5KSkpY1uxQIRgM4pe//CXmzp2L559/Pt3D6QZfXkObcvI0zsrKwp49e9h12WAwoLS0FIWFhVAqlXA6nairq2PNMKjr7v79+7F9+3bs3r2b3fbu3Ys9e/Zg06ZNePbZZ1FVVcUaOwIHrB94vzTyMqOGPXQ/Dz2Gfge6j/+aoIk9eeOWCdlC06dPx5dffol4PI6tW7emFOFUKhXLaKBbJkMZyuS9zPtgks8m0PV+GI1GZGVlwWKxIDs7G9nZ2SgqKsLIkSORk5PDrDSysrIwduxYlrlB8RIOh9mCTQjNmQn5Mc+aNavfPnNXX3011Go1tm7dmtBwRzD0oY0h+SiSSEylr6mEZBKT6fF0o0wmEpfJvzUYDLJ9DV+BwYuEmUJPa4/Bsibh1yZ8E2KylqAGseFwGM3NzTAYDPjtb3+LwsJCPPvss7j66qtx5plnYvHixbjsssvwhz/8Aa+//jrUajUuuOAC3HvvvbjooouQm5sLn8+HV199Fa+//jo0Gg3OOussZGVlwe/3Y+vWrVAqlZg1axZUKhV8Ph9sNhuArrUDNfOaPHlyQsNRasrFW8FptVoUFxez34/Phqd/M6lXRKbCW3LRZ5Nf/5F4QmILCUOlpaUYP348Xn/9dQBdXv4A8Je//AVmsxlLliwBALz11ls4++yzodVqsX//fqxbtw5lZWUYN24cVCoV/H4/9u7di+3bt+PHH3+EzWaDRqNBQUEBey26fpAIBABOpxPV1dXYu3fvIb23eQu49vb2BLGJ/xukk82bN2PevHkJNm/JUNY3fxso+OuyVquFWq2GSqWCVquFwWCA0WiEWq1mQvNzzz2H6urqgz5nY2MjALAqSXpfaE87e/ZslJSUICcnB2VlZSgpKWF9hTweD+rq6rBnz56E9zQnJ4fZ49XX1yMcDsNoNDJLJsps3r9/P6v6JA9wOozgK3EGmp7ioDfxAaQnRpKreWluIZGZ3hv6nOl0OgBd+zC9Xg+Px4MPP/ywV6/V3t6Oe+65B9u2bWOHluFwGNu3bwcA5ObmsucPBAIoKCgAcOAwzeVywWQywWKxsJiWy+UwGo3Iz8+H1+tFR0cH4vE4DAYDRo8ejcmTJ2PixImYMmUKixmXy4VYLIZAINAtXgaNyAx0+ZRkZ2fjySefRENDA37xi1/g8ssvx3//93+nfHxeXh4mT56ML7/88qgGKxAIBh7KMqEGJp2dnaiursbXX3+Nzz77DJs2bcKuXbsSJg5qtFFSUoKysjIUFBQMyeYFmXySCyRu6PhmgAaDAfv27WNWBOXl5VAqldi6dSs2btyImpoa1qjAZrNh9+7d2LVrF5qamtDR0YFgMAiJRIKCggLMnz8f55xzDs4++2yMHTsWsVgMb7/9dsIimQRgmkD5bOXDjQledObFAtp4UJYAgATfxHRy4YUX4uOPP8bMmTNx7rnnDqqmfweDGgJSRjO9N9SpmTqlUxa6Wq2GTqeD1WpFTk4OLBYLcnJyUFBQgOLiYlgsFuzduxfff/99QnZzYWEhDAZDwuJMCM2ZxVdffQUAOO644/rtNbKysnDppZcC6MpmFgxt+INREoeTs/74zD/KYOaFZV5wpuZyvLjMQ4eedF2j5m+ZyGDMLuM39/y/yfdRebNCocCOHTsAAJdffjneeust/PrXv8YHH3yApqYmNv/bbDb88MMPWLlyJW644QbceuutqKmpwcyZM/G73/0OixcvhkQiwcaNG1mmMmUXUiMvhULB5uVkgc/v92PPnj3Mn1UqlWLkyJHQaDSYOHEiJk2ahMmTJ6OyspKJUx0dHYjFYgnCskqlSsjkFqSGt1RJhvxIAXSzDyBRn+7/f//v/2Hy5MkIh8N4+umnceKJJ2LcuHEIBAJ4+umncfrppyMvLw9utxvvvvsu3n33XQQCARQWFrIMdqDrvdy6dSt++OEHjB07FjNnzkRRUREkEgncbjc0Gg0mTJjAHm+321FdXX1QoVgikSA7O5s1v7TZbIjH42zsvD1YuuitzZvJZGK3kpKSARsfXa+BLlFYJpNBo9GwdaZer4dWq4XVaoVer0coFMJjjz3GxL9kOjs7meUXJXnQ/CKVSqFSqeD1erFz507Y7XbWe0itVqOyshLFxcWQSCSw2Wz46quvsH79enZoOXLkSMyaNQtyuRwdHR1oa2tjiRNkUcgfqAFdhxbUtwboXr05UPQUB72JDyA9McIfSiXPNfzniq7XtLfYsmULLrroIgDAn/70J6xcuTLl7xUKhbB582Y8+uijuOyyy/D1119DLpfj9ttvh0ajwd///ne0trZCp9PhmGOOgdPpZK9ntVqZ+E0C8f79+6HT6TBq1ChUVFRg1KhRMBqNqK2thc/ng1wuR3l5OcaMGYPm5mYWX1VVVSguLoZer0+o5u3p34HmiFZPcrkcv//97/GHP/wBa9euxfTp0/Hkk08mPKa1tRUajQZGoxEulwtffvklbrzxxj4ZtEAwEGSy39BAQCIebey8Xi9sNhtqa2uZvQJfjuHxeGAymZCfn4/CwkIUFxcjJycHer1+yJaaZnpDN5pQ+ZJiqVTKyv1kMhlKSkqwZcsWthiSSCSIRqNoaGhAZ2cngC5Rz+FwIBwOw263J0y6CoUCp512Gq6//npMnjwZjz76KCvfodP/5E0DZTLzCzhaZPFiJZU30WNofOTFltxll9/Q0eMyAbVajTfffDPdw+gXyNaEhGSKN76UnSfZt5AvayfRgO8KTteQcDgMvV6PgoICNDc3M6E5+bkz4VBhOEIi89y5c/v1dW655Rb87//+L9auXYumpiYUFhb26+sJBh7aLNG1gPdWJmQyGauCoVJ6vqyeriUH21zRBpPmEWpETBYZmVx11dPaI5PXJPzmnn9f6H66LzlTdfLkyfjkk0+wbt06AMCCBQtw8skno6SkBFKpFB0dHaipqcH333+P77//HlVVVfjv//5vXHvttTj33HNx4oknIhqN4v3338fGjRsxY8YM9to0X3R0dDBRx2q1AugSm6lpGJGdnc2qvejn+WzmeDzOPJzp8eSfqdFoEg7ABalJzvbm14oAmBUKvQf0eBJyiFAohFtuuQXXXXcd/vWvf6Gurg5XX301nE4nmpub8fjjj2PixIkYP348bDYb2traUFNTg5qaGkgkEkyYMAFnnnkm7HY7tmzZApfLhY8++gjnn38+KioqYDKZsGPHDrS1taG8vJz5hO/evRs+nw/bt29n1xT6HeRyOVQqFQwGAzQaDXJzc9Ha2gqPxwOFQgGTycTWUZFIJK3XoN7avC1btox97XK5BlRolslkrL8MHRSRyMwfMmZnZ0Mul8PhcODxxx/HpZdeipNPPjkhrl5++WWEw2FUVFSwtSXFICVS0J7IarXCYrGwBpStra3Q6/WYO3cumpubUVtbC5vNhs8//xwlJSWorKxEUVERjEYjvv76a3i9XjQ0NKCiogJ79+5lzf3cbjcTHqmfmVQqZWvqdNBTHPQmPoD0xEhP+wD6HNJnLDs7G/v374fRaIRcLkdtbS3GjRuHU089FR9++CFefPFFrFq1CqNHj0Z2djZisRja29uxb9++BD/1SZMm4fLLL0dDQwMeeeQRRCIRqFQqXHnllQiHw9iyZQsAoKSkBDKZDHq9Hi6Xi+1t3G43qqurUVFRAa1WC4/Hg5qaGkSjUajVaowZMwYqlQr79u1L8P7u7OzE/v37kZOTw+y+eFtK2vOn68DqiI9SL730UlYqsnr16m4TZkNDA44//nhMmTIF8+bNwy233ILJkycf9YAFgoGiNxkh6SwV6i/4Zn5erxd2ux319fXYvn07Pv30U7z33nvYuHEj6urqEi5cWVlZKCsrw8SJEzF27Fg2oQ7lxXSmnvTz2V280AyAddcGuqpMNm3aBLvdDoVCgaysLOzbtw8//vgjOjs74fV60dzcjK1bt2Lv3r2or69nv6PJZGIT5Ntvv401a9ZAJpOxhT81TpHJZN02VvwCgB4DHDjY4Muage7+WGq1mgmKtPjjG0mSd7MQHAcG2jxR93X+/8ke2zqdjnVcp7JGs9nMFu1msxkWi4V53nm9XjQ1NWHfvn2oq6tDNBpNsM6ghkAUL+nO/hmONDY2oq6uDlKpFLNmzerX15o6dSrmzZuHSCSCZ599tl9fSzDw0GEjHW7z/+czkai6yu/3sxv1B+js7ITdbofD4YDL5YLf72dzk1arhcViQWlpKYxGIxOYqXGXSqVi80smczTZZenMPiR42yu6Zif7ZJLHbXZ2NhOYf//73+P222/Hsccei7y8POTk5GDcuHE488wzsWLFCqxcuRKLFi1CPB7H888/j23btgEAxo8fDwBs7UNNBPPy8hCJRNjjSkpKoFar4XK5UFVVxQRms9mMCRMmoKysjDXw4rPk/X4/HA4H6uvr2WuQWA10JWfJZLKMyWTOZFuVZGu0ZJE52beY1pdKpRJff/019uzZA4VCgalTp2L+/Pl44IEHoFQqsWnTJlx33XWw2WwoLCxEPB7H9u3b8e6772Lbtm0oKCjAcccdh1GjRrHvPfvss8jLy8O5554LhUKBzs5OFpe8AEno9XqMHTsWarUa0WgUbrcbTqcTDocDdrsd7e3taGhowM6dO7Fr1y4AYKKi3W5PEJbTbZkxGGze+J4ger0ecrmcWWfodDrWqFMul8NisaCoqAjRaBQrV67E008/zQ6Avv32W3z//feQyWS45JJLmJhM74VEIkl4P6hSd8aMGaioqIBcLofH48GOHTsgk8lw3HHHsetqfX09PvroI2zcuBEOhwPTp0+HRCKBy+WCSqVCbm5uwu/U0dHB9lF+vz8h6SYd9BQHvYkPID0xwmd/8w3maU9C36fPXl1dHU488UQAwPvvv4/Ro0fj97//PUaOHIlIJIJdu3Zhw4YN+Prrr1FTU4NwOAyLxYJTTjkF1113HSoqKrB69WqsX78ekUgE5eXluPnmm+FyufDGG2/A7/fDarWisrKSrV+ArrgqLy+HVqtFJBLBzz//DKfTyQRmvV7PbHyAA370vAd0IBBgVTi89RT/76DKZAa6skkAMHPyZI455hhs3br1iAcmEKSb3mSEPPTQQ1ixYkWaRti3kBBJGze32w2XywW73Y7du3dj+/bt2LVrF1paWtjPWCwWaDQalJaWYurUqSgvL2ddcfkmbkOVTD3p5ycWEppp8qETesrCCAaDMBqNUCgU+PTTTwGAdT2mRTAAVFRUYM6cOZgyZQrKy8uhVqsRj8fx7rvv4qmnnsKmTZtwzjnnsIMWEojNZjNisRib2CORSEJDQvLCpFLS5MxXEi35WJJKpdBoNAnZsnz2i8hoTQ90GEDvC7+YI+j9p7J0akJJ2YP0fvLvK8VvKBRCW1sb5HI5NBoN/H5/QgdlQrz/Awv5I0+ePJktfPuTX//619iwYQOee+453HnnnQmZhGniLJ4AAQAASURBVILBC4nKfDUEwXvAk5clL/CRZVKyBQYA1mBu4sSJLFbcbjfrR6BSqaBSqRIymDP9+nE02WXpzj7sCZrDyWKJBPIff/wRALBo0SIsXLjwoM+RlZWFZcuWQSKR4MMPP8RHH32EKVOmMOHXYDCgs7OTiczjxo3Dzp074fV6oVKpUFlZCZvNhn379gHoio0RI0ZAJpPB6XSipaUlIYMtFWSFYDQa0dbWBqDrfeGbEac7viiJ5vrrr8dpp52Gyy+/PGNsvPgS91Q9OCjTndayFO+lpaV46KGHAAAXXXQRLBYLAODcc8/FzJkz8de//hXvvfcefvrpJwBdB0vjx4+HXC6H0+nE119/jU2bNuH888/Heeedh1deeQWNjY147733cOWVV8JoNKKjo4MdglBGIQlVBD2v1+tFMBhMyCokezeqJty1axcqKiqgVqsRCATg8XiYf2u67BGICy+8EBdffDH++c9/YsmSJRkTHzy8RZter4fD4WDXc61Wy+yTzGYzOjs7oVQqMXv2bHz33Xf49ttv8fPPP+P000/H+++/DwBYvHgx8vLyumXTU/N0Ep0p5iQSCQoLC5Gdnc163NhsNthsNuj1ekyYMAFtbW1ob29Hc3NzQhYq0DWvlZaWoq2tLSGb2WKxsGQvs9kMIH1CYXIcPPXUU/jFL36R0fHBJy6Rz7pCoUA4HGZ7RGryW1xcjIaGBnR0dGDhwoX4/PPP8fXXX0Oj0eCMM86AxWKBy+WC2+1m+5dYLIaOjg60tLSwuUUikaCiogKVlZUIhUL44IMP2Gc4Pz+fZc7X1NTA5/NBKpXCbDZDLpejoqICW7duRTQaRXNzM/u5nJychP1NUVER2tramFUgPTeJ1sk9jAalXcbdd9+Nd999F5s2bcLJJ5+M//3f/8XNN9/c12MTCNJKbzJCMnWxfriQWTxlArlcLrag3rVrF3766Sfs378/YdFDp8Tjx4/HtGnTMGLECJjN5mHViZ1OcidMmICtW7fiwQcf7PYYWvCkg2QPKolEwuI4Ho/D6XRCoVAgPz8fr776KgCgsrISH3zwAVpaWiCVSrFo0SKcc845CU1vCN5TWaVSYevWrYjH4ygoKGCbK7PZjEgkkuDJnZzFQyXOcrmc3fgsluTfKVlYpseR1Ua6N3DDHRKb+TL35CaUBInNlP3MW7vw1xvKcAwGgwmd5oGubA+5XJ5QbTIYhKKhwkBZZRBnn302ioqK0NjYiNdff535NA91hqqFF2X20Oee/2xTdjHNGVRaSt7KHo8Hfr+fVTWEQiEolUqUlJSwhk9k7bVnzx4m/tBmn7yXKYN5sFTA9LT2yOQ1SbJwmPx/PqOZrv1arZYddp988sm9fq0xY8bgww8/hMfjQTwex4YNGwAAY8eOxWeffYZ4PI7y8nJEo1EmKE+dOhUej4d9nZWVhdzcXLS3t6c8vODXMSRcaLVamEwmJlzG43F2KErPkQm9IjLZVoVEZn7NwEPiMo2Hsk7lcjl2794Nk8nUTZMoKSnBww8/jFtvvRXr1q3Dm2++idraWmzevBkqlQrXX3893G43Nm7ciH/9618oLCzE+eefjyeeeII1i6PXVavVCAaDLKM5lWWTRCKBXq9ne8hkwuEwamtr4Xa70dDQgKKiIiYc9fQzA81gsHlL/gzSXl2v1yMSibB/o9Eos29tb2/HVVddhffffx9NTU1Ys2YNgK6qhv/6r/9iB5rhcBgajQaBQIBZM9EBA4nMAJjHv16vh0qlgtPphMvlgsfjYfstyqD2er2ssmbkyJHsgIKSM1QqFTtko+emigje8nAgOVgcZGp88LaKpEfw/RYouSUSicBqtbJK9Hg8jgsvvBCfffYZ2tvbe9VguqSkBMXFxdBoNGhra0togGixWDBlyhQUFhaipaUFTU1NbB4YM2YMs3Ch3gBA156ZGkLW1taivr4eJpMJRqMRer0es2bNQltbG2KxGHJycqDVarF7924AYEJ/qhiJx+MDPu8ctsj8j3/8A3/605/w2WefYcqUKfjtb3+LRx99FNddd13Gl5cJBIdDbzJC0ikg9gVkT+B2u2Gz2ViJqd1ux/79+7Fz505UV1cnTKhA14XzmGOOwZw5czB+/HjWdXmwbND6ikw9yU1elPObO8rCIf+6srIyNpHOnDkTW7duRUtLC/Lz83HPPfegvLy8x9dxuVxYvXo1AGDKlCn44osvAACjR4+G0+lkWciRSARGo5HFBi2kgsEgNBoNOxkm8ZCaxCXPKZTpxsM3axpOsTcYoIxlWqAnC8zJJdOxWIw9lkRmXqimBSLFDv9coVCIbf4IITQPDAMtMisUCtxwww24++678dRTTw0bkTmTsw+PFFqD8IdLwIFqFT4jyePxwOfzsUor2sS7XK5u/uxutxtnnXUWZDIZdDodqqqqWKMmvtkbNYoaTAIzMDizy/jrfSq/Xb5HA/0/Eomwa7pWq+3V60SjUXzyyScAutYimzZtwp49e1hD6traWsjlcsyYMYNV3I4cORIGg4EJBLm5ubBarWhqamLjNRqN0Ol0LJHiYLESiUTYupn2DgdraDfQ9NZWJR2VmiS4UQUckLimpTUgfeZpLUtVlmeffTY7YEomJycHv/rVr3Dttdfiu+++w1//+ld8++23+Otf/4q3334bhYWFWLt2Lb744gtMmDCBvR6Jg0CXCEQ2KkajsddxyaNQKDBy5Ej8+OOPCfsrqjwU9A7eXzcWi8FsNsPj8bDDQ0pw4Q8wfT4fNm7ciFtvvRWbN2/G+vXrodFocNVVVzGbitGjR2Pnzp3sMxsMBqFUKtmBRigUQiwWQ11dHbuPR6fTsXmN7x9DB1FGoxFqtRqNjY0J2e5msxltbW2ssiIcDsPlcjFhlPZMgkPDN4FXqVQJFbPhcBhqtRperxc+nw8VFRWoqamB0+nEtm3bMH/+fGg0GuzZswf19fVwuVyIRqPQaDSsYblWq0UsFkNra2tCDGi1WowYMYI1ArXZbGhoaGDfp+oYiUSC7du3s/lNJpOxXlZZWVksKz4SiaCjo4MdaslkMqhUKsjlcjQ2NsLn87FrpVKpRDgcTugZQGS8yPz+++/j5ptvxurVqzF79mwAXWWLjz/+OF5++WVcffXV/TJIgSAd9CYjZDATi8VYMz+6SHZ2dqK9vR11dXXYuXNngjUGUVZWhjlz5mDOnDkYOXIkLBYL68I+WDZofUWmnvTTootvcgAgoTkbnaZqNBo0NjYCAObMmYNnnnkGAHDXXXcdVGBubW3F8uXL0dbWhry8PHR0dCAQCCAvL48tmktKShAIBCCRSJCVlZWQcSKXy5lgIJPJoNVqmQjOZwyQ0EwLA/r9SLwcbjE3WOltiTBfKs+L07TpJI81hULByuXj8Tj0ej37OVq0Ubd2ESP9h9frZU1NBkpkBoBf/epXuP/++/HNN9/gu+++S2jmNVTJ5OzDI4Gu6XyTPwDMtoKft5xOJwKBALPxIu9lPsPUbDbDZDKhrq4OPp8PgUAAWq2WbfB0Oh0TDcm7k7fKGEzXicGYXcaTbH/F30fNYWUyGUKhEPLz8+F0OrFhwwaMHj36oM8bjUbxzDPPYNeuXdBoNBg9ejTeeOMNAF0H4du3bwcAnHDCCaiurkYoFILRaMS4ceNQU1ODWCwGo9GIwsJC7Nu3D/F4HDqdDvn5+b0Wh+PxOBwOB4ADiSh85VUmiMyZbKvCe9DSGo+PF7lczppEAwcEaPp98vLyDvkaEokEM2fOxLHHHovzzjsPO3fuxNatW5k4HYvFsGfPHvZ89fX1ALr8wRUKBfPyJSuDI4FvTM2Xu2eKb/dggW+Mp1QqWcaywWBIaARLewvy83/11Vdx7bXX4vTTT+/2nDNnzsTOnTvR1NSErKwsOBwOZGVlwWazMUuw+vp6tqehKl4SgoPBYIJ3ON+cNhwOJ4iGRFFREaxWK9ra2uByuVBeXs4akpJvcygUYlV/goPDJ6XQfoFsMtRqNWKxGDQaDXw+HxwOByoqKmCz2VBbW4uqqioAXYdSM2bMYB7rXq8XHR0dTC8hDAYDSkpKYDQa2cEAn50skUhYP5pYLJagrchkMuTk5CAvLw8SiQRer5dl0RcWFrLG6PRvNBrtdqiuVCpZjyTqfxMOhxPmmnSsb3otMm/evBnnn38+Hn30UZxzzjnsfqPRiF//+td4+OGHceWVV2bE5CkQ9AWZnBFyNFDmkN1uR1NTE1paWpjITBfYPXv2JGxUga7F8qRJk3DCCSdg6tSpyM/PZ+VB4nOfWfCZQWRdQPep1Wr4/X72tc1mYxOw1+tl96dqdEn3v/POO3jllVfg8/mg1+tRVlYGh8MBnU6HnJwcxGIxmEwmdrqan5/PFl/AgRJoapZBDVJ0Oh30ej0CgQCzRqDFFGUUiKZ+QxsqcwPQzW6Dz3aORCKQSqWskkSn00EikcDhcCAWiyEUCrEYEfHSf3z77beIRqMoKioaUKuovLw8XHDBBVi1ahWeeuoprFy5csBeO11kcvbh4cL7L/MCMzXgIyKRCOx2O2us5nA40N7ezjKQgsEgRowYgRNPPBFGoxHffPMNgK69iUajQW1tLZxOJ7PkIYGZMlLJKkNcH/qfZKGQ7qP1I91HwqJWq02I+VdffRU5OTk444wzUr5ftbW1+Nvf/sY8dxctWoS1a9ciFouhpKQE7e3tkEgkmD59OhMWFAoFjj32WDgcDni9XkilUpSVlcHtdjPRqrCwsNfxEYvF4HA42PxDoiUdkGdKdU0m26rwTbsUCgUT7ChBwmAwwO/3w2g0orOzk3kY0zXkzTffxIUXXnjIjM94PI7Vq1czO5acnBzWBHHKlCn46KOP2P/pcGLkyJEADhzeHU0PArp+S6XShOaFmZTxPhiQy+VsPlEoFLBYLEyEMxgMCQkK4XAYZrMZHR0daG9vx+rVq3HppZeyzE9i5MiRyM3NRVtbGzQaDWssTc07FQoF8+ItLy+HTCaDy+Vi31MoFExcpmxmmu/4Kj25XA61Wo28vDwmJJM/t8PhYL1HOjo6YLFYEIvF2L5LxMehIbE1Ho8zQVmlUjGhme73+/2w2+0wGo04/vjjUVNTg+bmZrS3t7OqhWSys7OZsOz1euFwOFhMAGD9HagSRiKRsD2wRCKBwWCAwWCAUqlEMBjsZkmaDB2G8/shfo9PVcBWq5XNN/z1JB30WmQ+5phjUi5oAeC+++7Dfffd12eDEggygUzNUj0aKHu5tbUVDQ0NaGxsRGtrK1wuF9ra2lBTU8O8dHlycnIwe/ZszJ8/H+Xl5cjKyoJGoxGbswxFJpMldNXlMz+TFyZNTU0oKytDdXU1Pv/8cyxYsAAff/wx7r33Xpx//vmYPHkyFAoFmpubsXXrVnz55ZdsLsjJyUFRURGArsyY0tJStoEnewyz2QypVMqaLFFZMv9/KkX0er3MdoUWBgRldwzHjPnhSrLdBp/JTPdTJrPT6YRGo2FZLMnWKkJo7h8+++wzAMCCBQsG/O/761//GqtWrcLq1atx2223YeLEiYf18z6fD8899xx++uknTJo0Cddff/0RlT4PFJmcfdhbqFKBhGU+w1Or1SZk99FhuNvtht1uh81mQ3t7OxOaacPW1NSEyspKtLa2Mv/UGTNmsEN0AGxTLgTm9EFrEdog0wacP1jU6XSIx+PQarXMpsDn86G8vBx79uzBU089hXfeeQfz5s1DSUkJYrEYmpqa8MMPPzAhUKlUYuLEiaipqQHQtU4hkXLq1KmQSCRobW2FVCrFjBkzoFKp2GMLCwtZ42Ogq0Fyb+IjFovB7/ezQ3Ogy1aOsm759UsmkMlJNMkiMx0Q0eGyyWRCW1sbW0dS0oTT6YROp0N1dTUuvfRS3HrrrZg9e3a3Na/dbscXX3yB//u//2MHEqeddhpeeOEFuFwujBgxAg0NDejs7ITFYoFer8fu3buhUCgwfvz4BK/dZFsOEhVp3D0RDoexf/9+AEgQRTUaDVu3ZEqsZDp0EEHWOiqVCrm5uWhuboZSqUywy6A1pMViQWdnJ/bt24eXX34ZF154YYIXtkQiQXl5Odra2lI2U6P7LBYL6w9A0CFZsn8yLzrzvsvhcBg2mw1arRYGgwHFxcXYvXs32tvbWeY8zYUUb16vF3q9XmS7HwKJRAKVSoVAIMCSUoLBILRaLbOZoMcFAgF4vV54vV4UFRWhsrISgUCA9Xqgn6cKKI/Hg46ODlbVAHR9fukwO9kvnH6O4iMYDCb8LKFQKKBUKhOswihu+X+Te9uQCE3XD41Gwx6Trn2zuIIJBMMAyl52OBxoaGhAQ0MD6uvr4XQ62X27d+/ulr0sk8lQUVGB448/HscccwwKCwthMpm6bQYFmQWJcACYQEfinFKpZJliBoOBZRDL5XLs2bMHo0ePRmVlJXbu3IkXX3wx5fPr9Xrk5uYiOzsbUqkUOTk5rJzUbDbDYDAwL0ypVMqyovV6PSwWS7fGP/wmgGKVfo9khF/d8ITihEQKEiwkEgkrRaQGYJQJQp2kCSE09z2ffvopAODEE08c8NeeOXMmzjrrLKxbtw433ngjvvjii15vunbu3IkLLriACVMAsGrVKnzxxRcZ03gpmUzOPuwNdG3nG3rx4lsoFILf72cCJNli8F3cm5qamJUST3NzM7PFmDx5MmQyGWvgptPpoFQqEwRmuonrwcBC7zOVA1P2IWWXUbaZxWJBIBBgJepKpRIjR45EY2MjamtrUVtb2+25JRIJ6w9CooLZbIZGo4FSqcSECRPYHEGezGazGbt372aZbbm5uUwwBnDQbFhqmO33+xPWzlKpFBaLhZXPk6WLUqnMGFEok5NokudsACw7lBp5UdJETk4O2tvbUVRUhIaGBmRnZyMajWLnzp249tprYTQaMWrUKBiNRgSDQTQ3NzNxF+i6Xk6YMAG1tbWQSCQoLCxETk4O9u7dC6VSiVmzZjHf7uOOO475qAJdhxcUtzabDZ2dnQnXJpVKBY1Gw+KPbB18Ph86OztZ1itVF1LjavobZEqsZDoymYyt+Uhsps9ya2srNBoNIpFIQgao3W6HxWJh/YeeeeYZnHTSSZg8eTLLjKaDJtrr8oIkoVQq4fP5WEKNRqNJaExOr0eZzHSdoCxaiUQCl8uFQCCA6upqjB49GiaTCSUlJaivr4fNZoNGo2FCucvlgtFoRDweh8/nYxV8gp4he8ZIJMIEe2rGChwQmOlgMxgMsqopgj6LVFHFQ/7fvOcxNf5Uq9Usy5i3beGfV61Ws+uEVCpFOBxmcyQ9FyXVEMn7YN6aBThwYE9fJ2fqDxSSuNixCwR9hsvlgslkgtPpTJlllA5owWyz2VBfX4/9+/ejsbERgUAAra2t2LdvH/Mb4zEYDDjmmGNwwgknYOzYscjNzYXBYBAbs6NkoGIkuaESnfJT11qXy4VwOIzW1lY2qe3atYt1OFapVNi/fz/a29sRiUTYfTqdjmX3mEwmFBQUQK1WQ6vVwmKxsA0jTZg0QfILaBILAXQrlaZTZ/qaBHH6Xejnh3pGcyZeSzIJ8nOlBTxfjkhltcABXzbamGZKufJQwO12w2KxIBqNYt++fRgxYsSAj2H//v2orKyEz+fDI488gttuu+2QP/PSSy/hpptugs/nQ25uLpYsWYIXXngB7e3tuPTSS7Fq1aoBGPnhEwgEcPHFF6OhoQFLlizBTTfddMifyZTrCP95Bbo2foFAgG2CeKLRKPx+P+sR0dTUxEpXgS7xZu7cudi+fTva2trYIWc8HseoUaMwZswY1m2dNujUcIn8nmnzJxjYGCFxBABrpBUOh+H3+1nGGG3kfT4f2tra4PP50NHRwbIAPR5PQl8JygwzGAxsvUDrEblcjpycHCbMAF3Zp9OmTUMsFkN9fT0TpMeNGwe1Wo3W1la43W7I5XKMHDmy23wRi8Xgdrvh8/kSNvvk863VapmgSGsZyjQbzHPPQMYJ2ZVoNBo0NTUhHA7D6XSy9z4ej6O6uhqRSATNzc2sv0draysikQg6Ozvh8XgSPNt5LBYLtFotyzyUSqUYM2YMe7xOp8P48eNZU8Fp06ahtLSUHUioVCqMGzcOLpcLnZ2d3Roa90Za0Wg0MJlM7DVzc3OZbQYJ04ONdM43dO0gSGSz2WxMoPV6vfB4PHC73XA4HAgGg3A6nQn2AtnZ2XC5XPB4PJBKpRg5ciSzqKDrikqlgsFgYCJvbm5uQmP1nqCEH6/Xy/YzRqMRTqcTdrsdCoUCEydOhFQqhdPpRG1tLaLRKGQyGasOVSgU0Ov1LEs3k6oQekM6YoTmHfpc0nWEklNof8w3iKQ5JvmzTJXBdPjA21HSeoNPruKhQyXa65LoTT7eqcRjfu9Mvwt/gEGPo30xLy4DYD2z0oFIRRQIhjCRSARutxvNzc1obGxEY2MjWlpaEAgE0NzcjOrq6m6dcSUSCUpKSjBv3jzMnj0bpaWlsFqtLNtVMDigxUhyd1m66fV6uN1u5OTkMIuUsWPHorm5mS2+zGZzt8YmcrkcZrMZ2dnZTFw2mUxssUObd15YTt5YkfDNj5V+jiZmEgT5100uGxKZqcMXyvShQxEqTyPBmeC71NP9QmjuG9avX49oNIry8vK0CMwAUFpaiscffxw33XQTbr/9duh0Otx0000p39/29nYsW7aMicgnnXQSVq1ahfz8fCxevBgLFizAK6+8guuvvx7z588f6F/lkGRy9mFP8PYYBGX1UVYrAFYKTyKizWZDS0sLGhsb0dDQwOaFSZMm4cwzz8S///1vtLW1Qa1WIzs7G/F4HCUlJQkCM81PQmDOHPimROFwGEajEXa7nXlNRqPRhDLf/Px8dHR0QKlUwu12w+v19uhFSiKvXq+HQqGA0Whk2WTxeBxqtRojRoyAXq/H/v37mSAll8sxYsQI+Hw+NDc3s7UJCUcENSQm71X6WY1G0635NYkWAFiZtZhzeg9530ajUeY/Txmp9L4VFRWhsbERBQUFaGtrY/0/AoEAO1wIBoMIhUJsDSCXy1k/EADMD5f6OABAcXExNBoN83KfOnUqpFIp826m6wqfTa/RaJCTk8MONshCwe/3s8QOfgwkBNFrZmVlsZiiA3HB4UHVKlTFRnOO1WplfrsE2ec4nU7I5XJm2RcKhVjDNqVSiby8PPh8PpYtTWtHSqahwy0g0Xedt0sh0ZK3QTGbzQiFQqyRbXZ2NjweDztMsVgsMJlMqKysxN69e+Hz+ZgoS0lBZP2gUCiEP/Mh4G0zgK5DJK/Xy6qbaF6huKH3TqlUdjtASr6OUyYz7Uf4gy2lUskOHmmOo1igg7RU41QqleyQqSeLjJ7gG4emu2eWUIwEgiFIPB5HMBhkHU6bmppgt9vR3NzMBOYdO3Z08wPSaDSYNGkS5s+fj8mTJ6OgoABGoxFqtVoskAchtKDt6b0ju4y8vDzW6XjEiBHIyclhZaDUTIBOz8nbknyXKWuZFu4kLlO2cvKkTFlH/GRJwjdt7uiElyZV/rSW/xnB8IZigI8Ps9kMh8ORIGoR0WiUxaPYxB096bTK4Lnhhhuwc+dOPPXUU7jllluwZs0aXHTRRRg3bhxUKhVaWlrwxRdfYNWqVXA4HJBKpVi+fDn+8Ic/sAX4vHnzcO211+K5557DXXfdhS+++CKtv9NQINkegzKA+Ca0ZO1BG3yXy4X29nY0Njairq4ObW1trLz14osvxtSpU/HBBx9g+/btkMlkGDFiBGQyGfLz8zF+/Hjs2bMHAFjVDW3ySGBWqVRCYE4zfDMmyuwCDlzHqTeDVCqFw+FAQUEBXC4XVCoVjEYjIpEIEw6BLtGGMstoPcJnNZPYTKXp1LSNSpoVCkVCcyeFQoG8vDxotVqWgR8MBuH3+9kaRS6Xs/VP8vqKRCBgaGQwp4PkgwiPx8NENRJp9Ho9CgsL0dTUhLy8PFgsFjQ0NLBswmAwyDLKaR1LsUKeubS3kUqlyM7OZnY6QJc4WVBQwHzwqek02XaQHYvRaGQCFWVS8+JRqvUGfV+lUsFkMjERiRpeing5Msj7lqoIaB1IMURCM73nMpmMNfzU6XTsoJPmKnqfyQKQDi3pekPiM71OqmQsOjRQq9UIh8MIBAKIx+NQKpUso9dut8NsNqO9vR1OpxMGgwE//fQTE5bJDoS8mL1eL4vTQCDAml8KekYul0OpVLKmrLy4bDAYEAgE2GeVhF0AKfegtI/gq3LpM61Wq6HT6dhhaTgchs/n67GvHXkw0x6ahOhUmc2HgsZEhxzpvo4IkVkgGGLQ5Gq329Ha2sqEZfJ9IosMXmCWSCQoKirCzJkzMWfOHJSXlyM7O1t0sB0C8E11gMRsZqBLaPZ4PMjNzYXb7WYezakWLbRxp8072VnwN1q40SRMkxzvUdYTlOEBoMcyR4JeI51s27YNN998M8LhMG655RZcfvnlWLduHf74xz8iOzsbq1atgsViwTXXXIOamhqcfPLJuPfee9M65qECH8N8hojH4+kmMIdCIea/RptN3rdccGS89957AIBTTjklreOQSCT4n//5H5SUlOCee+7Bhg0bsGHDhpSPnTJlCp555hnMmTOn2/fuvvtuvPDCC/jyyy+xZcsWTJs2rb+HPmRJLjWlDXnyNTsej7NmO3QQXldXh7q6OnR0dDDrgng8jsrKSqhUKnz++ecAgDFjxkAul8NisWDSpEksg1mlUkGv1zPBkReYxWc+/ZBYR76TRqMRoVCINQKk0ma1Wo2srCz4fD5IpVKYTCaWGcqLciQc8pmG9H+1Ws36UvBNBmkjzle4UCWWXC6Hz+dLyFjmx86XyfOQoMDPR6La6sig5Ah636ihr06nS/C5NRgMqKioQE1NDWQyGUaOHAkAaGtr61HU4bFYLOwaQZmDBoMBFoslQTyk70ulUuafSg226dAiFfw6mNYdfLzS7wEcEEjFIdjRQ4kwZMVDQqLX603wuKXPqEajYVnvZJUgk8nY9+RyOTuQouZvZBlIYuShehXRdY8yp8mTm66FdBDhcDigUCiYVYvL5WKHpXRoK5PJEAgEWIySpYbg4NAcEw6H2SEjHSKRjQUdBPTm70kxwh860MFkKusU2uPS46lCl6p7U+17k5O2eHjxO1WWdboRIrNAMESgxQoJzG1tbXA4HKx0qLOzE62trQlNTYCuRda4ceMwY8YMTJkyBaWlpTCbzaKkfIhxsMWP0WhkzSsMBgOCwSBbzPClYVR+Qxs4WqBRuRYvLqd6fcoe4S0MgMRGL6k2dfxz0kI9Exbi999/P1555RUUFxdj3rx5uPzyy/HEE09gw4YN+OSTT/Dcc89h3rx5yMnJwQsvvICLLroIjY2NKCoqSvfQBw20AEvOJOBP+GnTSZn3QNdikjYVlIVGMcxnNGdCHA1Gdu/ejV27dkEul2PRokXpHg4kEgl+97vf4aKLLsLKlSuxceNG7N27F+FwGNnZ2ZgwYQLOOeccLFq0qMdrYVFREc4991ysWbMGf/vb3/D8888P8G8xNEhVMprqcxaJRJg/Znt7OxoaGrB3717U1dWhqamJbbKJn376iTXtqqioYPPPhAkTsHfvXgAHBGYSDKn0VAjMmYVcLmfZzPF4HCaTidlmkBhM61TK5KMmgVqtNuG5eOGOFwOBA4fstD7hN+r0c3zFV7LdEj2OSqJ7slqhQxV+/hFr6COHDgqoqaJer2fXFGrMSnsbiUSCyZMno6WlBa2trQCAgoICAAd8eflyc1pTUK8G4ECzRo1GwwREPnOZ4oq3JuB7hFAWYk8WcfQ7AQfWNLwIpVAooNFoRLz0MbRPCQQCCIVC0Ov17LPOv7ckMFPvF7oGkYhI84harWZiM9lkAGB7ot5AWdMejwexWAx6vR6dnZ0IhUKsQR351tN1KxQKISsrCx6Ph2UuU+M/ytQX89uhofddIpEk+K/zTRn5hCk+meVQJM8dtHdOZZ9yMCGa91fOhGSqo0GIzALBEIAWuKFQCA6Hg01YdCJrs9nQ0dHBBLzRo0ezLrWjR4/GscceizFjxiAvL481bBMMPeiUFUBCZg8t4qlEh28ckLyBozKy5MkzOYMZOOAllZzBfLAyHj7LNBNPZnmeeeYZ5OTkMNHS4XAgKysLSqUS8+fPx3PPPQeNRoN58+YBAObOnYtvv/0WZ599drfnosYPxMGyY4YTyaWnyfAbN8pSNplMzPOMMtZoEcn7nFG36UyOsUzl3XffBQAcf/zxMJlMaR7NAUpKSnDnnXce8c/feOONWLNmDdasWYMnn3ySZcALegffkI02WcnriXg8zkp/HQ4HWlpasG/fPuzevRv79u1Da2srm5tOOukkfPXVVywbzev1Ijs7m23ox4wZg+bmZgAHBGatVpvgaygE5syDYoPWrVqtlpUv85YmtCahTHS9Xs826cCBtUSycJwsKtMaJXl9wY+HREL6Wb7smB7Dw/eI4BsYk3glODqUSiXL7gsEArBYLMzWjX+/qaG1xWJBUVEROjs70dTUhFAoxITgVIkJarWa2apotVp2jeCzE+k6QsIk75lKsZK8vj3YeiUZWleLfjf9h0QiYVWXfr+f/d/n80GpVDJrQBKYee9bulbwGcyUFU2fcT4LvrdQRjyJyWSHodVqWYNCEpwphuk1gsEgDAZDwjipGaXg0JDQnKoHEG9T0dvPMZ8Zz9+AA3NEKvs+gt8/8/PUUEBc1QSCQQ4vMLvd7oRycYlEAo/H061sTKVSYezYsaisrMTEiRNRUlICk8l0UP9eweCHNnbkhUj3UQYPCcipoJNVygzgrTGSu98StOGjSZYXuPkx8d5Wgyn+cnJyAAB33XUXLrroIrjdbpZlQ5kKqe5LxUMPPYQVK1YMzMAHEXwzHAAJiz+5XJ7QQBLoWiy63e6Ehqa0yaBGY7RYp8x6IUAdPu+88w4AYPHixWkeSd8yb948jBgxAnV1dXjnnXdw/vnnp3tIg4ZkgTmVVUA0GoXX64XP50NHRwcaGxtRXV2Nmpoa1NXVsU3flClTcMMNN2Dv3r345JNPmOCkUChYQzbqHQCAZSDq9Xq26aeb+HxnJnw2MwknZJVB6wvKXqZyYj4bla9GSd6k8/9PXpvwpfIH29QnNyg+FCRAinjrO8hSgg6JLRYLq3CgCjzKVKVri0qlwujRoxMy0/lqKFr/8sIun3XIVz/Q4QP5ulMlFD1XcgPr5IONVCQfeggGBrK8oGQOg8GAcDjMLDFSxQovMtPhA1nqAF3xeaSWOBRv4XCYicx8UoROp0MgEEAwGGQN1js7OxOaXhOiR83hQ59rvgKFzx5OFpr5+Sa5wpafl3gLHB5+PuI//0P5GiBEZoFgkEMTo8/nYxdFqVSKSCQCt9vNyjEow1KhUKC4uBhjx47FqFGjkJOTIxr7DSN4P0TK4pTJZCyOUmUd89lBtDE7mLicDJWbJpceDbYJ9sknn8Srr77Kvr7zzjuxZcsWtLW14aGHHoLdbmf+Xl6vlzUc4u8rLS1N+dx33HEHli1bxr52uVwoKSnpx99mcMD7MspkMnadAxJ9uykO4/E4HA4HgANlcKFQiHUI12g0TGjms6EGUxymG4fDwRrjDTWRWSqV4uKLL8bDDz+M1atXC5H5MEjO5uQ/UyRAUwOctrY27N+/H7t27UJ1dTXLRjYajbjxxhuxYMECdHR04LnnngMA5ObmQi6Xo6ioiGWB8Yd3dONL23uy6RBkBiTKkkAYCoVYMz23283WJmQvwGcw88+RvHGn+0kY4tcpNJ/05e/Ar4/EPNK30AExlbeThzcdJlPDLhJ/STDixV++1wcPn61OTSMpq5gsDbRaLRONkuOPL2nnG4AJMheyYSGLDNoPUezwh1nAAZGZBGY+250Xm48UErelUin0ej08Hg+0Wi08Hg87VKX+AnV1dQAAs9nMREy+OlVw+EilUqjV6gT7Ct6aMdWBYW8sNIZydvLhIERmgWAQQ75etAinCdTn87FJyGQysckzLy8PRUVFKCkpQVFREeuKLBhe0MKKDiDi8TjbKCV30k21iTuY79yhXncwT7RLly7F0qVL2ddr167F9u3bsWrVKgBd/ubt7e0IBAJYv349pk+fjunTp+Pdd9/F4sWLsWHDBjz22GMpn5vKgQWJULYbbfBSLe4oA59KHgmz2QyNRgOv14u2tjaW6aTT6ViHaT5bbjDH5kCybt06hMNhjB8/HmPGjEn3cPocEpnfffddOBwOmM3mdA8p4+G7sSd/lmKxGHw+HwKBANxuN1paWrBnzx4mMJM10Ny5c7F06VKYTCb4fD6sWLECLpcLer0eFosFer0eJpOJZTNLpVLWhI0yDXmRWXyeMx/ysw0Gg4hGo/D7/azJH3l7Uxl7qqxi3r+St7sADlS98HMG/39eBOAzT5MPR5J/jn9tEWP9DwnNVN5O1gAqlQp2u531CqFeIrwg3FMmIh8zJB7S69BhVbJnKq0zxIHC4IcOKlUqFXufe7MXpnjpq6pfiUTCKhypuZ/P52OHJ+QlzfsHU5IQb9Eh9vFHB11DlEplSg/35J4wfCYzn908XMXknhAis0AwiOG7WFP5DC2w+FPXwsJCaLVaZGVloaioCFarlZWCCYYnfKYPnaSnyjSmf6lsUEyiB1i+fDnUajVOPPFEAMCnn36KZcuW4YQTToBGo8HatWthtVrx0ksvYc6cOVi4cGGPmcyC1NChBp8lCRzIJKEDEgDsceSz19HRgeLiYtYIrKmpCcFgEF6vlzUS4heJwhexd6xZswZAlxg7FJk0aRImTJiAqqoqrFu3DldddVW6h5Tx0KE2CTj8/T6fD8FgEHa7nQnMO3fuRHV1NQKBABQKBW666SacdtppzNt++fLlqKmpYZVXarUaRUVFUCgUKCgogFKpZJUilJnG22MM5Tlq27ZtuPnmmxEOh3HLLbfg8ssvZ9/78ccfcdZZZ6G0tBRFRUV45ZVX0jjS3kHrCkqG4MVBEgL5tUjyITgvBPSmVPlw+j3wrytIL2RF4vP5WHxYrVaEw2F4PJ4Ef+Rkb91UCRN0n1qthkajYQIT+bISfFWEiIOhBYmLKpUqIW56qubsj/dfJpMxP3pqLOr1eqHVaqFWq9n+jOZVWrdaLBamAQgP+L6B91cWHD2SuDByEQj6DJfLBZPJBKfTCaPR2K+vFY/HWfdttVrNmv3Z7Xb4/X6EQiGW4azVamG1WpGVlQW9Xi8uoGlkIGPkcKDFOe+bnLwwFwwcmRon6YKaK/FNNCgbhWKTGp/GYjF4PB7E43GYTCbk5ORAIpEgGo2ioaGBWcVQExc+A1LE+cGx2WzIz89HNBpFdXX1kMxkBoD7778f99xzD0477TS8//776R7OETMQ1xHy1AWQ8BkKh8PdBObdu3fj559/xs8//4xgMAiz2Yz77rsPFRUVAIAffvgBf/7zn2Gz2SCTyTBq1CiYTCaMHDkSer0eBQUF0Gq1MBgMMBgMUKlUCdnLw+Hze9555+FPf/oTiouLMW/ePGzcuJF9b82aNQiHwwnC86HIpLmGhJ5kkfBw4LNVh3Opcl+TKXFCjUN5uywA3TyYk0Vm/pCBspEpOSfZcoAs3kRFxOGRKTEyGIlGo+wAJRaLsWbgybGpVqtZA1QAbC3bX/T1oaaIkeGDSNsRCAYxfBYHlRKqVCrE43F26qrX62E2m1lDHLFgEqRCnOAKMhnKPguHwyxjORaLwev1JmQxUkYIde52Op3MKkgmk6GgoAB1dXWsrBY4IGxEIhFhm3EIXnvtNUSjUUyfPn3ICswAcMEFF+Cee+7BJ598go6ODmRlZaV7SBkLrT14ywHaMPMCc21tLfbu3Yu6ujoEg0FYrVY8/vjjKCwsxL59+7Bq1Sps2LABQNc6pqysDBaLBaWlpbBYLMjJyWHiMpUN0wHRcPrcPvPMM8jJyWEWaTxVVVX4+uuv8fzzz+OOO+7A6aef3u3nSbwgyK4kEyAhkDLzkrOUD2ZbkdyMSTA0IVsLsg0gsZn6NvR2DZsqY5W3zxAIBhKZTMYaEwaDQWg0GuYXTEk/dGDGV3poNJp+Hdf999+PV155hR1q8iLzjh07sGLFisM61BQMH4TILBAMUpJ942ijp9Fo2Am9VqtlJTdi0SQQCAYzlF0UiURY1hI1FAuFQkyIJm9rjUYDv98Pj8eDQCCAnJwc6HQ61mCFBAsqR6RNp7hW9sw//vEPAMBll12W5pH0L2PHjsWUKVOwbds2vPnmm7j22mvTPaSMh7cy8Hq9CAaDcLlc6OzsRHNzMxoaGuDxeOB0OgEAl1xyCb799lts2LAB27dvZ8+TlZWFgoIC5ObmIj8/H7m5uTCbzTCZTDAajcweY7hWH+Tk5AAA7rrrLlx00UUJ35s8eTIuueQSFBUV4eSTT8aiRYu6Xc8eeughrFixYsDGezQI0VjQE9TIlywFkjPgkw8kevJRFY37BJkC35iQ/OhTXf/4Crz+vj4O5UNNQf8iRGaBYJCSXApGjS+kUinr0K1SqfrNR0ogEAgGGt4bHDhwwMYftJEYTf+nRqjNzc0J3nXkwUxZIWTFIcqrU/PDDz/ghx9+gFKpHBaZKxdeeCG2bduGf/3rX0JkPgj0WaTPTyQSQTgcRiAQgNPphM1mg8vlgtvtThB+nn766YTnMZlMyMvLg8ViQWFhIbKzs5GdnQ2TyQSTyQStVsua8wwH72XiySefxKuvvsq+vvPOO7Flyxa0tbXhoYceSnjsqaeeykqQR40ahY6ODuTm5iY85o477sCyZcvY1y6XCyUlJf34GwgE/Qf1FxG+tIKhglQqhVqtZn1H+P3+QM97w+lQU9C3iGM7gWCQwjc3CYVC0Gg0LNOHykmHUwmpQCAYHlD2ER2i0Y3vGC+Xy1lGCHm3AmCdxCUSCQwGAwCwxTvvRy7ozvPPPw8AOOecc5CdnZ3m0fQ/F154IQDgs88+Q1tbW7+9zrZt2zBv3jzMmjULL7/8MgBg3bp1mDlzJs444wx0dnYiHo/j6quvxvz58zNuw8Y3zQyHwwgGgwiFQnC73QgEAnC5XCyTyWAwYPLkyVCr1VAoFDAYDCgoKEBlZSXGjh2LMWPGYNy4cRg1ahRKS0tZRrPJZIJOp4NWq4VGoxlWh+dLly7Fhg0b2M3n82H79u3s88izZMkS7Nq1C8FgEPv370/5OVWpVDAajQk3gUAgEGQetN5NboLaXzz55JOYN28eu73//vv44x//iLa2NixdujThsaeeeioqKythNBrZoWYyd9xxB5xOJ7vV19f36/gFmYMQmQWCQQrvJUaiCDUAEN7LAoFgqEMZTJTVyDd6ksvlUCgUrKJDo9HAaDQykcpisTDhizIx6ZqZ3GhFANjtdiaADpes3lGjRuHYY49FLBbD2rVr++11yPPw66+/xt/+9jcAwBNPPIENGzbglltuwXPPPYcNGzYgJycH69evx86dO9HY2Nhv4zkSKHuJspmpRJa80/V6PbRaLbO6GTt2LMaPH48xY8agoqICFRUVqKysxJgxYzBq1CgUFhaioKAAVquVHZqLtU0Xy5cvR01NDU488UQsWLAA0WgUt956Kzo7O7F8+XJcc801WLhwIW6//XZhASAQCASCXiMONQV9hbDLEAgGKQqFgmXkkcAy3DdfAoFg+EH2GOSpzDeJUigUzF4jGo1CKpUyaw2y1yBrDXH97JnnnnsOXq8XkyZNwoknnpju4QwYF154Ib7//nv861//wo033tgvr5HseehwOJCVlQWlUon58+fjueeeg0ajwbx58wAAc+fOxbfffouzzz6723Oly/+QPnfkiUoeqdSYyO/3o7S0FG63m92vVquh0Wig1+uh1+thNBqh0+lgMBhYYy+6CQubA/z000/d7vvTn/4EALBarfjqq68GekgCgUAgGIIsX74carWarfs+/fRT3HbbbbjzzjvZoWY8HheHmoJuCJFZIBikHE4XZYFAIBjqUBYzDwlfJDLL5fIEMZo/nKOfFQvlREKhEJ588kkAwH//938PK7Hv/PPPx+9+9zt8+eWX2LdvH8rKyvr8NZI9D91uN/R6PQBAp9PB4/GkvC8V6fI/pM8TXwWgVCoRDoeRm5sLh8OBUCgEg8EAuVwOpVLJRGa1Wp3QpJhv6icOzwUCgUAgSA/iUFNwpIidlEAgEPSScDiM2bNns6+TfTMFAkFmQY0ClUplQtOw5AxJ8rqjm+AA//jHP9Dc3IzCwsJujV+GOiNGjMBJJ52EeDyOv//9733ynIfyPNTr9fB6vQAAr9cLo9GY8r5UpMv/kCoH6LOjUqmgVquh0+mgVquRm5uL4uJilJaWorS0FMXFxcwSIz8/H9nZ2SyTWafTDTvfZYFAIBAIBIKhghCZBQKBoBd0dnbitNNOw969e9l9yb6ZAoEgM+GbBdKNsiUVCgUTm0Wz1ES8Xi/uv/9+AMCdd97JrEWGE2ST8b//+78IhUJH/XyH8jy0WCxob29HIBDA+vXrMX36dEyfPh1ffvklAGDDhg2YNm1ayudOp/8hf0ijVCqh0+lgNBqRlZWF3Nxc5OXlIS8vD7m5ucjNzUV2djZMJhP0ej10Oh30er0QlwUCgSANiCQagUDQlwiRWSAQCHqB1+vFX/7yF4wfPx4Auvlmbtq0KeXPBYNBuFyuhJtAIEgf1BiQspkVCgW7CXErkSeeeAItLS0YOXLksGn4l8x//dd/oaCgAG1tbXjttdf6/PlTNXJbtmwZTjjhBDz66KO46aabcPzxx8Nms2HOnDkoLy9HaWlpn4/jaKCmmzKZjH2m1Go181qmm8FgSGjkR5nLarVaWGMIBAJBGhBJNAKBoK8RnswCAYAff/wRZ511FkpLS1FUVIRXXnkF69atwx//+EdkZ2dj1apVsFqt6R6mII2UlJSgpKSEfZ3pHpkCgeDgkKAlhK3U7N69Gw888AAA4IEHHhiWWcxAV5Pdm266CXfffTdWrFiBCy+8EHJ53y2fU3ke/vKXv8Qvf/nLhPteeOGFPnvNvoYEZvq/XC5HPB4HkPg5o8eRf7r47AkEAkF6oSSapUuXAuieRNOTyJyuRrMCgSDzEZnMAgGAHTt2YMWKFfjPf/6DV155BYA4xR3upPLN5Ml0j0yBQCA4UmKxGK6//noEAgGcfPLJuPjii9M9pLTym9/8BtnZ2aipqcFLL72U7uFkHGQ1Q9UB5MlMN41Gw5r8KRQKkbUsEAgEGUJJSQkmT57Mvj6cJBqTycRufCKOQCAY3giRWSAAUFVVhRdffBHHH3883n//fWGFIOjmm3n66acnfD+Vb2Yq0umRKRgYrrrqKvznP/8B0N3HLh6P4+qrr8b8+fNFRrtg0LBixQp89tlnUKvVePbZZ4e9IGgwGPCHP/wBQJc3dUdHR5pHlJlQFjN/o8zl4R5DAoFAkAmIJBqBQNDfCLsMgQDA5MmTcckll6CoqAgnn3wyXnvtNWGFIDgk5Jup0Wiwdu3adA9HkAZ27NiBdevW4aqrrgJwoALik08+wXPPPYd58+YhJycHL7zwAi666CI0NjaiqKgovYMWCA7CypUrcd999wEA/v73v6O8vDzNI8oMbrzxRjz//PPYuXMnlixZgjfffJNZRAgEAoFAMBhYunQps8ZIxeEk0ahUqv4apkAgGMSITGbBsCT5FFepVKKyshJGoxGjRo2CWq0Wp7iClFDGKtDlm/nNN9/gP//5D7KystI3KEHaeOyxx3DFFVcASN0McvPmzZg3bx4AYO7cufj2229TPo+oihCkm1gshsceewxXXnklgC6LiMsvvzzNo8oc1Go1Xn75ZSiVSrz99tu47rrrEIlE0j0sgUAgEAj6lOTmswKBQHA4CJFZMCxJtkJYuXIldu3ahWAwiP379yMnJ0dYIQgEgoPy3XffYeTIkawpaCofO+FtJ8h0IpEI3nvvPcyfPx+33XYbgK458s9//nOaR5Z5HHPMMVi5ciWkUileeOEFHHfccfjwww8Ri8XSPTSBQCAQCI4YkUQjEAj6CmGXIRAAWL58Oa655hrE43HcfvvtkEqlwgpBIBAk8OSTT+LVV19lX9fU1KCmpoaJcal87JLvKy0tTfncd9xxB5YtW8a+drlcQmgW9Bl+vx9vvvkm2tvb4fP54PP5YLPZsGfPHnz77bdwOp0Aug5CHn/8cVx//fXCQ7cHLrzwQmg0GlxxxRX47rvvcNpppyE3NxezZ89GWVkZcnNzWZO7hQsXorKyMt1DFggEAoFAIBAIBgRJPB6Pp3sQAsFQweVywWQywel0iqxmQUpEjAwNPB4PZs6cidzcXOzbtw9msxkbNmzAL37xC3z44Yf47LPPsHnzZhx//PF499138eijj+LCCy/EY4891qPQzCPiRNCX/PnPf8att97a4/ctFguWLFmC3/72t+Jwo5c0NzfjkUcewYsvvshE+mRGjhyJvXv3DvDIDiCuI4JDIWJE0BtEnAgOhYgRwaEQMTJ8EJnMAoFAIBAcJnq9Hjt27ADQVQmxYMEC6PX6bhUQVqsVL730EubMmYOFCxf2SmAWCPqaZcuWJWTKC46egoICPPHEE3jiiSfSPRSBQCAQCAQCgSAjEJnMAkEfEo/H4Xa7YTAYRKmxICUiRgS9QcSJQCA4WsR1RHAoRIwIeoOIE8GhEDEiOBQiRoYPQmQWCAQCgUAgEAgEAoFAIBAIBALBESNN9wAEAoFAIBAIBAKBQCAQCAQCgUAweBEis0AgEAgEAoFAIBAIBAKBQCAQCI4YITILBAKBQCAQCAQCgUAgEAgEAoHgiJGnewACwXCBzO4FmUkmNCEQMZLZZEKMACJOMhkRI4LekAlxImIksxExIjgUmRAjgIiTTEbEiKA3ZEKciBjJbA43RoTILBAMEG63GyaTKd3DEPSA0+mE0WhM6xhEjGQ2mRAjgIiTTEbEiKA3ZEKciBjJbESMCA5FJsQIIOIkkxExIugNmRAnIkYym8ONEUk8Ho/343gEAsH/z6FO6FwuF0pKSlBfX5/2C31PDOUxDoZT3KH89x9IBnOMAIM/TjJ9fMDQjxEg89+HTB7f0Y4tE+JksF9HgKE9RhEjfcNQHmMmxAgg4mQgEDGSfobyGDMhTkSMDAwDFSMik1kgGCAkEkmvPsxGozFjL0yEGGP/IGJkYBkMY0zFUImTTB8fMDjGmIrexgiQ+b9jJo8vk8d2KIbKdQQQY+wvRIwMLINhjKkQcTJwZPr4ekLEyMAyGMaYjIiRgaW/xyga/wkEAoFAIBAIBAKBQCAQCAQCgeCIESKzQCAQCAQCgUAgEAgEAoFAIBAIjhghMgsEGYJKpcK9994LlUqV7qH0iBhjehkMv5sYY/rJ9N8v08cHDI4xHi2Z/jtm8vgyeWx9xWD4HcUY08tg+N3EGNPPYPj9Mn2MmT6+o2Uw/H5ijOllMPxuYowHEI3/BAKBQCAQCAQCgUAgEAgEAoFAcMSITGaBQCAQCAQCgUAgEAgEAoFAIBAcMUJkFggEAoFAIBAIBAKBQCAQCAQCwREjRGaBQCAQCAQCgUAgEAgEAoFAIBAcMUJkFgjSzI8//ohRo0ZhwYIFuPTSSwEA69atw8yZM3HGGWegs7MzzSPsIhgM4pe//CXmzp2L559/Pt3DYbhcLhQVFWHBggU46aSTsHHjRsyaNQsLFy5EbW1tuofXZwyGOMnUGAGGR5yIGDk6hkOMACJOjhQRH5kTH0BmxggxHGJlMMSJiJH0MhhiBMjcOBExImLkUAyHGAEGR5xkaowAaYqTuEAgSCurV6+Or1y5MuG+E044IR4MBuPvvvtu/I9//GOaRpbIypUr43/729/i0Wg0fsopp8T9fn+6hxSPx+PxjRs3xu+77z729S9+8Yt4R0dHvKqqKn7dddelcWR9y2CIk0yNkXh8eMSJiJGjYzjESDwu4uRIEfGROfERj2dmjBDDIVYGQ5yIGEkvgyFG4vHMjRMRIyJGDsVwiJF4fHDESabGSDyenjgRmcwCQZqpqqrCiy++iOOPPx7vv/8+HA4HsrKyoFQqMX/+fGzatCndQwQAbN68GfPmzYNUKsWkSZOwc+fOdA8JQNff74MPPsDxxx+Pf/7zn/D7/bBarRg/fjxqamrSPbw+YzDESabGCDA84kTEyNExHGIEEHFypIj4yJz4ADIzRojhECuDIU5EjKSXwRAjQObGiYgRESOHYjjECDA44iRTYwRIT5zI++VZBQJBr5k8eTIuueQSFBUV4eSTT8Zrr70GvV4PANDpdPB4PGkeYRdutzsjx1VWVoZHH30UM2bMwKJFiyCRSNj34vF4GkfWtwyGOMnUGAGGR5yIGDk6hkOMACJOjhQRH5nzXgCZGSPEcIiVwRAnIkbSy2CIESBz40TESOa8FyJG0stgiJNMjREgPXEiMpkFggHmySefxLx589hNqVSisrISRqMRo0aNglqthtfrBQB4vV4YjcY0j7gLvV6fkeOaPXs25syZA6VSidmzZydcOKXSwXuJG4xxkqkxAgzNOBEx0rcMxRgBRJz0FSI+Mue9ADIzRoihGCuDMU5EjAwsgzFGgMyNExEjmfNeiBgZWAZjnGRqjADpiZPBG30CwSBl6dKl2LBhA7utXLkSu3btQjAYxP79+5GTk4P29nYEAgGsX78e06dPT/eQAQDTp0/Hl19+iXg8jq1bt2Ls2LHpHhIA4J577sHHH3+MWCyGzZs3Q6VSwWazYceOHSgrK0v38I6YwRgnmRojwNCMExEjfctQjBFAxElfIeIjc+IDyMwYIYZirAzGOBExMrAMxhgBMjdORIyIGDkUQzFGgMEZJ5kaI0Ca4qRfnJ4FAkGv+fHHH+PHHXdcfM6cOfG33norHo/H4+vWrYvPnDkzfsIJJ8RtNluaR9iF3++Pn3XWWfFjjz02/te//jXdw2E0NDTEFyxYED/uuOPizz77bHzjxo3xWbNmxWfPnh2vqalJ9/D6jMEQJ5kaI/H48IgTESNHx3CIkXhcxMmRIuIjc+IjHs/MGCGGQ6wMhjgRMZJeBkOMxOOZGyciRkSMHIrhECPx+OCIk0yNkXg8PXEiiceHkGGLQJBm4vE43G43DAZDQimCQECIGBH0BhEngkMhYkQgEBwt4joi6A0iTgSHQsSI4FCIGBk+CLsMgaAPcbvdMJlMcLvd6R6KIEMRMSLoDSJOBIdCxIhAIDhaxHVE0BtEnAgOhYgRwaEQMTJ8ECKzQCAQCAQCgUAgEAgEAsEw5dxzz8W+ffuwceNGzJo1CwsXLkRtbW26hyUQCAYZQmQWCAQCgUAgEAgEfU48HkckEkn3MAQCgUDQA6FQCGeffTa++eYbAMCDDz6I999/H3/961/x8MMPp3l0AoFgsCFEZoFAIBAIBAKBQNCnVFVVoaKiAhqNBn/4wx8g2sAIBOnH5XKhqKgICxYswEknnZTu4QgygGAwiP/3//4fTj75ZACA3++H1WrF+PHjUVNT0+PPuFyuhJtAIBAAQmQWCAQCgUAgEAgEfUgkEsF5552H3bt3IxKJ4KGHHsKqVavSPSyBYNizY8cO3HDDDfjPf/6DTz/9NN3DEWQABoMBxx9/PPs6Foux//d0OPjQQw/BZDKxW0lJSb+PUyAQDA6EyCwQoPupvvCiEhwM4VkmEAgEAkHPrFu3Drt27UJ2djZuvvlmAMDy5cuFdYZAkGaqqqrwwQcf4Pjjj8c///nPlI8RWarDG4lEwv4vlaaWi+644w44nU52q6+vH6jhCQSCDEeIzAIBup/qCy8qQSqEZ5lAIDhaxKGmYDjwf//3fwCAa6+9Fo888giysrKwd+9erF27Ns0jEwiGN2VlZXj00UfxySef4KWXXkJ7e3u3x4gs1eGNRqOBzWbDjh07UFZWlvIxKpUKRqMx4SYQCASAEJkFAgDdT/V740UFiJP+4YbwLBMMJiKRCMLhMMLhcLqHIuAQh5qCoY7T6cR7770HALj44ouh0+lYNnNPmZMCgWBgmD17NubMmQOlUonZs2enPNwUWarDm7vvvhuLFy/GNddcgzvuuCPdwxEIBIMMITILBOh+qt8bLypAnPSni3g8jlgshmg0yoS0SCSS8L71B8KzTDCYiEajCIVCCAaDcDqdokw9QxCHmoKhzmeffYZgMIixY8di0qRJAIDLLrsMAPDJJ5+kzJwUCAQDwz333IOPP/4YsVgMmzdvRnl5ebfHiCzV4cmLL76IsrIyzJ49G5s2bcLGjRsxevTodA9LIBAMMoTILBCg+6l+b7yoAHHSP5CQsMxnZ4bDYUSjUcTjccTjcfb/gUJ4lgkyGYVCAQAIBALw+XxoaWmB3W4f0M+IoDviUHNwQPOK+LwcPl9++SUAYOHChWyeHDNmDI499lhEo1G89tpr6RyeQDCsWbZsGR5++GHMnz8f5513HrKystI9JIFAIBAMIYTILBCg+6m+SqU6pBcVIE76BwISl8PhMEKhEEKhECKRCKLRKMtm5sXlgRQEhGfZ8Ka+vh4LFy7Ecccdh4cffjilt+6dd96JuXPn4sYbbxzw8UmlUmi1WsjlcgSDQbjdbuzfvx9VVVUIhUIDPh5BF+JQM/PgDzEp+5/mG/5rqpgRwvPBWb9+PQBg/vz5CfdfcMEFAIA33nhjwMckEAi6KCoqwueff46vvvoK119/fbqHIxAIMpTkfQ6R3FtEIEhGnu4BCASZwLJly3DZZZfhvvvuwxVXXIEpU6Zg8eLFkEgkePnll9M9vGFJsiVGLBZjt3g8DolEAqVSyR5L8IJNf0OeZSJOhif/8z//g+XLl+OEE07ASSedhI8//hjvv/8+Wlpa8PDDD+POO+/Enj178NVXX+G2227Dxo0bMWfOnJTPFQwGEQwG2dd9ZYUgkUhgMpkQi8XgdDoRCATg8XjQ2NiICRMmoKioaEA/M4KuQ81FixbhlFNOSTjUbGtrO+ShpkqlGriBDnFojuHnlVTZyxKJhN2oYkYikUAulx/0UGC44na7sWXLFgDdReZzzjkHt912G/7zn/+go6NDZFAKBAKBQJChJO9zbr75ZhgMBtZb5O677073EAUZihCZBQIcONXn2bRpU5pGM7xJFpeTM5X5x0UiEcjlBy5jUql0QASzF198EUBX2buIk+HLH/7wB5hMJgBd/scymQxWqxVWqxU1NTX44YcfMHfuXABdYsumTZt6FJkfeughrFixol/GKZFIYLVaoVAoUFVVBbfbjba2NlRXVyMvLw9nnXUWO7AR9D/iUDN9pBKWqSomlcjMC8wAEoTlcDgMpVIpDmmS+OGHHxCLxVBSUtLN0qW8vBxTpkzBtm3b8O9//xtLlixJ0ygFAoFAMFihuZtsE3nrREpEoptUKoVMJoNcLheHw4dJ8j6H9tzUW+Tjjz/GkiVLepzL+yuBRpD5iE+ZQCDICHhbDJqU6BYIBOD3+xGJRKDVatkCIR6PY/fu3TjxxBMxYcIEzJkzBzt27EjzbyLoD5I9ufkydipl5z26eWpqanDcccehoqICM2fO7LMYsVqtkMlk+Pvf/47p06d389Z1u93Q6/UAAJ1OB4/H0+NzDYQVgsFgwDHHHAO9Xs9ep6qqCo8++mjK7vLDjf6Kk2SSS5VFg53+gzaiZINB1wm6hvDzjN/vRyAQSLDKoO+Fw2HE43F4vV789re/xYQJEzBt2jTWzE5wgK1btwIApk+fnvL755xzDgBhmSEY2ixduhRlZWWQSCTYvn17wvcGaq4RDG5EnKQmHA6z5sd+vx+hUIgJzfwhMt0XDodZFZ/D4YDL5UIwGBxStlcrVqxIea05WpL3ORqNBkD33iI9NfMVvUTSTyAQwFlnnYWKigpMnToVp512Gvbt29fvrytEZoFAkFZ4cZnf4PObfrLLoA0/iXkymQxLly7Ftddei59//hm33XYbrrnmmjT/RoK+INkjlReR+azDnh4fiUTY96+//npcd911qK6u7vMYWbVqFdatW4eHH364m7euXq+H1+sFAHi93oP6cQ+Ud7dCocCJJ56IGTNmsL9le3s7HnnkEaxZswaRSKRfXncw0J9xIhgYUl03+IqY5AOqQCCAcDjMHsNfZ+jzTFUz0WgUy5cvBwBs374dW7ZswSOPPJLG3zYzIauMqVOnpvw+icwfffQR3G73QA1LIBhQzjvvPGzYsAEjRozo9j0x1wh6g4iTROig1+12J2QuBwIBuFwu2O12dHZ2JvzrdDrh8/nYQTEARCIReL1eOJ1OhEKhQS82//DDD9i0aRNKS0v75fn5fQ6R3Fukp0QV0UskM7juuuvw888/Y+vWrVi8eDGuu+66fn9NITILBIIBIx6P47jjjsPUqVOh1WoxZswYTJ06FX/4wx8SmiyRuMxnhhLhcBhAl8Bss9mwdetWXHrppZBIJDj33HNRW1s7ICd0gr6Dt0gJhUKYM2cOpk6dCr1ej3HjxmHGjBm49957Ex7Hi0WpMpjpMa2trfjhhx9YxmFfxsg333yDl156CWvXroVSqezWCHLatGmsAdb69et7zOwbaKRSKU444QRcdtllkMlk6OjogMvlwmuvvYZbb721x4yETKKna8ldd911RM/X1tbWb3Ei6H9ICOavB9FoFCeccAJmzJgBi8WCiRMnYs6cOVi+fDmCwSCrivD5fHC73fB4PPD7/fD5fPB4PHA6ndBqtaxU1OFwYNWqVbjnnnvY6xYVFaXrV85YKJN52rRpKb8/YcIEVFRUIBQK4d133x3AkQ0NRCOmvqev5xMAOP7441FcXNztfjHXDE3EmqR/icfj8Pl8zHpBKpXC5/Ohs7OTzd9erxcej4fdvF4vu8/pdLLHRqNRAEAsFmOPTbXf7I/foa+vM8FgEDfffDP+9re/9Yt1V/I+h7jnnnvw8ccfIxaLYfPmzSgvL0/58wOVQNNb+EQEuqWqgE0nfR0narUaZ5xxBouP2bNnY+/evX055JQIT2aBQDBgSCQSfPXVV4jFYigqKsK7776LoqKihOzTJUuWsEUU2WLQ5L9q1SqUlZUxf63m5mYUFBQk+GKWlpZi//79B22gJUgvNMnz//L85z//AQCMGDECH3zwAYqLi5loDACXX355jxPkq6++ivLycva8tbW1KCwsZD5iEomkz2LkgQceQEtLC04//XQAwIMPPpjgrVtWVobRo0ezcsd58+Yd1ev1JRKJBOXl5Vi2bBkee+wx7Ny5E/F4HPv27cPNN9+M66+/HgsXLsxY7zqJRIKvv/4aAJCfn49PP/20WxbHeeedh927d6f8+bfffjuhbK++vr7f4kTQf9ChU/L1hP7/0UcfAQDGjBmDN954A/n5+QC6DqECgQBuuukm1NXVsefjrZieeeYZyGQyJiTX1dXBarXi8ccfxxdffAGNRoMVK1YIQY8jFAqhqqoKQM+ZzBKJBOeccw4efvhhvPHGG7jooosGcISDH9GIqe/p6/nkYIi5Zmgi1iT9C1UfAV3ztM1mQzQaTfD8TfZk5tevMpkMCoUCsVgMoVAICoWCCZ7hcBhOpxM6na5f+5P0x3XmnnvuwWWXXYaRI0f2y5iT9zkLFy7E+eef3623SCY38U0WlnuC9++WyWQJ/TgGkv6ej5588kmceeaZfTfgHhAis0AgGBD4LNTGxkaEw2Hk5+ezUiUSmZ977jkolUoYDAZ4PB62eFAqlZDL5eziL5VKWaM/fhLIpNNIQReHEpUJmuAlEgmam5sRDoeRl5fHxGWga6G4evXqhIWkTCYDAFYOFwgEoFar2WslLxL6KkbefvvtbvclN4J88MEH++S1+guj0Yh7770Xa9aswbp16xCJRBAIBPDkk09i06ZNuPXWW6FWq9M9zB6hOElVJvj6668f1nP1V5wI+h7aMPD+iz1lIrW1tbH5Buh6X8kf/amnnoLJZILRaIRKpWIx0NHRgY6ODkgkEtaoJhaLYd++fRg/fjweeugh7NixA4sWLcKOHTuQk5MzAL915lNTU4NIJAKDwXDQ0t1zzz0XDz/8MN577z34/X7m8yg4NKIRU//Rl/PJwRBzzdBFrEn6nlgsxqznSGAmywvquUDJSvw6QCaTQaVSQSaTsf4MwWAQarUa8XgcHR0d0Ol00Gg0bF2gVquh0Wj6VVzsqxjZuHEjvvvuu4SKlr4m1T6H+Pzzz/vtdfsKqpBNXh/y2gF/OEFVcARpDfQzPcE3mkxuGH2k9Md89Mc//hE1NTV49tlnj2psvUGIzAKBoF+hizafcbZ161ZMmjSJXcj508Ubb7wR+/btS5gQ6AK/evVqVFRUIBKJQCqVori4GI2NjYhGo5BKpYjH46ivr+83XyrBoaFFMIk+yd7JBN/1mZ+QKcvgu+++w+TJkwF0vf8KhQIymQw+nw+XXHJJN/8veq7XXnsNBQUFCAaDUKlUKC4uRkNDA0KhEJRKpYiRFMjlclxyySWYOHEiHnvsMbjdbsTjcXzzzTe44YYbcO+99/ZblsTR8uOPP/ZYln84J/0lJSVoaGhAJBKBXC4XcZKh8M18kjcDQNd1gD+MlEgk+OCDDzBx4kRIpVKo1Wo0NzcD6GrGuXTpUuzZsyfhOah88tlnn8WYMWMQi8UgkUgwcuRISKVSXHLJJZBKpZg2bRpGjhyJqqoqLFiwYKD+BBnNzz//DAAYO3bsQTdYxxxzDEpKSlBfX4+PPvoIv/zlLwdqiIMeq9UKAD02YpoxYwYWLVqExYsXpzz8eOihh7BixYoBHfPB4CsP+M0+T0+JBPR/+j4JArSuOFz6aj45GGKuGdqINUnf4/f7AXR9zu12OyKRCDweD6tI4g/NgK7rAK0VfD4fgC7bBhKTqVkg7Qn8fj/0ej3kcjkCgQBCoRC0Wi0UCkW/iM19FSNffPEFdu3axdbnDQ0NOPXUU/GPf/yDZR4PV+LxOGvyTNAek7QIuk8qlbJ1I4CEpIWDJTAcCj4r+kjmpL6ejx5//HG88cYb+OSTT6DVag9rLEeCEJkFAkG/wJ8IJnf7bWhoQGFhISQSCTQaDTo7O9nPPfPMM+z/KpUKJpOJnWDr9XomKkgkEuTm5mLq1KlYtWoVlixZgrVr16KsrGxYlpKlC35TmGoy7klkTrWJ5H++ubkZRUVF0Gg0kMlkiMVicDgciMVieOGFFyCTySCXy1nTSHoOo9GIcDjM7s/Ly8OUKVPw8ssv45prrhEx0gNSqRRTp07Fk08+iYcffhjV1dWIx+Po7OzErbfeiiuuuAL/9V//lXH2GY2NjSl9L4HDO+nPzc3FtGnTsGrVKlx11VUiTjKMVOIyXXMkEglUKhWUSmW3hbzf70djYyMKCwthtVphs9kAdAnMhYWFWLt2LYCubBePx4POzk52PaFrD9BVSZObm4uFCxfi448/xplnnom6ujrU1tZi7NixA/zXyFyqq6sB4JB/E7LM+J//+R+88cYbQmQ+TKgR07p169h9s2fPhkajgVQqZY2YUonMd9xxB5YtW8a+drlcvRZI+wryUKdqhP6ANvhyubzX81ZfzScHQ8w1QxuxJulbqOk70FWFQX0UotEovF4vIpEIJBIJ8vLyIJPJEAqFIJFIoNPpoNfr4XK50NTUxCo4lEollEolW1PQfZFIBAqFAmq1GkqlEh6PB3K5HBqNBnK5vE/F5r6Kkdtvvx233347+7qsrAzvvPMOJk6ceNRjHMxQRSt/cEkNnun7/B6UzzqWSCRQKBRMdKYENv7n+J/nq2WTY4T2tOFwGBKJhFVk95a+nI/+/Oc/Y/Xq1fjkk09gNpsP62ePFEl8uNZeCAT9gMvlgslkgtPpTLu5fbrgvY+SxWW63OzevRsXX3wxZs+ejVWrVsHr9cLhcCAej7NsM51OB5VKhUAgAL/fD6lUCr1ezxYbCoUCQFd57jXXXIOOjg4YjUa89NJLmDBhQtp+/0MxFGKEF5VJ9OcnXNo09iQwHwqZTIba2lqcddZZmDVrFl5++WU4nU6Ew2FIpVIYjUb2/gNdApHL5WKLTZPJhEAgAKCr4cGuXbvwq1/9Cp2dnYMiRoD0xkkoFMLq1avx1ltvsUWZRCLBtGnT8Pvf/z6jStt//vlnLF68GLNmzcKqVauO+rmuuuoqcS3JIEiMopJYXmTmxSmtVguDwdDt571eL7Zu3YorrrgCc+bMwZ///Gc4HA5IpVJWEhsOhxMyoeh7tLEk8Vqn06G6uhrXX3897HY7ZDIZ7r33Xpx99tkD8rcYDCxZsgQvvvgi7rvvvkN6A69fvx7HH388zGYzWltb+9ULcyjxzTff4K677sK///3vhGvxrbfeikWLFuGUU07BqaeeijVr1vTKJ3MgryO04eZ9MfnDZT4zuacbX7qcqtw5lSUXZaodSizqy/nk5ptvxltvvYWWlhZkZ2dDr9ezzLPBNtcAw2O+6QvEmqRvY8Tv97NM5o6ODtb8z+PxMGE4Ho9j7969KQ+sCgoKMHr0aCgUClbVSJCYSIKiQqGAQqGAUqmEWq2GSqVi31epVAmWWkdDX8YIz2AQmfv7OhKLxRAIBNhcQFacyQkKyfA2nFQFdySQSE02Lfw+GeiqHOX7SB2MvoqThoYGlJSUYNSoUWydrFKp8M033xzxc/YGITILBH3IcF6E8aIjb4+RXM5MEzn9DH8CSFkndF8kEoHb7QYAtpmKxWJsEgDQ5yfM/c1gjRE+U5nfEPJey8mPJ2hjSFmGydmGfFM/iUQCtVqdcNobCoXgdDoBdJUK03uf/HoOhwORSIRlNYbDYbZ4jMfjCeVQmU664yQWi+Gnn37CX/7yF9jtdkgkEsTjcWRnZ+Ouu+7KWPuM4US6Y6Q/4TMdec/F5PmEIB9FqmIgcZiuCwBgMBigUCjQ2NiYsNEkaCNJGwyZTAaNRoNoNAqFQgGNRsN6CCgUiozL6s8EjjvuOGzcuBH/+te/cMEFFxz0sdFoFCUlJWhubsbKlStx+eWXD9AoBzdnnnkm9u3bxwRkasRkMplw2WWXIRQK4YorrsD111/fq+cbiOtIcukyrQ+PNpM52XaLSpPpM0wVdTxyubxPPr/Ja6JkgfxoxYpMYyjPN4K+oT9ixOl0soxjp9MJt9sNr9fLfHbb29uZCG2xWJCdnQ0A6OzsREdHB3seuVzOqiODwSBCoVC3/SMJyiQSKpVKZrNBgjOJzYNlL5Fp9Od1hARmPks9EokgHA4f1GYt2daC7uP3qgf7l+ae5LlMqVTCaDSyZAaa/yiWhjpCZBYI+pDhuAij7BESAHoSl3uLTCZjpUt0QaaTP3pOEg/If3Mwke4YSVXuQ6TaDCX7JKbyTkzV1I9/bqlUCqVSCZ1Ol7Cxi8fjrPwtGcokBAC3282a+aXKViTC4TAcDkdCNjPfUVqIzIdPR0cHnnnmGWzZsoW93waDAZdeeilOOeUUIbSlkUyJkb6EF5f5W3IzH7VajUAgwOYEtVqd8volkUhYQyBCKpUetLkL0HX9ocqZcDjMNpv0PEJkTk1WVhY6OzuxZcsWTJ069ZCPf+SRR3D77bdj3Lhx+OmnnwbdfD4U6O/rCM3zlEFGm22+6o1fSwCJTYBTNVBK9XWqx9J6kuKKv45Q1uLhisCUjd1TRlwytAYZLGuPnhiK842gb+nrGCGbPDosdrvd8Pl8rCl8e3s7IpEITCYTJk2aBI/HA5fLBYVCAavViqysLLS0tKC6urrHBqf8wRTtNWm+p6xmEpm1Wi00Gg0ToZMTYgSHpr+uI+StTdfnUCjEROZQKMQSBPiDQV4g5n39+Zg4HC9l2qPSWICuecZsNkMikSAUCrGK3EyqCO0vxCdDIBAcMal8Mg8lLidnstJGnTYf0WiUXZyBLkFZoVCw8kq+pHaoZIj0J6kykOl+4mB/x2S/Zb4Db6qy1FTwp8smkwkA2PtMP0/ZA1RKGwgEoNPp2Akw0HX6ezCSS2dT/a6CwyMrKwu33XYbPvjgA6xbt44t4D788EPs2bMH11xzzSHfF4HgUCSLy3Qd4D+7tMmTyWRwOBwJh460WaDyV8piIQsmmkN4H+fkzSF1oucPuPj5ib9+irmnOx0dHay/wpgxY3r1MzfeeCMeffRR7Nq1C/fff39GNaQTHD28NyZ9JklYpqoDelxyFhhvi3E48M3/6FoAgPmrJnt09jYrMR6Ps9JrHuonwa87+DUurYsPp0xaIBCArf1pXRAKheD3+xGNRtHZ2ckEZo/Hg5UrV6Z8jpycHIwaNQpjx45FKBSC2+2Gy+VidhvJB9h0KERCoEajYeKyWq2GVquFVquFTqdjfs60LhGkBzrIJHuMUCiEQCCAcDjM4oUSBpKv3wQlIaWqdCFffxKd+ccBSNi3BgIBZGVlwWKxwOFwMDs2OpCgeY+v5D6c3zPVfj5TK2eEyCwQCA4bPnM5lbhMZYo8fAljMrx3EWW8kDAAHFhokIDA+8QKUsM3XkyVgZxMqsYFyRnMqQ4RaKLkswCSG+2Qfxq9j+FwmHkmJ9tjyGQyeL1eFmMKhYK95sEWcfF4nDWIJKsM+hl+4yc4fJRKJRYvXowxY8ZgzZo18Pv9UCqVaG5uxuOPP44lS5agsLAw3cMUDEJ6EpeTvVv1ej3MZjOi0Sjsdju7JqhUKiYmkbDDZzfT8/K2OckHbOS/mHx9ICGKHkdjGWwWTQPFzz//DAAoKSmBTqfr1c8YjUY8/fTTuOSSS3Dfffehuroao0ePRnV1NaqqqhAOh3HSSSfh7rvvRkFBQX8OX9DH8AJzJBJhIgCt8UgQ4O2ykkm22qLn5f+lx/FZaXQ9oJJ4+uxHIhEmCtGhUyAQYMkMPUHj51+PhI2eRAsALBOSniMajaa81gi6k8qOJFX1XSrP7uT1bPJaNtUamLdzE2QG9Nki4ZASkex2O4LBICQSCaqqqliFUWFhITtI8ng8aG9vZ7fDhQ6ctVotVCoV9Ho9TCYTE5z9fj9rLhgOh5kILdYGAw9dWymD2e/3IxgMIhAIIBgMsoSmSCTCRGcg0epCrVZDJpOx6zztZWnu6Ol91el0yMnJgUajgdPphMfjQUdHB/Lz86HRaODz+VglbvL+ujexwq+Re2Mt1Vd2UH2BEJkFAkGv4YVGXmQmSEjmNwy8uJxcAkkbjWAwyE4IqYSRz5YFDnhl0TgEqUkWl/mM42TvQKDnMtPkDObk5+cnvmRrDLPZzMqDACRkElEJEwDmecq/LmUYksBEkyWJTj35MXs8HrbQVKlUCSJzNBpNWXIr6D1SqRSVlZX4zW9+g9deew0NDQ0Aug4QXn75ZSxatAhTpkzJiIWNIPPpSVymjJNkz2S73Q6j0QiXy5XgucwLSqleg+8Ozl+n+OsaLd6TrXx8Ph97LSLZVkNwABKZx44de1g/d/HFF2PHjh144IEHsGbNmm7fr66uxtq1a/HZZ59ldNMrQSLkmUqf6Wg0ykRn2vzz0GYfQMJa5Ujt1/i1JF1blEolE6tMJhMbH401OdM4OXuZ1ieUBUmPIdGZ/k9fkzUICRgkaievewRd0IEEL6rQejOVwMIfQvB7i6OB3mMSmMT7lD7IVicQCLC9QyAQYHYZLS0tiEQiUKvVsNvt2LhxI/tZiUSC0tJSlJSUwGAwwOfzwel0sutOJBKBz+eD1+uF3+9ndgqUEUo2GWSVYTab4fF4oNfrYbVa2XhCoRCzSAiFQtDr9SKreQDhDyspg5luXq+XJTU5nc4eLVMIo9EInU7HqjNpHUqNn7VaLROLKSa9Xi+8Xi8sFguKi4sBAB6PB52dncjNzYXP52PzC12n6Lp2sLUkiebJcx9/EMbPNTRn0rWT5px0IkRmgUBwSHjvPF5gJqisiDYRwIGFWvICkCCRINmQPxW0WaCx8M8hOAC9R6kaMB5Ncx1+o0cLsWRIyInFYujs7IRCoYBer2cnx0DX+0gLPD4rnc+GpoMGXnRSq9Xw+Xxwu90sE4lej8qh6PfT6/UsjvjMJCEM9Q1WqxVLlizBl19+iW+//RbxeFdDxS+++ALt7e1YuHDhQTPCBMMbvuEXLy5TpiB/QMlXrcjlcjidTnatoOoXyipObvaV7BWfPFeQH57f70/IdiZBirqT02vTYl5kMffMkYrMAHD//fdj0aJFePPNNxEIBDBy5EhMmDABkUgEd911F3766Secdtpp+PHHH2GxWPp66II+hm/qRwIzZZD5fL6EagMATDziP++UXUx+xvznLrnRHolCsVgMfr8ffr+fCbwKhYK9TjQahVarBdDVUMxkMkEqlSZUUNDrJa93aD1LHrH0uiRkJEPNQklUp2ZPlAFNYxKA/a3pPaMbvSfJJeLJ4jLvn8qXtfeUmcxnOicfLvKHA1KptE+adKVqEAmgx/2R4MDfjOKA/nU4HGyfQev//fv3s/dNpVKxdUZdXR3q6uoAdDUFLCsrg0qlwu7du+HxeNhrkeBMojb1kSHrLIPBwBoP5ubmwu/3Iycnp1svIp1OB5fLBb1eL9bBAwS9Z8FgMOFG+8WOjg5W/eZ2uxOsNOjzzdtykhBttVphsVggl8sRi8XgdrvhdrsBdGUvFxUVsR4U7e3tsNvtALqy6akxJb0GCeEk/PJJU8nXHxKX+T07XSMoU5tP7iItRavVsuodmnfTfZgpRGaBQNAjtEHnhWW+SYtMJoNWq4VCoUhYaKcyzad/+c0/+dTRRZfPNqOf4Tf19DP0fbEoOwBNWrzQTJNvqozmVBkgBL0HfJYPvzCm951O+Kl5Tjweh81mg8vlYqVkNCnTRA2APZ4/VKBx8Q04SKCmsncqg+I9uwmJRAKDwQCZTMa+zzeLFPQdSqUSJ554IoqKivD5558z79uqqio4nU6cfPLJQggSJHAwcZkXlwBAq9XCYrEgFAqxMledTpcgMPNZZsmlgXzVDMEflAJImHuSx8ELGfxBqRCYDw6JzBUVFUf08/Pnz8f8+fO73T9v3jzMmjULu3fvxm9+85sevTcFmQFl9NG/lHwQiUSYFRb1Wti9e/dRHYAno9VqUVxcjDFjxsDj8aCtrY1da0wmE7PV0uv1iMfjcDqdMJvNUKlUCXYeyZAtT2dnJztYpa+BA7ZfJE6TiEEZ05TNTWIlHdqnO9Ms3ZA4xPuo0v95D9VUTRZ5b1S+tP1IspBpXlGr1cyijdbTfr8fcrn8sK0Q6DmSq/16en06UBEJEV3QXE3rBT5GnE4nfD4fXC4XHA5HQkYyIZFIMGbMGBQXF2P//v2w2+1MCAS61hRTpkyBTCbDq6++2q16ip6DEld8Ph+ys7MRiUSQm5vbo4UL7Xv0en1CDyFB38Nn7tL11u/3M3vG1tZWuN1uOJ1OJjTzVXQSSVczProOt7e3w2w2Q61WIx6Po7OzE3K5HCUlJcjLy2ONKL1eL6qrq6FWq1FRUYGRI0eitrYWdrudWaiQKK3VauH3+5llBt8fxO/3JyRHJO9Xac1J9h+p4AV2yrYmexBqWp0uhMgsEAi6wYvL/EWcFwloo6DX69kJMHDAPJ/PKkiVxUwX02Th8WDwWQDDfXHOkyww8x6HlCF4tGKrXC6HTqeDTqfrcbHNC/8SiSTBr5Der+T3mSZYylqgRQAJxDQRU8dn/hSXMoloc0AlTPQ6dIIMCIuVvobsM/Ly8vDvf/+bLd4bGhqwZs0anHLKKSgvLxei3DCGn0do7uBFAxJiCCpDVavVCIfDaGpqAtCVgUSfY6pioOtGT1YZyfCl+LyQTNUVlGlC8JnRB+snIDhAdXU1gCPLZD4YVqsVq1atwpw5c/Dyyy/j17/+NWbMmNGnryHoO/jMPv7whgTmaDSK1tZWtjbhKxmopJj8UNVqNdRqNVQqFduIJ/s0k4Dr8Xjg8/lQXV2N2tpaTJkyBePHj0ddXR0To6xWK2KxWILQ7HA4YDabodVqmSjOZy+ThYbH42Hj0+v1aGxsBAAYDAZkZWUlXIeoMRkJHFlZWQgGg/D5fDAYDOzwnioqhiOxWAw+n49lkZJ3Kgky0WiUlaJTnFCWKYm+vKhCccOXifOHhKl8m/nKFyqxl8lkMBqN0Ov1CUK31+uFRqM55N6Dt4fh4a2W+IMV2teQiEpr3eE+3/AiM60dvF4vyyol+wu6AUB2djbKysrQ2dmJvXv3orq6Grt378Ytt9yCyZMno7m5GUBX34BRo0ZBLpfj4osvRigUwsiRI3HiiSdCo9Ggvb0dmzdvxu7du+F2uxGLxWC1WmGz2ZCbm4u2tjYUFhaivb09odKJ3mONRgOPxyN8mvsZuiZQ1jAdUoXDYdhsNrjdbrS3t8PlcrFrbkdHRzfbDIvFArPZzCpVgK4Dy6ysLMRiMdTW1qK2thY5OTkYP348tFot9u/fj0AggB9//BElJSXIz89HS0sLGhsbMXLkSBajRqMRANi4qLKG5rtUdhiko5DlB0EVwPz8RI8LBoPwer0sC5+uXb3RVvoLITILBAJGKnE5WRxIfjzvn8lnEhystJgWeHz5G2WWHWxc9FhRWnYAOq3kfQfpa97/WCKRQKfTMR9T4MApMJ0A0/vOe5JR1iBt4lpbW1nmqlqtRlZWFvLy8tj7wZe7+nw+AAeEZX5xnpwZSBlAfHmkWq1mmz7+eTUaTY8TJ58JT6/DZ3GnI26CwSAuuOAC2Gw2XHXVVfjVr3414GPoL6xWKy677DK8/fbb2LdvH1vovPbaa5g7dy7mzp0rDoSGGXzWMF/u2pPnsl6vR1ZWFvPBAwC3282uMSqVCn6/nx0c8deNw8364qtiSLDQ6XTsmkmPocfRnCY4ONFoFLt37wbQ9yIzAMyaNQtXXHEFXnrpJdxxxx345JNP+vw1BH0Dv/6gzzwJzIFAgGUXu1wuVrLOV7hRNRJt9gmLxYL8/Hzk5uYiOzsbWVlZCSJtJBJBa2srqqur4XQ68f3332PcuHGoqKhAXV0dy2S0Wq1sTFQh4XA4YDKZ2DUoea1Ah91Al6hMdjoqlQrZ2dnd1hUymQzZ2dksAzIUCrFDb75SLF1rknRDAnM4HIbP52PNsajE3WazwW63MwGar3ahxAKtVssOAUgkosxjlUoFlUrVq78v2SJQhiE1llOr1TCZTJDL5ez99vl8PYrAvFBMUFa1RCKB1+tl4nny61OmPO8vm+5S93RD+z3+YJq3wqFDJRKYf/Ob3+C8885j83VDQwOee+45fPrpp3jyySfx2GOP4aSTTur2OiQChsNhlJSU4IQTTmCx89133+Gee+6By+VCSUkJe22FQgGPxwOlUonOzk7k5eWxAwq+txA9nmJV0Lfw9pC0l/X7/SwuOjs74XK5EA6H0dbWxmJFKpXCYrEgEomwLGdKlBk5ciR0Oh27Lmm1WowYMQKRSATt7e344osvYDKZMHPmTITDYTQ2NqK+vh5ZWVnQarXw+XxobGyEyWRCIBCAw+GARqNBKBSC0+mExWJhjQZ5kZk/pCBbGN76US6Xw+12d8tolkqlsFqtrBmlx+OBxWJJyI5OV+z1+6v6fD5UVlbi/PPPx+OPP97fLycQCA4CLW6TfY15EZcvJUklLtOCyO12M6EYSMxg5jMFeoL34D1YOVkqgVls+g9Af7tkiwxeyKFyzY6ODrjd7oRJjRbjZHtBp6S0iOvJbxAA8yiz2WwYN24c5HJ5wnPT/0kIopjoKTOQ7qPXo8mRFv+8xyN5Gib7npHNRigUYkI0H+PpiJ1XX30Vp512Gq6//nqcdtppuPzyy4eUH6NcLsdZZ52Fn376CW+//TZCoRAkEgk+++wz1NXV4ayzzoLBYEj3MAX9DC1o6XOa3MCJPrcEZYqk2rDz8wpfHZH87+FuwvnH841XyLKJEPYYh8e+ffsQCoWgUqlQWlraL6+xYsUKrFq1Cp9++il++OEHTJ8+vV9eR3Dk8BVJJJjxzf7a2trg8/nQ0dGR4NPsdDoT1pkmkwlZWVkwGo3MS5OEgJ07d7LH6XQ65OXloaSkBKNHj0ZRUREKCgqwc+dO1NTUYNeuXYhGo6isrEQ0GmUZzdT0z+fzsXJ4h8MBvV4PrVbb7XNP2c3kf0kVUwercCALLxKZjUYj84CnJoDpFADSBR02kIDs8/ng9/vhcrkQCoXQ0NAAv98Pu93O/HdpL0K2EvR3N5lMMJvNCIVCcLvdGDFiBACwuYaaS9MBJW/fR7HKC8NqtRparTYhK9JkMkGn07ExU0YiJWDQPogXkOVyOROOvV4vE7cIWpMCYK9P3r+8oDqchWY6CKa1OwD2vlCFBNnxXXzxxbjwwgsTfr64uBj33XcfLBYLXn/9dfz973/H3Llz4fP58M4776C2thYqlQpTp07F/v370dDQgOXLl0OpVGLs2LHIzc1l7zcATJkyBZs2bWJWgA6HAxaLhcUJZdIHAgFIJBJkZWUBAPP5pQSs5D1y8k3QO3i9gpKrqKLX4XAgHA4zoba9vR1OpxN5eXm48sorMXPmTLYH83g82LZtGz7//HN88803qK2thVwux/HHH89E4p07d8JqteL/Y++946Ousv//1/ReMumNJCSANBVEQYoIYldU7GVtrLrW3bWtZV1BRfja1u5H18ZallVZWVdFURClqoAgTUoI6XWS6b38/sjvXO5MJhAgyUyS+3w88oAkk3m/k7lzy+uc8zqjRo2C1WqF3W7HN998g5KSEgwfPhzl5eWwWq0wGo0si9jj8UAqlTI/fppzqIk1JXzxZ1Jao/jkMK1WC5fLFRN0pZ+lgFRLSwtrOEkBVLKZpH4jyaDHrzpv3jyMHz++py8jEAgOQrxQm+j7/GTNZ7cSKpUKRqMRKpWKbeiAA2JAvADQXffNly4KgTkWPjBArx8AtjGSSCRIS0tDVVVVTMkNwZcIHgqdTgeDwRBjl+F0OlFTUwOHw4Hdu3dj+PDh7PG02eZLzmkTdbCDGZX6UIk9/W6UscJ75fl8vg5jgjZxVI6v0WhYNnOyPBA3btyI2bNnQyqVYvTo0di5cyfGjBkT85h4Ae5QXZBTDYlEgmOPPRb5+fl44403WBb73r178frrr+OKK65Afn6+2ET3Qw5XXKYDv8PhQG1tLVtDZDIZ9Ho9MjIyYDAY0NraCpfLBZ1OBwAdgkRH4l1JFTa01nVWqih8MQ8P8mMeMmRIj/3tioqKcPnll+PDDz/EM888gw8//LBHriM4cuJL3KlygRoweb1etLS0sKwuvvkWcEB840vg9Xo9xo0bh9LSUlb+TCXPbrcb+/btw759+/DDDz+gtLQUJ598MkaOHAm1Wo2tW7diz549UCgUKC0txZ49e+DxeOByuZhtBQnNFFgPh8PQ6/Uxa1V8FR0JFGTzwFdh8MQ3qgPAKigS+QwPBPhxQecMSn4ggbmhoQFut5tlJMb34SB7E8ouLigogMfjwc6dOzF06FDk5OSwTOj4MUZQbxHKMqWACO0bqZFkW1sbNBoNjEYjq+qjvTMQKxgDiEl+4JuFKZVK6HQ6JjjRuun1euF2u1lWpcViYXYboVBowDeQ45Oj+MZolEwDALNmzUr4sxKJBL///e/x6aefYs+ePairq8Orr76K8vLymMcZjcYY79utW7fGfD8zMxP79u0DAGRnZ0OlUsHtdiMzMxMtLS3svez3+9kZxmq1IjMzk42tRPuNeGQyGRQKBfMYF3ROfO8g/mzodrtZNZxMJoPNZkN2djaee+45mEymmOfR6/Ws6rKmpgZvvfUWfv75Z6xYsQJnnHEGpk2bhm+//Ratra344YcfMHbsWJSVlWHHjh2oqKhAc3MzJk2axGyZDAYDy3SnPgBkmUQVwzabLcbah86r/D5ZrVZDLpejubmZrSNGo5FVV9DfgDKxyZ6FrCrJi5mvUO5tenQEUxT5/PPPx7Zt23ryUgKBIAHxIi1Bm2c+EsiXNcd7U1LEnh5PGa8kRlNWxpHeY6KvxYvi8V1YBQcOMHznaioXBNoXpL1797LXKC8vDxaLhVlZUCkXLwjxgj6fjc775un1emRlZbEFb9u2bbDZbMyfjB4f78MNHDzzh38s35SLF9OpDI2yGeh3439WrVbD7XazjUeyD3TUBARoF+sTHXrmz5+PuXPn9vatdTvp6em477778K9//Qu7d+9GJBKBw+HAO++8gxkzZuCkk04SgaJ+QiJbDH6jT3Y9dDiUSCQIhUKseVY8lNFot9thMpmg1+sPGgQ70oAFjT8+OzE+ECY4PHrKjzme++67Dx9++CE++ugj/L//9/9QWFjYo9cTHB70nqJ5gPaSbreb+WEGAgG0tLTA7/dDo9Fg3LhxLGNMIpHAYrEgOzsbbW1t2Lx5MxwOB1auXInVq1fjxBNPxLRp05CZmcmep76+Hvv27UNdXR327t2Lffv2YeLEiRg7diyi0Si2bduGHTt2wGQyobS0FLt27WKij0wmY9mRlHVM2dUmk6mDLQ/tQ8iuwePxoLGxEXl5eR1EoWg0yoLFVBrNP9dAhQ9GkrBLYglVx5G1RENDA4ADDdii0SicTidsNhscDgfKysoQCASwf/9+jB07Fna7Hbt370ZtbS1Gjx6N7OxsVkJPojZlDlOGPInABoMBRqORNZimMUFWKYFAABaLBTqdjmUz843f6KxE64vX6415brr/trY2Zh+j1WphsVig1WrR2trKAh10DarIG4jQ+6Sz9Zj/22dmZnb6PEajEcXFxSgvL8eyZctQXl4OnU6HK664AoFAgFU9SKVSGI1GNi7D4TA7i6jVajQ1NUGtVsNisaCpqQkqlQo5OTloaWlhtj30Hqe5rbm5GXq9HiaTiYmJfPIMn6UNHGgI6vP5oFAooNPpBvx80Rl8vx1eM6A1hwKedB6+6KKLOgjM8RQUFOBvf/sbvvrqK7z22mtYtmwZRo4cifvuuw/ffvstVq1ahU2bNqG5uRkXXngh1qxZA4fDge+++w6nnnoqqqqq4HQ6YTKZIJVKmT0GVRJnZ2ezLPz45AuCglF+vx+NjY3sazTGW1pa2PlWqVTCYDAgIyMDLS0t8Hq9UKvVrIKCAnFUedHbHLbIHIlEMGLECFxwwQX4f//v/7Gvf/311zj//PPxwQcf4NJLLwUA3HvvvXj66aexdu3a7rtjgUBwSBKJtPEWFrSY8R2QqSM4/T/RJKhSqVBUVASDwYC2trZOReYjEfRoAx9/36LpUmL4vxdFyGlhValUqKmpYX5gZWVlqKysxObNm1lH9KNBKpWirKwM48aNQ2FhISorK1FTU8NKpTsrAz2c15FKI3kbEMpm5AXoRPfGZysmW9TU6/Usk9ztdjMPOJ4HH3wQd999N/ucPOD6InK5HNdccw22bNmCTz/9lL1+X3/9Nfbu3YvLLrssKRseQfdBG9f47t60npBwQIKT3++PEYt1Oh0yMjJgNpuZ/6Hf74fNZkNNTQ3sdjvLxIgv9+uOqpn4MkXB0UGZzD0tMh9//PE49dRTsXLlSrz11luYM2dOj15P0HX4vQj9n+YIh8MBh8PBvCb9fj9ycnJgsVjw+uuvJ7TkGjVqFK699looFAp89913qKysxLp167B+/XqMHDkSU6dORWlpKfLy8nDCCSegpaUFa9aswf79+7F69WoEAgGcfPLJcLvdqKiowObNm3H66aejpKQEu3btgs1mQ0lJCWw2G7xeLwwGA0wmE+x2OwKBAMt2Bg4Ex2l/rFAokJmZidraWoRCITQ0NCAvLy9GEHK5XPD7/ZBIJDAajUxw5hs3DcR9LS+08aKQ3W5nDROpBBwAxowZg1WrVmHPnj0AgKFDh+KYY47Bjh07sGfPHpx00klobm7Gpk2bMH36dFitVrhcLqxfvx5Ae9Zzeno6zGYzzGYzLBYL89V2Op1obW1FW1sbE5wzMzNZAMHhcMSUobe0tMBisUClUrFMZ8pMj2/8SOXter0eBoMBwWAQ1dXVMUk8JEQXFRXBbDajubmZidtAcn27k91LhM6rvMUEeVzzlhORSARVVVUoKyvr9LmysrJQXl7O1qlJkybhxBNPxPvvv4/m5maYzWbo9Xom7NPZgf72vA94U1MTZDIZJk+ezPoQ5OTkoLGxEYFAgD2PyWRiQQOXyxVTJcU3CqTsZQpQUHZ/MBiE3W6HwWBIalZzZ+Mg2eMjEXzggaAzcaJzVyIkEgnOPvts2Gw2fPDBB1i+fDlOO+00nHfeeRg+fDjee+89VFdX43//+x+uu+46fPvtt2hsbMSqVaswffp07NmzB3a7HZmZmWweSE9PZ0HR/Px8Nr/Q+kjZ71QFEQgE0NzcDODAXrmtrQ21tbUxv5/X64XdbodCoUBGRgYLzlGj3GAwyM7CVEHYmxz21aRSKR588EG89tprzCR7y5YtuPTSS/Hkk08ygfm///0vhg4diqFDh3bvHQsEgoPCZ5YBsd7HtKGjKH18N2efz4e2tja0tLTE+ALR5CeVSuH3+7F3796YBkp0HZ6uboriPTLjs5dTWWD2+/244IILMGnSJPzjH//o1WvzkXC+aQ69ZuQJpVAokJaWhi+//BIbNmxgAjPZT/BZfRRsoBIjjUaDtLQ05ObmoqioCIMHD0ZRURHzM9y9eze++eYbZGZmMu8pErsPVRZ2OPCWGInK2w9FvKVLbzN27Fj88MMPiEaj2Lx5c0Ihhuxo+I++jEQiwfHHH4977rmHZQNFIhHs3bsXr7zyCjtACvoefAk8icfkAe9yuVgmoN/vh8PhgN1uZz6FmZmZGD16NAYPHoy2tjasXbsWS5YswWeffYatW7ciKysL2dnZAMBEab66IlXXgoFOb4nMAHDLLbcAAN56660OzYgHCp3tPZK9JwEOlC/Tv16vl5UwezweuN1uVh33r3/9C8FgEEOHDsUll1yCSy+9FGPHjoVMJsO2bdtw//3346uvvsLNN9+MO+64AyNGjGDZya+88gpeeukl1NXVAQAyMjIwc+ZMnHLKKQCAn376CS0tLRg5ciRrHtrS0gKdTgeLxQIArKQZaK84UqlU7HPyTwYOBLwBMEFcJpMhNzeXeWPya1okEoHVagXQ3rCQeo8AYFWBwMDLao7POgRiszf9fj8TXQKBAPLy8rB27Vo0NDRAr9dDqVRi9+7d+O6773DsscciGo3il19+QVFRESKRCL799luEw2Ece+yxLPPPZrOhvLwcGzduxPLly/Hxxx9j0aJF2LJlC3Q6HUaMGIFx48YhJycHANDc3MzEZqB9jJAIFI1GWcYxna0S2SvZ7XZWeWcwGBCJRJjALJfLkZ2djZycHGal0NLSwipEgdhKwM5sDnsa6iWyatUqfPzxx12y1etOaM/PC8vUE4jOLpSssHz58oM+Fz2OLHgKCgqwaNEiVFdXQy6Xo7CwEEOGDEFpaSkKCwuRm5vLXqPs7GxkZGQgLS0NJpMJWVlZyM/PR0VFBXw+HzIyMthYkcvlyM3NBdA+nxQWFsJgMDB7lGAwyPZMZJPicDhgtVpRV1eH+vp6hEIhGAwGlrzF99FJBp2Ng66OD9oH8h/dQXxvDhLt6T1E36cq0nXr1nX5ucnHGUBMMkJpaSn+8Ic/QK1WY//+/di4cSPOPvtsWCwWeL1ebN68GUOGDAHQnnGsVquZtY5CoWCis1wuh9FoRHp6OjIzM5Gens5sNgCwAJVGo0FmZiZcLhdqampY9UNBQQFKSkqQk5MDhUKBYDCI+vp6lphBdjt8kiAf3Ootjmh1u/rqq5GRkYEXX3wRNTU1OPfcc/G73/0O9957L3vM+vXrsWjRIhQXF+Pee+/FP/7xDzz22GPdduMCgaAjfEYr79NLAjLvgebz+VjXZo/Hw6L5Pp8PLpcLra2taGhoQE1NDRobG5GRkYHjjjuORe9poThY05OukCi6y2/cUllUSOYmjD/Q8a81fb2pqQlA+wK5cuVKeL1eVpoXDodZZs/WrVuxY8cO7Nq1C3v27MHu3buxc+dObN26FZs3b2aPqaysRDQaxbhx43DhhRfizDPPhFKpRHNzM8rLy9nBjP4GfCkTgA5BicOF9zM8FHyzSsp45p+jt7n88svxzTff4KSTTsLFF1/cr5r+HQqj0Yh77rkHxx9/PBsDDocDb7zxBjZs2JC0A5Tg8KFDEr2/qNIlGAwy8YhsdchvlTIoCgsLccIJJ8BoNGLDhg3473//i82bN6OxsZEduMrLy7FlyxY2JniBB0DMAUKQWvSmyHzRRRchPT0dNTU1+Oqrr3r8eqnI0R78ewJ+T8LvRUlYIf9joN1f+4svvgAA3HPPPXjttddw22234dZbb8UzzzyDf/3rX7jwwgshlUrx9ddf45577kFOTg5uuukmPPDAA5g4cSIUCgUqKirwwgsvoKqqCkD7XDFmzBgMHjwYALBv3z7I5XImKpN9AQkPgUCAeb7TIZxELP5rAGIEQEKhULCgmMvlYnMXCetyuZw1Xqb7i7f3GqjwXrud7Re1Wi1qa2thsViwdOlSfPnllzj++OPhdDrx1VdfITs7G4FAAGvWrGFVdL/++isWL16M+vp6lJSU4IQTTsC4ceMwdOhQZGZmQiaTwePxYPv27fj444+xc+dOqFQqlJWVYdSoUZBKpaxKkwTDtrY2mEwmJjTHN/LjIQsQoD2LWiKRMB9yuVyOoqIipKWlwWw2M2GbRGkaY2TVwP9NepuNGzdi8uTJMb1E4ukpARE4IO7ReVAul0Oj0bA+KxqNhr13Fy9ezBIfE0H3xZ8FKioqIJVK8ec//xm33norRo4cCa1Wi8zMTBQWFqKkpATFxcUoLi5GSUkJBg0ahKysLOj1emahMWjQIOTm5jJL2Ly8POTk5LCkG6A9y5kSdAoKCpCXl4fc3FxkZGRAq9WyputkJ9bS0oK6ujqW2UxCc6qNg66MD6DdCtBkMrGP7qrQjBeZaX9ICVRarTbm8atWrcLXX3990OeMRCLYsGED7r33XixbtgwAMHPmzJjH5OTk4NxzzwXQrnUqlUqcfvrpkMlkqK2tBQBYLJaYPkkul4utOYn6I8VDgUzyCifbjPT0dJSVlbGmuNnZ2TjmmGPY+kbBKgBMD6A1LD6Jrzc4ohO3XC7HX/7yF7z44os455xzMHbsWLz44osxj5k/fz6qq6uxf/9+PPPMM7jpppvwt7/9rVtuWiDoDZKZEXK4UEkyX4IW77NLFhgUPaV/rVYrbDYbXC4XGhoasHv3blRUVKC2thbNzc1oa2tDTU0Nli1bBrfbzSY/PpsDQIxIfLi+lryw3BfEZSKZm7BEG3Ra0DweDxOEtm7dyjKTt2/fjtWrV2PXrl2smQqV11DZTnxmudvtRlNTE3bs2IEvvvgCTz/9NPbu3YucnBzWvK6iooIJpzQuOrNPOVJvZP6+qDyRfme+TJ8CJwCY1QaRrPJ4tVqNTz/9FD///DNuu+22pNxDMpHJZLj44otx/fXXQ6FQsPlq2bJl+O9//5uwVHqgkerrDc0n4XCYWSnRvy6XC4FAAB6Ph5WeRyIRaDQalJaWYsyYMZDL5Vi5ciW++eYbthHPzs7GiSeeiLPOOgvjxo0D0B4coywOqsDhK1r4ypaB2DQrFXE6nSybtDeqF1UqFa6//noAwBtvvNHj10tFjubg31t7Elp7SXSLRCJMZKbGWhdeeCHOPffcDvu9jIwM3HXXXXjuuedgNBqxa9cuvPTSSwDa541LL70UDz30EPPkjc9kpMxFEnfJl5PP9ALASomB2ED2waqfurI37Sy4ThmNh/Nc/RVeJOIDFMCBfSTNK2eeeSbzHn311Vcxfvx4eDwerF27FjKZDD6fDytXroREIkFWVhai0SgqKiqwbNkyLFq0CJ999hkaGxsxevRo/O53v8OZZ56J3NxchMNhrF69mtkemM1mJlY3NzezZufky5yWlgYAzLM3EdRzQ6vVstJ3qiCkzENCp9OxzHa3252wYi/Ve4n0hIAIHHg/UhYzicxSqRRarRYGgwFarRZKpRIulwvPP/98wr9VKBRiVivxZ6Xs7Gykp6ezZJtoNMqEvKFDh6KgoACZmZmwWCywWCzIyMhAdnY2CgoKkJ2dDalUygJXlF3a0tLSQcyTSCRQKpXQaDTQarWwWq3Yu3cv6urq0NjYyLLn6fwbCASY0CyRSGJ67fQ2nY2DrowPoN0KkHpt2O12VFdXd8t90figPaJcLmdivU6nY009JRIJRo4cCQB46aWX8PTTT2Pnzp3MgtHhcGDz5s149913cdNNN2HOnDnYs2cPNBoN7rvvPpx44okdrk2BRVo7TSYTy2CurKxkc0hbWxv7G9Gcxou9fHZ7orMqrVMkFFsslg5rhlQqRUFBQUyiFwUsyLKK6O2z1hGnhFx99dX405/+hGg0in/961/C207Q76CMkFtuuQVnnXUWfve736VkBiK/oSfxJt5GgffMpMWKss5IaHY4HExUDIVCyMzMxLhx46DRaLBy5UqEw2GUl5cjGo1CrVYzIZn3x+IjzwNh85zMhm7xrzEtJoFAAE6nE16vF7W1tayZVkNDA3uMy+VCbW1twqimSqXC8OHDMWXKFEyYMIF1/m5oaMCmTZtQX1+PDz/8EA8++GDMQksZGOSZzAc8eB9DAMwn6kgzi0k0JwuYRNladC3adNLmQ5A8SkpKcN999+G9995DQ0MDFAoFampq8NFHH+Hcc8+F2WxO9i0mjVReb+IFZspkpmZIJDDTfKLT6dimt76+HsuXL2elhxKJhGXwZGdno6ysDBKJJKbygnxMlUole9/yB06+m3hfCUj2Z+gAn5mZyUSYnuamm27Cs88+iy+++AI1NTUoKCjoleumCkdz8O+pPUl8IybaA1BAinzatVottmzZAqlUiquuuuqgz3nsscdi3rx5uPPOO/HNN9/gD3/4A1snzGYzysrKWHNjwul0MtEwPz8fbW1trKlgZmZmjJWFwWBgWamU+ebxeJgIRaI0//vx+5ZoNMpsMvR6PfueRqNhnpher5c9D99cmX5+IPntHspPn/421DCWXsfp06ezx2i1Wrz88st46qmn8PHHH2PXrl2s3Ly8vBzl5eUYNGgQSktLIZPJUF1dDbfbjQ0bNmDDhg0oKirCrFmzcO655+Knn37Cr7/+ijVr1qCgoABqtZpZlZFYQ+caSm4gUTyRoMnvR2k88d6q9N7k/x46nY41O6SfiU/4SAbJ7iVC50vK8CY7EWp2Rn8vi8WCxsZGfPPNNzj22GNx8cUXxzzP8uXL4XQ6kZaWxoRa2lvR37uiogIAMHHiRIwaNQqNjY3YsmULlEole+/SmSPeT5nuye/349dff2XXTUtLS7iHa2trY/sdEs2pkXlLSwu0Wi10Oh2zQMjNzWXJM3xjyd6is3HQlfEBtJ8pe6IHC/8a0OtA/5rNZtZ0j5Iexo0bh40bN+L777/H999/DwDsDMmj1Wpx+umn4+KLL2YZ706nEy6XC3a7HfX19WzPQ8IyAOaj7vf7Y4TleJslunc6m/OVMZSdr9fr4fV64XA4YDAYoFKpEAqFYLfbO2Ro0/Pl5eWxeSQnJ4clmwFgFkS9bbtyxCLzHXfcAaA9NftQA54yDgSCvsTGjRsxe/bsmIwQytwk4hvjdWepUFfgG62Q0MgfwOMzmj0eD9tA2+12WK1WVuZMQjNNuNXV1TAYDOzQqNFoEAqFIJVK2QRKC15nmWb9nWRuwuJFZlrASOxpbm5GOBxGU1MT2tra4Pf70dTUxA5XdP8ZGRmQy+Xwer1obm6G3+9nNhlff/01FixYgNzcXAwdOhQTJ07EggUL4HQ6UVtby8YGvyHmM3coa4eaAPINvKhhZFfHC23s6PeVy+XQarXMG5YeE/98vB2LIPmoVCrMnj0bGzZswI8//sjG3rJly3D88cdjyJAhA2b+4EnV9SZeYKamOFTVwle3qFQqFBYWQiqVoqqqChUVFSxzkTa59fX12LFjB3v+GTNmYPLkycxugeYHlUrFfDD59zX9nw9iiSSH5NKbVhnEsGHDMHXqVHz//fd466238Oijj/batVOBozn499aehPafNIdQNhbN78ceeywyMjIO+bwjR45ERkYGWlpa0NDQwETm8vJyfPPNNwDa+x4A7fuKpUuXIhgMIicnBwUFBVi9ejUAMBGxrq4OoVCI+bqSmGgymdh+GABr+EbQAZ3/WltbGwKBAKRSKStZpscYjUbY7Xa0tbUhLy8v5m+UbFItqBmfycxb+RmNxg5roUKhwMMPP4yTTz4Z8+bNg9VqhdVqxciRIxEOh1FVVYWqqioolUqMHz8exx9/PFpaWrBp0yZUVlbipZdewmWXXYYTTzwRNTU1aG1txZYtWzB+/Hj2+sZnVdMelm8GFw+JmCSKUmM/AMy3Nx6tVgu73R4jTqXCGKFeIiNHjsTmzZsxb968Do/pKQGRILs7pVLJRETa91MfE8owb21txXPPPQcAmDVrFiQSCbZu3cq+Nm3aNGzcuJFluQIH9ht8pihwIHCanZ2NwsJCVvHbGbyASD66WVlZB32sXq/H8OHDAbSPtdbWVlRVVTFxUKvVxgiLVKlJYmZv0dk46Mr46Gmo4SYv+JMgm56ejkgkgtzcXNTV1cHj8eCMM86Ay+XCli1bYuyNsrOzMWTIEBQWFkKr1aK+vh5///vfY/bb8ZSVlbGAhsfjwW+//Qag3dKC9r28XSNB8wYJzCSWU/+CQCAAg8EApVKJQCCApqYmpKens6pijUaTMCGHGvtFIpEOSVa0/6b1p7fOWEckMj/yyCP44osvsH79esyYMQNvvfUWbr/99u6+N4EgqSQzI6QrkHjHi8zUEZeylmlzTxu2YDCI1tZWtLa2wu/3syZ/fCZoWloa87bavXs3srKyIJFIkJubyzbiNJHzDRlokh9IYl4yN2GUEUP/klcqva5+v5/5bAeDQZSXlyMQCCAtLQ2zZs3ClClTkJOTE7PYUHOSdevW4eOPP8bu3buxZMkSXH311QAQ4zEll8tjumfz5aAqlQp+v58tnPzP8FnMNG67mmVMCzYJTCRC8RYM8Y8fSEGPvoJEIsGJJ56IoUOHYtmyZcyrcOfOnbDZbBg7duyA895NxfUmkUUGv5643W72vtfr9fB4PFi9ejXbYNN7MhgMoqamJiY7kAKfe/fuhVqthtvthkKhYA1mNRoNE5o1Gk1MFhkAJjSHw2EhMieZZIjMQHsDwO+//x5vvvkmHn744QE1ZxzNwb+n9iS8yMwnQNC+gcQVOriPGDGiS8/b3NzMguMk3FRVVeHNN99kTd5OOOEEBAIBfP7556wB0hlnnIHy8nK0trZCLpdjxIgR8Hq9zN8yOzub2RhYLBbWuBQAK7XmiReZg8Eg2wNlZmZ2GH8mk4mJhxRgp/0L/zdLxv4kmUHNRNnMvNUK0J5FTMLfcccd1+l7e/r06Rg3bhz+/ve/49NPP8X27dtRUFCACy64ADt37kRVVRVWrVqFVatWYcKECbjpppuwYsUK/Pbbb6wK+8QTT8TXX3+N7du3Y/To0TGZh3SuAsAabNH/41+3aDQaM36AA1nMRqOxUxGf92HmbVbiLVd6m8svvxxXXnkl3nnnHdxwww1JCULI5XL4/X521lQoFNBqtbDZbExsTk9PRzQahclkQkVFBZ599ll88skn0Ov1zAJj9OjR7H07duxYdualeZB+NxJ4aQ9zzDHHxFRCmM1m6HS6GA9l4EAyi1qtZmJfZ5CtgcvlYl69UqkUGRkZ0Ol0+O233+D1etk9ORwO5j1NVWS9udbFj4OXXnoJ5557bkqMD9oTkg6hUCigVCrh9XpZAAJot/Hau3cvqqurIZPJMGPGDBiNRtazitYYWg/45zebzTAajdDr9TAajcjIyEBpaSnLMN+yZQu2bNkCn88HvV6PoUOHMpsqi8XSYS+v0WhYxbhEIoHFYmHNY6mi3GazwWQywWq1sjmYtJnKyko4nU5kZ2fHVNq0tbUxMTl+T0yJYL2dmHHYo/TNN9/Es88+ixUrVuC4447Dn/70Jzz11FO4+eabY3yGBIK+TrJLhQ4G78EcLyyTuOz3++HxeFiZYktLC/PhpcmUNrtKpRLjxo3DmDFjsGbNGrS1tbEyLqA9A0Sv18NkMkGtVrOFlCZ2EpcHksAMJG8TFt9ghz6oLIgONk1NTQiHw6ipqUEgEMC4cePw4IMPdijZI6RSKYqKilBUVITKykqsWLGCBRzC4TD+85//sKyBnJwcfPvttwCA3NzcmGwPvV4Pv98Pt9vNypX4iC1tkmjzHAgEWMbiwaAmGHzmfme/B//8gtTEZDJh1qxZ2LRpE2pqaqBUKtHW1obNmzdj6NChnZbf9UdSbb0hYSjeg9ntdsPtdjMPOfJ33bdvH/tZyvayWq0xzXhyc3MxYcIEDB48GG+++SbL0CkvLwfQngFClTLUfIuEZ6/XGyMq89nMyRJqBO2QyNwbfsw8s2bNYg0Aly5divPPP79Xr59MUvHgH5+lywfB6XMe/oB8MN5++21Eo1Ecf/zxsFgsqK2txeuvvw6fz4fS0lJcffXV8Hg8+Oyzz9DU1ASFQoGZM2ciGo2yqolRo0ZBrVZj9+7diEajMBqNzL5Do9FAoVDECIS0943/XYADIjOJrnwDMh5KxqC5k+Yo/sCf6n67PRXUjK82i2/szP9tDjWGjUYjHn30UZx11ll49NFHUVNTg4ULF+KNN96AXC7Hl19+ibVr12L9+vXYsWMHHnzwQRiNRvz0009YtGgR/vjHPyIzMxPNzc3Ytm0bTjzxRBYQcLlcMa8Vn0wRj8vlYhZOWq2WVY6STUtn0DmM9sb0/0T2LL0J9RJJJiSm8p679GEwGGCz2VjCUygUwllnnYWVK1eisrKSPQf1fHjvvfcgkUhwzjnnYM2aNQDAKino/UviMi/IkfBYWFjIGk02Nzezx3YGiZ8qlQparZZVRqjValaZUVNTg2OOOYb9DPWx2LVrF9ra2pCZmYlgMIi2tjaYzWYEg0EmZvYWBxsHyR4fiXyZpVIp1Go1PB4PcnJyUFFRgXA4jIkTJ7IKh927dyd8PvLbtlgs0Ol0UCqVMWdNCjpt2bIF33//fUzgzWKxYOrUqfjtt99gs9lYwpXH44FGo2FisUajYWPHYDCw9USpVMJiscDhcDALOoPBAIfDwQItJDRTsqBarWYZzxQ4ycjIiKmooL9PMtaawxKZly5dittvvx3/+te/MGHCBADAnXfeiWeeeQbvvfcebrzxxh65SYEgGaRCqVAiKLuMJjuKbAYCAeYHRiXMTqcTLS0tcDqdsNvtaG1thcvlYt+jsoybb74Zer0e77//PpqamiCRSFBYWAiDwYBBgwbBbDbDbDaz31ehULCoIW0Wu7oR4kWBvi4MJGsTxnsf8pkWtDBR2Q0A5veWnZ2NRx555JAb9nA4jIULF2LFihUAgKlTp6K+vh6ffvopKisrIZFIcP7552PdunVobGyETCZjUWK6nsFgQGtrKwKBAGQyGXQ6HRuT9H4JhULs/zSmu5LRzGdBxzfQoUBHXx9XAwnKJBo0aBB+++03yOVyhMNh7N69G/n5+R2y7fsrqbTekMBM6wpltLndbjidTmaTYbPZEAgE2Ps3Go2itbU1pqyUMghPOukkFBUVwefz4d1332UCMx3K09PT2UGMOsjLZDKYTCZ2gKB5TafTDYgx0VdIViYzNQB89tln8cYbbwwokTlVD/6JxEPe5gY4IBquWbMGV1111UGz8hYvXoyvv/4aEokEs2fPRn19Pf7v//4PHo8HxcXF+P3vf4+2tjZ8/vnncLlc0Gg0mDlzJsxmM1auXIloNIr8/HwUFRWhqakJbrcbUqkURqMRLpeLZaqRpQHNQfHw2ce016VDfWfl63x1VXxm6sEaC/YGyQxq0pjgBSJeTATahRgKQvz8888s4/NgjB8/HosWLcLdd9+NTZs24dFHH8XHH3+MoUOH4qKLLsJLL72E/fv3Y8GCBXjuuefQ2tqKvXv3YunSpZg6dSq++eYb/Pbbbxg3bhyMRiNaW1vhcDigUChYFQ+f5cwHN0OhEBtDRqMRUqmUrYMmk+mgSXgkOKlUqphkjWSPkVSAAsoUcKbEJvIsJpuMoqIi7Nu3D7t27cKtt97KrBIKCgqwdu1avPfeewDaA5PRaBTbtm0DANYQjrf5k0gkrGEjeaoD7a+r3+9HTU1NTMCJ9+zm+yPxFaYulwstLS1IT0+HyWRCfn4+rFYr8/rlx7bRaER2djYaGxvR1tYGnU4Hn8/H5hKyLBtIlTudwc8hfFYzBffC4TBGjx6NzZs3o6GhAUVFRTjllFNYw1vy+6YqWavVyhoUdpWsrCyUlpZCrVZj586dbE+bmZnJgkxqtRrhcJjdF9AuKsfv56VSKUwmE9xuN7M21Wq1LFELaBeRKYDl8/nYOkT9TpRKJRO/aVxTtS//N+sNujxCN27ciEsvvRRPPfUUZs2axb5uNBpx5513YsGCBbjuuutE2aKg35AKpSDx8OXLJDDTREoN3wKBAOx2O5qbm+FwONDS0oLW1lZ4PB7Y7XbWFI5oaWnBwoULmXAtl8tRVFSEzMxMDBo0CEajkXVYpoWeNoUymYyVZxxsI8SXT/IbbbFIHjl8xgfvxR3fPZbGLWXyHIzt27fjtddeY35kl156KbZv347NmzcjGo1CpVJh0qRJ2LFjB1s8TzrpJFRXV8Pv90OhUCAtLQ0ymQx6vR5OpxNutxsZGRmQSCRwuVzw+/0wGAzMo5G8xg6njGcgZs33d6jL9/bt25l9htVqRSAQQEFBQb/fW6TSekMZwyQwU+DS4XAgGAyipaWFlfu5XC44HA4mWFBDQMruKysrwznnnMPmg4ULF6KhoQFyuRw5OTmsTNRoNLJu8VqtFjKZjM0lQLvoQB56h2ORQfOKCD71DJFIhHkRkr9kb3LzzTfj2WefxZdffonq6upeqSQTJIYXlElEpMCxUqmERqOB0+lkmV179uzBI488gj/+8Y/IycmJea6Wlha88847WLp0KQDghhtugNFoxKuvvgqXy4WCggLcfPPN2LdvH1asWIFwOAyLxYLzzz8fOp0Oa9asgc/ng8FgwPHHHw+fz4e6ujoA7WsNZe6mp6ez/6vV6oQZyTyJLB4SEY1GYbPZWAUXZVoCB6x+4p+vN0lmUJMXhPi/De0LAbDxotFoYLPZ8MADD+Dpp5+GRqM56HPL5XKMHj0amzZtwr59+9haUVRUhMcffxwPPvggampqsGrVKlxwwQV49tlnsXPnTlx++eVMAGptbWUis9vtZpV5tF8FwM5gJB7TGFKpVOweeXGyMyKRCBOndTod+39Xz1YDAaVSiXA4DLVazcYkZXhSEk0oFMLgwYOxb98+/Oc//4FCoYBer4fNZmPv05kzZ2Lo0KF45513WGVEfn4+QqEQ6uvrARywsuCDByQoK5VKJjCr1eoOdgU8/HmMfLmDwSCam5vh9XqRnZ3NBHKr1dohgFJQUACHwwGv18v8p1tbW5GRkcH2WL3tzZyK8GsNBTKVSiWCwSCzYfP5fJg8eTLWrVuH+vp61NfXs3kn/rxMz2k0GmEymViVS3xAjLLpZTIZ01kIrVYLjUbDnttoNCIcDjOxmdYEg8HAgo9UnUvX12q1UCgUcDqdbLxR0gedj6jyDziQ6U/NAuk+6J5JRO/t+aTLCs8JJ5yQsJwGAB577DE89thj3XZTAkEqkAqlQjwkMMdnmIXDYSYcu1wuNDQ0oLW1FU1NTWhubobNZoPNZjuogb3D4YBKpYLZbEZBQQHy8vKQnZ0Nk8kErVbLNnxqtZo9j0KhOGSnUr7LuKD76ErZCwn4JFatW7cOv/76K4499tiYx/n9fvz000/4/PPPsXnzZgDtgs7xxx/PxGYAyMvLg8lkQkNDA3tMUVERW1ylUimGDh3KDlDxzQ6ofIk2zvzvIfxVBUD7mD322GPZHCaTyRAMBlFbW4usrKyUCPT1FKmy3tB7lzazfr+fCclerxdNTU0IBALMCiMcDsPn8zHxiLfHAIAff/wRFRUVuPnmm7Fw4UK0tbVBLpejsLAQGo0GWVlZ0Ov1MBgM0Ol0LJhpMpli5gI6SPBVHPz34qFsbN5SpyuWPILDo6qqCl6vF0qlEoMHD+716w8dOhTTpk3Dd999hzfffDNpPTIEsRmqVOlGmVhkT0FC3pAhQ7Bz5078+OOPuOaaazB8+HAUFRWxpqHbtm1jthLXXXcdSktL8fLLL7Og4w033IAffviBBThKSkpw5plnAmjf69hsNiiVSkyYMAFSqRQVFRWIRqMwGAzM61ev17MAt1wuZ4f+zn434MC8IpfLodFoEAgE0NLSglAoBLVaDYlEwhI+KMPMYrGwIByVXwNIar+IZAY1eXs9uVwOpVLJLACcTidMJhMikQjcbjfS09NRX1+P1atX44orrsBtt92GU089NUb8bmpqws8//4yVK1di1apV7O8+a9asmDVELpfDYrGgpqaGWblRVnkgEIixNqHXm29qTX0BaL/Kry2039VoNB08p3lxOh6yLaSkHbpP/m810FGpVGzuoA+fzwez2YzW1lakp6ejqakJoVAIw4YNQ2NjI2w2G9uLDBkyBFOmTEFtbS0+/PBDAO1N22bNmgWn04nvv/8ebrcbWq0WBQUFCIVC7JxLr7FKpYLb7WbVmXl5eTGvDVWU8kIh9ZPQarWwWCyw2+1oaWlhwfL09HRme0ANk4EDQf7Bgwdj586dMT2TXC4Xy3gnm8GBDtkokm83nSOp94ff74fNZsNZZ52FvXv3Yv/+/awyAWifkzMzM5GdnQ2LxQK1Ws3sJyipj29kSwkW8RgMBpYxTPdD/UQkEgn0ej37Oap24BvixkMVN5StTGI3BS8SCeT0c3wwjr9ub9saizRCgaAPkEhg9vl8CIVCsNls8Pl8qK+vR1NTE4vUkQczQRH+vXv3smi5SqXCoEGDoNfrkZ+fj/z8fOTl5SEtLQ16vZ5FjimqFu+3d7DGFHzZUCLE5qn74KO5dFigv29jYyNKS0tRXl6O++67D6WlpSguLgYANDQ0YM+ePWyRk0qlyM3NhdlsZlkYaWlpMJvNbNFSqVQwmUwwGAwseGAymZCXl4doNIqGhgbWPIOitQCYhyvdL3/vvL9qvAid6IPuVWQ09z+oyWh6ejpqa2vZWLHb7czeRwiFPUMkEmGb13iB2eVyoampCR6PB/X19fD7/XA6ncwfjqegoAClpaX4+eef4fF40NjYiDfeeAMejwcKhYKtOdnZ2dDr9dDpdNDr9VAoFKzze/xrzB/g+EzARO//REEuQc9ADW6GDBmStMqkm2++mYnMjzzyiKiQShK8SEYNmHw+H4xGIzweD3Q6HTIzM9HY2IhgMIjS0lJ4PB5UVVVh+/bt2L59e8zzjRo1CjNmzEBFRQU++ugjAO2WLCeddBI++eQTeL1eSCQSTJgwAePGjUNTUxO2bNkCr9cLhUKBiRMnQqvVYt++ffD5fJDL5az0nC+ppobWB1tXqMkwNWcymUxIS0uDz+dj2a/xSCQSpKWlsf07ZajxzeOSRTKDmvT+JIGZhDvKHjabzXC5XLBYLAgGg8jJyYHVakVlZSX+8pe/QKlUoqCgAHK5HM3NzR0CmyUlJbjuuuswc+ZMBINBVFZWYuvWrVixYgXq6+shk8kwfvx4fPbZZ4hGo6w5l8PhYK8Z2b/x/qnkr0prDz/PkL2Cw+Fgv5fJZEJLSwsaGhoQCARgNptZZn84HGaN14H2xpG051ar1Un3Y04l6FwTjUaZTQbtB0gMJqsumm8yMjJgMBigVCpht9uxfPly9lxTpkzBiBEjsGrVKuzbt48JgtOnT4dcLkdNTQ0AxGSjarValiHKB8BDoRCzNegMfjwolUrU1dXB4/Gw7NNQKAS73Y60tDTU19djx44dCIfD0Ol0yMrKQltbG/OPpwxcuVwOt9vN7AMHMpSlS4EaGi9ut5udW/1+P3bv3o2MjAyUlZWx9yAAZmficDhivLwPhUqlgl6vZ2I2vyclSzc63/LvaYPBwLKOeQ2FxlR8ryW5XM7WUPp95XJ5TIU4H9zlNRe9Xp/Q2qW3EDsxgSDFibfIIIE5EAjAZrPB4XCguroa9fX1qKqqQn19fYy4PHLkSJx55pkoKSnBiy++yATmtLQ05ObmMqP7goICZGZmsi7IarWaict8JD8R8SWEB2vKRpOpEIqOnPiSVLlcjkAgwHyftFototEosrKy0NTUBK1Wy3yTy8vLWaMtgjKNLBYLlEol84WyWCxQqVRs0TQajSxTg6K0KpUKMpmswyGLosNUTkSbQT4bnm/OR1mKVEp0qKY4fOazEBX6H0qlEsXFxbBarWwDH9+oScwh3Qff6I9EZt4Ko7GxEXa7HfX19QgEAmhoaIDVagXQ/t494YQTMHnyZIwdOxZpaWnYs2cP1q5dC6B90+zxeKBWq1FYWIi0tDR2CDQYDCyYaTQaE26CqTwUONAsrDORmX6PeMR46RlIZE6GVQZx0UUXISMjA3V1dfjiiy9wwQUXJO1eBjK0FyHRRqFQsL2IxWJh+0KJRMLENZVKBZ1OxxoYKZVKVm7c3NyM7777DkD7+/e4445DIBDAqlWrALTvYadNmwYAWL16NduD6HQ6jB8/njUVJfEwPT2dZQWaTCa2D+HnnXgfZb68mHpNhEIhtLa2QqvVIjs7Gx6Ph2U5AojZG1EzZroOb5kxUBMtaP9IGcT09wqHw8jMzITf70dmZibC4TCysrJgtVqhVCrhdruZ9QDfaFYqlaKkpARDhw5FQUEBpFIpNm/ejGXLlqGlpSXmLGIwGDBr1ix8/vnnqKqqgkQiwdSpU5kIOWzYMLS1tcFut7OzCgUleJ9lauLGPy95+La0tECn0yEtLY0Jz5SxSuOJr+40m81MNAQ67pEFYNmlVD3AZ5LTHkCtVsNut7P+L/yZRKlUssZ9TU1NqK6uZt/Lz8/HxIkTodfrUV1dzZrCFRYWstdbq9Wy14fsT2iPRJDACRyYR/j+Fl6vF1qtFjk5Oaivr4fT6YTBYEBbWxvq6+thMpmYpy/Q7pVeUVHBxhoFxID2+YvGjF6vH9B7G9ITSGSlahEKIvKNXVtaWmKsLeKRSCTQ6XTME54XrnmLn0gkAq/Xy5pgA2BNq3mvdrVazcYE2XBIJJKY7HS6Dv8a8j23aCxRpQxlWB/sd5DL5SyDme5HpVL1+jgRs5dAkMLwXj004VDmBEXBq6qqsH//flRXV6OhoYFNKKeeeiouu+wylJWV4fPPP8c999wTU+qTl5eHwsJCDBo0CDk5OTCZTKxcmSwy4sVjmkzjN8fxk2NnAjMtBgN5QewO+EWLDivU0drn8yEzMxOBQADp6emIRqNobm6GVqvF8OHD4XK52MIjl8uh1WrZ4qNWq2E2m1mknsRnnU7H/BWpWUH8okjlsXRQlEgkMVkfQPtiykdu+ZJH8qBLJC7HH/aAA+OMmiwM9Gh+f4QaWfj9ftjtdjbmKNuWxqHg6KGgDQnMbrcbLpcLPp8PjY2NcDgcqKurg9frZRYJcrkc55xzDmbNmsW6tANAXV0d5s6di2AwCJPJxOaQgoICZGVlIT09HQaDgWWBUBmgz+eLyWKORqMsoxo4MMeQmMM3jyJ4Ww3+4CHWnJ4hmX7MhEqlwg033ICnn34ar776areLzDabDc8//zw2bNiA4uJi3Hnnnb3e5LCvQBYZdNCneQU4YD3Q1tYGqVTKBEP6HpUFU8Yg0P7aUvNXEgfUajWGDBkCrVaLzZs3xwScBg8ejGHDhsHhcGDnzp0sg5jWEaBd1KP/056X5pr4ABWfOUg+8Q6Hg5VNUzYhNTwGwOZREqWo7Jlvet3bDcNTDRonCoUCKpUKoVCIWWbwawn593u9XlitVhgMBhawoIAj/U337NkTY/FG6HQ65ObmIiMjIyZIoVKpMG7cOOzYsQORSAQZGRmsgRzQ3mCLXkOz2cwER41G08EPl5rX2u12Jj55PB7WY8DhcDC7OIIsCgGwxCCdTsfGMy9aDnSoKtJut7P3En2d7AHa2tqgUChgsViYEA0cyCSmSkvgQBIMJcJs376dZYoC7V7taWlpaG5uZo93u92s8X0kEmECM1VgdVZVRXsYsoBRKBRIT0+H1WplXr1utxvNzc1s/pk6dSrq6uqwb98+uFwuFojx+XyQSqVwOp1M7Pb7/f3aSu5QSCQHGjXSusNXifj9fpZJLpFImOgPHNhT0tmCzqDUZLorkCUKaTV0P3xiAwVP6exN1+5M+KXKGYVCwc47tH5QvxNKOqR75u2HADAhms72ydgDi9OZQJDC8Jt0XmBubW1FQ0MDqqurUV5ejoqKCrYxHz16NO644w6UlpaisbER999/P3755RcA7RuYoqIiDBo0CMXFxcjPz0d6ejrLJCGBOX6xpLIkvlyns0yy+ImMFwjFQb974MUTElk1Gg0ikQiMRiMikQjy8vLQ0NCAzMxM6PV6VvLOL75UxqPVallGoUKhYM0eacGisRHvuRgf4SXiPabogMZnLVNGNB+UiPdg5T/ikclkCAQCh8x4FvR9VCoVMjMz4fP5Yg5pfr8foVAoKRH6/gQfxKQDkdfrRSAQQFNTE7Nj8vv9qKyshM/nw6BBg/CXv/wFRUVFMc9VU1ODv/71r7DZbFCr1Rg0aBBMJhMKCwuRk5MDi8UCo9HIApl8ySitc+Q7R+9v4MBBkReBOstOpsCDGBM9TypkMgPAH/7wBzz77LNYtmwZtm3bhlGjRnXL8/722284/fTTWQk1ACxcuBCffPIJ8wAWHIAOx0D7+5PKhGlvQnsBrVYLm83GDt5kfRG/V+Tndp1OB4vFAqC9jwjf4KigoAAZGRnw+XzYuXNnjAclldgD7Rmn9D3a+/AerMCBvQcd3umQT3MVidRer5cJFomqJyQSCRMg+MeI9eqAgEp2GSTU2+12WCwWVkpeUFCAmpoa1sgtHA4zuxWCRBRKlKEkBxJhKOjY1NQEoH1MFBcXQ6FQsK8NGjQIhYWFqKqqAgDmC03Zh7Sn1Wq1MJlMMRWmdA8ymYw1C6NABAmXJCzy90zZkARVjvLjRHAACtaQXQ29H0nQIxHW4/GwvSG9hnwSDFUZSCQSFjggqE+ERqNhlimZmZnsdaQGffS6UZPzzt7PMpmM9TPy+Xzwer0s8GUwGOB0OqFWq+H1elFbW8veDzabDSUlJcjKysKWLVvgcrngdruh0+ng8XgglUrhdrthMBjg8XhYIGygQk38SLjlz4V0ViQoEYonPjEuPjkh3taCxpxUKmUVDHQtet/SekbNZHnRl7KXDwX/WLKJIU2I1hdeg4m3ijuYkN0bDNwRKRCkOBRN45v8BQIBVlpTXV2NPXv2oKKiAm63GzKZDLfccgsuvPBCRCIRfPLJJ3jnnXdYiU12djYGDx6MkpISFBUVITs7m1ljkOVBvKBHpT78ho4yToHEmWTxguNA30z3FFRuyHszUTTbbDbD4XCgsLCQlf5pNBpkZ2ezaCsvDvOZOFS2SpYpFB3ly4boNe3sdeYfE/94suOIb5wS/7sdrAsuWbIczBNc0L+gDRXfKIPEAcrqEIeyw4e3yaB/6aBGNjeNjY0Ih8NMcC4rK8MTTzzRoSP6tm3bMG/ePDidTqhUKpSWliIzMxOFhYXIzc1lAjOVL1JQi0oAyQqKh+YMADEH+oPZX4j5oPdIFZF58ODBuOiii7B48WI899xzePvtt4/6ORsaGnDaaaehrq4OZWVluPXWW7FkyRKsWrUKl156KTZs2IChQ4d2w933LyhoRFll5IsZjUaZkGaz2ZjHbTAYhMPhSBgs1mq1MVYa9N7W6/Uwm83MjsPn88UEAiiATgd5CmzTNdRqNfR6fUz2Mome8dYZwWAwJuGDFxT5+ZMPlpP4BSDmGvzzD2RoXqeMZPr7ajQaeL1e5OXlobq6GhKJBCUlJWhpaYHT6YRMJkNBQUFM1mB8xi8vKJEgZDQaWWMuep2CwSB0Oh2GDx8eY69gNpvZeYhf4wwGA7u/RM3M6ZxEYihl51OCTmcl7rTfBg6scWq1WoyTBND7zuVyMc9l2kvQ35oPJCWCHzP8B32ND2DRfoUsd0wmE8LhMAtKJeof0dk1edsGGvd8FQWN/0AggPLycmRmZkKn0+HEE0/EL7/8ApvNBo/HA71eD7fbzapLNRoN3G43s2IYiFB1iM/nixF6aV7gm3XGnx3jE6UOdvbkiddFaH2inyXLDX4sUrLE4b5OUqmUZdGT2NyZNRz9XrTWJHNMSKIiBUwg6DYowkolPUcD3+TP6/XC5/PBarWisbGR+UZVV1fDarVCo9HgiSeewKhRo/DDDz/gnXfeQV1dHYD2RXDQoEEoLS1FaWkp8vPzkZaWxrKXedsDmpxpwxefLRCfjdrVyVhwgJ4YI/RBApzP50M4HIbT6WRlWlSKGl8mREIPHco0Gg0TlfkyIt73KpHIfDB43+RESw7fyZvgr8lHk+M3Ccn0ZN6yZQtuv/12BINB3HHHHfjd736HJUuW4Mknn0RGRgbef/99pKWlYfbs2dizZw9mzJiBRx99tEvP3Z3jpL9BBzbKWCHUajUMBsOAKTHtjjFC87zf74ff72fissvlQk1NDex2OyorK+F2u7Fnzx6o1Wq8+uqryMrKinmO//znP3jvvfcQiUSg1WoxePBg5OfnMzumtLQ0FgjgM4kIEor4+SnRnHGoAJSg92hubkZWVhYr9+U7mieD9evX4+STT4ZCoUBlZSVyc3OP+Lmi0ShmzJiBFStWYPjw4fjhhx+QkZGBYDCIGTNm4IcffsDJJ5+MNWvW9Pmx2BNrDQWr6F9KknC73QiFQpBKpQgGg6itrQUA5s9L/6f9Cc3lUqmUiXyd+RnT1/l9C58JxgvUlBlGxJc3x0NNURMd6hNVdJFATb6sdI34DLq+RHePk2g0CrfbjXA4DLfbzcaJw+FgWcINDQ0sAEEZgy6Xi2WRU2BApVJ1eJ1pDUkktGRnZyM3N5fZtgAHst7pObRaLRtnlDkbLyzRc9OZKT5xgt+/8nZw/F46ft/LB0f6Gr21b6X9Anme+/1+Nn74jE/6m8dXRh6sspYEw3jhPysriwnclH1/JL9jJBKB0+lk90fnNKfTyWw4wuEwCgsLccwxx7Cs/B9//BFut5ud08iCgYIiGo0m6WtwV+jJMUKJJzT/0vm3s7m7M+L3pvHEByj4uZ/mIv59TSJxd51NEq0vdJ34ZLBk0jdnMYGgn0NCGonMgUCANTRoaGhAXV1dTEfr8847D5s3b8ZTTz2FxsZGAO0LZXZ2NkpLSzF48GAUFRWxxn60QJG4yEf+40vS6bn4hTnZ0TFBO5RVHr8hJX9Ts9nMbFaowQ4vztKBjLxteR8o3hIjkeDLj4dEi3C8OBz/GH4h7OxgdqhNQbKb5zz++OP44IMPUFBQgMmTJ+N3v/sdnn/+eaxevRrffvstXn/9dUyePBmZmZl4++23ccUVV6C2thb5+flJu+f+AC8c2O12Nn+Rj3BGRobIau4ifNCGNuO05sT7igLAjBkzYgTmbdu24c0332SlpWlpaSgqKmJ+/1lZWbBYLCyoSUIOCUkEZfoAsfMAQRUVYt1JHbZt2wYAKCkpSYnD7YQJEzBp0iSsWbMGL730Ep588skjfq53330XK1asgFarxZIlS5hPrEKhwIcffohhw4Zh3bp1+PDDD3H11Vd316/Qb+D7JCgUCvae1mq18Hg8zI/3uOOOQ2trKxobG2MO4GSpQVV2vOAMHAhCk4jIZ7OSgEfBcv7wr1KpYhIoKAPuUPsIEgmUSiWbm2h/Ej9XxXM45dEDCfq7kGcqjRGDwcAySXNzc5GWloaGhgb2WsVX0BwMEuL4D7lczgRJAKyknTIM5XI5GxMU3OAzken7iQQjEpsp8SN+79vZvpj+FqJBbdeg/QKfvUqNzylAQR+80Ay0v95dya/kLSEzMzNhMBiYjRjQnsB1JFCmu9PpRCgUgk6nY8F5apDsdrtZJv/QoUMhl8sxevRorF+/np3nqNm73+9n/XiS5b2bKtAc6/f72ZmWLHP4igki0Xk2UQAiUZ8gHlqjaB3hq717IqOY5qlUX1NS++4EggEIHfbjM1MDgQAaGhoQDoeZjxTZDnz88cfs52UyGdLT09khv6SkBLm5uay0kEouaNPLT57xky8vAvJlJYLUgMrGg8FgjA8pHcDI35T3YeY3WwBiDmN0WKPNdSIhOB5+k8w/hs825q91sOaPtHDypU2JFvdUidS+9tprrAu6RNLu75aeng6lUokpU6bg9ddfh0ajweTJkwEAkyZNwk8//YSLLrqow3NRJinBNz8SJEYqlcJsNrMGUuTjWFVVBbPZjPT0dDFfHQTedoYCOxTcobFIawLNIdFoFDt37kR5eTlWrlzJGr9JpVLk5+ez7OX8/HxkZmYiLS2N+S/zB+hwONxB2OHXPoLmBPE6ph7U6+H4449P7o1w3HPPPVizZg1ee+01PPDAA0eUKdXS0oL77rsPADB37twOlhj5+fl46KGH8PDDD2Pu3Lm44oorRGl7HPS+DQQCbI9CVXIkiJD9m1QqRXFxMQAwgSh+n8Ifqvlqhs4yyuiD39cAHS13Djdwxd8HiRb8R3wZNh+oF3SExgZvrRKNRmEymVh2s1arRUlJCat04ccI/zeOt0BI1DiP/LWBA96sZOFGIjatU/EZiRRoOJiwQ9emwAqf4Ry/n6XHiuqcI0cqbW94TlZqfBbzoaDXgu8Lw2ea8xnN0WiUicIAWMXnkSKRtHsyU/NAvV7PxgvdEzVZbm1tRUlJCYxGI/NsJhuGYDAIr9fLbIMCgcCAT7CgOd3v97P3LL3G5NvcGV2t0E6kj8Q/D4nOA/l9LURmgSDF4LO4aLGk8hmXyxWzAA4ZMgSVlZUIh8OsVDwrKwt5eXkoLCxEXl4eLBYL9Hp9jPcyn1VBmym+7CI+Y1WIy6lLvNBMrx0Jn3z0NtEGlxZKfoMeL+LGL6a0EeM3RZ014EtUTtSV34nfwKWq93JmZiYA4K9//SuuuOIKOJ1OlmWj0+ngcrkSfi0R8+fPx9y5c3vnxvsRVNaqUChYMyYAaG1thd1ux6BBg/p0iXJPwh+yeMGZf1/TvF9SUoJt27bhiy++wBdffMGeQyKRwGKxIDc3F7m5uSgoKOjgv8x7/lNgNH6uIHE5vsGKOHynLps3bwaQWiLzzJkzccwxx+C3337Ds88+e0Rz6v333w+r1Ypjjz0Wf/zjHxM+5s4778QzzzyDPXv24D//+Q8uvfTSo731fgdvSUGeqfQv2V6QF2kiL3be25jmDxLk4gPgvOgcLwDwHMxC4XChexQBhiOHMlLJBiAajUKn08Hr9bJzC2WPHmm1BC9E0x6ZxgDdAyXe0GP57PYjzTIW46P3oNeI9nq8tR4v7sefXRKJ/zxk5cJDPvHdcc96vZ6dCShjnpBKpfB4PHC5XNi6dWvM1w0GA9ra2mLO6ZRtP9BFZuBAw0W+aSu9/w+WlXw0awLNHSIp4gBCZBYIUggSmHlP5PhSPLlcjlGjRmHr1q2Qy+UoLS1l5TcWiwVZWVksc9lkMrESQdpAUfYGf03alMeXdQlxuW9Am+RQKBTzuslksg7iET0+0eEsXkzuisjDb9oSPX93CESpIjK9+OKL+Oijj9jnDz/8MH755Rc0NTVh/vz5aGtrYxtSasRBTTroa4MGDUr43A8++CDuvvtu9jk1bhR0DYVCgYKCAjQ0NMDpdAIAa6CSnZ2NtLS0lBlHqcLBSkZp3tfr9bBaraivr8eoUaNQWVnJypuNRiPS0tKQnp7OPC4zMjKY5z+tPTQPUDCMf37KnOYtmkRmV9+AMpnHjBmT5Ds5gEwmw+OPP45LL70Uzz77LG6//fYYe5dDQT0tAOD//u//YqqAeAwGA+6880489thjmD9/Pi655BIxXuMg4YcyyuhvSc2t+CA4X9LO70XiD+68wHyo4HX8XkjMKamJVCplDfU0Gg1rVk4VNSQMxdsf8D+faE8LdKywI+Iz42m/m8jPW1hY9D0OR+DnM875j3j/bLIW7M4zMQnNHo8HQLv3Nz+vyeVy+P1+tkdSKpXIysqC3W4HENvYLn4fNdDh37+UsEfJV139+c4E6M6Cm4JYhMgsEKQQiSKshEKhwKBBg1BVVQWpVIoRI0YgEAgwLyqTyYT09HQmbFHmMkXo+eg9Lzp2ZkcgJsy+Bb2+tFmioAGflRj/+PiMdf5Qd6QLcX/mrrvuwl133cU+X7x4MbZt24b3338fQLsfbXNzM3w+H1atWoWxY8di7Nix+OKLL3Deeedh9erVePrppxM+NwlygiNHKpUiNzcXer0e9fX1MfYZlNUssoo6h58DNBoNJJL2JkRpaWloa2tjQU3gQJf3tLQ05rtsMplispe1Wi0TlamUk6B1qLNGSoLUxufzYefOnQBSK5MZAC6++GKccMIJ2LhxI5588kk8//zzXfo5v9+PW265BQBw88034+STTz7o4++66y48/fTT+OWXX7B27VpMmjTpaG+9SyRqOEv8+uuvuPDCC5llzQcffNAr99QZcrmcVeWRr7Lf72f+qfGiYSJhmBeaO6uyiw9sD6R9SX9ALpczoZka31FzNapy4RvrxVe3HUxUjn9MfMCBPFP5apv4JoKC/kuyM84lEgl0Oh1kMhk8Hg9MJhMLfvh8vg6iNiVRqNVqNpblcjlLMhLEQgkOZGGTSPtIdB4WHD1CZBYIUohEkx+JyD6fD0ajEYWFhXC73YhGo1AoFNBoNNDr9azBBXlF0SaJLw+TSCSdelUdrqWBIPXgN0vxJfBA4o35wUpLBQdnzpw5UKvVmD59OgBg+fLluPvuuzF16lRoNBosXrwYFosFCxcuxMknn4xp06Z1msks6B4kEgkTOisqKtDW1sYOqB6PB8XFxdDpdMm+zZQifh6g7ML09HS0tLQgOzsbJpOJZSQqlUro9XoYjUaYzWbo9XoYDAYmLlNjWZqP4r3+aX7i/VFFiWHfYtu2bQiHw8jIyEi5RqYSiQTz58/HGWecgVdeeQWzZ8/G6NGjD/lz8+fPx2+//Ybs7GwsWLDgkI9PT0/HlVdeibfffhuvvfZar4nMiRrOEjt27MDcuXNjvpZsaC4gSy+yzOks846voopv6EeJEmKv2v+Qy+XMr5tPiKGqzu7K0iRRLj6oSVaCItApSAYUXHG73TAYDKwCmZowU5CF5kGaV9PT01kAvycawfWloOahEOfc3kUS7UqLTYFA0CUcDgdMJhPsdvsRNZwhMSQUCrGGf5Tt4Xa7WWYeQVnKtDjx/nW871yiTZPI+kgORztGBAMDMU6Onmg0ioaGBtalm7LFLRZLv2gKeLRjhDKJqYGM3++Hz+eDz+eDw+Fg65DH40EoFGIlo2q1GjqdLuZf8kMkr1WCr8yRSCQdKivowC/Wn77Fm2++iZtuugkzZszAN998k+zbSchFF12EJUuWYPz48VizZs1BM9V27NiB448/HsFgEP/+979x2WWXdekaGzZswIknngilUomamhrm09+TNDc3s4azU6ZMwdq1a9n3HnnkEaxduxbBYBAPPvggzj777A4/n6jJbGFhYY+vNeFwGIFAIOb9zydWJNqLCp/L1KG39iTkLRsIBGLOO531/Ui0dsT/XKJSd4KyHEWV09Ej9q1HTzQaZf1FKIs/EAggFAqxxoO0n6V+LzTGyW6jO7nkkkvw7LPPsqDmunXr2PcWLVqEYDB4WEFNMUYGDiKTWSBIIeJ9xVQqFTsMGI1GGAwGdljnP/iO27yfJYnLtKES2coCgWCgIJFImD99eXk523y3trbC6XSisLCwU8/VgQCfwRUOh1nDo2g0CqPRyJqm6PV6tiZRWTOJynRAV6lULIuG/AwP1lAHQEzzJUHfYtOmTQBSzyqD56WXXsLy5cvx448/4rHHHuu0CaDb7cbll1+OYDCI884777Ca+I0bNw7jxo3Dhg0b8M477+D+++/vrtvvlPiGszzHHnssrrrqKuTn52PGjBk444wzOrzHktVklqryeP/TRL0c4u0yBAML3kuVkm54ca2rz3Ew4v2YBYJUgWzK6Pzv9/vZPjW+GpU+l0ql0Ov1PTJfvvbaayyoGf9e2b59O9auXYt//OMfhxXUFAwMhMgsEKQQ/Cab95+jTXn8Zpz3EOP/31kjDLGZEggEAw2NRoMRI0agsbGRbXCDwSAqKytRUFDQLZ3C+yK8nQU1PQLAbC+onJ1fb/gqGb5ahrLPDnYtEezsP6xZswYAMGHChCTfSecUFBTg5ZdfxnXXXYfHHnsMo0ePxiWXXBLzmHA4jBtvvBHbtm1DTk4O3njjjcMel7feeitmz56N119/Hffee2+3H/QP1XCW58wzz2TZYYMHD4bVau3Q+DCZTWaT7X8q6DvwCTRAbFVMvAVcZ/9P5N8t1h5BX4DEZvKvp6qz+CapfN+lnqCvBjUFyUfYZQgE3cjRloHwXW6pm3Iio/rOmpzwmyexmUpNRKmQoCuIcdIzuFwu1NbWss9NJhO0Wm2PZYH0JN0xRkgcpvWGfP/is5DjswsPFrRMFNwU61D/wW63Iy0tDdFoFPX19cjJyUn2LR2Uu+66Cy+99BLkcjlefPFF/OEPf4BEIoHNZsPvf/97LF68GHK5HCtWrMCUKVMO+/k9Hg/y8/Nhs9mwdOlSnHXWWT3wWxxg8eLFWLx4Md5///0Oc9bFF1+MefPmoaSkBNOmTcPq1asPOa+JtUbQFcQ4ERwKMUb6Pp0FNcvLy/HWW2/FPNbhcLDX+YorrsCLL77YIaiZLHsmQfLpWycqgaCfw2cxU4SS/4gvUeZLvig7JD7DTCAQCATt6PV6DBkyBAaDgdlAeL1etLW1dVtjob4Edd6mtUOpVEKlUkGtVkOj0UCj0UCr1bKMGn794dck8rumZjX0nCLQ2f9Yv349otEoSktLU15gBoDnnnsOV199NUKhEG677TYMGTIEZ555JoqKipjAvGjRoiMSmAFAq9XiuuuuAwC8+uqr3XnrCZkzZw727NmD6dOn49RTT0U4HMY999yD1tZWzJkzB7Nnz8a0adPwwAMP9LnAmUAgEAiSx1133YXVq1ezD4/Hg23btuEf//hHh8fecMMN+O233+D3+1FVVYWMjIwOj1GpVDAajTEfgoGBsMsQCFIMPgssURZzoseKTDGBQCDoGlKpFLm5ufB6vXA6nQDam656PJ4OjesGAlSWTL93oqoZ+r9A8MMPPwAAJk2alOQ76RpyuRz//Oc/ccIJJ+DRRx9FeXk5ysvLAQDDhw/H22+/fdS2H7feeiteeOEFfP755ygvL0dpaWl33HpCtm7d2uFrzz77LADAYrEwKxOBQCAQCI6GOXPmQK1WY/r06QCA5cuX4/7778fDDz/MgprRaFQENQUdECKzQJDCCPFYIBAIuh+JRAKtVgulUsmEZolEAp/PF9PEbiAhxGRBV/jqq68AADNmzEjynXQdqVSKP//5z7jpppuwYsUKNDc3Y9iwYZg4cWK3HIyHDRuGs88+G0uXLsVLL72E559//uhvWiAQCASCJCKCmoIjRYQcBAKBoIsEg8GYjKclS5bgpJNOwjnnnIPW1tYk3plAIDgS5HI5zGYzdDod+1o4HE7iHQkEqUtjYyM2bdoEADjjjDOSfDeHj16vx8yZMzF79mxMnjy5WzOv/vSnPwEA3n77bdZgVCAQCAQCgWCgIURmgUAg6AKtra0466yzsG/fPva1559/HqtXr8Ydd9yB119/PYl3JxAIjhSJRAKlUgmNRsP8hAUCQUcoi3ns2LHIzs5O8t2kFqeffjqGDx8Op9PZoUGSQCAQCAQCwUBBiMwCgUDQBdxuN/7+979jxIgRAACbzYb09HQolUpMmTIF69evT/hzfr8fDocj5kMgEKQe1GxV+MoJBIlZtGgRAGDmzJlJvpPUQyKR4M9//jMA4KmnnoLH40nyHQkEAkHXEJWaAoGgOxEnKYFAIOgChYWFOPbYY9nnTqcTer0eAKDT6eByuRL+3Pz582EymdhHYWFhr9yvQCBITX799VcMHjwYp556Kq6++moA4kAnSH0aGxuxbNkyAGDjVhDLddddh+LiYjQ0NODll19O9u0IBALBITnSSk2RRCMQCDpDiMwCAcShX9CRF198EZMnT2YfS5cujfm+Xq+H2+0G0J7lbDQaEz7Pgw8+CLvdzj6qq6t7/N4FAkHqsmPHDsydOxcrV67EBx98AEAc6ASpz9tvv41IJIKTTjoJZWVlyb6dlESpVGLu3LkAgAULFqCpqSnJdyQQCAQH50grNUUSjUAg6AwhMgsEOPJDv6D/ctddd2H16tXs4+yzz475flpaGpqbm+Hz+bBq1SqMHTs24fOoVCoYjcaYD4FAMHDZvn073n33XZxyyilYunSpONAJUh6fz4cXXngBAHDHHXck+W5Sm6uvvhrHHXcc2traxN9KIBCkPEdaqSmSaAQCQWcIkVkgwJEf+kVm2cDm7rvvxtSpU/HUU0/htttuS/btCJLE9ddfj5UrVwLoWAERjUZx4403YsqUKSzDTTCwOfbYY/Hyyy/j888/x6OPPgq73S4OdIKU5rnnnkNjYyMKCwtxxRVXJPt2UhqZTIa3334bMpkMH3/8Md59991k35JAIBAwuqtSUyTRCASCzhAis0CAIz/0i8yygQeJiQBwwQUX4Mcff8TKlSuRnp6evJsSJI0dO3ZgyZIl7PP4CojVq1cjMzMTq1atws6dO1FbW5u8mxUkhfgDnVKpxPDhw2E0GjF48GCo1WpxoBOkLFu2bMHjjz8OAJg3bx4UCkWS7yj1GTt2LB555BEAwE033YSvvvoqyXckEAgE7XRXpaZAIBB0hhCZBQOS7jr0i8wygWBg8/TTT+Paa68FkNjHbuPGjZg8eTIAYNKkSfjpp58SPo+oiui/xB/o/vnPf+K3336D3+9HVVUVMjMzxYFOkJJs2bIF55xzDnw+H84880xcc801yb6lPsMjjzyCq666CqFQCOeffz6eeeYZBIPBZN+WQCAQHBJRqSkQCI4GebJvQCBIBnfddRfuuusu9vnFF1+MYcOGoaSk5LAO/SqVCiqVqrduWyAQpBA///wzSkpKEIlEACT2seuqt938+fOFncYAYc6cOZg9ezai0SgeeOABSKVSdqDTaDRYvHhxsm9RMECJRqOor6/H1q1b8cknn2DhwoUIBoMYMWIEFi1aBIlEkuxb7DNIpVK88847CIVC+Oijj3Dffffhtddew7XXXotTTjkFo0aNQnp6OqRSke8jEAiST3yl5gUXXJC8mxEIBH0aITILBBCHfoFAcGhefPFFfPTRR+zzPXv2YM+ePXjuuecAJPaxi//aoEGDEj73gw8+iLvvvpt97nA4hP1OP2X06NFYs2ZNzNfEgU6QbFpaWjB+/Hjs27cv5uszZ87Eu+++C7PZnJwb68MolUosWrQIp59+Oh566CHs27cPc+bMYd+XyWQ49thjsWLFCvH3FQgEAoFA0C+QRKPRaLJvQiDoLzgcDphMJtjtduGXKUiIGCP9A5fLhZNOOglZWVnYv38/zGYzVq9ejXPPPRdff/01VqxYgY0bN+KUU07BF198gaeeegqXX345nn766U6FZh4xTgSHQowRgUBwtIh5RNAVxDgRHAoxRgSHQoyRgYMQmQWCbiQajcLpdMJgMIiyUkFCxBjpf8yZMwennnoqTj31VPz3v//Fk08+ySogLBYLZs+ejZ07d2LatGl48sknu/ScYpwIDoUYIwKB4GgR84igK4hxIjgUYowIDoUYIwMHITILBAKBQCAQCAQCgUAgEAgEAoHgiBHdJgQCgUAgEAgEAoFAIBAIBAKBQHDECJFZIBAIBAKBQCAQCAQCgUAgEAgER4wQmQUCgUAgEAgEAoFAIBAIBAKBQHDEyJN9AwLBQIHM7gWpSSo0IRBjJLVJhTECiHGSyogxIugKqTBOxBhJbcQYERyKVBgjgBgnqYwYI4KukArjRIyR1OZwx4gQmQWCXsLpdMJkMiX7NgSdYLfbYTQak3oPYoykNqkwRgAxTlIZMUYEXSEVxokYI6mNGCOCQ5EKYwQQ4ySVEWNE0BVSYZyIMZLaHO4YkUSj0WgP3o9AIPj/OdoIncPhQGFhIaqrq3t8IejNa/X29Tq7Vn+I4g7E1603r5UKYwToW+Okv16rs+uJMZLa1+rt66XyXNIdWUOp8Lfsy9c62HXEGBHXOtS1UmGMAGK9SZXriT1J95Hs1623r5UK40SMkdS+lshkFghSFIlE0i0Tg9Fo7LVoY29eq7ev19u/W1foi2Okt6/XX691OPTFcdJfr5WM63UFMUZS63r9eYwA/fdv2VvXSsXxAYgxMpCvdTiI9Sa1rpeK46QvjpHevl5/vVZXEWOkf11LNP4TCAQCgUAgEAgEAoFAIBAIBALBESNEZoFAIBAIBAKBQCAQCAQCgUAgEBwxQmQWCPoIKpUKjz76KFQqVb+6Vm9fr7d/t95EvG5971rJoL/+Lfvz+O9txOvW966VDPrr37K3rtXfxwfQP1+3/nytZNCf/5b9+XfrTcTr1veu1duIMZJa1xKN/wQCgUAgEAgEAoFAIBAIBAKBQHDEiExmgUAgEAgEAoFAIBAIBAKBQCAQHDFCZBYIBAKBQCAQCAQCgUAgEAgEAsERI0RmgUAgEAgEAoFAIBAIBAKBQCAQHDFCZBYIBAKBQCAQCAQCgUAgEAgEAsERI0RmgSBF2bJlCyZPnozx48fjvffeAwAsWbIEJ510Es455xy0trYiGo3ixhtvxJQpUzB37txuu7bf78cFF1yASZMm4R//+Ee3PW91dTWmTZuGiRMnYsGCBVi3bh3Gjx+PadOmoaKiAgDw8MMPY9KkSbj11lu75Zpz5szBu+++2yvXSgbJGif9aYwA/Xuc9LcxAoi5pLsRY0SMka7Q38aJGCPdjxgjR48YI31rjABinHQ34gwsxkhX6G9zSb86A0cFAkFKcvHFF0f3798fDYVC0QkTJkSj0Wh06tSpUb/fH/3iiy+iTz75ZPSHH36I3n///dFoNBq9/PLLozU1Nd1y7X/+85/RV199NRoOh6Onn3561Ov1dsvz3nPPPdGVK1dGo9FodPr06dHp06dHrVZrdPv27dGbb745WllZGb388suj0Wg0et9990XXrl17VNdrbm6O5uXlRd95553oueee26PXShbJGif9ZYxEo/1/nPS3MRKNirmkuxFjRIyRrtDfxokYI92PGCNijByK/jZGolExTrobcQYWY6Qr9Le5pD+dgUUms0CQorz22msoKioCAEgkEthsNqSnp0OpVGLKlClYv349Nm7ciMmTJwMAJk2ahJ9++qlbrk3PK5VKMXr0aOzcubNbnvehhx5i9xsOhwEAFosFI0aMwJ49e7Bp0yZMmjQJANjveDQ89dRTuPbaawEAXq+3R6+VLJI1TvrLGAH6/zjpb2MEEHNJdyPGiBgjXaG/jRMxRrofMUbEGDkU/W2MAGKcdDfiDCzGSFfob3NJfzoDC5FZIEhRMjMzAQB//etfccUVV8DpdEKv1wMAdDodXC5Xwq91Bz31vBaLBTKZDG+88QbGjh2LSCTCvheNRrv1utXV1XA6nRg2bBgA9Oi1kkmyxkl/GCPAwBgn/W2MAGIu6W7EGBFjpCv0t3Eixkj3I8aIGCOHor+NEUCMk+5GnIHFGOkK/W0u6U9nYCEyCwQpwosvvojJkyezj6VLl+LJJ59EU1MT7rrrLuj1erjdbgCA2+2G0WhM+LXuoKeeFwDef/99LFmyBAsWLIBEImFfl0ql3Xrd+fPn4y9/+Qv7vCev1ZukyjjpD2ME6J/jZCCMEUDMJUeDGCNijHSFgTBOxBg5OsQYEWPkUAyEMQKIcXI0pMoYAcRcksqkyjjpD2ME6NlxIkRmgSBFuOuuu7B69Wr24fF4sG3bNmYon5aWhubmZvh8PqxatQpjx47F2LFj8cMPPwAAVq9ejTFjxnTLvdDzRqNRbN68mUW4jpYff/wRCxcuxOLFi6FUKqHRaNDS0oIdO3aguLgYY8aMwapVqwCA/Y5Hys8//4zrr78eCxYswIIFC3r0Wr1JqoyT/jBGgP45Tvr7GAHEXHK0iDEixkhX6O/jRIyRo0eMETFGDkV/HyOAGCdHS6qMEUDMJalMqoyT/jBGgJ4dJ/KjujOBQNBjzJkzB2q1GtOnTwcALF++HHfffTemTp0KjUaDxYsXw2KxYOHChTj55JMxbdo0DBo0qFuuffnll+PKK6/EO++8gxtuuAFqtbpbnveJJ55AQ0MDzj77bADAvHnzcN5550EikeC9995DcXExysrKMHHiRAwdOpT5Eh0JP//8MwDg3XffBQAcc8wxPXatZJKscdIfxggwMMZJfxsjgJhLuhsxRsQY6Qr9bZyIMdL9iDEixsih6G9jBBDjpLsRZ2AxRrpCf5tL+tMZWBKNRqNHdXcCgYBB/jUGgyGm5EAgIMQYEXQFMU4Eh0KMEcGhEGNEIBB0B2IuERwKMUYEh0KMkYGDsMsQCLoRp9MJk8kEp9OZ7FsRpChijAi6ghgngkMhxkj/Y86cOXj33Xexbt06jB8/HtOmTUNFRQUA4OGHH8akSZNw6623dvn5xBgRCATdgZhLBIdCjBHBoRBjZOAgRGaBQCAQCAQCgSCJtLS0MF/BefPmYenSpXjllVewYMECVFVVoby8HGvWrIHBYMC6deuSfLcCgUAg6G9cfPHF2L9/f8JAp0AgEHQV4cksEAgEAoFAIBAkkaeeegrXXnstAMDr9cJiscBisWDPnj3YtGkTJk2aBACYMmUK1q9fj5NPPrnDc/j9fvj9fva5w+HonZsXCFIMq9WKzz77DJWVldBqtRg5ciSmT58OjUaT7FsTCFKOQCCAyy+/nHm0UqCzoaEBCxYswOuvv57kOxQIBH0JITILBAKBQCAQCARJorq6Gk6nE+PHjwcARCIR9j3yMNTr9QAAnU4Hl8uV8Hnmz5+PuXPn9vwNCwQpzEcffYSbbrqpQ5DFYrFg7ty5uO222yCVDtxiXofDgeHDh2PIkCGQyWRYvnx5sm9JkGT8fj/+/Oc/4+233wbQMdDZ2c+IoKZAIEjEwF1hBQKBQCAQCASCJDN//nz85S9/YZ/zDXGkUin0ej3cbjcAwO12w2g0JnyeBx98EHa7nX1UV1f37I0LBCnG0qVLcdVVV8HhcGDkyJG4+eabcdVVV6GwsBCtra248847cdNNN8UEcgYaO3bswB/+8AesXLlSCMwCAIDBYMApp5zCPo8PdCZi/vz5MJlM7KOwsLDH71MgEPQNhMgsEKA9+pqfn49TTz0Vp512mvCiEhwU4VkmEAgEgu7i559/xvXXX48FCxZgwYIF0Gg0aGlpwY4dO1BcXIwxY8Zg1apVAIBVq1Zh7NixCZ9HpVLBaDTGfAgEAwW3241bbrkF4XAY1113HX799Ve8/vrr+OCDD1BRUYEXXngBUqkUb7/9NubMmZPs200a27dvx1dffYVTTjkF77zzTsLH+P1+OByOmA/BwCE+0JkIEdQUCASdIewyBAIciOo/8sgjAIDzzjtPeFEJOiA8ywQCwdESX6r8xBNP4E9/+hO0Wi3efvttlJSUJPsWBb0MrSnvvvsuAOCYY47BeeedB4lEgvfeew/FxcUoKyvDxIkTMXToUEyePDmJdysQpCYvvvgiqqurUVRUhFdffTVGHJPJZLjrrrtgMplw/fXX44knnsDpp5+OKVOmJPGOk0NxcTGeeuopnHjiiTjjjDNw3nnnITMzM+YxwnpnYEOBzqamJhQXFyd8jEqlgkql6t0bEwgEfQIhMgsEOBDV/+abb3DDDTd0yYsKEH5UAw3hWSYQHJpoNIpoNIpIJML+z5dbJiq9lEgkLHNGKpVCIpEwgYDPqOkPiKCmoDOuv/569v/169fHfG/evHm9fDcCQd8hFArhlVdeAQA89thj0Gq1CR933XXX4YcffsDbb7+N22+/HZs2bYJcPrCOwxMmTIBGo4FUKsWECRNQUVHRQWR+8MEHcffdd7PPHQ6HsEMYQDzyyCMxgU6BQCA4HIRdhkCAA1H9b7/9FgsXLuySFxUg/KgGGsKzTCCIhcTkYDAIv98Pj8cDt9sNj8cDr9cLr9cLj8fDPuh7/Af/GPoZr9cLt9vNHu/z+RAIBBAKhfq8l2Z8qTIFq0aMGHHIoKYoXxYIBIKO/O9//0NtbS0yMzNx+eWXH/SxTz31FCwWC7Zu3cqE6YHE3/72N3zzzTeIRCLYuHEjSktLOzxGWO8MTN59910UFxdjwoQJWL9+PdatW4eysrJk35ZAIOhjCJFZIEB7VP/kk0+GUqnEhAkTuuRFBQg/qoGO8Cwb2FRXV2PatGmYOHEiFixYkNCj++GHH8akSZNw6623Jvluu4doNIpwOIxAIBAjDvt8Pvh8PvY1p9MJu90Om83Gxj7/f/4j0dcdDgfcbje8Xi97br/fD5/Px8Rqn8+HYDDY50RnEdQUCASC7uVf//oXgPZqgEOV8Kenp+PJJ58EADz++OMDLmB39913Y8GCBZgyZQouueQSpKenJ/uWBAJBChJ/ziHie1kJBPEMrPoggaAT/va3v+GMM87A6aefjo0bN0KlUh3SiwoQflQHI14s6W8l74DwLBvovPDCC5gzZw6mTp2K0047Dd98802M7cHDDz+M8vJyrFmzBvfffz/WrVuHk08+Odm3fURQtnIoFGLv7Wg0ilAoxL4eDodj/qX/d2abQXMCWWVIpVL2IZPJIJfLYz6nf+l7crkcoVAIQHuQRy6XQ6FQpPxcE1+q/NNPP7HvHSqoKcqXBYLE0NwSiUTYB2/bQ8TPNzS3CPoubrcbn3/+OQAcMouZmD17Np577jns3r0bL7zwArMvGgjk5+fju+++S/ZtCASCFCf+nHP77bfDYDB0sH0TCOIRIrNAgPao/jXXXIPHHnsM1157LY477jjhRXWYUIYjf7iLp78d6oRn2cDmoYcegslkAgCEw2HIZLIYj+5NmzZh0qRJAIApU6Zg/fr1nYrMqerdHYlEmE0FECssUxYxLzQHAgEEg0EmNPv9/g5iMxEv9MhkMigUCiYUSyQSJibL5XImLvMCNAnLcrmc3atSqUxpsVkENQ+fzny+EwUuEgUtBH0Tes3D4TDC4XCH1/twnifR8xJSqRQKhQIymSxl5w1B53z99dfwer0oKSnB2LFju/Qzcrkcc+fOxZVXXolnnnkGt99+OywWSw/fqUAgEPQd4s855F8f38vqhhtuSPjzqXq2EfQ8QmQWCJA4qh/fdEcQCx3S6ADYlYMfPT4UCjGBSCaT9cLddi/vvvsugPaydzFOBi50IH3jjTcwduxY/PLLL+x70WgUTqcTer0eAKDT6eByuTp9rlTs5B4KheDz+QAcCCL5fD4mIJOYTJvISCQCv98Pl8sFj8eDQCAAv98f46UcjUY7CIEkGpPATIKPWq2GRqOBWq1mj1coFFAqlWzuUCgUCAaDkMlkUKlUkMvl7HpqtTolBUYR1EwMLyTHZ6MeibBIULCCxpYg9eGDWUcqKvPiNC8oSyQSFtQiUZnmLolEApVK1Sf3JQOZr7/+GgBw/vnnH1aQ4LLLLsOTTz6JrVu34tlnnxXNNQWCAUqiwDW/Vx2owcf4c45GowFwwPbtxBNPxBlnnIHzzjuvQ/NQIDXPNoLeQYjMAoGgy5DQFH9oI3jRKH5R5g99JB4EAgEmFB3pAv7VV1/hr3/9KwKBALRaLV5//XUcd9xxR/w7Cvoffr8f99xzD77++msolUqMGTMG77//frc89/vvv48lS5ZgyZIlOOuss9jXpVIp9Ho9amtrAbSX8x6scU6qWSHwArNEImFCMonK9K/P50M0GoXP54PNZmOezA6HA16vlzUE5EVmAB2sL/gsQpVKBY1GA51Ox76u1Wqh1+sRjUYRCAQglUqh0WgQDochlUqhUqkQiUSgUChYtq/H44FGo4kRjO666y589tlnqKysxNatWzFq1Cj2vT179uC6665DS0sLzGYz3n33XYwYMaLb/7YiqNkOCYn8mnCox9O/B7M/oLWEF6kp814mk0GpVB6xiHiw8SM4evjXioevfkokBvB7E97Sh74XP/dQsBsAC2jRY3w+H6uGOFLEOOk9otEoE5nPPPPMw/pZqVSKxx9/HBdeeCFeeOEF/PGPf0RWVlZP3KZAcNiIeaTnoDUj3gauM+Kro2ivQV9PlgjdG2OEP+cQ8bZvFRUVCUXmVDvbDER68gx8MITILBAIDkp8RlA8vFfqwRZZWoiprJ3EBcqCViqVh71It7W14ZprrsGqVaswfPhwfP/997j66quxbdu2w/49BT0D/1rHlyfTeOjpDdoDDzwAqVSK3bt3QyKRoL6+vlue98cff8TChQvx2WefQalUdvDoHjNmDBYtWoQ77rgDq1atwvnnn9/pc6WSFUIkEmECs0wmg8fjgd/vZ4JxJBKB2+1mwrHD4YDNZoPf70dLSwv8fj+cTiccDgd77Sm7OBqNxthc8GIfiT06nQ5erxdtbW3QarUwGo0Ih8NwOp0wm80wGAwA2oV7hUIBrVbLhCESnrRaLQDA5/NBq9Wy8XXJJZfg/vvvx+TJkzv83rfccgtuvvlmXH/99fjkk08we/ZsrFu3rqf/3AMO/mCXCJoP4q1YutrgUSqVsnFEvt38euP1eqFSqY5IRDzY+BEcOYnEZapckEgkrPlnV8cA7Vs6G2cUmJJKpey6BoMBEomEzVeUAX8kiHHSe+zduxeVlZVQKBSYOnXqYf/8zJkzMW7cOGzYsAFPPfUUnnnmmR64S4Hg8BHzSM8QjUbh9XoTrg3xZ5FEgcxEUIVMb/cG6ekxEn/OIeJt3+67776EP59KZ5uBSk+dgQ+FEJkFAkFC+ENaIh9VykILBAIxfqu00KrValbmzj/npEmT4PF4sHv3buTn50Oj0eDss8/GvHnzDvtAV15ejqysLAwfPhwAMHXqVFRWVmLTpk1d9uUT9AwHO+DT90lEkkgkMdmF8WNEp9PhvPPOwxNPPHHY9+F2u/HOO++gpqaGjcXc3Nwj/8U4nnjiCTQ0NODss88GAMybNy/G9qC4uBhlZWWYOHEihg4d2mcOCoFAAEC7wExl5Py/JDBLJBLYbDY4HA64XC60tLQgEAigubkZgUAAbrcbra2t8Hg8nWaJUMayUqmEwWBAMBhkNiM6nQ6vvfYagsEgrFYr0tLSoFarccIJJ+DRRx9lwrfb7YZOp2MZzhKJBIFAACqVCtFoFH6/H2q1GgBwyimnJLyPpqYmbNq0CcuWLQMAXHzxxbjjjjuwf//+g/okC7pOvL83cMDKgoKUwWAQPp8PXq+3Ux/dRA0jZTIZW5MikQg8Hg/cbjcuuugiBAKBmPXmrLPOwt/+9jcAOGyhubPxIzgy+H0EQeuBVCqFz+frYDMUXynFlzNTEIGCZASNMRKzI5EIvF4vC1KdddZZ8Hq92LdvH/Ly8qDVanH22WdjwYIFRyQWiHHSe9CcPXnyZOh0usP+eYlEgsceewznnHMOXnvtNfzlL39JmJEnEByMROebo9m7AmIe6QniBWbepk0ikSAUCsHtdiMQCLCgJm+vRIlVvL0XPS8FtP1+PwtkH+oMnOpjJP6cM23aNFx66aUdbN/S09N79D5Snfj96pEGGbp7jPTkGfhQCJFZIBDEQNHa+PIhWlz9fj/cbnenGUW00LpcLvh8PphMJlbmKpFIsHbtWgBATk4Oli9fjtzcXJZlJpfLcckll2Dv3r0Jn/t///tfTJnNkCFD0NzcjPXr12PChAn49NNP4XK5sH//fiEyJ4lEGWmJMpYpO57GGflhKpVKrFmzBhKJhI2RQYMGxVzjcMZIeXk50tPT8cQTT+Dbb7+FRqPBnDlzcNpppx317/q///2vw9fibQ/6mscjvX8BQKlUwmq1AgDL3qBNtFKphNvtZtnKLS0tCAaDaG5uht/vh81mQ2NjI3vejIwMZGVlQaFQwOv1oqWlBa2trfB6vfB6vQCAtLQ0jB07Fs3NzXC5XHC73XjggQfgdDrx+OOP4+6778aYMWPQ2tqKXbt2YcqUKbjmmmtQVVUVM7ZovlmyZAmbXygA1hnV1dXIy8tjgS6JRIJBgwahqqpKiMxHCc0JvJBIthX0Wnm9XrhcrhgBmuaNUCgEr9cb8/OJ0Ov1yMjIQDQahcfjgc/nw5IlS6BWqzFixAgsX74chYWFrDml3+/HlVde2eW5RNC98IEr4EDmslwuh0QiYd7uQLtITMGo+EM7jR2PxxOzL1Gr1SxYxVdMRKNRuN1uWK1WtlZ9++238Pv9GD16NJYvX47MzEy2FzrcfYmgdyGR+Ywzzjji5zjrrLNYNvNzzz2H+fPnd9ftCQYIic43R7N3FfQMtIcFAK1Wy/Z8kUgEzc3NcDqdh3wOOqvQB/UDoSotsl0KBoPMUoJ+rq+NkUTnHCLe9q2/E9/n4WA9QyjwzdsCdqUnSHePkZ48Ax8KITILBAMcPhqbyGtZLpdDLpcjGAzCZrN1OBDSoZBEHhIUKNvR7Xaz8naivr4ewWAQgwYNYuIVTdKffPJJl+/dZDJh8eLFTIiaPHkyRowYcVReioLDhx8/8eJyZ/6nJCAplUp20OfFZhIt4xdX4PDGSDAYxL59+zBixAgsWLAAW7ZswYwZM7Bjxw6RrZSA+M0SHwQg8Q4AsrOzsWrVKkgkEpZ1SkJiNBpFY2MjJBIJZs2ahYsvvhh5eXkdruVyubBr1y789NNPWLZsGdra2rBixQo89NBDqK+vx9q1a7FlyxbMnDmTbejUajXy8vJQV1eHPXv24LPPPsPWrVsBtAvZ4XCYNVuk8ndeLDoYnZVJCo4MPuDE++HyjdXC4TDa2tqYgCyRSKDRaKDRaBAIBNDQ0BCzJsXbrFDFRDAYZELjoEGDYLFY4Pf7YbVaUVlZGTOXKJVKNq7//e9/iyZvSYAseWhcUDNPeg8GAgE21+h0uhjLG8LlcsFqtcaUL8tkMuj1ehgMhpjSXh6JRAK9Xg+FQoHa2lp4PB5YLBZUVVUhGAyiqKiI+cjTvHE4a46g9wgGg1ixYgWAoxOZJRIJ/va3v2HmzJl4+eWXce+99w74zDzBkcGfb+IR80hyIfEXOLDm0NcbGhpYwgPfcJpvXM1bPFKfEoLWHpPJxK4TDofhcrmgVqtjsprFGOk7xFdtH87P8RXhwIFzL1+91xndNUaSeQYWIrNAMACJF5UTlXnwkyCVHgPtk6RWq4VCoYDb7WZfp6wh+lm5XA6bzQafzwedThcTwfv1118xZswYdi90TeDwo7innHIKVq5cCaDd3D4nJ4fZZwiODH588A2WEi2IfLkYwdtf0M/wpezRaDSm7IyaLPGC1C+//IJjjz0WwWAwRnwADm+MFBUVQSqV4uqrrwYAHHfccSgpKcH27dtx6qmnHu2fqt8R3zAr/t9oNAq5XM6yPdLS0rBv3z5Eo1E0NzcDAFpbWwEAt912Gy699NJOr6XX63HCCSfghBNOwI033ojnn38eS5cuxfvvv493330XkUgE69evx3fffYfRo0cDaG/Od+GFF6Kurg4tLS249tprsXXrVnZf5PkMAB999BEGDx7MxvHBKCwsRE1NDUKhEHue6urqhBs8wcHpzBYjXkikDHiywKCsU6lUCpfLhbq6OgDth8G0tDTo9fpOAwV+vx8NDQ3w+XxobW1Fbm4uyy7auXNnp81gL7vsMpSXlyf8nsgs6zlonpdIJFCr1R0yfPjDfiILBJ/Ph6amJgAHmqzqdLoOFl0HI34s7dixgzVNin+OVMsuE7Tz448/wuVyISMjA8cff/xRPdd5552H448/Hps3b8YLL7yAxx57rHtuUjCg4M838Yh5JHmQRRKtO7xPsN1uh9frhUQiQW5uLqLRKCorK9HW1tZh76hUKpkdJCVZBYNBhMNh2O122O12GI1GmEwm9nXKalar1ZDJZGKM9AEoSYISbAi+aTnZtdHjEz0HBSZIbwkEAjFJFXyWO/8cP/30E4477jj4/f4OlcB95QwsRGaBYADBe0bFQ43Y+C7uAGIEZhKSXS4X6uvrYyZEav6Vm5vLss2o1DkQCDBPVACora1FQUEBm8QBsGyyw43i1tfXM3+hxx9/HNOnT0dZWdlhPYegnURWF4cDlQTRaxkKheDz+Zh3bqJFWC6Xs8YQ5GEWCoVQV1eH/Px8JlZRkybg8MZIRkYGTjvtNHz99dc455xzUFlZiYqKCgwbNuyIfsf+zsHKufjXjyL6tOlRKpWIRCJQKBRoaWkB0F6C3FVUKhUuvfRSLF26lGVBFxcXY/369aivr0dBQQEAdBAuP/74Y2zcuBHhcBhZWVkIBoMxmczxQazOyMrKYh2Xr7/+eixevBjFxcXCKuMwofc8IZVKYywQiGg0ira2NkQiEcjlclgslpisIhIQjUYjcnJy2M+2tLSwYEZeXh5MJhOA9vGTlpbGsj8AsGzUhoYGtumORqMs214ikeCTTz5JWlf4gQwFJnnLlETf7yyowGej5efnH/ZrGI1G2TylVCrh9/tj5hma345kzRH0Ht9++y0A4LTTTutSKfLBkEgkeOSRR3DxxRfjpZdewgMPPMAayAoEXYXON4kQ80hyIMst2g/ylTF+v58lRmRkZMDpdGLv3r3ssSQoUpIMiYQOh4M9P1XP0OMcDgecTicMBgO0Wi0TGalZdXV1tRgjKQz1doiv3Kb9Co0nn88X09Q8Hho7VH1HZ2M++aqz7Oiamhrk5uayjHmqAlQoFH3mDHx0K7JAIOgz0OGaRBrKOKaoLE1e/Ead/JeB9kVZr9fD6/WiqakJ0WgUSqUSZrMZZrMZMpkMgUCAHdyAA02V4kXLSZMmYc2aNbjqqqvYvRxpyfIjjzyCY445BmVlZaisrMRbb711RM8z0AkGg/B4PDGvFS2O5DnGe5DxXmR8aZnX60VbWxsaGxuZvxkv6lCWO40zslRpbW1FY2MjHA4HpFIppk2bhh9//BGzZ89mGQiHU6rE83//93946qmnMHr0aFxwwQV44403eq3xQV+Db6jFf85H3En0B8BK2kn04QNKlInaFaLRKBYuXAgAGDVqFKLRKDZu3Aigfb5Yv349/v3vfyMrKwu1tbUAgPT0dNjtdoTDYchkMjZ2abMnl8vZJpHu/fbbb0dBQQFqamowY8aMmIDU66+/jtdffx1Dhw7FggULxFxymFDGDtD+9ybbi0Sd1p1OJ4LBICQSSYzADIB5JkskEmRnZ7OftVqtqKiogMvlgsvlwt69e2OCDjQWKeBhs9kAAFOmTMHatWtxzTXXsIAX0C5MH644ebDxczRUV1dj2rRpmDhxIhYsWIB169Zh/PjxmDZtGioqKgAADz/8MCZNmoRbb721W66ZTPi5JNHhjPYObrc74byv0WgAoMNh/1CQH3NtbS3b2xgMBni9Xpx00kn46aefcPXVV8c0fDoSemqcCGJZvnw5AHSbv+QFF1yAwYMHw2az4cMPP+yW5xQMLOh8c8011xz1c4l55Oihsw1VufEe/V6vlyVMaTQaOBwO7NmzB9FoFEajEWazGT6fD7W1taiurkZNTQ0aGhpgtVrhcDjgdrvh8Xhgt9tRX1+PmpoaeDweJkg7HA40NDTA5XKxirpgMIgxY8Zg9erVYoykGGRzwvedUqlU0Ov1UKlU8Pl8aGxsRE1NDaxWK9xud6cJVED7npiaoDscDrS1taGtrQ2tra2w2+1wu93w+XwxTShp33zKKafgxx9/xM033wzgQCb+oXqTJCJZZ2BJVJgOCgTdhsPhgMlkYuUyqUQoFGKHa4qqHeyATV6ZtPhSdmBtbS18Ph8MBgMyMzNjosE1NTUAgJKSEkilUtaQR6FQwGw2d3o/fJZqfyfVxkh88IEyD+PHR3zZDzXto38TNYKkEnnKUo7PZqQF2O/3w+fzxUSNTSYTtFptTHMo8msdCJmHyRwnJOgrlUo4nU4mzNHr5PV6kZWVhQ0bNrCNtNVqhcvlYkGm3377DVOmTMFjjz12yNcrGo3i5ZdfxuLFiyGTyfDKK6/AarXivffeg0KhwIMPPohFixYhEAhg1qxZ2LlzJ3w+H04++WS2wacGowqFgomHGRkZzOM7kadrXyfV5hLaLMtksk5tC8jX2263AwDMZnOHbEGXy4Xa2lqoVKqYTPLdu3fDbrcjIyODjbNRo0ZBo9EgFAox2xZq7ufxeCCTydg6xWemUFA1Vbj33ntx/vnnY+rUqUww+/jjj9HQ0IAXXngBDz/8MO6//34sWrQI999/Py666CKcfPLJHZ4n3ifS4XCgsLAwZcYIQQcmACzYzY+XSCSC1tZWNu8bDIYOQYHW1lYWSNBqtTCZTOwxfGNZ+ggEAjHrDNls0OdkzUFe0VRlI0hNXC4X0tLSEAqFUF5ejsGDB3fL8z777LO49957cdxxx+GXX37pd+vGkZJq640g9Ui1MRIKhWKax9L5IRQKobW1ldm+yeVy+P1+NDc3MzG6oaGBCXrkq8tbr/H+unQ2USqVrJmgTCZj/6f1TaFQQKfTsb0Hf08DhVQbIwQ1EaazsFwuZwkzLpeLNTkn6DWkMyzv3Q3ENv6jSnGyvKCzSqLXXaFQwGAwsJ4ydG9kuQKgQzPjVEXYZQgEAwTKBqLS5YMRiURgt9vZQYv3RKRJzmg0xkyQJBTTYsw3d4oXIHmbjPjsaUHvQc0r+LFBCx8tanw50KHgF1HgQHMnp9MZ40lFWc0ymQxKpRIajQYmkwkSiYRlONpsNoTDYRgMBqjValbqFgqFUkoc6o/IZDL2eqnVavj9fvZeVigU8Hq9aG1txaBBg7B//36YTCZYrdYYQUYqlWLVqlX49NNPMWvWrE6vFQqF8Nxzz+HLL78E0C62abVavPLKKwDafTJ3796NQCCArKwslk2g0WiQnp6O+vp6dj0A7B6oRBFAh+CGoGfg55FEf+9gMAiHw8FEUGroFk9nWaS0ZlBDN5lMxl5vm82GaDTKPA/pYGk2mzsIzGq1+pBrYG/z0EMPMesPysy3WCywWCzYs2cPNm3ahEmTJgFoz8xev359QpF5/vz5mDt3bq/e+5FApZ9kaRKNRmNEZKlUirS0NNjtdlZ+LJfLodfr2euflpYGqVSK1tZWeDwe9pp35dp0sOcFZo1GwwRmCpAKUpdVq1YhFAqhuLi42wRmALjxxhvxyCOPYMuWLZ2+zwR9B+olQftPvg8NX6VFdoFir9A/4Jv8kZgLgGWT8mOgubmZJVG4XC4mNlJyBQXF+WZuvIioUCigVquh1WpZdTA1L+a/r9FoEAwGodFooNVqWSWnVqsV5+AkEi8wazQayOVyOBwO2O32mMC0Wq2Gx+NBY2Mj25PyQYhE8wtZZcSfRbRaLXQ6HTQaTUwT69bWVrjdbmRnZ7MztVqtZsHzYDAoRGaBQJA68NHXgxEMBpkoKJFIOojJcrmclUXzPstAR99TPgrHw1t29IWJsr9CiyIAlmkMtEdmbTZbQmGZIvRSqZQtdsFgkPmUHU5xDGUyu1wuNDc3Q6/XIzc3l230nE4n25zxzTWEyNyz8MEhEvHoIEY2OYFAABkZGaisrEQkEoHBYIDD4UBaWhra2towatQo/Prrr3jxxRchkUhw0UUXdbhOIBDAE088gR9++AFSqRT33XcfTjnlFMybNw/BYBCjRo3ClClT8NprrwFoF9eqq6sBtFdLtLW1AQCrspDJZEwg0mq1TMxMNUGxvxO/DkSjUVZaShgMBva6xUPzDr82UMUFcKApnNFoZGOSDoFpaWnsOhqNBiqVignMlG2Uioc5i8UCAHjjjTcwduxY/PLLL+x70WgUTqeT/b10Oh1cLlfC53nwwQdx9913s88pkzkVofclBTp9Pl9MVZNMJkNaWhoTkEOhEGw2G1QqFQwGA6RSKcuEt9ls8Hg8MQFtEo34bDMAbG9Dj9Hr9ZDJZGy+oAOdEJtSG96PuTtJS0vDpZdein/+85/44IMPhMjcB4lEIjGVdoezL+UzVIXo3HchwQ84YK/U0NDAgpEymQwulysmkOl2u+H3+5klF3kwUzA0GAx2SJriK+ioIaBOp2ONislqkmw7DAYDgPbzF2XyktAszsPJgU+kograuro6JiJTkMJut2P//v0saEW+zGTHcihob0EfAFjFXX5+PgoKCuDxeNDW1sa8wjMyMtjPKpXKGDE81REnL4FggHCoCZAWQDrIS6VSmEymDouewWCA3++HzWZjh3x6fhIs6Wfoe/GLssgwTD4UNQXaMwPpwO/z+VgTDBLrqLRcIpGwBZA2YIlI9JrGe4DrdDpIJBK2oXM6nXC5XNi3bx9KSkoQiURYWT1lxSfa4Am6H/49LZVKmais1Wrhcrmg1WpZIKK0tBR79+5FVlYWnE4ndDody1Y9+eSTsW7dOrzwwgvYs2cPbrvtNiaUORwOPProo/jll1+gUCjwt7/9DVOmTMGbb76J1tZWZGZm4sYbb8SuXbsQDAaRmZmJsrIybN++HUB70zdqDqdSqRCJRJgwFN9MLBVFxf4IVUBEIhG2BlCDP8ooUqvVMBqNBxX+6RDIZ8Y7nU6EQiGWAQscECmDwSBCoRCbr2hc6PV6lpFP107lsfD+++9jyZIlWLJkSUzTTBJCyYvc7XZ3WmZK1kR9BdoDkCUSVSnQGiKRSFimj8vlimkkazQaWW+ArKwslmVG8xaVRXs8HpahTM9Jh0aa22jfQ9nxYl+S+pAf84wZM7r9ua+88kr885//xEcffYTnn39eBCpTHDp/kDVOZ/tEPqOZ5gogNqNZJpMxcZEsDshCTtB3iD9nWq1WeDweSCQSqFQqVFdXIxQKwel0wuFwIBAIoK2tjZ2DPR4PW5fIto/WHt42gyoyqbcRefjabDbo9XqYTCZ4PB7odDqYTCbYbDbWENBms7GeRm63m/WwEPQe4XCYBZjVajVCoRAaGxvZPoKsPcrLy1k1rdfrhc1mY+dot9vNhGp+fPABK7Lfop8H2gOaJpMJ4XAYVVVVsNlsKCsrg1KpZF7eWq2WVfzxCUCRSCSl97OAEJkFggEBLzDHH57C4TBcLleMmTwtkokmMKPRCLvdjmAwCLvdjrS0NADokEHE/0vfp6wzfnEWJAf+UE2bGvLhBtoXW7PZHNOgqaamJiaDTiaTMT9Ur9fLmmB0lbS0NBQVFWHQoEHM09vn86G6uhrFxcXw+XwdsuZJxEr1xbUvQ4ct2sjwZX8EiTMFBQWoqKhAKBRCXl4e6urqUFZWhl27dsHlcuGKK67Av//9b3z55ZdYs2YNTj/9dGi1Wnz55ZdoaWmBRqPBvHnzMHbsWJSXl2PDhg2QSCT4/e9/D61Wi/379wMARowYAQBsM0hCNxBbpUFl93zASwhGvQM/ZggS+ID29ztlFHWGx+NhmcgUkIhEIqisrATQ3imbnsPlcnWwXuLLGhUKRUw2eyqvNz/++CMWLlyIzz77jFkItbS0oKmpCcXFxRgzZgwWLVqEO+64A6tWrcL555+f7FvuNsjDm4TgQCDQQSiXSqUwGo2sOVM4HGaBblob4puWejyeDusVZZtR0JLGJnAg2Crmi9SnubkZW7ZsAQBMnz6925//tNNOQ2ZmJpqbm7F8+XKceeaZ3X4NwdHDV9PFJ9JIJBJWZcf7mR4KClRTdirNSSQmijmib8DvBagKBmjPaqY9q81mg9vtht1uh8PhgNfrZZZ91DeALC0oMEG2GnyWK2WmyuVyloilVCpZkNNsNrNxlJ6eDqfTyYLiNpuNWT9RPxQR6OwdyCYDOBCMaGpqYtZrNFYo2EBWgeFwGG63G263m/l6U2CKKiiAAwFtsimlPQrtY8m6paCgADKZDA6HA9u2bcPQoUNhNBrhcDjQ0tKC3NxcZi1KdoZ8k/VURYjMAsEAIFFUn/yq+EMYeUod7DBOTdlaWlrg9XqZyMyLfuFwOMbgnkrXlErlQQVvQe/AZ53zUXO32818uNPS0mK8lSsrK+Hz+SCRSGA2m5kPb3V1dYdutyRck88dWV4AYBkCXq+XLbBFRUUoKSlBUVERysvLmQcaZc5ShJ/PlBQic89C71vyZbbb7QiHw9BoNPB6vTAYDKzZX3FxMcrLy5Gbm4u6ujr4fD6UlpaivLwcDocDzz77LP7+97+juroan3zyCbtGQUEB5s6di9LSUgDAd999BwCYOHEiioqKAIDZIFDzNtpg8eViNKfQv3K5POaAIegdZDJZzAYbABOMSSCMh97PwWAQLpeLVVEYjUZ2wK+oqIDP54NcLkdeXh4AMJ9lp9PJsgwTrS1dtYlKNk888QQaGhpw9tlnAwDmzZuH8847DxKJBO+99x6Ki4tRVlaGiRMnYujQoZg8eXKS77h7oQx1ah4Z3wiQUCgUSEtLYw1JHQ4Hq7bgoabDQLt4rNPp2CGP9j6893d89YMgtaG1YvTo0cjKyur251coFLj00kvx6quvYtGiRUJkTiEosBgIBDqcbeRyORMFaT8LHMhWpPUpPhgKgJ1ZSBQi6x6FQgGNRgO1Ws2sdkR2c+rDr/0OhwNA+/u6qqqKJdS43W40NzezqkkSed1uN7xeLxOSKZjR1tbGRMV4VCoVLBYLs1GwWCywWq1MYKZqUJvNhvT0dDgcDha0sNlsyMjIYEIl7bXFmtSz8HOISqVCU1MTIpEICzLt2rWLZS9brVZmjUH2XKFQCC6Xi2XEH6xiXKPRsCq+5uZmAEBWVhY0Gg1qamqg0WiYnrJ9+3YMGjQICoUCwWAQjY2NyMnJYdnQlG2f6vsWITILBP0cmiCBA1l90WiUlZ4C7Qsv+RLS5o02W/GZgHxXeH5yI79LivRlZWWxzRiVGYlGOqkBjQeKihL0uvJdbQGwcneZTIbi4mJIpVJs27aNCYBKpRJGoxFutxttbW2wWq0Js0bS09NRVlaGcePGIRgMoqqqCnV1daisrIRCoUBhYSEyMzNRX18Pq9WKkpISlmVPzbDokCDKV3sW/m9Nft0k/ni9Xvb3dzqdLDjgdDpRVlaGvXv3YtiwYaitrcW+ffvgcDjwzjvvYO3atdi0aRMCgQBGjhyJGTNmsIxFn8/HMtNOOeUUdh/xFjxkx+FyuaBQKJhYRFnMfGALSH1xsT/Bl/KReMz73AGIyR7iPeF5dDodsrOzEQ6HsX//frS2tkIikaC0tJQFxTIzM9HU1ITKykoMGzYMAGLsdMgmgw+UpTL/+9//Onxt/fr1MZ/Pmzevt24nKXQ16ExZzWTL43a7O+wtqKKGGuvwz80fLEVmYt9k6dKlALrfj5nn4osvxquvvoovv/xSBLaTDJ1jyBqJh84pXq8XTU1NbO9JYjQJzvQ1yjbkm1ADBxpXU5IEZTRTfwfy19VoNDHZzZSlKMZH6sAn0lBDcaA96E2CoMfjQUtLC9xuN1pbW5llpMPhYGOMeiX88ssvrCpKo9HghBNOQHFxMWQyGaqrq/Hjjz/C7Xajvr4ekUiEWSyYzWbWtLapqQklJSUx1TN2ux1ZWVmsMicrK4tVcJLFhlibeoZ4mwyv1wu/388yzHfv3s3GUW1tLSKRCEuMCoVCsNvtcDqdLBnH6/WyOQRoH4Okn9Bel87YFosFCoUCjY2NrMmx0WiE1+uF0WiEwWBAZWUl0tPTmcUGCc3URJDu/1DVgclEnNIFgn4O3/xALpezJkm0ESOT+6amJjYB8lAkn36WzwAymUwxj7VYLKitrYXL5YJMJkN6ejrUajUrL4rvoMtv8AS9B5/BRfDepfGlynw2qVwuxy+//MJe4/z8fNTU1GDjxo0dMgm24760AAEAAElEQVR5L+VAIACr1Qqr1YodO3Zg2rRpGDZsGDQaDcrLy7Fv3z5kZGTAbDazrr0UmAgEAvB6vcwvi/yuxNjpOeK9v6j0Lz5DNBwOw2g0std5xIgR2Lt3L/bv348LLrgA//73v/Hpp59i1KhROOWUU2IEZJ6ff/6ZeS8PGjSIfT3+eiQuJcpgJS9gCqTxPy/oeeLtkajCgQ7gHo8HdXV1CYVlskwwGo3Q6/VwOByorq5mB7KSkpIYH+L8/HwW/KIsEApw0pzhdDphMBiYMCGEotSFypCBrlnc0PpC2YmJng84kJ3IQ+OPspUEfYtQKMSCMjNnzuyx60yePBkGgwFNTU3YuHEjTjzxxB67lqBzSJyJt+QjIdjpdDJ/VODA3NDU1MQyWPneH10JOJLArNFooNPpoFKpoNPpEAgEWJM2Enf4QLgIWqUGvGcyrf98fwiHwwG73c56zNB5gyqpysrKUFhYiEWLFrHzz/Dhw3HttddixowZHWwKPB4P3njjDbz11ltobGyE2WyGx+NBZmYmQqEQFAoF8+iNRCKw2WzIzc2F3+9nFTx+vx9erxd6vZ41saUeBYLuhbfJoObAjY2NAMAEXrL2rKmpQSgUQnNzM1wuFxwOB2w2G8tiJi/vQ80rUqmUJd9R0hTNIxRkMJlMiEQicLvdLMBhNBqh0+kQDAbR3NyM7OxsqNVqlqjBN0tONYTILBD0Y/jmbiTI8AKzSqXqkHVKkTi+QQZlIhNyuTzGG5NQq9XMx46uk5mZyUo+nE4nTCYTE4FERmpyoE1xvMUJ0L7g8l8PBoMxGc6//fYbXC4X5HI5TCYT1q5dyw752dnZrAETicFqtRo5OTkwm82orq7Gr7/+CqfTiS+//BJnnHEGCgsLYbVaYbPZsG/fPowcORJGoxE2mw0OhwNGo5F1d9bpdADAFv++spH3+/247LLL0NLSguuvvx433XRTsm/pkNA8wFvd8B2UQ6EQi6ZHIhEYDAbmLadWq+HxeFiJ/969e/Hqq6/irrvugtls7nCt2tpaLFmyBAAwdepU9rpStgAA9tpTAIT3I0vkxSgymZMDze28V7JSqWRicDQahVKphMViYRnytN643W44HA7s37+frTcKhQKDBw/u0OhOLpdj8ODB2LVrF6xWK9LT0xGNRuF0OpGeng6g/eBHzf5I9E51D7uBSHzwOlHFE78fIT9CGiOJGh2S1RI1qOUzwqRSKbPciV/vBKnPmjVrYLVaYbFYMGXKlB67jlKpxBlnnIHFixfjiy++ECJzEqAG0LTG84Gh1tZW2O129j2FQsF6xlCTLlpb6urqWHUNCXpUIRff/A8As+AhD3eVSgWTyQSn0wm9Xs/EZp1Ox+YWPpuR/HkFyYEC3AqFggUayLfb4/EgHA7D6XSyhm2RSIRZGFx55ZVoaWnBK6+8AqBdcP7zn/+MKVOmdHrm0Gq1+NOf/gSfz4cPPviAZcb6fD4oFAp4PB5oNBq43W6oVComItbX18Pj8SA9PZ3Zz5lMJmg0Gng8HgSDQdbQWtA90BiIRCKsAtvpdDLhl5LiALAMZnpt7HY7Wltb4fP52BkVOHAe4ZsWk3gNtGfQu1wuNDQ0AADz7tbr9VCr1eysSxnslN2ekZEBAMyehyxODQZDh/NZKtKjM6BcLseoUaMAAOPGjcObb77Zk5cTCAQcJA4DB2wRHA4HO/hLpVK2qFL5qUKhYJsu8v7hBUOyv6BFj4QE8mCmTZ5EImE+VzU1NSyaS6VrVP5BhzyxgPYenTVejLclIKgRoEajQWNjIxszwWAQGzduBACYzWbI5XLs3bsXP/30U8LrGo1GTJkyBTNnzsTKlSvR0NCAb7/9Fueccw6GDBmCn3/+GU1NTSgsLGQis8vlYoss36iQBIJUXVjj+eijj3DWWWfhlltuwVlnnYXf/e53fULsIqubYDAY0xwSADvAkfBDDUx8Ph+GDRuGLVu2YOvWrbjxxhuxYMEC1NXVYe7cuZg0aRJKS0vZxm7nzp346aefEAqFUFRUhKlTp7LnX758Ofx+PwwGAzIzMwEgxuKHRKlE3oqEmFuSg0QiiRGK6+vrEY1GodVqkZ+fz2wzqGyVLyEFDmR9ZGdnw+12Y//+/fB6vYhGo9BoNMjIyIDRaERxcTH2798Pq9UKg8GAaDQKq9XKMkJsNhssFktMQxbho5k6xAvMFBSgUlC+tD0RKpUKBoOhw9cpO8jr9TL/QvJDJMsfsv6iYIegb0AByfPPP7/HX7dzzz2Xicxz5szp0WsJOuL3+9legyohfT5fTEWMSqWC2WyG1+vFrl272FpisVgQiUSwadMmAO1NYylJAjiQhENnnvhKPDrTyGQyZtOl0+lYTxK9Xs9EIbPZzLINSRhXq9V9Zo/an+BtuuRyOfPmJu9csk/hz8j02p933nk44YQTcMEFFwAA/vCHP+CWW27p8jwzfvx4fPDBB3C73ax6y2QyIRgMxiRl0Vma7pe+F58URt8Xe5bug+xIgPbgAFV3A+3V2fv27QMAFoTy+/1wOp2sUs7r9aKhoSGmOWRdXV2Xrp2enh7zWre1tUGv1yMtLY1l06enp6O+vh55eXmwWq2Qy+WwWq0oLCxEIBCA3W6HXq+HXC5n95iq9OjqbDabsXnz5p68hEAgSAD5hQEHFjNeYI5EIiwCRxmCtbW1MdnKBJUxk8E8LeB0AIyHzO3T0tKYt1VDQwOMRiMrUaEFU2Qz9z58SSEvwCXa3Pj9flitVgDtGSR79+5lpTxWq5UJNr/++mtMNkh2djYsFguUSiXcbjeqqqrgcDjwxRdfYNu2bbjwwguxdu1aNDU1YdmyZTj33HORk5ODhoYG7NmzB8cff3yMjxUf0JDL5UxkpnLJVGfjxo2YPXs2pFIpRo8ejZ07d2LMmDExj4mvFqD3ZzKRy+XskEfjhS/5IzE3HA4zz123240xY8Zgy5Yt2LlzJ4477jjce++9ePPNN1FVVYVvv/0W3377bYdrjRo1Ctdffz0UCgVaWlqwbNkyVFVVAQBmzJjBNuyUCZCeno7q6moAiMk+ovEBdK3sXtB9kMclcMAPE2jf1JN/ZWZmJiorK9HS0tLh56k3gEKhQCAQQF1dHbZt29bpJtpisWDYsGFMaHY6nVCr1Wy90+l0bK3T6XQIh8PMtkmMi+QTLzBrNBqEQqGYvQoPCT8k/qhUKhYY9/l8bJyRF6JSqYRarWbNBFtbW6HRaFiJKnWM9/v9CIfDnTYbFKQO0WiUicwXXnhhj1+PGnFu2LABTU1NPdJkUNA5JBbyFSkkMCsUClYpuXPnTmZ1oFQqUVZWBrfbzRpEjho1Cj/88APLFJXL5fjtt98SzjO84EzNhtVqNctapoqbzMxM+Hw+1tjN4/EgNzeXJdT4fD62JxH0HnxCCq0HEokEgUCAfY9s4Gh/29TUBKD9/f7zzz8jGo1i5MiRuP322w/r2tRXxGg0wuVyMUGRqm00Gg2r/KMKUZVK1aG6NN4aRtA9BINB9p7XaDSQyWTM6oIsTfx+P0vCowz3cDgMq9XKfJmDwSDMZjN+/vlnAO1713HjxmHEiBHIzs5mQW673Y7q6mrs3LkTO3fuZOdpoD3opdfr4XK54PF4WFNruVyOtLQ0tLa2siQLlUoFm80GnU7H9Bfe0jBVETOfQNAPobIgoH3yI+8egiL5ZrMZTU1N7HMqHZFKpSyqRp12qXFGImQyGRMYvF4vvF4vGhsbmZ9ZOByGw+GASqWCUqmEy+WCXq9n9yrEoN6jMxsBPusQaBcOq6ur2eJbUVHBOjJT9qHNZmNjLS8vD1lZWTAYDFAoFDAYDMjNzUVOTg5CoRA2bdqE5cuXo7KyEm+99RYuvvhi+P1+2O12LFu2DKeddhrz0GtqaoJOp2MeeuRZ5vP5WNYaZdj3BU9NKrEEwH6veObPn4+5c+f29q0dFApQkacdiX/0fqcxFAqFmJ1BW1sbjjvuOIwaNQrbtm3Dxx9/jAkTJuC2227D/v378euvv6Ku7v9j783DoyzP/v1z9i0zkz1ACIQdURBQEQQUFahr3drXurUub6vWlm9rW6u1ttZqtWpbpa0WbW2t2lqXSq19XXDBIuICyCIgBAjZyZ7MviXz+yO/6+aZEBQ1ZOM+j2MOksnkeW4y19zLtXyuWuLxOG63m5KSEo455hjGjBlDZWUlK1asUA03bDYbp512GpMmTSIQCLB69Wo6OjrIy8tTUXxpUCo6iHa7vceAiebQY2woKgcoq9WqDv8+n4/t27erg57H48Hv96s1obm5mV27du138LfZbCpTzGw2EwwGaWpqoqWlhTVr1jBy5EhKS0upqKhQWSpSkup2u1UXbovFopzfOsOs/+ne2T0YDGYE2kTKorscjhwEJUO5p2A3oPYsUmpqMpnU/sTlcqkDv8xvIu+kD/UDl02bNrFnzx5cLheLFy8+5PcbNmwY06dPZ8OGDbz66qtcfPHFh/yemgMjUgdWq5VRo0ZhMpn44IMPaG9vx2w2M2rUKEaOHMnWrVtVpd3EiRMpLy8nEAiQm5uL1WpVCXDZ2dm4XC4CgYBqKByLxTI03a1Wq9JAdbvdRKNR/H4/iUSC3NxcUqmUcjRXV1czcuRIYJ/UXFZW1qA83wxGmTfYtw+RfQXsO+/Iv8b3Q869oVCIhoYGJk+eDMCWLVt49NFH+epXv3pQ719VVRV///vfga6MWAloRKNRtd8QyYy8vDzVjNDr9ap1T15ndDr3t+0cyA4Gm31I1jHsk90xJtxlZ2crJ7DNZlMVVLJPSSaTmM1mQqEQhYWFan756le/yg9/+EOloXwggsEgq1ev5qWXXuKFF16gqamJ1tZWTjvtNHbs2EF9fT1jxoyhra1NOZ9FXtTr9RIMBsnJyVHa3XLmGsg9ij71Tqqzs5PJkyfzwx/+MOP5l19+GbvdztNPP62eCwQCHHPMMcybN48333zz849Wo9F8IsYGbtIAScrHrFarmlBFD0oauI0aNYoZM2Zw5JFHqqYHxcXF5OXlqcYXxqZakk0qUXuLxUJWVhZ5eXlkZ2erA11TUxPRaFRF36TkWUqYABVt1hx6ZANmLL8yRnedTiednZ1UVVWpjU9TU5Nq3CdNDxobG1UmWDAY5J133uH555/niSee4C9/+Qu//e1v+dGPfsT111/Ps88+y9ixY7n66qspKCggFArxxBNPUFpaisfjob29nZUrV6rN+c6dO1WGfUtLi8oCEJuRjZjRSTGQycrKUg6PcDi8n74swE033UR7e7t6SJZuf2MMOsjfXTbqYkPRaJRhw4YB0NDQQDAY5Atf+ALjxo0jlUrx1ltv8Yc//IG3334bu93OkUceyfHHH8/UqVNxuVysWbOG+++/n6eeeort27eTTqeZOHEiV111FSNHjmTt2rW8/PLLBAIBXC4Xs2bNoqKiAkBJ98iY7Ha7mku0k7nvMGYuS4kq7Gs8a7VaqaurI5FI4HA4OOKIIyguLmbv3r289957bNmyRZUgWiwWsrOzyc/PV1URksXm8/k45phjOPXUUxk9ejQA1dXVbNy4kWHDhmG1WlUWmWS4dnZ2EgqFlI0MlnljKCN7B+iaY9rb2zOqJpLJJO3t7bS2ttLW1rbfo729XTXdkWv4fD7y8vLUHkQy1pPJJJFIRO1D5LAp2origO6eWa0ZeEgW8+LFi1X1zKFm0aJFAKxYsaJP7qfZh6zhxoxU2FeJJw3cLBYLxxxzDCUlJbzxxhusXbuWdDrN2LFjqaurY8uWLSpovmHDBqxWK+edd55qZL1r1y4aGxvV+UQQJ1MgEKCpqUn929bWRkNDAy0tLaqnSCQSIZVKUVdXp6oijFWlgw2ReVu1ahVPP/30fpJWAxHR3YZ9yU/ytfFf6QUhc0hpaSkAy5YtIz8/nyuvvBKAe++9l69//evs2rXrY+9bVVXF1VdfTSQSYerUqUo+QdY4GYfYc05Ojvp7ut3ujP4VcGD5wv7gQHYw2OxDPodms1mdKeXzbrVacbvdyvHf/Qwh5yD5vXHjxhGLxSgqKuKuu+76RAczdAUTTjvtNO677z7Wrl3L6aefruQ4pkyZQjKZpKioKOM+3Rufix1JtrUwUP0nn/oEZjabuemmm/j2t7/NjTfeSE5ODhs3buTLX/4yv/jFL/jyl7+sXrtnzx5GjBjBhx9+yJlnnsnmzZt7PFxrNJrewXjQl6wtieQ6nU5Vopydna20hWw2G5MnT8bpdFJbW8v27dt7LGX+NFitVoYPH052djbhcFg12hD9I9HmjEajSpt2sGSlGhmMkdzumy7Yl/ElWVxVVVXKoRsIBIhGo6rxQXNzs8psN2aeWa1WRo4cqRq7tbW1UVNTQ1tbGy+//DKvvvoqJ598MhdffDH/+c9/2LlzJ88//zwLFiygrq6OlpYWtm/fTl5eHpFIhJqaGpxOJ6lUimAwiNVqVY3g/H6/cjpJ9+WBGMUVZs6cyX//+1+OPPJINmzYwB133LHfaxwOR48NrPob0XMX3fXuP4OuCH1xcbGSPHnrrbeYPXs2X/rSl9i2bRvr16+npqZGZQsdCI/Hw6RJkzjiiCOIRqO8//77KgsWYPjw4Rx99NFUVVURi8VUSbwcHo06zbr0vW+R7u0mk0k58kwmk1p/JOPU4XAwceJEysrKVKAA9sk2tbe3U1dXpzJOesJutzNlyhSOPfZYRo4cyYYNGwiHw2zcuJEJEyYQDodJpVLEYjHlZBTpHpfLpQ7+g0EXfShidLxYLBbVdEeeN2ayy+faGOA2PqQRYCwWo7W1NcOhIwdH6S0h64Vc12q1qgoZY1PZWCymM5oHKH0plSEsWrSIe+65hxUrVgzYjLGhilQmSTKLBJWTySShUEjtP7OyssjKyuKNN96gqqoKi8VCaWkpa9euVRV3os+cSqXIz8/nD3/4g6qYmjt3LtOmTaOkpISioiK8Xq9yGK9bt47nn39eBbxyc3NpaWnBbDbT3Nys5Hukqgq6EjPEkRiPx7HZbINuPhmsMm+CSJ3AvvNO9wQJh8OhqjJzc3PZu3cvN910EzfeeCOFhYX85je/4d133+WCCy7g7LPP5rLLLmPixInqHu3t7TzzzDM8/PDDhMNhRowYwfjx49myZQuTJ08mFAopqUmjfRj3Hj31EDFm0vc3B7KDg7EPGBg2YtxzGBspyrhE1iQSiQD7V/yKb8JYAWexWKivr+fuu+/mW9/6ltpDHAzBYFBJtHg8HrXflftIskT391+CD8lkUiXXyB5oINhKdz5Tms8ll1zCz372M5YuXcpVV13FmWeeyWWXXcb3v//9jNeJvshRRx3FlClT2LFjB8cee+znH7VGo+kRY6dkyQ4CMhrd2O127Ha7ct6MHz8eh8PB2rVrKS8vV9cSvUNZIERg3ngP6cAsOodS8ppIJKiqqqKqqor8/HzVeCkcDtPR0aE657rdbuLxuHImGhfhwcBga+hmLGc36unKwup2u6mrqyMYDKoMZXEwS4m6bMiMGfGFhYXKiZRMJpk4cSKzZs0iLy+PLVu2sGLFCrZu3cqrr77K+vXr+cY3vkFOTg7vv/8+K1euZM6cOTQ2NlJfX68y5ltaWlRmc3NzMyNGjCCVSqnSMwlSyOZhIDpohQsvvJCLLrqIP//5z1xxxRUD2kZ6wqhzbMwalo1ae3s7sViMY489ltdee41AIMArr7yCz+cjOzubI488kiOPPJJwOKzmEjms22w2pZEqXZzffvttdW+TyaQ27ul0mrKyMrWhkkZA4lCWjaKxqYrm0GPcwBu7ucO+zXooFMJkMlFaWsq6detUQ9H8/HyCweB++pgmkwm/34/P51MyGZFIhIaGBsLhMBs2bGDbtm0sWLCA+fPns3nzZmpqatixYwejRo1SvQPE0SyHPAl0SibtYFpvhgrGgIRknEt2sWi+S0JKMBhUzmP5vU9zH6Mtit437KvesVqtSlJMSmjF0TzQg5eHG3v27GHDhg2YzWbOOuusPrvvvHnzcDqd1NTUsG3bNqZMmdJn9z7csVgsOBwO4vG4+kyKXmljYyM5OTlAl3OoubmZ3bt3YzKZGDVqFK+//rpqNi57WXH6iq5zaWkp8+bNY/LkyUybNi2jiajdbmfcuHGMGzeO008/nZtuuony8nIVSI1EIpjNZrKysmhtbcXtdtPa2kp+fj6BQACn06n2TpFIBI/HM6jmk8Eq8ybIeRe61gKRIMjLy6O5uZnc3FzVMDgYDKr3p7GxkR/96Ed861vf4rnnnuPee+/l9ddfZ/ny5SxfvpwRI0YwYsQIgsEgO3fuVHvjGTNmcPbZZ/Pkk09isVg4+uijWb16NaWlpYRCISUXCV3OTbHrUCik1iVxenZ3NvcnB7KDg7EPGBg2ImdfY+ABMp35sg8BMqrBYV9QQH73vffe4+KLL+axxx7jvvvu46GHHmL+/PnMmTOHY489liOPPHK/c14wGOTtt9/mX//6Fy+88ALJZBKv14vZbKa8vByPx6P8MiJTKPfvntFudIIbk8cGGp9pd221WvnhD3/Ij370I5599llmzpzJ0qVLM14jE67D4aC6upqtW7cyduzYXhm0RtMXDLYsVTk4wz49IaMEgmQn5+fnU11drb7Oyspi48aNysE8ceJEbDab0r77tJPXiBEjmDhxIhaLhaqqKpqammhubqa0tFRlEhkzmV0ul9LMlOjcQCgROhgGU6TfmOVudMCJg9lmsxGNRlUTBNGmkwxmcTBLJrNs4N5+++0DNuaaOXMml1xyCd///vfZunUrf/3rX2loaODee+/lm9/8JieffDJvvPEGa9as4eSTT6a8vJzKykomT55MLBajpqaGkpISYrEYDQ0NFBUVEYlEaG9vV8GSeDyusuAHYiQXuj5/zz33XH8P4zMjDQChK/hk7HbtdruJRCLs2bOHSZMmsWjRIjZs2EBVVdUnZi4LxmYYQl5eHiUlJerAVl1dnbHxczqdWCwWlelo7NztcDgG1WEOBt96Y8ToNDSbzcrh3P3f/Px8tmzZQmtrKzabjdzcXNatW6eCDuLwFceirEmi3T1jxgxOO+00amtrefvtt2lsbOTll19m2rRpzJ8/H5/Px7Zt26isrGTkyJFKR09KY61WK6FQCJ/Pl9E8ZbDZymDGuA7BPukS2Rc4nU5cLhfV1dU9NiIWJCDtcDhwOp0q81gqL+x2u1rHWltbiUQi6hBstVqVQ0GCDSKzI3JMxoxmbR8Dg3/9618AzJ8/n/z8/D67r8vlYv78+axYsYIVK1ZoJ3MfI59l0Tj2+XwEg0GSySSJREL1kJH1Ijc3lzfffJNEIkFbWxvBYJD29nbC4bDScRc2bNig9JntdjvnnnsuS5Ys2S8r0Si3NG7cOMrLy1Vigzi/W1tblSRhTk4OjY2NjBgxApPJRGdnp0qsGSzzycHKvF1//fXq+0AgQElJSZ+NsTvG7M7Ozs6MStm8vDxCoRBer1f5qIxOXWkQnJOTw86dO/nVr37FhRdeyH333cemTZt49NFHeeONN6itrVVyGACTJ0/m0ksvxe/38/vf/x6Aiy66SJ25x40bx8aNG4nH4+Tk5FBXV8fevXsZPnw49fX1NDU1qb+ZMdAKHPBs1ZccyA4Oxj5gYNiIsYLX+PmTc4yxjxXsy2AWORXxS7S1tXHcccfx/vvvs3btWi666CLWrFnDnj17ePnll3n55ZfVdUtLSykoKCCdTlNfX09FRUXGPSZMmIDD4eDDDz/EarUyYcIEIpEIeXl5hMPhHscpvWjk+4E+l3zmFI5LLrmE73znO6TTaf7+97/v5xTatm0bV199tcoeuf/++w9Ks0SjGSgcTJbqQHEgQmYWs5SgQpeDSxyJEllva2sDupqaNDQ0sGPHDgCOOeYYNmzYwEcffaSuKQ/JQJSMY4fDgc/nIzc3l9zcXGKxGHv37lULcG5uLieeeCKtra3U1tZSXl7OiBEjVHa0zA3xeFxtDKSxmDRpGugMpki/MVggf1vRxoYux1xNTU1G88aWlhYlkWF0MKdSKRU8ADj66KM59thjycrKorm5mU2bNrF582bWr1/P+vXrmTt3Lrfeeis/+9nPePjhh1m/fj3Lli3j9ttvJ5lM8tZbb7F69Wq+8IUvsGnTJsrKyjjyyCMJBAI0NzeTk5NDIpEgGAyqjWEgECAvL0+VQQ7UcqGhgDh0xJkfi8VUho5USbS3t7Njxw5Gjx7NnDlzmDlzJk1NTUpSRUpepcGW/CsbaofDgdfrJTc3l5ycHEKhEI2NjezcuVONw2az4XA4Mpr8iZPZqJ02GLNTB9t6Y0SCm7IBhkwZBJEokDlEHL5r1qxRzunW1tYD/n8ikQjbt29n+/btvP7661x66aVceOGFvPfee7z33nts2rSJ9vZ2zjjjDKxWK5s3b6a6upqSkhISiQTxeFyVH5pMJjo6OpT0glTSaPoGaRAM+7K2RF5FyopF/9JisZCbm4vX61Wfe6mwaW9vJxgMsnfvXhUQ7Y7dbic/P5/hw4dTXFysqnFEdkka6lgsloy+EOKoFj1WLb0zMJBA7Xnnndfn9168eLFyMv+///f/+vz+vc1gCmqaTCacTqdKpInFYhQUFFBbW0sgEMDv99Pa2qqqY1paWlTDcgkytbS0ZKydxcXFjB49Gq/XSywWY/fu3VRVVfHUU0+xadMmli1bhs/nIxQK8cc//pHXXnuNzs5OPB4Ps2fPpry8XPUvaW9vV6+V6gebzUZWVhZ1dXUMHz5c7XcksWYwzCeDVebN6GS22WyqN5HP51MZq2PHjmXnzp0UFBRkNApsamqis7OTBQsWsHLlSv7xj3/Q2NjI1Vdfza9//WvC4TBbt26lqakJt9vNxIkTycvL429/+xt//etfATjllFM499xz+elPfwp0VQvv2LEjowFgbW0tkyZNor6+nubmZsaOHavGLfIqwIDIUD2QHRyMfcDAsBH5vMn+Q76XvV8sFss4P8p4JXgdCAQ47rjjeOedd4jH4xxzzDGsW7eOtWvXkpeXx2mnnYbNZuOjjz5i/fr1qol1dy3v4cOHU1hYqHwF8XicwsJC/H6/CkLl5ubS0dGhHPEy9xkT8CQQbqxOHoh85pPYt771LaBLd6gnZ9AJJ5zA5s2bP/vINJp+5mCyVAeKA9E42YiDRTZUTqeTvXv3Al3C8+IYlGZ+0pRz9OjRrFq1SjkaLRYLFRUVB4ykRqNR2traqKysBGDs2LGcc845tLe3895779HS0sLy5cuZNWsWRx99NBs3bqS2tlZ1hI5Go2qs8q90kxf5g4E6cQqDKdJvPEDLAitZZKKdKiXmUl4YDAZpa2sjHA6rfzs6OqitrVU6vD/5yU961OGqr6/nH//4B0899RSrV6/mO9/5Dg8//DDf/OY3ufvuu9mxYwevvvoq//M//8Pu3bupra0lHA5TVFREfX09iURCNRAbMWKEar6Sl5ennG0dHR3KGTAQNmNDGamOgK7Pq8w5HR0dSqNQmnK53W61OZNyVylDl42S1WpVTmLRj5fggcxX0GWvknUotisZzFLqLlnMIp0xGBlM640RCfBAl41IQNP4c+jKCJFMM7PZzJYtW1Q2cWNjo3rNUUcdpRq3jRo1SpUif/TRR6xatYqGhgZ+97vf8Y1vfIPZs2dTUFDAyy+/TEVFBf/6178455xziMVilJWVUVdXR0FBgXIWit1IyWwsFlMVP1pepW8wlq1KgFACEy6XS9lIQUEBI0eOxGKxEI/HqaiooLq6mtbW1gOWEEugWuRbEomECnqbTCaGDRvGqFGjcLlcNDY2Eg6HVVm70+lUQQiRVZHxSkBL0380NTWxatUqAM4555w+v780/1u5cqUKPAxmBpvUm8hhSRWdxWLB6XSqagNAVUi0t7crOQtj7xCn08kVV1zB2WefzfDhw/e7x3vvvcdNN93ERx99xPe+9z0eeOABfv3rX/Puu+8CXU61q6++mldffRXoahZntVrZvn07drudjo4Ompub1VpisVhwu93U19czfPhwlaQzWCokBqvMW3epCXEyd3Z2kpubq/SyRSbjqKOOYv369dhsNkaMGEFtbS3Nzc1cccUVPProo7z++us0NTXxne98B7/fz3HHHaeuv27dOn7xi1+ozOYvfvGLXHbZZSoQajKZGDlyJKWlpWzbto29e/dm7JdFFiEcDuNwOIhGoySTSeXklOqa/rSV7nbw29/+ljPPPHNQ2Yf4GKSiwOVyqeQWce7LvJ5IJNSZorW1lSOOOIItW7aQTqcZM2YM5eXlJBIJzjzzTNavX09dXR2vvfYaJpOJGTNmcMstt5CdnU1TUxMNDQ00NTXR2NhIZWUlkUhEyewMGzYMv9+vzuBFRUVkZWXR0dFBTk6Omkfy8/MxmUxkZ2erYLrX683Ivh6oiTWfaVS33HIL//nPf3jnnXdYuHAhf/rTn7juuut6e2waTb9yMFmqA8WBaMwOkowt6Dp0GfUy3W43e/bsAbomrl27dhEMBrHZbFRVVVFTUwOgGlyk02k8Ho9yPCaTSdxuNyNHjmTq1Knk5eWxfft2PvjgA3bv3s3999/PV77yFa699lrefPNN1q5dy3vvvUc8HufYY49l7dq1VFZWMmnSJOLxuHI0S9myNOAx6uwO5I3YYIz0Gw/oEoiw2+3U19fT2dlJIBAglUrR1tZGLBYjEAgQiUQIBALq58FgkMmTJ7N06VLV6K87RUVFLFmyhNNPP51rr72WLVu2sGrVKk4++WSOO+44duzYQUNDA2azmSOPPJLa2lrq6+uZNWsW9fX17N27l2nTplFZWanK2KSJpGTVplIpFYgYCNplQxkJAHV2duJyuVSJeSwWo7Ozk/z8fGKxGMFgUG2kPg9ZWVkZXbdlDMYmKk6nUzX1ku8H8nzxcQym9caIOJhFqkDWGtkAy/eynphMJnbs2EEymVSBK5vNRmlpKeXl5Tz22GPKEQldwcsvfelLnHrqqZxwwgn89a9/ZceOHfzpT3/i+uuvZ9y4cZx33nn861//ora2lpdeeokzzzyTxsZG1fBJMlXtdrtyGkq2UDKZVOvPQA9qDqbswwMh9iLztXzvcrnUQb2goIBRo0YBUFZWpgISgsvlwuPxkEgkiMVi6mAuchs5OTmMHz+evLw8WlpaqK6uVg0l6+rqcLlcTJo0idLSUtVEVCS8JBghZfkShJDse03/8MILL9DZ2cn06dMpLS3t8/tPnTqVwsJCGhoaWLNmDSeddFKfj6E3GUxSb4JUUMrn3efzZWQgylxiTDpIpVJqDfr+97/PBRdccMDrz5o1iwcffJArr7yStWvX8otf/IJt27YB8N3vfpeFCxeSSqWU0/mEE07Abrezfft2ysvLmTFjBs3NzbS0tFBUVERLS4sKRjQ2NjJs2DA1dgmQD2QGu8ybIImQHR0d5OXl0dTURCAQYObMmbz55pu0tLRw3HHH8d5776nql6amJurq6rjhhhv4zW9+w6ZNm1iyZAkLFixgxIgRNDc38/7776skq+zsbK655hrlgN64cSPQlTHvdDoZP34827Zto6KigmOPPZaWlhaam5vJyspSsi/CQDvLfJwdDBb7MJvNuFwuotGoqnJwu91YLBY8Hg/BYJBwOExWVhYtLS2qQjIajXL00Ueze/duJX/j8/nYuHEjZWVljBkzhsWLF2dU7q5fv/6A4/B6vYwZM0btK8THMnbsWMLhMOl0moKCAnw+HyaTiYKCArW3drvddHR0qKouaRY4kM89n3pH/cc//pFf/epX/Pvf/+boo4/mO9/5DnfffXfGQVCjGQocTJaqSEYYH/2BbK5Eb8jYhEn+Dy6Xi7a2NnWgdrvdfPjhh0CX83n79u2kUin27t1LW1sbdrud9vZ23n33XdatW8dHH33Erl272Lx5My+++CJ33303jz/+OGeffTa//OUvmTVrFp2dnfztb3/j7bffZvHixZxzzjmYTCY2btxIIBBgzJgxQFdWitFRKOXzorsG+2s3DkQuvPBCVqxYwaxZs7jgggsGdCTXKJHRvbmBBCkkK1EchxI1FXsqKSmhtraWnJwcfvOb3xzQwWxkwoQJHH300QDs3buXZDKpMpJKSkro7OxUJUU+ny9DQ02yySSrWcZs3IR17wKsOXQYs7dEE1lKP6WJWkFBAQUFBeTn56tHQUEBhYWFFBUVMXz4cNU4pbCwkJycHNUgsLCwkNLSUiZMmIDL5crQEPf7/eqeZrMZv9+f4WCWzITBymBab4wY1x5jY9hoNKrm9o6ODlpbW0mlUlRUVJBKpWhpaSEcDuP1egmHwyxfvpyNGzeqz700dNq9ezd33303d911F3a7nSuvvJJRo0YRjUZ5/PHH6ezsZPjw4ZxzzjlYLBbKy8vZvHkzRx55JNCl9y3SGMaNvTTbEZsxHvIGKpJ9uGrVKp5++ukeJSIGMsZguDiNjZVSImsiDV/Xr1+vbMLv9zNjxgyOPvpoYrEY77//vpL2qqiooLa2lrq6OsrLy1m/fj0vvPACf/vb36ipqWH27NksWrSIiRMnKifVhg0b+PDDDxk3bpwKdMt8IzYi45GxDbTD/+HE8uXLATj33HP75f5ms1llM69YsaJfxtCbHKzUm9/vV4/+DmjCvoy9dDqd0YdBAtCwT94L9knfALz55psZwaqemDhxInfeeSdms5nly5era7788ssqG1bmic7OTkpLS5kyZQqdnZ3U19cr+Q1ZT1pbW1WD00gkohJO9Hxy6JC/qzjejEEIv98PdElw5eTkqEonn89Hfn4+iUSCKVOmYLFY+PDDDwmFQtx5552MHj2aUCjECy+8wEMPPcSzzz5LZWUlDoeDc845h6VLl3LcccfR2dnJhg0beOGFF4CuwAV0Jd1Al5SL6H1L01sZqzHpR1dm9j42m001kU6n00QiETo7O9U5UwLL0KXBPn78eKAr0H3uueficDioq6sjmUxy+umn4/f7aWpqYs2aNfh8PpYsWcJll13GnDlzGDVqFIWFhYwaNYqpU6cyZ84cjjvuOIYNG6Yq6CQrvqSkhEgkgslkYuLEifj9fkwmE3l5eep8VVhYqGTeRJ4UGPDBqk91InvxxRe57rrrePzxx5k9ezYA3/72twkEAjz22GOHZIAaTX8hWarpdJoNGzYwadKk/h7SATGK2sO+A5yUBkOXI1kylYcNG6YOby6Xi40bN6os1UgkgtVqZePGjVRVVeHxeDjnnHP48Y9/zD333MNPfvITzj33XFwuF5s3b+aaa66ho6ODr3/966rj95NPPklFRQVHHnmkyvh48803OeKIIzCZTDQ3N6vJXBZZOdhJpqr8bCAvthLhff/99/nmN7/Z38P5WIx6TkZHPuyzl+7fizxGe3s7sM++zjvvPPLy8j7xnul0mhdeeIG33noLgEmTJnH//fdTWVmJx+Ph+OOP58knn2T37t1YLBZmzpyp9MDz8vKUdriUSQJKC1i+HuiaVEMJcfCKU1caqnk8HnXgMwaO5CHOPWPmYTQaxWazUVhYyIQJE5g4cSLDhg0jmUwq3VSr1UpRURE+n09tsBwOBwUFBcpxOBQczDC41hsjRiez0ekph3FxOEPXxj2ZTBIIBIhGo3g8HrZt20ZZWRkul4uLL76YRx55hH/+8588+eSTPP3001x++eVYrVZWrlzJH/7wB+x2O1/72tdwOp1UVFSouWX48OHMmzcPgHfffZesrCxyc3MzSk2TyaRygkswTQ79EuQcyKxbt4558+ZlZB92RyRnjI+BgjEgKM0iZf6Wyoe8vDwsFgu7du2ivLwck8nE9OnTOemkk9i+fTvPPfccu3btUhk/06dP58QTT+SMM87gvPPO47TTTuOYY44hOzubZDLJ+vXreeihh9i4cSNHHHEEZ5xxBtOnT8dqtdLU1MQHH3zA6NGjAVQpvtiC7EWM9qPpeyKRCK+88grQf05m2CeZIWMZzBys1Ft7e7t6VFVV9fUw90PkbGBf9YzJZMLv9ysnktvtxmq1qooHcaavWrWKa6+9loqKio+9x/z581XF0JYtWzCZTGzdupVf/vKXWCwW5syZA8DSpUvZuXMnZ555JjabjerqajWX1NfXq+CV7E0keQcys601vUt3J7OcJ6WniNfrBboC0NLEs7y8nPnz5wNdge2FCxcCXc1Ga2pquPvuu7nhhhtYvHgxs2bN4tRTT+Xaa6/loYce4sILL6SqqornnnuO22+/nT//+c/EYjHGjx/P3Llzgcy1T5zJVqtVrXvSHFIkHGStMa4/ms+PZC6Lozkej6s9YEdHh7KNUCjEhAkT8Hg8RCIRdu3axUUXXURhYSGxWIzt27czdepUTj75ZFWF9dJLL/Huu+/S3t5OXl4e+fn5OJ1O4vE4zc3NtLe3YzabKS0t5fjjj6e4uJjm5maSyST5+fkcc8wxyk6GDx+uZAILCwuVTefm5qp9tWRnD2T7OOhT2bp16/jyl7/M3Xffzfnnn6+e9/l8fPvb3+auu+4aEF0wNZreYrBkqYpmE+xztBmdhhJRl67K0nhPSlObmpqUA0hKmjdt2kQqlWLBggU899xz/OhHP+Lss8/mxBNP5Mwzz+Smm27iH//4B6NGjaK+vp7f//73mEwmzjnnHI455hg6Ojp45plnADj++ONxuVyqgZw4J2W+kG66RukDKU81vk7z+ZGIpxyijdnNPf0Lmd1rjZv1+vr6A94nmUyyevVqlixZwm233UY6nebEE0/kiSeeYMuWLdjtdk466SQeffRRduzYgcVi4bzzzmPXrl3s3r0bk8lEaWmp0uZ1OBzK6WiUfjHqew6GRpGDHaM2aUdHB9nZ2Rn6gz6fT2U9Saat3+8nJyeHnJwcsrOzycrKUr8TiURoaGhg9+7dlJWVUVVVpSL6ubm5lJaWqo0ggN/vV00xZDMm2s+DncGy3hgxZqaKdi6QUZ0ieoOxWExlLwcCAaxWK3v27KG9vZ1x48axbNkyvva1r1FcXKzmHJ/Px0UXXcSPf/xjAP7973+zdetWcnJyVEDz5ZdfVk7sqVOnqhLq3bt3U1xcDOxzYIpjU2wnEolgsVj2C84OVAZr9qFgzNoyfoYtFotyePn9fhKJhKqymjZtGiUlJTz99NPquaOPPprTTz8di8XC66+/zl//+lceeOABHn30UXbu3Mnxxx/P17/+df7nf/6HkSNH0tHRwTvvvMOjjz5Ke3s748ePZ8GCBVgsFqXN7Ha7e6yQSSaTas4b6PYxVHnllVeIRqOUlpYybdq0fhuHOJ7Wrl1LS0tLv42jNziYoOZArJwxIg32AFVeLnuS7OxsfD4fXq9X7RtMJhNr167l/PPP58Ybb+Sdd945YODo4osv5qyzzlJ6qVarlXfeeYc//elPXHrppUyePJl4PM7vfvc7zGazkhqpr6/H4/EoWTHYF5ySTGhZb7ST+dAi+wiZvyV7vLCwEOiqrCwqKiI7O5tUKkU0GmXChAl0dnbS2NjICSecQDqd5h//+Ae/+93vCIfDzJo1i4ULFzJ58mSampp48MEHufHGG3nwwQdZuXIlzc3NOBwOFi9ezDXXXKMc3CJTWVBQoPoiSSWe0+lUexhxchqrkTW9iySmAKoPg7HyQfbe4XCYefPmYbfbaWlpYe3atSxatIgTTzwRq9VKbW0tFRUVHH/88Zx00kmMGTMGi8Wi5EVFmi07O1tVPEyYMAGz2UxjYyPxeBy/38/xxx/PsGHDlIb32LFjsdlsmEwmpccMqMaVMie53e4B7WCGT6HJfMwxx/S4oQW47bbbuO2223ptUBrNQGCw6lEZnc7G5n/iFCwsLOS9994Dug6qZWVlpFIpamtr6ejoYO/evXR0dLBo0SJuu+22AzpwioqK+MlPfsL//u//8sorr/CDH/wAp9PJ//zP/7Bu3Tq2bdtGMBjE6/VSXFzMzp07aW5uxuv1qkxFyUaUpmLyvRzsRL5BL7S9gzhUOjo6VARXdKC6vw72OXKzs7Npa2ujuroar9dLWVkZ5513HscffzyTJk1S2pVNTU2Ul5fz4YcfKqeB2Wxm7NixNDU1YTKZyMrKIj8/ny1btgBdEdvp06ezfft2lXU3ffp0FQTJzc1V1/J6vXR0dKgIrhz6B7p291DCarUqG0qlUuTl5REKhVQWoBFp1icN+UQDDbo20aFQSDXlkdd7vV7y8vLUnCAOwtzcXNXNXQ5tIp8xFBiM6013qRo5xBmlB+QzKrq38n56vV42btxIUVERd91118c6MObMmcPixYt55ZVXeOaZZ/jJT37C7NmzVbDr3XffZcGCBZjNZsaPH8/69eupra1V+ojBYBCfz6caL4lklHwv1RGiMz5QGUyNZnuie8DY6GSWQ7XH42HHjh2qnHTcuHE899xz1NTU4HQ6Oeuss3j//ff517/+td/1W1paePXVV1m7di3f/va3KS0tZfTo0ezcuZMVK1bQ0tLCU089xRVXXEF2djbjx49n+/bt1NTUUFxcrMpnJcsJuux3IDVhOhwxSmX059++uLiYI488ki1btvD666/zpS99qd/G8nkZTE27DkQ6ncZutxOPx9Xewm63qwoWCSqm02lVVi6SgS+//DIvv/wybrebY489lmnTpjFhwgSKi4tVM6758+fzwgsvUF1dzb333suvf/1rnn/+eS644AKWLFnC7bffTm1tLe+8844KaLa1tSkJKGMDOuOeqXtjOk3vIn9fWV8cDof6+4fDYQoKCqisrCQWi1FfX8/UqVNZtWoVO3bsYO7cuVRWVrJ3717Gjx/P6aefzooVKygvL1dNaXsiJyeHiRMnMmXKFKZMmZKxjwgGg0rHe+LEicrhbLFYSCaT+P1+VQVm/Fr3ATh0SAWEVLWJc1ia7tXV1dHQ0MDkyZM55ZRTePvttwkEArz77rsMGzaMSy65hHXr1rFlyxaly20ymZQkqDQ1Nvb6kPfV4XBQXFxMTk4OsVhMBR08Hg8lJSVEo1GlxyxN0V0uF06nU+2n3W73oEiu0dar0Qwhum9axHljPEA3NDSQTCbJyspS0gTiEPB6vWzdupXi4mJuvvnmT5zEjjrqKNxuN5FIhPr6ekaPHk1eXp7q2tva2qois4LRMSGTfPf7yEFP/k/6YNd72O12otGoWgCl+ZXT6SSdTuNwOMjKylJZc+FwWJUMBQIBioqKcDqdNDY2snr1alavXt3jfeQ6ubm5qlmb1+vF5/ORTqfxer2MHDmSRCLB1q1bgS5H25QpU2hvb6ejoyPDKSk6ZtBlp0ZJmKHiaBwsiK6pOHx9Ph9ZWVlEo1FisZjaYMlBTxx7oVCI7OxsXC6XOgzm5uaqYFL30kBxPNntdnUINjaa0xvw/sX4XiQSCZUlLJID6XRaBR9aW1tJJBJKl1J02K+44oqDypA799xzeeWVV3j//feVY3j+/Pk888wzbNy4kQULFgAonfhgMKjKDTs7O3E6nSqTTOa+zs5OJdsB+zKzB+paMxgbzQrGrPcDOZslu0hsY9KkSbz33nvs2rULq9XK2WefzXPPPcfOnTsBOPbYY5k3bx4lJSWk02nKyspYvnw59fX1PPDAA9x88804nU4mTJjAiBEjePzxx2lvb2fr1q3MmDFD6X5HIhEVyJaxyFh1tmH/kkql+Pe//w30r1SGsGjRIrZs2cIrr7wyqJ3MgzGoKZjNZiXF5Xa7VbPhkpIS9uzZg9/vV2cf0WR2uVw4HA68Xi/t7e2quiYSifDf//6X//73vwe835gxY2hoaABQ8k85OTnk5uZSW1urmtdC114lGAwCmZm0RgkH7WQ+tBgdhtD1N/f5fLS2ttLS0sLIkSMpKSlh9+7dVFRUMH36dIqKiqivr2fLli188Ytf5J///Cc7d+7E5/Nx1lln0dLSQkNDA8FgUO0fJOFCHuFwmC1btlBdXa16iITDYZV0I2fjVCqFx+NRTkeRVBAZB2MSxUDdiwx2JAFGKqqMfRkKCwvZu3cvgUCAPXv2MGrUKBYtWsTWrVvZvn07e/fupb6+ntLSUtUUcOfOnTQ2NmYEziXolZWVpeYf2XeaTCYlQylOZ7PZrDLahw8fDnTtP+x2Oz6fT9mF8Vw80NEnNI1mkCOTpThqjJOPOOJkw5WTk6M6n7rdbqLRKC6XS2niykbqyiuvzGiqcSDKy8tVebsc2Hbt2kUwGFTC9clkkurqagDy8/PZvn070LWAdt+MGRdUvbgeGkTzKx6Pq016IBBQOmBZWVmkUikKCgqor69XHdULCwtpbW0FurLhfT4f4XCYeDyuFlar1YrdbldRVylnFF08i8VCVlYWTqdTBSegy4E8atQorFarKkOVhdlkMuF0OtUm3hi0GOhND4YqYkPiUJYNt8fjyXDYGTWZpat6W1sbDocjI7Ak2c493Qf2yRzoOWFgYZRpkjVGgoJGbVt5Xt7P4uJiPvjgA4CDLn8fO3askstpaWmhsLCQcePGAWRI98iaJ01wrVZrhuRB9wN+IpFQEhTy/EC1s8GcfWh8X0QiSw5NYjter1dlmMn/TZrEzp07lyeffJKqqipcLhdXXnmlau4oHH300YwbN45f/vKXNDc3s2LFCs4++2yga42ZMmUKa9asobGxcb8xiZ1234tIkEIYqLYxVHnzzTdpaWkhLy9P6Zv2J4sXL+a+++5jxYoVA3quGMrYbDY6OjpIJBJKf1kaxvr9ftrb28nPz1cl506nU+05pdms9IUIh8MkEgmSyWSGZj90zQsOh4NIJMLjjz8OwOzZs9m+fTsPPfQQNTU1mM1mjjjiCJ5//nmgq+KqqqoKu92eUZ0hVXrGniKaQ4M4DMXRbLFY8Pv9BAIB1fOjqKiIhoYGQqEQH330ETNnzuTVV1+lubkZq9XK+eefz0svvUQgEFCVv4KsCT01C25paVHnXSN5eXmcffbZrFmzBujqi5RIJHC5XOo6Xq9XfW21WnUSxSHG2KxaAgKhUAifz8fo0aPZs2cPDQ0NtLS0UFRUxKRJkygtLWXjxo3U1taq7Han08m4ceMYN26c6jkjWuzdzzWyZshcJTJdMg673U5xcTGxWIzOzk5sNht5eXkZVemDyS4Gz0g1Gs0BkUNSKpXKcDL31NhNnEISRcvNzQW6DvEvvfQS0JWh/EmUl5fzgx/8AIATTzwRn8/HRx99xB/+8AegazPmdrt58cUXicViSqdVHJXS0bV78zZp8KU5dFit1gy9btH39Hg8pNNpsrOzMZlMDB8+nKamJqxWK+3t7VitVvx+P+FwGIfDgdvtzjiAS3ap3W5X2ac2mw2bzYbb7cbj8WQ4i3Nzc/F4PCSTSRXBleCEZHzIAcFkMqlIv/wf7Ha7tpV+Qhz8yWRSyQ8Y3wvZYMn77fF4qK+vV9nPxmzLRCJBMBhUmyyv16sOeBJAa29vx+/3Z+gZGiseNH2PMTtLDs5GzWN5r8QGZHMcjUbJzc2lubmZbdu2qYZ9H0d9fb1aJySQIfOXcc2T5rZ5eXlqTQT2y+6VsYsmn1TVDOTsssGafWh0KJtMJlXpIHYiwWafz8fbb78NQElJCf/+979Jp9OMHTuWV155hbq6OrKysliyZAnDhw+nvLycYDBIcXGx6vWQlZXF+eefz8MPP8zKlStZtGiRclh3710hjWW9Xq86xIktGZuLdbdnTd/x7LPPAl1ZzAPhcH3iiSdit9vZs2cPu3btYvz48f09pMMOm82mqlLi8ThFRUXU1tYSiURU49CWlhZycnJwOBy0trbidDqJRCJKwi8WiymHUCKRUNJJ8oDMOcDj8eB0OikvL1dyBy6XiwULFvD888/T3t5OTk6OkjMaNWqUKnt3uVyqD4FxvzKQ15rBjCQ9iNyax+PBYrFQWFhIXV2dSoKaOHEimzZtIhwOU1FRwezZs3n77bepr68nGAyyePFiAoEAu3fvpqWlhWQyic1mw263q7nIuKY4HA5sNptaM0TmoLi4mNzcXNatW0csFsuo3JIeIxaLRTk6Yf/9iqb3MfbicLvdtLW1kUqlqKurU/InFRUVxONxampqqK2tJTc3l6OPPppJkyZRVlZGbW2tmkuMSFWmNEW32+3KNox7ZGNFbkFBAXa7XSVf2e12CgoK1LXlLD2Y6P8VW6PRfG5E2wlQpR/GRnqCcfNknNygSy+opKSE7du388QTT/C9731vv0ypdDrNrl27WL58OcuXLyeZTDJ8+HC+8Y1v8Oc//1lJJ5SWlrJo0SL+/ve/qw3ZwoULVRb18OHDlca76OzKuKBrgjZmFmlHYu8j73sikcBisSgdOa/XSyQSwWw2Ew6HsVgsRCIRnE4nPp+PSCSiIrWykZLorDTgEwewlG6LQ9LtdmOz2TKisbKgWiwWsrOzMw7ybrdbOZIlE1rGLlkqmv7DbrerTseflNVldCoZG062trbS1NSU8drGxkbVrCc7O5uWlhZlk+KA7ujoIBaLDfjuyocLH9c0VGxD9NTr6uqYPn06r732Gvfffz9ZWVlMnz79gNfeu3cvt99+OwAzZsxQwTDJchUdvL179yqphfHjx9PY2Kh0O7s7DwUth3DokWCA2WxWBybZf8i84HA4qKmpUXIqcvgX7e7m5mb8fj+XXXYZ//nPf3jppZdUwBpg/vz5LFmyhIKCAo4++mhV/rxq1SoWLVoEoA7wEuAWLcS8vDz1M9FAFCTwLV9r+o7Ozk4VVLngggv6eTRdeDweTjjhBFauXMkrr7yincz9gDhuw+GwWlMkM1WyQ4uLi2lsbFSvDYVCKpFCmtFGIhFVIi+O5mQymaHlLPtao8M5Ly+P7Oxs4vE4GzduBLqqREtLS2lpaVEl8oFAgPz8/Az5g+7/D82hQRy2Itnldrtxu90UFBTQ2NiosponTZrERx99pHqDHHfccWzevJlwOMzatWtVE8nc3FxCoZA6r/SEMUBhfG7r1q0ZjarHjRunkndEvjArKytjndEBzUOPOIIl4aqgoEDJiTY0NGCxWCgpKVHVc6FQiObmZpqbm7Hb7QwfPpzS0lLi8TixWExJb/RUyQeojGXB7Xaryl7oqugyNoAUfW6pAhyM8inayazRDAGkLFgWObvdnuFkln+j0ah6nd/vB7rKe6xWK1VVVUydOpXt27fz/PPP88YbbzB9+nQKCgro7OykqamJ7du3q1JT6CpPnTp1Kvfcc4+aPKdOnUpeXp4qLxPtzIqKCpUNO3bsWJVxJnpULpdLbcak6Z/83zSHBomsxmIxla0Rj8cxmUzKMeNyuYhEIrjdbhKJhNKyk0wSY4DAWKJuzGgWR7Nxo+5wOHA6nSorQDb20GUzLpcrIxosmy6JCA8ENm7cyHXXXUcymeRb3/oWl112GcuXL+cXv/gF+fn5PP744+Tk5HDVVVdRVlbGwoUL+elPf9rfw+41jBumj/ucijMZUO93Op2mqakpo7JBSlPj8ThtbW0EAoEMjfdQKEQ6nVb6z52dnaq0Xs8T/Yvxs208IEnZ8PDhw6mtrSUvL4/GxkYaGhoYOXIk1dXV/PCHP2T69OmccMIJlJSUqDmnurqaDRs28O6779LR0YHP5+O6664jlUrxn//8h7Vr12IymTjllFNobm7mhRdeALq0fP1+vwp6FhcXq6xVYxdxY5M3Y1a2pvcQuRzo+ttKGbNkM0sWs9/vZ+3atUCXU7qurg6TycSePXsIBoPk5eUxcuRIrr/+euWodrlc5OTkUFtby6pVq9iyZQv33HMP48aNY/HixTz22GO88sorzJ07N6PqRhzJxmoumYdEz7OnTGbtZO5b3n77bfbu3Yvf7+fUU0/t7+EoFi9ezMqVK1mxYgXf/OY3+3s4hyUWi0X1CpHPZ1FREe3t7coRWFBQQEdHB4FAQAWo4/G4cjqKQzkWixGPx5XD2Sj1ZXQ2SwKFyWTKaEZdUlJCPB6ntbUVi8XC5MmTCQQCmEwmRo8erYLoUoGj15pDj5xnjI5mcf6bzWYaGhpUsszYsWOprq4mGo1SU1PD+PHjSSQSlJeXE4vFMpIgTCYTfr+frKwsJZth7OUgMhqyjzUmc0lTSbmeNHmDLjuS8+9AOd8MdaRSVqT8zGYzBQUFRCIRQqEQHR0dGVVW2dnZRCIR2traSCQSSl70k5Czrpx35ZxsbEQtuFwusrOzsVqt6owj8hqDcb7QTmaNZohgdNTJIiXN9eTfaDTKsGHDqK6uVhutQCDAqFGj2L17N9u3b2fatGnKISyZYt3vU1JSojKQJZJfUFCgFnXZgEkZq2hU2e12Jk2aRG1tLYDKBoAuZ7NkJYimltxPc+gQR7LxfbDZbESjUWw2G/F4XOkqx+NxtRnqHqkVO5MFVDblPT3EHiGzlFqcy0ZntcvlUod7cVYPFH7+85/zxBNPMHLkSObNm8dll13Gfffdx1tvvcWrr77KsmXLmDdvHgUFBTzyyCN85StfoaamRnUiH6zIe2bUjzvQBsjY9E026NBVqi6OnYKCAiXRkk6niUQiNDU1EY/HaWxsxOl0KpkWaSQnJa+S0awdzX2PsYu7fC7lsytSKn6/n8bGRvVZliBBKBTC6/VSWlpKRUUFGzZsYMOGDQe814wZM7jyyiupqKjgscceUwe1s88+m0AgwIsvvkgymSQ/P5/Zs2fzzjvvEAqFcDgcZGdnqzVHDoPiPLTb7QcdLNF8OtLpdIYMhRz4Zb4XzUyn08nu3btVQ6WysjJSqRTt7e0Eg0HV7V32I0cccQSXXnopxx9/PFarlfLycm677TbKy8v57ne/y9KlS5k1axavvvoqdXV1LF++nIsvvlg5eSSLTdYut9u9X7aybs7V/4hUxhe/+EWlqT4QWLRoET/60Y94/fXXVRNSTd8jjuZYLKb2otKEOBgMqqSa3NxcfD6fcv45nU5yc3NJJBLK0Sh7CZmf5CEBJ1kzbDYbLpcLt9uN2WwmEokop3Zubi5FRUWqSnP8+PFqbNKPxJjNqNeaQ4vZbFYJCalUSmWr2+12RowYofaYEsSU/ab0hZk0aRIOhyPjnCPSbsZ7dA8+ijPb2KtGKrnk2iNHjlR2IBWeslZqJ3PfYazkloCBzWYjJydH6b7L3NDR0YHNZqOwsFDJ/hkbTEoQyiipIvtMCaz3pOMt0pPS0E8qLQDlYB6sAW69Mmo0QwTjwV4ObrFYTDXokuwwWXhDoRCFhYXU1NSojqqtra0kk0lKSkrIy8tTGyToWihlMpTsL5vNRlZWFm63W5V8yKHe5XIpZ6XJZKKgoEB1goauqL5ssjwej5qopZxN/k+DdXIdTEgpjmysrVaragAo2rjJZFK9Rhbj7pke8n6JE/njNtHGjGeRvjD+jjR0keekM+9A4sEHH1TZMiaTiba2NvLy8rDb7cyfP59ly5bhcrmU5uzcuXN57733OO+88/a7lmTSCNIoZqAh2cPGrMAD6cclk0laWlrU3ycnJ0fNCVIRkZ+fr5qGAipz3e12097eTlNTk8o0ys3NVSVl6XRaZSZ1dHQQjUZxOp16vuhD5G8tcgeC2IQc0H0+nyodFuew6Lx7PB7Gjx+vSp9Fhsdms+H1esnLy1Pvu+j9Q5f27qxZs6iqqmLTpk1A18HtqKOO4p133lH6iaNHj1YOZglYuVwuta5JhQagJXh6GZHSkQN2R0dHRnMtccbY7XZlF1VVVco5Lfqp27Zto6WlBa/Xy7e//W0WLVqU8T6NGTOGpUuX8oMf/ICPPvqIH/zgBzz44INceOGFKuh33HHHMWzYMAAqKioy5glZ57qXthp/ZnxOc+hJp9P885//BAaOVIYwY8YMNZetXLmShQsX9veQDluM87mUlkswW8rNxWFjtVpVBrM4fCTwlU6nVVahZBiKU7InjBqsBQUFFBQUqD2c2Wxm/PjxmM1mVUFTUFCA2Wzer2JPc2gRJ53Yh7FhnwQaWltb1TxfXFxMKBSivb1dOYR7wuv14vP5VFWenInErkTrG8hIxJFeNPI6i8VCfn6+sidJwtH0HTabTTWIlv0J7EvAkgQ46T8j2cVSqdsTcq3uyFnW+JDAUyKRyEh4kPsPZnvQM5xGM4QQrVLJLLNYLNhsNlKpFA6HQx3ic3JyVAlRcXGx0iZ0u91Kd0r0VgVxYlssFux2u5p8JXs1KytLOYfEMSjNMiQ7VcYoDmgpV5FJVCZUmWh1Y7e+wyiRIVIYsvjKAihOA3EwS5mY8Rryfhmdxt2fk697QpokCOKwGoh2UFBQAMCPf/xjvvKVrxAMBsnKygL2NVPs6bmeuPPOO/nZz37WNwP/jIgzV+gug2IkHo/T0tJCOp3GYrGQk5OD3W4nnU5TX18PdG3UjQ5mQQ6K2dnZeDwe9u7dSzQapbm5WR0MYrGY2rTLxi8ajQ76TdlgQuZ0CWqKNIYEE10ul3L+i1OxqKiItrY2FewMBAKqlLUnYrGYchJDV/WLrF+7d+8Guuxw3LhxAOzYsQPockL7/X4liSC2Kusi7NPmlUOgziDqPbpXqLS3t2c8JwfwnJwcdu7cCezTbDaZTDQ2NmK329m7dy8tLS2UlpZy1113KUdxd7xeL7/85S/51re+RVVVFbfeeiv33Xcfc+fOZfXq1Tz55JN873vfU03BWltblX22t7cruS7Ze4jTQYIVRk15zaFn7dq1VFZW4vF4WLx4cX8PJwOLxcIFF1zAsmXL+Pvf/66dzP2MnE3EURSPx9W+1OFwqAoayWQGVI8Qv9+vHL/yc6NjWZxLIj8oiRRyBrLb7YRCIeUkzM7OZsSIEYRCITVfFBYW4vV6SaVSav+kzzZ9i9VqxePxqECCZJeazWYKCwuJRCIEAgE6OjpwuVz4/X7i8TihUEjZg8vlIisrC4fDoZIbjE5B2He+dblcGecjY4KMsZFbUVFRhpZzd91uTd9gdBpLwEmkvsSfYrValcO5J0kduY4kWknAwFjFK9Wa8vsSADMicpIfVyE6WNBOZo1mCCETpVGzMBwO43K5AFQWcjQaJT8/H4fDQXt7O8OGDSM7O5tAIIDT6VSLaveGXt01dR0Oh+qeapQ3EMeysfRUSkjEuSzOQ4nmy9hk0Ra9YE3fIl2OZXNtXGCNzmXjoftAGnNiP8ZFuKf7yQGhe6aylBwNlIV26dKlPPXUU+r7m2++mQ8++ICGhgbuvPNOWltb1aYhHA6r0k3jc6NGjerx2jfddBPXX3+9+j4QCFBSUnII/zefjnQ6reYVyXw/0OczlUopB7Pdbic3N1e9NhwOk0gk1Obe+N4mEgnKysqUzMGwYcMoKipi5MiR1NfXEwgEaGlpUYeCaDSqAlyS3awdzX2HHLrFeSh6gyKJEQ6HMxyHDoeDUCiE2WzG5/PR3t6eUUHR/eAmQSmjxruxMZvH4yEnJwer1ZpxcPP5fBnauiIBZLPZVMBKnOLGw522md5DnMlms1kFJ+VAJk1lARWQsFgs7N27F0BVdIwcOZINGzbg9/u59957yc/P/9h7+v1+7rrrLr7xjW/w4Ycfsnz5cs477zw2bNhAXV0dW7ZsYcyYMezcuZOPPvpISXk1NDSQm5tLIBAgFoupijD5V+ao7g4FzaHjySefBODMM89U+9eBxEUXXcSyZct49tlneeCBBw5YzaPpO+T8I4k1khloDCLKXsTY/E8+3yJzINmpkpEaDAZ7/OzHYjG17mRlZTFixAhSqZSqQrPb7RQVFanmbsY5UdtL32MMRkjClew3HA4HRUVFBINBFWgwVt+JPUgyA3SdXaSKV/SYjXuZA+FwOFTDt0QiofYzTqdzwFVrHo6InUjSgQQk5PFZKhA+LkhtlJbs6Rw8mNFOZo1miCGHeVnk3G53huNFJtBIJKKcYJK9LFFUY1mrsemNcRI0yhiIU1mi8/Jao/PQ6DCQjGgZS/cNlzgTNP2D0bEjC2x3zcpPez3j7xodzj05oMVGBprTZ8mSJSxZskR9/+yzz/Lhhx+qJpc5OTk0NjYSi8VYtWoVM2fOZObMmfznP//hrLPO4q233uKee+7p8doStBmoGN8nCSodCGnQZ7fbycvLy3itZBKJI9BIU1OTyvSOx+NUVFRgMpkoKiqiqKiIdDpNMBiktbWVwsJC2tvblUNashKl1H4gOiaGGnKolxJjl8tFbm6uKjOVMlUpE47H4/j9ftWwSQ79kpkuZcoHkuExat6Jk1hwOp3qM2R0GshaYswMkUaixo7vunS5d5H1wmq1Koms7g30ZO8BXaXLZWVlpNNp1VBHfu+LX/ziJzqYheLiYq6++mp+/etf889//pMvfelLLFiwgP/85z+8//77zJ8/n507d1JZWcmcOXOorq6mqalJNSMOhUIMGzYsIxtSAhGSgTTQ1qWhRiqV4oknngDg0ksv7efR9Mz8+fMpLi6mpqaG//u//+tRAkvTPxidRN21U+VhMpmUhnI0GlX9HoxZpRLA9vv9mEwmpasq2Y2yrkhTUdnbmM1mcnNz8Xq9JBKJjOo1WbsGSuLE4YjxfCP7EjnrZmVl4fV6CYVC+9mD/K7b7VbBCKNsoOyHHA4HXq8XQGUzS3BV9h3itJZrSiBcM/CQygXJcjZmMRvf34+T0+ouJWnMdh7Kc4HeVWs0QxCJlMfjcUwmEy6XS0VmbTYbsVgso7RMMstEFsGYAWCcOLsf9I0lX+JQNjqQu5eLGCdYWeSNBzZxTA/lSXewIQss7DtkGxfW7shz8lrjcx+XCWa0rcFyiL/11ltxOp2ccsopALz22mtcf/31nHTSSbhcLp599llyc3N59NFHmTNnDieffPIBM5kHEx0dHR/rlDPq3Xb/LItjORqN0tHRkeFoFvkCIyKxIM7mSCSiMiPdbjeRSIRgMEh+fr7qFC0bQB2oOvTIQU2kVOx2OwUFBarxklQjSGaoHNRFc1lkeIyH94O9r0g3GfXapTmTrEPytbFpkyCHO20nvYtx7peMciDjsA5kVE0Z3wOpaPqsDbJmz54NQG1tLR0dHYwfPx6AxsZG5awOBAJkZ2cDXXOMBBqMGsziSEqlUhmSGYNlfRqsrFixgvr6evLz8znttNP6ezg9Yjabueiii7j33nv585//rJ3MAxRj1aVRM1eC0dJrxOPxqIocqZKSnxv3rcYMR8EoIebz+VTTMHE6A6oSRwczBxayjzBqekslVnZ2dsaeRM6x8tyB9Lq7n42MMoHdNXfFnvSZd3BgzDruzoGczIfze6tnO41miCIl7VLiI5lcUqouDmUpBZJDvmzGDnTYlwic0SkoGWLdSz6MGaziPOypKdxgcy4ernzcAtsTsuj25Jg26jUP1mju5s2b93vunHPO4Zxzzsl47pFHHumrIR0y5DMsm3E5NPX0mbXZbKoLd/dDmc/no7W1lXg8zp49e1Tnd4vFQnZ2tsoojMfjSrPOOAYpr08kEvj9flXaKA4ho9NTOw8PPeKolQ7cRv1Ct9utGjVKsEoyg+TwLr0C5PAlc0RPgazu2SDynHHdMQYwjdJM3QMY3YOkmkOD8e8rX4ujJRKJKKdvU1OTaqaWl5fH3r171R7k2WefZe7cuUyaNOkT79fQ0MBvfvMbAEpLS7FYLJSVlQEoORdAaTDDPtkXmTtknMb1q/tzmkPHX//6V6BLkmIgZ/f97//+L/feey//+c9/qKysHBIB5KGMOJyl34xo80YiEbWfsdlsZGdnqwCUyCpI8o0xK1quabfbcTqduN3uDFkx2NcwXe9FBi5GGQ15r3tyInfXYDaedY3SYd33L93PPIMtg3Xjxo1cd911JJNJvvWtb3HZZZepn23atIlzzz2XUaNGUVxcrCpQDkcGy/vZl2gns0YzhJHyHZG/MOrtiraQiNzLxqmnDuvG6xm/NmYpG7VzjXIHZrO5x4OZ8Xf15Dw0kff10zimNQMXkSKQg5Zk/RmrF6CrCZc4HRsbG5WcjsPhwGazMXLkSOrq6kgmkzQ2NtLY2IjH48Hj8ZCdnU1BQYFy7BjnBilrBQ7YKE47g/oecTSLDqasKYBq8GcymUgkEqo0VV5vbKLS/WB2oGoJY0WM0clsrJgBVJaz0YbE0aDno75BNP0lG9iYnRyJREilUkpiJSsri6amJvW5b21tpaioiPr6eq677jrOOOMMTjrpJMaMGYPb7SaRSNDS0kJNTQ1lZWVs2LCBTZs2qaa11157Lf/+97956aWXADjqqKNYuXIlABMnTmTbtm1AVwPXeDyunEPd5Z3EeaA59NTV1fHss88C8LWvfa2fR/PxTJo0iZNPPpk33niDhx56iNtvv72/h6Q5CIzazbFYLGM/A2QEMaFrLyFnGvl948/EqWh0SspZS68zgwdjr4buwQT5uTGxqqcEC+OZZ6jw85//nCeeeIKRI0cyb968DCfz1q1b+dnPfpbxnEYjaCezRjPEkQwdKfMR57JRT8q4mH5cFpn8230TZpTJkMZM3aUSgAwngHYsazSDC9Fgl4qI7hqHUg5qNpvJz8+nvb1dNccRB47MFfn5+Rndu0UTUV4jGnVSdWG8hnTvbmtrA/Y1CZXmYjJWTd/RvalOd21lOahLMFIc0pJNaswale+N0jvG+wAZFTHGg53co/shz2if2jYOLcas4EQigdPpVA0foathVk5ODi0tLdTW1jJ8+HAikQixWEw5nHNzc2lubiY7O5tkMklLSwvPP/88zz///Cfef9y4cUybNo2nn35alaxPnDiRDRs20NnZSUFBAZFIhLa2NqxWK+PHj2fXrl0Aqoko7HMyy17J+Jzm0PDAAw+QTCaZO3cuxxxzTH8P5xO59tpreeONN3j44Ye5+eabdS+AQYRICUpfAXEyH6xkU0/Xk/2NlsUYvBglVnraexxuPPjggxQUFKh9vpEtW7bw9ttv8/DDD3PTTTdx+umn7/f78Xg8oxGiNMbUDH30LKjRHCb05ASQTGORzOhJ1qAneQNj2U/3RaenyK+x8Z9GoxncWCwWXC5XRjdt0TgUmQur1UpOTg6pVEo5kGTeMWqzSkaRNJiUDBJjsxwjXq8Xj8dDY2OjOgz6/X4Aksmkek4f8voHY1Md4ICBTLEhKTHuqby0+9fdK2k+aS2CfRrN2rnct4j+dncZG+kVkU6n8Xq9BINBwuEwhYWFNDc3ZzR7tFqtNDU1UVRUhNfrpa2tjVgslnFglUZaEvzKysrCbrezfft2oCsg5fP5VH+KvLw83G43bW1t2Gw2pkyZwp49e1RVhsi3GPcsFotFHbC1k/nQEY1G+cMf/gDAd7/73X4ezcEhpeKVlZX8+c9/5pvf/GZ/D0nzKRE5pQMl2HSnpwrP7jJOmqGBfj+7Kn0AfvzjH/OVr3wl42fTpk3j4osvpri4mIULF7J48eL9Avx33nknP/vZz/psvJqBgz6FaTSHGcbMZsn8k8P/55U16ElzSi/SGs3QpLvGoTib5Wtx0LhcLjwej3Ikd9flNQajRN6nJx07yVYWB7ToOFutVmKxmHJeOxwO7QwaIHQPZPaU5Syv6837dZfOGMj0pHm4fPlyfvGLX5Cfn8/jjz9OTk4OV111FWVlZSxcuJCf/vSn/T3sj8XYfFi0t41IPwifz6dkM/Lz87Hb7WrOaG5uxuFwEAqFcDgceDweYF+1VU9OHQmmOxwO3G43TqdTZSz6fD7VjDQnJwe/3097ezvQ5awWR5OM3WQy4Xa71f5Ia3gfWu677z6ampooLS3l3HPP7e/hHBQ2m40f/OAHfPvb3+buu+/m61//+oDWkdYcGNmHaDSHM0uXLuWpp55S399888188MEHNDQ0cOedd2a89gtf+AI+nw+AsWPH0tzcTGFhYcZrbrrpJq6//nr1fSAQoKSk5BD+DzQDBe1k1mgOU4zOG2Mm88dpYcrvGX/f+LU+gGk0hx9GjUNjtrJRm9eINMsRx5Nku8rvHmgekTnJZrOpMldp3CPIODQDD2OWs6wx4mw+UImycY3p/u9QWX960jy87777eOutt3j11VdZtmwZ8+bNo6CggEceeYSvfOUr1NTUUFxc3N9D/1gkm10yj42NQkVyB7qqE1KpFNFoFL/fT1ZWFu3t7TgcDqLRqMp2TiaTSqbHGIQyNiE26nXb7XYV4JJ7Z2Vl4fF41NwjDZLFqSzZzPK8oEvgDy1GB8bPf/7zQeXsu+qqq7j99tupqKjgT3/6E9dcc01/D0mj0Wg+E0uWLGHJkiXq+2effZYPP/yQxx9/fL/XXnHFFdxxxx2MGTOGyspK1cjXiEiPaA4/9I5Jo9EAOoqv0Wg+H92diMambj05Eo2OIvk90cHrruluzGaW542NdnRDt8GFMch5uNNd87CtrY28vDzsdjvz589n2bJluFwu5s2bB8DcuXN57733OO+88/a71kDTPxTHbzweVw35pD+EOIIlKCUSG/F4XFUpiFZ7LBZTGt7GBsXdKx2MzmYJoDscDlwuF06nU9mbPG+z2dTeR7KojY2L5bU6cHVo+f73v08wGOTYY4/l4osv7u/hfCpcLhc333wzS5Ys4ZZbbuErX/kK2dnZ/T0sjUaj+dzceuutOJ1OTjnlFABee+01brjhBm6++WZuvfVWrrrqKtLpNDfeeKPez2ky0E5mjUaj0Wg0vYo4jrvTXW/XWEEhzqOPC3gZKyyMTiXtXNYMVrprHgaDQbKysgDweDyEQqEen+uJgah/KFnBUqkAmVrsxgaiotssTQMdDgder1cFlYxSK0J3bW7jvNC9KaQ4jGWOkbnDbDbv1zRSnOB6bjm0PPnkkzz22GOYzWbuv//+QemouOaaa3jwwQfZtm0bt956K/fdd19/D0mj0Wg+N5s3b97vuV/96lcA5Obmsnr16r4ekmaQoJ3MGo1Gc5Akk0nmz5/PO++8A7CfbmZubm4/j1CjGdh0lz3oTnfHs1G6p7sjabDKI2gObz5J87C1tZVwOAxAOBzG5/ORlZWV8dyoUaN6vPZA1T80VisYHcvSGBAyZXOSySROp1NlORtf373KQa5v/FrmCHE496TVLV/L90aHtGQy6znm0LJp0yauvvpqoOtzcMIJJ/TziD4bNpuN++67jy984QssXbqUc889lwULFvT3sDQajUaj6RcGX7hYo9Fo+oGWlhZOO+00du/erZ4T3cxvfetbLFu2rB9Hp9EMDcRBJFmHot0sDQal3F47fzSDlSVLlvDWW2+pRyQS4cMPP+Thhx8GICcnh8bGRmKxGKtWrWLmzJnMnDmT//73vwC89dZbzJgxo8drOxwOfD5fxmOgITIWTqcTt9uN2+1Wn3GHw4HT6SQrKwuv15vx//D7/WRnZ5OTk6P+lUd2dnbGw+fz4fV68Xq96lrytcfjwePxKF13yW42NgvUc8yhZ8+ePZxxxhkEAgFOOukkfvKTn/T3kD4Xixcv5n//939Jp9N87Wtfo6mpqb+HpNEcNMlkktmzZ6vvly9fzqxZszjjjDNoaWnpx5FpNJrBiHYyazQazUEQDof5zW9+w5QpUwD2082U7ObuxONxAoFAxkOj0Ry+bNq0ibFjx7JgwQIuueQSQB/oDmduvfVWysrKOOWUU1iwYAEdHR1cf/31nHTSSdx9991885vf5MQTT6SpqYk5c+Ywbty4A2YyD0Yky1ka9bndbqWhLI5oo6O4u/O5+/PGf71er3IoGx3Z4syWezkcjgx5Dc2hZevWrcydO5eamhqmTJnCc889NyQaK/76179m3LhxVFZWcv7552f0DdBoBio6iUaj0fQ2g39F12g0mj6gpKQko+x4MGtkajSa/mPr1q387Gc/47LLLlPPyYHu1VdfZdmyZdx00039OEJNX9KT5uE555zDOeeck/HcI4880ldD6ldEL1lkNIyyOd0ldD7pOkaJHe1AHhi8/PLLXHzxxbS0tDBlyhReeeUVcnJy+ntYvYLX6+X5559nzpw5rFq1iosuuognn3xSN47UDGgkiWbJkiXA/kk0B3IyD7RGsxqNZuCgM5k1GnRmmWZ/li5dyrx589TjxRdfzPh5d43MA5Ul33TTTbS3t6tHVVXVIR+7RqMZuGzZsoW//OUvnHjiibz44ou6KkKj+RjESWxs3me32z/xYbPZVCM/7WDuf6LRKDfddBOnnXYaLS0tzJo1i//+978UFxf399B6lSlTpvDMM89gt9v55z//yUUXXUQymezvYWk0B6SkpIRp06ap7z9NEo3f71ePgaD/r9FoBgbayazRsC+zbOXKlTzxxBPAwZUK6UP/0KW7bubpp5+e8fOedDN7YjBoZGo+H5dffjkrV64E9g9OpdNprrzySubPn68z2jUATJs2jd/97ne88MIL/PSnP6W9vV0f6DQazZDlhRde4Mgjj+Suu+4C4Nprr+XNN98kLy+vn0d2aFi0aBHPPfccdrudZ599ltNOO43W1tb+HpZGA+gkGo1Gc+jRTmaNhs+eWaYP/Yc33XUzNYcfW7duZfny5er77sGpt956i4KCAlatWsW2bduoqanpv8Fq+oXuBzq73c4RRxyBz+dj7NixOJ1OfaDTaDRDjt27d3P22Wdz9tlnU15eTnFxMc8++ywPPPAATqezv4d3SDnjjDN4/vnnycrK4vXXX2fOnDmsX7++v4el0egkGo1Gc8jRmswaDV2ZZRdffDHFxcUsXLiQp59++qAyy2666Sauv/569X0gENCO5iGOZKxCz7qZmsOLe+65h69+9atAzzp2LpeLefPmATB37lzee+89zjvvvP2uo7Xthi5LlixRWocAF1xwAZMmTWLMmDFUVlZSUFBw0Ac6h8PRV8PWaDSaz0Rrayt33nknS5cuJR6PY7Vauf7667nlllvU3vpw4Atf+AKrV6/mrLPOYvv27Rx//PH88Ic/5Ac/+AF+v7+/h6fRHBBJonG5XDz77LP9PRyNRjPI0E5mzWHJ0qVLeeqpp9T3P/jBDzjiiCMAPlVmmT70azSHL++//z5jxoyhs7MT6FnHTjeI1HTn1ltv5aqrriKdTnPjjTdiNpv1gU6j0Qx6Kisreeihh3jggQeUPMSpp57Kb3/7W7XHPtyYNm0a69at45vf/CbPPPMMd9xxB7/73e/48pe/zAUXXMCcOXO0w1kzINBJNBqNprfQTmbNYUlvZZZpNJrDh+7BqbKyMsrKyvj1r38N9Kxj1/25UaNG9XhtXRVx+DB16lRWr16d8Zw+0Gk0msFATU0Nb731FtFolGg0SltbG7t372bt2rVs3LiRdDoNwFFHHcUvf/lLTj/99MO+8WJBQQFPPfUUzz33HLfccgtbt27lj3/8I3/84x8BmDhxIhMmTKCkpITi4mK8Xi8ejwePx4PVasXv93PKKadgtepju0aj0WgGPnq10mjQmWUajeaTMQanQqEQs2bN4otf/CJ79uxh+fLlvPXWW/sFp2bOnMl//vMfzjrrLN566y3uueeeHq+tqyI0Go1GM9A56aST2LVr1wF/fuqpp3LNNddw3nnnYbFY+nBkAxuTycT555/Pueeey8qVK/nHP/7BihUrKC8vZ8eOHezYseNjf/++++7j//2//9dHo9VoNBqN5rOjncwaDTqzTKPRfDqysrLYunUr0BWkWrBgAVlZWfsFp3Jzc3n00UeZM2cOJ5988gEzmTUajUajGejs3Lmzv4cwqDGbzZxyyimccsop/T0UjUaj0WgOCaa01DVpNJrPTTqdJhgM4vV6D/vyQE3PaBvRHAzaTjSfhLYRzSehbUSj0fQGei7RfBLaRjSfhLaRwwftZNZoNBqNRqPRaDQajUaj0Wg0Gs1nxtzfA9BoNBqNRqPRaDQajUaj0Wg0Gs3gRTuZNRqNRqPRaDQajUaj0Wg0Go1G85nRTmaNRqPRaDQajUaj0Wg0Go1Go9F8Zqz9PQCN5nBBxO41A5OB0IRA28jAZiDYCGg7GchoG9EcDAPBTrSNDGy0jWg+iYFgI6DtZCCjbURzMAwEO9E2MrD5tDaincwaTR8RDAbx+/39PQzNAWhvb8fn8/XrGLSNDGwGgo2AtpOBjLYRzcEwEOxE28jARtuI5pMYCDYC2k4GMtpGNAfDQLATbSMDm09rI6Z0Op0+hOPRaDT/P4c6QhcIBCgpKaGqquqQLxRD8V6HQxR3KL5vfXmvgWAjcGjtZCi+b315P20jg/t+h9NcotebgX2vw8FGoO/+nkPtPjAwbAT0ejOQ73U42AgMvfetr+81EOxE28jAvpfOZNZoBigmk6lPNkg+n6/PopFD9V79hbaRwXOv/qQv7GQov2+Hg50MNRvp6/tpG+k9hur7pm2kd+mrv+dQu89AQK83g+de/YVebwbPvfoLbSOD514Hg278p9FoNBqNRqPRaDQajUaj0Wg0ms+MdjJrNBqNRqPRaDQajUaj0Wg0Go3mM6OdzBrNEMHhcPDTn/4Uh8Oh76XpkaH6vmkb6T2G8vum7aR3GMrvm7aR3mOovm/aRnqXvvp7DrX7HC7o9UZzMAzV903bSO8xVN+3gWojuvGfRqPRaDQajUaj0Wg0Go1Go9FoPjM6k1mj0Wg0Go1Go9FoNBqNRqPRaDSfGe1k1mg0Go1Go9FoNBqNRqPRaDQazWdGO5k1Go1Go9FoNBqNRqPRaDQajUbzmdFOZo1mELNx40bmzZvH8ccfz2OPPZbxs02bNjF27FgWLFjAJZdc8pnvEY/HOeecc5g7dy4PP/zwJz7/eamqquLkk0/mhBNO4K677lLPBwIBiouLWbBgAaeeemqv3W+o0xc2An1rJ9pGehdtI5qDYaitN9pGeh89l2gOhqE0l2j7ODQMJRsBbSeHgqFmI6DtpLfRe5J+JK3RaAYtF1xwQXrPnj3pVCqVnj17dsbP/v73v6f/+te/fu57/PWvf00/8MAD6Y6OjvSiRYvS0Wj0Y5//vHzve99Lr1y5Mp1Op9OnnHJKOhAIpNPpdHrNmjXp2267rVfucTjRFzaSTvetnWgb6V20jWgOhqG23mgb6X30XKI5GIbSXKLt49AwlGwkndZ2cigYajaSTms76W30nqT/0JnMGs0g5sEHH2T06NEAmEymjJ9t2bKFv/zlL5x44om8+OKLn/ke69atY968eZjNZqZOncq2bds+9vnPy49+9CPmzZsHQEdHB1arFej6/7z00kuceOKJ/PnPf+6Vex0O9IWNQN/aibaR3kXbiOZgGGrrjbaR3kfPJZqDYSjNJdo+Dg1DyUZA28mhYKjZCGg76W30nqT/sPb3ADQazWenoKAAgB//+Md85StfyfjZtGnTuPjiiykuLmbhwoUsXrwYi8Xyqe8RDAbJysoCwOPxEAqFPvb5z0tubi4ADz30EDNnzsTlcgFQWlrK3XffzXHHHcfixYs566yz1P9fc2D6wkagb+1E20jvom1E28jBMNTWG20jvY+eS7SdHAxDaS7R9nFoGEo2AtpODgVDzUZA20lvo/ck/Wcj2sms0Qwili5dylNPPaW+v/nmm/nggw9oaGjgzjvvzHjtF77wBXw+HwBjx46lubmZwsLCT33PrKwswuEwAOFwWF3zQM/3Bo8//jjLly9n+fLl6rnZs2fjcrkwm83Mnj2b8vJyvcD2QH/YCPS9nWgb+exoG9E2cjAcDuuNtpHPh55LtJ0cDEN9LtH28fkZ6jYC2k4+L4eDjYC2k8+D3pMMIBvpb70OjUbz2XnmmWfSF110Ubqjo2O/n51//vnpbdu2pWOxWHrOnDk9vuZg+Mtf/pL+/e9/n+7s7EyfcsopSk/oQM9/Xt555530woUL05FIJOP566+/Pv3SSy+lOzo60gsXLkw3NTX1yv2GOn1hI+l039qJtpHeRduI5mAYauuNtpHeR88lmoNhKM0l2j4ODUPJRtJpbSeHgqFmI+m0tpPeRu9J+g9TOp1O95+LW6PRfB6mTp2K0+nE4/EA8Nprr3HDDTdw8803U1NTwzXXXEM6nebGG2/ki1/84me6RywW46KLLqK6uporrriCcDjMmWeeydixYzOe/+Y3v9kr/6ezzz6bPXv2kJeXB8DJJ5/Ml7/8Zfx+P5deeimJRIKvfvWrXH311b1yv6FOX9gI9K2daBvpXbSNaA6GobbeaBvpffRcojkYhtJcou3j0DCUbAS0nRwKhpqNgLaT3kbvSfoP7WTWaDQajUaj0Wg0Go1Go9FoNBrNZ8bc3wPQaIYS6XSaQCCAjt1oDoS2Ec3BoO1E80loGxl63HrrrfzlL39hzZo1HH/88Zx88smUl5cDXdqCc+fO5dprrz3o62kb0XwS2kY0Gk1voOcSzSehbeTwQTuZNZpeJBgM4vf7CQaD/T0UzQBF24jmYNB2ovkktI0MLZqamnj44YcBuOOOO3jxxRf5/e9/z1133UVlZSW7du1i9erVeL1e1qxZc1DX1Dai+SS0jWg0GuGCCy5gz549PQY6Pwk9l2g+CW0jhw/ayazRaDQajUaj0fQjd999N1/96lcBiEaj5ObmMmXKFMrKyli/fj1z584FYP78+bzzzjs9XiMejxMIBDIeGo1Go9F8HIlEgvPOO493330X2D/QqdFoNJ8G7WTWaDQajUaj0Wj6iaqqKoLBIJMmTQKgs7NT/SydThMMBsnKygLA4/EQCoV6vM6dd96J3+9Xj5KSkkM/eI1GoxkkBAIBrrzySrxeL2PGjOGJJ57o7yENCOLxON/97ndZuHAhsH+g80C/o4OaGo2mJ7STWaPRaDQajUaj6SfuvPNOfvjDH6rvTSaT+tpsNpOVlUU4HAYgHA7j8/l6vM5NN91Ee3u7elRVVR3agWs0Gs0goaOjg/POO48///nPhEIh9uzZw6WXXsr999/f30Prd7xeLyeeeKL6vnugsyd0UFOj0RwI7WTWaOiKbBcXF7NgwQJOPfXUz6RFpdFoNBqNRvNpef/997n88su56667uOuuu3C5XDQ1NbF161ZKS0uZMWMGq1atAmDVqlXMnDmzx+s4HA58Pl/GQ6PRaDRw77338vrrr+PxeHjllVe44YYbAPje977H6tWr+3l0A4vugc6e0EFNjUZzIKz9PQCNZiCwdetWrrnmGm655RYAzjrrLF588UX27t3LXXfdxbJly/p5hJqBxAUXXMCvfvUr6urq+M53voPb7eaRRx5hzJgx/T00jUYzwAkEAhxxxBFMmDABi8XC7bffrueRw5z3338fgL/85S8ATJ48mbPOOguTycRjjz1GaWkp48eP54QTTmDixInMmzevH0er0Wg0g4vW1lbuvPNOAJYuXcqiRYtYuHAh1dXV/O1vf+OKK65g48aNuFyufh7pwEACnQ0NDZSWlvb4GofDgcPh6NuBaTSaQYF2Mms0wJYtW3jppZdYsWIFV1xxhdKiys3NPaAWFXTpUcXjcfW91qMa2iQSCS688ELlEJDGGB8XjNA2ojnckVLLdDq9X9llT2WYJpMp4zHU0EFNzYG4/PLL1dfdm/vdcccdfTwajUajGRr89re/pb29nalTp6p51mQy8cADD/DGG29QVlbGz3/+c37xi1/070AHCLfccktGoFOj0Wg+DdrJrNEApaWl3H333Rx33HEsXrw4w7FxIC0q6NKj+tnPftYXQ9QMAKQxxiOPPAJwUMEIbSOaw4l0Ok0qlaKzs5OOjg46OzuVtt/BOJgh08lssViwWq3YbLYh43DWQU2NRqPRaPqGjo4OHn74YQBuvPHGDPkHv9/PAw88wHnnncc999zDhRdeyNFHH91fQ+13pJqmtLR0v0CnRqPRHCxak1mjAWbPns2cOXOw2+3Mnj37oLSoQOtRHW58lsYY2kY0Q5V0Ok1nZyeJRIJoNEooFCIYDBIOhwmFQuoRDAZpa2ujra2N1tZW9fi459rb2wmFQoTDYSKRCKFQiHg8/rFBv8GCBDVfffVVHn300YOaR0A32dFoNBqN5tPy8ssvU11dTW5uLueff/5+Pz/33HM5//zzSaVSfP3rX6ejo6MfRqnRaDRDB53JrNEAP/nJT1i8eDGLFi1i3bp1OByOT9SiAq1HdbhzMMEIbSNDl6qqKr761a8Sj8f54he/yEknnbSftu7NN9/MypUrmTZtGg8++GB/D/lzk06n6ejoIJlMkkqllARGKpXa79HR0UFHR4fKbD5QRrPxOZPJhNlsxmw2qwxmu92Ow+FQ93W5XFgslj79f/cms2fPxuVyYTabmT17Nu+995762ScFNa+//nr1fSAQ0I5mjUaj0Wg+hieffBKASy65BKfT2eNrfvvb3/Laa6/x/vvv89vf/pbvfOc7fThCjWZg0v2cc+ONNwL79xZ57bXX+nmkmoGGdjJrNMD111/PpZdeym233cZXv/pVjj76aK1FpflEDqYxhmbocv/993Prrbdy0kknceqpp7JixYoMbd2bb76ZXbt2sXr1am644QbWrFnDnDlzerzWQJdCSKfTJBIJEomEymAWR7P8K49kMqkekhEkzunOzs4Mh7Lx+iKRYbVa1dc2mw2n04nT6SSRSODxeOjs7MTj8QxaR7MOamo0B4fMNRLMMn7dk8Y77JtXZA4xm81YLBbMZvOQkdzRaDQHRzKZ5N///jcAX/7ylw/4uhEjRnDPPffwjW98g5tvvpkzzzyTCRMm9NUwNZoBSfdzznXXXYfX692vt4hG0x3tZNZogOLiYt54442M5w43LSrjAa4nhnozrs+CboxxePOjH/0Iv98PdGn+WSyWDG3d9evXM3fuXADmz5/PO++8c0An80DV7hbnsjjAU6kUiUQiw5EsjmVxQkOX0zwcDhONRonH4ySTyU91X4fDgdvtxu/3q/tLRnRWVhYmkwmPx/Oxmb8DFR3U7MIYeOjuPIT9nYQWi0WvPUMcY1VER0dHr8rjSNDKbrdrO9JoDhNWrlxJW1sbhYWFnHDCCR/72quuuoq//e1vrFy5knPOOYc1a9aoPZ5GczjS/ZxjtXa5Drv3Frniiit6/P2BnkCjOXRoJ7NGc5jT0dFBIpEglUod9O9YLBZsNpvKODzULFmyhOeff56Kigo2b97MUUcdpX5WVlbG1772NZqamsjOzuYvf/kLU6ZMOaTj0Y0xBhcvv/wyP/rRj1T27Q9+8AO+9rWvfe7r5ubmAvDQQw8xc+ZMPvjgA/WzdDpNMBgkKysLAI/HQygUOuC1BqIUQmdnJ9FoVDkC4/G4ciSL09e4gUwmkwQCAYLBoHqtOJpljhHJjJ6CVuJQtFqtuFwuvF4vra2t+P1+CgsL1e8bf8/j8fTaHNRXc8nhGtTsLrVi1KI+WIxZqT39TH5+qNal/lhvDgfEudxdd90YdDAGI4xBCWC/uUSylsVW5HUSIHM6neqw3NtoG9EcDNpO+oZ//vOfQJfu8idVP5nNZv72t79x3HHHsW3bNi6++GKef/75QVs1pekZY0B7sCdPvfTSS/z4xz8mkUjgdrtZtmxZrzau7H7OcblcwL7eIscddxyLFy/mrLPOoqCgYL/fH6gJNIcT8Xic733ve7z88svY7XZmzJjB448/fsjvq53MGs1hSjqd7jHDsPvhvfuBD1Baq2azGYfDccgOa8KXvvQlbrjhBubNm7ffz66++mq+8Y1vcPnll/PMM89w1VVXsWbNmkM6Hs3gIZ1Oc/HFF/PGG28wbdo09uzZw+TJkzn//PPxer2f+/qPP/44y5cvZ/ny5Zx22mnqebPZTFZWFjU1NQCEw2F8Pt8BrzPQpBA6OzsJh8PKMRiNRkkmk0SjUVKpFJFIRGUtR6NR1dgynU4TCARob29Xr5FsZtFwhkzt5e4Zq3a7HafTqQ7f0lxw5MiRhMNhldVqtVpJJpPY7fZe+T/rueTQkUwmicfjGQ2VepJAgMxggxzuJavVqO19IMxmM06nE5vN1uv/D20jvU86nSYWi+0X6BaH8GcJRnTHarXidrvV/aLRqLYRTb+i7eTQk06neeGFF4AuJ/PBMHz4cJYvX878+fP5v//7P2666SbuvvvuQzhKTV9hDGYa1xWRabPZbIOqYqq1tZVLL72UVatWccQRR/Dmm29yySWX8OGHH/bqfYznHKF7b5Hy8vIencwDMYHmcOPGG2/EbDazY8cOTCYTdXV1fXLfwVdnqtFoPjdyyBIHsxzAsrKy8Hg8GY+srKyMh9vtVk4dyXSMRqMZTb1OOOEEpk+fjtvtZsKECUyfPp0f//jHn3m8J554IiNHjtzv+YaGBtavX8+ll14KwAUXXEB5eTl79uz5zPfSfDxGR09Pj89b3nwo7Aegra0N6Nrg5OXl9YpD99133+XRRx/l2WefxW63K43urVu3UlpayowZM1i1ahUAq1atYubMmZ/7nn1BOp0mEokox18kEiGZTCqncSAQUJnJTU1NVFZW0tbWRnt7OxUVFdTV1dHQ0EBNTQ01NTVUV1dTWVlJRUUFe/bsoaKigsrKSqqrq9Vrm5ubaWtro7W1lfr6empqamhra6OlpYW6ujoikQj19fUkk0lisRixWIyFCxdy7LHH9oqd6Lnk0CDBikgkkuFgjsfjBAIB2traVPZ7KBQiFAoRDAZpb2+nubmZhoYGQqEQVqtVrT9OpxO73Y7NZlPyB1JZI/eMRCJEo9FenUu0jfQ+shcRB7PVaiUej6tqCGPlA6CqUeLxuJoHEokEHR0dmEwm7HY7brcbr9eLx+NRe5VUKkUgECCVSqnARSwWI5lMDjobqaqq4uSTT+aEE07grrvuUs8HAgGKi4tZsGABp556aq/dT9P7+xI9l/QNu3btorq6GpvNxkknnXTQv3fssceqisV77rmH1atXH6IRavoKCWYaz6uyrqTTabXHlf3ugRpUGx9y7pEG11KhdaBzUG/PI7t27aKwsJAjjjgCgJNOOomKigrWr1//ma7XE93POcJPfvITVqxYQWdnJ+vWrWPcuHE9/r7D4cDn82U8hgqShCPVnZK493kC471tI+FwmD//+c/84he/UPY+fPjwzzy+T4POZNZoDjPEaSST4MFk80j0V8pPHQ4HdrtdTaiyuErm2RtvvEFnZyelpaU8//zzjBo1CqvVqkqTvvSlL7Fz584e7/Xvf//7oKOcVVVVjBgxQjkXTCYTo0aNorKyUjfi62VEi9foKPo4jKXrVqv1oLVzTSYTb7/9NgDDhg3jtddeY9SoURmv+TT2YzKZeOqppzj//PPxeDy0trbyz3/+s1eyX2+//Xb27t3L6aefDsAdd9yRoa1bWlrK+PHjOeGEE5g4cWKPmfgDkVgspuYH+VpkM0KhkJKsEOdvKpWipaWF1tZWEokELS0tRCIRAoEALS0tB20zADk5Ofh8PtLpNC0tLSrLubW1FZPJRE5OjpqDnnvuObxeL5MmTfrcdqLnkt5HHMxiS1arlVAoRCQSAdgvMzmdTqsMZrvdTmdnp8qaj0Qi2O12srOzcbvdB8w0koOkyLq8+eab2Gy2XplLtI30PsbmoA6Hg9bWVvW9y+UilUoRCoUyNB0PBqkkycvLw2azEQwGVbBD1iRZ01avXo3JZBo0NqIbMfU9vb0v0XNJ3yCyVLNnz1aVDAfLhRdeyKuvvsof//hHvvOd7/Dee+8NmgxXzf5IZQygzrAmkylDxkvWo2g0CqCymj+pZ1FP2Gw2HA5Hxtmnt+eRCRMm0NjYyDvvvMPs2bN57rnnCIVC7Nmzp9eSWrqfc04++WS+/OUv79dbJC8vr1fuNxiQoET3jHgjUpVps9k+1bzR2zaya9cu8vLyuP3223n11VdxuVzceuutfRKI1k5mjeYwQ5xGJpMJl8t1QK0xOayHw+GMA57FYsHr9aqMMpvNpq4pzmaAvXv3kkwmGTlypCp3TyaTuN1unnnmmV77/3SfvHuzUZCmC2Pjt4NFnEdSmibOI6PD2dj4SwIQIoUg9tN9cQU+lf2kUinuvPNO/vWvfzF37lzef/99zj33XDZv3qy0xj4r0rHcSHdt3TvuuONz3aOvkUxB6PpsSUO/zs5O9Tl3Op3s3r2bSCSCw+GgurqacDhMKpWioaGBeDxOY2MjwWAQgPz8fBYsWMDEiRPxer0kEglaW1upq6ujpqaG+vp69u7dSzAYpLW1ldbWVubOnUt9fT0tLS243W4CgQA5OTkEAgGcTqeS7Kmtre0VO5H/rxE9l3x20um0cjCbTCacTicNDQ3Ktsxms8pWPRA5OTkMGzaMaDRKMBgkkUjQ0NCQIdNk1PCWzbxoBiYSCWKxGI2NjdpGBiAi2QVdwe5gMKgaqPr9furq6jLsw+FwZGSxWywWdfg3Nh6Vg59kyI8ePRq/34/VaqW9vZ1QKERRUZHKOuvo6BhUNqIbMfUfdXV1g8ZONPuczCeffPJn+v077riDJ598krVr17JixQoWL17cm8PT9BEdHR1qzusuTSdSGVarFYfDoc6qcj45GIyfZfkcSwKWy+XaT1Kyt+YRv9/Ps88+y4033kgwGGTevHlMmTKlV2WgejrnCN17ixwOpFKpjGx4IKNPiOwpJFgRi8Ww2+3Y7fZP1ai8t2wkmUyye/dupkyZwl133cXGjRtZuHAhW7du7VHepDfRTmaN5jDC6AQ2OpjT6bRyJEmETjIXBWPEt62tjUQiQXZ2NhaLBbfbrSZVcSps376dGTNm4Ha7VdMwyaK+/PLLeyWTuaSkhOrqapVFnU6nqaqq6nFS1nw2jJszKU03NskwNoQTzV1xFovTR+xCnACfhNlsZsOGDcyYMaPHn3+aKO6GDRuora1l7ty5ABx33HGMGDGCjRs3fuaDx1BG3h+LxaKyOeT9i8Vi6nXBYBC3201jYyPhcBi/38+2bdtUNkgwGMThcPCd73yHCy644KA2vc3NzTz99NMsW7aMDz/8kBNOOIGysjISiYTKPBQnZSqVoqOjg82bNzNt2rQer/dp7ETPJb2LrCcmk4msrCzlxJPNeH19PYD6udPpxGKxKKmLUChEa2srsViMyZMnk52dTSAQIBAIqMz67uTk5JCdnQ10OS1lDL01l2gb6V1kLyK67DK/5ObmsnfvXuLxOBaLhby8PHw+30H3fpAAeUNDA9FolNbWVoYPH47b7SYYDCqntMwpHR0dbNq0adDYiG7E9PFI4EF03HtqMttdDx7ICHIfKPNsMNnJ4U46nWblypUALFiw4DNdo7CwkCuvvJKlS5fym9/8RjuZByEdHR2qekqqpKDLPqR3CHQ5nyV5yuFwZEgASp+I7nOInHvkrCxVdmazWSVlRCIR3G53xvrVW/MIdMlJip3H43GGDRum5DM0vYecgYxJODabTSVBpFIpzGazalpuMpmU30MCu8ZeNGJP3b8XestGRo8ejdls5pJLLgHg6KOPZsyYMWzZsuUzz4sHi3YyazSHETI5ShaQPNfc3NxjxNZsNuN2uzMWyFAoRCAQIBKJYLPZyMrKyogECw0NDZSUlKhNu8ViUTqvjz32mDoYfR4KCwtVl9TLL7+cZ599ltLSUl1u2EvIYR1QUX4JNkQiEYLBYI/OHiN2u11lkUkgwohxgZXM587OTsrLyykuLu7xmp8miiuHue3btzNp0iR27tzJrl27mDhx4kFf43BC3h+bzaYa+XUPTknTiGHDhvHBBx+oKH08HqekpIQ33ngDs9nM7373O4499tiDum86nWbr1q28/vrr6tqtra1AZjNScQiIk6Curo4RI0b0eM1PYyd6LuldZK1xOp1K8gK6Mm9kc1xYWMjw4cN7dB6Gw2F27txJNBqloaGBYcOGKSeyBLUk+CEb+NbWVlwul5qnLBYLHR0dVFdX96jpD9pG+hPjwdzYHwK63n+AUaNGfWr9fHEEyPVl/jA6CIzPp9NpampqBpWN6EZM+xBnsaxT3RtIfhak+Wx3Z/Ngs5PDmR07dlBXV4fD4WDOnDmf+TpLlixh6dKlvPzyyx+7lmgGFuLck8QJqagymUx0dnZSX1+fUdGRSCQIBoPq3Ct7CavVmpFUI/Jdcp7tCZH2MpvNav+TlZWl1pzemkegK+NVNHZ//vOfc8oppzB+/PhPdQ1Nz8j5R5JnBJvNRiKRoLq6ej+5jGg0SktLCz6fj/z8fBWMkKBnT+dg2JdR73A4sFgsvWYj+fn5nHrqqbz88succcYZVFRUUF5ezqRJkw76Gp8V3fhPozmMkInNeKgPBAJ0dHRgNpuVhpTb7SY3N5dhw4bh9/tVRqrJZMLr9Srhfmmk0xNz585l9erVqrGJRPiAjMjvwXDdddcxcuRIqqurWbhwYcYCumzZMpYtW8bEiRO56667+NOf/vTp/iiaAyKZgIBy3ESjUdWszahb5nK5cLlcOJ1OlQ0g12hsbKS1tRWLxbJfI0mPx6M2cy6XC4/How7JRvv5rBQVFbFs2TK+9KUvcfTRR3P++efzwAMPHNCBfbgj77c4/btvoJxOpyqvlvd/0qRJbNu2DUB9NmfNmnVQDuZ0Os0rr7zCl7/8ZZYsWUJZWRl+v5+ioiKamppU8y6n06m6f8O+8sRZs2bx7rvvfm47AT2X9CZGB6IEqpxOJ6FQCOhyNpeUlBwwO9Xj8ajggQQbYN9B0efzkZOTQ15eHiNGjFB6m8Zse7GROXPm9MpcAofORro3c1uzZg3HH388J598MuXl5QDcfPPNzJ07l2uvvbZX7jlQ6O74NTqHP412fiqVoq2tjcrKSvbs2aMqICTzNx6PZ+yBjM2fuu9XPg+Heh7RjZj2VViFw2ECgQChUIhYLKb2oxJkkuQHqaAwZjQbv5fXiC2Kw8noWID997WfB73eHFqklH/OnDlqP/pZGDduHPPnzyedTvP3v/+9t4anOYQkk0lCoVBGZZ6cLVKplKqUMZlM5OXlUVBQoJzAnZ2dhEIhGhsbqa6upqqqSv1bWVmpqvekh4TT6cTj8eDxeFRAVKS95B7QtV8Wp3RvziO33HILkydPZvz48VRUVOh55HMiTmFZW6QRJOzLVG9paaG+vp7Ozk7sdjsFBQWMHDmSYcOG4fF4gC7/SHl5OS0tLUBXgo4xeGG32/eTjxS7jcVinHDCCb1mI3/4wx+4++67mTp1Kueccw4PPfRQnzT/M6W1CJRG02sEAgH8fj/t7e0DcuMuGqlut1tlMjc1NRGPxzOcx59EOp2mqamJRCKhNJrFWSCROlmAJSonSCd5m832uTZ+g5WBbiOC6E4BKprf2tqqbMhkMuHxeEilUup543LicrkoLCzEbrdn/MzpdCqHoRzwpEGCbMaM5W2yMTzc6A87SafT6v31eDzU1dWpDXcikSAUCpGdnc2mTZuIRqMUFBTw1ltvMWvWLP75z3+STCY599xzuffee5kxYwaPPPLIx96vpqaGW2+9lbVr1wJdtlFaWqrK5O12O4WFhdhsNoqLi8nKylKBL5/Ph9frxev14nQ6ycrKOuR/n4HGQJ5L2tvbAfB6vbS2thIIBPD5fASDQZqbmxkxYsQnbnJjsRhbtmzBbDYfsGxQ2Lt3L9FoVEkrQFfVTUdHBy6Xq1cafR5Kvv/973P22WerZm4ATz/9NHv37uX+++/n5ptv5oYbbuDJJ5/khhtu4Lzzzjuo7LyBbCOSZSYOvqamJkwmE4WFhezatYvOzk6GDx+u9Ie7k06niUajhMNhwuFwRoABumyvsLAQq9VKLBajra2NdDqN2+3G7/crR0FPmpkDmbPPPps9e/aoRkvSiMnv93PppZeSSCT46le/ytVXX31Q1xvINmJEssqMwQIjYkdARhMv2Y9K6btR2s2IBMs9Ho+qlIF9Gq666dvg4itf+Qr/+Mc/uPXWW/npT3/6ua61bNkyrrnmGqZPn84HH3zQSyMcevT3XCLVl8beD3J+SaVSBINBdR4xm80UFhaSSqVob2/vUSISyAhQAaoHhNVqJZlMEolEMnoL+P1+JRkHqIxm+flA34scavrbRg6EOJe77yNEFkP6iLS2tiopuNzcXBXcTqVSOJ1OcnNzcTqdNDY27nctuZ7ZbFaSkhKksNlsxONxFSgV2ZbBzODZVWk0ms+NMTtRNuNut5t4PK40Vg/msGUymcjJyaGpqUlpNH8cubm5KovZZrP1Wkmj5tBgdDCLPUgTAoCsrCySySSVlZUZmT6ycIqmd0VFBR6Ph9GjR6smkrFYrMeF12KxkJubi8fjUfIqHR0dpFKpw35T1h/Ihrr7v4lEArfbTTQaVVnFra2tFBQUUFtbS35+PtClhV1WVsaECRN6vP7rr7/OT3/6U0KhEHa7XWWWp1IpLBYLRUVF2Gw2bDYbBQUFKktEtM6MMjyHYxBiMCHOGqmWAQ5amx3YzxnUHZlvABW4FKeS8ToDme7N3GQ+zM3NpaysjPXr1ytd+fnz5/POO+/06GQeTE3dZA+SSqWUjqXoHubm5tLU1KSa//l8PrUmRKNRIpGIaixpROYIn8+nmhK3t7cre7Pb7fh8PtXYSeaSwcTh2IhJdNiN+0ZxKotkTiAQIBaLfWKVnDiQjfOSyKuEQiFCoRAulwufz6euDRyWSRGDFaMec2/03rjgggu49tpr2bBhg5bMGKBI0FHmCGkQK9J+xj2HZJHu2rVL7R1Ex10ciN2rH2S+SafTNDQ09LgvaW9vp76+npycHHJzc2lvb6etrY38/Hw1l0hlsGbgII2qjVVUopUfjUYztLuhy37S6TTbt2/fby9bVVWlsps9Ho/qASF2aQx0SsZ0c3MzHo+HYcOGqaB4LBZTzu3BinYyazSHEbIhTyaT6rDvcrlUJDYQCKjS0k/CarVSWFhIKBTKyCwxCth3dHSQTCZpa2tTWljGZoNyyNMMHIwOZtlQ7d27l87OTqVVVltbq15js9koKioiOztbNdXo6OigsbGRmpoawuEwH330ESUlJRQXFxOJRDL0qaRESH7HZDKpTPueso00hw5jJrpsrI1NT6AruzQ7O5vm5mbl/K+urubII4+ktraWPXv2sGjRIlasWMFtt93GI488ktH0L5lM8vvf/55HH30UgOHDh2O321UZ2siRIzM24iUlJTidTsxmM8OHD1fliVJmJg/NwMXo7JWMc9H7/rj53/i+iuO1JyTIacwSMjb0GgxOxO7N3IzZclJhIH87j8ejZEe6M5iauok8gWSoZmVlqeaO+fn5JBIJAoEALS0tquS0O2azWZUqZ2VlYbFYiMfjRCIR1XhJyMrKwuv1Kq1OIKOCRjPwMDaiFmS9kKzE7gkLxoxlOdzLQ/Yd3XE6nXi9XtXEOhqNEo/HVbZ4PB7/1PItmv5j27Zt1NfX43Q6Of744z/39fLz85k9ezZr1qzh//7v//jGN77RC6PU9BbdHcwul4tkMkltbW1G0EnOJ1KJK0HNeDxOLBbLCEDJOVacy0bZHfmZUSrDbrcTDodpaWmhtbWVRCJBbm4u0WiUQCCg1p5EIjHoM1SHEt0dzE6nk0gkoppVG19nNptJJpM0NDQoW3M4HBQWFio5uMbGRhKJBDU1NRn3MSbHiPaySGdIElZlZaXqZSVO6MEc3NROZo3mMMJmsymHnjgNTSYTfr9faex+Gsev2Wz+2HKXzs5OVXIv19UKPQOXRCKhDt+yyDU3NwNdm7NEIsGuXbuAfUGGSCRCWVmZKkW2WCwMHz6cSZMmMXXqVHbv3k0gEKCiooL6+nqKiorIy8vLcDym02mam5sJhUK0t7crfVVN/2HUrhT9UmmYYsy4tNvtRKNRxo8fz4oVK3jvvff4+c9/zpo1a/jwww+54447+MlPfoLZbGb79u38/Oc/Z8uWLQAceeSRxGIxTCYTM2fOVPrwNpuNcePGYbfb1Tw1duxYtcEXZ6I4ogeDE/FwonuwwuhklkoF0Z7zer0HvI5IpiQSCXbs2EFJScl+sihyHYCcnJyM54GMpj0DHWMzt9NOO009L855ObSEw+EDrruDqambyWRSDUOlQiIWi5FIJGhpaaGgoEDJrYjDWIJRxobEIo3Q2tq6n4auBEbF7qRxk/zMuA5pBhapVCojM1ne+7a2NiXHI4jzRhpyGZGKKGkqbAycSmDcWGGVk5ODz+cjlUrR1NSkqnOi0WiGhqZm4CJZzCeccEKvOfTOOOMM7WQeoCQSCeX0k3WkqakJ2KfHHAwGVcKMVEaEw2GSySThcFglwHQPUEtFnfFh1G8XpCJvzJgxVFRUEA6HVYKEXNdkMpFIJHRwcwAhMimyH5HqKdi3lxW5QOPzct5tb2+npqaGjo4OJaVis9kyGtLKeiNrjtlsVs2NzWYzJSUlKqDa2NhIfn7+fpU7gxHtZNZoDiMOJENgdNIYpTQ+L8YmLEa9XdhXoqjpfySzy+iYSSQSquGWw+GgublZLYo5OTkkEgk++OCD/UpTOzo6qK6upqamhvHjxzNp0iRaWlqorq4mFotRUVFBZWUlOTk5FBcX43a7MZlMZGdnq0XcWOqubaTvkA1V97+5fFalo7I4+lpbWxk9ejRlZWXY7XY8Hg+BQIBwOMwdd9zBd7/7Xf71r3+xfft2XC6Xys70er2cfPLJbN26FavVyhlnnMGHH34IwJgxYzjqqKMoKyujs7OTrKwsJkyYQCQSwWQyqWxFaegEgyNT9XBC5gSjxAqgSv/8fj8tLS0qu+dAmEwmRo8eza5du4hEImzfvp2CggJKSkrUtcXBLFrvsM/hJPccDEgzt+effx673Y7L5aKpqYmGhgZKS0uZMWMGTz75JN/61rdYtWoVZ599do/XEf3YwYLMKXLAMspwNTU1kZ2dzahRo9TrRXNTZDB6ClpLIEo0DcVWksmkciSaTCZcLpdeXwYgkl1oDBg4nU5SqRTV1dUZ80s0GqWtrS1jH5JMJlXAPBwOZ8jH9ITIM+Xl5RGLxWhtbSUSiTB8+HAVvMjNzVW2p4PgAx+RjekNqQzhzDPP5JZbbuHVV18lHo8Pqnl2KGOsTHE6napnEHRVrzidTnbt2qXmE5F4lPlB5JRkrkgmkxnnD2k4bbPZVHKDBKdkvZV1rLy8nOrqaiZPnkxTUxPNzc0MGzaMVCpFOBwmKytLVe4Mlr3JUEYC1NC1F5H1xWQykUwmaW5uVmuLrEsdHR0EAoH9AtqCaHF/HHa7nZycHCWnUVFRoRpdB4NBlTAhlTeDdZ+incwazWGGdG83Hs7koP5x+qZGDTuj0/jjEKekSGUYNwODqdHOUEYOTkYds3g8rhzMTqeTuro61eQxOzubnTt3qvJVv9/PmDFjKCoqwul00t7ezvbt26mrq6OsrIzKykomTZrEUUcdRWtrK42NjUQiEVVSNmbMGAoKCjLsSUpdQTsQ+5LuTmajZiXscwjJITsUClFcXExZWRl1dXVMnjyZdevWsWPHDs4880zuuOMObrvtNj766CN13UWLFvGFL3yBBx98EIBLL72Ut99+G4B58+YxceJEVq9eDXR1dR8zZgxVVVVAl0yA1WpVchnG5pGagYPMJZKxbMwSEWciHFwAyefzKSmW5uZmGhsbSaVSjBkzBpPJpLIWjc5qo97uYDnI3X777ezdu5fTTz8dgDvuuIOzzjoLk8nEY489RmlpKePHj+eEE05g4sSJzJs3r59H3DuYTCZVLppIJLDZbOTn59PS0qIcfKFQCJvNpspHu2fKGzPMREqh+3piXOPMZjMul0tnpA5ApJGWYLPZcDgctLe3K8kUyQIzSqiIDFxbW1vGIV8CC8bmf+I4EltJp9PU1tZSW1vL+PHjcTqdxGIxGhoayMvLU80G7Xa7kt/Q+9eBS2dnp8pkXrBgQa9dd/r06QwfPpy6ujpWrVrFwoULe+3ams+OMZnJZrNlnF2ys7PZsmULyWQSp9OpHHqdnZ1qrgiHw4RCIWKxmMpm7r7GyHxhfDgcDpxOp9pjFBYW4vV6SSaTfPjhh5SWlhKLxWhpacHn82Vo9xolKzX9g0isQJc/Yu/everc2dzcrORTRK6pvb1dBR9kHTCZTMTjcaLRaMa+0yipIn4VkVuRiqr6+noARowYgc1mo7a2lpEjR9LZ2UkwGFRBrN5M/Otr9Cqp0RxGSAQVujbqovUom3Kfz6fKm8PhMNFoVB3+updtSOmix+MhOzt7v0mwo6NDHRY8Hg+AWrzF4aDpX6RkTBZOyRaSw5vL5aK6ulo1ZjKZTGzevBnoKkmbOnUqw4cPzzjQ5+TkMHv2bOrq6ti8eTPhcJhNmzbx0UcfMXHiRKZMmUIsFqOmpobW1lbKy8vxer3qwC/BiMGkpzoUMb4HgrwXokUXi8VU2f7evXsZN24c69atY8+ePQCcdtppHHvssbz99tuk02mOO+44vF4v3/ve9wA4/fTTaW1tJZVKMWHCBE499VSeffZZAKZNm8aRRx6psp+lQ7d0CpfAla6IGFgYncg2my0j0Gg2m6mvrycYDKrO3AeD3W6ntLSU7Oxsdu/eTWtrK16vV+n2yvXl/hLINGaxDnR6aub2zjvvZHx/xx139NVw+hQ5fImGrsPhID8/n2AwSCgUysg2gq55yOl0qrLUT3qPjR3bxUEwWOzicCKRSGT0g5BAQENDg9qjymFc9pIWi4XGxkblWALUHrOtre0Tm1JDV4BqzJgxBAIBdu7cSWlpqdLlzM7OxmQyEQgEKCwsVNlsHo9H29AAZevWrTQ1NeF2u5k1a1avXddkMrFw4UIee+wxVq5cqZ3MAwzZC8paIZV1yWQSu91Ofn4+77//Pul0WjUDlDUmEAgouchoNEooFCKVSpFOp7HZbEp72SjZJr0R3G43drud+vp6GhsbGTlyJOl0moqKCgoLC9X52Wq1Eo1Gcblc6tp6Duk/jPKg0hiys7OT5uZm5URuaWlRfoxEIqGCoLKvhX0NI419howJO2azWVVfyvtts9kYPnw4ALW1teTm5uL1emlublZVvS6XS0m7DNZzsHYyazSHCbJ4yqRqMplobm5WB3KPx0MymaSqqopIJPKJ2sniRI5EIrS1tTFq1KiMyKxkR0vEV7KQYHAd/ocq0txG7EGcNI2NjcC+DOZUKoXT6SQcDqvI69ixYznyyCNJJpNUVlaqDZnNZsPj8ZCbm8uwYcMoKiqioqKCHTt2EIlE+PDDD6msrGT27NlMmDCBjz76iEAgoDKGoGvxFYfAYNJTHcrIeyDZW8lkEr/fTywWU3azd+9ejjvuOACl2w1dDXO++MUvAl2bsbvvvpvW1laKi4tZvHgxv/3tbwE4++yz2bFjB8lkkvz8fI499lh27dpFOp1WwS/YJ38gDVy0fQwsjM06rVarWgeysrIIBoNUV1cDMHLkyB4bmkjWqTSAEyejzWZT80pdXR21tbVKGxz2BUCM99eBzMGBZKDJQU2e8/l8eDwelYkqjdcOxrFsRJwOxqwzzcBC9iPQ9bmVIGJjY6NyMHs8Hnbt2qV6ATQ3N6s9idlspqCggGAwyK5duzK0Mx0Oh2psHYvFsFgseL1esrOzSaVSBINBNm3axNFHH00gEGDPnj1MmzaNVCpFfX09I0eOVPrQsu7o5l0DF5HKmDt3bq+vAQsWLFBOZs3AQBJUZO2XvYAxOcrj8aiKOK/XS1VVlZLICIVCKtkmEons17DNeJ+cnByys7NVgo7b7VZ9Q3JycrDZbFRVVVFUVKT047OzswkEAmRnZ6s9syRzDVbn4WDHKA+ZTqdVlnJbW5vKXK6vr1dVtVJ9C13nGGkEG41GD0oeA/btaex2O6lUisrKSlwuF7m5ubS0tCi/jEhIyj7W2Lx4sKGdzBrNYYA4mI16yI2NjSoCZzKZaGhoyJjMbDYbbrdbdT81NjyRbDUpBRKxetEUkm6tgNJvNcpk6INe/2LMMjcuamIDVquV9vZ24vE4NpuN9vZ2mpubMZvNzJgxg2g0yvPPP09tbe0B7+H1epkwYQLTpk1j9OjRVFZWsnXrVgKBAG+99RYLFiwgPz+fQCBAMBhUDiOr1TrkpDLi8Tj/8z//Q1NTE5dffjlf//rX+3tIPSKfb2PjE+Pz8n7E43FycnKor68nlUrhcrmIRqN4vV5MJhNVVVVUVVVlNBzr6Ojg4YcfZt26ddhsNv7f//t//Pe//yWdTnPkkUcybNgw1qxZA8CUKVMAVNPJ3NxcAoGAyoaFTDvRDByMWsgiUSAN1rZv3w50vZ95eXlKlzkSiajmb9LgrSfa2tpU0zdjxQXsy14Um9ABqsHFgYLa0rTp89C9OkYz8DDuD8UJI42AoStY+dFHH9HR0YHb7WbPnj0EAgEsFgsTJ06kvr6etWvXqs+/7CfKysr2084U+YumpiasVivTpk1j7969bNy4kZkzZ9LS0sKOHTsYP3680oaWbGbRaY7FYioDXzOwECdzb0plCHLN9957j3A4/LnnJs3nR6QJxPknGcahUEidP40NQSXwLedfed5isVBTU4PdbmfRokVMmDABgOrqatauXcuePXtobm4mGo0yffp01adGmr1JhZXL5VJNzqFrbpMqUZGrlP2rnj/6HmNvBovFQl1dHYCq3A6HwzQ2NqpgogQyZR2Rn8G+/h+SsSySTPKAfc2v5b0Xe83Pz1e+lMLCQlpbW1XTSofDoZIrBvM555A6mcvLy7nyyiupr6/HYrHwzjvv6AlZo+ljjJIIEpULBAJ0dnaq8g/BZrPh8/nIyspSHXelqYoc9nJycpQeqsvlwul0UlFRQTAYVBOpaBNJWZEs5oDO/uhnjJsqs9msHMzBYFBlEkmpKXRtyJqbm7FYLIwcOZI333wzo7N7Tk4Ofr8fq9VKMpkkGAzS2tpKMBhk/fr1bNy4kdmzZzN9+nSKiopYtWoV4XCYDz/8kCOOOALo2gAaHcuyOA+VDdhTTz3FaaedxtVXX81pp53GZZdd1mMWZ38jkXTZKNlsNpLJpHLsyr/BYJBhw4bx0UcfUV1dzcSJE9m4cSPbt29n1qxZvPvuu9x///1897vfZdiwYWzdupW///3vlJWVYTKZuO666wiFQmzYsAGTycSpp56aobE5cuRINTeJHh50zR1GzTM4sHNK0/eILiqgMg2hS1qnqqpKSSHIftCoUWjEbDbj8Xjw+Xx4vV6l2VtZWanWMqfTSVNTk9pTyrU1g4+Ojo4eezVIs6R4PJ6hpSu6hvK7ksEm8gnSOFGuJX0G5B46w31gYZRxkyq3dDqt1oPs7GyampqUs6axsZFAIKCawr700ktq71JcXIzZbGb9+vVKUsPj8ajriWOwpKQEp9NJdXU169evZ+bMmdTX17N582bGjx9PNBpVY5ISeMl8lCCXyGZoBg6dnZ28+eabQO82/RPGjBlDSUkJVVVVrFmzRktmDACkEjMSiSjtZaleMOrmypohFZyBQEDNEfn5+axevRqbzcaDDz7IxIkT97vP5s2bWbp0KR999BEffPABN9xwAy+++CLt7e04HA6VsexyuYCuxtiFhYVKXzeRSKhKCKvVOqgzVAcrxvOv1WpVyXapVEolVslzgUBANRdOp9Ps3bsX2KeRnEqlVL+ig8Vms1FUVKRkoPx+P1lZWbS0tFBUVEQgEFB+GOlRMZjt5JA6mS+//HJuv/125s+fT0tLiz4AaDR9jGyEZZIUfWV5CB6PB7vdTigUYs+ePRkC992xWq2MGTOG0tJStbhbrVZSqRShUAifz5fhMDSZTBmNoHSznf7D2OjA6GBOJBLqQOd2u6moqFAR2ubmZvW1NGhzuVxMmzaNyZMn4/V6CYVCtLS00NHRoYIU1dXVbNiwgbq6OlavXk1TUxOnnHIKxx57LG+++SaVlZWMGTNGZSAYS5eGGuvWreOqq67CbDYzdepUtm3bxowZMzJeY3SCAAQCgb4epnLiJJNJVQ5sdDLL5zoajTJu3DgsFgvt7e1MnTqVjRs38v7773PJJZewbds2Kioq+M7/x95/x9ldVfv/+PP03qamTTqJJCGQ0BJS6BERpIgilyJFxasIXK6CyEcRlCL3p15BRVSQKgKCgHApAYEkmISQ3tukTZLpM6fN6XN+f8x37bzPyaQAycycyX4+HvPI1HN25uzZ5bXWeq2bby54fJfLxXe/+13MZjN//etfga5mf0OGDGHVqlUAVFZWqqwQ2NMtHFBjM3rZlXKUv78h81cuc3KYz+Vy6rCeSCSUJY9YYMj+I/NOMg1FYAyFQgwcOJDKykoWL15MIpFQARHp1p5IJJSADV17nwQ9NX0Xo02CZLyLhYF8vvj794fMnWg0isvlwufzqYtaJpNRgrW27Oo7dNeDQSobTCYTPp+PtWvXAl3nk1WrVmGxWBg2bBgvv/wy+Xye6upqqqqqmDdvHrFYTL3GTU1Nqkxe6OjooKmpCavVyqxZs1i1ahVLly5lwoQJqjIPYNu2bQwfPlztOYAqe89ms2SzWZ2N2MdYuXIlra2teDweTjjhhEP++CaTqcAyQ4vMfQNpzCfVUMFgkIaGBhKJBH6/n3A4rIKO8rdsTH6Szx133HHdCswAxxxzDL/73e/4/ve/z9KlS1m4cCE/+tGPuPfee2lsbOTkk09myZIlNDY2MmzYMHXPlsQrCZ5Lj5tSFg9LEfHihq59RirooCsgILYZ0FWRFw6HsdlsNDc3E4lElN3F+vXrC862JpMJl8u11z4mjSHdbjdWq5W2tjaam5upq6vD6XQyYMAAwuGwSpqJx+PY7XZCoZA6C3s8HuX1XIrnlcMmMq9evRqbzcaMGTMADrrBi0aj+exICYZRtOro6CCbzRbYZkgDgu3btxcY2UPXpu12u5WPYS6Xo729nWQyycaNG2lvb+fYY4/FbDYTCARoaWmhqalJNeOBPR1YjSb4mt7D2HjR5XKp8vLGxkblXdjQ0KDKzlpbW5U/aiaTwWKxMHnyZI4++mi2bdvGW2+9xebNmwua7kDXej9z5kwuvPBC1qxZw5w5c1i/fj3t7e2cddZZKhNk2bJlVFdXE4vF6OjoUIK3bNj9xbMsGo2qsj0p5Svmvvvu46677urpoe2FiH1yEI7FYuoiLUGESCRCQ0MDY8eOZc2aNdTV1TFu3DjWrFnDK6+8ws0338zrr7/OkiVLVCbZKaecwimnnML8+fPZuHEj0NXc79xzzyWZTLJixQoAVaIo65FkjcEekRkoEA+1mNj7GD33nU6nqoSwWq3s3r1bZatms1ksFgujRo3C5XKxefNm1q1bR3Nz834vXR6Ph5kzZ3L00UerLEWx5HA4HEQiEfx+v8pylYwV3aCr7yLnEUBVPcVisQKPQ6M/r1zijHY+xi7u8jkJ2En5q9/vV/udVEjkcjnVWE7TuxjPh/K3Kpd4t9tNa2srnZ2dar0AqKmp4c033ySfzzNmzBhqa2vVfmM2m2lvb1f7rN/vZ/To0QwaNIhjjjmGYDDISy+9xMKFC3n77bc5++yzWb16tQrs1tbWqmxmmR/Nzc1UVlaq5oSSWCHrmaZvIFYZ06dPP2y2fKeeeipPPfUUc+fOPSyPr/l0OBwOFVzO5/O43W46Ojq6rVwxmUw4HA61zoh9woHOCna7nauuuoqlS5eyYsUKysvLOfPMM3nppZeor69XvWZcLpcKkkHXXme329X+Bf0zoaYvk8lkVEWUzWZT3tvSLFI8uQGVtZxOp1XT13A4rBqRS6bxhg0bDjrRxefzccUVV7Bt2zbWrl1LQ0ODely32008Hsfr9aoGyMZEQOl5VGp8YpG5s7OTcePGccEFF/CLX/xCff6tt97i/PPP55lnnuErX/kKGzduxOv18qUvfYm6ujouueQSfvSjHx3SwWs0mr2RjGURZsTbTszsRZCx2+1s3bpVCdHGTTccDtPe3q4WXLvdzoABAxg/fjxer5d169bR1NTEihUrmDhxovJMzWQybN26lcrKShVVbm1tJRgMAqVtYF/qSDYydB3GxD+qsbFReYXF43E6OjpIJBK0tbUVeCKWl5dTVVXF8uXLeeWVVwpeS5PJpCwz2traaG1t5eWXX2b58uVceumlnHfeebz11ls0NDTw7LPPMn78eOX7LBd9aZBh3FjFk6rU8Xq9SjSNx+P4/f69vuf222/nlltuUR9HIpECT+OewmKxKJsMafooQl5HR4e6UDc1NTF69Ghqa2tVI7/m5mYaGxv561//ygknnMCsWbPI5XLE43HWrFnDk08+qZ7j7LPP5rTTTqOtrY0PPviARCJBIBBQWSSSWe/3+5UQZbPZCixdtNdq38BYIWG1Wsnn80SjUVWSKmKiCEU1NTUsX76c7du3FzyOxWLB7Xbjdrvxer1YrVbS6TT19fXE43HefPNNzjnnHCoqKmhublbl61KC2t7eTllZGW63m2g0qoTtvmhNc6RT7ItoMplobm5WWTtS4dLe3q7OMkaMwnLxucJut6vggrEMNhgMYjable93PB7XzQD7AMZeIYIIxB6Ph9raWmCPBY/JZGLFihXkcjmqqqqYP3++mksdHR2q+kqqYFasWMHy5cvVY1922WX87Gc/4//9v//HRx99pJoBbtu2jeOOO46mpiY1lsbGRoLBILFYjEGDBqlMSZ/PB2ihqK8hDfkOh1WGcMoppwCwaNGifnNG7Y5S6SUiSPJMPB4nm83i9XoLes/InmHs6yHZzbLerFmzRtlaHAh5HAlOOZ3OgqCpPG8xsmaUytqxr3lQavPDaOUm9zGXy6Uq6+Q1E7uTAQMGsGjRIqV/iOXvxIkT+dvf/qbOHaNGjWLSpEkMHz6csrIylaUu9+ht27bx0UcfUVdXx8MPP8zf//53Hn74YWpra1VgJJVKqec12n/JOeiIEZnNZjO333473/ve9/jhD39IKBRi+fLlfOUrX+Hee+/lK1/5CtD1Ys6dO5dly5ZRVVXFOeecw4knnsjZZ599yP8TGo2mCykHkcVPhDyxzZCmbp2dnWzYsAFAlRhv3ry5oJGSkXQ6zfbt29m+fTsDBw5kypQpbNq0icbGRhYvXswxxxxDTU0NO3fuJJVKUV9fr7rAZzIZYrGYet7+VF5YSpusXOSMHretra2kUilMJhMWi4WWlhaSySRtbW2qIZ+Una5Zs6bgolZeXk5ZWVmBDUswGOSUU04hl8sxe/ZstmzZwq9//WtmzJjBrFmzWLFiBdu3b2flypW4XC48Hg/19fWUl5cDe6K10nBQ/DZLfb5MnjyZOXPmMH78eJYtW8Y999yz1/c4HI4+YyklTUoku1387CRD1OfzEY1G2bp1K1OnTuX9999n586dfO5znyMYDLJhwwYWLlzIwoULCx7XbDZz7LHHMnPmTOLxOG+99ZZqHulyuTjrrLOwWCxEIhF1cPd6vSrQIYcsEZi1yNw3SCaTan1xOp00NjYCXedAabgjArPJZOK1115Tr5kEEZqbm5XICF3CY01NDSeffDKnn3468+bNY8OGDbz33nucf/75NDc3k8lklN+uw+FQ5Y3SfCeRSKi1pNTXkP6EUWA2m80q6xi69im5dAnGBjrGi9e+kDJlsVqQPaqpqYlQKITH41FBD7ET0z7NvYfRTg1Q5wnoes1l/ZcGTSaTidbWViwWCytXrlRVVlu3bgVQgQT5GGDMmDGUl5ezcOFCnn32Wa655houueQSPvroI9asWcPUqVNZsmSJCkg1NDQQCARoamqiurqaZDKp5qQEQjR9i1wup/yYD0fTP2Hs2LEEg0Ha29tZvnz5YbHl6AuUSi8RI8ZzqjEQKZmibrdbWRxIQoVUyIVCIdra2nj99de56KKL9vkc//rXvwAYP3487e3tKqN94MCBzJkzB5PJVCBuy7jkY/lcqawh+5oHBzs/+oIVIFCgi8j+4na7C8bWHUOHDuXDDz8E4D/+4z+44447yOfznHfeedx00037tFcxPq9UeEJX4HLo0KHU1tbu8xxjnDciMpcin6pO7PLLL6eiooIHH3yQuro6vvjFL3LllVfy/e9/X33PkCFDOPHEE6mpqcHhcHDuueeybNmyQzVujUZThGSTSTmIlBDLhU3EOrPZrA7f1dXV1NfXs2jRIlpbWzGbzUpU2r17N5s3b2b79u2kUimVqbp7927+7//+j7KyMiwWC21tbcybN4+NGzcSCASorKzEbDarLDMpLxTx4JOY5Pd1ZJOdO3cuL7zwgro490WKfbKTyaQqS/Z4POzcuZNMJqPKTKPRKJlMhnA4zJYtW8hkMlRWVnLUUUcRDAZZs2YNs2fP5p133mHevHl89NFHvP322zz44IP8/e9/Z8aMGQwZMoRUKsU777zDH/7wB9asWYPX61V+WC0tLXR0dNDW1kY2myUSiZDP54nFYupQVtwZvhS59NJLmT17NieddBJf/vKX+/xBXTJCAGV3IWsKdB3SRIhuamrilFNOwWKx0NDQgMvlYtasWUyePJmhQ4cyaNAgxo0bxxlnnMGFF16Iz+fjrbfeYu7cuUpgHjFiBBdccIHyuhQ7Dem4DBAIBAoy8WVsmt7FWNYnF7lkMqmagMqeJN52S5cuJZ/P4/P5iEQifPzxx6xevZqGhoaCssNcLsfWrVt57rnneOqpp5g+fTqhUIhkMkltba0KTIl3s9Dc3Ew0GlWXR/keTd9AAt6wp8pK5ojRh1vsVyKRCK2trbS2ttLS0kJrayttbW3K/gtQzf6cTid+v5/y8nKVxSoVVmIN1dzcTCKRwO12q6BVKpUquX0mlUpxwQUXMG3aNP70pz8d8PN9FWM/BpvNRj6fVw1DfT4fdXV1QFfwateuXWSzWbZu3apeSylz3rp1q2oIunnzZnbu3InH4+H666/ngw8+4I033uDRRx9VtlUdHR1UV1cDXaLH+PHj1fsmk4m2tjbcbjew5+xkvOiLOKDtVvoOy5cvp729HZ/Px/HHH3/YnsdsNjN16lQA5s+ff9iep7dZvHgx06dPL+glUkwqlVJJAcbkgN7E6L0sa7z8K5WTfr9f+fLb7XZMJhOjRo0C4JFHHikIUBlZv349//d//wfA+eefz4svvkgqlWLYsGEqCWfYsGGqgkpEb0m6KhaZS+EMu695cDDzA7qsAAOBgHrrjQpNKLRlMvaZkfuYBJplP5LgZlNTkwoabN68mXw+z5lnnslvf/vbAwrMW7Zs4cILL1QC8/jx43E4HMyZMwdABdeN88Nms6nxybzt7OwsyUrwT7U7Wq1WbrvtNh588EHOPfdcJk+ezIMPPljwPSeeeCINDQ20tbXR2dnJnDlzOProow/JoDWanqDUDuvpdFodhh0OhyozzWaz6pIVCAQKPO2WLFnCrl27sNlsDBw4kLq6OlauXMm2bdtUuXEymWTbtm0sWrSIHTt2YLVaSSaTfPDBB+oy39nZyc6dO/n4449VAy8RqSSK2dHRobKUSnGx7I5SPITJAUc2ULfbzc6dO1U3XbFLEasT8YkaNGgQq1atYvbs2SxbtoyOjg6sVitVVVWMGjWK448/nmnTpuHz+WhoaODRRx9l+/btjBw5koqKCgDa29tZt24d27ZtUxYubW1tJBIJotEo2WyWdDpdELk1enqXKk6nk3/84x8sWrSI73znO709nIPCarUqMdfY4FOyUaWDdyaTobW1lalTpxIIBNR6EY/HqaioYNCgQQDs3LlTiYn5fJ6ysjImT57MV77yFc444ww8Hg/pdJrVq1eTSCSw2+34fD7l1R0MBguCIsYDen+46JfafgOFnbrtdjudnZ3KP7Wjo0NlOJtMJhobG6mrq1OXrMWLF1NfX6/WkGAwiNPpJBAIcOyxx3Lddddx3nnnYbVaWb16NW+99ZbKGFu5ciWDBw9WdgiyjggiJMrlQYQoTe9ibPInlipyJhBxOZvNqsBjJBJRAQzxXpbH6ejooL29nebmZhoaGti1axd1dXXU1tayfv16wuEwfr9fZbV2dHSoC1s4HCYSiaiLHaAqvUqFfQW4Dybw3ZfOJPL6WiwWrFarCjpIUFOqIkR4lr9lmQNy9oSui/jatWsxmUxcfvnlvPfee9x6660MGTIEgEcffZRIJMKgQYM4+uijaWhoACAYDKq+QfF4XJ1dBTmLGDPhjEF7Td/A6McsQuPh4kgQmQ+2l0hfEBCNyD5h7Oki/8q8kGZsXq9X/b1Ho1HGjh1LPB7nhhtuYM6cOQV7wpo1a7jtttvI5XKceuqp+Hw+5s+fj8lkoqamRu05EtSWhKtQKFSQuGE8r5bC3WZf8+Bg5gd0WQGGw2H1VtyItacwivvG17yyshLoWudNJhOpVAqr1UpHR4eqkJOApOw1Igjvjw0bNnDRRRexfPlyfD4fN998M5///Oe555576OzspKqqCofDsddclL3PbDZjtVr3arxeSnzqVfjyyy/n5ptvJp/P8+yzz+610VqtVu69915mzpxJPp9n1qxZnHfeeZ95wBpNT1FKpUKdnZ0FTZckC1XKQaErK3Dt2rXk83mqqqpYt24d7e3t+P1+gsEg77zzDtDVtM3r9VJXV0dbWxt2u52KigqVmbZmzRoGDx6M3W5ny5Yt6mdCoZBaB7Zu3YrVamXIkCFqbFIWKw0R+urv8pNQSg3djBus+EXBntLlRCJBIpGgvb2dzs5OwuGwyl7esmWLCh4EAgEANm7c2K29ynHHHccxxxzD+vXrlcWKIBYbkiGdSCQoKyujvb0du92O1+slnU7jcDjUvyIm6uZdPY8Ih2JLYDzkJBIJdSCSUvSjjjqKfD7Pli1bVIBCsNlsVFdXU1NTw9ChQ5VQnMvliMVitLW1sXPnTtVIadCgQUo4lOYY0LW+OZ3OggaA/UFkLqX9Bgp9mCVTZ/fu3WptkWxmERMbGxuV97o0VSkvL2ft2rUqc11YtmwZr776Ktdccw1XXHEFjz/+OB988AEnnXQSZWVltLa2smrVKkaMGEFtba3a41pbW9WFrqmpicGDB6vS2f6y55Qqxj1H/JClQbFYVsgZQy7ewWBQrQ/JZBKTyaT8uv1+v2q+Jv6F4v0tVk/RaJQhQ4aoRjrhcJjy8nISiYRam/x+v/JAzGQyfcay6EAsXryY6667riDAPWnSpH1+3khfOZMY+0TIXtPc3AxAKBRSYoTb7WbTpk1ks1kaGhpIp9M0NTWRTqdVo+JcLkddXR3l5eX85je/USKgsHv3bn73u98B8N///d9YrVb+/e9/A13ZZcb9ReaGBNBkT5IziAhVxow4Te/z7rvvAnDWWWcd9ueS+SVzqD9SSr1EusMo4Bp9mGFPNZzX6yUWi6lzRU1NDZ2dnWzcuJE77riDyspKhg8fTnt7uzqnjBw5kttuu00lA0yaNEklbw0cOJBIJMKAAQOArrORePRKE2upKJZ1q6+vIfuaBwczP6DvWAEaf+der1fZeUryTCKRoKKiQtlqNTU1EQwGaWhoKNA2AGbPns2SJUuYPHlyt89VW1vLFVdcQVtbG8cccwy33norjzzyiBKpBwwYgMvlwmazEQwGcblcKslCqn3dbndB5nsp8qlF5htuuAHoyhjZVyT3C1/4Al/4whc+7VNoNL3KwRzW+4rXkGRamM1mddHK5/NqbH6/n2g0qryRXS4XW7duxWKxMHbsWJ555hmg6+D0r3/9a6+GTPLxyJEjMZvN7Ny5E5PJRFVVFWazmVwupwTHsrIylbm6detWBg4cqHx2xQNLSpT6+uZ6IErpEGaM7hsFOmmSE4vFiMfjqlmWNEqqra2lpaWFiooKhgwZwksvvVQQ0BgzZgxlZWU0NzezZs0ali1bxrJlyxg5ciTHHHMMJpOJ3bt3Ew6HaWlpoaWlBavVyogRI0in00SjUaxWK6lUSjWY83q9ZLNZbYvQy0jGsogwLperILNDhGaxP5FM41GjRnHssccWZHuZzWYVyFi5cuU+o/Jut5vKykq1lopFgpQ+SsaZrG2l2AyjO0ppv4Gu4JS8hm63m9bWVmWdIa8zdK03u3btwmQyKYFZgkgff/wxABUVFQwbNoxQKER1dTVr165lyZIlPProo9x8882MHz+e1atX88orr3Duuefy2muvsXbtWtxuN4MGDWLXrl0qE9XYaLalpYVQKEQikShZT7v+gAQkJMDZ0dGhBMZUKqWC4SLqyXll0aJF+33d/H4/gwYNYsiQISrwDV3ZsVu2bKGpqYm6ujqGDh2KyWQinU7T3t6uglbxeByHw4HdblfNkvvCZfhg+CzZZX3lTCLBAehax9va2sjlcsrqRs6UxmAWFIq80jdgw4YNVFRU8Le//Y0RI0bs9VwPP/wwyWSSE044gQsuuIC2tjbeeustAM4880xVviy/MwmmO51OlVEmSH+Rg8lm0/QM6XRa+TH3hMh80kknYTKZ2LZtG7t372bgwIGH/Tl7mlLrJSLs72+yeA2R7xcxeMeOHZx44omcdNJJ/OMf/6CpqUk1hjOZTMyaNYubbrqJlpYW1qxZg9lsZuDAgezatYsRI0YQiUSUHUcymaSmpkbdfZ1Op7ICFMGzFKpn9jUPDmZ+9CUsFouq7nY4HFRXV1NXV0ckEmHIkCFs2rQJp9OpEmqM/6ZSKRWIGDp0KNu3b+eKK67gxhtv5Nxzz2XQoEHKyumtt97ij3/8I7FYjDFjxnDppZdy1113kcvlKCsro6ysjM7OTux2O2VlZTidTioqKpSFi2RZBwKBgsbGpbjXfCqR+cc//jGvv/46CxYs4KyzzuLRRx/lu9/97qEem0bTq5RSlqpcxGw2G+3t7QAqs8dqtVJeXq7KukaNGsW8efOArqxTOWjPmDGDN998k7q6OsrKyrjgggtIJpOEw2FaW1v56KOPqK2txWq1MmzYMHK5HPX19WrhCwaDKmLb2trK4MGD8fv97N69m0GDBimzfblA9IfMslI6hBlFZmMTQGmOJV6Z4mMJXQEDad46adIkfvOb3wBw/PHHc/nllzNt2rSC/1tDQwN/+9vfeO6556itrVVd4ceMGcPMmTOZNGkS7733HqtWrWLbtm1UV1cTj8eV364xK80oLJfi5tpfkOxByTz0+XxqLZTqBGkmKY3ePmkJtpSKBYNBlc0IXYcsKWGTigqZs5Ld3l8adpXSfiON1KBLhJFgUS6XIx6PK1HIarWqBrNms5n6+np10dqxY4c6ZC9YsEDtSSaTiQsvvJAvfvGLvP766zzyyCP8+Mc/Zt26daxbt45hw4Zx0kkn8dFHH7F48WKGDBnCyJEjVYZjPp9XFTodHR34fD5gT8fuUg9slhoyV+RCLf7LmUyGVCqlmhUnk0nMZjNlZWUsXbpUzX+fz4fH4ylo1iRWTrLOrFu3TpUsT5w4kYEDBzJ27Fg8Hg9bt25l+/btjBs3jpaWFmXd5fF4iMfjKosN9oiepbDffJbssr5yJpHLs2QJy7ofCoVUtUNlZaXq5yN7kDSzrq+vJ5VKqYq6Bx98sFuBubm5mb///e8A/Nd//ReZTIb777+fRCLB2LFjMZlMrF69WgXdAXU2DQaDqoGk7IUy7v6y9/QHFixYQEdHB1VVVUyYMOGwP5/f72fChAmsXLmS+fPnc/HFFx/25+xpLr30Ui677DL+8pe/cM0115TkfU3WcvlX9v9sNquymEeOHMnmzZsZMmQIW7ZsUecKuc81NzfjdrsZN26cSqB68sknga5gg9y5KyoqqKurY+DAgep85Pf7aWpqUoKhjEEEz1KwQCieBw899BBf/OIXS25+SLKb/N49Hg8VFRU0NzcTj8cZPHgwdXV1BINB0uk0fr9f/StzIBqN0tnZqX7uvvvu47777uv2+T73uc8xZswYHn30UQCmTJlCPB5XDSgDgQAOh4PKykrcbjehUIiysjJsNhsOhwOPx1NgF1mK1kyfWGT+85//zC9/+Uv+9a9/ceyxx3LzzTfzwAMP8K1vfavfZBRpNFBaWapy6JXLm2QLASpal06nVXl7Y2MjVqsVq9VKc3MzXq+XcDhMXV0dVVVVqoOq0edy+vTpBINBNmzYoEqDvF4vHo8HQAna0WiU8vJy5fMbCoVobm6mqqqKTCajFngpTS2FC92+KKVN1vh7lvkiBxy5XIvQK/YIctE7//zz+dnPfgbAd7/7Xb7xjW90+7pVV1dz0003cc011/Dqq6/yxhtvsHr1ajZs2MCGDRs45ZRT+O1vf8ttt91W4MtlFJdlrMbHL+U50h8wm824XC7lgSkCXjKZxOVyKX9MsU2Q6HsulyOfz6tMMKvVqhqhyMdWq1VlOoowLf7x0sTU5XIpIciYlSrrWX+glPYbeV2BgnVCGqjJutLc3Ew+nycUCrFw4UKg6zWTLOSWlhZWrFgBdJWZer1eNm7cyD/+8Q9OOOEEjjrqKDZu3MjTTz/NF7/4RV599VXeeustxo0bxzHHHMOaNWuoq6ujoaGBCRMm0NHRQSaTUVnzsh+JMFQKpan7I5VK8dWvfpXm5mauvvpqvvnNb/b2kA6I7Pmyv8jrIAHNZDJJMplUVQpz584ll8vhdruxWCysXr16r8aNLpeLCRMmMGrUKJLJJHV1dYTDYWXPNH78eKZMmcKQIUPo6OigsbGR7du3M2LECHbv3k1bWxvDhw9XjYmNlIrI3B+yy4z7vZwJoSvIJsKNx+NRHs3Nzc3Ks1sCSsKXvvQlTj755G6f58EHHySVSjFx4kSGDx/OrbfeyqpVq3A4HFx00UVKBKisrCSbzeJ2u3E6nbhcLuWXKWc7aQYoe5mmbyB2f2eeeWaPrfGnnHJKvxaZpZdIqSHrtzGoLP/m83lls1RTU8PatWtV9mg8Hufoo49m69at1NXV8Yc//IFQKMT48eMZMWIETU1NtLW1sXr1apYuXYrJZOLcc8/l5ZdfBvZkR0sGrMPhKKj2MorMxqSfvs7+5kEpzQ+5d0i1t8fjKbBvFL0CuoIDUg0uPWRaW1uprq6mqalJzbFEIqEqs2BPwozD4SCZTLJy5UpsNhsXX3wx69atIx6PEwwGVePhyspKXC4XFRUVuN1uFbgUn2g5+0hzylLjE63Eb7zxBt/97nd5+umnmTJlCgDf+973iEQiPPXUU4dlgBpNbyGH9Xw+z7Jlyxg7duxe3+NwOPD7/QVvvYEscMYO6fl8XjXnEh+ho446isWLFwMwbtw4PvzwQ6ArGvvPf/4TgIsvvphbbrmFtrY2hg4dyuTJkzGbzcybN48333yTYcOGKd+qcDhMfX09DQ0N1NbW0traSjQaZefOnXR0dLB79251IZDLnFw6gZIvYS6lhm7dbVDGS1qxJYWxTCeZTCoB8Otf//oBNzu/388VV1zB008/zcMPP6wyNBcvXlzwuMVNnaSMTDLWZNyluLn2N4wXbckCCQQCKovZ5/PhdrvV62iz2dRF3eFwYLFYVHVFKpUiHo8rC5XW1laV/SoZzTJHfD4fZWVlKuPRKDAf7uY+PUkp7TfGyhnx1JWDuxyKpQGoxWJRZe8jR45kyZIlKmtx27ZtVFZW8sc//pF3332XV199lSeffBKv18vHH3+s7FrWrFnDxx9/zBe/+EUsFgtr1qzhxRdfJJvN4nK5yGQyLF26VHlAi62IZK0KpXCh2x8H09StL9HZ2VkgEMvrI3/r0BVQsVgsDBkyhHnz5pHL5aisrGT37t0sWbKEVCqF3+/nqKOOYsSIEco7cdGiRfztb39jy5YtfP7zn+eSSy7hc5/7HACrV69W55wRI0ZgNpuJxWJqrTL2iYDSbKhz6aWXMnv2bE466SS+/OUv89BDD7FmzZq9Pl8Kge/iyiWpkIE9ZwRJYjKKRZ2dnaox4LXXXtvtc7zyyisFdnDf/OY3WbVqFW63m3POOYe//e1vpFIp5YNps9kIhUJKADCbzQSDQcxms8pmBvr07/VIRETmnrDKEI6E5n+liDHIIOuG/N2mUimVjexwOJRNz4QJE7DZbLS0tDBw4ECOP/54XC4XbW1tzJs3j6eeeopf/vKX/OIXv+C1114D4LzzzmPQoEFKGJTnNfa7MVoddJdEo60AexZJTJFeD/l8nurqanVHFZ/kQCCA3W5XDandbjfl5eXY7XaqqqoYNGgQoVCIqqoqhg0bxtChQxk6dChDhgwhFAqp3hHHHXccp556KosWLVIJeBJAF6sMyV6W/cbtdqssZjmb9IXKo0/DQd/QFi9ezFe+8hUeeOCBgoid3+/ne9/7Hvfffz9f//rXdWRX028opSxVQS7/IjYHAgHq6uqU0T10+SSLn3JtbS0Wi4V4PE4ymWTUqFG8/vrrJJNJZsyYwRNPPIHdbmfz5s3cfffdvPvuu8yePbvgOcWsXkSkZDJJVVUVTU1NDBkyhKamJlVu73Q6lSeSCE66AqLnkINOd5kekm0qnXelKR90NVSRSO4f/vAHvve97+1T+M3n8yxfvpw333yTf/3rX+oSCPCVr3yF3/3ud+zcuROr1Yrdbsdmsym/MrvdrjZTERBLOfOwvyFZyqlUStndVFdXKz9eq9WqLC2Mjd/k8i6vtclkUhmNkm0i/pYdHR2qNF4Oa8ZGYbCnM3h/opT2Gzn4Wq1WZWuQy+XU62mxWFQVzNChQ3n77bcBVDl8ZWUlc+bMwe/389RTTzFs2DD12CeeeCK/+tWv+Na3vsWrr77KjTfeyLvvvsuHH37IunXrmDZtGrFYjK1btxY04jGbzdTW1jJ8+HCVRWK325WnuPGSV6qUmm+3iOBms1m99lKxYrPZVAb8qFGjmDt3LtlslurqatavX09TUxM+n49TTz2VTZs2sWXLFkwmEyNHjmT06NGqGe3y5cvZsGEDV155JTNmzGDAgAG8//77LF++nDFjxhAIBAiFQrS0tNDe3o7b7SYSiSibHxmTUCoBzf6QXWYU+Y37vLFEWKo7igPi8hpms1kGDBiwl0VCPp/n8ccf59577wVg+PDhah2SqomlS5cCXdnSfr9fXfSdTicDBgxQdj4Oh0MlbEDX717fdfsOkUiEjz76COjKZO4pJNlu8eLFpNNpbZ/SR5CGnOKbDl13Yr/fTyQSUQ3VWltbmTBhAkuXLqWuro7jjz+edevW0d7eTiwWY+zYscpTedeuXeqcW15ezrRp09TrL3drWRPa29sZMGBAQXVmJpPp9q5bKvtNf8FsNiurLLlXeDweBg4cyLZt20in01RVVbFz505CoRCNjY2UlZXR0NCAy+VixIgRbN++nVwux5AhQ1T1rzGAIPcdi8VSYB04ZMgQdf8uLy9XiToej4fKykp1rq6srCywpCvl/lUHfUs7/vjju/UIBLj77ru5++67D9mgNJq+QCmVCskFWjY1EZldLhcrV64Eug7ZixYtArq8ghYsWAB0ZdC9+eabQFdk/ic/+Qlms5l77rlHHZpGjRrFE088waJFi3juuedYunQpu3btIhqNqnJX6OqYms/nsdvtWK1WIpGIagomohHsKbeWxVlvtD2DzBPjBclsNqvXWYIAfr+fRCJBfX09Xq+X+vp6qqqqaG1t5bHHHmPJkiVceumlHHfccYRCIaLRKJs2bWLBggW88847qoMudG2Qo0aNorq6mmXLlimP3VAohMViwe/343Q61VuxyKwvc30LEZrT6TTJZBK3201VVRWRSIRoNEo6ncZisVBVVaVeSylxFjsF2BPUkMah4rMszyHZZEaBWTwx++OcKKX9Rg7DZrO5wPZAgpwVFRXq0u9wOMjn8wwaNIjly5cDexrJfve73y0QmIUZM2Zw3XXX8eijj/LYY49x9913qyY8r776KtAlFFVXV9Pe3k5tbS0VFRUEg0G2bdvGsGHDcDgcyrpDgpmlLjKXkm+3BB0ECSYVi82BQID29nZaW1txOBykUinlYTlt2jT+8Ic/FFharFmzBoDTTz+d66+/ntdee426ujoee+wxrr/+eo466ig2bdpEXV0d69at4+STT8bv99PS0kIsFlNZbMaLoaCrZnoWqVrK5/OqvFyqD2w2G+l0WonMEjiRRpESwARURY2wYcMG7r33XubOnQugKj5sNht+v181GrbZbPh8PlVxIyXLAwYMwOFwEAqFcLlc2O12vF4vJpMJm82mxcQ+xr/+9S9yuRyjR4/udj85XIwZM4ZQKERbWxvLly/nxBNP7LHn1uwf8d+Vil6xdotEIiQSCeW/29raysSJE1mxYgXbtm1j5MiROJ1OFi1aRH19veojMX78eM4991xGjhy5V4KDrFHS/0GeSxqpA6ofEaCSLOR9Tc9isVjweDzK/q9YaM7n86p5tPx9S7O/bDbL0KFDcblcxGIxZdFmDCjAnkCH2CrJXmcymSgrK8Pr9eJyuZQvs/SzCgQCOJ1O1bvCZDKVbBYzfMrGfxqNpm8iZYey2IkQZDab8Xq9ykv5qKOOUpf1yspKGhsbCQQCqovuGWecwciRI/d6/BNPPLHgIJVIJNi8eTMvvfQSf/7zn1UjQJPJhNfrpaOjg1AoRDqdxuPxqOxHEcTNZrMWmXsQYxMMi8VCLpfD5/MRDofx+/2qvFkavEWjUfx+v2rSVF1dTXNzM8uWLVPNeLrDarUWlBnl83mVtSaXN6fTSTAYxOPxEAwG1efsdrvy+QZ0pnsfQw49EsxKJpN4PB51OGprayObzdLU1ERZWRm5XI5wOHxQjy1BB8k0AdR8NJlMeDyeko3o9xeM+4vJZFLeqEZR0e12k0wmVVkidDXQkvJEsW8677zz9vk8N910E8uWLWPx4sXcc8893H///WQyGebOncvKlSvZvXs3u3fvprq6mkAgQHNzs1pvEokEDodDlRv2lwtdKfl2S8DBarWq8mHZ761Wq1oTBgwYoMrNR48erc4lZ511Fr/5zW9Ip9OMGzeOs88+m87OThYtWsTChQt57733qK2t5Y477uCVV15h7dq1PPvss/z3f/83Y8aMoa6ujm3btnHyySer/UR846HQIsM4nzU9h4i2Yqfm9XpJpVJEo1EcDgfpdHov2wypSoCuYKTFYmH9+vVcd911DBkyhDVr1rBkyRL1+KFQCL/fj8fjUYFth8OB2+1WlkvBYBCv14vf76eyslIFOaWySv7OLBZLv+oD0F+QNeOLX/xijz6vyWRiypQpvPHGGyxYsECLzH0IEZlzuRx+v59kMkkul1MN27LZLFVVVTQ2NhIOhznhhBNYtmwZu3fvxuVycckll9DY2MjHH39MOBxm8eLFLF68GJvNRk1NDVVVVdhsNnbu3KmqbGpqatTaY7fbC6rvioOacibR59neQYTmWCymGhK73W6CwSDt7e2qT5XoGKlUismTJ7NlyxZaWlpUsNzlcuFyuQ74fHJ/8fl8ygO8oqJC9aKQrPuKigqSyaTa71wuV0nPES0yazT9AMkGKfa1k0wnsc0Q/6Ht27eTz+cZNWoUa9euBbrKzCTz4/Of//xBPa804JkwYQLBYJAHHnhAbZ6JREKVpBR7HhaPVdMzGC9HEokVkdnlcpFKpZQtht/vx2w2E4lEVBYzdAkdUk5mLMuW0nSv14vX61Ubo2S1S8mp3W5XXlcej4fy8nLVXdfn8ykbDeg6KJbyBttfEa9cieCLRYLD4VBWOZlMRs0Z2NMQQ7JAiu0yZF4Y52hxVYaeC30LWcdlfTc2P5F/RdSTrxkvV5It0h02m42HHnqI6667jrVr1/LNb36TMWPGMHnyZK677jo6Ozv5+9//rnyf7XY7u3btYsSIEUSjUeXrbfSAL/X5czBN3SQrprcxiszyvswBh8OhhGen00lDQwOA6tw+YsQIFi1aRDqdZsKECfz4xz9W8+jUU09l7dq1/PKXv2Tbtm088MAD/OhHP+Khhx6itbWV+fPnM2XKFEwmE+FwmPb2diUyGz2sjeXtxoZMmp7FbrerkmNpBppMJnE6nWo+wJ7XRuxOBgwYQH19PcOGDWPLli28//77BY/rdrspKyvD5XKpjGSXy4XX61U2XT6fT2WUiVemz+fD4/FgsVjUx5JRZgx+avoGuVxOeeR+6Utf6vHnnzp1Km+88Qbz58/ne9/7Xo8/v6Z75EyZTqfJ5XIqI1WyVNvb2zGbzUpobmxs5JRTTmH16tW0tLTw/vvvM2jQIC6++GLy+Txr1qxh7dq1RKNRamtrqa2tLXiuc845h/Xr1wNQXl6u7kayb0nFnnxO1rX+WJVXKoh1RiwWUw3vy8vLVRNAScAbMGAA27dvp7m5mUmTJlFVVUV9fb2qqkkkEsRiMaV3GJP9JKFLLC+sVit+v1+JzcFgUAVbBw8erCygYI9neCmjRWaNph9gzFAVjAKN3W5X3bqrqqrYtWsXAGPHjuWtt94C4JhjjmHOnDkAbNmyhXA4TDqdZuvWrezYsYNMJsPgwYNVQ4Rixo8fD+zxgzb6HMolrrvGB/rQ3nMYOxp7vV5isRiZTEZd2ILBIBaLBYvFQjQaxWw243K5iEajWCwWAoEA0WhU+ZwaM9LlNRUxWQRleU6bzaYiuE6nE5/PpzKYJRPWbrfj8/lU87i+IJZousfoeyciM1DQrMJmsykhWf4Vr7JPig5K9Q26y8YpXsPl7zaTyagS0ubmZux2O6lUSpXFr169mmOOOWafzxUKhVTDnRdeeIENGzawYcMG/va3v3HmmWfyi1/8gv/6r/9i165djBkzRlVcGNcN415T6iJiKfl2G4VbmSfF5wCr1aoCUcFgkC1btgBdTYn/+Mc/AnDZZZep9ULsT44++mjuvvtufvSjH7Fp0yaee+45zjrrLF588UXef/99pkyZwpAhQ9ixYwerV69m2rRpqjJHMqjFsxv2ZDXrs0jPI/6V0hDa7XYru4zuMJ4bpQx+0KBBqvTZZrMpiwu5zLvdbuW5LA2ZxCJDvsdmsykfVZPJhN/vJxAIqMCErqLpmyxcuJCmpiaCwSAzZszo8ecXX16xH9T0HaSaSc6nsgfIXSYcDmOxWBg4cCC7d+9m69atTJgwgVgsxrJly9i1axe7du3CarVSXV3NKaecAqBsEsTix+v1smnTJpXdOnz4cJqamlSfIkAlZUDXmicitF5Tehe5Z6ZSKZLJpGo03tLSooKRmUyGUaNGsWnTJjZt2sS2bdsoKyujo6NDveaw59xrtVrVudPY1F4CnVJZLnuKNBWUqk2gwDqylNEis0bTDzBeoo1da+Vylkwm1aHd+H4sFlOlgBs3buRLX/oS//73v/n973/P73//+26fy+VyMWvWLL7whS9w3HHH4fF4WLlyJT//+c+BPVlsxsxEGZN8zbj46otdzyEHGrnMySXNbDarg5Z4RElWckdHB06nE7/fT0dHhyppFa9TEZktFovKXJTNWbKY5fPyuNJoRy58Ho9H+R5KtNfpdOq50YfpLkM0lUrR3NysGntVVFTQ2dlJW1sb6XRaNcEwBiGk4V93mEwmlY2SSCQwmUz9ruFfKVP8uomgKBkhLS0tQNe639TUxFFHHcXq1aupqalh06ZN3Hnnnfz5z39WDUaLSaVSrFixgoEDB3L++eezdOlSZbXx7rvvctttt3HUUUcpr3egoLGp8YAPpZ81VEq+3d1ZUMjnRNS12+1qjpSVlalGbGazWVXVjBkzhmQyyS9/+UtefvllMpkMxxxzDFdffTU33XQT99xzD2+99Rbjx49XVTizZ89m0qRJ7NixgzVr1jBkyBAGDx7MunXr2L17N5WVlXR2dpLNZlWmdanPjVJGBJhsNqu8MmGPrZfYM0nDJslErKioUH1BJMtYKqrcbrfKRJazjN/vx2KxEAwGlV2GyWSiurqayspK2tralMVGKBRSlYC60V/f5ZVXXgHg3HPP7ZWsv5NOOgmTycSWLVtoaGigurq6x8eg6R6TyaS8kTs7O3G5XMqDV/zZpSnbsGHD2LZtG1u2bMHv9/P5z3+eXbt2sWHDBjo6Oti5c2dBr5nucDqdDB06VNlODh06tMBWQQRusY2C0j+T9Aekf0dnZ6ey+IxEImQyGSorK9m1axfZbJZx48ZRV1dHJBJR1Vcmk4nBgwczePBgoKsaS/avYuTO43K51H05GAxitVoLetX0p74z+ram0fQDjJdqo9+uHLrC4TBHHXUUALW1tZx00kkAzJ8/n+nTp7NgwQL++te/cvHFF/Nf//VfPPfccyrbedCgQQwfPhyHw6Euaa+88oo63BkRAdHhcODxeNTB3+v1YrPZlOAoDb+0YNSzyO9bStwrKiqor69XAo1kBEUiEVwuF9lslkQiQUdHB4lEgkQiofyi5FJoLAkSewsRD+U1l/eNzf2kdNVqteJ2u3E4HCoTen/Co6ZvIJ5hIuQZBWaHw0F5ebkS+CorK0kkEkSjUeXjLBliUiommWWSBSBIQwxpHuh2u/W60YsYXxvjfiP/ZrNZdu/ezdFHH828efNYsWIFxx9/PAsXLlRZgiIYrV27lvPOO4+vfe1rnHzyyUrYWb9+PQsWLGDevHn7PLAPHDgQl8vFpk2bgC57JqMdD+yx65A1SK8pvYMx8A2FIrN49ZtMJhX8rKurA/ZUR91xxx28++676vFWrlzJf//3fzNjxgzOPvtsZs+ezcMPP8w111zDm2++yQcffEBZWRmf+9znWLduHbNnz2bq1KmqZLqtrQ2fz0cikVC9IkSA0PQ8xtJ2s9msssdCoRCtra1UVFSwa9cuysrKCprDxmIx7Ha7Or8Aai+RM4WIy5KNaCxB9vv9jBo1CqvVqs67wWCQ8vJy1VhQhGtN30T8mHvDKgO6rAjHjRvH6tWrWbBgARdccEGvjEPTPWJ709HRQTabVfY30rdBhOZ0Os2oUaPYvn07kUiEVatW4XA4mDhxIiaTiY6ODuLxOB0dHQWeuWLvJ+cLOdcOHjxYvV9RUaHOMT6fT61Xcm7S9C7GYEQ2myWdTjNgwABlMSqWKh0dHQwaNIixY8cCeywiw+EwjY2NBVV+Ho8Hp9OpEi+M1o+ijxjPpFKZ09/OqfqmptH0A4oztURkjsfjKrunublZdUhdtWoVY8aMYcOGDcyZM4cTTzyRRYsW8dJLLwFdUd1jjjkGk8mkxKFwOExlZSVVVVVEo1Ha2tqUd5FE58rLy3E4HFRXV2O32ykrK1MNwaQ7t1EE0If3nsWYGSq+hwMGDKC1tbWg/KuiooJMJkMmk1HZQJlMhkQiocqKpHTZKDIbBWV535jJLN7MEskVYVGEylLLGFq+fDnf/e53yWQy3HDDDVx55ZW8/PLL3HvvvVRUVPD0008TCoW47rrr2LhxI2eddRZ33nlnbw/7M5PP59Uh2263k8vlaGlpKRCY4/G4OrRJNlogEMBsNpNMJkmlUqppnDR+ikQiWK1WFaCSQ7jL5SKRSKighy5d7j2MNim5XE51wna73eRyOfU6Dh8+nMrKSpqamrDZbCr7sKamhu3bt1NVVUVLSwttbW08/PDDPPzww90+X0VFBcceeyxVVVXKN2/AgAFMmjSJ++67j1gspqov5OAue434E0sgQ9NzGPtEiB+lsQkSdAmCYpchl/ChQ4eycuVKACZMmMBLL73Eu+++i81m49e//jVjxozh+eef54knnmDu3LmsX7+esWPHsnv3bv76179y6qmnsnLlSv7xj38wceJEBg8ezM6dO/n3v//NyJEj8Xq9ZDIZ4vE4Xq9XCZsSANH0DtLor7Ozk2AwSFNTkxKLoSuo1NLSgtlsVh6YEoA0+l/KZV2qpKxWKwMGDMDhcKizhdfrZfjw4Spjbffu3erz5eXlwJ4gan+79Pcn1q1bx7p167BarZxzzjm9No6pU6eyevVq5s+fr0XmPohURoo4HAqF1FlF7hxtbW10dHQwcOBA0uk0zc3NpFIpFXwyPpbX6+32eUwmk2p4LgHLUCikgmbys7LX6ftv38Fqtap7RjqdVvvG7t27VYCytbW1IEGmGGkca7PZiMfjBb2o5F5TXKG7r340/QUtMms0/QARY/L5PE6nU/kxp9NpKioqCIfDtLW1MWjQIMLhMOFwWJV57Ny5k0gkwsiRI+no6KChoYGWlhZVxmpESjqsViuVlZVUVFQAe3wvPR6PauI2aNAg3G63OuDLgd/YCK4/Lqp9HREFc7kcyWRSlY9KB2YR/0SQ9ng8ZLNZUqmUyvqSzFKjl6Vkpov4ZxSZxedZ5qnYZkhGaqlmL//sZz/jmWeeYciQIUyfPp0rr7yS//3f/2XevHm88847PPLII0yfPp3Kykoee+wxvva1r7Fz505VWlWqGLO8rFYrLS0tKlrv9XrZsmVLQVNII3LQ9ng8qvGbBC6SySTZbFatUeKlKQdAKXtMpVIH1dFZc3iQzu3pdBqv10sikVBe2w6Hg2QyydatWxk3bhwff/wxsVhMXd46Ojqorq5WHs3y81IZYbPZVJBBMuGLS1XXrVunGn0ZA5ihUAir1YrP51NZJrLP6Oz33sGY3VMsMlutVuWRLOeN6upq3nzzTQBGjRrF17/+dQBuvPFGpk+fDsANN9zAOeecww9+8AO2bNlCe3s7o0ePpr29nTfffJOTTjqJnTt3smLFCqxWK0cddRTpdJra2lrVaDaVSqn9SLJhRWzWAayeR4LMcoGXoJSUE8OekvNYLIbb7Safz5PNZlVFlbGayul0EgqFcDgc6ud9Ph9Dhw5V3qzbtm1TZxiPx0N1dTUmk0lZgYEWgvoyzzzzDADnnHMOgUCg18YxZcoU/vznP2tf5j6M9AeRSrrKykoaGhqUZZKcSTKZDCaTSVm9STBdrAGNAS2jHVd395/Kykr1nGazWe07gEq60fQdZK2XhBaxkRRrjPLycjo7O9U9Gbrurm63G4/HQy6XUxW/gPIBl7uK8Sxk1EJK7d77SdAzXKPpB8hFKZVKKVFPMsySySRDhw5l27ZtRKNRampqcLlc1NfXKzE4HA4Tj8cJBAL4fD7luWvcSMVHSjZcaeQlWWJS6u7z+aioqMDr9VJTU4PT6aS8vLzA+F6yXTU9j2SGSvaoCM6wp7TM6/Wq11qyniUSK4cmY2YaoA5bRjG5GNmMjQ3DJKu5FHn44YfVQdJkMtHe3k55eTl2u50ZM2bwyCOP4HK5lDgybdo0PvroIy666KK9HiuVShUIs+IV1xcxNhRNp9Nq3D6fj+3bt5PL5VRzFZvNRjabJZlMqpLF9vZ21YhULHa8Xi+hUIhEIkE8HieTydDR0UFHRwfBYBCv16uE5kwmg8Ph0GJQLyF7jWQyizjk9XrVxSydTlNfX8+YMWOora0lHA5TXV2tRMWBAweqcneZT/tCurEb1xuHw0FZWZmqlCgrK8PtdlNZWanWMQlk6YBmzyNBbyOyz4iAJ6+7y+VSFhnijThkyBDee+89wuEwI0eO5LLLLit4rNGjR/PUU09xxx138MEHH7BmzRpGjRpFMplk7ty5VFZWUlZWRiqVYu3atXg8HpU9LyWwYrGSyWRUDwEtMvceEgCX1yMUChEOh5WvckdHB1arFb/fX3DZN5afS6DL+DcfCoUYPHiwamgse4/8TFlZGX6/X81ZEQnkLK3pe+TzeSUyX3755b06lqlTpwKwaNEiJVpq+hZS4SCicTKZVMKynGUkw1nuzbKeHOxdVRKtpApP9jebzUZlZWVBYo7uOdM3kWb10kgWYMCAAUojkaZ9xdnssmdAV8BSEh0kIcf4+EfS3UWvhBpNP0HEHED5C0rX1Fwux5AhQ2hqaiKRSFBWVobL5aKlpUVtjCLuyAYs5YKCbLjFi6N46Bn9mMvKyqioqMDlclFRUaEyEcVPVW+wvYvR+1gEY2Npj7Ghm4hIIhxJBlh3GIVmYzNAm82mOj3LvJJASClvtpWVlQD8v//3//ja175GNBpVhw+Px0MsFuv2c91x3333cdddd/XMwD8DEnyArjWnra0N6Co1bm1tVcLj0KFD97psSWONWCxGPB4vyGBuaWlRWfXl5eXkcjkikQipVIr29nby+Tw+n09ZAUmpo6bnMWYdJpNJQqGQanbj8/lUcFICCwMGDMDr9bJz504VhIlEIspGQwJexnXH6N9vtF6QA7v4QZvNZsrLywkGg6oiQ/YiyYbW2Yi9h/GCJa+vnFNkL/D5fGodkUZJY8aM4e233wbgyiuv7Fa48Xq9/OpXv+J3v/sdjz32GJs3b2bgwIH4fD6amppoampS1VWxWIxEIsGQIUNobW1VNlCpVEpluRmbmWp6Hgk6i8e/1WolFAqRSqXo6OhQzbrkHOLxeEilUnu9ZmazGZ/PRygUUlZfsocIIgR4PJ4Cz3CxDZOxaPom8+fPZ8uWLXi93l7zYxY+97nPEQgECIfDrFy5kkmTJvXqeDTdY/TezefzJJNJysvLSSQSyvrRYrHg9Xpxu917ZS8XP5a8GROxTCaTCrSbTCb8fn9B4hbQrxq79Uek2lJsITs7O/H7/fj9fuXJbWx6LwFq6QliTNCCPfft/mqJsT+0yKzR9BNkAxW/J2m6J5f9fD5PRUUF6XRaddEWDyIRw8T/Dii4cMnmuS/E/sDr9eLz+dRlwOfzKYFZmv9JR29N72M2m3E4HMAeMUAOVcYyMfleEZyN3weFBy7jY8v8k+wg+f5SDTQ8+OCDPP/88+rjO+64g6VLl9LY2Mh9991HW1sb8XgcgHg8jt/vx+v1Fnxu6NCh3T727bffzi233KI+jkQi1NTUHMb/zaej2CJFDlJOp1N5Ww4aNKhbUag4CyCbzRKPx4nFYsRiMeXt3NbWxuDBg6moqCAajRKJRJR9hghCxqCIpucxZh2mUinKy8uVUCiHcYvFQjKZJJfLKR/U9vZ2TCYTPp+PZDKpMtOlmSjsCVxKJqGsIXJ5gz1ZhmKnEgwGqaqqUnuPzBWdxdw7FDf7M74vf7vGYFXx1zweDzt27ADguOOO2+fzWCwWbrzxRo455hjuuusudu/eze7du5kwYYIKZsTjcYYNGwZAY2MjgwYNoqmpSXkyi8DcnZig6VmMDZDkMu9yuVTGeSaTUdnKQMG6YOz90NnZSSaTUZ7f0LVmybnUKPKI/ZeckwEVoNL0TZ5++mkALr744oJ7S29gNps5+eSTefvtt5k/f74WmfswZrMZj8ejMlUlY7mysrLgHCJ2CZ/m8aXvjNvtVk0GjeuKznTv+5jNZtxut7JXkXOnVGN3d76BPQF0OHIsMfaHnukaTT/CZDKpTrpWqxW3200ymVSZIdlsVjXmkyxCKS2WTVb8iOTgbmyoIm/Gxm7ipSvROpfLpRpzifgsF34tMPdd5HUpjrBLVLbYIuNABzDZlI1ioMyRUt10b7zxRm688Ub18YsvvsiqVavUhUcyOqVke/LkyUyePJnXX3+d8847j3nz5vE///M/3T62lPiWCvL6yetsFGgONnPUarUSCAQIBALk83lisZj6/dXV1TFq1CglRqbTaRKJhMou0xmHvYsx00/En0AgQCqVIhKJqGCjxWJRmYey9wQCAdUY8rNmC3o8HkKhkGr6JwKzzmLuXYzN/owd1I3/yl4jXtzSaBZg9erVBINBYrEYtbW1jBgxYr/Pd/rpp3Psscfym9/8hldffZVVq1bh8Xi47LLLmDNnDtu2bWP06NEkk0kSiQQWi4VUKqX2sVISmbtrOCusWLGCCy+8kKFDhzJ48GBlKVBqWCwWZc8llQ7SIEmajBqDXLIfSMWVIDZfPp8Ph8OhfJwluFBswyNnaC0E9V2SyaQK9l9xxRW9PJoupkyZwttvv82CBQv4zne+09vD0ewHEZqlr4QxeG1sTF8c2DbuZbDvbGbYU7VnfM5StgY8UhGtQ864IiJ3d04wVu8a/bmPZPQuqtH0M+SQLA1UJBpnLDPPZrMqK9Xv95NKpVTE7mAu5SIwi1+V3W5XDVdEhLbb7cp/WXyISlFYPNKRpllWq1VdwveVxWz04TQGKWCP3Up/mgM//elPcTqdnHHGGQC8++673HLLLZx66qm4XC5efPFFysrKeOKJJ5g6dSqnn376PjOZSwWjuCzNliQoZbVayWazNDc3U1lZ+Ylea8lulcaByWSSWCxGWVkZNputIONQ0zcweqpLxrI0vIlEIiqoJLYaclCXvae8vFw1FRW/wuKmOkYR0FiGKnY74pcolTRSTSOZKP1pvSkl5HUTf3bj5+TvWYJqTU1NjB49mrVr19La2ordbqe2tpYhQ4ZQV1fHAw88wJgxYw5Y2VFWVsZdd93F5Zdfzt13382qVav46KOPOP/883nllVf2ypY2Zh0JpTBfums4K6xZs4a77rqr4HOljPTyMGYyi++t+K0Hg0FsNpuqvoLCaggRlqUnQHdIQOpILGkuNV544QVaWlqoqalRZ6/e5pRTTgHggw8+2EuM1PQ9pGJCbPwk4FQcdJJ7y8FgFKQFWVdKsbG5cCQENfeH9LwSKwzjW/H9V1OIFpk1mn6IbKCS5SGbnDRUEVFIOnNLlFUaqogPUXFmjxzsjZuuCALGCJ4syJLNrJv89Q+MUfv9fQ8cONO5P7By5cq9PnfBBRdwwQUXFHzuscce66khHXYkkJTP58lkMrhcLqLRKNFolIqKCurr62lubiYejxMKhfB6vQeVFSaPJz7MgBIIjB8bRQRN72OsTkgkEirDUATfaDSqrHakFFUCnSJKS8O1T/q84t3scDjwer1KaBaLJp011HvI7z6Xy2Gz2UgkEntlMEejUSorK2lqasLv92OxWKitreVzn/scK1asoLGxkUAgQENDA5dddhnXX389F1100V5Nd6Arm37r1q1s3ryZDRs2qAaTxrLnXC6H2WxWc80Y9JT5VAoXxeKGs0ZWr17Nv//9b/70pz9x++2384UvfGGvny+lJrOC8aIvZ1SprpJzrNhoyH7T2dlZkK0oGBsUFwezNH2f3/3udwBcf/31fWaNnz59Ona7ne3bt7NhwwbGjh3b20PSHASSFNXdHVXuvsZ7cHGSw74+L4Hw/rCuHElBzQOhxeRPhhaZNZp+ihzKrVaryhQzHtLlki+CsnwsF7F9UdzYTS76IjIbs5l19rJG07+QdUWECo/HQzweJ5vNYjKZqKiooKWlhUQioTouy1og65F47Mr6I8Ewo7WKiJThcFg9ttPpVCWIOnDVt5DydqmIkUuXz+cDuoQtEaHltZdgp7HpWnGFhFCcPWLs/C5WTNJIVBqwaHoPORtIcxx5zaQyRrLfa2pqaG1tpb29nVGjRrFp0yZisRgDBw6kvr6eUChEJpMhFovxy1/+kgcffJBx48YxcOBAzGYz4XCYHTt2sHPnzr182r1eL8cddxyvvPIKgDoDSdWVCJLGpk19RbTaH8UNZ41MnDiR//iP/2Dw4MGcddZZzJo1a6//U6k0md0X8toZmwmL4GwsUS9Gzr+l8Bprumfx4sUsXLgQm83GN77xjd4ejsLj8TBjxgzeffdd3n77bS0y9wOK7Z2OVI7EoKbm0KBFZo2mnyNZysbMZdjjm+p0OpXAXOzF3J0lglwQjWKzoP2INJr+j91uV8GpRCJBKBSira1NNW4LBALkcjmVRSZC8sHgcrkIBAI4HA6amprUzwWDQVKplBIZtcjc95AAhM1mUxcLo0+3vGYSzJQS1e68UQ/muaQcXqpmtJ9q38LhcKjAgtvtJh6PK/sUj8dDJBIhkUgwevRoamtryWQyDB48WDVrGzhwIK2trVgsFiKRCO3t7WQyGZYvX87y5cv3ej7pQ+F0OlWG+0cffQRAeXk5NpsNt9uNx+MhEAgUVFpJn4C+eG45UMNZI5///Ofx+/0AjBw5kpaWFqqqqgq+p1SazB4Mct6UrOVMJrNXuXupl6tr9vD73/8egEsuuYTq6upeHk0hs2bNUiLz9773vd4ejkZzSDjSg5qaT48prw0ONZpDRiQSIRAIEA6H1UG/L2LMJMtms3uVBRV7Sxk9Mo12CMUZzfoQf2BKZY5oepe+Pk86OzuJx+MFntvSVMuIrDXFZYXFDVOMmY/FzSIDgQAmk0l93uPx6Gw0+v4cEQsUY8OUA32/cX7s6+vFHnhi2aH9VPemt+eIBJry+bxqACmfS6fTRKNRNU/a2tpoaWkhn88TDocJh8OkUini8bgSpI1VD+IJL4GGYs9/k8mEx+NRwrLT6WTgwIG43W6GDx+u/HzFC168wvs6L774Ii+++CJPP/30XqL4l7/8Ze655x5GjBjB6aefzrx58w4onPf2HDnUFK8Tmv7Brl27GDFiBOl0mnnz5jFt2rTeHlIBy5YtY9KkSXg8HuUtf6TR39aSI5F9BTU3b97Mo48+WvC9kUhEvc5f+9rXePDBB/cKanaXyVxTU6PnyBGATvfQaI5Aipu5ieAsGWXFlxKjv1R/bOCm0Wg+GdKhO5lMqrVDmnwaA1jdldh1h2S2CpKJKI8nuN1uLTCXCMUNUyR4KVnLxooZCWrub64Yv2a0yyj1/ai7xjovv/wy9957LxUVFTz99NOEQiGuu+46Nm7cyFlnncWdd97Z28M+KOS1z2Qy6n1jxnAwGCQejwNQVVWF3++nvb0di8VCIBAgFosRiUTw+Xxks1nVQNLYJLLYW9fYI0K+7vP5KCsrw+l0MmTIEKxWKz6fTzV/kozXUqC7hrO33nord9xxBz/96U+57rrryOfz/PCHP+yTmdmHGy0u90/+f/+//x/pdJrp06erRnt9iYkTJ1JdXU1DQwPvvfcen//853t7SBrNJ+bGG2/kxhtvVB+/+OKLrFq1iqeffnqv773mmmtUUHP79u1UVFTs9T1ibaQ58tAis0ZzhGMUnKGwkYH2pNJoNPtCrHjEF1OEH+mobezGLCJid8VTxR2ajeuNCMwWiwWXy3VEiib9AaOP8r4w+i7Lx8WP0R87eXfXWOd///d/mTdvHu+88w6PPPII06dPp7Kykscee4yvfe1r7Ny5k8GDB/f20A+IZJmbzWZSqZQSdOXckUqlsFgsSkAW7/XKykoikQjRaBS/308ulyORSChLBKPIvK83yXAWr+5AIEBVVRU2mw2/368EZnkrlbWlu4azv/zlLwEoKyvjww8/7OkhaTSHlaamJv7whz8AXWX7fXH9N5vNXHzxxTz88MM899xzWmTW9At0UFPzadEis0ajKUALyxqN5mAxduc2NhA1Zqt+Wq9TEYrEM1XTv+lv4vHBUtxYp729nfLycux2OzNmzOCRRx7B5XIxffp0AKZNm8ZHH33ERRddtNdj9cUmO8VNiOXvOpPJYLFYVANQaeSWyWRIp9PYbDZCoZBq6JZIJJTHe3GTSPnXKDybzWbVFNLr9ap1SqwxXC4XHo9HfV6j0fRNfvWrX5FIJDjhhBOYNWtWbw9nn1x66aU8/PDDvPTSS/z+978vCfsdjWZ/6KCm5tOib20ajUZzkGQyGWbMmMGCBQsA9ippLisr6+URajS9h2QwF1NsjWDMVjVi9HsXj/cjUXTUHFkUN9aJRqN4vV6gy388Fot1+7nu6MtNdqTyQURlCVAV2+vI+/KWyWRwu93q658UsVUxZi2L/7JkOut1RqPpm2zfvp3//d//BeDHP/5xn/5bnT59OjU1NezYsYPnn3+eq666qreHpNFoNL2CFpk1Go3mIGhtbeUrX/kKtbW16nPFJc233357L45Qo+mbiFWCRqPZd2OdxsZG7rvvPtra2pRPcTwex+/34/V6Cz43dOjQbh/79ttv55ZbblEfS5OdvoTYpkjmsojHEowqFp3lTb5mDFjtD8lmNvp3W61WnE5nged7XxatNJojndtvv51kMsmpp57K+eef39vD2S8Wi4Vvf/vb3HHHHTz44INceeWVen3RaDRHJNo8RaPRaA6CeDzOr3/9a8aNGwewV0mzZDcXk0qliEQiBW8ajUajOTK58cYbmTdvnnrr6Ohg1apV/OlPfwIgFArR1NREMplk7ty5TJ48mcmTJzNnzhwA5s2bx6RJk7p9bIfDgd/vL3jrq0gms9hWeDwe3G43brcbj8eDz+dT/4dAIEAgECAYDBIMBgmFQoRCIfVxd2/yM4FAAL/fj8/nw+v1qudzOBxaANJo+jALFy7kr3/9KyaTiV//+tcl8ff6zW9+E6fTyeLFi3n99dd7ezgHTSaTYcqUKerjl19+mZNOOolzzz2X1tbWXhyZRqMpRbTIrNFoNAdBTU0NEydOVB9/kvJl42W3r2WVaTSanmXFihWMHDmS0047jcsvvxzQF7ojmZ/+9Kds3LiRM844g9NOO41cLsctt9zCqaeeygMPPMB3vvMdZs6cSXNzM1OnTmXUqFH7zGQuVYweyk6nE4/Hg9fr3etfr9eLz+dTArRxbxUx2ShM+3y+vR7D5XLpygqNpo+TTqf55je/CcDVV1+9z8BaX6OyspKbbroJgO9///skEoleHtGBaW1t5Zxzzum2UvOGG27gkUce6fbndBKNRqPZF9ouQ6Oh69J/4YUXMnToUAYPHswzzzyj/XaPcLoraf7CF76gPi4uX95XxlgplC9rNJqeY82aNdx1111ceeWV6nPaeufIpbvGOhdccAEXXHBBwecee+yxnhpSn0AaBHZHd1YZ+/J5L4XsR41GU8jPfvYzVq5cSWVlJb/4xS96ezifiB/+8Ic8/vjjrF+/nptuuolHHnmkT69DUql54403AntXau5LZO7LPQA0Gk3vojOZNRr2XPrff/99nnnmGeDgoria/ktxSbNRYIbuS5q7o5TKlzUazeFn9erVPP7448ycOZM33nhDW+9oNJ8QEY+Nb9Iw1PjWl4UdjUbTPa+99hr33HMPAL///e9Vc9RSIRgM8uSTTwLwpz/9if/+7/8mm8328qj2zaet1Lz99tsJh8PqbceOHT0yXo1G0/fRIrNGg770az4dxSXNmiOTq6++mvfffx/Y2/Ygn89z7bXXMmPGDJ3xoQFg4sSJ/Pa3v+W1117jzjvvJBwOa+sdjUaj0RzxfPzxx1x22WXk83m++c1vcskll/T2kD4Vs2bN4qGHHgLg17/+NTNnztznXbKnefDBB5k+fbp6e+ONNwq+frCVmjqJRqPR7AstMms06Eu/5uARMRG6SpoXLlzI+++/T3l5ee8NStNrrFmzhpdffll9XFwBMW/ePCorK5k7dy5r165l586dvTdYTa9QfKGz2+0cffTR+P1+Ro4cidPpPGjrHZ01pNFoNJr+RmdnJ08++SSnn346sViMU089ld/+9re9PazPxA033MBzzz2H3+9n/vz5TJ06lVNOOYU//elPhMPhXhvXoarU1Gg0mn2hRWbNEYm+9Gs0mkPB//zP/3DVVVcBe/vYLViwgMWLFzN9+nQApk2bxkcffdTt4+iqiP5L8YXuySefZN26daRSKbZv305lZaW23tFoNBrNEUUikeC9997jzjvvZPz48Xz9618nFotxxhln8M9//hO73d7bQ/zMfPWrX2XlypVce+21WCwW5s+fz7e+9S2qqqr40pe+xFNPPdWrgvO+0JWaGo3ms6Ab/2mOSG688UbV4ADgy1/+MmPHjmXEiBGf+NLvcDh6atgajaYPsWjRIkaMGEFnZyfQvY/dwXrb6QYqRw4//elPue6668jn8/zwhz/EbDarC53L5eLFF1/s7SFqNBqNRnPISKVSbNu2jcWLFzN//nwWLFjA0qVLC7yKA4EAP/jBD7jtttv22fSzFBk6dCiPPvooP//5z3nqqad4/PHHWbt2Lf/85z+VmD5r1iymTZvG+PHj8fv92O12crkcuVxO/Y58Ph9+v19Vz7pcrkPqO19cqVncfFaj0WgOlv6zgms0nwF96ddoNAfiwQcf5Pnnn1cfb9y4kY0bN/KrX/0K6N7HrvhzQ4cO7faxb7/9dm655Rb1cSQS0fY7/ZRjjjmGDz/8sOBz+kKn0Wg0mv7AO++8w09+8hMikQjJZJJ4PE5DQwP5fH6v7x04cCCnnXYaZ5xxBl/96lf7dYXOwIEDufXWW/nBD37A6tWreeGFF3jhhRdYu3Ytr732Gq+99tonejyr1aoE52AwyE033aQq6zQajaY3MeW7W/E1Gs2nIhKJEAgECIfD/fqgpPn06DnSP4jFYpx00klUVVWxdetWgsEg8+bN44tf/CJvvfUW//rXv1i8eDEzZ87k9ddf54EHHuDSSy/lf/7nf/YpNBvR80RzIPQc0RwIPUc0B0LPEY1GcyjQa4nmQOg5cuSgRWaN5hCSz+eJRqP4fL5DWsKk6T/oOdL/+OlPf8ppp53GaaedxiuvvMK9996rKiDKysq47rrrWLt2Laeffjr33nvvQT2mnieaA6HniOZA6DmiORB6jmg0mkOBXks0B0LPkSMHLTJrNBqNRqPRaDQajUaj0Wg0Go3mU2Pu7QFoNBqNRqPRaDQajUaj0Wg0Go2mdNEis0aj0Wg0Go1Go9FoNBqNRqPRaD41WmTWaDQajUaj0Wg0Go1Go9FoNBrNp8ba2wPQaI4UxOxe0zfpC00I9Bzp2/SFOQJ6nvRl9BzRHAx9YZ7oOdK30XNEcyD6whwBPU/6MnqOaA6GvjBP9Bzp23zSOaJFZo2mh4hGowQCgd4ehmYfhMNh/H5/r45Bz5G+TV+YI6DnSV9GzxHNwdAX5omeI30bPUc0B6IvzBHQ86Qvo+eI5mDoC/NEz5G+zSedI6Z8Pp8/jOPRaDT/H6UcoYtEItTU1LBjx45e34Q+KQc7dh3F/WzoOdJzlOo8KeU5Agc3fj1HPjulPE9KaS3Rc6R30HOkZ9BzpOeQeVKKv/P+Pua+NkdKkVKcI0IprSV6jvQOh2uO6ExmjaaHMJlMJbfwFOP3+0v2/1AKY9dzpHcplbGX+jwpld/zviiF8Zf6HIHS+D3vi1IYu54jvUspjF3Pkd6lVMZePE9KZdxG9JgPL3ot6V1KYex6jvQuh3rsuvGfRqPRaDQajUaj0Wg0Go1Go9FoPjVaZNZoNBqNRqPRaDQajUaj0Wg0Gs2nRovMGo3mgDgcDu68804cDkdvD+UTU8pjLyVK+fdcymMvJUr991zq4y8VSvn3XMpjLyVK+fdcymMvJUr591yqYy/Fcesxaw5EKf++S3nspUQp/54P19h14z+NRqPRaDQajUaj0Wg0Go1Go9F8anQms0aj0Wg0Go1Go9FoNBqNRqPRaD41WmTWaDQajUaj0Wg0Go1Go9FoNBrNp0aLzBqNRqPRaDQajUaj0Wg0Go1Go/nUaJFZo9FoNBqNRqPRaDQajUaj0Wg0nxotMms0mv2SSqW44IILmDZtGn/60596ezifiEgkwuDBgznttNM488wze3s4/RY9RzQHopTnCOh50lOU8jzRc6Rn0HNEcyD0HOk9VqxYwciRIznttNO4/PLLe3s4+6UU50mpz49SoxTniKDnSs+g50j3aJFZo9Hsl+eff55zzjmHuXPn8sILL5BMJnt7SAfNmjVr+Pa3v83777/Pu+++29vD6bfoOaI5EKU8R0DPk56ilOeJniM9g54jmgOh50jvsWbNGu666y7ef/99nnnmmd4ezn4pxXlS6vOj1CjFOSLoudIz6DnSPVpk1mg0+2Xx4sVMnz4ds9nMMcccw9q1a3t7SAfN6tWrefPNN5k5cyZ/+ctfens4/RY9RzQHopTnCOh50lOU8jzRc6Rn0HNEcyD0HOk9Vq9ezeOPP87MmTN54403ens4+6UU50mpz49SoxTniKDnSs+g50j3aJFZo9Hsl2g0itfrBcDj8RCLxXp5RAfP8OHDeeCBB3jnnXd44oknaGpq6u0h9Uv0HNEciFKeI6DnSU9RyvNEz5GeQc8RzYHQc6T3mDhxIr/97W957bXXuPPOO8nlcr09pH1SivOk1OdHqVGKc0TQc6Vn0HOke6yH7JE0Gk2/xOv1Eo/HAYjH4/j9/l4e0cEzZcoUXC4XZrOZKVOmsGXLFiorK3t7WP0OPUc0B6KU5wjoedJTlPI80XOkZ9BzRHMg9BzpOR588EGef/559fEPfvADjj76aABGjhxJS0sLVVVVvTW8/VKK86TU5kepU4pzRNBzpWfQc6R7dCazRqPZL5MnT2bOnDnk83mWLVvG2LFje3tIB81PfvITZs+eTWdnJ4sXL2bUqFG9PaR+iZ4jmgNRynME9DzpKUp5nug50jPoOaI5EHqO9Bw33ngj8+bNU29PPvkk69atI5VKsX37dioqKnp7iPukFOdJoVHfsAABAABJREFUqc2PUqcU54ig50rPoOdI95jy+Xz+kD2aRqPpdySTSS677DLq6uq45ppr+M53vtPbQzpodu7cyRVXXEE6neaqq67i+uuv7+0h9Uv0HNEciFKeI6DnSU9RyvNEz5GeQc8RzYHQc6T3WLlyJd/+9rfJ5/P88Ic/5Etf+lJvD2mflOI8KfX5UWqU4hwR9FzpGfQc6R4tMms0h5B8Pk80GsXn82EymXp7OJo+iJ4jmoNBzxPNgdBzRHMg9BzRHAg9RzQHg54nGo3ms6LXkSMHbZeh0RxCotEogUCAaDTa20PR9FH0HNEcDHqeaA6EniP9j5/+9Kc8/vjjzJ8/n5NPPpnTTz+dLVu2AHDHHXcwbdo0/vM///OgH0/PEc2B0HNEczDoeaLRaD4reh05ctAis0aj0Wg0Go1G04s0Nzfzpz/9CYB77rmHN954g9/97nfcf//9bN++nc2bN/Phhx/i8/mYP39+L49Wo9FoNBqNRqPZG2tvD0Cj0Wg0Go1GozmSeeCBB7jqqqsASCQSlJWVUVZWxsaNG1myZAnTpk0DYMaMGSxYsICpU6fu9RipVIpUKqU+jkQiPTN4jUaj0WiOcJqamvjHP/6BzWbj4osvJhAI9PaQNJpeQWcyazQajUaj0Wg0vcSOHTuIRqOqK3lnZ6f6mngYer1eADweD7FYrNvHue+++wgEAuqtpqbm8A9eo9GUFJFIhMGDB3Paaadx5pln9vZwNJp+wVNPPcXw4cO5/vrrufbaaxk/fjxr167t7WFpNL2CFpk1Go1Go9FoNJpe4r777uO2225THxsb4pjNZrxeL/F4HIB4PI7f7+/2cW6//XbC4bB627Fjx+EduEajKTnWrFnDt7/9bd5//33efffd3h6ORlPyPPfcc1x11VV0dHRw3HHHMWrUKHbu3MlFF11EOp3u7eFpND2OFpk1GvaO6nfXdEejEb785S+zdetWPU80Go1G85lZtGgRV199Nffffz/3338/LpeL5uZm1qxZw/Dhw5k0aRJz584FYO7cuUyePLnbx3E4HPj9/oI3jUajMbJ69WrefPNNZs6cyV/+8pduvyeVShGJRAreNBrN3mzdupVvfetbANxwww0sXryYBQsWUF1dzfr16/ntb3/byyPUaHoe7cms0bAnqv/jH/8YgPPOO4833niD+vp67r//fh555JFeHqGmL5BOp7n00ktZtGgRsKc5k54nGo3mYIlEIhx99NEcddRRWCwWfv7zn3PzzTfjdrt57LHHGDFiRG8PUdPDyJ7y+OOPA/C5z32O8847D5PJpEpwR48ezSmnnMKYMWOYPn16L45Wo9GUMsOHD+eBBx7gxBNPZNasWZx33nlUVlYWfM99993HXXfd1Usj1GhKg2w2yxVXXEEkEmHq1Kn8+te/xmw2U1FRwT333MM3vvENHnjgAb73ve9hs9l6e7gaTY+hRWaNhj1R/dmzZ3PNNdfs1XRnX+gmO0cWqVSK//qv/+Kxxx4D9m7OtK+f0XNEozl48vm8euvs7Cz4OJ/Pq+8zmUyYzWYsFgtms7nAYqAvo4Oamn1x9dVXq/cXLFhQ8LV77rmnh0ej0Wj6I1OmTMHlcmE2m5kyZQpbtmzZS2S+/fbbueWWW9THkUhEe7xrNEXcc889fPjhh/h8Pp555hms1j3S2lVXXcUdd9xBQ0MDr7/+OhdeeGHvDVSj6WG0XYZGw56o/jvvvMMTTzyxV9OdfaGb7BxZ+Hw+Zs6cqT4+mHmi54hGs4d8Pk8ulyOTyZBKpUgmk3R0dBCPx4nFYkSjUaLRKLFYTL3J54xfi8fjdHR0kEgk1M8mk8mCv8m+SnGpsgSrxo0bd8Cgpi5f1mg0Gs1n4Sc/+QmzZ8+ms7OTxYsXM2rUqL2+R1vvaDT758MPP+Tuu+8G4OGHH96rCs1ms6nAsSQnaTRHClpk1mjoiupPnToVu93OlClT9mq6sy9KpcmOZARms1kymQyZTIZsNksul9uviK7ZPwczT0pljmg+OTt27OD000/nlFNO4f777+/Wo/uOO+5g2rRp/Od//mcvj7Z36OzsJJ1Ok0gklGAsArGIyyISy9fD4TAtLS00NTXR3NxMU1MTjY2NNDY2qvebm5tpaWmhpaWFaDRKMpkklUoRi8VIpVJ9el3TQU3NkUQ+nyeTyRQEhIyBomQySTqd1ucRjaaHuOWWW7j//vuZMWMGl1xyCeXl5b09JI2mpAiHw1x++eV0dnZyxRVXcPnll3f7fVdccQUAs2fPVs17S4nie45Q3MtKoylG22VoNHRF9WfNmsXZZ5/N4sWLcTgcNDc309jYyPDhw/f5cw6HA4fD0XMD/RTkcjnS6fR+L28Wi0W9lUrJeV9AmjPtb56UwhzRfDp+85vf8NOf/pRTTz2VM888k9mzZxfYHtxxxx1s3ryZDz/8kFtvvZX58+czderU3h72YUdEpUwmo4SjXC5HNptVb7lcTn1NAmDZbLbgffkZo2UGdAV3LBYLdrsdh8OB3W7H5XLhdDrxeDykUilyuRwul6tPrmfFpcofffSR+tqBgpqlWL5s3Hvy+bx6Tfria6M5dMg60F3Qx/i3bET+tq1WK1ardb9/DxqN5tMxePBg3nvvvd4ehkZTkuTzea699lq2bdvGiBEj+N3vfrfP7x0/fjzDhw9n69atvPPOO1xwwQU9ONLPTvE957vf/S4+n28v2zeNphgtMms0dEX1r7jiCu6++26uuuoqjj322IKmO6VKNpslnU4Dey51uVxOZc6ZTCZMJpMSgQCsVis2m00LAAfBj3/8434xTzSfjh/96EcEAgGg6+/KYrEUeHQvWbKEadOmATBjxgwWLFiwT5G5P3h35/N50uk06XRaicXysQjORvFZPjaKy8ZKC3mTx8vn85jNZux2OzabDYfDgdvtxuVyFVRneL1eADo6OnC73X1uLevPQc18Pq9eh87OTvW67Qvx0hZ/bfHYls9pSpdUKqXOH9BV1SCfM55BjL7qIi5ns1mAAsHZYrH0yv9Do9FoNBrhF7/4BS+99BI2m41nn312v1YyJpOJ888/n4ceeojXXnut5ETm4nuOeE4X97K65ppruv35/nC30Xw6tMis0dB9VL+46U6pIYIPdF3UstlswUJvJJvNqoueiAQ6k2jfPP7440BX2XupzxPNp6esrAyAP/7xj0yePJmlS5eqr+XzeaLRqBI8PR4PsVhsn49V6p3cc7kciURCCccdHR0qi1FEZhGcJUNZPi/isvF7jeJzd9jtdrxeLx6Ph0AgUBA8A9Shvy8Kzf0xqCkCooiDsMd/uzgTXRAh2SgyG8VlndFaukgFFXT5UobDYZXRbKxikMz24gCDzWbDarVit9vJ5XKkUinMZjM2mw2bzabng0aj0Wh6nN/85jfcfvvtADz00EOcfPLJB/yZc845h4ceeoj333//MI/u0FN8z3G5XMAe27cTTzyRWbNmcd555+3VPBRK/26j+fToU5pG008pzhQSgdnhcODxeHC73SpjWUrWRQgS0SeZTJJMJslms73qlXjjjTcyfPhwTCYTq1atKvjaxo0bOeWUUxgzZgwnnXQSa9as6aVRavoqqVSKG264gaOOOorx48crj7RDwdNPP83LL7/M/fffv5dHt9frVR5s8Xh8v9kOpezdnU6nicfjZDIZYrEYkUiEaDRKe3s77e3ttLa20tLSov5tbGykvr6ehoYG6uvr2bVrFzt37qSxsVH9TFNTE7t27WLbtm3U1tZSW1vLtm3bqK+vJx6Pk06naW1tVW/i8ZpIJJTILYJWLBZT4mdfWEskqPnhhx9y/fXXM2XKFBYsWMD8+fMZPXr0YXnOw4m8/rJPGP13xW9XslmSySSJRKLbN/Ho7ujoKPh+8e0+kO1TT/Dmm29ywgknMHHiRKZMmcLy5ct7dTx9FaPALK8noOZGdwEm8W3v6OhQf8/hcFh5rkugXL6WyWR6bT70hXVEU9q89dZbHH/88UyaNIkJEybwxBNP9PaQNH2M/a0zmp4lHA5z7bXXcvPNNwNw22238a1vfeugfnbatGmYTCY2bdrErl27DuMoDw/Ge45Q3MtK+tAUU8p3m/5Cb51JtMis0fRTjJ6HIjhbrVY8Hg92u52Ojg4ikYi6AErWoJStp1IpdQmUxl3JZJJMJrPP7MLDxSWXXMK8efMYNmzYXl+7/vrr+da3vsWGDRu49dZbue6663p0bP0dyVAUAcgo5hnLnvsyP/zhDzGbzWzYsIHVq1fzP//zP4fkcRcuXMgTTzzBiy++qHyBm5ubWbNmDcOHD2fSpEnMnTsXgLlz5zJ58uR9PlapdnIXITCTyRCJRJTIHIlElGDc3t6umvS1trbS1NREQ0MDzc3NRCIRJTw2NjaydetW1q9fz44dO2hubiYajaomgdFolObmZrZs2aKyIkWUjMViao0SoVmErXw+r+bvBRdcwNy5c/VacogQAVmQBoySwZxOp9XrJ69PseBsbABZLDwb1xoRnHtLbG5ra+OKK67gqaeeYsWKFfziF7/YZ7OfIx3ZFywWS0EFRyqVwmQy4fF46OzsJB6Pq3VDmgDK/JA5IW/hcFg19sxms0qUTqVSR9SZRDdiKn3y+Tz/8R//wV/+8heWLl3Ka6+9xvXXX080Gu3toWn6EPtbZzQ9x7/+9S8mTpzIX/7yF0wmEz/72c+47777DrpCLhAIcNxxxwGoO0GpUHzPEX7yk58we/ZsOjs7Wbx4MaNGjer250v1btOf6K27jbbL0Gj6KVJO2tnZic1mA1BemQCJREJ9r2SfwZ4mgGazWZW1Gn0Tiz+2Wq0FG20+n2fatGl0dHSwYcMGBg8ejMfj4bzzzuPnP//5p/q/zJw5s9vPNzY2smTJEt5++20AvvzlL3PDDTewdevW/Xqbag6MlCjLfDFibNaWSqWwWq04HI5DUsJ8qOdPPB7nL3/5C3V1dWqeDhw48DOPE+DnP/859fX1fOELXwDgnnvuKbA9GD58OKNHj1YR5OnTpx+S5+0riMCYyWSIRqMFwqAEryKRCNlslmQySTQaLbBTsNlsOJ1OduzYQV1dnfq8yWRi2LBhjBo1il/+8pdks1m2b99ORUUFgPKHmzlzJq2trcTjcdxut7LqkFJ9Wac8Ho/6/IknnqiqN4xCpV5LPjmyRkDXfhONRpX9gQj7RsRXV7y2ZR2RnxG7Jnkd5WdsNhu5XA6z2YzD4VBWUE6nU/kDdsehXks2b95MVVUVRx99NACnnnoq27ZtY8mSJfsNIB3JGPcPOXPU1NSwefPmgjOIBLgB9bdrfO3T6TQWi0XZaCQSCeVRLqK03W7HbrfvtQ/1tzOJbsTU8xyOOQTQ3t4OdAUIysvL+7znvmbf9OQ6o+kZcrkcP/vZz7j77rvJ5/OMHDmSJ5544lOd5WfOnMnSpUuZM2cOl1566WEY7eGh+J5z+umn85WvfGUv27fy8vJeHmnfQ+4Zcr6RM46ceeWOYtRRDvU60pt3Gy0yazT9FGNTv3w+r5rpiE+sCMZOpxOv16syyozCsix8ZrO5oDmgUYTOZDJKbBY/xX//+98ADBgwgHfffZehQ4cWjO2SSy5h06ZN3Y77n//8JzU1NQf1f9yxYweDBg1SQoPJZGLo0KFs375dC0OfEmPzNkECD7CnUZeIzOKvm81mcblc+xV9DoZDPX82b95MeXk5P//5z3nnnXdwuVz89Kc/PSSZXv/85z/3+lyxR/c999zzmZ+nL5LL5UgmkyobUcrdJds9lUoRDofJ5/NEIhFlG2K32xk3bhzDhg3j//7v/5g/fz7Q9bofe+yxTJkyheOOOw6fzwfAtddeC3TNhQ8//JChQ4fy2muv8eyzz9LQ0MDzzz9Pa2urWn9kbTKbzTz22GMMHjyYVCpFIBDAarUWlNgnEgl12NNrySdHBGaLxaLsSex2OxaLhaamJgDlme1yuUin02zcuHGfvQGgK+vF6XRiNpvVGpPNZrHZbNjtdhKJRMH7VqsVp9PZbUbRoV5LjjrqKJqamliwYAFTpkzhH//4B7FYjK1bt2qRuQg5X3R2dqpziLxGUhkFMGLECMrKypTFTSQSoa2tTfmyZzIZ5cstgrN4Nssa5HQ6cTgcat9yOBzY7Xb1fP3tTKIbMfU8h3oOmUwmnn/+eS6++GI8Hg9tbW289NJLBZmCmtKiJ9cZzeGns7OTa6+9lieffBKAb37zm/zqV79SfVY+KTNnzuQ3v/kNc+bMOZTDPOx0d88RintZHelITyu5G3eXpLUvJFnLarUe0nWkN+82WmTWaPopchETj2WXy0U8Hqezs5NoNIrf76etrU0JReXl5fj9flWaaswmAwrEG0Bd9ozZZ9KYx2KxsHv3bjKZzF6LI8Df//73Q/r/NNLbnp2lTD6fV96XgNqUREQU7HY7wWBQZY/KHEokEodEaAYO2fzJZDLU1tYybtw47r//fpYvX85ZZ53FmjVrum1SoTkw+XxeCbRiTWG0Pshms0q4kLJ3s9nMlClTVJOUO++8k7q6OiwWC2effTbnnnvuPjMhiudCKBQCusSqyy+/HJPJxMCBAwkGg/h8Pnw+H06nE4/Hg8lkIpvN0tLSgs1mIxQKqYCJWME4nU5AryWfhM7OTnWAFvEeIBgMKs/B8vJy1TQGoKGhQa0jbrdbZSVLprtURqRSKWw2G8OGDVOWCGLT5HQ61fsOh4NsNks8HleH8+7E5kO1lgQCAV588UV++MMfEo1GmT59OuPGjVOVQpo9yNkjl8vhdruJx+NqnjQ0NODz+YhGo2zbto1kMklVVRWVlZVUVVWpwFRjYyMtLS3q0pbJZHA4HFgsFuXpbLPZyOfzpFIpXC4XdrtdVVcUZ7r3lzOJbsTUexyqOZTNZrnvvvt45ZVXmDZtGosWLeLCCy9k5cqVBWumpvToqXVGc3i56aabePLJJ7FYLDz66KN8/etf/0yPN2PGDABWrVpFa2ur/jvvB8g5WJKt9iUqi24iZxnJaJaKcLPZrL5mtVpxuVw0NDQcsnWkt+42WmTWaPoxktkngrE0IhNh0OfzEYvFSKfTNDY2Ul5ejs/nw+v1FvgwyyJqFBXk8iYLq3wsXeCXLVvGpEmTuh3XoYrm19TUUFdXpxbmfD7Pjh07ul2UNftHyttl83M4HMortZh0Ok1TUxNVVVVYrVbcbrcSiQ5GaC4uHzJueJKBv3Tp0kMyf4YNG4bZbFbeqcceeywjRoxg9erVnHbaaQf8vWj2Rg5JIgqKj6p4dEtWq8/nY9euXZjNZr761a8yYsQIoKt5Wl1dHcFgkNtuu+2Af68rVqxQc6Gzs5N//etfAHi9Xp5++mna2tpUNYUEwsxmMw899BDDhw/H7/crUaqxsZHKysqCrEqHw6HXkk+IBKJkfwFUFrOQy+UKMli9Xq/KcJY54vF48Hg8VFRUkMvlaGtrU6JyOBxWQvPu3bvVHlScxWo2m0kmk1gsFjUG46HaOH+K+aR70cyZM1WH+FQqxYABA5R9hmYPYm0hQrAEo6WiClBWF7t372b37t3YbDZ8Ph9utxu3282gQYMYNmwYzc3N7N69W/UGsNvteDweZaORzWZxOp0qwCWiq3yvw+HAZDId0nmwL3pqHZFGTC+//LL63JQpU3C5XCqgt2XLlm5F5ttvv51bbrlFfRyJREoug1ISG4xnCHkz/u3LeaKY4s9Jtr08thERAiwWyyGbQ8uWLWPXrl1MmzYNgBNPPJFBgwaxfPlyTj/99IP4DWj6Kj2xzmgOL8899xy//e1vlfXdZZdd9pkfs7KykjFjxrBhwwYWLlyo7Cc0fR+5s8oZVN7vTqyVKnC5j4hVYLF9nBGr1YrP51NiczweP2QaSm/ebbTIrNH0Y0wmE3a7nWQyqWwQRBCUpn4ej4dEIkEul6OxsRG/34/X61V+h5JpJuXwcqmTn5WDvZShWywWOjs72bp1KwMHDiSXyxUID3DoovlVVVVMmjSJp59+mquvvpoXX3yR4cOH6/L2T4hkIYvA7HQ6lccudJW8i8VKZ2cnLS0tZDIZ4vE4gUAAk8mE0+lU80hK2o2ZhcUb9IHYunUr1dXV6rGMwtEnmT8VFRWceeaZvPXWW5x77rls27aNLVu2MHbs2E/6a9KAyhoEVKaiRPFFZE4mkwDK8uLoo49WAjOgMl2nT59+UAednTt3MmTIEABeeukl1q1bp8Sqyy+/nLKyMgKBAIFAAI/Hg81mY9y4cdTV1ZFMJkkmk8rvMpFI0NLSAuwRGnK5nF5LPiHGxm4iMsvvMxAIkEwmaW9vJxqNqqxlh8PB0KFDaW5uVo0Ajb68xbjdbgBcLheDBw9m+/bt6rWMxWLkcjk6Ojpwu92YTCa19pjNZux2u1p/jPOnmE+6F+3evVt5uv/sZz/jjDPOYPTo0Z/oMY4UnE6nWu+9Xi+xWAyn06kyjcU/2Zip3NraSmtrq3oMk8mEy+XC5/MpWxYJGI0YMYL29nay2SwdHR2qIiGbzeL3+zGZTAWVXIdyHuyLnlhHpBHTq6++ulcjplmzZnH22WezePFifvCDH3T78/K3WGp0dnaqc+snKUM+VJjNZrZv335I5pBc/NevX8/YsWPZtGkTmzdvZsyYMYdquJpeoifWGc3hY8eOHXzrW98C4I477jgkArMwZcoUNmzYwIIFC7TI3IeRIKbREnJfGPtTibVoOp1WDY2Ne5XR9ksCorKntbW14fF4cLlcdHZ2smXLlkOyjvTm3caU1/WgGs0hIxKJEAgECIfDfaqDqghDxhINKXUHVEm5+PBKVM3lcu2V8ZHNZgmHw0pIGjBggMpmBFQ22fr167nkkks44YQTePzxxws8mz8p3/3ud3nllVeor6+noqICr9eronjr16/n6quvpqWlBb/fzxNPPMH48eM/3S+qB+hrc8SYwSwX+ng8rrqcl5WVqcwwoaOjg7a2Nmw2G1VVVQWPZbTb2B9GgU+ECNmOTCYTmzdv5j/+4z844YQT+POf/6yafn2aOVRbW8u1115LS0sLFouFO++8k4suuugTPUZP09fmiSCCTmdnJ+FwWFlkdHR0qMZ+ra2tuN1uKioqWLBgAZMnT+bzn/+8eoz33nuPP//5zwQCAX72s58dsGHI+vXrVem3iNUjRozA4/HgdDopKysjFAoRCASw2+3k83mOOeYYnE4n9fX1qrnSwIED+f73v89bb71FU1MT5eXleDwe1q9fj81m02vJJ0D8+yUQ2draisViUVni4XCYlpaWfYpB4utubP6Xz+eVQBwMBgkEAgV2Gi0tLeTzeWpqarDb7bS3txf4QosPr5QmSpC1traW888/n5NPPpmnn376M/2/v/GNbzBv3jyy2SxTp07loYceIhgMfqbHhK6L7VVXXUUqleJLX/oSp556KjfffDNut5vHHnuMESNGcMcdd/D+++8zceJEHn744YN63N5eR6SyQTJOpepBqqvEZkUudca9YF+ZQsKgQYOoqalh586dyp7H4/Go1z8UChVkqG7fvp2LLrrokMyD3jyTnH/++WzdulWtm9KIKRAIcMUVV5BOp7nqqqu4/vrrD+rxenuOHAhpMFp8rhC7NllLjE1EZR0wNoHc11wqrqQy/mvMYAPYuHEjl156KVOmTPnMc+jZZ5/l3nvvVefxH/3oR3zta1/7TI95OOnr86SvIOeVw73OaA4Pl156Kc8//zxTpkxh7ty5h8T+T/jDH/7Af/7nf3L22WerRmxHGn11HZEzhwSm97VfyF5jzGg2JlEVI83Hc7kc7e3tqleNEAqFCIVCxGIxAGX3t2HDBr72ta8dkr2mt+42WmTWaA4hfXXxBFS2sfGgbrFYVAarHM4l6xn2XNIlQgfsdTkcMGAAFouFTCZDLBZTjyNZaMWLbnGG65FGX5sjIhbJa9bZ2UlDQwPQtfnJ62hEbAfEC9f4WspGnclkCqK1cuGT7DLx8jX+nGB8PJvNVpDJXJyl2F/pa/NEkNctk8kQjUaJxWIFQrP4sft8PsaNG8c//vEPfD4f//mf/6kqGtLpND/+8Y+pq6sjFArx7W9/mwkTJnT7fPl8nlWrVvHMM8+wY8cOAIYMGUIwGMRqtVJRUYHf78ftdnP88cfT3t5Oc3MzVquVkSNHUlFRQVNTE/X19djtdsrKyujo6CAUCqnmcm63+5BeJHqK3pwjiUSCbDarvJBlzQgGgyooJQJjIpFQItH+Du8Hg9PpZMiQIUo87OjoIBaLFVRHyB4j641UWvTl1/j73/8+559/PqeeeqpqSvrCCy9QX1/Pb37zG+644w5uvfVW/va3v3Hrrbdy0UUXMXXq1L0ep7umbjU1Nb26jkjwUdZ7CWinUqkCcbk42CiBB9mfALX22Gw2xo4di9/vJ5/PU19fT0tLCyaTibKyMhUwLy8vV1nuQIF9hqaLvrrXFM8b2JM1lslk1Fw3NinuDskeM2abGRtjy3MZ35evW61WZcGTyWTUc5nNZtxud4GI3d/pq/NEozlUfPDBB5x22mmYzWaWLFnCsccee0gfXywQpCfSkbR+CH1tHRFtxJiIJ1gslgJbQKN+IntGsV2TVHTLa5tOp5U2IthsNsxmszqryX1FEmIqKirU90qviVKk7564NRrNZ6I7n1s5LKfTaXV5czgcKnoHqDJKKX0vvrQa8fv9Sjiy2Wz4/X6i0SidnZ2q0Y+UxIrgKEJ3sX+npucxRl7Fy1E8mB0OR7cCM6AEXikLMpbeGv2oip8rFosRj8cLxIPuIsBms7mg9EhEBdlok8mkamxpvDRqDi/yWgDqQFaccShzJplMMnr0aLxeL9FolI8//lg1/bPb7dx6663cf//97Nq1i/vuu4+jjjqKE088kZqaGlwuF9FolC1btvDRRx9RV1cHdAmM1dXV+Hw+bDYbZWVleL1e3G43o0ePpqqqimAwSCKRIB6Ps2HDBiwWC8FgkPr6erXuwZ5MWuCIPOh/VoxBR7vdruwQ2tvbSSQS6nButFGyWCwq87k4i1lei2KPdkDtIx6PR9kgQNdr6PF4cLvdKtAhImQmkynwaE4kEtjtdpXt3Nf40Y9+RCAQAFAWU2VlZZSVlbFx40aWLFmi/FtnzJjBggULuhWZ+2JTN6mQsdlsBZcql8tVYH8gAYhi4dn492m1WvH7/YRCoYKGnQMGDCCdThONRslkMng8HuLxOC0tLVRVValzj7EZYV+cB5oujBnwgPL0jsVi3XpbSgC6eB2RKr2Dqa7aHzLvPB6PanTc0dGhmstqNJrSJp/Pc+uttwJw/fXXH3KBGWDChAl4PB4ikQhr167t05VyRwL5fF71qRJEs4hGo7S2thbsKUa7UDnbHuzz2O12ysvLqaysVAHKRCLBhg0b6OjoYNu2bQwePJhYLEZbWxuVlZWqz5Gcn0sNLTJrNP2M7jKWpaxQLCucTmfBImk2m3E6nSr7FFCCnoiJspgW+w8lEgl1wLdarQQCATo6Ogoy10TgNnoRpVIpZX+g6R1kjsi8gD3iUSaTIZFI4HQ6u22S43K56OjooKWlRWUvdtfBVjLcxZrFWJK0L29m2cxlPDabTT2Ww+FQUWARDWBPk0t50xe/Q4+xtF3WGGMTR+hq7mb83pkzZ/J///d/zJ07lzFjxhAKhYCuDMO7776bF154gXfeeYeNGzeycePGbp/XarVSVVWF3+9X4lQgEFAZzKNGjVK+uHa7nWOOOYbNmzfT1NTE7t27lcWGxWIpsFeQ+a/nyifHarWqA3cul8Pr9SpP5O6Ckka7JGMGyL4sEYqzPhwOhwqEGRHbFhGkJINE1plEIqGyrSXI0BczWaXT/B//+EcmT57M0qVL1dfy+TzRaFT9bXk8HlVaWUxfbuomwUdjNZQI/1Dogyj+uyIWyllCzi2NjY00Njbi8XgIBAJ4vV4qKiqIRqPE43FqampUr4Hm5mYGDBiAy+VSfQPi8fgRl4laKkiSgojEVquVtrY2ZckGqCC2nFOKM54FCUYX93WQ5yluQmz8OeOZWSyBIpGIalIqQrMOWPQ/ijPcu2sKaWwqqRMdSp/Zs2fz0Ucf4XK5uPPOOw/Lc1itVk488UTef/995s+fr0XmXqRYYHY6ndhsNqLRqLJmA5TNlzFBSqqx5IxSnM1sfA7jv9u3bwe67h9+v5/q6mqGDRvGli1bSCaT1NfXEwgEyOVyKtNbzisej6fk9BItMms0/QgpLyy+tBsvaZL9KYduo7BsNKU3HtiLO3Tvy3sI9mTASoMfY0a0NB6Ug70WmvsGxtfW7XYTj8dVEyabzUYgENirUVAgEFBCQVtbG5FIRGWrQdflz1heJHMymUyquWOxWPB6vQXlzHIhlMYJ4sfY2dmpMuFSqZQqIZLHLZ6TxjJXzWdHstZhj4Dc3TogQph838SJE1m1ahXbt2/nhRde4Atf+ALV1dXYbDZcLhdXXXUV559/PgsWLGDdunXU19eTTCax2+0qK1rWKrPZTCAQwOfz4fV6sdvtHH300Xs1sDCbzYRCIZqamsjlcsqv1el0Ku9xEZl1QOLTIQKMZFq4XC6CwSAej0fZIFgslgJ7JPlbLp4zxZd4434FqPfj8Ther1etGfl8nt27d+8zs1Hmj4zHZrORyWQwmUx9svHZ008/zcsvv8zLL7/MOeecoz5vNpvxer3s3LkTgHg8vs8y01Jo6iaBQPl7NGYti6goiOgse02x4ByPx1X1jfHxTSYTFRUVNDQ0kE6naWhoYMCAAXg8HhWQEKFZ7xF9h/z/1ytEkh/MZjP19fVKcLbZbMTjcZqamvbbQFhEYMlkTqVS3a7zxZ6axckUNptNPa80MK2vry+wYdFCc9/FGMzsrgeAfI/8252YfLBIso3sjZIMoSkNfv7znwPwrW99i+rq6sP2PFOnTuX9999nwYIFfOMb3zhsz6PZP8YGstLYvqWlRVlW2O12UqkUzc3NwJ47UCwWU3cho+AsZ5hiobm4R4CsDW1tbbS1tbFu3TrVTFrsK71er0rg83g8qopH7tilstdokVmj6UfIJUwyh40lzcbsIClxlmZqkjlqLE2Ui57xUCZ01xxFFli50EsZu5TEFmcQWSwWldHaXbas5vAj80MyUmUzlIywWCxGJpOhublZCUjGn62srFTfJ3YY3WGz2VR2O3QJwFIytnnz5m4DFsFgUGWjtbS0qMxZGUMikVCZ1uK3Khu+UYhwOBwl62fVlzBe0iRQYBQCZe3w+XzqZyS75/zzz+fJJ5+kpaVFNbAwXsZEdLLb7QwYMKAgY02+1+124/V6VcDDarVy7LHHMmDAgG7HK8KT0+mktbUV6BLgstksLperQEzQfDqcTqcS7Do6OlRWqgSajIgYLXRXHWHE7XZjs9nUniUZ0rFYTGWzx+NxOjo61JollghyCYjH42o86XRaCZjyfl9aFxYuXMgTTzzBq6++it1ux+Vy0dzcTGNjI8OHD2fSpEn87W9/44YbbmDu3Lmcf/75vT3kQ4JcvGTOGEUhWVvEZkWC38lkUs0NEfokuG61Whk0aJB67KqqKurr68lms9TX11NVVbWX0FyKGUL9FWPSg81mY9euXarUWDK9BAlESjPaWCymziLdIYFKCVDJnrY/JCDldrtVJZXP51MVXJKZr4Xm3sdYCSEC0oFe3+4CnLB3lnux+NydcGSsoBERymaz9fl+ABqYM2cOc+fOxW6384Mf/OCwPteUKVMAmD9//mF9Hs3+Me4zFouFjo4OJTCHQiEaGxtpa2sDUE3E5Qwr59LW1taCyj2jFmLUTGSdMCa1uFwuvF4vZrOZXbt2YTab8fl86rHlLpXL5dT7iURCWVSWQl8iveppNP0IOVDJoimIkGO0KZCLmhyOnE6nOqB1t0AWZzNLNqBx0ZRmYBLVk6/JRV+Exo6ODrWYGq0z+vqC2d8weitLljugMka9Xi/hcJhEIkF7e/teApLJZMLv9+P1ekkmk8oiReaGZB3JRm0ymVTDhw0bNhSMw+PxqEYIstm3t7czcOBAhg4dSkNDA8lkklgsRiAQUIJSMpkkmUxisVjw+XwqU14yJlOplLoEaD49xuZKxgxmuZhJdrP4pAJqrvj9fr7+9a/zwQcfsHHjRuVpKYep7pDSegkiBAIBhg4dqhpOOp1Otm/fzvr168lms5jNZoLBIGPGjMHj8Shh2Wq1kkwmlRgAXXYDxoCa5tMh2cISQJTyQalGEAFfbAug6/VwOBwF4n6xOCAZI5lMRgUqXS4XkUhECcvi2w1dVRXBYFA9XllZGdFolKamJmWRIVkpsifJxaCvCM0///nPqa+v5wtf+AIA99xzD+eddx4mk4mnnnqK4cOHM3r0aE455RTGjBnD9OnTe3nEhwfjuUIqVSQTFVAZ0BJEgK7g0eDBgwssWQSLxUJ1dTUNDQ1KaA6FQng8HjVvRSDUa0HvIvs2dP1dSsmyw+EgEokoAaCsrExdzLdt29btY0kfEDnPyrwy2hrIvtXZ2VmQLS/BC8k4c7vdJJNJnE4nfr+fbDaL3++nvb2dQCCgqv+0BUvvIOvDvjxS5e4ijRtlbzmQCF3cO6Q40ab4/mOxWNReZbfb1flGbIG6s3vS9A1++ctfAnDNNdcwePDgw/pcIjKvWbOG9vb2grOLpuco9vuXO4PcUdva2jCZTIRCIdauXUtnZ6fSKTZs2LCXlZecTfdXBSF6iJyDo9GoqroVyzmjPiL32UwmQzAYVPuaVO8ZLSJFy+lL6Fu3RtOPkMNOdwcnOTSLl60cgERcNC5Wxkyi7qL8Qi6XUwsk7BG3RYQyXtpEkJDn7ejowOv1KisEESe00NxzSBmoeKsWZyBaLBZCoZDKXu3o6FDNqYxIpqmUp4tPan19fYGwB7B582aVoVZWVkZVVRWpVEo1N6ipqcFut1NXV0d9fb0qhx8/fjytra20tbURDocJBAJUVlaqhl+5XI729nZyuRx+vx+Xy6XsOjKZjBaZPwNykII9Pt7FFzDomi/GZpFSpQBdGc7nnXeeKofevn07O3fupLm5mWg0WpAhZOzMXFlZydixYxkxYgSbNm2iqakJ6CorK7ZJqK+vp62tjRNOOEGVmsn3iDggF0ERqPR689kQf3a56ItAnE6n1cFX1gAJZnaXxSxBBZlTkpmaTCbVnHI6ncTjcSUcyWvo8/lIpVLs2rWLSCRCPp/H4/EQDAaJRCIqkGG329W+A3uaV/aFAOc///nPvT63YMGCgo/vueeenhpOn8HY4DWbzap54XA48Hq9tLS0kM1maWxsZODAgd2+jlarlQEDBtDS0kIikaC1tZVEIkFZWdleTdz62iXtSMJYiSeJEBIEb29vx2QyMXLkSNavX6/2AegSBcrKyshkMjQ1NVFfX68CUJ8UWX9kHBaLhXA4rOyA0uk0wWCQzs5OJUYEg0GVWS8BCz2PDj/y+y629pO9RJIWwuEwsVhsrwZeRqG52FPV6NFttNuQ5zBmJUryheyDUm1TUVGB3W5X4nY2m1Vl+Zq+w7Zt23jttdcAuPnmmw/781VVVTFq1Cg2b97MRx99xKxZsw77c2r2TzqdVpZKJpNJVcyUlZWxevVq8vk8FRUV7Nq1i127dgF7sp3lY9mzJMlOqseNVcISuBRbM0mYEZu5aDRKMBhUCRbGrObW1lY8Hg8+n0/tUcWWhVL11V01YW+gb90aTT9CLvTZbHavqJZ8XjKEpPRURAGjuCOZn0bLjOJyMclsNC5wRo+j7g7Z4i8UDodVNE4aDoqgrX3Meg7J4IF9i21SLmr0V94X0qygra1Nfa/dbsfj8SgBCLpK4X0+Hzt27GDJkiV7BS/Kyso4/vjjmTBhAmvXriUcDrNmzRomTJiA3W6noaGBcDisyqH9fj+xWIxIJEI0GsXn8xX47n4afz3NHuRvWvwtjcgBSr4vm81SVlZGa2sr8+bNY9KkSfh8PtLpNC0tLezYsYPt27cXXAwlOOV0OrHb7TidTrxeL0OGDKGmpkY9/siRI7FYLESjUZXx5nK5GDRoEGazmRUrVpBIJFR2m8vlUl7eckH0+/1KuABtl3EoKBYC5fJefACWjAxjZUx3FTKSwS6Zpsafl++RwITNZiOZTFJbW1sQXA2Hw4TDYWWFYBSaY7EYPp9Pjac4+7q3BWfN3sgck7JWEYEGDhxIY2MjqVSK1tbWAi/NbDZLNBpVc04yDKU6R7x1YU+2vbY86D3kTCmvEXQFp6VZ0rBhw1i7di0tLS1YLBaOOuooBg0axNq1a1m4cOFeNktGy519+cGLDVMmk6GlpYXGxsaCdUTWBQlwhEIhOjs7VfZhKBSivb2diooKJY6LBYveWw4f6XS6IGtQhBur1UpnZ6fyOy1uVtzR0aGCk8aKGeN+ZUyyEaGoWGSWhByxUvF6vcTjcRwOh/LLr6+vV829ZM2KxWLqDKLpG/zxj3+ks7OTM844g8997nM98pxTpkxh8+bNzJ8/X4vMvYTxPCl3U5fLxdatW4Guu8KaNWuUwLxx40bC4TA2m43y8nIWLVpEZ2enSp7YsmVLgSWc0Z7UWN1rrHyQu04ikSASiRAIBFTvqoqKCpXBLAGqfD5PR0cHfr9frTPGtUt0FYfDUVBV2ltokVmj6UdIdF0ywZxOp/IJkwwv2CMcSkmX0ce2mGL/ZSjMlBYh2li+KuKeiEGyyIo/mcvlUhkIUspsbABkzKrWh7HDg0RuYU8JTzHGgzns21ogm83S1tZGe3u7mhtWq5VAIKAaG8jziLi8evVq9fPSzE0ykVpbW5k9ezYTJ05kwoQJrFy5kvb2dmpraxk9ejRms5ndu3fT1taGx+PB4/Gocvrise9v3D1FKpXiq1/9Ks3NzVx99dV885vf7NXxfFK6E2Tlb9PhcJBKpXC73XR0dLB06VImTpzInDlz2Llzp2pWVowcroyPbRSrRSS02WwMHDiQzs5OtmzZwvr162lpaSl4rJUrVzJjxgyCwaCydpESOOg6LGYyGdVo0hg000LAoUPWeBF2jN6YUFgO393PdrfHGF8fo6e7WPDY7XY2b96s+gAMHjwYs9lMa2srTU1NxONxlS0iGWpSpihCkFFslv+D3W7Xe08fRMRiyWgHKC8vZ9euXSor2WQy0draqhrGGZFAt3g5NzY2UlZWBqCEyL5wOTvSMIp4VqtVnTnE5kACWC0tLVitVk499VSy2Swvvviiylr2er2MGTMGn89HbW0tmzZtKtgrrFarsvcSj+/m5mY2bdqkvseYdSYWDFarlUQioWyWjI2LI5EIoVCIlpYWBg0apOw2tNB8+Egmk0rMkeopmR8NDQ20tbXt1cCxsbFRidLSLDQejxfYfRntVYwiswjNxj1JBCOHw4HH41GVFeXl5aRSKSorK7HZbEQiEWXZE41GVbajy+Xqtd+fZg+pVIo///nPAHznO9/pseedOnUqzzzzzF7VSpqew6hjyB7S0dGhqvC2bt1KZ2enEpul2imXy7Fw4UIAhg4dyrp166itrQW6ziI+n48NGzZQW1u7l6Yi1cF2u131iInH48qKKZPJ4HQ6KS8vJ5PJUFFRQS6XIxAIqMxlt9tNOBwmEong8/kIhUIqoSaVSqm7vdls7nU7uMMmMq9fv55LL7204ONnn32WCy+88HA9pUZzxCNioZSUitdxsdWFlCGLl60xg0su4sbvNf5rfC4RmCVbyHhgK24CZyyjhi4fRUBlMRc3KjRmT/ZVv6FSRTYi2BNNNZlM6vWRg7VcmKDrdZAycyGdTiv7CpkfdrtdZRavX79elfU4HA5aWlrUZmy1WhkzZgxjxoxRr6vYpyxbtoz169ezYsUKAI4++mhWr17Nrl27CAaDVFRUKDGxublZ+WvK8wMFgkNvb7TPP/8855xzDtdffz3nnHMOV155ZUkKGcbMUfnbFwFfKiDC4TDRaJSjjjpK2ZuIT7f4K8MeP3DYY6XjdrvVOrBjxw6g63VsbGzk3//+t8pug64MMq/XSzQapb29nfnz5zN06FBgj42P0b9VvNXMZrMKPuis1cOHHHAl8xS6ss2MjWSL96TiPUYERUA19wIKKl9ESAyFQtTU1NDc3KyE5erqauXjnEgkyGazOJ1OJTRLY0Dp3l3sB60bNvVNpOJKhJ/iIGJTUxONjY0Aas0xNh+Ws4mUube2thIIBJSVmJyJND2HrMlWq1WdO8xms8pODgQCKmA5atQo3G43Tz31FNFoFL/fz4wZMxg+fDhvvPEGr7/+erfVS9lsltbWVuW9KeTzeeWBuWvXroJsNOjKbpNztLFZlJQ+iwDd2tpKVVWVEjNFlNB7zKFDrNWg6w4h+0Nra2tBFrrD4SAYDNLQ0KDOErlcjpaWFiX2yr4gvv3FwnLxOdgoMounqsvlIhaLKauMZDJJdXU1TU1Nyr6nra1NWbjF43EVyNLzovd58cUXaWxsZNCgQVxwwQU99rziy7xgwQK11ml6DuPfs9wVoevsIIGoVCqFy+Vi8+bNdHR0EAqFaG5upq6uDqvVyvHHH88//vEP0uk0FRUVXHnllfz1r39V1ivQVSkTCoXI5XK0traSTqdpbm5WXw8Gg2rvSSaTeL1eQqEQ2WyW8vJycrkc5eXlBedTyW4GVPVuWVkZgUAAl8ulAqTSd6s315nDdnoeO3Ysy5YtAyAWizF8+HDOPvvsw/V0Go3m/0PEnGKhWTKcxa9HsjSKS5qLbTPEf6x4oZKyjGJvIKMo6XQ6Vcaj8RCXyWRUVppkBRg98IwClNFYX4QLvSF/erLZbEFZqmTj7K9hgWSdysE8lUqpJgfG7/H5fITDYdWMTQSlWCymLnYWi4VRo0ZhsVjYsGEDb7/9thqPx+Ph+OOPV1mpCxcuZMWKFVRWVjJkyBDq6urYtGkTwWCQ8vJy2tvblQeWXEZdLpcSoWRcvX2YX7x4Mddddx1ms5ljjjmGtWvXMmnSpILvSaVSBVmexqzs3kZEnM7OTjwej7IqkCxmu92uSrxEyDE2zRCKG3KJJY+sEeXl5QwfPpy6ujp27NiBxWIhmUwyd+5cVfEg5WQWi4UxY8YwaNAgnn32WZLJJK2trWo9kTVE/pWMd2M2rRaSDj/GrD9jYMW4rhv3jOKGSiLWGH27I5GIsjfo7OxU2emzZ8/eq5GkzWajqqpKHc6z2Swul0sFTERU9nq9KktFehaIeKT3m76FMSBhMpnU2i9WOiIwV1RUkEwmqa+vL2hcKgFrKWd1OByEw2F8Pp9qFGr0hdccfmS/lgbR0CUUSkDA4/GoAHVVVRXr169XmVyXXXYZLpeLF154gUWLFgEwYcIETjjhBGpqagBYt24dO3bsoLW1lWg0qmwTJCNMPDih68wwYMAAzGYzdXV1JBKJAuseu91OW1ubqsiTcmkRLKWqR9YQufhrPhvGhlcSPMrn8wVWbE6nk8rKSux2O8uXL1eBab/fz6pVq9Qe0NraqhpVS0NyucNIVYvsT/vCZrPh8XhUY2IRhhoaGhg4cCC7d+9m+PDhqkJv6NChak9Lp9Mq0UbTezz88MMAXH/99T0aUJ44cSIul4v29nY2bNjQYzYdmi7k79psNqu1Q4JNAG1tbZjNZtrb24nH45SVlRGLxairq8PtdvOlL32JX//616TTaSZNmsT3v/99/v73v/Puu+/icDj49re/zQUXXKD+5gEVxFyyZAlz5sxh9uzZqsG93+9XFVXpdFoJzBUVFXslYHi9Xpqamhg8eLC6I0lvioqKChwOh/q/dNdrqSfpkb+oV199lTPPPFM1ftJoNIcXYxm7HGgk4i7RueLuxxKtF2HwYA7FRuHYeOmT5l7FZe3GsUn2tDyflLNL2ZvR51O87iTaJ6K55pNTLLA1NjYWNDuRf40dtWOxWEH01YgEEsLhsPLCzWQyJJNJYrGYEgDy+TyBQIBEIsE777xTcHiXuRaPx5kzZw6bNm3i61//OpFIRPktXnDBBTQ3N5NMJtm5cyfDhg1Tl7l4PF4gnMv/UTz6eptoNKqywD0ez15Z/gD33Xcfd911V08P7aAw2vBIFL29vR273a7+7q1WqyrRku8zZgsYm+WIsAwoW4JUKsXu3bsJBAKsX78e6LoYfvTRR0BXGdr27duVXxrA6tWrufbaawvsGCQ4Jt7O8n7Z/5+98w6Pusr+/3t6r8mkJwQIvQiIItIXBESwYe+urmV1XcvqV5dVFGV1dXXVLXZdV11dEWVtiMAqUgUpUoVQ05Mpmd7L74/8zuUzkwQCJpkZcl/Pk4dkkkxumJtbznmf9zGbkwKTmaBw7wkIg8wAWNDY5/O18kc9FrQukeqEPOIp4NPY2AigRTlCr7XdbkcoFEJtbS2kUinMZjMLYlGjpng8znyayXNVmCwRNq/kZAaU2AZa1iay6VIoFEztqtVqUV1dzSqshCXvpEQMh8PsHER7nU6nY+pU7s/cPQj3CXpdgJbXk5LTVKEHtPyNkzp1yJAhUKlUqK6uxqZNmyASiXDNNddg2LBhiMfj+OSTT/DFF1+0Sj4JocSTWCxmgYQtW7ZAr9fjjDPOQEVFBVavXg2PxwOZTAav18vOHtS0ieaJy+VCQUEBC0BTQ9RMaC6a7ZCCmTz7ASQFmPPz82E2mxGNRvHDDz/A6/VCJpNh4MCBLAFpNBrx008/MRuUpqYmZpNDohkhtO/QOYX2sWAwiEgkAqfTCbVa3ary0ul0wmKxoKmpCSaTie151N8kEAjw/jNpprKyEmvWrIFYLMbNN9/crT9bJpNh9OjRWL16NdavX5/RQeb27P6y2QaQ9hhKUsfjcWYpSuuJ0WjEwYMHIZVK0bdvX3z00UeQyWS444478K9//QuhUAinnXYa5s2bB5lMhgMHDgAAKioqMGvWLPTq1SvpZ4rFYpSUlKCkpATnn38+/H4/PvnkE/zjH/9AQ0MD3G43ysrKoNPpYLVaYbFYYLPZkJeXx2yigBYhlVQqRW1tLfr06YPc3FzYbDbWg4Q+TyLCrAoyx+NxDB48GBdccAH+9Kc/sceXLVuGOXPm4L333sOll16a9D0ffvghrrvuup8/Wg6H02HEYjE70FBJqTBQpFAoWNCFMvqpUGCI3qd/6YKXGiikLuCp3U4lEkmS9zNZdQBgZWe0EMZisSR1MwWfKSBAvkMUQOJ0HKEVikQiYaVBVJYqVHu19b1C31RShFqtVvba+nw+BINBpoomW454PJ7k1wwAFosFw4YNQ//+/ZnScN++ffjss89QV1eHpUuXYvbs2Th06BC7+PXq1Qt79+5FfX09ysrKoFQq4ff7kxr+CEsaM0WpSk1hgJZAOjVsEPLQQw/h3nvvZR+73W6mwsoEqNSTgjRGoxFut5tZCpC6WNhVmQI3BAWA6e9bIpEgNzcXBQUF2LVrF9xuN2sKqFAomFdmSUkJtm7dyrosDx8+HDt27IDb7caOHTuQSCSYOlEul0Mul7NSZvoZ5FNP4+EBpK5HWF5O6zmVDBKUlKDAYWo1BX1MSS8qSSbvfqfTyRTsQ4YMQSQSwaFDh+D3+6HT6VBcXMxKCpuammAwGKDRaFgSRKVSIRaLsaCF3W5HTk4O5HI5s9jgZA6plQhCdSOddaRSKRoaGthe4PP52gwySqVSVuYuXKtofeb+zN2DsFk02XYBYBUyAFhDNwAs0AuAqb/27NkDABg2bBiGDRsGAPjyyy+xZMkSAEBBQQG7wCuVSthsNlRWVmLfvn1M+Z6K2+3GypUrsXnzZlx44YXYuHEjCxhTNR4JOWjPol4j1H9EeK7ic+nkEfbYIGW42+1mAaGysjJWbbd9+3Z4vV7I5XKMHj0a+/btY+cusssqLi7Gjz/+yPYAElFQhdSAAQNQX1+PZcuWtSmwKCwsxOjRo7Fnz56kniVUGUhe0SKRCHq9HhKJBPX19ejVqxeronC73dBoNBlzTu1pvPPOOwCA6dOno6ioqNt//tixY7F69Wps2LABN954Y7f//I7Snt1fttoACn31aW2muzHZu0mlUlYFMXLkSHYXmTJlCkpKSlhVzYUXXsj+fi+66CIsWrQIu3btwowZM9CvXz9MmTIFU6dOxahRo1rFK9RqNa6++mpcfPHF+Mtf/oI33ngDVVVV6NWrF7RaLbNfcjqdyMvLg8vlgkKhgNVqxaBBg+B0OnHkyBGMGjUKoVAIHo8HHo8nqXKmverk7uKEg8xisRgPPfQQfvOb3+DBBx+EyWTCjz/+iEsvvRR//OMfWwWY3W431q5diw8++KDTBs3hcDoGBdroQk1BIFKCORyOYy5CwiBBe1BZs9PpTLockDm91WpNslWQyWRQq9VJamS6yFM3cOCouoWyd16vFxqNhv0OmeBrlm2ZXKGqNBAIIJFIsBJjCh7qdDokEglWUirsvi2EFPJUSkrKQr/fzz4WKkzj8TgMBgN69eqFXr16wWw2Mz9UmgeDBw+GTqfD66+/jm3btmH8+PGoqKjAzp07UV1djXHjxmH//v2sySQpUSkRQQErAGn3ohIyatQofPfddxgyZAi2bduGhQsXtvoahUKR0eWTYrGYKTxJCazX6xGNRhEIBFgQgBrntBVkFlrv0OdtNhtkMhkr77JarQBaDn+xWAyFhYWoq6tDJBJBr169MHfuXAQCAdZ4g8qcFQpFUpCZPqYOzcLyOJVKxSshuoFUxSntESKRiFXWtOXdL+wDQG/CpkwAWHM3j8cDqVSKIUOGYO3atW0GjPLz8zFkyBBUVVXB5XIhGo3CbDYjFovB5/NBrVYnNWNqbm5GTk5O0hgyZS3pyVCCmuaAQqFgQSaZTMYafgnXKFLCisVitlYJfVvJe1u4v/l8Pmi1WoTD4YxontMWp5K6jNZmqVTKzhUA2L4AJDcAJZUWfQ0A1gi0uLgYQMtcWbZsGQDgmmuuwfTp0yESiXDo0CHMnz8/qfGwWCzGwIEDcdppp2Ho0KGoqKiAyWRCXV0dnnzySezdu5ftM/RzhdZwlOigxrMUHBD2h6B1L5P3+ExGKFCgvZv+tnNycphl0p49e+BwOCCRSDBq1Cio1WpWGTVq1Ch89tlnAFr2hGg0irKyMmzduhUAcPXVV+O8886DVqvFf/7zH7z33nsAAJ1Oh1GjRqGgoAAejwfr169HfX09vvjiC1x66aXYvHkzq45xu93MPsVms0GhUKCxsRH5+fkAgCNHjjArFrKSo2RXJlTd9RTi8TgLMqdLBEm+zOvXr0/Lz+8o7dn9dcQGEMg8K0Dab4T9QlIbTefk5LDAcmlpKdsvdDodAKBfv36wWq149tlncc4552Do0KHMuu/ll1/Gt99+i8rKSlRWVuLVV19FQUEBLr/8clx77bVJFoJAS9Ls97//PUaMGIF77rkHR44cwZAhQ5ivskQiYWcRSqwHAgGWxHS73VCpVKw3lpB0n1tPakW7+uqr8dhjj+HFF1/ETTfdhPPOOw/XXnstfve737X62v/+97+YMWNGVmQ3OJxTEaEfEC2ukUiEdVOVy+WslJ8aYVBQkS7XQgUiBYlIuexyuZLUJrm5uWhqasLOnTvbHE8kEklq4KXX65lyiBZ8g8HAgsmkMhOJRPD5fNDpdGxsbTX96U6yNZMrvMiRikuhUKCoqAgHDx5sFaihcmMKOAubKEUiEdapOxQKsQQGBZ2Fl34ArJmfkLy8PEyaNAkzZsxAaWkp+vfvj3379mHbtm0YPHgwdu7cCbvdDrFYDI1GA7fbjUAgwC5sQqW+sOw2U7j88stx5ZVX4q233sKNN96YFXOkLSQSCTQaTVIDT7K0Se3ILrwUAkgK1CkUCpjNZrhcLjQ0NKC5uZnNF/IQq6urA9DiXffWW28BAObMmQO3242PP/4Y0WgURqMRoVCIVTooFAoolUpWGWE0GpNUsxR8TvfBqydAAUGgZY5QlQMApsYQBpFpXTlWUpNsleLxOJqbm9m6VVFRgWXLlsHv90OhUGDEiBEwGAyw2WzYuXMnGhsb4fV6MWfOHGzfvp01BiwoKGCly9Q8lALNqdURPCmRHmifp/2HEhD0OlGSgs4jlDSlqiqg5Yxx8ODBVmsSlazTmYKgc45Wq0UwGGTWXZnEqaQuE1Y7kL2J8NxKnwOOvjZCezYg2dMZaJkX9PpPmTKFJRxuueUWOBwOqFQqTJ8+HZMnT8aoUaNaNTUGWgIN1157Lf7whz+gqqoKAFolTemxcDgMvV6PQCAAn88Hh8PBEumkvic7qUybS9kGJanpvkBBm8OHD7Nzw7Bhw6DT6bBz5044nU7IZDKUlZWxuwe9BgaDgYlZLrzwQnampHOqVqvFF198gdzcXPbz/X4/Hn/8cfz3v//Fhx9+iGnTpuHQoUPweDyQSCRQKBTsZ1qtVuTn56OxsZE9R01NDbv30JnJ4/FALpcz2xZO17J69WocPnwYer0eF154YVrGQEHmnTt3wu12t1nhmAm0Z/fXERtAIPOsAIVJTQrK0rou3GeETUX79++PPXv2YOnSpbBYLPjVr36FhoYGHDx4EJ988gk++eQT9vxKpRKTJk1CLBaD0+lEZWUlGhoa8MILL+CNN97Avffei2uvvbbV3/msWbPQ0NCAhQsXor6+HoWFhcz7n2zbQqEQpFIpvF4vcnJyWNKdnkvYP4d+j3RyUrdwqVSK//u//8Pvf/97LF68GKNGjcKLL77Y5td++OGHuOWWW37WIDmcdJCNipBUKDgIHFVeUBYMaFkMKaAjVBufKGKxGGazGSKRCLt27WLPn5eXh+LiYgSDQTQ2NrJLnUajQSgUgsPhYCVvFouFKUFcLhfUajUMBgPL5tGCT4EE+l3SefnPtoZuwg2HNiEKwuXm5qK6upoFmC0WC5RKJWpqalBfX58UdCElMXkhx2IxeL1eFmgWXsRIsUqNIIVq9UgkArfbjaamJixatAjr1q3D7373O5x22mnYt28fdu/ejTPPPBMAmN1EakNI4WNCa5d0b65ClEpl0iEkmyE1llwuT+rATk326JKW2pwrHo+zBo1ks0KXQ/r7EFpZJBIJqNVq5ObmsmqMt99+myXHyFdeJBJBq9Wy8mTyYhYGDbJBvXwq7DcE2TDR60nrBVnz2Gw2Fkgi31Lh95LynNYOuoin2vkYDAaIxWJ8+eWXiMVisFgsuPDCC5naBABGjx6NxYsXw263Y8+ePZgwYQK+++47diEib1VqxBSJRJjPr1qtZmPI5LlzKiFsIiy0USGEDYXtdjs7Awg9FoGjqliDwYBDhw4hHo+juLgYFRUVCIVCqKmpYRUSzc3NMBgM7PuF/vEqlYqdZzIpOPhz1GWZdCZJtfAS+mtTAIDWeSGkEhb+HkLoeWhPAFruow6HA3379sU//vGPpMBhe9DzU6UXrQOU1KaxUCA5JycHdrsdLpcLkUgEFosFCoUiqfKLN5U8cVLPrjQ3KGjvcDiY8nDgwIEwGAz47rvvsHv3bgDA6aefjs2bNzPfdbJZicfjKCgoQENDA9544w3ceuutkEgkuOSSS/D555/D6/XikksuwTPPPIMzzjgDQMuesXDhQsjlcixatAgrVqzAwIED2XmW+k6QjVNTUxMsFgusViv0ej3UajWzcDKbzcy+iUQc3EKj6/nXv/4FALj00kvZ3bK7KSwsRK9evXDkyBFs2rQJU6dOTcs4jkd7dn8dsQEEMs8KUBiAFfaUEf5rtVrRp08f1NTUYN26dbjgggtQXl6Ow4cP4+WXX0Z5eTlmz54Nj8eDyspKVFVVoampiQmshPflXr16QS6Xw263o7q6GgsWLMDKlSvx3HPPtdqDrrnmGjz33HNwOBzo3bs3QqEQEwXq9XoWZBaJROwMq1KpWCUPNUMVWlClk5P+6VdffTXz3Xz//ffbPIC7XC5s3LgRM2bM+FmD5HDSASlCVq9ejUWLFiUtGtkCXdSpgZHwoi6TyVizNmHHZsrueb1eNDc3w263w+FwwOVywePxMEUaKU7IomDfvn3YunUrU4OdccYZ6NWrF9auXYsvv/wSmzdvxu7du/Hjjz9i3bp1aGpqwtixY1n3VavVioaGBhQWFkIsFsPv98PtdkOtVgMA81ONxWKtVC7poqMN3QwGA3tL5+Yq9FRO/T8UiUSor68H0GJbYTKZsH79ehw8eJCpuWKxGKxWK2pqatDc3MwCxFVVVbDb7fD7/XA6nbDZbKitrWWv944dO7B9+3b88MMP2LNnD5xOJwoKCjB37ly8+OKLuPXWW2E0GlFbW4snn3wShYWFkEgkcDgc7P+USuXpwk/+h8LfRXgB5HQtpO5TqVTQarWsyzo1sxFWQJA9AiWdJBJJ0lqUmiygQDUpF6dOnQqxWMwCzHl5edDr9ZBKpVCr1VCpVFCr1VCr1WwcdFikpFamz4mO7DehUIgl5YR+lJkEWeaQVUooFGJe3nRYpvfJukImk0GhUMDn86G+vh5WqxXNzc1wuVxwuVxobm5mHzudTrhcLjQ1NWHr1q3YvHkzYrEY+vbti8suuywpwAy0XITGjBkDAGhsbIRGo8GIESMAAFVVVejTpw9rPEmKWWH/ACD9vnanOhSA8/v9zKaJ+kTQ+YXeKChcU1PD1n+z2cwuu4TwIklVD+eccw4aGhqwZMkSbNq0iVXHUM8AUqGSBVQgEGBrFPWuyJS58HPUZZl0JhGe4UQiUZLKTHg2EVYt+f1+lhQge6VU6PcWNpzftm0bAOCqq67qUIAZOFrKTudQurST57/wZ9hsNhgMBlgsFpZAq6urQzQaTbKHy4S7RCgUwgUXXIBx48bhtddeS/dwjouw6Z6wUopeD7LEKCoqgkqlwqJFi1iAuaKiArt27cJXX30FAJg4cSKbH8Kmb1999RUeeOABbN68GSNGjMA777yD8vJyNDU14eabb8bmzZvZeMRiMebPn4/LLruM2XSQhz/tWaRo9/l8aGxsRDQahdvthsPhYJUXdrsdjY2NLHFGjbaP1aiS8/Pw+/1YtGgRgPRZZRBjx44FkNmWGWT3l0gksG3bNgwYMOCYj6eiUCiYcl9YuZxuhBVKlGgIBALMBtDr9aKoqAjhcJgJaseNGweJRILDhw/j008/xTfffIOamhrIZDL06dMHw4YNw/DhwzFixAiMGDEC/fv3h1KpRDQahV6vx+jRo6FUKrF27VrMmTMHa9euTRqTMKlOydG2zhy0P1KvKrJj0ul0STaF6b73nHSQ+c477wTQsqm290sYDAY0NjZmpJ8Zh3M8Nm/ejPHjxycpQlLJ5Es/lSDTv3RBA8CCulQ2rFarEQ6HceDAARw+fBiNjY3sYu9wOGC1WtHY2IiGhgb2Vl9fj5qaGlRVVaG2thZ+vx9isRhFRUUoKyvDjz/+iEWLFqGxsRFSqRT9+/fHqFGj0LdvX0gkElRXV2Pz5s0YMmQIzjrrLMjlcng8HjQ2NqJPnz5MTUZNvYQL7fF8oruLjjZ0o2CJy+ViXdHTBQXfaN2mwB4FhkQiEUwmE1avXs2UOKeffjprMuDxeBCNRuF0OlFXVwev18u8Lvfv34+DBw8yRXSqyigej6OxsRGbNm3CO++8g9/85jc477zzsG/fPjz88MPMamXRokWsMy+VqQJgymmRSAS5XN5K3Uy/UyZZZfQE6AJI5Z5arRZarTZJPUwqZqlUyoIg7TWapAZ/0WgUy5Ytg8FgwMUXX4yzzjoLvXv3hlQqZY11NBpN0r8KhYI1viD7jGywx+jIfpNJwaG2oHWAkoGkECYFHzUCpUAe0HK4b2xsxIEDB9Dc3Myew+l0ora2FocPH0ZtbS0aGxthtVrR1NTELFbi8TiKioowe/ZsnH/++W36nYbDYea5ST7LFGAKh8OQSqUsYEUBC9prKJCR7mTmqQop3j0eT5K9EtkxeTweOJ1OuN3upD1U2IDUYDCwuUDNP4Gjr6Ww30MgEMC3336LRCIBuVyOYDCI2tpa9nq7XC5mn0IVVD6fj503QqEQ/H5/Rpw/fo66LNPOJMDRBLiwUolsPijhQH+/+/btQ1lZGQDgp59+Yokq4OieQhVZwmAync/z8vI6NKbq6mp88803bAwAWDUW+e4CYOuH3W6Hx+OBTqdDUVERE2yQnzN9vdBbPl1ko4iGznVUbQKANdDzer0Qi8WwWCz49NNP4fF4oNFoYDAY8O2332L37t0Qi8UYNWoU9u/fj8WLFwMAhg4dijPPPBP33XcfVCoVKisr8dhjj+H//u//kJubi8WLF2Pq1KmIRCJ4+umnk8ZDgWYKUtfW1sLr9bI9zu12w+/3s+bUNpsNSqUS4XAYdXV1zBYwFAqhtrY2qcqP7j6czmfJkiXweDzo3bs3xo8fn9axUJB5w4YNaR3Hsbj88suxfPlynHnmmZg7dy7++te/Yvfu3a0ez3RbJkJ4NqAEIVUQxONxJmAhy6Py8nLE43Fs3rwZjY2NmDBhAsaOHYt+/fqx/ZXOux6PB263G06nk/UgKS4uxogRI5iH8oABA1BcXIympiZcd911+OUvf4n3338fS5YswU033YRgMIiioqKkfhP0f5sqfMjLy2Pe9CaTiVlOAWCK53RyUjfxhx9+GF988QU2bNiAadOm4Y033sAdd9zR2WPjcNJKRxUhmeQ1JIQuV8KLG2G32wG0XPD9fj/27dsH4Khvs8/ng8vlOu4hR6hWpJ914MCBpK/p06cPJkyYkKQoWbduHbZt28bGYTQaMXz4cPzwww+or6/HkCFDYLFY0NjYCKfTCYvFArfbjVgsxjJ46S4DAbKzoRtZDwitJYCj6ppEIgGbzQa/3w+5XI5f/OIXeOmll+B0OqHValFSUoLly5cjEolAKpWiX79+WL58OVMU5eXlYerUqTjjjDNQUVGBvLw8lsl1uVxobGzEoUOHsH37dnz77bdobGzEc889hx9//BF33nknFixYgHXr1mHu3LkAwDz2RCIRmy8mk4mVB5FyVlhSy4PM6YfWBZpv5PVOdhbk5S6TyVhFBAWkm5ubMWDAAPz444+w2+1YvXp10nOTNzRd+DUaDbPPoI7u5L+cLXRkv8m0skMhFDAEWvYB8ooLhULM7oLeB1peQ6/Xi5qaGgBgauK6ujpmUSFsKNqeX3NdXR2OHDmCoUOHYsKECUlBtVAohI8//hj19fVQKBRM0UxBNVKPkRCCyieFQWahz3u6OZUsVSjpRMTjcRbYTbXMSG0gKkTY36GgoACNjY0AjiqTaM6QXRfxy1/+Evv378eKFSvQ2NiI0tJSdvaRSCTMa14qlbKydlJVU9PZdPq7t3f2yLYziTCoTMF/oEVRlpubC4PBAJfLhcrKSvTr1w92ux0HDx7EyJEj2Tq5ZcsWZr1E1Vg2mw1Ai+0XQWdQ8mo+FpFIBAsWLEAsFkP//v2RSCSYTRT932m1WjanyA5h//79GD58OOtxUVNTg3A4DK/XC51Ox3qNRKNRbvV2gshkMlY+TvOEqhCAliTi+vXrEQ6HYbFY0NDQwM6PJSUlcDqd+PLLL9nzjR07FlOmTAEATJo0CcOHD8fHH3+Mr776Cj/99BN+//vf469//SvmzZuHlStXYseOHYhEIklWFiKRCPfeey9kMhleeuklWK1WNjYKKjkcDuTk5LB1Q6vVwmazobq6GqeffjoOHz6McDiM5uZm5Obmwu12M3VipvydnkqQVUZbvrjdDfkyb9iwIWMbDB/L7i8bbQDpThIOh6HRaJi1EdlRRKNRWCwW2Gw2eL1eSCQSnHHGGTh48CDsdjsOHTrEnstkMiE/P5+dF6jxONkJHjx4EHV1dXA6nejVqxd8Ph9qa2thMBiQk5ODnTt3YtWqVVi1ahV7ToVCgf79+8NmsyEnJ4edRwC0SqQrFArWeN1oNCbFejLBcueE/7pef/11PPvss/jss89w2mmn4e6778bTTz/NSzs4pxzZqgghqOxC6GsoFovZpcxoNMLhcKC2thZAy0ZitVpRW1sLp9PJLvqk1rbZbGhsbER9fT3q6+vR0NCAxsZGNDU1oampCXa7nWXxtVotBg8ejIsvvhgzZ85MCjDv3LmTlS0Ky2voMEubrFDtSIfx1CxeujfkbMzkkpcg+cZRQNbv97OSUArmUlmQ0+mETqfD9ddfj//973+IRCIYNmwY5s2bhy+//BJWqxUlJSV45pln8PXXX2PevHmYPn06+vTpA61WC6lUCqVSifz8fAwfPhwXXHABHn74YXz99dd4/PHHIZfLsXLlShw8eBADBgxAIpFg2VkKtmm1WqYIMhgMzDqBxk+HcZlMlvZ5wUmGDmBAy98zXcKETT1JHUZ2B1VVVSgvL0dpaSkMBgOzwyA/RbVazRTTarWa+TDTvM62y1lH9ptMLTsEwM6A1PgzFouxfwOBQJICNBwO48iRI2ydAYBDhw6hpqYGoVAIXq8XtbW17JBPz0OBKJlMxg7QsVgMdXV1+Prrr/Hkk0+isrKSPefy5ctZgPniiy+G0WhEdXU1duzYAaClQ7hIJGKBIpqjqetHpijKslF92BbCALNIJEIwGITL5WIKYp/PxxSAPp8PXq8XXq8XHo8HHo8HXq+XqQOj0SjkcjmKioqgVCrZa0meqz6fDyaTCbFYDLt27UJ5eTkA4D//+Q+KioowfPhwAMnlsjR3hQ1sm5uboVAoktYxocq5uzlV1GVC9XIkEoFKpYJMJmNVT+Xl5Swh1dTUhN69ewNosb4YMmQIgJYADamcd+/eDY/Hw9YHOicALapVAPjggw+SEg6peDwe3H///diyZQtLXgJgexUpxCiwHQwGUV5eztTxFOgWVkmQtYtw3Uon2WarAhxdn1ODcfRaisVi1NfXM9/2uro6qNVqTJ8+HT/++CP279/P1MwPPPAAbr755qRAv8lkwk033YSXXnoJxcXFcDgcWLVqFbubyOXydhMDd955JyZMmJBk50dNiYGjVTR2ux0DBw4EANYokNT2fr+fJciB9M+RU5G6ujosX74cQPqtMgBgxIgRUCgUsNvtzPKF07VQJSTQsnbk5eUx209q1gq0/M0ajUZmE1lcXIxp06bh9NNPR0FBQVL1DAnzKIZy6NAhVFdXIz8/H+eeey7y8/NZ5Vzfvn2ZTVdFRQUGDhyI8vJy9OrVC0OHDkW/fv1gs9mYjRzZDVJyWKfTQSQSwWg0sn3FaDQyS0sSRWRCkPmE5F5Lly7FHXfcgffff59lX37zm9/gz3/+M9555x388pe/7JJBcjjpINsUIceCFh0KOisUCsRiMdjtdkgkElgsFuYJpdVqUVxcjO3bt7MA9IlCTXUcDgfGjBmD4uJiAC1KiDVr1gBo2VyHDx+OQCCAffv2McUBHWTpYNeWkiy10Vu6yNaGbuRnTNlWUtcolUrmqyyRSBAIBJhCWafToaamBsFgEAUFBbjvvvvw+eefw+l0oqSkBB999FGbHdqPhUQiwUUXXYQ9e/bg3//+N7Zu3cou+6ROiUajkEqlrDmKQqFgAQo69JM9Aj3GyTxkMhkrExYqnNVqNTweD8vQJxIJ6PV6ViIPtF5n6WOVSgWlUsksOkjVLpwP2UJH9ptMhi7EVDkTDoeZGjkSiTBLlerqahYwlsvlrMSYkpoNDQ1svc/Pz4dWq0VtbS2qqqpgtVoRjUYhFotRUFCAIUOGYOzYsdDpdPj2229RU1ODV199FXfffTe0Wi327dsHkUiEiy++GIWFhThw4AALMJeWlqJPnz7MlgFoXeVBZEqQORvVh6lQEgJouew5nU7W6DEYDDIFH/mxk80XzRkKSpK6h/o3kHIVaDkX5OXlsSZsRUVFcDqdOHDgAAYMGMCswEjRBhxtJGq321lwiCpwQqEQxGIxrFYrUy3RWCk5292q1FNFXUZBNZq3UqkUeXl5qKurQzAYhN1uR//+/bF//34EAgH2t0+2bUVFRairq8OWLVvY+x9++CGmTZsGAPjxxx/x6aef4rzzzsPll1+OxYsXY9euXbjyyitx/fXXY+LEiSwAaLVasWLFCrz99tuwWq2QyWSoqKhALBZj1TL5+fmQSCQoKSlhAXyq6iorK8P+/ftZ0h1AK4GEULmdTjoqosmkyhkK8NM5gt6n/YLWFY1Gw2zWZs2ahTVr1iASiWDgwIG48cYbj+vHnZubi9NOO40Jbn744QcAwJAhQ9q9c4hEIpx33nnMZo4SJZQEo8o7vV7PlJAajQZisZhZvGTqvnMq8d577yEej2PcuHHo27dvuocDuVyOiRMnYvny5fj8889ZAoLTdZCohc6dkUgEhYWFaGhoQCwWg8FgYIlupVKJ3r17o7q6mlmiyuVy9OrVCwMGDGC9ZWgdojOLx+NBbW0tHA4HHA4H8vPzUVxcjK1btyIWi6GwsBDhcJgJLVLvzhqNBnq9HnK5HGazGTKZDHq9Pqk/AY2Tvh5Akoo5E+5AHQ4yb968GZdeeimefvppXHzxxexxvV6P3/zmN3jqqadw/fXXp91kmsPpLC6//HJceeWVeOutt3DjjTdmvCIkFeECQ4cVCgQID2EVFRWsVGPgwIHo3bs3Xn31VXZwHjBgAMrKyqBWq/HTTz9h//79aGpqQiAQYOXEVAJP5cfxeBw2mw02mw3r1q3DmDFjcOGFF6KqqgrxeByFhYUYO3Ys6urqsHPnTnZILC8vR79+/RAOh1nZK5WwpP5OQPqDzNmKsPyGDsN+v581s4lEIjCbzazJn1gsRl1dHQs42+12VFVVMW/Duro6/PnPf8Y555yDAQMGICcnp8Mb3LZt2/D1118DaMnGbty4kf0M8pcSlghptVpmlyKTydj8I+9uPicyE6GHInA0oCeXy5mKoKSkBNXV1UgkEuzQRRdKYWCJlPHkAa3RaFhygewyso1s328IYQVNJBJBJBJhf6OkMtbpdEllhzKZDDabjSWWBg8ejEGDBuG9995rU90Tj8dRV1fHVElDhw7Ffffdhy+//BL79u3D22+/jcsuuwxAi6KVLhAUYO7bty+GDh2KWCyGgwcPAmhZe1KbjmXaPMp2Cy/gqGcuJTEpgExe3tQ8i4LOx6uSpBJVsVgMqVQKnU7HurmXl5fDbrfD7XZj9OjR2LRpE/bu3Yv+/fsjEAhgz549iMVi0Ol0OOuss3D48GHY7XammKcqKgpmiUQiNDU1oaCgABqNhvUxCAQC0Gg0GTdfsgW5XI5IJML+L9VqNQoLC9HY2IhIJAKHw4GysjI0NTWxhFBubi5sNhvEYjG0Wi28Xi80Gg2kUin27t2LYDCIadOmYcWKFVi0aBHWrFmD6dOn45lnnsH8+fNRXV2NJ554AsDRC7qweWROTg5yc3OZKIOCzMTpp5/O1g5aq9vqNUDPmapOTfc5JVtFNNQ0OBaLMZEEKZxp/aazLABW7g60BKGPHDnCmnpWVVVh7969OHDgANxuN3JzczFjxgxMnjyZKQQ1Gg127twJoOU1PxZkO0jjoLuQWCyG1+tljYpJvNOvXz+43W4WaCLVu/D7OZ1HIpHA22+/DQC4/vrr0zyao1xwwQVYvnw5lixZgt/97nfpHk6PQCwWQ6PRsP4O4XAYhYWFaGpqYtXYCoUCzc3NCAaD6N+/P8LhMKqqqpLiE+0hk8kwZswYRKNR7NixgwlmBg0aBJfLhdraWiaUoXNOIpGARCJh665EIoHBYIBGo4FOp2PVnGRFSH2JqMIq06wygBMIMp9++ultHmgBYMGCBViwYEGnDYrDyQSyVaVKkDqVfJNJKQa0HIJpMSK/VJVKhUmTJuGll15CJBJB//79ccUVV0Cv12PPnj2YP39+klroWJSXl+OMM86AWq3Gzp078f333zNVM9Dyf9vc3Izt27cDaClTGzRoEFQqFRoaGthFTy6Xw2g0sp+b6iOc7oN6NkNqdqVSyS5SoVAIOp0Ozc3NrDwnFAqxhnzr1q1DaWkpu6DNmTMHc+fOxeLFi/HRRx/ho48+Ys9tNpthMBhgNBphNBphMpmg0+mgUCgQjUZhs9mwY8cO/PTTTwBa5szu3bsRiURgNBohFouTbBBoQ5ZIJKwckg4KpHDLtAsR5yipSi7hv2azGTabjdmwHD58mCkwhQ0qhUFm8scUBphVKlXWrgnZvt9QokAkErVqnGc0Gtla36dPH2zevBnNzc2QSCTIycnBli1bEIvFoNfrcfnll8Pr9eKRRx5BOByGSqXCjBkzMHbsWJSXl0Oj0SAQCODIkSNYs2YNvvrqK+zcuRN33HEH5s2bx2ycNm/eDKAlMBuPx1kjxd69e2Po0KEIh8OorKxEMBiETCaDxWJhzcJov5RIJBmlJstG9WEqqZ7ciUQCoVCIXZBIEUSfA1ouTNREKx6PQyaTscAyBZqAljUiGo3C7/dDIpGwYF8oFMKgQYMgFouxceNG1NTUQCqVYsKECcjPz0dZWRmkUilLgtCeQmMRKvGj0ShTNKvVanZJFfrEck4MKgcmSx2fzwe1Wo3i4mLWH8Lr9cJkMjFbN6lUynoz6PV6RCIR+Hw+5OXlwWq14siRI0gkEujfvz+qqqpQX1+Pt99+GxKJBBMmTIDP58PevXtx5MiRpAbCxcXFLFhMzf30ej1UKhXzVB4xYgRqampYxVdRUREAMIsvCkb7/X5m5aDT6VgwA0h/34hsTWrSeSAWi7E5Q4/Rmuh2u5mVyYoVKzBlyhTs3r0bhw8fxj/+8Y92n9tqteLdd9/FoUOH2GtYW1vL/q4rKyuT7L6IRCKBzz77jFVG0BmVxDdarRZisRhlZWUsoDxo0CCIRCLWk8BgMLBmx7Se8fWkc9m6dSt27doFhUKBSy+9NN3DYZx//vm48847sW7dOjQ2NiI/Pz/dQ+oRSCQSdqYia9CCggJYrVYEAgHIZDJWNdPc3AyZTIYRI0ZAIpEgGAwiFAoxMQKdF/x+PxwOB4LBIKvOHjVqFAKBALZv3w6fzwepVIqioiLmL08+0ABYbxoSz0gkEuj1ehiNRtY0XSQSIT8/nyVcab+iQDWQ/v2FyIxRcDicTocOXuS/S0ocoCW7r1KpWAdkQiQS4ciRIwCAuXPnQq/XIxqN4tFHH4XNZkN+fj6mTJmC3r17M1+gQCAAh8OB6upqVFZW4qeffsLhw4dx+PBhlJSUMB/fyspK5rdKzd8AoKioCEOHDkVDQwNTVwMtF72ioiKWAZRIJEytJPTF45wcwosdeTHTBkXNdqj5IpXuuN1uSCQSGI1GOJ1OLF68GDKZDDNmzEBTUxOqq6tZuTF5dx8PiUSCiooKljWmS51UKmVlQmazOWmDVSqVTBVCZHOAsSdASS0K3Akv2zk5Ocx/t7CwEEOGDIHP54PD4WA+72KxmF3cSMGu0WhYWRg9zkkPUqmUBeNoXaYgMwUMjUYj7HY7mpubodPp0LdvX/z3v/8FAAwbNgxXXnklmpub8bvf/Q7hcBhjxozB//3f/zF/XUKr1cJisWD06NG46qqrsHDhQvz44494/PHHceedd2L58uX4/vvvUVpaytQntI8MHDgQwWAQe/fuRTQahUwmQ+/evdnln/Y8AK32zXSTrepDIalVVaR6J29mAKz5X25uLkKhUKtmwmSnAbTsYzqdjq0PFKAWUlJSAolEgsGDBzNrMPLA3Lt3b1IAGQDKyspw5MgRNn9pnoRCIahUKoRCIbhcLhiNRmb1QGXxnJNDIpFArVazRAIFmvPz8+H1emGz2ZidRnFxMVOC0VnEbDbD5XLB7/fDZDIhGAzC7XbD4/HAYDAwtXQgEGCJbbVajQEDBjABhrASisZDCma9Xg+ZTIa+ffuiubmZ9Tg544wzoFQqUV9fz3qy5OfnIxKJsKSVwWCAVCplwWw6x6aTbE1qClXLWq0Wdrsd4XCYnVnz8/PhdrvhcDhQWFiI+vp6LF++HMOHD0csFkN9fT08Hg8UCgUKCgrQr18/9O/fH7m5udi0aRMWLVqEtWvXMruT5cuXY+rUqZBIJFi1ahWmTZuGCRMmoLS0FGKxGI2NjdiwYQO7zwgVhySO0Gq1TDgBtFTqJBIJNj+MRiMKCwsBHPXupiQap/N45513ALQoh41GY3oHI6C0tBSjR4/GDz/8gM8++ww333xzuofUY0hVNAcCAeTn58NqtbLHSkpKYLVaEQqF2BpPVb905qCKBb1ej+LiYkilUhw5cgR1dXWora2FRCLBuHHjWAVFY2PjMe8r1GBWp9MlJaskEglLdpLHs16vZ9VgANLakDgVHmTmcE5RRCIRZDIZOwzTgUUulyd1LXY4HJDL5QgEAjh48CArw7VarcjJyUFtbS2sViuUSiXefPPNpJLBtnC73VixYgXef/991NTU4Nlnn8Wdd96J//3vf9i6dSvKysrg9/vZpb6oqAhVVVVJDd5IheBwOJhKlQ6XwksA5+dBamEqUQWOKkxNJhNcLhcKCwuZTYZcLofD4YDJZIJMJoPX60UgEGCH5dLSUhQXFzNFGpU10uUdONq0hZTq5KULtLz2OTk5kMvlyM/PZx6IpGgnn0yySaB5TTYtnMyDAsoUZJbL5aybMyU66HDmdrvZZU04ZyigTAFqkUgEg8HAPqYSM076oCSg0NeY7G7o8CsSiXD48GEAwOTJk/HPf/6TvX/hhRdCJBLh5ZdfRjAYxIgRI7Bw4cLjBmPy8vLwzDPPYN68edi0aRP+/e9/4+yzz0ZlZSVCoVCSTQc1Ot21axei0ShUKhXy8vLQ1NTEGgpKpVJ2eKeAaKasLdmqPhSSWlWVqhRXqVRobm6GXC5HWVkZvvrqK4hEIowcORK9evWCVCqFx+NBQ0MDa0qb6jtNDXOUSiWMRiO0Wi1LplosFsyZMwe1tbWorKxEXV0d81SUSCQYNmwY+vfvjyNHjrDHyGec/hWJRHC5XMyegYLMqQ3JOCeGMNCcSCRYoFmn00GpVKKhoYGptSjQrFQqmaLZbDZDoVDA7XYzSyW/38+SlEDLGYPKk8nShz5HZwmpVAqVSsV6QWi1Wmg0GpjNZpbcsFgsGDRoEEKhELZt28YSJBaLBUajEXV1dYjH4+zsQsFz8gPl8+TkINEMJaZovuj1ela1Qt7cEomEWSWReEYqlaKiogImk4l5qdO8Ovvss2GxWPDqq6+ipqaGnUlWrFiB8vJy1NfXw2aztRmcF4vFrGKPVO8UXKaGvSKRCP3790ckEmHB5IKCAphMJohEIqbkp3MRp/OIRqN4//33AWRGw79ULrroIvzwww947733eJC5m0kNNPv9flgsFkgkErjdboTDYRaToCbEwooDIdTEGGjZC8rLy7Fnzx40NzezNUitVqOiooL1IaCzECW7qTEhrQMUZKZmswqFgu1pBQUFiMVibD3JtLsQDzJzOKcwUqkUsViM+e6GQiEolUp2UKYDWk5ODurr67F69WoMGTIE33//Pf773/+irKwMOTk5kEqlCAaD2LRpEyZOnHjMn6nX63HxxRdj+vTpmD9/PrZu3Yp///vfmDZtGnbu3IlgMAiNRgOPx8MCnF6vF2KxGKWlpazskaBFlwIYFFjMlIt/tiMMNFMgj8qBzGYze23IU06j0bAyUa1Wy4JIoVCI+bHG43HW5fZ45ebCjrlkiWGxWFgASKVSwWQysYsfBQ9IvSqXy/mFLUOJxWIIBoNMtSiXy5m6DAC7XPl8PqhUKng8nqTSd7VazVTtbrebBZRJ2Sq8kPE5kF7IroZsJqjRIzVIA4Dm5mb2OrlcLni9XohEIsyZM4c9Tn6VkydP7rDaTy6X45FHHsGNN96Iuro6FnxtbGxkScycnBwkEgk0NjaykmedTsd8oGldob1Rq9UmeTRnAtmqPhQiVAwDrZXN9DmtVouGhgYALRYnp512GvserVbL1OxutxtNTU2w2+0seUXd3qlZ3/79+wG0BHQGDRqEXr16oaSkBCUlJUk+0Gq1OklxCgDFxcWoqqpiZyev1wuDwYB4PA6n05nURIwHmX8+lFAMBALs8kyJ5eLiYjQ2NrLXq7i4GA0NDRCJRMjJyWFnFQoYhEIhJrSgvYgEFjTfhGcUUsPT2Ya8LqnZIz13nz59mN0OQQprpVKJuro6lrTKy8tL2gPJU5Nz8iiVSvb65uXlsV4v+fn5aGhogFKpZJ7eVFoOtOw5gUCA+fm3RUlJCWbOnInvv/8eDQ0N0Ol0LPhUWFgIv9/PXk9hgpsSTlT1ZzQaYTabodFoYLFYIJPJUFJSwtYnuu+QHziV3wN8jnQFK1asQGNjI3JzczF9+vR0D6cV1157Lf7whz/g22+/RWVlJfr165fuIfUoaG2nZoA+nw9GoxEqlQp2u52dS6hCQVhZTVWUpIR2OBxwuVywWq2w2+0oLy9Hnz59UFNTA6vVmqR8pruLsEITONo3idYBsmyi+7VMJkN+fn5SI2XaOzPpDJIZJ2cOh9Ml0OJFixJd9im4rNPpEAwGIZfL2WNisRg6nQ5WqxUvvfQSrr/+elx88cX48MMP8fjjj+OGG27ApZdeetzSUK1Wi8ceewzXX3896urqWCbQbrcnqWcp4ESdUmmMFOykg7+wlJGXpXYuFKgjdSltdnQYpuAuJQfkcjlMJhP8fj/8fj/zlkqFGijRxkgXOvIJp9eXfgZtpFqtFmazGUqlkpWaqlQqKJVKdvEjT2ZOZkGKv3A4nFQSLxKJkg5rNJeqq6uTmiEVFhZCqVSywxcFj4CWQGBOTk7Sc/MLWeZAazb5rtMBHDi655DKYseOHVCpVAgEAlizZg0mT54MoMU2Y8uWLXj55ZcRj8cxe/bsDq33Op0O1157Lf7yl79g8+bNqKioQFVVFQKBALM2AMD8/ZVKJUKhEEtmAEeTFjqdLsk7mCc0Ow+ynaAgHAX46F/aR6jpHoAkW6Tm5mZ89tlnzMu0I1ATnXA4jPr6euTk5OCss85Cfn4+EokE9u7dm+THTWMwmUwoKipCQ0MDwuEw80yleez3+5mdCu1xfC36+QgT35SoikajUCqVKCgoYJf4aDSK3Nxc+Hw+5tlM6jBq3kk+yBRgJuUYqZjJd5vs1yhZRmMoKChge1avXr2gUqmSepPodDqYzWbI5XJ4vV524VepVMjJyWFzndYZvpb8fGQyGauYCYfDTNUOtFS2UDKzpKQEXq+XeWXn5OQgFotBrVaz86RUKoXf70djYyMaGxtRU1PD1pbi4mJ4vV6WvIpEIiyRSlCgSSaTMdW7Wq1mAea8vDyo1WqW5KQqmbKyMiiVSpbkogAzJVQ4ncu7774LALjiiisy0lattLQUM2fOxNKlS/H3v/8dzz//fLqH1OOgJrJCRbNEIkFBQQFCoRCr3BVWYoVCIfh8PrZvqFQq9O7dG5FIBFVVVazqCmg5xwwcOJCdFUgNTefQ1LGQ9Q71mqB9xGg0stgNjUOods4keJCZwznFEYvFUCgUSQE/oZLDZDLB4XAgJycH0WgUPp8Pubm5iEajqK+vx7PPPouJEydiypQp+Oabb/DGG2/g008/xfnnn4+pU6eioKCg3Z+t1WoxbNgwfPfddwiFQqyRHB0OATC1CQB2SRNm8ITBZoB7lXUVlJCgcnEqdaeggEwmQzgcZp7JgUAAPp+PKY4ouEgKZgoop/5LCA/n5CGlVqthMBhYkJG8p1QqFSsbEn49J/3QhV34lvo5ChIIVe7CEncqf6XyYo1Gg1gshqamJnb5osY4dMgDwC/tGQb5pVMjUWEwh147atrW3NyMgoICHDp0CJ988gmampowc+ZMXHXVVdi1axd++OEHvPjii3jnnXcwc+ZMTJ06FX379j3m3/3AgQMBtCiYZ8yYgaqqqqTgpUQiQSgUalUeT8+pUCiSkrFCpQmnc6DkIs2RSCSSZE1C//c+n481tKEgXzAYxL///W+4XC5mmSMWi9m6IvQDJ49CaqwDtPid0n72xRdfYPDgwejfv3+bqkadTofhw4czxeGBAwfg8Xig0WgQiUSYWt7r9bKkijD4xPl5UOKbAsPRaJT9X1MC2mazsaChQqFAMBhkaxA1AyQrFAouU0UfqVGFF3XCbDbDbDazILREIsHAgQPh8XjY3MvPz4fJZEIgEEAgEGDBZWpISNV/wNF1hO9VnQOdFckeIxaLoaSkhAVzcnJyWHLRZDJBq9UiEAjA7/fD5/MxT3eXywWxWIz8/HyMHz8eZrMZ9fX12LVrF/Nkp7lF9ip01qW1RqhmpGZdJJig5ANZZgAtyfXi4mLWz4aSEEBLgDkbLZAyHa/XyyqArrnmmjSPpn1++9vfYunSpXjllVfw4IMPHvNuzekaKNBMyUrhnYYqFCg4TPsLWTgFg0EEg0E0NzdDLBYjNzcXJpOJWXrR51OhuzclqCjJRPY5BP18oT0GrYWZUm2XSmaOisPhdCpCnx460AjVQWazGc3NzczwPhgMwmKxsMVx5cqVkMlk+MUvfoEtW7bAarXijTfewBtvvIHevXtj+PDhqKioQElJCbv4NTc344cffsC6desAAOXl5di6dSv7malNdehfCjALfYnodxAGnzldAwVeKKhMQV1hgCgajSIQCDBlVywWY5c5umwLA82pQWahkpnmJgUMSVVP6jMKOFMjhExSefz444+44447EIlEcOedd+Laa6/FkiVL8Mc//hG5ubl49913YTKZcNNNN6GyshLTpk3D/Pnz0z3sn4UwA08XLoLKksmHmxJJ9HEkEkm60Ov1ephMJmg0Gvz000+s4iIWi6GxsZH5Zebm5kIulzOvTq4Ky1wooCKcCzRPKKGZm5uLxsZGpkS02WxYu3YtNmzYgCFDhuDiiy/GyJEjsWTJElitVrz//vt4//33UVhYiDFjxmDkyJEYNGgQLBYL2x/sdjvzXKRLPICkADHtJ/QvJSxpXRN+LU9mdR3UA4IuRmShQXsOBaEpuExrxurVq5kq/ZprroFer0963lgshh07dmDbtm04cuQIs9hRq9VQKpUs0UWezzt27EBDQwNLiKhUKpx++umtVEEWiwXV1dUIh8OsYTIlzX0+HwsMCS+EnJ8PqcOkUilLLoZCIWZ3U1xcDLfbDbfbDZFIxC7ptD9RgBpo2Zso4SBMPNA6RUkKEjTQ3mU0Gpl6GgBTqvn9fvYYPa7T6dgcJjU8VWHxdaRzIaWf1+tliczCwkK43W40NzdDo9Ew71K32w2ZTMaaulIllNfrhd/vT2pSTf1A+vbti0AgAI/HwxSM9LqmVrkIxQ9klyGTyZifON1dcnJy2JqRqoZWq9UZqbA9FViyZAn8fj8qKipw5plnpns47TJ9+nSMGTMG33//PZ555hk8++yz6R5Sj4QSldS/iu4twoAz/U3r9XpIJBJWMUPJLLLcAMAqcNuq5hXGOShoTXsP/RydTgeDwcASUzQGmUyW8XsLDzJzOD0EKgcEkKT4oUCNVCqFw+FAQUEBnE4nXC4XcnJyoFQqWRbO6XSivLwcPp8PHo8HjY2NOHToEGvW1R5Tp05lQSMKYKYeqGihpUs/fUyLMA8udy+kICZ1M2241DSHFH/CADNd1lKDjx35WTQvKNhDB3c6fJP/cibx+OOP47333kNJSQnGjx+Pa6+9Fs8//zzWrFmDFStW4JVXXsH48eNhsVjw5ptv4oorrkBtbS2Ki4tbPRdlzonUZlbphC7owgsWcDTgLAwsU1KBPk5VN5PSi1Q+AJhPGTVeo7VCIpGwQCId2LgqLPMhOxsASYkmACzIV1BQAKvVygI7Ho8Hfr8f27dvx/bt2wEAFRUVGDRoEBoaGnDo0CHU19djyZIlWLJkCQAwO51oNMoayYrFYlx11VVYvnx5qzHRnkP7iTBAQHuiMNHJ6RqEZxGlUsmCh2SNRYkmahhJnuybN28GAMycOTMpwJxIJLBu3Tr85z//YU1q2yM3N5d9r81mQyQSgV6vZ0r7yspKjBo1KuniJhaLYTKZmMcrAJaAFcKVzF0DeU1ScDiRSLC9koK7VFkVDAYhFotZslqYFCVrOAAs2EjBgXA4nKQo1Wg0LGAtbOSk1+vZWgO0JEppPgn3cApU8MBh10GBZmruGA6HWR8Hu93O+oiQFVw4HGaPAWAiCQAIBAJobm5GOBxm1htCqGksIRKJoNFo2JlFpVKxvUOlUiWJJijoDCApgCSVSln1TCYHirIdssq45pprMvr/WSQSYf78+Zg1axb+/ve/49Zbb0X//v3TPaweCwmuqDpPeKcRWjEJv54CwrRXCfeEtqzXqAKYoLsvVe+qVKqkPYqg9SaT5zPAg8wcTo+CAocUzBVm07xeL/Ly8uD1eln2zO12swN+MBhkmX+dTgedToe8vDx4PB5Wcki+d6Qeys/PR69evRAOh7Fq1SoALcoQOoDTgiq89AsVrrypW/qhOROLxdhrQhsobbhC+xVC6MMsDDSllhoKfbcBsIA2HdzpZ2ZSx1zipZdegsViYV6uTqcTOTk5kMvlmDBhAl555RWoVCqMHz8eADBu3Dhs3LgRF110UavnevLJJ/HYY491969wTITqL2HwWBhwFlpg0Jsw2ELfQ5c0pVIJo9HILvuBQACNjY0AWspcSeEqkUiQl5fHfh7AVWHZhFwuT6pcEL5RQrOoqAgulwtut5s13aPGSvQ+0HKgHjBgAHw+HxKJBPPZTC0/HDRoEKZOnYpVq1bBbrdDqVRCrVazpkwUJKD3SY1CislsOLSfCtC+TzZMwj0fOFptRa+tQqHA5s2bEY/HUVZWhj59+rDnisfjePXVV9n5QqfTYcyYMejfvz/zYG1qamIWLDabDTabDYWFhayUlfYxg8GAxsZG7Nu3j1mv0HiEAW9KuJLvL10ShaX0nM5FmHgW2l/QXiQWi2E2m5nNF1liCIPOKpWKBZyFSnoASUlxev2EzZ5MJhNTv9JjZrMZIpGIjUU4Tn527R6EwV5SEIbDYeTk5CA3N5dVY9LrQsmKWCyW1OBTp9Mxex4ASWIJobUbebaT8h04qjaktYGsXnQ6XdLZVpjE5HtN99DQ0MASzplslUHMnDkTM2fOxFdffYXbbrsNK1eu5PMkAxDGJICj60Nb1bsE/Z1rtVp2DxIKLug8TM9NZ1J6/ng8zhoREnQHyhYRBA8yczg9DFICUlM3YWCXDuTUZVWhUMBoNDKVCHmlhkIhFgwQNu9pC2qoQwpGarpBhzrhwi30X+aH9MxBqCgn5bJcLk/aZCm4KLTGSH0faH2ZEwabSeFGthjC9zMRi8UCAPjDH/6AK664Ah6Ph11U6G+orcfa4qGHHsK9997LPna73SgtLe3i36B9qFMyHYxSEwvC1x1oP6kgzNITgUAAgwcPht/vx+HDh1lSKhAIsI7tFouFKc0AJM0JTnZADbSAZPUwXcY9Hg+kUilr+krWAzR/qDSe1CB0ADcYDCgsLGSXfVKbhMNhrF69GgCYFyapS4CjTSKFY6CAQKZ62p2qkDeysEs7rRVCtTDQMnf27dsHADj99NOTnmfx4sVYtWoVxGIx5s6d226jyKlTp8LtduPTTz/F0qVLUV9fj0AggIKCAni9XrYXGQwGHDhwAMXFxdDpdPD5fDhw4AD7GrPZzJoVU5msMODE6VooUCeVSpOqqEgNT8hkMtYcmAQQZHNCn0uttqF1RxhUpDMpzU1qeky2GDy4nBmQMIFsLYR2JxaLJcn6QlhFIVS5U1ITaFt1KDzLUFKDlIb0nFqtFnq9PumcyxXL6eODDz5APB7H2LFj0bdv33QP57iIRCL84x//wJAhQ/DNN9/gzTffxE033ZTuYXFSoIb1wn5SqUIbYXCY7tDtkaqKToXWkGw7p2bXaDkcTqcg9LqjAzV5IVJzPqlUCp1Ox3yGyCZB+DEZ49OhTtitW3hYJ89dMtUnGw7K9JP/Ll04+WEsM6GNki54wiyuMKjcXuO/9hAq2YSl69T9O1N48cUX8eGHH7KP582bh61bt6KpqQlPPvkkmpubmTrG5/NBr9ezbsX0WFlZWZvPrVAoMkatTZ2V6XWkixspyOgQlVo61h60phASiQRWqxWNjY1IJBKsmiGRSEAmk8FgMCSVHfMgYPaSqlSl4C4pCancmRKPlNCgPYXehMEACj7TnAHA5gv56ZJXXm5uLtRqNbRaLauIoCAVrTd8bnU/tM9HIhHWOE+o6gHAKhh8Ph+zLCgsLGTPEQqF8PnnnwMAbr/9dlYx0h56vR7XXHMNxo4di+eeew4OhwMSiQT5+fnweDzMlkmr1aKqqgqFhYXYu3cvS3z17dsXDoeDVfUQFGwGwJXM3YTwLEJrBq0RAFrZNEkkEphMJgAt84bOsB3526ceAGSBIEyuAjwBmimQQIZ6iNB5hRILFosFhYWFCAaDLOBMn1OpVKzMXXimaesuQ+cVavhFamqz2Zx0FqIqQG6Zkj7eeecdANmhYiZ69+6NBQsW4P7778e9996Lc845p917AyczSA06A0iq3k0VXKW+CWlPfJWN8JM1h9ODoUMQlafTpY+UHuRbR9l6UjHTG5UqCj8WNgQTeiyTzx2VrBkMBkgkEmi1WvY1wss/J7MRNu2jg7XwcJ66eQqVzG2V0RPCsthM21jvuusu3HXXXezjxYsXY+fOnczzzWQyscaZq1evxqhRozBq1Ch88cUXmD17NtasWYNnnnkmXcPvEFQaLizXossaKbdI0ZwKlXKRJ6FKpUIoFEJjYyMLApJXL3WCp8AfBZPJmkX4fJk2DzgnhkQiYR2whT7IKpUKwWAQCoUCWq2WJSxpX6H9hEoNqfyd7DSEaw79HNo7aI+hxqEGg4GpE0lxSAlOTnog6yVhIoLsEIQcOXIEGo0GLpcLdXV1MBqNAFqqPSjZcCINnfr27YtHH30UjzzyCOx2O/Ly8tjz0dyw2WzMnsVoNMJsNsNqtbK1yWw2J9l5kJ1Gd9BWw1li+/btuPDCC1FWVobi4mK899573TaudCFUEbdn3ySsqqFzp16vb9PmKfWCLxaL2ZmWnqO9hqGc9COVSqHVallSkpoOU9JK2EyLVO6BQACBQKBDZejCQLJWq2UWgMJKDOpnwudG+ti9eze2bNkCqVSKyy67LN3DOSHuuecefPzxx1i/fj1uvvlmLFu2jM+lLCP1btsT4UFmDqeHIyw/FHZeJlUI+e2S/52wS7sw6EyBqPagYBI1cqODPgUq6aKZqdYInPahLG6qKihV2SwMQBN0oaPvF3rYZTqPPvoolEolfvGLXwAAVq5ciXvvvReTJk2CSqXC4sWLYTab8fbbb2Ps2LGYMmVKxisS6O+fgs3CSgUK9NEFS6PRwGQyQa/XM19EoEUp1tTUhAMHDrASMLp4icVihMPhJB9cqnAgZSpvmnTqIXy9qWlaNBplNhfCoHJqcEg4BymZmUgkkuZlPB5nawhVQFAwgTyZKfFBpdWU3OCkBwriAWDnDDpHKBQKpiD2+/0wGAxwuVxYtmwZTCYTCgsLme+qzWbDCy+8gNtvvz3JV/VYWCwW3HrrrfjTn/6EAwcOYMiQISwYRX7glByXSqWw2WwAwJrRUoBZWBrfXZfKthrOErt378Zjjz2W9FhPIlUNDxztCyC0xwDAbA5Sm7oJoe+l5xbaaPC1I3MR+h8L9w9hAkL4teTdL5wrqaIJAElWKnK5nD0foVAomE0LJ71Qgu3cc89Fbm5umkdzYkgkErz11lsYMWIEli9fjtdffx2/+tWv0jIWntTknCyixLHMVDkczgnhdrvZZUjY/TybEDb3Eh62hG+k6BA2AKPvTS0lFAYPAbAgAB3U6bBGj5/qh7NTYY5wup50zBNqqkZqc6fTydTMAFjDJLVajb59+zKvW6ClPNnhcMBqtcLpdLLHSfElXAOESlKlUpnk3SuTyZI+5rRPNq8llLRMTWoKLViEAefUi7+wekYYNKAAgEKhYEkNstCgADOVMFPz2VOZTJ8jwuY2brcbfr+f9YBwu90IBoNwuVxIJBLs82KxGGeeeSbOPPNMHD58GE8//TQikQh0Oh1mz56NX/ziFx0KNicSCdx2221wu92YMmUKbDYb9Ho9TCYTTCYTjEZjkgKf5gvNGUquCRXN3aGMt1qtrOHshAkTsG7dOva5hx9+GOvWrUMkEsFDDz2Ec889t9X3CzveA0f9/zN1jnQ2QrVze8lv4GjyPBsT4F1Bpq8lHUGYbDjWa3+i0N8+r8LMDOLxOPr06YMjR47gP//5T9YpmYnnnnsO9913H3Q6HXbs2IFevXp1+xguueQSPPvssyypuX79eva5Dz74AJFI5ISSmqfCOsLpGFzJzOFwkhAqO4QdVIUXf/qXmv4Jg9JisTjpwCbsmkqBJVIZ0GOkZuaBJQ4nfQhtbkiBTH+Ter0e9fX1AFpsQaLRKGw2GwKBANxuN9xud6u/ewouC21zqISUKhtIQUZBHH5J6xmQVQWpwYR+/rTXtBVgFlbYUEVNW9AeQ9YclNgkZWpPCDBnA5RoCgaDUKvVbQZ+KMCs0+lYefuGDRuwceNG9OvXD9deey2++uor1NXV4f3338dHH32E0aNHY+zYsRg2bBiz6EnF5XKxALFQySoMJAu9WOnsQv68NBYASQ2Mu5rUhrNChg8fjquuugrFxcWYNm0apk+f3mpNffLJJ/HYY491y1gzkbbUzpyeAb3uqX+rbVXdtdW4Gji6PtDdJps9U09VvvvuOxw5cgQ6nQ5z5sxJ93BOmt/+9rf4+OOPsXbtWtx999345JNPun0ML730Ektqps7zXbt2Yd26dXjttddOKKnJ6RnwIDOHw2kXoZl9asBZ+H40GmWXNDqQ0WYk3JSEAWdShpCKmR/SOJz0QjYYYrGYvU9/18FgEEajEQ6HA7W1taitrW31/cJSUgrICMuLhYFFuuBTgomXH/dMUgM+7XXppsRm6ltq40mhn2pqEpOSHtznO7OgknYAUKvVrYI6tK54PB4YjUaEQiF4vV5EIhHs3bsXAJCTk4OcnBzU1tbC4XBg/fr1WL9+PSQSCfr06YPevXujqKiI+fDW1tZi1apVCIfDKCkpSbLDoDVLeEYhSx/qQSD0jRb6yncFx2s4K2TGjBlMHdanT58kz2nioYcewr333ss+JiUzh9NT4YmHU4vXX38dAHDllVdCpVKleTQnj0QiwWuvvYZhw4ZhyZIl+N///sfs+boLntTknCw8yMzhcDqEsGyQlGfk40zd1SkwkNrITRhUFv5LwSgOh5N+6O82kUhALpezBoBSqRThcBiJRAJqtRqRSCQpqEeXM+EaIQwsUyJJ2MxRKpWyxzkcIrVLd1t+qvQ+zUOh8kwYZBY2lKV5mInNmNryPFyyZAn++Mc/Ijc3F++++y5MJhNuuukmVFZWYtq0aZg/f366h91pkAc7IUxQ0VswGIRMJkMoFEIgEIBSqUQkEkEgEEAwGGRB6qKiIphMJrhcLvh8PgQCAVRWVqKysrLNn20ymZCbm8sqs0jtTnOFkhQGgyEp+UbzqTsa1B6v4ayQG2+8EQsXLkTv3r1RVVXVphdpd9l6cDgcTnfjcDjw0UcfAUDafIw7k0GDBuHXv/41/vrXv+Luu+9mzQy7Cp7U5HQW/HbH4XBOCAoqUcdtCipRAECoQqKystRAlFDJyOFwMgOhmlSlUsHr9SIWi7GATigUalPtIywbpQAfBWyEVQpCCw2eXOJ0BGGwkZrDUS8A+pjUzMLvoTlJ72ey339bjdyef/55rFmzBitWrMArr7yC8ePHw2Kx4M0338QVV1yB2tpaFBcXp3vonQYFmuk1MhgM8Pv9LDlFntqhUIg1gKT3aW2ixyUSCdRqNYCWRqY+nw/BYJD5f1PyQa1Ww2AwIBaLQSqVwmQyQafTQaVSJQWaNRpNUk+JdHuvttVw9oEHHsC8efPw6KOP4qabbkIikcCDDz6YsXOew+FwuoL33nsPoVAIp512Gk4//fR0D6dTmD9/Pt59913s2LEDr7/+Om677bYu+1k8qcnpLHiQmcPhnBR0+ZdIJIhEIuxjYekycFSZRpf9TFSScTico036KGhjNpvhcrkQDoeZ3UCqYpSSSBQIJAWzMLDMqxY4nQUlOciTmd7agtTymZ7QTPU8dDqdyMnJgVwux4QJE/DKK69ApVJh/PjxAIBx48Zh48aNuOiii1o9Vzb7H4pEIhbADQaDzJ6CAsTUKJKSDPR+LBZLCjjT+kWNIbVaLfP7ToXsUzQaDfR6PfR6PbRaLQs0U5NIACzwnG527NjR6rFnn30WAGA2m7F27druHhKHw+GknUQiwawyfvWrX50yd82cnBw89thjuOuuu/Dwww/jiiuugNFo7JafzZOanJOFB5k5HM7Pgpp4UXCK1MxCKHiV6Zd9Dqeno1Qqk/zX1Wo1VCpVq79toS2BUK2cWuZ+qhzyOZkFNfaj5rPUpIk+RwmQbCDV89Dj8UCr1QIANBoNvF5vm4+1xangfyiVSqHRaBCJRFhiOhaLIRwOJwWWhUkG8kmmIDN9fSgUYl8jbC5JSW9qDknN/DQaDXufAs3U7I9b+3A4HE7msmrVKmzfvh0qlQpXXXVVuofTqdx222146aWXsGfPHjzxxBP485//3C0/lyc1OScLPzFxOJxOgYLNAJI6w2fTZf94RCIRTJgwARs2bACAVr6ZZrM5zSPkcH4eFFChgA49dqxyt1S7jFPl752THZCaPltUNMfzPGxubobP5wMA+Hw+pq4VPlZWVtbmc58q/ofC5DVZpJDqmALKQn9uCh4Le0MIGxMLG0WmNhWUyWSsIalSqYRCoYBarYZCoYBYLIZareYJcg6Hw8lwKPh5ww03wGQypXk0nYtMJsNzzz2Hc889Fy+++CJuu+02VFRUpHtYHE678CAzh8PpdLLlsn8iOBwOXHrppTh48CB7LNU386GHHkrjCDmczoGCynK5nAVtUj1vyQaHB5U5nBPjeJ6HJpMJVqsVwWAQq1evxqhRozBq1Ch88cUXmD17NtasWYNnnnmmzec+1fwPhZURpFanYLHQmos+J3xLDTYLv4Ysf4CjFizkt0yWHVKpNKO9vDkcDofTwk8//YTPP/8cIpEI99xzT7qH0yXMnDkT5557LpYuXYr7778fn3zySbqHxOG0Cw8yczgcTgfw+Xz4y1/+woIDbflmtkU2e2RyejbChp2czmP79u248MILUVZWhuLiYrz33nu8KqIH05bn4b333otJkyZBpVJh8eLFMJvNePvttzF27FhMmTKlXSXzqUxb65HQKkUYQBb+S18n/LxQzSxsZkxBbblczu0xOBwOJ0uYP38+AOD8889Hv3790jyaruPZZ5/F119/jSVLluB///sfOzdwOJkGP0FxOBxOBygtLU0qO+5JHpkcDqfz2L17Nx577DFce+217DFeFdFzacvz8IILLsAFF1yQ9Nibb77ZXUPKGjpildJWgFkYaKbnoAA2r8zgcDic7OH777/Hhx9+CJFIhAULFqR7OF3KoEGD8Otf/xp//etfcfvtt2Pbtm1QqVTpHhaH0wpeA8bhoEVZ1qdPH0yePBlXX301gBa/3TPPPBOzZs2Cw+FI8wg53c2LL76I8ePHs7elS5cmfT7VI1Ov17f5PA899BBcLhd7q66u7vKxcziczGXXrl345z//iYkTJ2Lp0qWtqiLI8z2VUCgEt9ud9MbhcI5NapM/hUIBpVIJlUrFmvspFAreqJTD4XCyjFgshrvvvhsAcP3112P48OHpHVA3sGDBAhQVFWHfvn34wx/+kO7hcDhtwoPMHA6OKsu+/fZbvPfeewCOKsvuvPPOY1oh8Ev/qcldd92FNWvWsLdzzz036fNt+Wa2hUKhgF6vT3rjnFrccMMN+PbbbwG0Tk4lEgn88pe/xIQJE7iinQMAGD58OP72t7/h888/x/z58+FyuTpcFWEwGNhbNjZ043A4HA6Hw+kMnnnmGWzYsAE6nQ6PP/54uofTLRiNRrz22msAgOeeew7//e9/0zwiDqc1PMjM4eDklWX80t+zId/Mp59+Gr/+9a/TPRxOGti9ezeWLFnCPk5NTq1ZswYWiwWrV6/Gnj17UFtbm77BctJCalWEXC7HoEGDoNfr0adPHyiVSl4VweFwOBwOh9NBlixZgt///vcAWs7eJSUlaR5R9zFr1iz85je/AQBcc8012Lp1a5pHxOEkw4PMHA5OXlnGL/09D1KsAi2+md9//z2+/fZb5OTkpG9QnLTxzDPP4LrrrgPQuhnkhg0bsHnzZowfPx4AMG7cOGzcuLHN5+FVEacuqVUR//rXv/DTTz8hFAqhqqoKFouFV0VwOBwOh8PhHId4PI6//vWvuOSSS5BIJPCrX/0KN954Y7qH1e08++yzmDJlCrxeL6ZMmYLvvvsu3UPicBi88R+nR/Liiy/iww8/ZB/ff//9GDRoEACckLJMoVBAoVB0/YA5HE7GsWnTJvTu3RvxeBxA280geYNITiqPPvoobrrpJiQSCTz44IMQi8WsKkKlUmHx4sXpHiKHw+FwOBxORhAIBPDTTz/hf//7H1577TXs3bsXAHD11Vfj73//e4/005fJZPjkk08wZ84crF69GlOmTMHdd9+NBx54APn5+ekeHqeHw4PMnB7JXXfdhbvuuot9PHfuXAwYMAC9e/c+IWUZh8PpOaQmpyorK1FZWYnnnnsOQNvNIFMfKysra/O5H3roIdx7773sY7fbze13TlGGDRuGtWvXJj12wQUX4IILLkjTiDgcDofD4XC6j127dqG2thbhcBihUIj9a7PZ0NjYiIaGBtTV1aGysrJVpbDBYMDjjz+OO+64A2Jxzy3MNxgM+Oqrr3Drrbfi3XffxXPPPYe//vWvmDhxIs444wz06tULZrMZSqUSSqUSQ4cORVFRUbqHzekB8CAzhwOuLONwOMdHmJzyer0488wzcf755+Pw4cNYsmQJ1qxZ0yo5NWrUKHzxxReYPXs21qxZg2eeeabN5+ZVERwOh8PhcDicU50VK1bgnHPOOaHvMRqNOOuss3Deeefh+uuvh06n66LRZRdqtRrvvPMOrrjiCixcuBDr16/HypUrsXLlylZfq9Fo0NTUBLVanYaRcnoSPMjM4YAryzgczomh1Wqxe/duAC1JqsmTJ0Or1bZKTpnNZrz99tsYO3YspkyZ0q6SmcPhcDgcDofDOdWZNm0aEolEuodxSnHeeefhvPPOS/cwOBwAgCjB/8I5nE4jkUjA4/FAp9P1SH8ozvHhc4TTEfg84RwPPkc4x4PPEc7x4HOE0xH4POFwOD8Xvo70HHiQmcPhcDgcDofD4XA4HA6Hw+FwOCdNz3VK53A4HA6Hw+FwOBwOh8PhcDgczs+GB5k5HA6Hw+FwOBwOh8PhcDgcDodz0vAgM4fD4XA4HA6Hw+FwOBwOh8PhcE4aaboHwOH0FMjsnpOZZEITAj5HMptMmCMAnyeZDJ8jnI6QCfOEz5HMhs8RzvHIhDkC8HmSyfA5wukImTBP+BzJbE50jvAgM4fTTXg8HhgMhnQPg9MOLpcLer0+rWPgcySzyYQ5AvB5ksnwOcLpCJkwT/gcyWz4HOEcj0yYIwCfJ5kMnyOcjpAJ84TPkczmROeIKJFIJLpwPBwO5//TkQyd2+1GaWkpqqur077Yd4RTabzZlMXNpv/3bBorkPlzBOBrSbrhcyQ9nErjzYR5wveb9MPnSHrIpvFm+hwB+H6TCbQ33myaI0S2/N+fSuPMhHlyKu432TRWoHP3G65k5nC6CZFI1OEFRq/XZ8ViRPDxdg4nMkeAzP092iKbxgpk9nj5WpIZZPJY+RzJHDJ1vHy/yRwydbyn8hwBsmu8mTxWvt9kDpk63hNdS4DM/V1S4ePsHE7l/Sabxgp0znh54z8Oh8PhcDgcDofD4XA4HA6Hw+GcNDzIzOFwOBwOh8PhcDgcDofD4XA4nJOGB5k5nAxCoVBg/vz5UCgU6R5Kh+DjTQ/Z9Htk01iB7Btve2Tb75FN482msR6LbPs9+HjTQzb9Htk0ViD7xtse2fZ7ZNN4s2msxyLbfg8+3vSRLb8LH2d6yKbfJ5vGCnTueHnjPw6Hw+FwOBwOh8PhcDgcDofD4Zw0XMnM4XA4HA6Hw+FwOBwOh8PhcDick4YHmTkcDofD4XA4HA6Hw+FwOBwOh3PS8CAzh8PhcDgcDofD4XA4HA6Hw+FwThoeZOZwMohIJIKzzjqLfbxkyRKceeaZmDVrFhwORxpH1ppQKIQLLrgA48aNw2uvvZbu4RyXuXPn4vDhw1i/fj3GjBmDKVOm4NChQ+ke1gnD50jXwedIeuDzJD1k0zzhcyQ98DnSdfA5kh74PEkP2TRP+BxJH9kyT7JljpxKcwPInvkBZM8cITp9riQ4HE5GYLfbE7/4xS8SFouFPTZp0qREKBRKfPHFF4k//vGPaRxda/71r38l/vGPfyRisVjinHPOSQQCgXQPqU1CoVDiwgsvTBQXFycOHTqUOO+88xJ2uz2xa9euxC233JLu4Z0QfI50DXyOpBc+T7qfbJsnfI50P3yOdA18jqQXPk+6n2ybJ3yOpIdsmieZPkdOtbmRSGTX/EgkMn+OEF01V7iSmcPJEHw+H/7yl79g8ODBAACn04mcnBzI5XJMmDABGzZsSPMIk9m8eTPGjx8PsViMYcOGYc+ePekeUpuEQiHcc889mDZtGgAgEAjAbDZj8ODBqKysTPPoTgw+R7oGPkfSC58n3U+2zRM+R7ofPke6Bj5H0gufJ91Pts0TPkfSQzbNk0yfI6fa3ACya34AmT9HiK6aKzzIzOFkCKWlpRg+fDj72OPxQKvVAgA0Gg28Xm+6htYmmT4+QqfTYeLEiezjeDzO3k8kEukY0knD50jXwOdIesmGMQJ8nqSTTB8fwedI+sj08RF8jqSXbBgjwOdJOsn08RGn0hwBsmueZPLYgFNvbgDZNT+AzB8f0VVzhQeZOZw08eKLL2L8+PHsbenSpUmf12q18Pl8AFqyd3q9Ph3DbJdMH197iEQi9r5YnNlLIJ8j6YHPke4lG8bYFnyedB+ZPr724HOk+8j08bUHnyPdSzaMsS34POk+Mn187ZFNcwTI7nmSyWNri2ybG0B2zw8g88fXHp01V6SdMRgOh3Pi3HXXXbjrrrva/bzJZILVakUwGMTq1asxatSobhzd8Rk1ahS+++47DBkyBNu2bcPChQvTPaQOoVKpYLPZ0NTUhPLy8nQP55jwOZIe+BzpXvg86XqyfZ7wOdL18DmSHvgc6V74POl6sn2e8DnSPWTzPMm2OZJtcwPI7vkBZN8cITprrvAgM4eTwdx7772YNGkSVCoVFi9enO7hJHH55ZfjyiuvxFtvvYUbb7wRSqUy3UPqEA8//DBmz54NkUiEd955J93D+dnwOdL58DnSvfB5khlk8jzhcyQz4HOk8+FzpHvh8yQzyOR5wudI5pCp8yTb5sipODeAzJ0fQPbNEaKz5oooka3GLBwOh8PhcDgcDofD4XA4HA6Hw0k72WHKwuFkCYlEAm63O2tN9TldD58jnI7A5wnnePA5curx6KOP4p///CfWr1+PMWPGYMqUKTh06BAAYN68eRg3bhxuv/32Dj8fnyOc48HnCKcj8HnCOR58jnA4HIIHmTmcTsTj8cBgMMDj8aR7KJwMhc8RTkfg84RzPPgcObWw2Wx47bXXAAALFy7E0qVL8fe//x1PPfUUqqqqcODAAaxduxY6nQ7r16/v0HPyOcI5HnyOcDoCnyc9g7lz5+Lw4cNtJjqPB58jHA6H4EFmDofD4XA4HA4njTz99NO47rrrAACBQABmsxmDBw9GZWUltmzZgnHjxgEAJkyYgA0bNrT5HKFQCG63O+mNw+FwOJxjEQ6HcdFFF+H7778H0DrRyeFwOCcCDzJzOBwOh8PhcDhporq6Gh6PBwMGDAAAxONx9rlEIgGPxwOtVgsA0Gg08Hq9bT7Pk08+CYPBwN5KS0u7fvAcDiercLvdKC4uxuTJkzF16tR0D4eTAYRCIdxzzz2YNm0agNaJzva+hyc1OZz2SSQSeO6551BWVobhw4fj66+/TveQug0eZOZwOBwOh8PhcNLEk08+if/7v/9jH4tEIva+WCyGVquFz+cDAPh8Puj1+jaf56GHHoLL5WJv1dXVXTtwDoeTdezevRu33XYbvv32W6xcuTLdw+FkADqdDhMnTmQfpyY624InNTmcY7NgwQLcd999qK6uxo4dOzB79mxs2rQp3cPqFniQmcNB66z+yXhRcTgcDofD4ZwomzZtwg033ICnnnoKTz31FFQqFWw2G3bv3o3y8nKMHDkSq1evBgCsXr0ao0aNavN5FAoF9Hp90huHw+EI2bVrF7766itMnDgRb731Vptfw1WqPZvURGdb8KQmh9M+mzZtwoIFCwAATzzxBM4//3xEIhH89re/7RHNMaXpHgCHkwlQVv/hhx8GAMyePRtLly5FQ0MDnnrqKbzyyitpHiEnk5g7dy6effZZ1NfX4+6774Zarcabb76J3r17p3toHA4nw3G73Rg0aBD69esHiUSCJ554gq8jPRxStvzzn/8EAAwcOBCzZ8+GSCTCO++8g/LyclRUVODss89G//79MX78+DSOlsPhZDPl5eV4+umnccYZZ2D69OmYPXs2LBZL0tc8+eSTeOyxx9I0Qk66oURnU1MTysvL2/wahUIBhULRvQPjcLKARCKBu+66C/F4HFdffTXmzZuH+vp6LF++HOvXr8fSpUsxa9asdA+zS+FBZg4HR7P6y5cvx4033si8qMxmc7teVEBLpj8UCrGPeab/1CYcDuPyyy9nAQFqjHGsZASfIxzOsTlWRl+opjlV4ElNTnvccMMN7P3U5n4LFy7s5tFwOJxTkbPOOgsqlQpisRhnnXUWDh061CrI/NBDD+Hee+9lH7vdbm6H0IN4+OGHkxKdHA6n43z99dfYsGEDVCoV/vznPwMACgsLcfvtt+O5557DK6+8woPMHE5PIDWrLwxsHCsAwjP9PQtqjPHmm28CQIeSEXyOcHo6iUQCiUQC8Xgc8XicvU+PHw+RSMTexGIxe6PHsg2e1ORwOBxOunjkkUcwffp0nHPOOdi8eTPuv//+Vl/DVao9E6qmKS8vb5Xo5HA4HeNPf/oTAOC2225DQUEBe/zmm2/Gc889hy+++AKNjY3Iz89P1xC7HO7JzOGgJas/duxYyOVynHXWWR3yogK4H1VP42QaY/A5wulJJBIJxGIxRCIRhEIhBAIB+P1+BAIBBINBhEIhBINBBINBBAIBBAIB+Hw+eL1eeDwe9ub1euHz+eD3+5O+X/i9fr8fwWAQkUgk6W8x06Gk5ooVK/D22293aB0BeJMdDofD4fx87r33Xjz11FOYMGECLrnkEuTk5KR7SBwOh3NKsHPnTnzzzTeQSCS45557kj43aNAgjBkzBrFYDIsWLUrTCLsHrmTmcNA6q69QKI7rRQXwTH9PpyPJCD5HTl2qq6tx3XXXIRQK4fzzz8ekSZNaeevOmzcP3377LYYPH46XXnop3UPudCioTG/CIGnq5+iNFM2pH9P30N8VKZclEgkkEglTMEskEkilUvYvqZrFYjGkUimkUmlGK5xTS5U3btzIPne8pCYvX+b0RITVEKkVEML1gtaBTP7753DSTXFxMb755pt0D4PD4XBOOf7+978DAC688MI2z+iXXHIJvv/+e3z++ee48847u3t43QYPMnM4aMnqX3PNNViwYAGuu+46nHbaadyLinNcOtIYg3Pq8sILL+DRRx/FpEmTMHXqVCxfvjzJW3fevHk4cOAA1q5diwceeADr16/H2LFj0z3sTiEejyMSiSAajbLHUoPK0Wg0KZhMX0+fo/cpOC0MHgkDRhKJBDKZjAWV6U0mkyV9LJfLEY/HEQ6HIZPJIJPJMjLYxJOaHYPmQuq/qUFFzqkJrSe0TpwIqdY62Wyvw+FwOBwOJ/NxOp3417/+BQDtBpBnz56N+++/H9988w08Hg90Ol13DrHb4EFmDgdtZ/V7qheV0Du1vc+nkqog6ikXOd4Yo2fz+9//HgaDAQAQi8UgkUiSvHW3bNmCcePGAQAmTJiADRs2tBtkzha/3UQigUgkgkgkwj6mQFAkEmkVGKLHKcAcDofZ+/F4nD1GXx+Px9kaQsFlhULB1MwymQxyuZz9K3w/HA6zx+hnyuXyjFM286RmC8KkBO079HhHvLoJoZKd5kkmvd6cE4fWhdTKCABJ3u5C6DWnoDLNLyHCuULzhc8VDofD4XA4P5e3334bfr8fQ4cOxaRJk9r8mgEDBqCiogL79+/HihUrcNFFF3XzKLsHHmTmcDgAjioTT1Qx1BZtXeK6+iL32GOP4dFHH8WOHTswdOjQLv1ZvDFGdhEMBnHFFVdg9+7dUKvVKCgowMsvv/yz1edmsxkA8Oqrr2LUqFHYunUr+1wikYDH44FWqwUAaDQaeL3edp8rGxpEJhIJBINBFgyMx+MIBoMsOCwMHofDYYTDYcRiMYTDYYRCIfY58mAOBAJtfi8AtnbI5XKIxWLIZDJoNBro9XoWdFYqlZDL5VAoFFAqlZDJZEgkEohGo1AqlZBIJCyArVAojmlFAQBfffUV/vCHPyAcDkOtVuOVV17Baaed1un/jz09qUnzI1UFn2qHkKpgJlKbQJLFCqnlATClu0Qi6dSx33XXXfj0009x5MiRbtlrehqJRKLV3BCJRIhEIm0GnVNtM1ItrMhOh4LLtL4IzznCSonOpDvPJJzsIxQK4b777sOyZcsgl8sxcuRIvPvuu+keFieD4PtNZiJMeLa1D3Vn8rKyshLXX389bDYbjEYj/vnPf2Lw4MHd8rM5ySQSCbz22msAgNtvv73dOSASiTB79mw8//zz+Oyzz3iQmcPhnLpQsIigCzy9DyR7paYiDBDQx8ISeQoSdfYljtiyZQs2bNiAsrKyLnl+zs9DODeAo8GA7lSQ3XLLLTj33HMhEonwt7/9Dbfccgu+/vrrn/287777LpYsWYIlS5Zg5syZ7HGxWAytVova2loAgM/ng16vb/d5Mt1vNzXAHIvF4Pf7mbKZAkChUAjhcBjxeJw1/qP1oLm5GW63m60LHo8HPp8vScHdFgqFAlqtFjqdDk6nE2q1Gjk5OYjFYggGg9BqtYhEIlCr1YjFYlAqlYjH49BoNBCLxYjH4wgEAlAoFJBK2z72NDc345prrsHq1asxaNAgrFq1CldffTV27tzZ6f+XPZloNMr2mlTVe1uq1fYCiW0FE4X2KGTHIpVKoVAoOm2tueSSS/DAAw9g/PjxnfJ8nKPQ+kEBYKlUikgkAp/Pxz5PSSlhQkqIMPkglUoRi8XYay+01hGJRGxu0VlFIpF02lzhZxLO8XjwwQchFouxb98+iEQi1NfXp3tInAyD7zfpp63eIh1pNE17kFwu77K7LwDceuutuOWWW3DDDTfgo48+wk033YT169d32c/jtM/333+PXbt2QaVS4eqrrz7m15533nl4/vnn8dVXXx0zvpLN8CAzh9PDoYsd0LIpknIQALvM0YVOWKZKF3+hZ6pSqWRqIWFzLwo4SaVSTJ48GX6/H/v27UNxcTE0Gg1mz56NJ5544qTGHwqFcMcdd+Df//43pkyZ0jn/KZyfDc2d1OCREJo3qQrTRCKBcePGddo8USqVmDVrFvv4rLPOwvPPP3/Cz5PK999/j7fffhuffvop5HJ5K4/ukSNH4oMPPsCdd96J1atXY86cOe0+V6b77aYGdSjATPYXFMil19zn8zE1ot/vR2NjI/ucy+WCw+EA0DJPSNUsDFZT0FChUMBgMCAUCqG5uRlGoxGJRAJ+vx8vvPACotEoamtrYbFYoFarMWXKFNx///1QqVQssE9rUigUQiKRgEwma/X7HThwAHl5eRg0aBAAYNKkSThy5Ai2bNmCUaNGdcP/8KkPJQUImjsAkgKHtGe0t26IRCIWLKTLGynnJRIJ1Go1xGIxotEoJk2ahEAggP3793fKfjNx4sST+j7O8RFWUikUCgQCAQQCAQAt5wy/35+kcAaSlWQUYKagQGqCm84y9HwqlQoKhQITJkyAz+fD/v37UVRUBK1Wm9FnktSGsw8++CCAlsTkoEGD0K9fP0gkEqxcubLTf3ZPpbPPJD6fD2+99RZqampYcKGwsLCzh83pRjp7jgB8v+luSDRBZ5Bj3V8IoSBLWIUlrOiTy+VQKpUA0KlzpKmpCVu2bGGCmblz5+LOO+/E4cOHeZ+gNEAq5ssuu4xZKbbHhAkToFarUV9fjx07dmD48OHdMcRuhQeZOZweDm2iIpGIqXii0WiHFIZC6DKoUCigVqtZIIcCUaRY++677yCVSlFQUICVK1e2Uvpccskl2L9/f5s/47PPPmul7nzkkUdwzTXXoHfv3ifya3M6gbYac1GwqL0Sd2GCQqggEwabRSIR1q1bBwCdNk+EvPjii8cM+HaUJ554Ag0NDTj33HMBAAsXLkzy1i0vL0dFRQXOPvts9O/fP2vVKHTwBlrUgE6ns9XjwWCQBXAowExKRFJzK5VKHDx4EH6/HwCg1WqxZ8+e4/pP22w29O7dGzKZDA6HAyKRCEajEb/97W9RWlqK66+/Hn/605/Qr18/SKVSdrinwzYFIoWJsdQ50q9fP1itVmzYsAFnnXUWPvnkE3i9Xhw+fJgHmTsBUsIDrQOGZKEiLEFtK9BMQUSaVzT3dDodVCoVU8F6PB4olUpoNBoWaOvbty9WrFiBXr16JY3r56wjnM5DuJZQA086UyiVStjtdiQSCYjFYuh0OgQCAbhcLoTD4VbPRfY5wuQSAKjVaigUiiTbnmAwiNWrV0MkEqGoqAiff/45+vTpA7lczp4v084kqQ1n77jjDuh0OuzevRu33XYbHn744S75uT2Zzj6THDhwADk5OXjiiSewYsUKqFQqPProo5g6dWrX/RKcLqWrz62criMWiyEUCrE9KBWywKA3ocCC7jukWlapVACO9hWgf+PxONRqdafOkerqahQVFbEKPZFIhLKyMlRVVfEgczfjdrvxwQcfAABuvvnm4369QqHA5MmT8eWXX2LZsmU8yMzhcE49hKWp5H3odDqTAjKkGBN6GxLkgUqBZGpgplQqodVqIRaLoVAokjbbxsZGRCKRNktJP/roow6Pff369di0aROeeuqpn/m/wOkI9Fp3tFxMeChLnTNCpTO9yWSypCZt9fX1nTJPhPzxj39EZWUlXn755ZP6fiGfffZZq8dSvXUXLlz4s39OuhEG+ygpJRaLWRm7TCZjTfbIe1mn08FkMrEg34gRI7Bs2TL4/X7k5+ejrKyMeZubzWZMnjwZgwcPhsVigVQqhcvlwv79+/Hdd99h//79qKysxBlnnIFoNAq73Y6CggKEQiHs3r0bsVgMFosF4XAYYrEYkUgEIpEIr7/+OoCWIBWpoSmpQaoSwmAwYPHixXjwwQfh8Xgwfvx4DB48uE3VM+fEoYsYBf6i0ShTnZJXuUQiQTAYZMHF9hCLxTCbzRCJRAiFQvB4PAiHwyguLkYwGITX60UwGIRYLIZGo8GhQ4cQiURQUFDQ6rlOdh3hdC50DiGVusfjAdDyt0tVE0qlEhaLBYcOHWJrDwCWoKT9hM4gMpkM+fn5iEQiCAaD8Pv9CIfDKCgoYBUUVFlB61ZpaSkikUiS9UqmnUlSG85SgGHXrl346quvsHz5ctx444248cYb2/z+bGky25mk+rwDR63gUsuUj1W23FlnkkgkgoMHD2Lw4MF46qmn8OOPP2LatGnYvXs3LBZLh5+Hk3l0xbmV0zVQJa9wPUy98wqrpXw+H/x+/zEFWGKxGCaTCQaDgZ2NKalO5+TOnCOp69WJNE3mdB7/+c9/4Pf7MWDAANbw/XhMnz6dBZnvv//+Lh5h98ODzBxOD0fYaCuRSMDlcrGScq1WC5lMhnA4DL/fD7/fz0qJaCOTSqVQqVTQ6XRMoRYMBhEMBhGJRGA0GplSlcpYN2/ejJEjR7Y5nhPJ4q5atQo//fQTUwzV1NRgxowZeP3115m6lNM5UAO3Yx1gSGlIgWX6PmrwlkgkmO8lHeJIdSpsPEmWLdu3b++UeUL8+c9/xscff4wVK1ZArVafxP9Cz0S4RggVzRRsJjVhbm4u8zAePnw4K+EbOXIkgsEgPB4P8vLycMstt+Cmm24CAMyZMwe33HJLknKQOP3003HZZZdh+fLl+Mtf/oJNmzbh17/+NVatWoX9+/dj9OjR2L17N/r16weg5eJOfsxAi5rg8OHDbLzC3+Xzzz9vdcCfOHEivv32WwAtgZiCggJmn8H5eQiTmcIAYkNDA4CWREN1dTX7OpVKxSpihMohj8eDUCgEm82GPn36IDc3Fw0NDQiFQnA6ncjNzWVqe7/fD7VajX379uG0005DJBJJstgAuLIsUxAmtamaCmhR+9jtdgBATk4OnE4nfD4fJBIJiouL2fmCCIfDcLlcaGpqQjgcRl1dHQYOHIhYLAabzYZoNAqr1YqioiLI5XLY7XbEYjFs2bIFI0eOZNU28XicPW+mnUlSG86Scq68vBxPP/00zjjjDEyfPh2zZ89uM2CZDU1mOwthU9kTCb6kNq+mRHlnnUl69eoFsVjMfDtPO+009O7dG7t27cLkyZM7/gtyMo7OPrdyugbqK0JnQqlUyipgKClJFm50RxFClm60TggbYdvtdvj9fhQUFDDbN+pXIpfLO22OlJaWoqamhlUOJhIJVFdX814AaYCsMm6++eYO+yvPmDEDALB69Wr4fD5oNJouG1864EFmDqcHI1R20MYaj8chFothNBoRj8fR1NTEytvbIhqNwuPxwOPxwGg0wmg0QqVSweVyIRaLwe12w2g0QiQSQS6XIxAIoLa2FkVFRW0+34lkcR988EHmRwi0XLI+//xz3oG5kxGWppPSDDjq0yucR8JmSpFIpM2LncfjgUKhgE6nY425KMtPBzWpVIrq6mqUlJS0OaYTzfY/99xzeP/997FixQoYjcYT+l5OC8KDE70fj8eTmq3R55RKJVwuFwCgT58+TNE8evRo1NXVwefzwWw249e//nUrT+7Unzl9+nT8+OOPWLFiBWw2G5RKJYLBIGsEmJubCwBsvlGg8qWXXmJKZIvFwpJgANpMMtTX1zNfzMcffxy/+MUvUFFRcZL/Wxwh9JqQNy5wNOBP/ruxWAxqtRp9+/Zt1588kUjgyJEjsNlscDqdLNDc2NjIXlulUgm5XM4uhw0NDWwdCYfD7CIJcGVZpiEMNgNH5w3QshbQa2yxWJCTk9Pq++VyOSwWC8xmMw4cOACfzwebzYbi4mIUFRWhqqoKoVCIBZFpPtbW1qKkpKSVpROQmWcSYcNZ4qyzzoJKpYJYLMZZZ52FQ4cOtRlkzvQms52BsJxdiLCR9fG+X9i8GmhRzNfU1HTKmSQ3NxdTp07FsmXLMGvWLBw5cgSHDh3CgAEDOvwcnMyE1pK24PtNZkDqYqBlTVCpVIhEIu3ed4W+/2TBJAxQA2ANqQ0GA5qbmxEIBNDQ0IDCwkJW5UdVoJ01R/Ly8jBy5Ei8++67uOGGG7B48WKUl5dzq4xuZvv27di0aRNkMhmuu+66Dn/fgAEDmL3JqlWrknoHnQq0f7PjcDg9Cio9BsCUMcINV6VSwWQyIT8/H8XFxSgtLUVJSQny8vJYwIbUYzKZjAWWKRNMP0Mmk2Hs2LFYt24drrnmmjT8ppwTQRhgJmWX2+2G3W6Hy+WCx+OB1+uFz+djpWSBQCBJ9SyTyaBWq6HRaJhildSIHo+HKeeVSiULLkSjUYwePRpr1qzBVVdddVJqJKKmpgb33XcfnE4npkyZghEjRmDMmDGd8d/T46AEg7BMm15Tt9vN1BROp5Nl5R0OB3Q6HYCWQC6tF9TsryOYTCYAyd7PkUgEffv2xc6dO/HUU08xtRnNIWHwmi4H9H5bSoOHH34YAwcOREVFBY4cOYI33njjBP5nOMciNXgofEwikbA9Iicn55gNMN1uN/MEVyqVSCQSSbYtqc8NtDTa2bBhA26++WbWG+BkueOOO1BSUoKamhpMmzat05IQ1dXVmDJlCs4++2w89dRTWL9+PcaMGYMpU6bg0KFDAIB58+Zh3LhxuP322zvlZ2YStLdQw2B6LYPBIJsPXq+XrTVer/eYe4FEImHnGPq7F55D6GxCc2H8+PFYu3Yts5jI5E7v1HB28eLFSRUgjzzyCJYvX454PI7Nmzejb9++bX6/QqGAXq9PejuVID9v4bmFPNo1Gg3z5haLxcxexefzwePxwOVywe12JzWjpXkWiUQwcuRIrF27tlPOri+//DKefvppDBs2DBdccAFeffVV3vzvFGDcuHGdNke6ar/pqSQSCQQCAXavpX3Cbrejvr6eWTORNSBV4DkcDthsNlitVthsNmaxRIksakZdXV2N/fv3Mzu2QCCApqamJHFOMBjs1Dnyyiuv4JVXXkH//v3x1FNP8XNrGvj3v/8NAJg9ezby8vI6/H0ikYipmZctW9YlY0snogQ3b+FwOg232w2DwQCXy5UVB3c6YAMtWVibzYZ4PA6TyYRoNIrGxkaIRCIUFBQc8+IPAHa7HR6PBxqNhqlnPB4PAoEAs9MAjm7yQEtwijbenkI2zRHy0AZagotkhQIgKatPUGkp2WFQOVnqhT0SiTAfVaBlo6XLHz0nqZrbQiKRQC6XZ3Qg4OeSSfMkFoshGAyy5qBWqxXA0SaAYrEYLpeLVUHYbDbk5uYiHo/j+++/h9lsxtixY/HCCy9ALBbjt7/9LebNm4fGxkbcdNNNuOyyy4758xOJBO68807s378fV1xxBXbs2AG1Wo2Kigr4/X6cfvrpcLvd0Gg0SY1HtVotgJZ5SZ7N0Wi0TU/mbCST5sjxIN9ltVrNLArUajUaGhpYw5yGhgaYTKY2g2OBQAA1NTVMHa/RaFBRUcESVQBQVlbGPHzJZ9ZisbAAprCxj1qtPqaCvrv53e9+hzlz5rBmbgCwaNEiNDQ04IUXXsC8efPwwAMP4IMPPsADDzyAiy66CGPHjj3u82bTHAkEAkxhTE0+qaGS0+mESCSCxWLB/v37kUgkYLFYUFxc3KYnpdVqZQ1H+/bty+ZXPB6HwWCAXq9nvScUCgUMBgMLGJCyLVP3lzlz5uDw4cNMyT1lyhRceumlMBgMuOaaaxAOh3Hdddfh1ltv7dDzZdMcORbUPFLYDJLW+WAwyP7+20tstuXXTO+r1eqkgD71GhFatZzqnCrzhNN1ZPIcSbXHIFu+hoYGJmARi8VwOp3sXkwVltFolFVu0r9CRCIRdDodu9NSc2qpVMqa1ZpMpiTRVlsWcZzsI5FIoHfv3jhy5Ag+/PBDXHrppSf0/YsXL8Yll1yCAQMG4KeffuqiUaaHnhXd4XA4SQjVZeRDCLQE8UgdRoGbYz1HKBRiwUfhxZ3eTz28S6XSNjdqTuZAXslAy3zweDzsciYWi+H1epMuc6nIZDLmq0pBP7q4yWQymEwmhEIheL1eRKNRpoZWqVTQaDRMpUjlZTQ/6bFoNMqbsnUTwr9julyHQiGWSIhGoyxoQypBm82G0047Ddu2bYPD4YDD4cDw4cOxfft2fPDBB7jyyivx/PPP41//+hdGjx6NPn36tPvzt2zZgv3790Mmk7EEVZ8+feD3+6FUKtk8TFUwSyQSxGIxtn6RapHPm/RCrwsFaKLRKHJyctDQ0IDm5mYEg0EWHIrFYqitrUVTUxP7/ry8PJjNZtTU1LA1iRKhXq+XBbS1Wm1SEEgul7P1JBAIsKRWJpDazE0ikcBsNsNsNqOyshJbtmxhzWQmTJiADRs2tBlkzuambkLbFGra6PV6EYlE2JrT3NyM4uJi1NTUwGq1wuv1Ijc3l+0Xfr8fDoeDnUcsFgtEIhHq6+tZQFmlUqG5uRlAS6JMp9MlBR8zPYHZVsNZ4ptvvunGkWQGdCYQ+qaSR6rdbm91TmnrLJEafKZEpFwuZ+dhsmiiRHggEIBYLG7VsJjD4WQW0WiUqZQpiRiLxVBXV8cSmz6fD263m/1tB4NBtqZQApL6xpBNHPkxS6VShMNhyGQyVrlJ+41Go4HH40EsFoPBYEAkEmEJVYVCwdeNLGfDhg04cuQItFotzjvvvBP+/qlTp0IikWDv3r04cuQIevXq1QWjTA88yMzh9GDI75AO5IRw00stdqCLHFkiCA/wYrGYXZQBtLJZEH4dAB5kzlDIzxBoea2oiSN9zuFwAABTtgrnC2X96UBGQQ4KTsrlcshkMsjlcigUCpjNZlauSgfBQCAArVYLtVrdSulOalQ+d7oPChzT5Vyj0bDEktlsZgFACjibzWbY7XZUV1fj7LPPxjfffIONGzdi1qxZOHjwIOrr69G7d2+ceeaZ2LhxIxYsWIAXXnghae0gwuEwXn75ZQDApEmTsH//fubvHgwG0adPHwSDQXbQp0M/lUIDLWo2oV90JilYewKpSUbaD+gSFgqFIBaLodfr4Xa7UVNTg4qKCoRCIVRWVrKAodFoRFFREbxeL+rq6gC0zLmCggIolcokBRLZ8wghr/BAIMAqajJFsZrazG3r1q3sc4lEAh6PhynzKfjaFtnc1E0sFjMv7UgkArlczj6WyWRsXxGJRCgtLUVtbS0CgQCqq6vbfK78/HwAYJUXKpUKKpWK/d/J5XIWYBYmoHpadVW2QsploaUFlbmTYpE+Fgaij5UcJ2KxGLMAo/4RsVgMTqcTWq0WSqWSBZsosUPnmkxYTzidD9kopPYgodebmpby1z+zoL9loOUuqlarEYlEUF9fj3g8DrlcDqfTyYQzbrcb4XAYgUAAHo+H9Sqi/Yf+7un5qK8MVTxotVr2/Xq9HpFIBFqtlq1XRqORibOoWTVPUmUvH3zwAQDgggsuOKmG8kajEWPGjMG6devw9ddf41e/+lVnDzFt8JMUh9NDSVUuCxsAUhAHaCkzpINUIBCAzWZLasYDtByu1Go1Kw8CwA70AFopoflmmtkID1EAmHpULBazADOVfjU1NSUF8KgsTKPRsOYadEgLBALsuQixWMzsVNRqNfx+P2smGQqFYDQaW3nrcrofYUMsKgmkjtYymQyRSAQGgwF2u501VnM4HOjXrx9KS0tRXV2NzZs348orr8Rrr72GdevW4aqrrkJVVRXq6+vx6KOP4sknn2xlY/HPf/4TVVVVMBqN0Ov1qK+vx4ABA+B2uyGRSKDVatHc3MwUZqROJT9W8oGnQGVb9i2crqWt0lIgOcgcDodRWlqKXbt2wel0wuFwoKamhgUYy8vLodfrWdNIADAYDKzpo8PhYOuQXq9v97BP6w0FoTKpIkLYzG3mzJnscbFYDK1Wy+wffD5fu6XI2d7UTSqVstclHA5Dq9WyJsIUXA+Hw4jFYigvL2eBgHA4zJKeZIVCyjWgZU4Ik6dULSNUv5IKjZPZUDM+obc6WWxRAonmTygUYl8nbOZHSkRhk2JKpAs9q/1+P1ufzGYzq+IKhUKsTwAFsek5FAoFn0dZBnnB05tQ7Z4aWD4elPCWyWSsTwQnPQjtGSUSCTQaDeLxOLNOUigUCAaDrKKyubkZkUgEzc3N7O+cbB9pbtDfOyXMJRIJxGIxlEoltFotq8g0mUwIh8PQ6XRMDa3T6WC1WqHX6yGXyxGPx+H3+1mCVSaTcRFEFhGLxfDhhx8CAK644oqTfp4ZM2Zg3bp1WLZsGQ8yczic7IcOUEDLoUgYKATAupSTj5VEIkFjYyOAo5s1KVPbysKSouxYyiBhx15OZkCXJaDltSMlslwuZ4qw3Nxc1NbWsmAPcDQI6Xa72fcYDAYYDAZWsiy80JGiKB6PM8WQRCJBTk4OdDodCyY0NzfDbDa36grP50z3kvr/T8rCWCzGOnNTMikYDMJisaCpqQmNjY2YPHky3n//fdTV1WHEiBGYOHEivvvuO3z55Zf4wx/+gAcffBC7d+/GggUL8Mgjj7BA8//+9z8sXrwYAHD99ddjxYoVAFqsEWpqatC3b1/m0ZtqlUHjozJ6GjdXKXY/tLfQhVtY0k6qZppHeXl5aGpqwsGDBwG0BHwGDBgAuVwOt9sNn88HkUiEwsJCpg6y2+2IRqMQi8UwGo3H9ToUKmYzJchMzdw+/fRTyOVyqFQq2Gw2NDU1oby8HCNHjsQHH3yAO++8E6tXr8acOXPafB6FQnHc/gmZDCW46UIfiUSYFQ/5KZOFht1uh1QqhcFggEgkYj0mhIlMCjoLq6r0ej1EIhFLPNHX8bUhsyHVsFDkQI/RaxkOh5n/stDKjZp4dSRQGAqF2BmmrKwMOp0OHo8HdrsdFosFYrEYkUgEVqsVRqMRarWaBZFoPJSM5WQeQpsUSjq0Ny9Sg8zCryPlcmpQkOYCJb5I4d6T/LszBXqNAbDEMwljqG9HdXU1qxaKxWJobm6Gx+NhTYZjsRjcbjer3k1FJpNBoVCwALNSqYTRaEQwGIRer0c8HkcwGITRaEQ0GoVWq2UiCZ1OB4lEwr4mGAyyHhVc3Zz5rFq1ivUSmT59+kk/z4wZMzB//nysWLGCiXdOBU6N34LD4ZwwdOmijUx48QeONmMjRamwWUJBQcFxs61Cf8NUhB6vkUiEqwszCGGWXhhwppJUlUrFgj1SqRQVFRVM0RMMBtHc3AybzQav1wuXy8WCgELkcjmUSiUraReLxXC73YhEImhqaoJOp4PFYmGqAp/Px0rFeZA5sxCWh9LBKJFIICcnB01NTXA6nejXrx+GDh2Kbdu2YceOHZg1axZ27twJh8OBw4cPY8GCBfj973+PzZs345577sGVV16Jw4cP4/333wcAzJ07lwWOhgwZApvNBgAoLS1FVVXVMYOEMpmMXTK4qqj7SU1aCT+WSqWt/p71en2S/zL5uQNHKyqMRiNbD0ipKBKJYDabO3w4b6tfQDp54okn0NDQgHPPPRcAsHDhQsyePRsikQjvvPMOysvLUVFRgbPPPhv9+/fH+PHj0zziroMUyaQeE4lE0Ov1cLlciEQiyMnJQTAYhMvlYn7+qd+vVquhUqmSmj2qVCpotdqkhsdk48TVY5lNJBJJ8hoHwHxTAbDydFK5e71eVhVFgV8KAlNgkHoKyOVyaDQaGI1G5OfnQyKRoK6uDrW1taiqqkJeXh5KS0vR1NQEq9XK/N/JIzwcDsNkMkGlUjGrF2GAkZN+hGr11MoaUqcKVe7CXiAdQRhMpnkFHLWeC4fDzFbhVAkgZQPCe6hYLIbP52PN9/Ly8rB//34AR20BA4EAWzucTie709Ad2OVysXWE7kkajQZarRZerxcqlQoGgwGhUAharZYFj3Nzc5knMzU9VqvVrHE2nXNI8CVsUM3nS+ZCVhlz5879WUnF0aNHw2w2w+FwYOPGjTj77LM7a4hphc9cDqcHIrRDENpbAMmXbzrAU0kQAKYu1ev1xzxA0/NQcDK1WzeV2NPhjmf60w+VmQIt84IOYwqFAna7HQCg0+mwd+9eAED//v1hNBoBHO3CrNVqYTQaEYlE4HK54PV62eWP5hwdukktJJPJUFZWBqlUCqvVCo/HwxoyUUCbSqA56SE1GCgMFNLlX/j3S364tIYMHjwY27ZtYyX/s2bNwrvvvotVq1bh4Ycfxh//+Ec89thjOHjwIBYuXMieZ8aMGbj66qvxxBNPAACGDx+OTZs2seYqAJhFBiWrhB7zVH6fOj5O90DzhEpLydNQuAcAR/chSiAolUp2wauqqkJpaSn7GrK6EL7Owj2qI2RasqqtZm4bNmxI+lj4d3GqQw3VKDBEDWF9Ph+8Xi9MJhOMRiMCgQA7Y5BCXdiojZ5LWJ4sXK9446XMRxhgFovFCIfD8Hg8AFrOHRRsJjUiKRKFAaMTSSb16tULkyZNQmlpKTZt2oSmpiZotVoUFRWhrq4ODQ0N6NWrFwwGA1wuF6uuMJlMrIqAxszViOmF5oFQ/U7nXLJUofWDHqM7SaplBiFs+EaevDKZLKlZNtByrtVoNGzdoeempth8XnQ99LrTXdXpdAJoSVTTOiISieDxeNj6EY/HWeWM2+1mtn27du1iDWOFkEWcyWSCyWRCIBBIssjIyclBNBqFxWJBPB6HTqdjyQedTgepVMoSpVKplAWcyUuaqpP4fMkswuEwq7T8OVYZQMtZZNq0afjwww+xbNkyHmTmcDjZSaqSjAJ3dDiizZhUQ+QHSYd7n8/HDvJUJkQNeihbDLSohii46HK5oNfrk4KEFBAKh8OsrJFUAJz0ICwblEgkSZd0KkenUlBS/kSjUezatQu1tbUdasZHnqgajQZqtZoFCQ4cOICioiIUFxejtrYWzc3NMBgMkEgkiMViCAaDLJgIZI4CsacgDMrRhQk4qgARIlQG0vcZjUZWdtzU1IQRI0Zg6dKlsNvt2LRpE8aPH4+XX34ZixYtwvbt26HX6zFjxgxMmjQJ3333HUKhEPLy8phaoKCggK1ZcrkcsViMBRlTg4/CsXK6D6EHLr1uFBwi325SJ1NDProE9u3bFx6PB1VVVSzxVFxcDJFIhFAohKqqKuTn57PLVzQahd1uh8Fg6JB6sL2mtJzMgZIS9LesVqsRjUaZnYHZbE5q7kj+mzSH6Hs0Gg3bN4RrAb+4Zz7CpIBUKmWNuOjMSOsJKdvJh9npdLLgMtllCK0R5HI51Go19Ho9zGYzK2Gvr6/HkSNH8P7772PWrFk4/fTTsXHjRhw8eBAWiwX5+flobGxEVVUVBg4ciJycHNjtdqZiVCqVkMvlSc0G+Zm2+6G9J/VsEgwGWZKSKhqo2oH6yAjFL6kN0YWBZVIlCyu5NBoNVCoVu2eRUjUnJwdSqZQlzchjnp9JuhZhhW4kEmHzwWAwMEsu6vVACQmy0SKhTWFhIerq6tDc3IySkhJceOGFKC0tRTAYxL59+7By5Uo0NDSgvr4efr8fOTk5AFr2mtzcXDQ1NTH7OIvFAuBotU1zczOUSiXrKRKNRuF2u5NsoGj9S+1Xwkkvy5cvh8PhQH5+PiZPnvyzn2/GjBksyJytzZtT4Tsfh9PDEB6c6DKe2qQvGAyyi5rZbGYXcYvFwg7jFBykyzpBWXq1Ws2CSuFwGA6HA1qtNuliJ5VKIZFIWIkjlRjyi3/3k1rWThc5iUTCDjkajYZl8ik7v2HDBjZXKHFAJaptIfRgBoDCwkIWWK6rq4Ner2elZ9TQjdTQQjUzXRazIUgQCoVw2WWXwWaz4YYbbsj6xg5Cj106tMvlchYwpDJB+hrCYrHA4/HAarWipKQEkyZNwscff4xvvvkGY8aMgdlsxq233pr0s9xuN/NinjRpElNCFxUVsZ8tlUoRi8XY3BD+TLJ9AXiQubuhPYVK02nPAMD8C0mRrFQqceDAAQBgpecqlQpyuRyHDx9GMBjEgQMHmAIskUigurqalaoGAgEWaNZoNMe8wLdVycPJPFLV5tRYls4LqX7aoVCIKcLkcjm0Wm2r11f4umfD3tHTESqYKcBMZwhhQ2GHw4FEIgG/3w+r1crOM263mwWiU6EmxkDL2WXkyJGYPXs2tm7dipqaGnz22WeYPXs2+vTpg4MHD2Lbtm2YPHkyAoEA3G43qqqq0L9//ySFPSXPqJ8JT4Z3PzQPhAklahBKyQlhMNHv97OPSYFOgeDU/gFyuTzJ8kIul8NgMECpVCIajTKLOIPBgPz8fPj9fubfnZubC41Gw4Lcfr8/KQHG6VqECe1YLMaqM+m1pCqE1H/79++P5cuXA2hpRN27d++k550/fz6++OIL/OlPf0JDQwN8Ph/69OkDoEXlnJOTA5vNhry8PNhsNuTn58PtdjOBFnkxy+Vy5OXlMRGHw+Fg6meqiuDnlcyBrDIuu+yyTolZkKfzpk2b4HA4YDabf/ZzppsuvXH95S9/wZAhQzB48GDcddddfLPlcNJMaiCRLut0MaMLGzV4ows8AOZ9GAqFoFAomC+mSqWCQqFgiywd7BsaGmC325nPFJUeOZ3OpK7g1OiHNk9SynK6l1QVMwWByYMZaFGFkcey2WzGzp074XQ6IZPJMHbsWMyYMQPTp0/HzJkzceaZZ6JXr14sQ08liRqNBiUlJSgqKoJIJEJ9fT3q6upQWFgIAKiurmYWHB6Ph2XvKaFB84xKGbOBDz/8EDNnzsTq1auxaNGiVuqabEPocUy/i1qtZnOG1oxU8vPzAQB1dXUAgDPPPBMGgwHNzc345JNPWr2eNpsNr7zyCnw+HwoLCzFo0CBUVVUBAMrLy9m8pDkhVDLTv6llrpzuQbjXkNKcgj06nQ5isZhd8gwGAzweD0tWFRUVATi6JuXn57Ou8H6/H36/nyWeXC4XGhoaWFIskUjA5/PBZrO1+3cmnL888ZCZCFXwwgvcsQJ3QmsWKkNORWjjlS37R0+FlMcAmAoxHo8nBZudTiccDgdbG5qampBIJCCTyVBXV8fOEEOHDkVZWRm0Wi0TQuTk5KCwsBB6vR6RSAQbN27E3//+d0ilUvTt2xfxeBxffPEFNBoNdDodQqEQtm3bhtLSUohEIlbVR2p6mq/Cswnfc7qXaDQKj8eTdMew2+1wu90IBoNwOBxwOp1wu92wWq2or6+HzWZDQ0MDampqUF1dzfy4SfhQX1+PxsZG1oi1uroadrud+XxbrVY0NTWhsLCQNbl2uVw4fPgwCgoKYDAYALScZ+LxOJsvlCjjdD2UXABagszU90Gn0zFbQDq3pnrr7tq1C/379wcA/O1vf2v1mkkkEpx//vn46quvMG3aNESjURw4cABisRh+v59VBbtcLsRiMbhcLsTjcVitVmi1WpSWlkImkyEcDqOmpoatT4lEAk6nM+luzMkMAoEAlixZAuDnW2UQJSUlGDJkCOLxOEtqZDtdlhKxWq3429/+hl27dkEmk2HixInYsGEDxo4d21U/ksPhHAehPzI1XfL5fGzzUqvVaGpqQiwWg0wmQ05ODitvb08NQlD5qUwmY91yKbMvkUhYqWskEoHD4UgqYyV/TrpARiIR3pm7G0lNPlAHd6E1AqlBEokEa1hRU1MDoKVpAWXrd+3ahUOHDh03kJqTk4PTTz8d+/btg91uh9lsZhc3AMwmIxQKMX9W8uKUSqXMT4+UQ5nM5s2bcdNNN0EsFmPYsGHYs2cPRo4cmfQ1VLZJUAA1U6CElDAwQ+XAAJL8dYVJo2AwyKwsevXqhfXr16O6uhoejwc6nQ6XXHIJ3nzzTWzYsAE1NTUYMmQIJBIJampqsGvXLsRiMeh0Olx77bX47rvvEI/HUVRUBJlMBq/Xm9S0VCKRMPU9kNxYjgcTuxeaC+RdSUpD8tJ2OBwIhULML3ffvn0AWtTucrkc+/fvR1VVVZKXJgC2d1HJMlk+UWk0Kc3ogqZUKltZNXGP7syGlFt0VpHL5cz2gM4hbVlrkeVKIpGA3W6HQqGAWq1OUi3LZDJmZRAIBCCTyZJswziZgzBpQCpUv9/PgrlWq5U1VHM6nbBarRCLxcjNzcXGjRsBtCQyQ6EQvvzyy3aTCiKRCH369IFer4fVasW3336LgoIC9O7dG/X19fjiiy9wzjnnoLKyEo2Njairq0Nubi6sVivsdjtKSkoAHF1XUhP2nK6H1geaG2Qz4Ha7kxIToVAILpcLoVCIVWxSFYzX64XP52u15wjRarWsF4Tb7WbnFY/Hgy1btmDmzJkoLS3Fnj17EAgEUFtbi/LyckQiEfj9frjdbuTm5kIulzMlNW8O2XXQuTX1jkNVDPn5+di9ezcAsAbm1CDa5/PBaDTCZrOhoqICBw4cwJIlS1BfX48///nPLBlO6HQ6/OMf/8ADDzyAJUuW4ODBg+jbty/cbjez+iPRjVKpRDgcxt69ezF16lQUFhaiuroa9fX1qK6uxsiRI1FTU4NwOMz2JmFTdk56+fLLL+H1elFWVoazzjqr05531qxZ2LVrFz799FNcfvnlnfa86aJLT1XRaBTBYJAp0PLy8rryx3E4nGMg9FGlQw01RQFaFKt2u51tajk5OaipqcGBAwfYxY4CBOQ7JvRgprJFt9uN5uZmlr2lQBQ1Y6FDt9/vh8PhSDoUUmD5WHYLnM4n9VJEynYKBgEtasOGhgYALQHinTt3AgAqKiqg1WqxcuVKfPTRR9izZw8L9pSWluK0007D6NGjcfbZZ2PixIkYOnQo5HI57HY7vvnmG6ZgPnDgAFMSNDU1QafTAQCam5uZ8oMuBkIVPnl6ZzIej4f9bhqNhv3/CnnyySdhMBjYW2lpaXcP85jQ363wb5gSEUDL3z89Ho1GmT9lPB5nymWz2Yzi4mLE43GsWrUK8XgcQ4YMwVVXXQW5XI6amhosW7YMX375JbZv345YLIb+/fvjl7/8JdavX4+amhpIpVKMGzeOXQzMZjNLaKQGD6kZD8CDzN1J6oXO5/MhGo1CJBLBaDTC5/MxFXNubi5qamqYJ39ubi42bdqEQ4cOsYawBoMBWq2WJRFI1ej1ellzUSpXpzdqNBoMBmG325NsnYRzlpNZUCM3em0UCgXi8ThcLldSgJnUgcKGWlKpFGazmZ1vQqEQmpub4XA4WIk6XfCFiuZAIIBAIMCUiVQqn+n7yqmMsAkxJRxobgBgZ1WNRsMCzHK5HMOGDcOmTZsAADNnzoTf78fnn3+OeDyOUaNG4aabbsI999yD66+/HpMnT0Z5eTkSiQQOHDiArVu3Ih6PQyqVoqGhAVu3boXRaEQsFsPKlStZUGn37t1JgcbUPSa1WpDTtcTjcbbmAy2vg9vthtvtZqIW8mpvamqCz+djKmaPxwO73Y66ujq43W74/X40Nzfj/7X35vFRluf+/2f2fckyWUnCEhbZRUWUCEhxwwWXIrjg0Wo9PWqt2lqlal1OxapHq+g5alGrVYp1Qa2loFW/iClGEBAkbCGQfZ3JZPZ95vdHftfNM0MQpCQzSa736zUvMpMhz5M819zPdV/L5+rs7ERTUxOampqErVEguqOjQ/ik1dXVKCkpEfJx+/fvh1arFf4bDZYzm80ADkk2SH0opu+Q7k+l6zntey0Wi/AfCwsLxYyR8ePHQy6Xo7CwEAqFAg0NDTj11FOh0+nw9ddfY/78+fjb3/522PEUCgUee+wxTJ06FV6vVxy/q6sLsVhMDBSsr68XMbH6+npRhEFFE+T7AMk2wvekzGDVqlUAgEWLFp3QvcWll14KAFizZk3SENGByg+uZI7H4xg/fjwWLFiAxx9/XLz+8ccf4+KLL8bKlSuxcOFC2Gw2/OpXv0JpaSmUSiV+9rOfYdSoUSf05BmGOTakradyuRwymQzd3d0iCKxUKuF0OoU+ptVqxcGDB0VlZU5ODmw2G7xeL9rb28VQFak2q1qthlarFRs40s0DegJrSqVSbOBMJpNohezu7hZD4Kh9OR6PIxKJCE0spm+h60i6qaRvSwP3ZDKZ+FqpVIrkgNFoRHFxMd5///2kgV0jRoxAY2MjqqurUVlZmdSeXlZWhpNPPlkMyti8eTPKysrEsB65XI729naMHz8e3d3d8Hq9sFgsQvfQ5XIhJycHarVaVFxnejUI6c8CgM/nExsOKUuXLsVdd90lnrvd7owKNNMQLqnzKw0sB4NB8Xt2dnYiKysLw4cPx549e1BTUyMG/5111ll455130NDQgLVr1+Lss8/GKaecgjFjxuDbb79FS0sL4vE4srOzYbPZ4HQ6sXbtWsTjcajVapx99tk4cOAAPB6PqECMxWIwGo1JHRFAT4CKvuYgc/+RWnFDtm82mxEOh9Ha2gqgZ4NH2usymQxFRUXYtm2bqDAdP348bDYbwuEwGhoaYLfbRcAagBiqRfc3ajOV2qlOpwPQs8mzWq1C2kkaSORAUPqh5IG0Ap6q/bxer9hc6/V6xONxtLe3C8kEKQqFImkgFw3wouQD+SlUHU2yGfRIhc6DK1L7F2lghQJz9K9MJoPf7xfaqp2dndDpdJg9ezb+9Kc/IZFIoKKiAllZWVixYgVkMhl+/vOfY9asWVizZg2eeuop1NTUAOi5viNGjEBRURG6urrQ0tICuVyO4cOHIxAI4LvvvsO4cePg8/mwdetWjBgxAj6fD01NTeLeQ+dFz6XdPUzfQvsMSiCpVCp0dnaKtcTpdCIWi8HpdArtZUo6kj9JHZahUEgkP6XQ2qFSqTB9+nQ0NTVh9+7dmDlzJhobG1FZWYnzzjsPDodD/P/UDiqyhdSBs5l8DxoMs0ToM0n2ASSvLdR5C/TY0siRI7F//36YTCZYLBa4XC6Ul5ejsbERPp8Po0aNgs/nw8GDB3HnnXdi586duPfee5P8S7Vajcceewzz58/Htm3bMGvWLFGsQwFt0lgGICQI29vbxX5crVYn2QonIzIHl8uFNWvWADhxUhnEjBkzxHDZ9evXC53mgcoP3nXJ5XIsXboUL7zwghgAtX37dixcuBDLli3DwoULAfRUn/39739HXV0dmpubsXHjRmzYsOHEnj3DMMdE6gALp9N5WJVxPB4XraMUYFapVCgvL4dGo8HXX3+NqqoqHDx4EE6nMynARwN37HY7mpqa0NDQAJ/PJxwrqjKj6lO3250UrPL5fHC73aKdOvWcmb5DurmWy+Wiylan0wltbqvVKgauZWdno7GxEQAwceJE/POf/xSJgksvvRQmkwmvv/461q5dK9rdaWhKLBbDgQMH8N5776GmpgYmkwnRaBT19fUIh8PC4Y/H46itrRXB2JaWFigUCqhUKtEGL5fLkxz3THbCpk2bhg0bNiCRSODbb7/F2LFjD3uPRqOB2WxOemQSlJwCej6blAAiB93n8yErKwsymQxerxfNzc0oLi5GdnY2YrEYtmzZArfbjezsbJx77rlQKBSor6/HG2+8gTVr1mDHjh1QqVTIz8+HTqdDQ0MDqqqqsHfvXsTjcRQXF+O0005DXV0d3G43FAoFzGazkPYhJ58qzEhKgaUR+hdpFbNSqRTVpxTcbWlpEZI7AER3RGFhIfbs2YNAIACdTofTTz8dVqsVX331FV577TV88skn2Lp1K/bu3Yva2lrU1tairq4O9fX1cLvdIoFJ1Wg+n09oN9Oa4nQ6EQwGk3TdM3ndGCqQXqbUbpRKpaheJgkchUIBu90Ou90uKpNJwoeCexQ8crvdcLlcIhlO7w2Hw3C73XA4HEl+j1qtTpLOkFa7U0cm039IP5d07ajogYIypaWl2LNnD4CeNuNPPvkEoVAIo0aNwvnnn4+XX34ZALBgwQLMnj0bzz//PO69914RYKaqwdraWnz55ZfYv38/jEaj8FNIlmvv3r3QaDRCgxnomR1B/qu0OlVaeMGJzb6HEk0ymQwGg0EEkMlPpFkwfr8foVAIHR0dQlrJ4XAgGo1Co9EI6ROlUokZM2agoqIC06ZNw+zZs7FkyRJMmjQJkUgEGzduFP7bzp07oVKpkrosyBZI7owqnskWepPvytTq1MEwS0RayUxBXSqmAnoSCFRRvHfvXpxxxhlQKpVobW3FlClTYLVaEQqFUFBQgOHDhwPo8TErKioAAK+88kpSwSUxZswYzJ49W7yfjiuFbIS6gOvr6wH0aPNS4oT2PUQmJiOGGqtXr0YwGMS4ceMOkz38d5HL5ViwYAEA4P333z+hPzsdHNcd8JprrkFubi6WL1+OpqYmXHjhhViyZAl+9atfifd8+umnKC8vR3Z2NnQ6HS688EJUVVWdsBNnGObYSK1ipgAvtR5Kh+tQO1kikYDZbMbIkSNRU1ODTZs2icpBm80GrVYLj8eDxsZGseFvampCd3e3aCtzOp04ePCgqDKlagJyqILBINxud1Jrq8fjERqewKEK20wlFAphwYIFmDlzJlasWJHu0zkupJsi0rNUKpXwer0iEeDz+RCJRKDVakVGvqioCG1tbejo6IBarcYll1yCnTt3YvXq1QiFQigsLMTll1+Oe+65B7feeisWL16MhQsX4uyzzxba39u3bxc6ep2dnWITR+3LLS0tIrDc3NwsdM2i0Sh8Pp/QZgUO6Y1nIosWLcI///lPTJ8+HVdccYUYZjiQoCohoOdvTRWi0q+dTqeovm5vb0ddXR0mTpwIk8mESCSCb775Bu3t7RgxYgQuv/xy5OfnIxaLob6+Htu3b8fWrVuxa9cutLS0iE6GkpISTJgwQdhMPB6HVqsV8iNKpVLochsMBhGcoGABgKQ1helbpEkr6b3HaDSitbVVJCh0Op1IVuXn56Ourk4EmE899VSEQiG888472LZtG6LRKLKysjBhwgSceuqpmDp1KkaOHCmqltva2lBfXy+ChiSlEQqFEAgERPUzBR4ikYiw5YEgtzOYIS1V+qyq1WrhP9C9iZKfNDCJgsDhcBgejwcej0dUG9Jzv98v7i2BQEAkHsg/oW4pn8+H7u5uoc0aj8eFlrNOpxOBiXA4nFEJiSP5HoPBJwEOBZlTB+ip1WoRyKN7fl5eHqLRKA4cOACVSoVrr70WH330Ebq6ulBQUIBFixZh+/bteOmllwAA//Vf/4XKykrs2LEDn332GR599FFMnjwZoVAIW7ZsET5HXV2dmCXS2dmJRCKB+vp6UR1JiQdp9T2db29DJ5kTC32GgZ5AHq35VOlOySdKVNHgPYVCIQa/FRQUYN++fYhEIpg8eTJOOeUU/PWvf8Wrr76Kt956CytWrMADDzwAl8uFiooKJBIJVFZWwmAwwOVyiUQDBQjpuBR0Jlmf1I4qmUyWFADNRLZs2YKKioqkWSKpkO619JFJSKuUqTAiGAwiOzsbQE+yqLy8HCqVCt3d3di7dy/OOeccyGQy1NfXC1+DrlFFRYXoBP7xj38MAHj55Zfx8ccfH3bsM888E8ChTi7q9qRgPdkIyXHE43FYLBYx3wboKfCRFkpwkDn9rFy5EkBPLLQvrsdll10GAPjwww8zdm04Vo5r16VUKnHPPfdg+fLlmD9/PqZNm4bly5cnvaekpAQbN24UzuP69et7rd5imExlsDjrUl0fChxGIhGxuabhf1QBoFKpUFpaCoPBgH/961+igpUm4H799dfYuXMn2tvbkwaVBYNB2O12NDQ0oKGhAaFQCPF4HG1tbWhvbxcZfRq6QcMBXS4X5HK52GwGAgHhoFNgIFM5lkx/JjthUt1DuVwunCGqzgCStZgLCwuFcz569Gjs2LEDQM9wHZfLhU8++QRAjxbi7bffDo/Hg1/+8pf46U9/invuuQcPPPAAnnvuObjdbmRlZSGRSKClpQWdnZ1CP9PlciEQCMDr9Qqbok1dW1ub0Gr2+XxIJBJJ0g2ZekPWarV4//33sXnzZtxyyy3pPp3jhj6XVFlIUgU6nQ4ymUxU/FGg2el0Yt++fRg3bpyoaN6xYwd27NgBrVaLyy67DFdeeSUqKiowefJkjBs3DmPHjsXYsWMxevRolJSUiAEpQE+AwWg0QqfTibZ4vV4PuVwuqkUAiHZ4aQIl0xks9xtpNSqtJ3q9Hm63Wwz6y8rKEptym82G9vZ2+Hw+aDQanHLKKfD5fFi9erXokLjwwgvx4x//GCqVCt988w3WrVuHL7/8Eg6HA1arFTabDYlEAq2trejo6BAavR6PB+FwGH6/H36/XwSlnE5n0gY/k4KHQwmqTqV1W61WC31toOdzS5XH1BED4LCgMF3vcDgsdJVJv9/v94vEJbXE0+vBYFAEnGnYoMfjgd1u73XAIGkDZwJH8j0Guk8CIGnAbG8D9FKlB2QyGWprawFAJCT/8Y9/AACWLFkCtVotAgOXXnopbr31VlitVshkMuTn52PBggVYuXIlHnnkEajValRXV4sgVG1trUiuUzKV1gvyf6UBRJ4B0H9I7+/U+QD0dOFR5yQF9igpZTAYUFdXBwCYNGkSvv76awA9drF//3688cYbCIVCmDBhAq6//nqcd955UCgU+PTTT7FmzRoUFBTA7/fD5XIhkUjgwIEDiMViqKmpEddfOmuGAptkK9KB5pk+G2AwzBKhz2EsFhOf32AwiIKCAshkMrhcLrS3t2PGjBki+dDW1oZzzz0XcrkcBw8exLhx40TAuKWlRXTs19TU4OqrrwYAPPTQQ+K+RdCel9Yw0oGWSmYAPfZLxTtlZWVCNkOr1cJisSTJGTLppaWlBZ9//jkAiGt/opk7dy7MZjNaW1vF+jRQOe674DXXXAO/349EIoFVq1YdtombMWMG5s+fj5NPPhmTJ0/GqFGjcMkll/zbJ8ww/cVgaBWiFlLazMViMVHJQ6/RgD6lUomioiIMHz4cdXV12Lx5M4LBIAwGA8aMGYNvvvkG3333HeLxOIqKisTAt2AwCJfLhXA4DK1WC5PJhHg8jsbGRiG34Pf7hc6VXC5HIBCA0+kU6wY5hPQ1gF71szKNY8n0Z7ITRs4LBQgBJDnrZrNZBJWzs7PFZjQvL0+0IcrlcowdOxaVlZUAeqQh5s6dizfeeANPPfUU2traoFKpUFxcjKysLMTjcezatQtffPGFqGz0+XxobW0Vlcwkx0LDXEiaKRQKwe/3iwFgVLUirVZg+g5pNXM0GhUbkHA4jJycHFFB5Pf7UVZWJjRVa2trUVhYiLKyMgA9Vc5VVVX48ssvUVNTA7fbLRLS0mQBHU+n04nhb2q1WgSbtVotFAqFqHCXyWTQaDSwWCxJP2cgaGMOhvuNNGkFHAoCaTQasYmy2WyiasdsNgvdTIVCgZNPPhmxWAx/+9vfEAgEkJubiyuvvBIA8OSTT+Ldd9/F/v374fV6RafDN998g6qqKmEPbrdb6HpTFVskEhF6nHTvc7lcGRk8HEqQXjLQE3yhawX0BAfIr6ChwCSBQd020oGAHo8nSSKFXqc2ebp3eDweIc1BAyTpPXRcoKftvaurC9FoVASGMkle5Ui+x0D3SYDe7+PSivKsrCwAh9aXzs5OUd1sNBpRU1MDv9+P7OxsTJ8+HQCwbds2ABCtyKnIZDJcfvnlWL58OWQyGaqqqlBaWnpYcAo4PLic+nOAzJVAGExIE5pSPXdpgJeKVqhYhq7Z1KlTRbCIOs2+/fZbWCwWPPfcc3j//fdx33334fnnn8eHH36IkSNHor29HVu2bAEANDc3IxaLibVFenySWpGeI9kO2RKQ+dIHxzpLxOVyiQd1J2UK0mSPQqEQf3+S1QF6JLvcbjcqKiqgVqvhdDpht9sxf/58yGQyVFdXY9q0aTj55JPFvueUU05BLBZDfn4+SktL0dHRcVhxAK05BQUF4vjAofWNfCV6rtfrRQGYXC5Hfn4+gGQ7Z9LLW2+9hUQigTPPPBMjR47sk2Oo1WpceOGFAIAPPvigT47RXxx3kPm2224DANjt9iNWCT366KPYvXs3qqurxY2bYQYKA71VSNqqTG1lpF1LwzKCwaCYoDtmzBg4HA588cUXaGlpAQCMGDEC2dnZWLduHTweDywWC04//XTs3bsX//jHP7B161Y0NDSI1vgdO3Zgy5YtIgvr8XhQX18PpVKJWCyG3bt3Q61Wi+E9DodDOAGU5aWvpdXMmcqxZPoz2QmTVoKQE6xSqcS1MBqNIsBbVlYmkgZFRUUiEJ2VlQWVSoX9+/cD6Ekw1tTU4K233gIAXHfddXjnnXfwyiuvYNWqVXj99ddx/fXXw2q1wm63o7q6WiRDqKLR7/eLtnZqdQ8Gg6IKMbU6JNPbDgcTFLAljTuqIA4Gg8jNzRU6lt3d3cjNzYXVagUAtLa2Ih6PY8qUKcjLyxOVRRQwpOE3NJjLaDSKYYE6nU5ILJhMJuj1eqhUKvEatc/T/5GufSqVakD4HgP9fgMgqSuC/v5arVYMbtNqtQgGg2KAqM1mE1Vl48ePh16vx7p160SAaMGCBdi3bx+ef/552O12WCwWLFiwAL/85S9x77334j/+4z8wfvx4oXXucDhgNpsRDAbFOhuPx+H1ehGJRIRsAlW+0lBTqb0w/UM8Hk/6jJJ2NgAxtwE4FJTp7OwU8k0KhQKdnZ0i4KxWq5GVlYXc3FxkZWWJpBMFgUijmTotKLDs8/lEEj4ajYoqakpeURCbgtpA5vgjR/I9BrpPAiCpGpju7TRcGoAIMtvtdtFO3lvA2Wq1JlUyAjiqVFVFRYUYtkT3DapqJ38jVcJDen/J9OpUYqB3zkgTmlKfVavViq/pWuj1ejEYtqGhQbzm9/sxfPhwJBIJbNu2DWazGatWrcL555+fdE3Hjh2Ld999F6eeeqooiIhEImhpaRHddqFQCM3NzWLGCNkqDQIk+5QmvCkRkan+yWCYJSKTyZLWbvJHXS4XrFarCDR3dHTA6/XirLPOgkqlgsPhQCKRwKmnngoA2Lx5swj81dTU4LzzzgMAbN26FXfffTcA4KWXXsJ3332HRCKB1atXY/Xq1QB6fBsAyMnJAZDcEQgcWitIggXoKepRKpVJUpcDoSNvMJNIJPDnP/8ZQE+hbV9y6aWXAujRZR7ICcvjCjI/8MADWLNmDaqqqhCNRvHKK6+c6PNimLQz0FuFaPMkbUmlKi5qE9VoNBg7dizUajU2bNiA2tpaxONx5OTkYObMmejs7MT/+3//D/F4HKNGjUJhYSHefPNNtLa2QqfTYc6cOfjpT3+K22+/Hddddx2mTZsGuVyOmpoaHDhwQGix7tu3Tzj3VIlG7YhdXV1iI0CBRNr8A4eGvmQix5Lpz1QnTPp3pQ05cMgZNhgMYqNvtVqh0+nE72qxWMT7KYFAnw+bzYbPPvsMADB79mxcffXVSRu7/Px8LF68GK+88grmz58PAKiurhZ/l87OTkSjUREEoGCQVGOPNheZsuEfSkirmcPhsKggBXqq/6Q27vP5IJfLUVxcLORYGhsbodPpMHbsWAwfPhwFBQWwWq2wWCywWq0wGo1CD1Wn08FgMMBsNsNgMECr1UKj0UCr1UKn04mBf3q9HjabTWgy0zoiPddMZ6DfbwAktXVSQIaSjUDPukGdEUVFRaivrxeaqgUFBfj222/R3t4OjUaDCy+8EDU1NXjjjTcQi8UwadIk3HPPPTjttNNw4MABrF+/Hk1NTbjgggvwn//5n0Lzub6+XgzraW5uFmsb6cqTRq/UToCedY/Xk/5DOgtCWgVI8l1Az33H4/GIlvecnBwhZwH0VIhNmTJFtMBv2rQJX3/9NbZv347m5mb4fD6hfUrDlTwejxgWRvctj8cjBgvGYjHY7XYRzKaEPAUGyK9KN0fyPQayT0KQbyENqtCcEHqd1nqq9qPf+cCBA2KQ14EDB0Q1IQV6Pvroo6Men9ZVWrekHV8AkgLf0n+lw8UyeUYEMPBlVaTBN6nUm16vF0kGul50fTQajfg9qbPmtNNOw5o1awAAd9xxB0aPHt3r8UwmE15++WWcdNJJYj4NJScTiYRIXpKEDwWXOzs7EQ6HxTlIq98z2T6AwTFLBEDSZ5J8S6AnAZCXl4fy8nIAPVIIarUakydPBtCzV6XBbm1tbTAYDKJAKi8vD3K5HA6HAzNmzMDs2bMRDofx4x//GNOnT8fdd9+NeDyORYsWYd++fQB6BvoBEH6edAAxPY9Go5DJZDCZTEnDTjUaTcYmI4YKmzdvxvbt26HRaLB48eI+PdYFF1wAjUaDmpoaVFdX9+mx+pIfHGR++eWX8dRTT+Gjjz7ClClTcMcdd+CJJ57gycvMoGOgtwqRM0P6dtINdDgchlKpRHl5Ofx+P7766isEg0Ho9XqcdtppOPXUU7FhwwZ8++23AHqqU8PhMP72t78hkUjg9NNPx89//nO0trZi2bJluPnmm/G73/0O9fX1uOKKK1BcXAyv14vvvvtOSCRUV1fDYrFAJpOhpaUFra2tsNlsAHq0W2nSt3TTn+mSGceS6c9UpNVC9PdVqVRiLdfpdMLBIadIOpCRHDXaiEmTAtSa+H2Tdw0GA26//XYsWbIEAMTfT2oDbrdbaGVKW5Vpg0Ebfm5R7V+oOpg2WWazOUk6A+iRVKEEhM/nQ1lZmahA83g86OjogNvtFq2B0spkk8kktJd1Oh20Wq3YHGg0GqhUKqFXl52dDa1WKyQRyFZkMpkYCDgQGOj3G+nnU5q0Sq3GIV1mvV4vOiPKy8sRDAZFK3JFRQV8Ph/efPNNxONxnHbaabjmmmvw7rvvYvHixXjkkUewYsUKPPPMM7j++uvxl7/8Bddeey2ys7PhcDjQ3NwMs9ksZg3QsNBgMCham8lOKBAJIEkfOBMZ6NWHhFQHW6FQCLuX6njn5ubCbrfD5/NBoVBg+PDhaGlpgd/vh1qtxrRp02AymfD3v/8dX3/9Ndrb25Mqob1eL5qbm7F3717s3bsXfr8fBoMBMpkMgUBAVChrNBokEgkhjUE6mna7XdzjUoNWmWAjR/I9BrJPAiSvIySLBfT87aXJy9LSUgBIqhgtKChAJBJBVVUV5s2bB6AnMffee++JoMBbb72FzZs3H/H44XBYJMkpuSH1N+hcgEPBK+qsisViUCqVA6I7YiDLqkj/tjR7gT6jer1eBJLJNug6+Xw+4afQWhEMBkXic+LEid97XIPBgGXLlgEA6urqoFKp4HQ6xaC/cDgMu92OcDiMtrY2kaRqbm4WAVqpbm+m+62DZZYIFRpQFy9VFNMgWJvNJiqcu7q6UFJSIhLlsVgsqXNCaj8kg1FXV4dnn30WZ599NqLRKLq6umAwGPCLX/wCo0aNws6dO4XMG3CoE0Oqzy1Fo9GIbrCBViwxmPnjH/8IAFi4cKEokusrTCaTqJb/61//2qfH6kt+UJB57dq1uPXWW/Hmm29ixowZAICf//zncLvdeOONN/rkBBkmXQzkViGpo06BuNQAXX5+PpRKpdBZttlsmDVrFmw2G/7xj3/g4MGDUCgUuOiii+BwOITjfcUVV6CgoAA33XQT3n77bbS0tCAWi8HpdOLzzz/H/fffD41Gg3HjxiEcDmPbtm0oLCxEIpHAli1bYLPZRLurw+GA0WhEIpEQwU1y/qTZ/0zY1PXGQM70Szdv0q+lG2naPNGGnG6s+/btQ3Z2NhQKBfx+P7q6uoST1tbWJv5fb9WYqVx11VWwWCzwer2YMGECgJ6KAmqDpUdv09ypUj/TnfXBBmkfAxA67waDQdhEPB6H3+9HTk4O9Hq9COJYrVZMmDABJSUlsNls4kF6yxRE1uv1IrBMQWXSYbZarbBardBqtaLlnmSACNJxHkjDlwby/Qbo/bNHiUP6mjb/BoNBVHplZWXBYDBg586diEQiyM3NRXl5OVauXIlwOIwxY8Zg/vz5+PWvf43XX38dgUAAw4YNw7x583DyySdDLpdj48aNuO+++3D++efDZDKhtbUV4XAYarUadrtdnAPJJdAQOKo2DAQCYh3MZH3mwaDbDRxav6UyTUqlUiQss7KyhMayQqFAeXk5mpqa4Pf7odFoRNv62rVrEQgEYLVaMWvWLCxevBjXX389LrnkElRUVGDatGnIy8tDIpFAU1MTtm3bBrlcLoY+0QDB/Px8yGQykfTSarUiAUE+SDQazShZplTf47nnnsOuXbsGtE8CHLqnAxD3EgBJw12DwSCKiooA9PgYVCFYXFwMAPjqq6+gUqlw2mmnIRaL4S9/+Qs++OAD/OhHP0I8Hscdd9yBPXv2HHbsSCSC++67DwcPHoROpxODsBUKBbRaLbRaLcxmM9RqNSwWizhPSkxQYQTdGzO5mnkgy6pI5y2QVjsAcc2AnvWEfj/S/08kEigsLARw6H61Y8cO8drevXuPemxpwoGqnun/03WndYKKNNrb25N0vaW683R+TN8hl8uT5i+QjwngMFmmSCQCuVwuPtN0fwF6kk40eNzlcgkfbcuWLaLSfcOGDfjwww+xdu1auN1u/O1vfwMA/OhHP0JrayuUSqUILtPanLp/UavVSVXMA6lYYrDidruxatUqAMDNN9/cL8ekxOiqVasy9j5yNI55B7ZlyxYsXLgQTzzxBC6//HLxutlsxs9//nP8/ve/z9hqQ4Y5Hgaysy5dkKQVzUCyHEIgEIDb7YZMJsPJJ58MpVKJHTt24ODBg1AqlbjssssQCAREO9miRYvgdrvxyCOPIBAIYPLkyXj88cexevVqvPTSS0Kz6v3330d9fT3GjBmDcDiMb775Rojk00AVAGhoaBA3e5/PlyTpQTd7IHOdsMGS6e+tQtnlciErKwtyuRwejwe7du1CaWkp5HI5Ojs7UV1djeHDhwPoaSMibbNt27aJipCPP/5YOEpHoqqqSjh65AhSNRCQ7IBJJ85Lg+EcZO5/FApFUqCZBuzk5uYKG/L5fEIGA+ipEunq6oLFYkFubm6Sdq9CoRDBZqVSKZxxs9kMm82GrKwsURkdjUaTgpf0XgpMq9XqAeeUD+T7TSrSv700gSXVaZYOAkwkEqKSbsqUKfjmm2/Q3NwMvV6PRYsW4be//S2qq6thMBjwm9/8Bn/6059w77334sknn8SKFSswatQodHd34/e//z0uuugiyOVy7Ny5U1Te1dTUiM0htTOHQiExbJLuOQCS5HgyjcGg251aqSoNlFMlqLTKvbS0FKFQCJ2dnZDJZJg8eTIUCgU+//xzxONxjBw5EgsXLsS4ceOwdetWPPHEE3j66afx2muv4e2330Z9fT1GjBghho7u3r0be/bswaRJk6DT6RAMBlFfXy98ErvdLgIMUpmMTAsyp/oed999N8aPHz/gfRKpbUg7IhQKhQj4Aj0+C1UkFhUVQSaTweFwYPLkyeLz39bWhilTpkCv1+PgwYNoa2tDWVkZXC4X/uM//gPvvPOOSDhVVlbi6quvxtq1a8XPVigUMJvNUCqVsFqtUCgUMJlMkMlkQqZD2n5PwU+FQiFsJVO7fAeyrIpUlkkmk4nfw2QyiWC5wWBI+hyTJAIFnmmeVH19vei4e+WVV0SiS0oikUB1dTUeeughXHvttQCAmTNn4uDBgwAOBZlpL0PnR/NDKOlOdkKa0ZleRDOYoMBuOBxGPB4X/oDP50vaW9A1ke4pqPK4q6tLfO4bGhpw1llnAQDWrVuHDRs2iEHlmzdvxl133YXNmzdDqVTinHPOEUmtM844Q3RXpcrt0DFVKpXwSbiKOTNYuXIl/H4/TjrpJFRUVPTLMS+55BLo9XrU1taKLr+BxjGPqjzllFOOWJX2yCOP4JFHHjlhJ8UwmQA56wOR3qpUCenNkxxgauVJJBLYunUrAOCss85CYWEhXnzxRQDAnDlzUFBQgAceeABATwXqLbfcIpzp/Px8TJw4EbNmzcJvf/tbVFVVwWq1orS0FA0NDdi1axcmTJiA2tpabN68GVOmTEFzczOam5thNBqTKsikgUN6Lg0sMv8+UjsgvS/SFFSr1QiHw2htbUVJSQkaGxtFNUtBQQFaW1vR3NwMk8kEuVyOhoYGjBs3DkDPIIyf/OQnsFqtaGxsxNKlS/HTn/4UY8eOFbbi8Xiwc+dO/POf/8TGjRsB9DhflMwYNmwYnE6neD85ZORspbbRpga12E76B9rk0Wc3EAgIGQuFQiH0TnNzc6FSqdDZ2Qm/35805FMmkwlNZhq2olQqRcUYAFGtTJB+qnQzP9AZyPcbIDnBI11bpMkgacBIqtPsdDrhdruhUCgwYsQIPPPMMwCAefPm4aOPPsKuXbtgNBrx9NNPHzbRu6ysDE8//TTuvvtu7Nu3D6tWrcJ5552Hjz/+GFVVVTjjjDOwf/9+tLa2Ijs7G+FwGOFwWAyZjEajQrLBarUK3WZpoitTOFbd7ocffri/T+2YkVaqSjtRKAFhMpnQ3d2NRCIBg8EAq9UqNAkLCwthMpmwZ88e0fo+d+5cyOVyrFq1SvgudK8IBoPCx8jJycE555yD7777Dh0dHaisrMT555+PLVu2IBAIwOl0wmw2w+12i6CVNPjDycz+QVq5nIpMJoNer4fH44HH40F5eTkcDgfsdjumTZuGLVu2wO12o6ysDH6/H+3t7aKDRq/Xw263Q6VSoaioCC0tLXj44Yfx3//930nBbJVKhfz8fGF7BoNBJE5tNhvUajVKSkqEPEN+fr7QBaYiCbVaLWSCMjWASJ0zEyZMwLfffotHH3003ad0zKTub6QSb5ScMhqNIigeDAYxYcIEbN26FV6vF2q1Gi6XCxMmTMCOHTsgl8uRn5+Puro6zJ8/H+effz5yc3Ph9XpRV1eHbdu2ob29XRx/xIgRaGtrg0wmQ3l5uRh6bTKZ4Pf7k+QZ1Gq1kF1QqVRiHgCdv/T3YfoOpVIpNPfD4bAYDhuNRoUsBXD4ME+aGQEAzc3NmDhxIjZu3IgtW7Zg/vz5OOOMM/DVV1/hqaeeOuyY5eXlKCoqQk1NDYCeOBrZ5+jRoxEKhYQNS/fqarU6aVBkpvkhQ41EIoGXXnoJQE8Vc39dD4PBgIsvvhh//etfsWrVKjGEciAxOHZnDMMcEWmgDjjU0uXxeETmPRQKicouqnwaP3489uzZg66uLphMJlx66aV45ZVXEIvFMHfuXNx66629bgQqKiqwbNkyyOVyrFu3DpMnT4Zer0dDQ4MYnNDd3S3Op7OzU5yHtFIVSNZizlRnfaAiraJIJBIieCENCkajUXg8HmRlZSW1qhqNRshkMng8HlFNtG/fPowePRrxeBzvvPMObrnlFuj1euzatQt33nknLrvsMixZsgRXXnklFi5ciIcffhgbN26ETCbDqFGj4PV6ReCArrvZbBYVs9LpymTD0qFO9Jwdsv6FWomlwzvj8TgMBoMIEvv9fqHpSHYkl8thNptRWlqKnJwcEahWKpXIzs4W/1cqk6JUKoVGMw39YzID6edO+rX0GknXd+pw0Ol0aGtrA9AzyM3lcgk9yylTpuDdd98F0DOUKTXATBgMBvz2t7+FVqvFd999JyRVXC4XDAYD5HI5WlpaxFpF9hQOh8V5UIUtbfYysTNvoOt2A0eeBSCtcqcklMViAXBIG5eqyJqamgAA48aNg1KpxO7du7F161bI5XIsXLgQjz76KH73u9/hwQcfxGWXXQaz2QyHw4H33nsPJ598MtRqNdra2lBdXY0xY8YAgEiaAj3rFVUwSwPiHBTqP+hvTNeB7IO6Yjo6OpCTk4Nhw4YJOaZJkyYJWQSdToeioiIxG0Cr1Qo5J6PRiLKyMlgsFpH80mg0yMrKQmlpKUwmE3JycmCxWJCXlwez2Yz8/Hzo9Xrk5+eLc8vOzobVau01wZbptjKQO2e+728rXV9UKpXwa6nTKRQKiapmmgmzfv163HDDDSgqKkJbWxtee+01/M///A9efPFFrFu3Du3t7VCpVCgsLMSwYcNEArKsrAyRSAQ+nw82m00Eosk/JdkfAEmDs8m3kfqtTN8ilbGh4C75oh6PJ8lXBQ5VpTudTpSVlUEmk6G9vR25ubnIycmB1+vFW2+9hTvuuANXXnmlqHbWarU45ZRTsGDBAiiVSjQ0NEChUGDGjBno7u5GIBBAbm6u+LxRRx9JNAE99iMNMjPpRTrw77rrruvXY1911VUAenSZB2IMhHdoDDMIkTph5BClDilxOBxQKpViI9fc3HyYs9PS0gIAGDt2LFQqlRiYcsMNN3yvYzRjxgxcffXVAIB3331XyGhs3LhR6O62tbVBrVYnDXpLXUTj8XhGtagOJqTtWuFwWATvgB5Hy2QyiUrlRCKRpEeoUqnE90grl65jdnY2XC4XPv74Y1x++eWYPn26cO47OztFEsNgMCAvLw+jR48WgSCz2Yzc3FwoFApYrVYYjUahr6vRaESVNQUnqP1QKrnA9D/kJEtbhKVOPF0fjUaDoqIilJeXY+TIkcjLyxNOtFSHs7fApFwuF8kGJvNIDTLTZ5EkEKRI5SiUSqVYE7KyskRQtLS0FPv27UMgEEBBQQFmz579vccvKCgQg1L+9a9/Yfr06QCAgwcPCs1WssNIJJJ036Fzj0QiSSADSwkAADaNSURBVOedaQx03W4guQpR+tmWtitLh0bGYrHDBtBS0JmCRF9//TWAnu6r008/XdibyWTCzJkzcffdd2PMmDGIRCJYvXo1pk6dCqBH2omSYdFoVGzsKQghPV+mf5B+/ihYDEDIWuTk5IggzP79+zFhwgQh2+X1epGdnY1Ro0ahoKBAaK8WFhaKqmSbzSY0//Pz8zFq1CiMGjUKZWVlyMvLg8lkQn5+PqxWq/h/VEGfl5cnpJjy8vJE8InWE+n5Z3oAcSDLqkglbBKJhAgQ+nw+4XPY7XbEYjGh3b1v3z6RUKL319bWYvr06UgkEli9ejWGDRuGiooKTJ48GWPGjEF5eTnKysowbNgwlJWVCZ+4pKRESGQAELYG9KxJJPVHvk1OTg6cTqeoZtbr9WKehPT3YfoWpVIp/MdwOCySih6PR+yDHQ4HPB6P0Hffv38/otGo6NT87LPPsHDhQsjlcmzbtg3PPPMMSktLcc899+D+++/HokWLEIlEsH37dgSDQRQWFmLixIlobW1FNBpFcXExSkpK4Ha7xbBrAOJctFptUqU772nST38O/Evl/PPPh9VqRXNzMz799NN+PfaJgHdrDDMIkTq2UokBAEISIRKJoLOzU2gR1tbWCs05ADhw4EBSNlda3XUs2VXKwO3du1dshltaWsQi7Xa7xQ1WWi0kJRaLsW5ZH0LXMR6PIxKJwGw2C81B0hY0mUwwGAwiyGs0GmE0GqFWq2EymYSWLg1is1gsyM/PRyQSwY4dOxAMBjF69GiMHTsW5eXlGDNmDCZMmCA2gjTMbdiwYcjNzYXBYEB+fj5MJhP0er0YCKfX60XAOZFIQKFQwGAwJMmscNY/fUi141I32NIkksPhwMGDB1FbW4uWlhahiSetMvF4PElBZ/q/maqVyyTfc+geA0BsrKXvkUqf0MR3oOdaUxu6yWSC3W4H0NOefCzBmvHjxwPoSWBSEMDpdIoqI9INBw6XbaDzpk1oJgYXB3L1IXG0gbOpckfSYD9dO7IRnU6HaDSKffv2AehpRyaam5uxZs0afPLJJ/B6vbjhhhswbNgw+P1+bN68GTk5OYhGo6iurhYV7iQ/QsEf6fnSeaa+xpxYaL1PJBJCeoLsnNaDkSNHQqlUIhAIYO/evSgqKsLMmTPFAMd4PA6dTocRI0ZgxIgR0Gg0sFqtyM3NhV6vR05ODrKzs2EymWA2m2E0GmGxWFBQUCCG0RYWFiI3N1cEmvPy8kTQesyYMRg2bJiQ2SB9eb1eL5LydK/iINGJh6SygJ7OKdqzdHV1idkNgUAAu3fvFrbicDhEIryrqwslJSVIJBJoa2vD6NGjodFo0N3djY6ODqETT4lto9GIkSNHYuLEiSgqKoJCoYBarUZhYSGKioqgVquhUCiEzIpcLhcVqjQjIhQKiaGjQE8hRyKRSApGM30LSWQBPX6mTqcThU5+v1/sTXfv3i0SUrFYDJWVlZg8eTJ0Oh3sdjt27NiBa6+9Fnq9Hq2trXj33Xfx4osv4s0338QXX3wBt9sNs9mMiRMnQq1Wo7u7G0qlEpMmTYLBYIDT6YRCoUBJSYnwj8jvMJlMSTKWmZqkGiqkY+CfFI1GgyVLlgA4FOweSHD6jGEGIbRpk1YyU1Y0FovBbDajq6sLbW1tKC8vh16vh9/vR3V1NcaPH4+vv/4alZWVuOCCCyCXy1FbW4sdO3Zg6tSp2LJlC1588UU88sgj3+tA041TqsUJHNogkmYdcLikB8Etqn0LVSKHw+GkyiGdTodAICAmYatUKpF1D4fDwjH2+XxJshlOpxN+vx96vR5FRUUIh8Pw+/1JGqdKpVL8LJI9UKvV0Ol0oqpMo9FAo9GI16ndlZwuqnSWaqmRrjSTOUgrdWKxGFpaWpIGQZI+s0ajQXZ2NgwGA3w+H3w+H8LhMCwWixgCGI1GEQqFEI/HWacuQ6H1nu47oVAI0WgUWq1WrPsAhHZ3MBiEx+MRA5q6u7tFBZHD4RDJybq6uqSE45Goq6sD0COzQNWu0jZllUol2mGlgU26V6ZK72QaA123G0gOMksh+YxYLCb8AalUDg2Ci8fjwpb0ej0OHDiASCQCk8mEwsJCxGIxvPrqq1i9erU4llwuxxVXXIFFixbh2WefRU1NDX70ox/B4XCguroa55xzDlpbW+F0OkXbcmoCItUXYe3/voECQaSdTvJJbW1tiEaj6OzshM1mw9ixY3Hw4EH4/X40NjZCo9GgvLwckyZNQnNzM+rr68Vnvby8XAyOVKvV8Pl8UCgUSYULSqUSBoMBZrMZOp1OBKRJz5mCijqdDrFYDE6nU9yTaO2gakjypejnMicerVYLn8+HSCQiOt1CoRDa29sxcuRI1NbWwu12Y8+ePRg9ejT27NmDzs5ODBs2DA0NDQB6OmecTifC4TDy8/MRCoVEVbpcLhe+B80A8Xg8kMvlGD9+PBQKBVwuFwAIzW7ybbOyskRCwmQyiaRrYWGh0P8njXfSBmb6Bwr4k5a3zWZDc3OzkJ8Kh8Pwer3Ys2cPCgoKEA6H4XK58M0332DGjBn417/+hebmZtjtdpx//vlCkopsw2AwQKVSCd14ACguLobBYBDPjUYjCgoKhF+Sn5+PcDgMhUIBvV5/mKwKkz7SMfAvlZ/+9Kd47rnn8OGHH6K9vV3Ihg0E+O7HMIMUhUKBaDSKWCwGnU4Hv98PtVotBk9QQKeurg7jx4/HN998g6amJpSUlMBiscDlcuGzzz5DRUUFNmzYgFdffRWzZs3Ct99+i/Xr1+MXv/gFbr/9dtGCJiUQCOCJJ55APB5HaWkpdu3aBaBnCnhtbS2AHgfP5/OJyg/gkEPem74nB5n7BqnmYTweRzAYFEFkcnKolZgCzBqNRlR70Obf6/UiKysLWq0WHo8HMplMSGxIoYpXGjZJweTeXiM9ZpVKJc6TguAUfCCtNZZRSD+UTKJgHW3y1Wo1mpubhf2QTIbH44HL5UIoFEJrays0Gg0MBoMY9Ge325OmxJMdUoUb2R6TGdB1l1YyUxCAgjJyuVwEBYPBIFpaWkRgubGxEaeccgpkMhmam5uRnZ0No9GI1tZWvPLKK7jpppuO+DnftGmT0G+eMWMGNmzYAKCnCpruOVQtT22z0qGE0kGAAFcg9jfSCnLpZ5ruM1TN6nQ6hX3p9Xps27YNwKEq9meffRaffPIJAGDKlCkIh8PYvXs33nnnHdTV1WHOnDn49NNPsWnTJowYMQJ+v18EIP1+P4xGY5L8Aa1ppNdNcJC571CpVIhEIkK7XavVwmazobOzE+FwGO3t7SLQbLfb0drailAohIaGBqhUKhQUFGDkyJFoamrCrl27EIlEhFxGR0cH1Gq1GPJJXVEkxWWxWMQ9x2q1QqlUComO1KG1hFqtRnZ2NpRKZZLePCdD+w6lUikCy8FgELm5uejo6EAkEoHL5cKwYcNgt9vh8/ng9XqRn58vKuHz8/PhdDohk8lEEjIUCkGlUiUlCKTYbDZMmDABJSUl2Lx5sxggOGbMGHi9XshkMmE7CoUCZrNZrFlarRYFBQWIxWKiW0Imk4n3Mv0H7ReCwSCCwaCQ7evo6BAygWq1Gl1dXbDb7WIuTFdXFw4ePIgJEyagoaEBDocDW7ZsET9XWiFNckuUqJLL5QiHw0LXO5FIiEByaWmpuMfl5OQkFWbwnia9pGvgXyqTJk3CjBkzUFVVhddeew333HNPWs7jeOAgM8MMUlKDzFT9Q44ZOdXhcBh2ux2jR49GTU0NGhsbUVZWhv3798PhcECtVmPkyJE4cOAAPv/8c8yePRsbN27Etm3bcMMNN2D06NGYPHky8vLyEIvF0NjYiI0bN8LlckGhUOC8887DRx99BAAYNWoUmpubk6pICgoKhCYnLeJ0c+UqkP6BHJpIJCLa11PlSaiqI5FIIBgMiopiGsgnk8lESyC1MdNmnd5LP0P6oOAyVUsrFAoRVJa2kWk0GiGVIQ0EpbOlbPv27bj11lsRiURw2223YcmSJfjggw+wbNky5Obm4s0330RWVhZuvPFG1NTUYN68eXjwwQfTcq59jVTOQqlUwuv1igovqhZSKBQoLi4WAR2FQgGbzYZQKAS32y0cdEo0kDMeCARgNptFJT1t5CkZwsHmzEBagUqbLpLhoUpVk8kEl8slrldbWxuKi4tRVFSElpYWbNq0Caeeeio2b96Mv/71r7j22mvx4osv4u2338aOHTtw8cUX46STToLFYoHP58P+/fvx+eefo7KyEgAwffp07Ny5U8hkOBwORKNR5OXlibZ2ak8mO6MBotLKZt789w1HksuQfi3daDscDgCHNCspYVBUVASv1yuCzKeeeireffddfPLJJ5DL5bjnnnuEjndlZSWefPJJbN68GVqtFjk5OXA4HCKAvGPHDkyaNAlOpzNJl5kGElLygQKSsVhMJLqYEw8lqAOBAKLRqPhb22w22O12RKNRMYTLZrMhOzsbdrsd7e3tiEQiaGxsRGtrK7Kzs3HGGWegrq4OjY2NUKvVKCoqQjAYFF0xlPSmripKjFMlKiU4pXMf1Gq18F/IdyEbDgaDIgHB9tG3aDQaIfUWCoWQl5cHl8sFr9crBlSTPAEA5OXlwe12i+AyJQ2kRQvUpSDVi8/Pz4fZbIbT6cSBAwcA9ASdy8rK4HA4IJPJUFBQILr1KOFAgWTycVIHGHMQMT3Qeh6NRuHz+aDT6VBYWIiOjg4xQyIvLw/d3d1i/c/Ly0NnZyecTqeYHdPd3Y2urq6k62o0GoW0oNTPsFqtYo8F9CQeioqKRLCZZAHpOSXEmfSRzoF/qdx8882oqqrCH//4R/zqV78aMP4pR3AYZpBCm2bSUjaZTHC73SIwE4lERGsoVQeWlpaiubkZfr8fBQUFcDgcIlhcUlKCpqYmOBwOUQHU2NiImpoa1NTUHHb8nJwcnHTSSdi0aRMAYPjw4Whra4NMJkNJSYnQx6QbvnSzT2g0GhG4Yoesb6EKZQowU9u79AEc2gBKtb4pKEPV8tJgNf1s0tGjYLK0FVH6Lznn9LNJJkMa+KaNYbqDi//93/+NlStXioExS5YswTPPPIPKykp8+umneOmll1BRUQGbzYZXX30VixcvRnNzs6jcHCwkEokk6ZtoNCoqvmKxmEhK5ObmorGxUUgZEDKZTGzsQ6GQaJXWarVQKpWIx+Po7u6GRqMR7aeUwKBgM1U2M+lDOrRLep8JBAIi4AtAbLbMZjPcbje2b9+OadOmoaOjA01NTSgsLBSVi9988w0uvPBCfPbZZ9izZw/27NnT67FlMhmmTp2KRCKB+vp6aLVaFBcXw263Q61WCw1e6o5Qq9WiJRroGURK9xqW3uk7pPcR6YAj2nzL5XKxdmi1WrS1tQHoCeoEg0Hs3LkTQM8w4k8//RTRaBRlZWUIBoP405/+BAD42c9+ljQosqKiAkajEffffz++/PJLXHLJJXA4HNi9ezfGjBkDv98vNvculwsmk0lUtYXDYZHwCgQC0Gg0QsaDK1X7DvIVKIBIwd28vDzY7XZEIhF0dHSIORL5+fmw2WxwOBxob29HOBxGR0cHgB77Gj58uAgqSodr0RpASWz6WurjyGQyEbA8kn4u3Yvo52q1WraNPoaCxQCEnZA9dHV1iYrSrKwshMNhBAIBMUMkEAiImR5UvRyJRMQjFoshHA4jGAyis7MTnZ2dAHoCiRMmTEAsFoPD4RA6y3Tvs9lsSfq6OTk5onsUgJCCY9tIHyRT4vf7EY1GhV0UFRXB7XYLGRSLxYJIJILu7m4oFArk5uYiEAjA6/UKTeeioqJeZwsASJL/o9f1ej2ysrKgUChEVTvJ80ilJLnAKv2kc+BfKldeeSXuuusuHDhwAGvWrMEll1yS1vM5VtiKGWaQQpUUpItJreg+ny+plVmv1ydVdthsNni9Xni9XuTm5kKj0cDhcECn06G0tBTd3d1wuVxQq9UwGo3weDxJgzJoE0/fIyeMNmT5+fmiAtZms4kWaqpIpffR0EFuX+5f5HJ5rwF9SlakZu29Xq9ofacqLwoAUkUPbeRSA8pkB9IKQko8UCafdDoJClBngpP+wgsviOEgMpkM3d3dyMnJgVqtxllnnYWXXnoJOp1OaHnNnDkTmzZtwmWXXXbYz6IqXoKq+wcC1DEBQLQaAhAyKgBgtVpRW1ubtAlXKBRifSLHXqfTwWw2i3ZGCkDHYjERgDabzdDr9WJDSDIvVEHCpAdpYjMSicBoNAqddovFAo/Hg3A4DL1eD6/XKxJboVAI+/fvFxXMra2tyM3NhUKhQFtbGxoaGjBmzBgkEgnY7XY4HA6h12q1WoV2N1Unmc1mZGVlCZsoLi4WgW+j0ZgUSKL7lbSqkW2o75AmCuleQkEehUIBt9uNRCIBrVYLh8OBSCQCjUaDnJwcbNy4EeFwGNnZ2dDr9aiqqgIAnHvuuXjqqacQj8cxe/ZsXHzxxYcdd+rUqfiv//ovPPfcc1i7di3OOeccNDQ0wOv1QqvVYt++fTjppJMQCASEJiv9S8mJQCAAnU6XZONcrdp3SCvIg8GgSDrm5eWJdcXtdsPr9cJgMIgq9dzcXLhcLnR1dcHj8SRVnh/peklnRVAgmXwcGubWG2QH0mGRtK4wfQ8FmuVyufAPFAoFCgsLhZ6uz+cTwV3S0aZuOyqGoGtIfhj5rzREkirW4/G48FVIJoUKKLKzs4WdWK1WmEwmMdgYgBhezaQf2mPS9Y7FYmJWhMFgEHajVquRm5srOupMJpNINJK90ZB0etD+hWxBrVYLXXcAwm8FeuwkOzs7KUFFiRMmfaR74F8qBoMBN998M5544gn84Q9/4CAzwzDph256dEOk6gqv1ysCdaSZqVKpRCbVYrFAo9EIbV29Xg+XywW32w2NRoOsrCx4PB4EAgFRZZoKaedaLBYhiZCTkyOqQSibS8MSqHKF/qXBUMCh4DWTPigArFAoROW7XC6HyWRCIBAQWme9VTHT9SObo6CztMKZqpKpwo2SCwRtAjMhuEzYbDYAwP3334/FixfD4/HAaDQC6HEKvF5vr6/1xmOPPYaHH364f078BCKtYqY1hILuVB1oNpvR1NSEWCwGvV6P4cOHC0eaKgQdDgfsdjsCgQACgQCKiopEFbTL5YLVahXrlcvlQjQahdFohEqlEg46Vbxlko0MJaSJzXA4nDRE1OVyCd3MaDQqXicJnHA4jK6uLowdO1YM0jEYDCguLhYt0EDPkKXc3NxeNXH1ej2MRiNMJhNkMpnYwNG6Rfai1+uTtN5pXaINItM3SLVOpUlI8jsMBgPa29sB9HRC7du3D0BPF5TL5UJ1dTUA4Mwzz8S6desQj8cxceJE1NbWoqGhARaLBbfddtsRP//z589HVVUVNm/ejM7OTsjlcjQ1NWHChAlJ7exUxRwIBGA0GkXrfTweF+3T9P5MuycNJqSyGXQv0Gq1kMvlyM7Ohk6nQ3d3N2KxWNKgLfIrjEYjjEajSD5Fo9GkLi3yTaT6pwqFAjqdTgSFehtGTQl3aXKVjqvRaNhX7WfITqibjrSPtVot8vPzxXwIv9+PYDCYlESk60iBPwoakh8SCAREZ5X0eBaLBXq9XnTbUSJCLpcjNzcXSqVSrGtyuVy8l8kcyG7UarUYck5FLSaTCUajUchhkJyFdIDxkYLB5AfRvjc1aa1SqcSQURqQTAUmLKOSGWTCwL9UbrvtNjz11FNYv349vv32W0ydOjXdp3RUOMjMMIMc2hBRO59SqURWVhb8fr8IykSjUbHJjkajCAaD0Ol00Gq18Pv9QtoiKysLXq8XPp8PWq1WOOtSZzvVaddqtTCZTDCZTKKSjDQ6aXgPVTJLK8yk7ffcXpY5kANFwyyokoSqQUjeQPp+CiLT5q83J0q6WZP+31QJjXSyfPlyvP322+L5fffdh23btqGjowOPPfYYnE6nCKzStGqj0Zj0Wmlpaa8/e+nSpbjrrrvEc7fbjZKSkj78bU4M0gGMNDmdiMfjIggcjUah1WoxZsyYpM0WJbH0ej0KCgrQ2NgIp9OJlpYWjB49GlqtFt3d3eju7kZeXh7UajV8Ph98Pp+Yxk2DXEgXmquF0gdVFFNQyGAwiHuP1+uF1WoVwSCZTIZgMCg24MFgENFoVGhg2u12sRG0Wq0IBAJJXRIARCUyVZsBPW3KVqtVbAK1Wq3YTEq/pmQm0HOf4Rb3vkUaZJbqt5N8Dukxkx9CUhWFhYVYv349EokEhg8fDo1Gg+rqashkMsyfP18k5xYvXiy0m3tDJpPhxhtvxObNm7FlyxYsXLgQ1dXV4n7V1NSEsrIyhEIhUcVIQUWp5q7FYkmSIuPK976DgnjBYBCxWAx+v18EcvV6vQj8BAIBkWzsbaYEdSmkQvJd0g4p8mnp3kb//0jDp0kaKFP8lKGKSqWC0WhMSkoEg0ER7KN9BPkJFFiWBvlIPoGKX2gIcapcHPmxRqNR3Dd0Oh2sVqtYuwCIew7bReZCSQDyU8LhsLg/5ebmikIHmUwGk8kkuuhSE1bSDk263lLddrJDhUIh1phAICBsi2S8mPSSKQP/UikpKcHChQvx1ltv4ZlnnsFrr72W7lM6KuwZMcwghypMw+FwUoUotQ6So0VVHhQopLYyg8EAvV4v9Mk0Gg3MZrOoPpO2rNNmTNp6SAFGnU4nWpVpQy8d8EabCRoQBaR/sBtzZFKHBdJ1BpKDCb1dO6pYln5PWpkoHSaYSdf+9ttvx+233y6ev/fee9i5cyfefPNNAEBWVhY6OzsRDAbx5ZdfYtq0aZg2bRrWrFmDiy66SAyg6g1q5xxo0GeVgsnU9k66qllZWWhsbATQM6zr+6p5VCoVRowYAblcLiqbR44cCZlMBqfTia6uLpSVlUEul8Pj8cDr9Yr2dWpvP1IggOkfpNWHJGNCnSp+v19UgqpUKpEoIO1MqiiS3neoTZUSn71dXwog0KAuCvbQZ4ruRbThlw4cBSCGlWbSWjMY6U2PmYK4qWsG6XcXFRVBJpOhrq4OADBlyhTs3bsXADB69GghwwMAc+bMOeo5DB8+HMOGDUNTUxMsFgsAwOl0ipkVJpNJ2COdK3BI5iMSiQgfh+59/R1k7m3gLLFjxw5ceumlKC0tRXFxMVauXNmv59YX0KA0GqqXOng4NYBIgebe5klIO6tICoWCiL115BGp6440qCSV/GLSi0KhgMFgEMFj2tNIEwYE3XMoCBwMBuF2u4W9hUIhMWdEilKphFarFVWnJNuUqi0v1fVmMh/yA8gPobVBoVAgLy8P4XA4qTgrFelcGenaIEVaEU9IK6qZ9JNJA/9SufPOO/HWW29h1apV+P3vf4+CgoJ0n9L3wkFmhhkCkDMlHZhFTjPdEMk5p6+pFZTa0Kmi2Gw2J1UBpDr0FBgk2Qvp9G1pVlcqj0FaqtIgI32PyVykwwKpzSx1Mybd2NFjsGzIHnroIWi1WsydOxcA8Nlnn+Guu+7C7NmzodPp8N577yE7Oxuvv/46zjjjDJx99tlHrGQeiEg35qRZCvRUjko1lskmeqtWP9LPlf5rsVjgdDqFPqder4fH4xGVJFQZAvSe1GD6F6rqoupD0k+2WCzw+/0iUEfD9ihQRFWsOp1OtChTyzsAUf0jleJJHRRKAWS611CHDH0tbWmn+xRXmvUv0sCftCJd2jLc2toKoKcqXap5m5ubi++++w5AjwwPAJFgogGT3wdJXgAQVezk61BnDp0XVa+m/gsg6X39TW8DZ4ldu3bh4YcfTnptMECVy9LK0lRJrVSkvigFleVyudBRJjk26TGkcyKOlATPtOQ3k4xUroAKa1Kr26UJCHpdpVIhLy8Pfr8fHo9HdFpSZ47Uluhrk8kElUqVtKbxcL+BDSUqqKuXbIgq12n4tPSaH2lfk1pQI/03VUaQyQwyaeBfKtOnT8eZZ56JjRs34v/+7//wyCOPpPuUvhcOMjPMEEHqIJFjRQ9pJapKpRKDbujmKm3vIYdLrVYfdYMl1dtVqVQiKECBbWohSg0uD6ZA5FCANm9DrWqDgh1SFixYgAULFiS99uqrr/bXKaWN1HZiqRZ8dnY2WltbUV9fj+7ublgsFqEDTwGicDgMr9cLh8Mhgk02mw3hcBgdHR0AkCTTIj0urWF0XCb9UHWOVOcSOLQBp0ozCgZQoE96f6HgACWwUivVpRu11KGiFGymZCWtT9L7DSVB+V7TP0iruuhrup7UQQVAdEyRpn1OTo6QRaitrcWwYcMAADt37sTcuXMxdepUbN26FY899hjuv/9+5Ofn93r8WCyG119/HR0dHWLOBABkZ2cjHA6LytbekCYygOQBhv1N6sBZKdXV1di4cSNWrFiBpUuX4oILLjjs/w/UIbPUnaBWqw9bE6QBH0IaRDzSdWWpi8GLVG7tSJBtUKdnLBYTXTGhUEjIsJBvS0lJaWIyVe6ANXUHB0qlUgSbpdXw0mIqKamygDwfZOCRaQP/euPOO+/Exo0b8cILL2Dp0qUZPSiSg8wMMwSRBpyBQxWJFFQmp5ucL9r8K5XKpGrE3pz71KxtansiOX1Sp56es3PGMAMH+qyTNikN/gsGgzCZTOju7kZHRweys7ORk5MDh8MBl8slgjtHQqlUIj8/Hz6fTwSY5XI5bDab0McDIDZ6FDChtYbJDKiijKQFpC3LdI+hjX1qt420Aq23ADNwqHpImsykQHPqwNHedBLZVvoXqawN/e1JboKS2QBgt9thtVrhcDhQV1cHo9GISZMmYdOmTdiwYQOmT5+OsrIy1NfX47nnnkNFRQVqampQU1ODm266CRUVFZg2bRqKi4uhUqngdrtRU1ODzz77TEj3nHnmmdiyZQsACA3o4uJiOJ1OAId3U1BggWZRUNAyHUmt1IGzUiZPnoyrr74axcXFmDdvHs4999zDznGgDpklvi94KL1evRVTENQ9wYGgoQ3ZAe1xSNeb1iWr1ZqUEEutiKb7Ft9PBiek5S6tVpdKQ6Y+mIFNJg78S+XSSy8V/s/KlStx0003pfuUjogswQKGDHPCcLvdsFgscLlcopVzoEFay1LNRBqIkFo1kqp3R/9KH0fK7FLAeag5+YPBRpi+Z6DYCQ31o82aw+EQA/8o4AwcSmxRsJECieSs99bZQOh0uqS2VQBiEGksFhPBSxrIM1QYKDZCUDKCrqPU/ZRKYUgrhig41FsyM1WCJ/V+I+2IkSY4h1IwINNshAI50q9jsZgYgBSNRoU2cywWE1W2+fn5aG5uxoEDBwAARqMRLS0tIgml1+vR2toqgshHQqvVYsSIEWKdKCgogNFohEajwbBhw0SlImlzUseFRqOBXC6H1WoV509SX33tvxxp4GxtbS1eeeWVpPe63W5xnRcvXozly5cjLy8v6T29VTKXlJRkjI30FamSbswPI9PWkr6A1iBal44EzYvpzV8ZygwFG2EGJ4lEAieffDK2b9+OP/zhD7jjjjvSfUpH5Omnn8Yvf/lLTJgwAd99913GrkEcZGaYE8hgusGSlmbqxl6qvdtbBbN0U5/qzEsrzjJ1UexrBpONMH3HQLET0kIFID7X3d3dSesD6dr9EKS67NKgoEwmg16vFxO+6efShm8oMVBs5EhIpTCk7ewUjO4tyCy9z9A9RhpsJqSJTb7fZI6N0OA2qgaktYHkU+j75HvQsCX6v9FoFE1NTUKX1+PxwOl0iueBQAA+nw/hcFgkxpVKpZBpsVgsoro9OzsbZrMZGo0GBQUFYjYEDSimgZU04MtkMon1hgZ7pcOu3nvvPbz33nt48803D0uYXHHFFXj00UcxYsQInH322aisrDxqUiXTbITJTIaanVDHgrRylZKVQylR+UMYajbCDB6++uornHnmmdBoNGhpack4PWYpLpcLJSUl8Hg8WLNmDebPn5/uU+oVlstgGKZXqKKH9KiAnk2etHLs+3JUqVIZXD3CMIMPGqRGlT8ymQxWqxVerzepwljatgzgiEmq3gZESltSlUol4vF40tCmoRhgHgxItdyl2qnRaPQH3Su+r4J5ILB9+3bceuutiEQiuO2227BkyRJ88MEHWLZsGXJzc/Hmm28iKysLN954I2pqajBv3jw8+OCD6T7t44J0dYPBIBKJhFg76HuhUEh0JJA2Mw0VJSmv0tJSuFwueDweyGQyGI1GeL1eeL1eMSDuSGg0GphMJphMJjGMMicnJ0lrlSqYlUqlOBetVpuk+57OwV69DZz99a9/jfvuuw8PPfQQbrzxRiQSCdx7770cDGOY44S6sxiGGfw8++yzAICrrroqowPMQM8w9J/97Gd48skn8eijj+KCCy7ISJ+XK5kZ5gQyWLO4VEFE2pi9IQ0O9VbFzPQwWG2EObEMNDuJxWJJgV8iEomIwOGxkqqte6R1hFpWh2ogZaDZyLEirWDuTQMzNRkx0K//j3/8Yzz11FMYNmwYKioq8NVXX2HOnDn45JNP8Omnn2L79u2oqKjA3//+dzz++ONYvHgxnnrqKRQXFx/1Z2eqjVCiiJJNNP+BqpepMjkcDovBkdRdRc+p6tnr9cLv94tK6HA4nCStQxq+JHmh0WhgNBphsVjEgEhpUJnkMUi+RzockhJdg8m3yVQbYTILthPmaLCNMAORpqYmDB8+HLFYDNu2bcPUqVPTfUpHpbW1FSNGjEAoFML69esxe/bsdJ/SYXAlM8MwR6W34QfS70n/ZRhm6EEVhBTgIVLXjVQdXuJYBqhQcFE6xZsZfKQOph3svPDCC7DZbKIToLu7Gzk5OVCr1TjrrLPw0ksvQafTiUE0M2fOxKZNm3DZZZcd9rN609vNRORyOXQ6ndBnJ2kcqb47DRvWaDRCUkWtVguJlUgkArVaDb1eLyRWKAgtTYhTkJmCx2RbFECmBz2noZQ0GA44NCyS/RyGYRiGGTz87//+L2KxGGbPnj0gAswAUFhYiJ/85Cd44YUX8Oijj3KQmWGYgQ9XKDMM0xvUCi8NBFEl6g9ZN1K1dqX6uwwz2LDZbACA+++/H4sXL4bH44HRaAQAGAwGeL3eXl/rjcceewwPP/xw/5z4vwnJ4CiVStHxQEFeWj9oDZE+7+0h7bSSDiim40gTVDQAkh6UCJO+RhJfQ71bgmEYhmEGKx6PB3/84x8BAL/4xS/SfDY/jLvvvhsrVqzAP//5T3z66aeYN29euk8pCQ4yMwzDHCORSARnnXUWqqqqAOAw3cxM13FimP6AKgeVykMuxtGqmOlfDiQzg53ly5fj7bffFs/vu+8+bNu2DR0dHXjsscfgdDrh8/kAAD6fD2azGUajMem10tLSXn/20qVLcdddd4nnbrcbJSUlffjb/PuQhAVVL1NFt1KpFOuGNIAs/Vr6SA0yE9IOCQoySzsiKKgsleeh9YuDywzDMAwzOHn++efR1dWF8vJyXHLJJek+nR/EiBEjcOutt+LZZ5/FnXfeiW3btiXtu9INe08MwzDHQFdXF84//3wcOHBAvPbMM8+gsrISt912G1566aVe/18oFILb7U56MMxQI1VHl4I6FNjhSmVmqHD77bejsrJSPPx+P3bu3IkVK1YAALKystDZ2YlgMIgvv/wS06ZNw7Rp07BhwwYAQGVlJU4++eRef7ZGo4HZbE56DBRIlkur1UKv10Or1Yrgs0ajgUajgU6ng8FggNFoFAP86Pe0Wq3f+8jKyoLVaoXZbIbJZILRaITBYBDH0Wg00Gq10Ol0XL3MMMyQIhKJYMaMGeL5Bx98gOnTp2P+/Pno6upK45kxTN/gdrvx5JNPAgAefPDBASnR9tvf/hZZWVnYuXOnGF6YKbAHxTAMcwz4fD784Q9/wPjx4wHgMN1Mqm5O5bHHHoPFYhGPTK8qYximb9mxYwdGjhyJOXPm4JprrgHAG7qhzEMPPYSamhrMnTsXc+bMQSwWw1133YXZs2fjiSeewC233IJZs2bBbrfjjDPOwKhRo45YyTxYoKpj0lHW6XTQ6/XQ6XTQarWHPXQ6nQhASwPP0oCy9P/T+6U/lzSYOdnFMMxQ4niLaBhmILNs2TI4nU6MGzcOV111VbpP57jIzs7G448/DgD4zW9+g+3bt6f5jA6ROTXVDJNGduzYgUsvvRSlpaUoLi7GypUrWQqBSaKkpCQpQHysGpkDsX2ZYZi+Y9euXXj44YexZMkS8Rpt6D799FO89NJLWLp0aRrPkOlPvvvuu8NeW7BgARYsWJD02quvvtpfp5SRSLWVjwTJZPBwYoZhmGODimhuv/12AIcX0Xxfp+ZAGDTLMKlUV1fjqaeeAgA8/vjjA7KKmbjpppvw0Ucf4aOPPsIVV1yBjRs3Ii8vL92nxZXMDAMc2vSvX78eK1euBMBZ3KHO8uXLUVFRIR5r165N+n6qRuaR2pIHcvsywzAnnurqarz22muYNWsW1q5de8xdESy9wzDfjzQQLZXh4QAzwzBM75SUlGDy5Mni+Q8ZNMudmsxAIxwO46abbkI0GsWCBQsGnBZzKjKZDC+//DKGDx+O2tpaXHTRRRnREclBZoYBb/qZw0nVzbzggguSvt+bbiYzNLn++uuxfv16AIfLHiQSCfzkJz/BWWedhYcffji9J8pkBJMnT8bzzz+Pv//973jwwQfhcrl4Q8cwDMMwTJ9zoopoli5dCpfLJR6NjY19fu4M8+/yq1/9ClVVVbBYLFi+fHm6T+eEkJeXh3Xr1iEnJwebN2/GmWeeib1796b1nDjIzDDgTT9zfKTqZjJDj127duGDDz4Qz1M7ICorK2Gz2fDll19i9+7daG5uTt/JMmkhdUOnVqtx0kknwWw2Y+TIkdBqtbyhYxiGYRimzzlRRTTcqckMJBKJBJYtW4bnnnsOAPDGG28MqvkWY8eOxfr161FSUoK9e/di6tSp+N3vfgePx5OW8+EgMzMk4U0/c7xQxSrQo5v59ddfY/369cjJyUnfSTFp48knn8R1110HoPdhkFu2bEFFRQUAYObMmdi0aVOvP4e7IgYvqRu6P//5z9izZw9CoRAaGhpgs9l4Q8cwDMMwTEbARTTMQCKRSKCpqQkNDQ1wuVxJcxkAoKmpCVdffTXuu+8+AD1FghdffHE6TrVPmThxIqqqqnDuueciGAzigQceQElJCW6++WZ88cUXiMVi/XYuPPiPGZLcfvvtYsABAFxxxRUYO3YsRowY8YM3/RqNpr9Om2GYDGLz5s0YMWIE4vE4gN517H6Ith3LaQwNHnroIdx4441IJBK49957IZfLxYZOp9PhvffeS/cpMgzDMAwzhEgtokkdPsswmUA4HMb+/fuxfft2bN26FVu2bMHWrVvhcrnEezQaDQoKCpCfn494PI6tW7ciHo9DoVDgf/7nf3DHHXek7xfoY4qKirBu3TqsWrUKDz/8MPbt24cVK1ZgxYoVsFqtmDt3LubNm4czzjgDY8eOhU6n65Pz4CAzw4A3/QzDHJ3ly5fj7bffFs9rampQU1ODp59+GkDvOnaprx2pNWvp0qW46667xHO3283yO4OUSZMm4V//+lfSa7yhYxiGYRiGYZgefvvb36KyshJ+vx9+vx8ejweNjY29VuQqlUooFAqEQiGEQiHU19ejvr5efJ8q86dPn96fv0JakMlkuPrqq7Fo0SJ88cUX+Mtf/oL33nsP3d3dWL16NVavXi3eV1RUBKvVCrPZDL1ej/z8fPzhD39AXl7ev3cOidR6coZhjhu32w2LxQKXy8WtzEyvsI0MDrxeL6ZPn468vDzU1dXBarWisrISF154IT7++GN8/vnn2LJlC2bNmoU1a9bgiSeewKJFi/Dkk08ekwYY2wlzNNhGmKPBNsIcDbYR5lhgO2GOBtsIwzAEB5kZ5gSSSCTg8XhgMpkgk8nSfTpMBsI2Mvh46KGHMGfOHMyZMwcffvghli1bJjogsrOzceONN2L37t04++yzsWzZsmP6mWwnzNFgG2GOBtsIczTYRphjge2EORpsIwzDEBxkZhiGYRiGYRiGYRiGYRiGYY4bebpPgGEYhmEYhmEYhmEYhmEYhhm4cJCZYRiGYRiGYRiGYRiGYRiGOW44yMwwDMMwDMMwDMMwDMMwDMMcN8p0nwDDDBVoIAKTmWTCoAq2kcwmE2wEYDvJZNhGmGMhE+yEbSSzYRthjkYm2AjAdpLJsI0wx0Im2AnbSGbzQ22Eg8wM0094PB5YLJZ0nwZzBFwuF8xmc1rPgW0ks8kEGwHYTjIZthHmWMgEO2EbyWzYRpijkQk2ArCdZDJsI8yxkAl2wjaS2fxQG5ElEolEH54PwzD/P32VoXO73SgpKUFjY2NabhDpPP6JPPZgz+IOluuUzmNngo0Ag3MtGSzHZhvpOwbTvS4T7GQw2ki6j882cuwMluuUzmNngo0Ag3MtGSzrGNtI3zJYbDQT7KSvK5nTda0Gy3G5kplhMhSZTNani4vZbE5rFjKdx0/3736i6GsbAYbudRosNgIM7rVkqB77RMM2MniPf6IYzDaS7uOn+3c/UbBPMjiPfaIZzGtJuq9Tuo9/ohjMNpLu46f7dz9R9Mf9Bkjf32uoHZcH/zEMwzAMwzAMwzAMwzAMwzDHDQeZGYZhGIZhGIZhGIZhGIZhmOOGg8wMM8DRaDR48MEHodFohtzx0/27DySG6nViGzl2hup1Yhs5dobydUr38QcK6f47DWUbHUgM1evENnLsDOXrlO7jDxTS/XcayjY60EjX32uoHZfgwX8MwzAMwzAMwzAMwzAMwzDMccOVzAzDMAzDMAzDMAzDMAzDMMxxw0FmhmEYhmEYhmEYhmEYhmEY5rjhIDPDMAzDMAzDMAzDMAzDMAxz3HCQmWEYhmEYhmEYhmEYhmEYhjluOMjMMAOQ7du3o6KiAqeffjreeOONpO/t2LEDI0eOxJw5c3DNNdec0OOGQiEsWLAAM2fOxIoVK476+omksbERZ599Ns4880z8/ve/F6+73W4UFxdjzpw5+NGPftQnxx6opMNO0mkjANvJD4XXEraRo8E2wjZyLAy1+w3byA+H1xK2k6PBNsI2cjTYRthGjpX+tpV02UhG2keCYZgBxxVXXJGoq6tLRKPRxIwZM5K+t2rVqsSf//znPjnun//858T//d//JWKxWOKcc85JBAKB7339RPLLX/4ysX79+kQikUjMnTs34Xa7E4lEIvHVV18lHnnkkRN+vMFAOuwknTaSSLCd/FB4LWEbORpsI2wjx8JQu9+wjfxweC1hOzkabCNsI0eDbYRt5Fjpb1tJl41kon1wJTPDDEBeeOEFlJWVAQBkMlnS96qrq/Haa69h1qxZWLt27Qk97pYtW1BRUQG5XI5JkyZh9+7d3/v6ieQ3v/kNKioqAACxWAxKpRJAz++7bt06zJo1C3/6059O+HEHMumwk3TaCMB28kPhtYRt5GiwjbCNHAtD7X7DNvLD4bWE7eRosI2wjRwNthG2kWOlv20lXTaSifah7NejMQxzQrDZbACA+++/H4sXL0763uTJk3H11VejuLgY8+bNw7nnnguFQnFCjuvxeGA0GgEABoMBXq/3e18/kWRnZwMA/vjHP2LatGnQ6XQAgOHDh+OJJ57AaaedhnPPPRcXXXSR+PsMddJhJ+m0EYDt5IfCawnbyNFgG2EbORaG2v2GbeSHw2sJ28nRYBthGzkabCNsI8dKf9tKumwkE+2Dg8wMMwBYvnw53n77bfH8vvvuw7Zt29DR0YHHHnss6b3nnXcezGYzAGDkyJFwOBzIy8s7IedhNBrh8/kAAD6fTxznSK+faN5880188MEH+OCDD8RrM2bMgE6ng1wux4wZM3Dw4MEhe4PNBDtJt40AbCffRybYCJB+O2EbOTJsIz2wjXw/mWAnbCOZTSbYCMB2ksmwjfTANnJk2EZ6YBs5Oum2lXTaSMbZR1pEOhiG+bd49913E1dddVUiFosd9r3LL788sXv37kQwGEycccYZvb7neHnttdcS//u//5uIx+OJuXPnCk2hI71+IqmqqkrMmzcv4ff7k16/6667EuvWrUvEYrHEvHnzEna7/YQfe6CSDjtJp40kEmwnPxReSw7BNtI7bCOHYBs5MkPtfsM28sPhteQQbCe9wzZyCLaR3mEbOQTbyPfT37aSLhvJRPuQJRKJRP+EsxmGOVFMmjQJWq0WBoMBAPDZZ5/h17/+Ne677z40NzfjZz/7GRKJBO69915ccsklJ+y4wWAQV111FZqamnDDDTfA5/PhwgsvxMiRI5Nev+WWW07YMYmLL74YdXV1yMnJAQCcffbZWLhwISwWC6699lqEw2Fcd911+M///M8TfuyBSjrsJJ02ArCd/FB4LWEbORpsI2wjx8JQu9+wjfxweC1hOzkabCNsI0eDbYRt5Fjpb1tJl41kon1wkJlhGIZhGIZhGIZhGIZhGIY5buTpPgGGYRiGYRiGYRiGYRiGYRhm4MJBZoZhGIZhGIZhGIZhGIZhGOa44SAzwzAMwzAMwzAMwzAMwzAMc9xwkJlhGIZhGIZhGIZhGIZhGIY5bjjIzDAMwzAMwzAMwzAMwzAMwxw3HGRmGIZhGIZhGIZhGIZhGIZhjhsOMjMMwzAMwzAMwzAMwzAMwzDHDQeZGYZhGIZhGIZhGIZhGIZhmOOGg8wMwzAMwzAMwzAMwzAMwzDMccNBZoZhGIZhGIZhGIZhGIZhGOa4+f8AD4xMuvDd7VIAAAAASUVORK5CYII=\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1500x1000 with 81 Axes>"
       ]
@@ -849,7 +849,13 @@
       "    Subset 0: ($X^{1}$ -1)  gives pval = 0.97777 / val =  0.001\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{2}$ -2) -?> $X^{1}$ (13/18):\n",
+      "    Link ($X^{2}$ -2) -?> $X^{1}$ (13/18):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: ($X^{1}$ -1)  gives pval = 0.96279 / val = -0.002\n",
       "    Non-significance detected.\n",
       "\n",
@@ -861,13 +867,7 @@
       "    Subset 0: ($X^{1}$ -1)  gives pval = 0.93372 / val = -0.004\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{7}$ -1) -?> $X^{1}$ (16/18):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link ($X^{7}$ -1) -?> $X^{1}$ (16/18):\n",
       "    Subset 0: ($X^{1}$ -1)  gives pval = 0.99454 / val = -0.000\n",
       "    Non-significance detected.\n",
       "\n",
@@ -1287,13 +1287,7 @@
       "\n",
       "Testing condition sets of dimension 4:\n",
       "\n",
-      "    Link ($X^{2}$ -1) -?> $X^{2}$ (1/6):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link ($X^{2}$ -1) -?> $X^{2}$ (1/6):\n",
       "    Subset 0: ($X^{3}$ -1) ($X^{1}$ -1) ($X^{1}$ -2) ($X^{3}$ -2)  gives pval = 0.00000 / val =  1.000\n",
       "    Still subsets of dimension 4 left, but q_max = 1 reached.\n",
       "\n",
@@ -1309,7 +1303,13 @@
       "    Subset 0: ($X^{2}$ -1) ($X^{3}$ -1) ($X^{1}$ -1) ($X^{3}$ -2)  gives pval = 0.22099 / val = -0.055\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{3}$ -2) -?> $X^{2}$ (5/6):\n",
+      "    Link ($X^{3}$ -2) -?> $X^{2}$ (5/6):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: ($X^{2}$ -1) ($X^{3}$ -1) ($X^{1}$ -1) ($X^{1}$ -2)  gives pval = 0.10135 / val =  0.074\n",
       "    Non-significance detected.\n",
       "\n",
@@ -1712,7 +1712,13 @@
       "    Subset 0: () gives pval = 0.20119 / val =  0.058\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{6}$ -2) -?> $X^{4}$ (20/27):\n",
+      "    Link ($X^{6}$ -2) -?> $X^{4}$ (20/27):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.18668 / val =  0.060\n",
       "    Non-significance detected.\n",
       "\n",
@@ -1827,13 +1833,7 @@
       "    Subset 0: ($X^{4}$ -1)  gives pval = 0.00000 / val =  0.733\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
-      "    Link ($X^{7}$ -1) -?> $X^{4}$ (15/18):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link ($X^{7}$ -1) -?> $X^{4}$ (15/18):\n",
       "    Subset 0: ($X^{4}$ -1)  gives pval = 0.00050 / val =  0.156\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -2163,7 +2163,13 @@
       "    Subset 0: () gives pval = 0.11534 / val = -0.071\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{7}$ -2) -?> $X^{5}$ (23/27):\n",
+      "    Link ($X^{7}$ -2) -?> $X^{5}$ (23/27):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.21199 / val = -0.056\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2552,13 +2558,7 @@
       "\n",
       "Testing condition sets of dimension 1:\n",
       "\n",
-      "    Link ($X^{7}$ -1) -?> $X^{7}$ (1/19):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link ($X^{7}$ -1) -?> $X^{7}$ (1/19):\n",
       "    Subset 0: ($X^{7}$ -2)  gives pval = 0.00000 / val =  0.668\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -2680,7 +2680,13 @@
       "    Subset 0: () gives pval = 0.17912 / val =  0.061\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{2}$ -2) -?> $X^{8}$ (8/27):\n",
+      "    Link ($X^{2}$ -2) -?> $X^{8}$ (8/27):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.18026 / val =  0.060\n",
       "    Non-significance detected.\n",
       "\n",
@@ -3182,13 +3188,7 @@
       "    Link ($X^{5}$  0) o?o $X^{7}$ (58/86):\n",
       "    Iterate through 1 subset(s) of conditions: \n",
       "    with conds_y = [ ($X^{7}$ -1) ]\n",
-      "    with conds_x = [ ($X^{5}$ -1) ($X^{6}$ -1) ]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    with conds_x = [ ($X^{5}$ -1) ($X^{6}$ -1) ]\n",
       "    Subset 0: () gives pval = 0.93945 / val =  0.003\n",
       "    Non-significance detected.\n",
       "\n",
@@ -3399,7 +3399,13 @@
       "    Link ($X^{3}$  0) o?o $X^{4}$ (8/17):\n",
       "    Iterate through 1 subset(s) of conditions: \n",
       "    with conds_y = [ ($X^{4}$ -1) ($X^{3}$ -1) ]\n",
-      "    with conds_x = [ ($X^{3}$ -1) ($X^{1}$ -2) ]\n",
+      "    with conds_x = [ ($X^{3}$ -1) ($X^{1}$ -2) ]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: ($X^{2}$ 0)  gives pval = 0.00000 / val =  0.374\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -3728,10 +3734,6 @@
       "    Variable $X^{8}$ has 1 link(s):\n",
       "        ($X^{7}$  0)\n",
       "\n",
-      "-----------------------------\n",
-      "PCMCIplus algorithm finished.\n",
-      "-----------------------------\n",
-      "\n",
       "## Significant links at alpha = 0.01:\n",
       "\n",
       "    Variable $X^{0}$ has 2 link(s):\n",
@@ -4136,7 +4138,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -4171,7 +4173,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -4221,7 +4223,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -4273,7 +4275,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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dsPsRkc94Tzf//UhlJa79+CfAvLmwrVmNDocDTU1NfPD4kiVLwHGcIH9HZtIy4Sfjvb3899MZSiFGcPh7mUykZcIn9ADiAdQx8GgkNUolcvPzkZ2djZycHKSlpaGtrQ02m02Q9gn5d+MPAU1tb9q0Cf/5n/+Jt99+G9u2bcOLL76IBx54AM899xx/TmpqqssIZGdnJ+bNmxd8i/1EyP94u92OwbPfgRPpL5sQJza5DP1JSeg3JqE/yQirWgXcvOl2nsFgECxG0Wa3Y+DbM4DFIsj9iOjEm6EU8gXfZrdj4JvTAI2yEz5gVqlgXbsaAKA2mZAwMICE/kEkDAxAZTYjfvZs5D7/LBIyMgTX8vj4OFiWFV2sekBGUiaT4YUXXsC///u/49ChQygvL8fPf/5zl3Pmzp2LmpoadHV1QavV4vjx43j55ZdD0mh/CEdOp6lgtBoyksSUsAyDYb3+lnE0JmFYrwd8GGVUKBQCtO5vSLRasGQkiRAw2VA64uOm/1AIYbQacGQkCR/gJBJUXLqMhIEBKK3umhm5dg3XfvRjxP3VUDrihNWyw+GIDiMJ3BqV/Od//mcAwK9+9Su3pMMymQw/+9nP8OSTT4JlWTz//PMwGAzBtTYSYMT1CybEgVml+qtxNGIgKRF2D3nIpkPw0BARrg4kIhtzUzNGqquBpUuEvTFpmfARzdgYND4MBpmbmzFafQPc4kUCtErcBGwkX331VQDAwMCA18oV69evx/r16wO9RUiQSCSCjkpyAg5zE+KFAzAaF4ee1BR0p6ViTKcL+pp2u13QUUnOQlomQoMsLg6Z2x9HxsPbINNqUV1dHdKSiNNippF1IjTI4uORtf1xpG97iNdyOJOgT0Zso5FAgEby17/+NY4ePYoPP/wQTz/9ND7++GPs2LEj1G0LCWq1WrBAWIlEAoVWA2ipXGIswgEYUqvRmZCArgQ9xoJM1s0wDDIzM5Gfn4/CwkKwLCvYalepRAKFTgeEwAATsYG1pxeY9NI+2UA6UavVwmlZKoVCHw/o4wW5HxH5eNTyJAPpRKVSCZJiELg10yvGkrN+G8mPPvoIu3fvxltvvYXS0lI89dRTeP311/HII494LBs006jV6rCVLZqMRqNB+Sd7BLkXIQ44jkNraysqKytRVVUVdLk3jVSKorIyFJWWIj8/36W+and3t2APX41WizLSMuEH3z38KCytrQBuPXQzH3/MzUA6EdJIajQa0jLhF9899DAsf62z7c1AOlGr1YIZSY1GnINUfhnJY8eO4Ze//CV+9atfYf78+QCAJ554Am+++Sb27t2Lbdu2haONQaHT6QTLX9nZ2YmqqiqsWbNGtL9wInhYlkVzczNfSjCYB6KEZWEYGkbBrFmY/+ADSMvJ8RoLqdPp0N3d7fFYqGlvb0dVVRVWr15NWiZ8ZjoD6URILbe1tZGWCb+ZzkA60el06OnpEaRNOpHOEPlcIvHatWt44okn8KMf/QhPPfWUy7H/83/+D7744gt88cUXohx2dSZzDjdffPEFzGYzlEolVq1ahWXLlolylJbwH4fDgcbGRlRWVuLGjRtBVeVISUlBQUEBCgoKoKqsROq6dZD50EFwHIe6ujpBtLx//35YLBYolUrcfvvtWLZsGWSysBXCIqKAzs/3w7hu7ZQPXSccx6G2thZWqzXs7fr8889htVqhUqlw++23Y+nSpaRlYko6930O4x3rRKVlhmFQWloqSo8VtlrbYmJwcBCtf51yCRfDw8P48ssvXfbFx8fjjjvuwNy5c0VZH5OYGrvdjoaGBt48BpovTCqVorCwECUlJSgoKEB8fOCxWgMDA2hrawv4874wNDSEr776ymWfXq/HHXfcgTlz5pCWiZDQ39+P9r9OH4aLwcFBHD582GWfXq/H+vXrMXv2bNIyERKE0HJSUhJf21tsxISR5DgODQ0NYUtOzjAMPv/8c68jRWlpabjrrruQn58flvsTocNms6Gurg5VVVW4ceNGwG+ZcrkcRUVFKCsrQ1FREZRBLrxxwnEc6uvrw5YEl2EY7Nu3z+u/Oz09HXfddRdmzZoVlvsTsYMQWv7ss8+8lvtMT0/Hhg0bkJeXF5b7E7FDuGeLpFIpiouLRTkaCcSIkQQAq9WK2tpaj6Ubg6W1tRVnzpyZ9ryysjJs2LCB6nGLjPHxcdTU1KCqqgo3b94MeJW/QqFASUkJysrKUFhYGLawhnBqubm5Gd99992055WXl2PDhg2irPtKRA7h1HJjYyPOnz8/7XkVFRW46667SMtEUFgsFtTW1obl2jk5OUHNZIWbmDGSwK3p5+bm5pBe02g0Qi6X4/Dhw6iqqpr2fJlMhpUrV2LlypUUPzmDWK1W3Lx5E5WVlaitrQ04D5hKpUJJSQnKy8uRn58vWOzV0NAQWlpaQnrN5ORkyGQyfPXVV6iurp72fJlMhlWrVmHlypUUc0YETLi0LJVK8dVXX+HGjRvTni+Xy7Fq1SrcdtttpGUiYMKh5ZSUFKSkpIT0mqEmpowkEFozaTQakZqaysfZtLS04C9/+YtP8ZgJCQnYsGEDSktLKU5HIMxmM27cuIGqqirU1dUFnKheo9Hw5nHWrFkzNt0Qyk4rOTkZKSkpvBabm5vxl7/8xad4zISEBNx9990oKSkhLRMBEU4tNzU14csvv/RJywaDAXfffTeKi4tJy0RAhHJNRkpKCpKTk0WvxZgzksAtQ9Ha2hpw/JtUKkVGRobHqRCO41BVVYXDhw+jv79/2mvl5+fjnnvuQXJyckBtIabG4XDgxo0buHz5MmprawOupqHT6VBaWory8nLk5uaKprpAuLVcWVmJw4cPY2BgYNprFRQU4J577oHRaAyoLURsEwotZ2ZmepwC5DgO169fx+HDhzE4ODjttQoLC3HPPfcgKSkpoLYQsc3Y2BhaW1u9xudOx1RaFiMxaSSBW7kA+/r60Nvb6/PIFMMwSEhIQGpq6rTTHw6HA+fOncPRo0enDcCVSCRYunQp1q5dG7JFGbEMx3Ho7OzEpUuXcPXq1YCD+ePj41FWVoby8nJkZWWJxjxOJlAtGwwGpKSk+KTl7777DseOHfNJy8uWLcOaNWtIy4TfsCyL3t5e9PX1hUXLdrud1/J0hlUikWD58uVYvXo1aZnwm3BrWUzErJF0wnEchoeHMTw8DLPZ7PYGIZPJoFarodPpkJCQ4Pc05tjYGA4fPowLFy5Me65Wq8Wdd96JefPmiX4oW4yYTCZcuXIFly9fDjgJfUJCAsrLy1FWVobMzMyI+j2EW8smkwmHDx/GxYsXpz1Xp9PhzjvvpNRXREA4y4EODw9jbGzMbQGc0Fq+6667KPUVERAsy2J4eBgjIyNh0bIYiHkjORmWZeFwOMBxHKRSach+qe3t7Thw4IBPsRNZWVnYuHEjMjIyQnLvaMbhcKCmpgaXLl1CTU1NQFPXSUlJ/MhjWlpa1DwswqnlL774wqeYs+zsbGzcuFG0+c+IyMDhcIBl2ZBrua2tDQcOHPBJyzk5Odi4cSPS0tJCcm8iNgmXlmcSMpICwnEcrly5gq+++sqn2pwLFy7EnXfe6VJvmbjFxKnrQPKDpqSk8OYxEoKZxQbHcbh8+TK++uorn6r8LFq0CHfeeSdUKpUArSMI3+E4DpcuXcLhw4en1TLDMFi0aBHWr19PWiaIv0JGcgawWq04duwYzpw5M+0Imlarxb333ovy8nKBWidexsbGcPXqVVy6dAmdnZ1+f95gMGDevHmoqKigBSEhwmKx4NixYzh79uy0WtbpdLj33ntRVlYmUOsIwncsFguOHj2Ks2fPTpvXUqfT4b777kNpaalArSMI8UJGcgbp7e3FwYMHUVdXN+25paWluPfeexEXFydAy8SDw+FAbW0tLl++jBs3bvg9dS2Xy1FRUYH58+cjJyeHRh7DRE9PDw4ePIj6+vppzy0rK8O9994LnQ/1xQlCaLq7u3Hw4EE0NDRMe255eTk2btxIWiZiGjKSMwzHcbhx4wYOHTo0bVoKpVKJu+66CwsXLox6Q9Td3Y1Lly7hypUrPk2dTiYvLw/z5s1DeXk5FApFGFpITIbjOFRXV+PQoUMYGhqa8lyVSoUNGzZg/vz5Ua9lIvJwpnE7dOgQhoeHpzxXpVLh7rvvpkWSRMxCRlIk2Gw2fPPNNzh58uS0VVby8vKwadMmJCYmCtQ6YTCbzfzUdUdHh9+f1+v1mD9/PubNmweDwRCGFhK+YLPZcOrUKZw6dWpaLc+aNQv3339/1GmZiA5sNhtOnjyJU6dOTZvCJT8/H/fffz/1PUTMQUZSZAwODmL//v3T1uyUyWRYu3YtVqxYIdr8hr7Asizq6upw6dIl3Lhxw+9qM3K5HOXl5Zg3bx7y8vJoREBEDAwMYP/+/dOGbshkMqxbtw7Lly+PaC0T0cvAwAA+//zzaUM35HI51q1bh2XLlpGWiZiBjKQI4TgOV69excGDB6dNpp2eno7NmzdHXEqKnp4efuralxXsk8nJycH8+fNRXl5OyYJFjDNTwaFDh6bVckZGBjZt2hRxWiZiA2emgkOHDk2bmD8jIwObN29GamqqQK0jiJmDjKSIMZlMOHToEK5evTrleQzD4LbbbsOaNWsgl8sFap3/jI+P48qVK7h06ZJPedsmEx8fj3nz5mH+/Pk0FRphmEwmHDx4ENeuXZvyPIlEwms5kio7ELHD6OgoDhw4gMrKyinPk0gkWLlyJVavXk1aJqIaMpIRwM2bN7F///5pg74TExOxefNm5ObmCtQy3+jv78fZs2dx6dIlv+voymQylJWVYf78+Zg1axZNXUc4N2/exOeff46RkZEpz0tKSsLmzZuRk5MjUMsIwj+qq6vxxRdfTKtlo9GIzZs3Izs7W6CWEYSwkJGMEKxWKw4fPozvvvtu2nNXrlyJdevWzWjGfI7jUFdXh7Nnz6Kmpsbvz2dnZ/M5Hynxb3RhtVrx1Vdf4dy5c1OexzAMVq5cibVr10ZF9Qci+rBYLPjqq69w/vz5Kc9jGAarVq3CmjVrSMtE1EFGMsJobm7Gvn370NvbO+V5aWlp2Lp1K5KTkwVq2S2sVisuX76Ms2fPoq+vz6/PxsXF8VPXSUlJYWohIRaampqwb9++aXWSnp6OrVu3UhJ5QrQ0NjZi37596O/vn/K8jIwMbN26lfo3IqogIxmB2O12HD9+HKdOnZoyQbdMJsNdd92FJUuWhH1KuK+vj5++Hh8f9/lzUqnUZeqaVjrGFna7HceOHcOpU6emrCYik8mwYcMGLF68mMIbCFFis9lw7NgxfPPNN1NqWS6XY8OGDVi0aBFpmYgKyEhGMF1dXfjss8/Q3t4+5XmFhYV44IEHQl59geM41NbW4uzZs9OmK5pMRkYGFixYgNmzZ9PUNYHOzk589tln0+YPLSoqwubNm6mSCCFaOjo6sG/fvmm1XFxcjM2bN0Or1QrUMoIID2QkIxyWZXH69Gl8/fXXU45OajQabNq0KSS1YS0WCy5duoTvvvtu2qmciUgkEsyePRtLly5FZmZm0O0goguWZfHNN9/gyJEj02p58+bNKCkpEbB1BOE7LMvi1KlTOHr06JRa1mq12Lx5M4qLiwVsHUGEFjKSUUJnZyf27NmDnp6eKc9bsGAB7rnnnoDKBvb09ODs2bO4cuWKX9PXOp0OixcvxqJFi2gkiZiWjo4O7NmzZ9o44EWLFmHDhg1UApMQLe3t7dizZ8+0ccCLFy/Ghg0bRJ2+jSC8QUYyirDZbDh8+DDOnDkz5XmJiYnYsmULsrKypr0my7KoqanB2bNnp63qMJns7GwsXboUZWVltFKR8AubzYYvv/xy2iwFiYmJ2Lp1K41wE6LFZrPhL3/5y7RZCpKSkrB161ZkZGQI1DKCCA1kJKOQuro6fPrpp1NWjGEYBmvWrMHtt9/ucYGLxWLBxYsX8d1332FgYMDne0ulUsyZMwdLly5Fenp6QO0nCCc1NTX47LPPptXy2rVrsWrVKlqsRYiWmpoa7N27FyaTyes5EokEa9euxcqVK0nLRMRARjJKGRsbw+eff46qqqopz8vKysK2bdug1+sBAN3d3fz0tc1m8/l+8fHxWLx4MRYuXEjB40RIMZlM+Pzzz1FdXT3lednZ2XjooYd4LROE2DCZTNi3bx9u3Lgx5Xk5OTl46KGHEB8fL1DLCCJwyEhGMc7asAcOHJgyplGlUmHp0qVoaWlBQ0ODX/fIycnBsmXLUFpaSm/QRNjgOA4XL17EwYMHp3zB0Wg02Lp1KwoKCgRsHUH4DsdxuHDhAg4dOjStlh966CHk5+cL2DqC8B8ykjHAwMAAPvnkE7S0tITkejKZjJ++TktLC8k1CcIX+vv7sWfPnmlrta9ZswZr1qyhPH2EaOnr68Mnn3wyrZbXrl2L1atXk5YJ0UJGMkZgWRYnT57E0aNHp0yWOxV6vZ6fvtZoNCFuIUH4BsuyOH78OI4fPz6llgsKCrB161bSKiFaHA4Hjh8/jhMnTkyp5cLCQmzZsoW0TIgSMpIxREdHB7788ku/p6/z8vKwdOlSlJSU0PQ1IRpaW1uxZ8+eKReDxcfH4+GHH/YpQwFBzBQtLS345JNPptSyXq/Hww8/TBkKCNFBRjIGaG5uxsmTJ1FTU+PX57KysnDffffR9DUhWiwWCz777LMpF5VJJBJs2LABS5cupelBQrRYLBbs3bt3ykVlEokEd999tyBlbwnCV8hIRikcx6G+vh4nTpxAU1NTwNcpLy/H5s2boVQqQ9g6gggdHMfh22+/xZdffjnl9GBFRQU2bdpEWiZEC8dxOH36NL766qsptTx79mxs2rSJkvETooCMZJTBcRyqq6tx8uTJaWtw+0pSUhIefvhhpKamhuR6BBEOmpub8fHHH2NkZMTrOUlJSXjkkUeQkpIiYMsIwj+amprw8ccfT5k/1Wg04pFHHkFycrKALSMId8hIRgksy+LatWs4efLktGUSnUilUsyfPx9z5szB8ePHp6xcI5PJcP/992PevHmhajJBhByTyYQ///nPU8YBy+Vy3H///Zg7d66ALSMI/xgdHcWf//xnNDY2ej1HLpdj06ZNmDNnjnANI4hJkJGMcOx2Oy5duoRTp05hcHDQp88oFAosXrwYy5cvR1xcHIC/rYQ9duzYlJ9dtGgRNm7cSCUPCdHCsiyOHj2KEydOTHne4sWLcc8995CWCdHCsiyOHDmCkydPTnnekiVLcPfdd5OWiRmBjGSEMj4+jvPnz+Obb76ZcvpjIiqVCsuWLcOyZcugVqs9nlNbW4s9e/bAbDZ7vU5eXh4efvhhSkVBiJqamhrs2bMHFovF6zmzZs3Cww8/7PXvgSDEwM2bN/HJJ59MqeX8/Hxs27aNtEwIDhnJCMNsNuPs2bM4c+bMlGZvIjqdDitWrMCiRYt8WmgwNDSEjz76aMpEuQaDAdu3b4fRaPS57QQhNIODg/joo4+mjBdOTEzE448/TlomRM3AwAA++ugjdHR0eD0nKSkJjz/+OJKSkgRsGRHrkJGMEEZHR/Htt9/iu+++m7Lc4UQSEhJw2223YcGCBZDJZH7dz+Fw4NChQ/juu++8nqNUKvHwww9TOTpC1Njtdhw6dAjnzp3zeo5KpcLDDz9M5egIUWO323Hw4EGcP3/e6zkqlQqPPPIIZs2aJWDLiFiGjKTIGRoawqlTp3Dx4kXY7XafPmM0GrFq1SrMnj076JiZa9eu4bPPPvNaE5ZhGNxzzz1YunRpUPchiHBz9epV7Nu3b0otb9y4EUuWLBG4ZQThH1euXMG+ffu8PhMkEgk2btyIxYsXC9wyIhYhIylSent7cerUKVy5cgUsy/r0mfT0dNx+++0oLS0NabLazs5OvP/++xgeHvZ6Di1cICKBjo4OvP/++1OmCFqyZAnuuecequJEiJr29nZ88MEHU2p52bJl2LBhA2mZCCtkJEVGZ2cnTpw4gcrKSp8/k5OTg9tvvx0FBQVhq3YwOjqKDz74YMq4SQr2JiKBkZERfPDBB1PGTRYUFGDbtm1QqVQCtowg/GN4eBgffPDBlHGThYWFeOihh0jLRNggIykSWlpacOLECb/KGBYWFmLVqlXIzc0NY8v+hs1mw2effYZr1655PYeCvYlIwGazYe/evbh+/brXc4xGIx5//HEkJiYK2DKC8A+bzYZPP/10ysEH0jIRTshIziCBljEsLy/HqlWrkJ6eHsbWeYbjOJw4cQJHjhzxeg4FexORAMdxOHbs2JS5U9VqNR555BHk5eUJ1zCC8BOO43D06FEcP37c6zlqtRqPPvqoYAMPROxARnKGaGhowJEjR9DS0uLT+QzDYO7cuVi5cqUoSmJVVlbik08+oWBvIuK5fv06Pv300ym1fO+992LRokUCt4wg/OPq1avYu3cvHA6Hx+MSiQT3338/FixYIHDLiGiGjKTAtLS04MiRI1OWcJuIVCrFggULsHLlSiQkJIS3cX7iS7D3qlWrcMcdd4QtdpMgQkFbWxs++OCDKZP7r169GmvXriUtE6KmtbUVH3zwAUwmk9dz1qxZgzVr1pCWiZBARnICHMfBarXyb3MSiQRKpTIkK946Ojpw5MgRn2MgPZUxFCO+LFxYsGAB7r//flo5KCDh1HK04svChYULF+K+++6j/0cBIS37z9DQEN5//310dXV5PWfx4sXYuHEj/T8KSLRqOeaNpN1ux8DAAIaHh2GxWODpv0OhUCAuLg6JiYk+VYaZSE9PD44ePerzKmxfyhiKDV+CvUtLS7F161bI5XIBWxZbhFvLscD4+Dg+/fRTVFVVeT2nrKwMW7du9TvJP+E7vmhZqVQiLi4OBoOBtOyB8fFxfPLJJ6iurvZ6Tnl5ObZs2UJaDiNOLQ8NDcFqtUallmPWSDocDnR2dmJgYMCvz2m1WmRkZEz7y+7v78exY8dw5coVn67rbxlDseHLwoXc3Fw89thjlIYixNjtdnR2dmJwcNCvz/mq5ViD4zgcOXIEJ06c8HpOXl4eHn30UdJyiAlUyzqdDunp6aTlSXAch6+//honT570es6sWbPw6KOP0v9diIklLcekkRwZGUFra6vXgGRfSE1NhdFodIsxGRoawvHjx3Hx4kWPbx6T0Wg0WLlyJZYsWRIVo3WXL1/G3r17vf7bU1NTsWPHDlFP10cSw8PDaGtrC1jLDMMgNTUVSUlJFC81iYsXL2Lfvn1etZyWloYdO3ZAp9MJ3LLohLQcPi5cuIDPP//cq5bT09OxY8cOaLVagVsWnQwNDaG9vT1mtBxzRrK3txednZ0huVZ8fDyys7PBMAxGR0dx4sQJnD9/3ifxKJVK3HbbbVi2bFlEvXn4ws2bN/HRRx95XQWbkJCAnTt3Uq7JIAmllvV6PbKysiKi0xKSGzdu4OOPP/aqZYPBgJ07d1J+viDp6emZMp7PH0jLnqmursbHH3/s9fmUmJiInTt3wmAwCNyy6CKUWk5ISEBmZqbotRxTRrKvr2/KQPpA0Ov1qKmpwenTp32qhS2Xy7F8+XKsWLEiYmIgA6GlpQXvvfceLBaLx+MajQY7d+6ckVyY0UC4tEwPYHeam5vx/vvve9WyVqvFjh07SMsBEsoXIieR8gAWmsbGRnzwwQewWq0ej+t0OuzcuROpqakCtyw6iFUtx4yRNJvNqKurC8u1+/r6cPTo0SnPkUqlWLJkCVatWhUz0wfd3d145513vKYHUigUeOyxxyhxuZ+MjY2hvr4+LNdOT0+nkWIPdHV14d133/WqZaVSiccee4wSl/tJOLWckZFBI8Ue6OzsxLvvvus11ZVSqcTjjz9Oicv9xGQy+ZzWz18yMzNFPVIc1jXnL7/8MpYsWYJXXnklnLeZFpZl0draGrbrJyUlIT4+3uMxiUSCxYsX45VXXsHdd98dMyYSAFJSUvDcc895NSbj4+N49913/aorHuuEW8udnZ0YHx8P2/UjldTUVDz77LNetWy1WvHOO+9MudqbcCXcWu7o6CAteyAtLQ3PPvusV5Pt1PKNGzcEblnkEm4tt7e3w2azhe36wRJWI/nEE0/gX/7lX8J5C58YGBjwOpQfKlatWuXyM8MwmD9/Pv7u7/4O9913n1ejGe3o9Xo8++yzyMzM9Hjc4XDgo48+wrlz5wRuWWTS398f1ocjx3Ehn5qJFhISEvDMM88gIyPD43Gnls+fPy9wyyITIbQcqli1aMNgMOCZZ55BWlqax+N2ux1/+tOfcPHiRYFbFpn09fWF1eiJvV8Oq5Fcvnz5jI/AcRyHvr6+sN9HrVZDr9cDACoqKvD9738fDzzwgKiHo4VCo9HgySefREFBgddz9u/fj2+//VbAVomDlj+8BXNbm0/nCqXl4eFhUb/9ziRarRZPPvkk8vPzPR7nOA6ff/45zpw5I3DLZp7m3bthmaIwwUSE0vLQ0JBPseuxiE6nw9NPP+01tIjjOHz22Wf47rvvBG7ZzNP85m5YfIxB5zgO/f39YW6RuLXst5FkWRb33HMP/u3f/s1l/4kTJzB79mwcOHAgZI0LBWNjY4JNb9x111146aWXsG3bNhiNRkHuGSkoFAo8/vjjmD17ttdzDh06hNOnTwvYqpmn58svcWH7DtT88/9nWkNpMpkEM3j+5j6LJZRKJbZv346Kigqv5xw8eDDmzGTPob/gwuPbUfO//te0hlJILfubKziWcGq5vLzc6zlffPEFzp49K2CrZp7uAwdw4bHHUfsv/zqtoRwdHY35ftnvdPYSiQS7du3Cq6++ihdffBF6vR7V1dX44Q9/iL//+7/Hxo0bw9HOgJmq3mioUavVSElOBseygt0zkpAwDLY8+CC0Go3Xh+xfDh0Cx7JYsWKFwK2bITgOcDjQ/cUX6D50CCl3342sp56E2kMogJBaHh0dhZEW3XhFwjDYumULNGq11xGbgwcOgHU4sHz5coFbN0NwHDiHA92f70fPgYNI3ngPsp94EqpM91AA0rJ4kEokvJa9hRgd+OILcCyLpUuXCty6mYNzONC1bx+6v/gCKfduRNYTT0DlIaxFcC2LcJAqoFXbdrsdd999N7Zs2YJt27bhkUcewfr16/GLX/zC7dwzZ87g3XffxX/8x3+EpMH+0tTU5HWlZaiRMAwGdr0kyL2IKEYq9WgoGxsbva60DDUSiQQDL+4S5F5E9MJIpR4NZUNDg2APYNIyEQoYqdSjoRRSy1KpFGVlZYLcyx8CipGUyWR44YUX8Pbbb+OFF15AeXk5fv7zn4e6bSHBW+63cMByHJgEvWD3I6KUv45QTp7yFlTLLAsmRheIEaHDOUJ5Yftfp7zbbk15h3vx40RYlgUTQ9kyiPBwa4Tyc1x4fDtq/+Vf+PANIftlh8MRVEW+cBFwHkmTyYQVK1YgNzcXf/rTn6DRaNzOee6553D9+nWYzWbo9Xr853/+J+bOnRt0o/2hqqpK0P/4kf/5C7Dd3YLdj4gBpFKkbLwHts2bwQoYNjHy8/8BtrdXsPsRMYBUitR7N2J80yZBtTz83/9vcAIsiCBiB0YqRcr998N670afyiGHipKSEtGVU/Y7RtLJq6++CuBWILNUKvV4zhtvvBHo5SMWio8kQgrDwLh2LTIffRRNAr75AqRlIsQwDIzr1iHj0UfRODYm7L1Z8Y3iEBGMRALj+juQ8cjDaDSZBDWSYiQgI/nrX/8aR48exYcffoinn34aH3/8MXbs2BHqtoUEuVwu6IgkJ1A8JhHlMAyMd9yB7Kefguav6TlkNTXCalmgeEwiymEYGNevv6Xlv1b+kd28KWiycG5UuAURRBQjkSD5zvXIeuopaP5a+UdoLXsbuJtJ/DaSH330EXbv3o233noLpaWleOqpp/D666/jkUceEd1wK3BrJbVQMQxyuRwZW7YIcq9opLOzY8oSU9nZ2cjKyhawReGne/9+2Ce+fDAMjHesQ/bTT/MG0olarRYstkwhlyNj61ZB7hWNdHR0oLHRu5ZzcnKQmZklYIvCT9fnn8Mx8eXDg4F0otFoBHv4KhQKZGzbJsi9opGOjnY0NjZ6PZ6Tk+u14ESk0rVvHxwTF9BIJH/T8qTSkWq1WjAtK5VKSCRhTf8dEH4ZyWPHjuGXv/wlfvWrX2H+/PkAblWvefPNN7F3715sE+Efq1qtFiyPmEajQfbfvSzIvaKRWQBk332HL774wuPxGgBrZldg7dq1QjYrrAycPn3LSHoYgZyMWq0WLI+YRqtFFmk5YGYBkJ096zWvbg2AdXPnYPXq1YK2K5z0nzx5y0gyDIx33onsp550M5BOBNWyRkNaDoJZACTffotDhw55PF4DYP38eW7V3SKZvmPHbhlJDyOQk1Gr1RgaGhKkXZ7WoogBn43ktWvX8KMf/Qg//elPsWHDBn6/TqfDzp078dprr2HLli2iG3aNj49Hu4/VFoLFWdmGCJwlS5aAYRjs37/f4/Fjx45BJpNFT6c1cdTGi4F0Eh8fjw4fqy0EC2k5eJYuXQqGYby+GB05cgRSqRQrV64UuGVhgmFgvOuuKQ2kE9JyZLF8+XIwDIODBw96PH748GFIpdKoyf/LSKVI3nDXlAbSSXx8vGDlC8Wq5YBXbUcSLS0tYX9jkMlkKCkpAcMwYb1PrHD+/Hl8/vnnXo/ff//9WLRokYAtCg/Wnh4ok5N9Pr+5uRnDw8NhbNGtEI3i4mLScog4d+6c1xcjANi8eTMWLFggYIvCA2k5+jk7xSg7ADzwwAP8bGUk46+WhchXLWYti2+yPQwIkQk+OTlZlL/gSGXRokXYtGmT1+P79+9HZWWlgC0KD/50VoAwWjYajaTlELJ48WLcd999Xo/v27cPVVVVArYoPIhRy9Qvh5alS5fi3nvv9Xr8s88+Q3V1tYAtCg+kZf+ICSOpVqvD+otWq9VITEwM2/VjlYULF2Lz5s0ej3Echz179qC+vl7gVs0sGo0GSWEs90ZaDg+LFy/G/fff7/EYx3H485//POVCs2gk3FrWaDQwGAxhu36ssmTJEq8vRhzH4eOPP55ycU40otVqw9pvil3LMWEkASAlJQVKpTLk12UYBllZWaJ9U4h0FixY4PUN2OFw4IMPPkDbXyu/xAqpqalQKBQhvy5pObwsWrQI99xzj8djTi0LFc8tFkjLkcnixYtx9913ezzmcDjw/vvvCxYDKxZSU1PDkrlGIpGIXssxYyQlEgny8vJC+otmGAa5ublhMajE31iyZInXldo2mw3vvvsuenp6hG3UDOLUskwWcD0BNxiGQV5eHmk5zCxbtgxr1qzxeGx8fBzvvvsuemOomlA4tRwOg0r8jeXLl+P222/3eGx8fBzvvPMO+vr6BG7VzCGVSjFr1qyY1HJMLLaZiM1mQ2NjY9D5+KRSKXJycqClGq6CwHEcDh48iLNnz3o8HhcXh2effRYJCQnCNmwGIS3fqqM8Pj4Oq9U65Tb5HJZlwXFcWDcAHvczDAOWZWG32z3+m+RyOUpKSqDVaiGXywPaxJhrbirGx8fR1NQUEi3n5uaKNk1KtMFxHPbv34/z5897PK7X6/Hss88iPj5e4JbNHOPj42hsbAw6t2QkaTnmjCRw6+HT29uL7gBrYsfHxyMjIyOkbx7E9HAch08//RRXrlzxeDwpKQnPPPNMRBqiQGFZFj09PQGPyM6Ulh0Ox7Tmz5sJnHyMcEcikXg1mVqtFhqNBlqt1uOmVCpnZBotWC3r9Xqkp6dTvywwLMtiz549uH79usfjRqMRzzzzTEQYolDBsiy6u7sDnl2INC3HpJF0YrVa0dfXh4GBAZ9qZcbFxSEpKQk6nU6A1hGecDgc+NOf/oSamhqPx9PT0/HUU0/F3BStxWJBf3+/z1qOj49HYmJiyLVst9sxMjKCkZERjI6O8t9P3EZHRwWrNkX4j0Qi8WoyPRnQUMeFBaLlpKSkmHqBFBvOuMi6ujqPxzMzM/Hkk0+Kfoo21FgsFvT19WFwcDCqtRzTRtKJw+GAyWSC2WyG2WyGw+EAx3GQSqVQqVRQq9Vh6TCJwLDZbHjnnXfQ3Nzs8XheXh527NgRMW9zoWSylp3Tp8Fq2eFwTGkMnd+bzeZw/LMIEaNQKKY1mxOP+TrtHi4tE+FhfHwcb7/9NlpbWz0ez8/Px+OPP079chRqmYwkEZFYLBb84Q9/QFdXl8fjJSUleOSRRyIuVkxonB3cdAZxbGxspptKRAFSqRR6vR4Gg8FlS0hIgMFggEqlmukmEkFgNpuxe/dur+EJ5eXleOihh6hfjjLISBIRy+joKN58802vtdSXLFkyZfLcWMEZwuHc+vv70dfXh6GhIZhMpplunmBIJBKoVCooFArIZDIwDCPoBsBloY3NZuM353Quy7Iz/L80s6jVajdz6dzi4+NFV4KXcGd4eBi7d+/2Wkt9+fLlXlMHEZEJGUkiohkYGMCbb76J0dFRj8c3btyIpUuXCtwq4bHb7bxBdH51bpFuFhUKBZRKJf/V123y+WKfUuvv78ebb77p9fd1++23o6SkBDabzc2I+rpZrVaMjY1FZAgCwzD8aOZkk2kwGKBWq0Wday+W6Ovrw+7du71qOVpK3BK3ICNJRDzd3d3YvXu3xwUcDMPg8ccfR1FR0Qy0LLSwLIuhoSGPo4ve3v7FgkwmQ1xcHL/pdDqoVCqfzGAsTYN1dnbiD3/4g8c0OAzDYMeOHSgoKAj6Pg6HA2NjYzCZTB63ycdsNlvQ9ww3CoXCzVw6TWdCQoLoXySijY6ODrz11ltetbxz507k5+fPQMuIUENGkogKWlpa8Mc//tFjbj6FQoHnnnsOKSkpM9Ay/+A4DqOjox7N4sDAABwOx0w30QWpVOpmECf+7NxmKqVMJNLc3Iw//vGPHn/XSqUSzz33HJL9rAUcLOPj416Np6f9Ypyi1+v1SElJQWpqKr8lJSXF1IuK0DQ1NeHtt9/2quXnn39ekDrVRHghI0lEDdevX8fHH3/s8Zher8fzzz8vmtRNNpsN3d3dLobRaRrFkBtRIpH4ZBBVKhUZxDBw9epV7Nmzx+OxhIQEPP/886JNEcJxHCwWi5u5HB0dxeDgIAYGBjAwMOA1HEVIpFIpkpOTkZqa6mIyxdJPRANXrlzBJ5984vGYwWDA888/H1M5JqMRMpJEVHH8+HEcOXLE47GsrCw8+eSTgqdYsFgs6OzsREdHB/+1t7fXp7xi4UAqlSIxMZFfwODJIFK82cxz9OhRHDt2zOOx7OxsPPnkkxE9XWuz2VyM5cDAgMvPMzmdrtFoXEYuU1NTkZycHNH/3zPJkSNHcPz4cY/HcnJy8MQTT9D/bQRDRpKIKqarfjN79mxs3bo1bCbJZDK5GMaOjg6vq8rDiXNhQlJSksuWmJgIvV5P03kRAMdx2LNnD65du+bx+Ny5c/Hggw9GpeHnOA5jY2MuJnOi0RweHhb8RYxhGCQlJbmNXur1+qj8HYQSjuPw5z//2Wv1m3nz5uGBBx6g/8cIhYwkEXXY7Xa8/fbbXhOWr1mzBmvXrg3qHhzHYWRkhDeLTuM4PDwc1HX9RafT8QZxomE0GAz0hh8F2O12vPXWW16TPK9btw6rV68WuFUzj8PhwNDQkEeT2d/fH3TNbn9QKpUuxtL5faxV15oOm82Gt956C21tbR6P33HHHbj99tsFbhURCshIElHJ2NgYXn/9da+jgVu3bsWcOXN8uhbHcRgYGHAzjUIl6VYqlTAajW6GMTExkR5WMYDJZMLrr7/udWX+tm3bUFFRIWyjRI7ZbHYzlz09Pejq6hIsBjkhIcHFWGZmZsb86OXo6Chef/11DA0NeTz+8MMPo7y8XOBWEcFCRpKIWnp6evDGG294HJ2QSqV46qmnkJ2d7bKfZVn09va6GMbOzs6wj3DIZDK3UUXnzxqNJqYfPsStFFdvvPGGRxMkk8nw1FNPISsrawZaFllwHIfBwUF0d3ejq6uL3/r7+wWZKtfpdMjOzkZ2djaysrKQnp4eczMHXV1dePPNN71q+emnn0ZmZuYMtIwIFDKSRFRTX1+Pd955x+NDQq1W44EHHnCJa+zs7PSYQiiUGI1GpKenIz09nU9BEh8fT2aRmJLa2lq89957HrWs1Wrx/PPPIyEhQfiGRQE2m40fsezq6uKNZrhnHaRSKTIyMpCVlcWby7i4uLDeUwzU1NTg/fff96hlnU6H559/Hnq9fgZaRgQCGUki6jl37hz2798v+H0lEglSUlKQlpbmYhwVCoXgbSGig7Nnz+LAgQMej6WlpeHZZ58VPCtBtMJxHEwmk8vIZVdXF3p6esKaJzMhIYE3ldnZ2UhNTY3KxXFnzpzBwYMHPR5LT0/Hs88+G3OjtZEKGUkiarFYLGhubkZTUxOuXLkS1rx1MpkMaWlpLqaR0oUQ4eDAgQM4e/asx2Pz58/HAw88IHCLYguHw4G+vj630ctwLbSTy+XIzMx0MZdqtTos9xISjuPwxRdf4Ny5cx6PL1y4EJs2bRK4VUQgkJEkoobR0VHeODY1NaGrqyss91EqlS6GMS0tDUajMSpHDQjxwbIs3n//fdTW1no8vmnTJixcuFDgVhFms9kt9rK7uzss+TCNRiNvKrOzs2E0GiMyNIZlWbz33nuoq6vzeHzz5s1YsGCBwK0i/IWMJBGRcByHoaEh3jQ2Nzejr68v5PfRaDS8WXQaR4PBEJGdNhE9WK1WvPHGG+jp6XE7JpVK8dxzzyE9PX0GWkZMxJnxoa2tDS0tLWhpaUFXV1fIF/aoVCpkZWXx5jIzMzNiMjpYLBa88cYb6O3tdTsmk8nw3HPPIS0tbQZaRvgKGUkiIuA4Dr29vbxpbGpqCvlUEsMwKCgoQGZmJm8a4+LiyDQSoqS3txevvfaax9WvCQkJePHFF6NiCjTaGB8f541la2srWlpaYLFYQnoPhmGQkpLCT4fn5uaKeiFWT08PXnvtNY+jtwaDAS+88AJpWcSQkSRECcuy6Ozs5E1jc3OzIHkbCwoKsH37dpqmJiKCyspKfPTRRx6PFRUV4fHHH6cXIZHDcRz6+vr4EcvW1laPI83BkpiYiIKCAhQUFCAvL090I5bXrl3Dn//8Z4/HiouL8dhjj5GWRQoZSUIU2O12tLe386axubk5pImDlUolcnJykJubi5ycHFRWVuLbb7/1eO7q1auxbt26kN2bIMLJoUOHvGp57dq1WLNmjcAtIoLFbDa7TIe3tbWFtD9kGAbZ2dnIz89HQUEBMjIyRPHyfPDgQZw5c8bjMap8I17ISBIzAsuyaGtrQ21tLZqamtDa2gqHwxGy62u1Wt405ubmIiUlxaWjZFkWb7/9NhobGz1+fvv27SgqKgpZewgiXDgcDvzxj3/0WhJ0586dKCgoELhVRChhWRbd3d0u0+HeqnYFgkqlwqxZs3hjaTAYQnZtf3A4HHjrrbfQ0tLidoxhGOzcuRP5+fkz0DJiKshIEoJhMplQV1eH2tpa1NbWwmw2h+zaCQkJvGnMzc1FYmLitNMgo6Oj+N3vfucxLZBKpcKuXbtEHVdEEE5GRkbw+9//3qOW1Wo1du3aRQmeo4zR0VHeVLa2tqKtrS1kL+OJiYm8qczLy4NKpQrJdX1heHgYv//972EymdyOaTQa7Nq1C/Hx8YK1h5geMpJE2OA4Du3t7aipqUFtbS3a2tpCdm2j0egy4hjoQ7K5uRlvvfWWxwTDlBSXiCSamprw1ltveVwRnJGRgWeeeYa0HMU4HA50dnby0+EtLS0YGRkJ+roMwyArK4s3lpmZmWGfBm9oaMDbb7/tUctZWVl4+umnIZVKw9oGwnfISBIhxWw2o66ujjePoVggwzAM0tLSXGIctVptCFp7i2+//RaHDh3yeIyS4hKRxDfffIMvv/zS47HFixfjvvvuE7hFxEwyNDSExsZG1NfXo66uzuMon78olUqXafDExMQQtNSdU6dO4auvvvJ4bMmSJbj33nvDcl/Cf8hIEkHBcRw6Ozt549ja2hp0jjSJRILMzEx+mjo7OzusKww5jsOf//xnXL9+3ePxBx54APPnzw/b/QkiVHAch48++ghVVVUej2/ZsgVz584VuFWEGOA4Dt3d3airq0NdXR2amppCMhVuMBh4Uzlr1qyQTYNzHIcPP/wQ1dXVHo9v3boVc+bMCcm9iOAgI0n4jcVicYl1DLb0oEwmQ05ODj/imJmZKXi9YKvVitdff91jUly5XI5du3YhKSlJ0DYRRCBYrVa89tprHhP0KxQK7Nq1K2yjSETkYLPZ0NzcjLq6OtTX14ekEhjDMMjMzHSZBg9mCtpiseC1115Df3+/2zGlUoldu3bN2MIg4m+QkSSmxfkmW1NTg5qaGrS0tAQ96piYmIjCwkIUFRUhLy9PFLFbUyXFzcjIwLPPPktxOURE0N3djddff92jlrOysvDMM8+IIt0LIR5GR0f5KfD6+vqgBwiAWy8us2bNQmlpKYqLi6HRaPy+RldXF15//XXY7Xa3Y9nZ2Xj66adJyzMMGUnCI1arFfX19fyUdbBB21KpFLNmzeLNo1hHRK5fv46PP/7Y47Hbb78dd9xxh8AtIojAuHr1Kvbs2ePx2Jo1a7B27VphG0REDM7BA6exbGpq8mjk/IFhGOTl5aG0tBSlpaV+rby+fPkyPv30U4/H1q1bh9WrVwfVNiI4yEgSAG51HD09PbxxbG5u9riS2R8SEhJQVFTEjzoKPV0dKPv27cOFCxfc9jMMg6effho5OTkz0CqC8J/PPvsMFy9edNvPMAyeeeYZZGdnz0CriEjDbre7TIN3dnYGfc3MzEyUlpairKzMp7ChTz/9FJcvX3bbzzAMnnvuOWRmZgbdJiIwyEjGMA6HAw0NDaiurkZtbS2GhoaCup5UKkVubi6KiopQWFiIpKSkiCxpNT4+jt///vceY8z0ej1eeuklQfOqEUSgjI+P43e/+53HGDODwYBdu3aJrlQeIX5GR0fR0NDAL9wJdho8JSWFN5WpqakenxtWqxW//e1vMTg46HYsMTERu3btgkKhCKodRGCQkYwxHA4HGhsbcf36dVRXVwedFFyv1/PT1bNmzYqaP+T29na88cYbHkdl586diy1btsxAqwjCf9ra2vDGG294jGueP38+HnjggRloFREtOGeznKOVjY2NQU2DJyQk8KYyOzvbxVS2tLRg9+7dHrW8YMECbN68OeD7EoFDRjIGCKV5lEgkyM3N5c2j0WiMyFFHXzhx4gS+/vprj8co9QQRSRw/fhxHjhzxeGzbtm2oqKgQuEVEtGK329HS0sLnE+7u7g74Wlqtlo+pnDVrFqRSKY4ePYpjx455PP+RRx5BWVlZwPcjAoOMZJTCsiwaGhpQWVmJqqqqoMxjXFwcH+s4a9asmJkKY1kWf/zjH9HU1OR2TKlU4qWXXqISikREwLIs/vCHP3isYaxSqfDSSy9RCUUiLPT396OqqgrV1dVobW0N+DpKpRIlJSUoLi7GN998g/b2drdz1Go1XnrpJSqhKDBkJKMIlmVdRh4DrSrDMAxycnL4UceUlJSoHXWcjsHBQfz2t7+F1Wp1O5abm4snn3ySUk8QEcHAwAB++9vfYnx83O1YXl4ennzyyZj9OyeEYXh4GDdu3EB1dTUaGhoCTiMnk8ngcDg8fj4/Px87d+4kLQsIGckIJ1TmUavV8qOO+fn5tJhkAlOlUVm/fj1WrVolcIsIIjCuXLmCTz75xOOxO++8EytXrhS4RUSsYjabeVNZV1cXdHqhiWzYsAErVqwI2fWIqSEjGYE4zaNz2joY81hWVoaKigrk5OTQyNoU7NmzB1evXnXbL5FI8NxzzyEjI2MGWkUQ/sFxHPbs2YNr1665HZNIJHj++eeRnp4+Ay0jYpnx8XHU1taiuroaN2/e9DgD5A8Mw+Cxxx5DcXFxiFpITAUZyQiBZVk0NTXh+vXrZB5nAIvFgt/+9rceUyQlJSVh165dEZMnk4htLBYLfvOb32B4eNjtmNFoxIsvvkhaJmYMu92OxsZGPq4y0GcdcCutUEVFBebMmUOlFMMIGUkRE2rzWF5ejtzcXDKPAdLU1IS33nrLY1zOihUrsGHDhhloFUH4T2NjI9566y2Px1auXIk777xT4BYRhDssy6KlpYU3lcHkOs7KysKcOXNQUVEBrVYbwlYSZCRFhtM8OqetTSZTQNfRaDT8yCOZx9Dx9ddf48SJE277GYbB888/T1PcRMTw1Vdf4dSpU277GYbBCy+8QFPchKjgOA6dnZ28qezp6QnoOgzDoKCgAHPmzEFpaWnU5D6eSchI/hWbzQaz2Qyz2QyHwwHgVsyQSqWCWq2GQqEI2yowMo+Rg8PhwJtvvukx9URqaipeeOEFSKXSGWjZ35hJLRORg8PhwBtvvIGOjg63Y2lpaXj++ednVMscx8Fut5OWCY/09vaiuroaVVVVHvtjX5DJZCgtLcWcOXNQUFDgUe9NTU1ITk6GRqMJuK0cx8Fms8FisUSllmPaSLIsi6GhIfT19cFisUx5rkwmQ2JiIhITEyGTyUJy/+7ubly+fBlXrlwJuMSU0zyWl5cjLy+PzKMAdHd343e/+53Hqjfr1q3D6tWrBW+Tv1pOSkqCwWAImZaJyKSrqwu///3vPWp5pjISsCyLwcFB9PX1TbvogrRMAMDQ0BC+++47jyPsvqJWq1FeXo45c+YgJyeHN3XvvvsuxsbG8OSTT/qdQ9kfLcvlciQmJkaklmPWSI6MjKC1tZV/M/CH1NTUgCu6mEwmXL16FVeuXPE4EuALarWaH3kk8zgzeKuuIJVKsWvXLiQnJwvWluHhYbS1tfmtZYZhkJqaGrE10YnQcOTIERw/ftxtv1Qqxfe+9z0kJSUJ1hbSMhEMhw8fxsmTJ4O+jl6vx+zZs1FQUIC3334bHMchNzcXO3bs8Hkh2tDQENrb22NCyzFnJFmWRXt7u8fC7/6gVquRnZ3tU3yF3W7HzZs3cfnyZdTW1np8+/flfmQexYPD4cDvfvc7j3E62dnZeOaZZ8LeCbAsi7a2tqAC0IFb2srJyaGVujGK3W7H7373O/T29rody8nJwdNPP01aJiICu92O3/72t+jr6wvL9YuKivDoo49OGfIRi1qOKSPpjEUMNAZxMjKZDPn5+R7NJMdxaGtrw6VLl3D9+vVppxs94TSPzmnrmY69I1xpbW3FG2+84fHYxo0bsXTp0rDd25lLNJjUGBOZSstE9NPS0oI333zT47H77rsPixcvDtu9SctEKGlubsbu3bs9HispKUF/f3/AC3UAoKKiAlu3bvU4mONwONDY2BhUSeKJyOVyzJo1S/RajhkjyXEcmpqaAo5F9IZcLkdBQQEf0zA4OIgrV67gypUrAb0VqdVqlJaW8iOPZB7FzcGDB3HmzBm3/QqFAt///vfDUr9YKC0TscWBAwdw9uxZt/0KhQIvv/xyWOoXh1PLhYWF1H/GKPv378e5c+fc9iuVSnz/+9/H2NgYrl69imvXrnnMpzodCxcuxP333+8yUs9xHBobG0M2UOVEoVB4XQgkFmLGSPb39we8sms64uPj0d/fj8uXL6OxsdHvz8tkMpSVlWHu3LmYNWuWqAVDuDI+Po7f/OY3HkMlCgsLsX379pBPC/b19QUcXzsder0e2dnZYbk2IW7Gx8fxX//1Xx6n5IqLi/HYY49FlJYTEhKQlZUVlmsT4sZqteK//uu/PJrEkpISPProo2AYBhzHobm5GVeuXEFlZaVfM4erVq3C+vXr+Z97e3vR2dkZkvZPRuxaDpuR7OjowD/+4z+ir68PUqkU3//+97Fx48Zw3GpaxsfHUVNTE3CBeF+oqanBlStX/PpMbm4u5s2bh/Lycr9XgxHiob6+Hm+//bbHY1u2bMHcuXNDdi8htJyTkxOW0SdC/NTW1uLdd9/1eOyhhx7C7NmzQ3YvIbScm5uLuLi4sF2fEC81NTV47733PB7btm0bKioqXPY5HA7U1tbi6tWruHHjhk+1v++66y7cdtttsFqtqK2tjVkth81Idnd3o6+vD2VlZejr68OWLVtw8ODBoHIxBUpHR0fYgm+dOGvYTofBYMC8efMwd+5cKtkURezduxeXLl1y269Wq/Hyyy97raTA2u2Q+DGV3N7ejv7+/kCb6RMqlQqFhYVhvQchXj799FNcvnzZbb9Go8HLL7/stQ8nLRNiY8+ePbh69arbfq1Wi5dffhlqtdrj525UVeGDDz/06R6bN29GSkoKBgYGgmrrdKjVahQUFIT1HoEStqW/KSkpKCsrA3CrFrFerw96FVMgsCwb9l8wcGvJfkpKisdjSqUSCxcuxDPPPIMf/OAHWLNmDZnIKGPDhg0ezaLZbMbBgwe9fu7Srr9D8x/fgcOH4GyHwyGIli0WS8gWPhCRx4YNGzyaxbGxMRw6dMjr5y698D00v/0eHObppweF1HKoFj4Qkcc999zjUcsmk2lKLTf/4/+NzNoGSDyk7tFqtUhLS0NxcTEWLlyI0dHRoLPA+IIzMb8Y8dtIsiyLe+65B//2b//msv/EiROYPXs2Dhw44PaZq1evguO4GSm5NTw8HFC6nUCYOIXJMAyKiorw0EMP4R/+4R+wadMmlySnRHShVqtx7733ejx27do11NXVeTxmGxhA3b///3D6vi3TGsqRkZGwTp1MRIiOkRAnGo3Gq5avXLmC+vp6j8fG+/pR96v/g9P3PzitoRweHiYtE2FHo9Hgnnvu8XjM25oGlmXBjI6i4FoVVh37Bhs1cXhq+3b88Ic/xM9//nP85Cc/wa5du/D4449j06ZNmD17dsxrOaCp7U8++QSvvvoqjhw5Ar1ej+rqamzfvh0vv/wynnvuOZdzBwYGsGPHDvzTP/0TFi5cGLKG+4oQ09pOGIbBkS++QLExGUVJRmhEvmSfCD2nT5/2uKhLp9PhrrvucksZUfv//T8Yn5C/T24wIOfpnch8+CFIJ027CDEV6ESpVCK+rkGQexHigwNw+pvT6Ohw13J8fDzWr1/vruX//e8Y7/ubPuWJBuQ8/SQyt22FVK1yOVdILauUSsSRlmMWDsA3p055XAgTHx+PO++8022Ap+ZffwXbhBFzRVIicp5+EhkPbXHTcltbmyCj64B4p7cDMpJ2ux133303tmzZgm3btuGRRx7B+vXr8Ytf/MLlvPHxcTzzzDN4+OGH8eCDD4aqzX5RV1cn6HBwzzO7AB+CdAliKuSJBuQ89QQyH97KG0rBtfz0i0AAlZ8IYiKeDGVtbW1AuXUDgQHQ/dQLgEAzU0T0ojAmIefpJ24ZStUMaJlhUF5eLrqZzYBiJGUyGV544QW8/fbbeOGFF1BeXo6f//znLudwHIf/9t/+G5YvXz5jJhIAbDaboPeTJIQ+byARe9j6B1D37/+B0/dvQfMf34XDbMb4+LigbZDoaeU2ETy2/gG3KW8h+2UOgESkq12JyGK8tw+1//vX+HbTVrS8+z4cFoug/TLHcQGVdQ43AS+22bRpEz868qtf/cot9+H58+fxxRdf4KuvvsIDDzyABx54ADdu3AiutQEgdJpMhpI5EyHEaSiv/vgfBdcySMtECHEayms/+Rk4oUcHSctECHEayms/+W+C98tiTP0d8F/Xq6++CuBWDKSnBNqLFy9GdXV14C0LEUIPAXM2mtYmQoe2qBCzdj0P47o1qBb6RYy0TIQQXXER8nY9D+Pa1be0LODICmcXdmaKiG50JcW8lquqqgQ1d2Kb1gYCNJK//vWvcfToUXz44Yd4+umn8fHHH2PHjh2hbltIUCqVPiUWDQUMGLACBd0S0c1EA8n8dVGDUqkULC0PwzBgRbpCkIgsJhpIp5YVCoVg8b4Mw4AbHhHkXkR0M9FAOg2dUqkUTMsSiUSUle/8Xmzz0Ucf4Z/+6Z/w1ltvYf78+fjNb36DDz/8EH/5y18gl8vD1c6A6ezsRO+EVbHhRKVSIdmHHGpEbDBmMuGjjz6C3e4+8rJy1UqUlpai8mc/h2VCiThtUSHyXnwOyXes5R+6ToTMQKBWq2EcE2fOMkJ4TKOj+Oijjz3GZ91+++0oLinG9Z/+X7B2dfP7PRlIJ6RlYqYYHRnFxx971vKaNatRWFSEaz/+mUs2DU8G0omQGQg0Gg3y8/MFuZc/+DUieezYMfzyl7/Er371K8yfPx8A8MQTT+DNN9/E3r17sW3btnC0MSiErKSj1WqhpyoKxF/RA1hgG8fXX3/tdux4YwPm3n8fJKpbpTE9jUBORqPRCPbw1Wq10IswzQQxMzi1fOTIEbdjxxrrMef+eyH5a7ozXXER8l56AcY1t5OWCdGhBzBv3IJjx465HTvaUI/Z990LifKvWp7CQDrRaDSCGUlvFdJmGp+N5LVr1/CjH/0IP/3pT7FhwwZ+v06nw86dO/Haa69hy5Ytoht21el0kEqlgqx0omo1xGRWrFiBS5cuuXU0Y2NjOHr0KHKKCpH/8ktTGkgncXFxkEgkgiTYJy0Tk7nttttw6dIlt5x5JpMJR48eRVZJMQr+/pUpDaQT0jIxk6xcuRKXL192S/A9OjqKY8eOIaOkGIX/8KMpDaST+Ph4wbSckJAQ9nsEQthqbYuJrq4u9PT0hPUeYh1yJmaemzdv4v3333fbzzAMdu3ahdTUVJ+vRVomZpIbN27ggw8+cNvPMAxeeuklr2ViPSFE2JFWq8WsWbPCeg8iMqmqqsKHHuppSyQSfO9734PRaPT5WkKEauh0OuTl5YX1HoEStlrbYiIpKSnsI6X+mAEitiguLkZRUZHbfo7jcODAAb9W/Amh5bS0tLBen4hciouLPVbW4DgOBw8e9EvLRqPRrTpOqKF+mfBGaWmpxxdmlmVJy34SE0ZSJpMhMzMzbNdPSkoSbewCIQ7uvvtujwawqakJ169f9/k6MpkMGRkZoWyaC0lJSYLGFRORBcMwuOeeezw+NBsaGlBVVeXztcLdLxuNRtIy4ZWptFxXV+dX3mu5XB7Wfjk5ORnqSSVzxURMGEngVhxDOGJlVCqVqN8UCHGQlJSE5cuXezx2+PBhv1JU6fV60jIxYxiNRq9a/uqrr/yKR4+Pjw9L3JdarfZrmp2ITZKTk7F06VKPx7788ku/tKzX68Om5eTk5JBfN5TEVLr/jIwMsCyLoaEhnz/Dsiy+/PJLjI+PY+nSpTh79iwUCgXuuusuqNVq5OXlhX1Im4gOVq9ejStXrmBkxDWn3eDgIM6fP49ly5b5fK1Qa1mj0cSEljmOg81mA8uy4DhuRjaJRAK5XA6ZTAa5XO62RcLvwKnl0dFRl/0DAwO4cOEClixZ4tN1GIZBZmYmWJbF8PCwz/cnLROhYu3atbh69SpMJpPL/v7+fly8eBGLFy/26TqxrOWYWGwzEY7j0NPTg+7u7ulPBnDo0CGYTCYoFApYrVYolUqMj48jLi4Or7zyiuhWqRPi5sqVK/jkk0/c9ms0GrzyyitQKpU+X4vjOHR3d/u8+CaStcyyLKxWq9s2Pj7ucf9U54q9y5NKpR4N5uTNmxGdblOr1SGpjnH58mV8+umnbvu1Wi1eeeUVKP6aDsgXQqXl+Ph4/OAHPxC1lgnxcfHiRXz22Wdu+3U6HV555RW/cmTHopZjakQSuPXWkJKSgri4OLS2tsJqtU55/vj4OBQKBe69915cu3YNs2fPxhdffIHx8fGI+AUT4mLOnDk4ffo0Ojs7XfaPjY3h9OnTWLt2rc/XYhgGqampiI+PF72WOY6DxWLByMgIRkZGYDKZpjV8EzebLXZK3DkcDjgcDlgs4SluwDAMNBoNtFotv03+eeKmUCg8Gs+5c+fi9OnT6OrqctlvMpnw7bffYvXq1X61KRRatlqt1C8TfjNv3jx8++23bgNMo6OjOHPmDFatWuXztZxajouLQ1tbW0xoOeZGJCfCcRzGxsbQ19eHkZERjyMVXV1dOHnyJIqKirB9+3a89957qKmpwRNPPEEpUoiAqKurwzvvvOO2X6FQ4JVXXglo4RbHcTCZTOjv7xdUyxzHwWq18gZxZGQEo6OjLj87NyFyuRKhRyaTeTWcIyMj+Pbbb90+o1Ao8MMf/jCgxS4zpWUitvGWpk2pVOKHP/xhQItdnFru6+vD6Oho1Go5po3kRJwPRLPZzD/wJBIJfvOb30Aqlbq9LTAMg5/97Gcz3GoiEuE4Dm+//TYaGhrcji1duhQbN24M+vqh0PL4+LhHQzjZLMbSaCHhOzqdDllZWS7m07no0WAw+BTGQf0yIRQcx+EPf/gDmpub3Y7ddtttuOuuu4K+frRqOeamtr3BMAxUKhVUKpXLfoVCAbPZzA81Nzc3Y3x8nNL9EAHDMAzWr1+P119/3e3YuXPnsHz58qBWZfurZYVCgb/85S9uI4nj4+MBt4EgRkdHUV1d7fW4RqPhTWVCQgL/vcFg4KuFeNOyXC6HxWJx07JOpwv3P4uIUpz98u7du92OnT17FsuWLUN8fHxQ149WLdOI5DR0dHTg97//PRiGAcdx/NeKigpR1hYnIoePPvoIlZWVbvvnzp2LLVu2BHXtiVMqfX196O/vR2trK5qamty0HG3IZDIolUqPm0KhgFKphFQqBcMwM7KxLAubzTblZrfbpz3HZrNF5e8PuDVS40xzNdlkGgwG9Pb24s0333TTcij+dojY5v3338fNmzfd9i9YsACbN28O+f2am5uxe/duNy3PmzcPDz74YMjvFw5oRHIa0tLSYDAY+Pqyzo67rq4OLMuKflk+IV7WrVuHqqoqNzNw5coV3HbbbT7ldLRYLOjv7+cNo9M09vX1eQ3ydt5PbCZkOvPny6ZQKCImQD1YOI6Dw+HwyXA6N6vVirGxMZhMJpdtugUBQsOyLAYGBtzqejtRKpUu9Y2dWq6pqYHdbodMRo82IjDWr1/v0UheunQJK1asCHlOx+zsbOh0Oj6V1kSP4TSWYof+2qaBYRgUFxfjzJkzLvstFguam5tFW/uSED9GoxELFizAhQsX3I4dPnwY27dvBwDY7XavZnFy7jMxoVarodPpEBcX57bpdDqoVCoXAxgJHaaYYBgGMpkMMpks6KoXdrvdo8E0mUwe9/uTQD8ceDO+ZrMZ//zP/8zHYk4czUxMTERycrJfaYmI2CMlJQXz5s3D5cuXXfZzHIcjR47gkUceCen9nB5j8nNgdHQU7e3tYa3+FCrISPpASUmJm5EEbr39kpEkgmHNmjW4cuWK24O5pqYGr732Gkwmk19Jx4VAqVS6mUJPZpFGhSIHmUyG+Ph4n2LAnEndJ5vLvr4+nD59esZHujmOw9DQkNe/m8TERKSmpiIlJQWpqalITU2FwWCgFxmCZ+3atbh27Zpbpomqqiq0trYiKysrpPcrKSnxOKBQU1NDRjJayMnJgVKpdHsLbmpqmqEWEZEKy7Lo6+tDR0cHOjs70d/fD7lc7nGEp729XdC2yeVyxMfHT2kQdTodjejEOAzDQKFQQKFQuC0K4zgOp0+fdvtMZmYmNm3ahKGhIX7KenBwkP9eyJX//f396O/vd6kLLpfLeWM50WCKub4xET4SEhKwePFijwNIhw8fxpNPPhnSF49Zs2ZBJpO5PQcixWOQkfQBqVSKvLw8tyLuHR0d/KpXgpiMw+FAd3c3bxo7OjrQ1dU1Y+lyFAoFjEYjhoeH3UrbMQyDn/70p35VcCCIyaxatQoXLlxwe+lua2vD0NAQiouL3T7jXBjmyWAODAz4VW4uUGw2G9ra2tDW1uayPz4+3sVYpqamIikpKWbicGOZ1atX4+LFi27ZKxobG1FXV4fCwsKQ3UsulyMnJwf19fUu+1tbW+FwOESvNzKSPpKTk+NmJFmWRWtra8QkDSXCh81mQ1dXFzo6Onjj2NXVxS8GEAqpVIrExEQkJSXxX52bVqsFwzA4deoUvvrqK5fPcRyH1tZWzJo1S9D2EtGFRqPBypUr8fXXX7sdO3LkCIqKitxGchiGgU6ng06nQ3Z2ttvn7Ha7y0jmRLPZ09MT1kT3w8PDGB4eRm1tLb9PIpEgOTnZbfRSp9PR9HgUodFocNttt+Ho0aNux77++msUFBSE9PftyUja7Xa0t7d7/LsQE2QkfSQ3N9fj/qamJjKSMYbFYuFHGJ1fe3t7BYsNYxgGCQkJHs2iM//eVEylZTKSRLAsW7YMZ8+edRv17uzsRE1NjcdRyamQyWS8vifT1NSEP/zhD277nVPug4ODIf+7ZFkWXV1dbqUh1Wo1byonTpPTKH/ksmLFCnz33Xduixo7OjpCPio5Vb9MRjJKSE9Ph1wud5uWjJQYBiIwTCaTi2Hs6OjwmpIkXDjfjI1GIxITE2EwGIJayJKenu4xHsdTRQeC8BeFQoHVq1fjiy++cDt2/Phxj6OSgZKZmQmpVOo2KpmYmIidO3eCZVkMDQ2hv78fPT09vAHs6ekJ+cpzs9mMxsZGNDY2uuxPSkpCamoqMjIykJ2dzT9LCPHj1PKBAwfcjh0/fjyko5LetNzU1ORXre+ZgIykj0gkEuTk5KCurs5lf2trK+UtiwI4jsPIyAhvFp3mMdzxWSqVih9tMRgMOHv2LMxms8s5Y2NjSE1NDdnbr1QqRXZ2tluJxpaWloiIxyHEz4IFC3Dy5Em3v5+2tjY0NDSEbBZHJpMhKyvL7YW+ubmZz/PrTP9TUFDAH2dZFv39/byx7O7uRldXFwYHB0PSrok4U3Y5iw9IJBKkp6cjOzsbWVlZyM7ODqpiChFeFi5ciJMnT2JkZMRlf0tLC5qamkKWuUUulyMzM9Pthb6lpUX0OavJ/fiBJyPpcDjQ3t6OnJycGWoVEQhjY2Nobm5Ga2srP9o4NjYW1nvGxcUhPT0daWlpSE9PR3p6OuLj413eaLVarceRnBMnToR0GiUnJ8fNSNrtdnR0dIQ8tQURe8hkMtx22204ePCg27ETJ06ENBwoJyfHzUjabDZ0dHR4TZ0ikUhgNBphNBpRUVHB77darbypnGgwQ5mwnWVZt4U9er2eN5XZ2dlITU2lFzqR4NTyoUOH3I6dOHEipCkAc3Jy3Iyk1WpFV1cX0tPTQ3afUENG0g+mimEgIyluhoeH0dTUhKamJjQ3N6Onpyes9zMYDC6mMS0tzafaqQsWLMDx48fd4suam5vR1NTkVYP+MpWWyUgSoWDhwoU4ceKEW3xZY2MjmpubQ9Zn5ubm4sSJE277m5qa/M7Bp1QqeTPnxJmXcqLB7OrqQl9fX8jiL515L69fvw7glnnJzMx0MZcajSYk9yL8x6nlyYMN9fX1Ic0rmZubi5MnT7rtb2pqIiMZLXiLYaDYMnHBcRz6+/t589XU1BSWKSvg1sKXpKQkfoQxLS0NaWlpAeefk8lkWLFiBb788ku3Y4cOHcKGDRvAsiw4jvPr6+R93mptV1dX8znNnJtcLue/p1WphK/I5XIsX74chw8fdjv2l7/8BXfeeWdA2vX01RPV1dXIy8sLWsvOxW0JCQkuC4XsdrtL3KXTaIai2pTdbuf7LieJiYm8qczKykJycrKopzujCYVCgeXLl3vMRnDixAk8/vjjIblPdna2x365ubkZy5cvD8k9wgHDzXQZgghj9+7dbsZRoVDgZz/7Gf1RzxAcx6G7u5sfbWxqanIb0QsFEokEKSkpLlPTqampLnlEOY6DxWKBxWKB1WrlN08/j4+Pez1P6LRBviKVSt0eyDKZDEqlEhqNBmq1GhqNxutGpRAjB09ank7XnvbHmpZHR0d5U9nd3Y3Ozk50d3eH/P9BqVQiKyuLH7XMzMyESqUK6T2Iv2G1WvHrX/8aFovF7diLL74YshHD3//+9+jo6HDZp9Fo8JOf/ES0fSeNSPpJbm6um5EcHx9HZ2cnMjIyZqhVsYXD4UBHRwdvHJubmz3+cQeDTCbjRxedI416vR5msxkjIyMYGRlBc3Mzrl+/zv/s3MKZ126mcTgccDgcAceMSaVStweypwe2VquFXq+HSqUSbecZqXAcB6vV6qbbkZERjI6OkpZ9ZDot63Q6pKSkYNmyZdBqtejv70dLSwtaW1vR0tISdEy21WpFXV2dS9x+SkqKyyKexMRE+vsJEUqlEkuXLsXx48fdjp08eRIPP/xwSO6Tm5vrZiTHxsbQ29uL5OTkkNwj1JCR9JOp4nHISIYHm82G1tZWfrSxtbU1pNVhlEoljEYj9Ho9tFot5HI5WJbF6Ogoenp6UF9fj5GRkRmrSBNNOBwO3qT4glKphF6vR0JCAv/Vuen1emg0GnpQTmB8fNzNDHraSMvBE4yWKyoqoFAoMD4+jpGREfT29qK3tzfoNnV3d6O7uxvnz58HcGskKysrC7m5ucjPz0dqair9vQTBsmXL8O2337pVu6msrERPT09IjF5ubi6+/fZbt/1NTU1kJKOFrKwsrzEMK1asmKFWRRcWi4VPrdDc3Iy2traQTgsplUqo1WowDAOLxQKz2eyxPBox8zhX0XZ3d3s8LpfLPRpN5/fRWG3EZrPx9aKdqWWc34ciPo8ID75oWaPRQCqVwm63w2QyBT0iPDY2hps3b+LmzZsAbmWFKCgoQH5+PvLz8xEXFxfU9WMNjUaDxYsX45tvvnE7dvLkSWzZsiXoe3hbhNbc3IzFixcHff1wQEbST5RKJdLT09He3u6yf/LPhO+YTCaXFdWdnZ0hvb5EInExos7YLSLysdlsU47mSKVS3lQajUYkJycjJSUFycnJAS+IEgKHw4HBwUHeKE40i0LUniaEx2azYWhoKKz3MJlMuHLlCq5cuQLg1lR4fn4+CgoKkJubS4nSfWDFihU4e/asW0L7q1evYs2aNUhMTAzq+hqNBsnJyW6ZRcTsMchIBkBGRobbL3V4eBhWqxVKpXKGWhU52O12NDc3o6amBrW1tSGZ0pkKsQb7K5VKqFQqKJVKflOpVFAoFPz+hoYGt0oZADBr1izcdtttYBgGEonE768sy8Jut+Prr79GdXW12/XvueceALcebna7nd9sNhscDofLfpvNBqvVirGxMYyNjYnq/9vhcPCjd5Pr2MbFxSE5OdnFXCYnJwu2YMGZVmayUezv78fAwIBgJTdDgS9arq+v91gJLFRaPnz4MG7cuOF2/Y0bN4LjuIjXcjhwjpB+++23kEqlyMnJ4Ucs09LSom40PxTodDosXLgQZ8+eddnPcRxOnTqFTZs2BX2PzMxMNyPZ398v2oIRZCQDwFucQm9vr995y2KFoaEh3jjW19dHVYyWSqVCXFwcv+l0Omi1WreH6sSffV29vHDhQvz61792m+JqbGzEpk2b+JrCgZKbm+vRSObk5AS0CtG5kMP5IPa0mc1mt30zgTO+bbLBjI+P92gwg3lJdDgc6Onp4SsmObfJsVYzjVqtdtOyRqMJiZbnz5+P//iP//BYAm7z5s1ISEgIqu05OTkejWROTg7S0tL8vp6vWjaZTC4/RyoOhwMNDQ18oQKNRuMyDU7Vd/7GbbfdhnPnzrm9aFy6dAmrV6+GXq8P6vpGo9FtH8dx6OvrQ0pKSlDXDgdkJAPA0y8ZICM5EYfDgebmZtTW1qKmpibsCcDDgUKhcHmoetp0Ol1Yp4Ocb7/fffedy36O43D69Gnce++9QV3fm5Z7enoCMpIMw0ClUkGlUvk8xcOyLCwWi8sDenh4GENDQxgcHOS/ChX/Nzw8jOHhYbcqVnq93sVcpqene8zl58zi4KyY1NnZiZ6enhldAa1UKt1060nP4Sz1Gh8fj/nz5/MLQZywLItvv/2WHwUPlKle8AMxkqHQsslkwsjIiIuOh4aGIiKWdWxsDFevXsXVq1cB3Pr/nTgNPjHtWayh1+sxf/58XLhwwWU/y7I4c+YMNmzYENT1p/IYZCSjBG8dViSapVAyPDyM2tpa1NbWoq6uTnSjLRNhGAZ6vR5JSUlITExEQkKC24NWLGEKK1euxPnz5z2+/a5bty6oWL+pHr5CIZFI+JQpU+GMIRscHHR7MA8ODvq8ejZQnNVHamtr+X0ymQyJiYlQqVTgOA6jo6MYGBgIazsm40yYnZiY6FHLcXFxonnor1q1ChcuXHCbtr948SLWrl0bVFiBGPrlQLU8Wc/h1nIg9PT0oKenB2fOnIFUKkV2djYKCgpQUFAQk9Pgq1atwsWLF920fOHCBaxZsyao54cYtOwPZCQDwGkyJi/YEPLhKwZYlkVLSws/Zd3V1TXTTXIjLi6ON4tJSUn89waDIayjL6FEr9dj7ty5uHTpkst+m82G8+fPY9WqVQFfOz4+nk9DMhExalkul/P1kT1ht9sxPDzs9mB2LloJxyiQ3W73ugo31HjSclJSEhISEiJGywkJCZgzZw6/2MPJ+Pg4Lly4gNtuuy3ga+v1esjlcrewmWjQ8kQ9i2F1vsPhQGNjIxobG3H48GFoNBp+CrygoCAmpsENBgMqKipw7do1l/1WqxUXLlwIKotLQkKCxyp6YtQyQEYyIBiGQXJyMlpbW132i/VtIZSMjo7y09V1dXWiWP2s0WhcTKLza2JiomhGYoJl+fLlbkYSAM6ePYsVK1YEHIDNMAyMRqPb4rFI1LJzdNDbNOTY2Bh6enrQ3d3Nj650d3fPWIymJzxpOSkpCQaDIWq0vGLFCjcjCQBnzpzBsmXLgtJyUlKSW9aHaNWyU8cT9TxTWh4bG8O1a9d4U2U0Gvn4yry8vKjR7mRWrFjhZiSBv2k50Gp3EokERqPRbXBGrFomIxkgRqPRzUgODAzAbrdHzOiAL7Asi7a2Nn7UcXLGfSHR6XR8tZnk5GT+IRsLZcFSU1ORn5/vtjBkZGQE165dw7x58wK+ticjKeYVgoGi0WiQm5uL3Nxcl/0mkwmdnZ2ora1Fa2sr+vr6BFk04Yy9y8zMRH5+PnJzc0WdkihUpKWlYdasWfyiDifDw8OorKzEnDlzAr52cnKym5Hs6+sDy7JRVcJWo9EgLy8PeXl5LvtNJpNHgyn0IiBnSi7nNHh+fj5KS0tRUlICrVYraFvCSUZGBnJzc92yEQwNDaGqqgoVFRUBX9uTkRSrlqPH8QiMt1VV/f39ogyG9QeTyeQS6zgTKxENBgNfmtBZplCn0wneDjGxYsUKNyMJAN9++y3mzp0LhmFgNpv9NiOe4nFYlkV/f79oKykEi9Vq5aslNTc3o7W11S0vXLixWCxob29He3s7vvvuO+h0OsyaNYvfgl3FLGaWL1/uZiQB4PTp05g9e3bAWvbUL7Msi4GBASQlJQXc3khBq9Xy+nHCcZxXgxnq0rKecDgcqKmpQU1NDT7//HPk5OSgtLQUZWVlQa9uFgMrVqzwmNbq9OnTKC8vD6mW7XY7hoaGgs7WEWrISAbIVMGwkWYkOY5De3s7/8cuZOJTZ5jARNOYlpYWE6OM/lJQUOAxUW1nZyfOnz+P9vZ21NbW4sc//rFf151qhWC0GMnR0VHeNDqT3ostT+Po6KjLKtmEhAQXYxlNL1JFRUUwGo1uMV8dHR24cOEC2traUF9fjx/96Ed+XXeqfjkWjKQnGIaBTqeDTqdDfn4+v9+5OKyjowOtra1ob29HW1tbWM0lx3F88YlDhw4hPT0dZWVlKC0tjdi+pri4GImJiejv73fZ39bWhosXL6KlpQWNjY344Q9/6Nd1p9IyGckoYaqHbyTgDJa+fv06bty4IUhsjVwuR2pqqssoY0pKSlSFAoQThmGwYsUKfPbZZ27H9u/fDwABjWJN1WGVlZX5fT0xMDIygrq6Or5a0uROPlgma1mj0cBisaCjowPt7e3o7OwMOt3P4OAgLl68iIsXLwK49XvKy8vDrFmzkJeXF9HT4AzDYPny5fj888/djjn3BVIhJNL7ZSFhGIZf1V9cXAzgb7Nqra2tfNnYzs7OsCVm7+joQEdHB77++msYjUZ+pDI9PT1iVoE7tfzFF1+4Hdu3bx8ABPQSM5WWnb8vsUBP8ABxrpScPB0m1mBYwNU8VldXh33KOikpCTk5OcjJyUFmZiaSkpJEF9sRacyZMweHDx/2umozkJQTBoMholYIesLhcKClpYUPyQh1BgGj0YicnBxkZ2cjKysLiYmJHrW8YMECALemoLq6uviRntbW1qDNrHM60plTND09nTeWkZjXb+7cufj666+9vsQGomXn72Wy8RFzvywmnAuWkpKS+Lhru92Ozs5O3li2tbWF/MUMuNXfnDx5EidPnkR8fDxvKnNyckT/3Jg/fz6OHDni9ZkayAxbUlISGIZxmzkRo5bJSAaIRCJBUlKS2wNLbA9flmXR0NCAyspKVFVVhc08MgyD9PR03jjm5OREVVC1GBgYGMCZM2emzM8ZyMPXqeXJaWzE2GFNZHBwkDeODQ0NIctbKpFIXLScnZ3tt5ZlMhkyMzNdChSYzWaXh3FbW1tQMwHO0ZzTp09DIpEgMzOTN5bZ2dmiHunv7+/H2bNnp6xwFYiWpVIpEhMT3fphsfXLkYRMJkNWVhaysrL4faHW8mSGh4dx9uxZnD17Fmq1GqWlpSgtLUV+fr7odN3f348zZ85MGWMdiJZlMhkMBoObaRejlsX1G4kwkpOTPRpJjuNmdFieZVl+5DFc5lEulyMrK4t/2GZlZUXciEgkYbfb8emnn6K5uXnK8wKNLTUajW5GUgxanojNZkNTUxNvHvv6+kJyXblcjuzsbJfR83BoWa1Wo7CwEIWFhQBuTSMODg6ira0NTU1NaGxsDPgh4czp2tLSghMnTkAqlSI3NxfFxcUoLi4WVUyVzWbDp59+ipaWlinPCzShc3JyskcjKSYtRzretOxcwNbQ0BCyv0+z2cyHeMjlcpSUlKC0tBSFhYUzXjTCZrPhk08+ccvgMplgtExGMsrx1Dk7HA5YLBbB45cmmsfKysqQB0xrNBqX0ca0tLSoSg0jdmQyGZ544gns27fPYw4+J4F2WJ7i0ex2O6xW64wtfHLWlnUax6amppCsrHamAcrOzkZubi7S0tJmZOqMYRgYDAYYDAbMnj0bwK3YTme944aGBgwNDQV0bYfDgfr6etTX1+PgwYNISUlBcXExSkpKkJmZOaOGSi6X48knn8Rnn33GLyzyRKC689Qvj4+Pw2az0ctumJioZWf6puHhYTQ2Ngat5YnYbDY+X6VEIkFBQQHKyspQUlIybTWhcODU8t69e3H9+nWv5wXaL3vSssViEV2aQfG0JALxNt01NjYmiJF0mserV6+iqqoqpMnBDQaDi3F0xmsQM4dMJsODDz4Io9GIr7/+2uM5gXZYU2lZSCNptVrR0NDAm8dQPHwMBoOLcUxMTBStluPi4jB37lzMnTsXwK1whoaGBv6BPDo6GtB1u7u70d3djZMnT0Kr1fIjlfn5+TNirmQyGbZs2QKj0YgjR454PCccWiYjKRzx8fG8lp0jlhNfkoKtzsOyLJ9pBABycnJQUVGB0tJSQSvryOVyPPTQQzAajTh27JjHcwLtQ6fSspiqB5GRDAJvb0AmkylsqSac5vHSpUu4ceNGyOLCFAoF8vPzUVhYiIKCgqjOYRfJMAyD22+/HUlJSfjkk0/cRugCfSv39rmxsbGAVs/6w/DwMKqqqlBdXY3m5uagV4gqlUq+VFthYWFE56pzjvIsXLgQHMeht7eXfxA3NjYGNPNgMpn4qUKZTIb8/HzeWMbFxYXhX+EZhmGwevVqJCUl4dNPPw27lk0mE/VrM8TEEcuJWq6vr+dLLQY7i+ZM7XXgwAGkpaWhoqICZWVlgqR9YhgGa9euhdFoxN69e920HOjA0lRaJiMZJUz1thBKWJZFU1MTLl68iOrq6ikD1P0hLS2Nj3PJysqiqeoIory8HAkJCXj//fddRqlC3WGFKy3U4OAgvwBsuvgiX0hPT+eNY7Rq2ZlzNTk5GUuXLgXLsujq6uKNZVNTk999g91ux82bN3Hz5k0Atyp1OKfAU1NTBRm5raio4LU8cZQqUC0L1S8TgTNRy8uWLQPLsujs7ORfkALR8kQ6OzvR2dmJw4cPIzExEQsWLMCcOXPC/lI5e/ZsXssT9RbtWiYjGQThfPg6zeOFCxdw48aNkJhHlUrFG8eCgoKoSnAci2RkZOC5557De++9x6+wDnWHFez000T6+vp48xhsqU1nsH9BQUHMatm5ujw9PR233XYbHA4H2tvbUV9fz5d79BdnpZ2jR48iPj6eN5V5eXlhjcnKzMzktexcTBApL0VE8EgkEmRkZCAjIwMrV66Ew+FAW1sbGhoaUFNTg7a2toCv3d/fj8OHD+Pw4cNISUnBokWLMGfOnLCFn2VlZeH555/Hu+++yy84inYtk5EMgqmGnQPBaR7Pnz+PmzdvhsQ8ZmZmoqioCIWFhUhPTxd9Pi7CPxISEvDss8/iT3/6ExobG0XVYXEch56eHlRVVaGystJtVbg/MAyDrKwsftSRtOyOVCpFdnY2srOzsWbNGphMJn60sa6uzu/+ZHh4GOfOncO5c+egUChQWFiI8vJyFBUVhSXW0GAw8FpuamoKy3QgERlIpVI+Pn/NmjUYHR1FTU0Nbty4gbq6uoAX3XV3d+PAgQM4cOAAMjIysGTJElRUVEAul3v9TCCr/Sdqubm5OSxhGmKCjCRuregbHh6G2WyG2WyGw+EAx3GQSCRQq9VQq9XQ6XRQq9UuggrFw9dZMuq7775DTU1N0OZRpVKhpKQERUVFyM/Pj+jqF4RvqFQq7NixA/v27YNCoUBvb6+blqVSKVQqVVi1DNzSc1dXFz/yGEyqiri4ON44kpb9R6vVYsGCBViwYAHsdjsaGhpw48YN3Lx5EyMjI35da3x8HJWVlaisrIRMJkNxcTEqKipQVFTk8SF87tw5lJSU+B1zqVarsXPnTnz22WcBazlSpgMJ39HpdLyWbTYbGhoacPPmTVRXVwdsqtrb27F3717s27cP2dnZWL58OYqLi91eUD/++GPMnj3b7ypfGo0GTzzxBD799FOftTy5H44ULce0kRwdHUVvb6/XlZAsy2JkZAQjIyPo7u6GUqlEUlISDAYDGIaBTCaDQqFwW/Diyy+5r68Pp06dQmVlZdCrrZOTkzFnzhwUFxcjJSVFtCtSifBhsVgwf/58jI6OetQzy7Kw2WwuWjYajUhISADDMJDL5ZDL5W4vMr5o2Vmr3WkeBwYGAv535OTkoLi4GIWFhaTlECKTyVBUVISioiJwHIfOzk7eVPobZmC323lTKZfLeVNZWFgIuVwOjuNw8uRJnDlzBk8//bTfydzNZjMWLFiA4eFhDA8Pux33RcueKjWJ7eFLBIZTc8XFxbjvvvvQ0dGBGzduoLq6OqBZD+dMYFNTE6RSKfLz83HbbbchNzcXHMehuroaVVVV2L59O58301fMZjMWLVqEwcFBr/eeqGWVSoWkpCRey0ql0mOlJrFpOSaNpDOWyN/UIlarFe3t7RgYGEBWVhaUSiW0Wq3PRtJsNuPkyZO4evWq3yMCk0lNTcX8+fNRUVEh6EpLQlzY7XZ0dHQEpGVnqTOnljUajdt1vGmZ4zi0tLSgsrIS1dXVAafpYRgGeXl5KCsrQ2lpKWlZAJxVqNLT07F27VoMDw/j5s2buHHjBhoaGvyqEW6z2XD9+nVcv34dCoUCJSUlSElJ4fXwxz/+EU899ZRPU3uh1LJWq3UzoWJ7+BLBwzAMH1u5bt06DA0N4ebNm6iqqkJTU5PfGSAcDgefUkihUMBoNPLXeO+99/D0008jJydn2uvY7Xa0t7d7fBGaCovF4rFfnjw4IDYtx5yRtFgsaGxsDCqxsdlsRk1NDbKzs6HRaNxGYCb+klmWxYULF3DmzJmgM9Knp6djwYIFKC8vp/KDBCwWi98P/smYzWbU1tYiKysLWq3W7SE+edqop6cHV65cwdWrVwM2jxKJBLNmzeLNI2l5ZomPj8fixYuxePFijI+Po66ujo+t9OeBNT4+7pZgvLu7G++88w6efPLJKXPpmc1mNDY2hkzLGo2GjGQMotfrsWTJEixZsgRWqxX19fW4fv16QGsOxsfH0d7ezv/McRzeeustPP/880hPT/f6uVBq2ekxJhtJipGcQSwWC+rr64POU+ekpaUFGRkZbivKTCYT6uvrcfz4cbS0tAR8P+fb1sKFC1FWVkYxYgRPKLXsHF3MyMhw6TiBWw/fkZERXLt2DVeuXEFnZ2dA95BKpS5VKEjL4kShUKCsrAxlZWVgWRZtbW24ceMGqqqq3Eq1+UpHRwfeffdd7Ny502OScbPZjIaGhpBrebJWxfbwJcKLUql00/KlS5eCKhvMsizeeOMNvPTSSzAajW7HzWYz6uvrwXFcsM0Hx3Fobm5GRkaG25S92F6KGC4U/+IIwDlkHYoSaxPhOA5HjhwJKi5sIs7VqYsWLUJJScmMlacjxIvdbkdNTU1Qb7ye4DgOX3/9tUs8jzNGMZBuwhmXV1ZWhuLi4hmvi0sEjnMRlXMaO5D+Li8vD9u3b3dZnBNOLR8+fNhl1FylUuFnP/tZSO9DRB4cx6GjowNnz55FdXV1QGsU5HI5/u7v/s4lKbiQWtZoNPjpT38a0vsEQ8wYydbWVq8Br8HAcRxMJhO+/PLLoEYes7KysGTJEpSUlFAZL2JKWlpaQlI6cDIcx2FkZASHDx8OWMsKhQJFRUUoLy9HYWEhaTkKcT6Ir1+/jsrKSr/61aKiIjz66KN8wvhwa/mrr75yeQn6+c9/HpXJ6onA4DgObW1tOH36tN9ZU1QqFX7wgx/w8b/Nzc1+x0T62sbh4WEcPnzYRcv/43/8D9GkQAubkRwdHcVTTz0Fu90OlmXxxBNP4JFHHgnHrXxqS2NjY9iuz3EcqqqqUFVV5fNnnAHvS5cuRXl5+ZR5rAjCiRBadi6g8RWlUomSkhKUlZWhoKCAtBxDOFfsO0cqfXmQlpeX46GHHoLJZEJTU1NY23b9+nXcuHGD3/fTn/404Jx+RHTjTMV3+vRp1NfX+zR7qdVq8corr8BisaC5uTmsbbt27RpfgQoAfvazn4lmxjJsMZJqtRrvvPMO1Go1zGYz7r//ftx1110wGAzhuqVXgl3kMh0Mw6CwsBA3btyYdiQnKSkJS5YswaJFi8JaKYKIDAYvXYN+XoXPaW6cFWzCxUQtT/WOKZVKUVJSgrlz56KgoIC0HKMwDIPMzExkZmYi2WTFZ5fPA9NoubKyEgqFAnPnzg1724qKinDz5s2QxKwR0Y0zg0ReXh4+/8/f4Hxv17RaNplM+M1vfoNNmzaFvW1FRUWoqakRpZbD1vtLpVI+oN5qtYJl2Rn5DxgfH/eaJzKUKBQKZGZmoqWlxe2YVqvFnDlzcPvtt9PbMOHCpVf+O2RxOhT/w/eQvG7VlIbSarUKsmBAqVQiMzPTY4m9vLw8zJ07F2VlZaJ5GybEQcsv/jdKJEDH4nIMZ6UCDAONRoOEhATEx8dDr9fzX3U6XdAp0HxBqVR6XBBJEFMx/p9/RIlaifbF5RjJTAEYBhKJBAqFgk8ertfrkZiYiOTkZEEWv6hUKqSnp7stiBQDfhtJlmVx7733Yv369S7BnidOnMD3vvc9/Nu//Rs2btwI4FaJrZ07d6KpqQn/+I//iMTExNC13EeE6KyAW/8vGRkZvJF0LjRYt24dkpOTBWkDEZkMXryKszu/j4QFc6Y0lEJr2WkkjUYj5s+fj9mzZ0Ov1wvSBiKyuFWlQwJdVy+KvjgJ7dwylPzD95F+5xqPWu7t7RVEz04tk5EkfMXhcNwqNtLVh+L9J6CdV47Sn7yMtDtu96jlnp4edHV1hb1dTi2L0UgGFCP5ySef4NVXX8WRI0eg1+tRXV2N7du34+WXX8Zzzz3ndn5vby/+7u/+Dv/5n//pccl8OAnXIhtPjI2N4ev3/oSE+lbMS0yBnKb7iGnoOfYNHCbXt1lvhjJcCxM8YTKZcPq3b0LX3oPZ6Zk0dU1MS8+RU3BMSquSsGguiv/h+0hec5uLlsO1MMETo6OjqPmH/wcAUFxcBKmUtExMjSctGxbNQ/E/fB/GNStmTMsjIyP4y1/+AkBcMZIBGUm73Y67774bW7ZswbZt2/DII49g/fr1+MUvfuH1M7/4xS+wfPlyfrRSKG7evOlWeSacNG99Huwo5SsjgmeyoRRay00PPgtuLLB8awQxkcmG8saNG34niA4UjuPQ/MAz4CzBlaIlCMDdUAqt5U8//RQsy4rKSAa0dlwmk+GFF17A22+/jRdeeAHl5eX4+c9/7nLOxBrWo6OjOHfuHGbNmhV8i/0k1DmdpkMSR1U6iNDgnPI+df8OdH99QnAtS0nLRIgYPH8FZ7e/hFObd6L76ClBtcwwDCQ60jIRGgbOX8aZ7bvwzeYn0HP0G8G1LMaUagEnIdq0aROfHf5Xv/qVW26uzs5O7NixA5s3b8bjjz+OHTt2oLS0NLjWRgKMOPI6EdGDqb4RQ1cqhV+sRlomQoyprhFDl68Lf2MfsyIQhK+M1jdi6OoM9MsiJOBgkVdffRUAMDAw4DHB6+zZs7F3797AWxYiJBKJoG8MLE0FEiFCro9D/q6nkPfsdsjj43xKLxVKSMtEqJAnxP9Ny3E6VFdXC6plCtEgQoXcoEfBS08j75nHIdNpUV1dHfKKeVMh5L18JSAj+etf/xpHjx7Fhx9+iKeffhoff/wxduzYEeq2hQS1Wi1Y/MK41Qqz3QboNIiPj4OERnSIabB0doOb9KIz2UA6UalUgmnZarHAzDr+quV4SGhEh5gGj1o26G9p+ZnHIY/T8fvVarVgWQgsZjPMDEjLhM940/JEA+lEpVIJkmIQuFXLOyqM5EcffYTdu3fjrbfeQmlpKZ566im8/vrreOSRR0RZ0UKtVguyoorjOPQPDODajnsBAD/+8Y8RFxc3zaeIWOfo6s0YrW0A4N1AOhHq4TtZyz/5yU+g1VKMGTE1X6+8D2MNt6p7eDOQToTSMsuy6Ovv57VMlW0IX/h6+T0Ya76VMsqbgXSiVqsFMZIcx6Gvry/s9wkEv4zksWPH8Mtf/hK/+tWvMH/+fADAE088gTfffBN79+7Ftm3bwtHGoNDpdILkeAIg2H2I6GI6A+lEp9Ohu7tbkDaRlolAmM5AOhFKywzDCPY3Q0QX0xlIJzqdLuwVx5yIVcs+G8lr167hRz/6EX76059iw4YN/H6dToedO3fitddew5YtWzzGS84karUaKpUKFoslrPdhWTasdWOJ6KTg5WeRtnH9lAbSiZBaDmfdWCI6KfrB80i7764pDaQTtVoNpVIJqzW8KXlIy0QgFP5wFzI2bZjSQDrRaDSCaNnhcIhWyz4H8c2ePRsXL17EU0895Xbshz/8IQ4dOiQ6E+kk3EnQnZ2VUPFrRPSQ/eiDPplI4NboSlJSUljb43whIi0T/pL92BafTCQgnJYbGxtFGVNGiJucx7f4ZCIBYbUsdAo4X4mJ1SB6vT5scTEcx8Fut+P6ddeUFmLM9UREPgkJCXwN+1BDWiaExGAwhFXLNpsNlZWVLvtJy0Q4MBgMYUsOznEcxsfH3bQspjUpMWEkGYZBZmamxzqZobj2hQsXXIa1pVIpdVhEWGAYBllZWWHT8vnz512q58jlclF1WET0ILSWFQoFlfokwoJTy+G69vnz511miVQqlahmgGPCSAKAUqlEdnZ2yK/b29uLtrY2l30ajSYsnSNBALe0HI5Oq7u7G+3t7S77aIUrEU7CpeWuri50dHS47CMtE+FEpVKFxWN0dHSgs7PTZZ/YtBxTr2fx8fHIyckJWcCq0WjE2bNn3fZTqhQi3Oj1egBAS0tLSK6XnJyMM2fOuO0XW4flK87pIKvV6nFjWRYcx4V1c7Zj8sYwDD/SG8gmlUqj6kU1HFo+ffq0237ql4lwo9frwXEcWltbQ3K9lJQUnDp1ym2/2PrlmDKSwC0zWVBQgNbW1oBXWUmlUmRkZECv12NsbMztuNh+yUR0otfroVAoQqZlk8nkdlxoLTscDjfTN5Uh9LZNnNKMRnw1nTKZzOVnhUIBjUYDrVbLbyqVasaNaai0nJmZifj4eOqXiRkjISGB13Kg/dB0WhbbS1HMGUngVuqJgoIC9PX1obe31+eVUAzDICEhAampqXysDXVYxEwSjJYNBgNSUlKm1HIwHRbLsjCZTBgZGeG30dFRjIyMwGQyeTSAtMLWN2w2W8hW1kskEmi1WjeD6WnTaDRhi/92arm3txd9fX0h1zL1y4RQaDQaFBYWBq1ljuM8ajlci9QCJSaNJHCr80xOTobRaMTw8DCGh4dhNpvd3iBkMhnUajV0Oh0SEhJcAlxZloXZ7F7DlTosQkhCoWXnSOBkPGnZ2blNNIiezOLo6Cg/xUuIF5Zl+d+dL8jlcjdzOZXx9GdRgEQiQUpKCoxGI0ZGRjA8PIyxsTE30zyVlu12u8eRIOqXCSGZqOXh4WGMjIz4rWWbzebx5ZpGJEUGwzDQ6/V8nA7LsnA4HOA4DlKpdMpO0JOJBKjDImaGYLTs6a0XuLUAZ//+/bw5dBpElmXD8m8gxI/NZsPg4CAGBwd9Ol+lUvHGMj4+HgaDwWWLi4uDROK67lMikbho2eFw8HGtgWqZ+mViJpBIJEhISEBCQgKA6NRyzBvJyUgkErdOzRueYsoA8b0tELHJZC3b7XYMDAygr68PfX19/FvyyMiIV1NQX1+P+vp6gVocPTAM43VjWTamEr5bLBZYLBavdYKdD1qDwcB/nbg5U534OrJJ/TIhZkKhZTKSUUSkvC0QsQPLshgaGkJfXx/6+/t509jX14ehoaGommqWy+VQKpUeN4VCMeUxmUw2pdkLdAPg08IVjuPgcDj4WMdANrvd7tM5YodlWfT396O/v9/jcZVK5WYunYZTr9e7PZSpXyaiBW9aFttLERnJIKAOi5gJOI6DyWRyMYlO09jf3y/aMlpOGIaBTqeDSqXyavZ8MYe+zhyIEYZhIJPJ+PiocOGs8OLcrFYrxsbGYDKZptzEtOrdYrGgo6PDLS8k8Ldwjonm0lusJ/XLRKQRKR6DjGQQ0BQKEU6c04GTzWJfX5+oHvROGIaBVqtFXFwcv+l0Opef4+LioNFoItoERhIMw0ChUPi90tput3s1mZ6M6Ey9vHAc53O85sWLF9Hf34/U1FQYjUaqckOIHprajgEi5W2BEC82m83FIE783pu+ZoKJBtGTOYyLi4NWqyWDGCXIZDKXxS5T4Uz+Pt0o5+joKAYHB2dsuv3ixYu4ePEigFsG22g0IjU1FSkpKUhNTUVqairi4+NnPKcmQTihqe0YwNNbMMMwosvxRMw8HMdhZGSEn6Lr7OxEZ2cnhoaGZrRdarUacXFxGBsbw+joqMsxhmHwyiuvIC4uTlR1XQlxwTAMH26QmJg45bkcx2F0dBQDAwMYHBzEwMCAy+ZrCqJg4TgOPT096OnpcdmvUqlcjKXTaIYrdyZBTIUnj+EsMiAmyEgGweROCAASExNpVCbG4TgOAwMDLqaxo6NjxkYYtVotkpKS+C0xMRFJSUkwGAx8h/Taa6+5GcmkpCQ+ZQVBhAKGYfgR7JycHLfjdrvdzWBO/DncIR0WiwXNzc1uZXQNBoPb6KXBYKC+nggrnjyG0WgU3ag5GckA4TgOvb29bvuNRuMMtIaYKViWRV9fH28ancYx0DJvgaJUKt2MonNTKpVTftablpOTk8PVXILwiEwmg9Fo9NiPchwHs9nsNorpNJvhzErgvE91dbVLW1NSUtxGMCm0iQgFzmfLZMToMchIBsjo6KhHsyDGXzIRGhwOB7q7u92mp4WK+ZJKpV7NokajCfgtdXh42ONID2mZEBMMw0Cj0UCj0SAzM9PtuMPhQEtLC9566y23Y1KpNOQLgux2O9rb29He3u6yPy4uzs1cGo1GCg8h/GJwcNCjZsXYL5ORDBBPQ84AjeJECzabjTeKTuPY3d0d9oouzlqrk41iUlJS2BYCeNOyGDssgvCGVCr1muj9gQceQG5uLrq6utDV1YXu7m50dXWht7c35H/TziT/dXV1/D6JRIK0tDRkZWUhOzsb2dnZPi1kImKXSPIYZCQDhB6+0YPFYuENo/Nrb29vWJN3y2QypKWlISUlxcUsGgwGwUcuPE1rA+LssAhiKrz1yykpKYiPj0d8fDyKior4/Q6HA729vW4GM9SLfliW5Ucvz549CwCIj493MZZpaWk0aknwRJLHICMZIN4evmL8JRN/wxkP2NTUhObmZrS2tmJgYCCs91QqlUhLS0N6ejr/1Wg0iiZQP5I6LIKYCk/9MsMwSEpK8ni+VCrlp58nMjY2xpvKiSYzlGEsw8PDqKysRGVlJYBbL5cZGRku5lJsaV4I4fBXyzMJGckA8fRLjo+Pn3ZhAyEsLMuis7MTzc3NvHkM5+ppjUbjYhjT09NhMBhEt8puIp60nJCQILoUEwQxHd607G/ycY1Gg7y8POTl5fH7WJbFwMCA2+hlqF5E7Xa724pxg8HAm8qsrCykpKSI5gWUCC+etJyYmCjKUWsykgHiaRSHpgJnHmcAvNM0Njc3hy1lSFxcHG8WneYxEhMae0sxQRCRhDM35GRC1S9LJBI+BKW8vJzfPz4+7jJ66fzeYrEEfU/navErV64AABQKBbKysvhRy6ysLKhUqqDvQ4iLcGs51JCRDACz2eyxdBE9fIVnfHwcLS0tLlPV4SjXZjAY3EYao2HayWQywWw2u+0nLRORhslk8mjewq3liebOCcdxGB4eRnd3Nzo7O9He3o6WlhavJe98ZXx8HPX19aivr+f3JScnu0yHJyUlRdzLLOHKyMhIRGXSICMZABRTNnOMjY3xI41NTU3o6OgI6aIYZ+m0iYYxLS0tat/6aaENES2IaZUrwzB8iUnn4h5noYLW1la0tLSgpaUF3d3dQfdfzgo9zvKParXaxVhmZGRQZZ4II9I8BhnJAKCHr3AMDw+7xDd2d3eH9PpGoxFZWVm8aUxNTY2pTldMD1+CCAaxa5lhGCQmJiIxMRFz584FAFitVrS1taGlpQWtra1obW0NekrcbDajpqYGNTU1/H3T09NRUFCAgoICZGVliTLOjvgbkeYxyEgGgNg7rEjF+cbuNI1NTU0hXVHNMAzS0tKQm5uLnJwc5OTkRMX0dDBE2psvQXgjEjNpKJVK5OfnIz8/H8Dfsko4RyxbW1u9/rt8heM4PvXQiRMnIJfLMWvWLOTn56OgoICmwkVIpPXLZCQDoLOz022fs+IC4TvOgOKmpiZ+m1zvORikUikyMzORk5OD3NxcZGdn06r6SXjSslarhVqtnoHWEETgeNJyXFxcRP3NMwyD5ORkJCcnY+HChQBujTBOnA5va2vzmnjdF2w2G27evImbN28CuJVtpKCggDe09BybeTxpWa/Xi3a2jIyknzgcDrS2trrtT0tLm4HWRB7OYPGamhrU1tZieHg4ZNeWy+X8SGNubi4yMzP9TvsRS9jtdrS1tbntJy0TkYbNZotaLavVahQVFfGxlizLoqurix+xbGlpweDgYMDXHx4exsWLF/kYy8nT4NSHCsv4+Dg6Ojrc9otZy6QQP2lvb/eYlDYnJ2cGWiN+nFM1TuPY1NQUspJkarXaxTimp6dTjjU/aGtr87jCPTc3dwZaQxCB09ra6rFfiUYtSyQSPqZ76dKlAIDR0VGX6fD29vaAs1c4S8KePHkScrkceXl5/DS40WikafAw403LYvYYZCT9pKmpyeP+aOywAmV8fByNjY18wPfQ0FBIrhsXF8fHN+bm5iI5OZk6tSDwpmUxd1gE4YlY17JOp0NZWRnKysoA3Jpt6OzsREtLCxobG9HY2BhQPl2bzeaycCcuLs5lGjzWY8zDQSR6DDKSfjKx6oATiUSCzMzMGWiNOOA4Dv39/fyoY2NjY0hyOSYmJvKmMTc3FwkJCWQcQ4gnLTvjSgkikvCkZWfJwVhEJpPxuS1XrFjBh2TV1dWhvr7eYxiAL4yMjODSpUu4dOkSgFvTrc5p8OzsbJoGDwGetCyXy2lqO1pgWdbjLzkzMzPmysnZbDZ+1LG2tjYkq6tTUlJcRhzj4uJC0FLCE960TDFRRKThcDjQ0tLitj87O5vS3PwVqVTKv5DfcccdMJvNfGLzurq6gGeNOjs70dnZiVOnTkEmk7lMg9OMkf/Y7XaPazDErmV6YvhBV1cXrFar234xDzmHkv7+ftTW1qKmpgaNjY0eY0X9QafToaioCIWFhZg1axatFBaQjo4Ojys/Y2UqkIgeOjo6KG7dT9RqNSoqKlBRUcHPKDlHKxsaGgKaBrfb7aitrUVtbS2AW/27cxq8qKiI+ncf8LYGQ+weg4ykH0Ri7EIw2O12NDU18aOOfX19QV2PYRhkZ2ejsLAQRUVFSE1NpTfWGcLTaCQQvVomopdY65dDDcMwfA3xpUuXwuFwoK2tDXV1dairq0N7e3tA1XdGR0dx+fJlXL58GRKJBHl5eSgrK0NJSQnNNnkhUrVMRtIPPD18neYoWhgcHOSNY0NDQ1D5yoBbOQmdo44FBQVRW2ow0vDUYUWblonYwJOWJRKJS+1rwnekUimfDWPdunUwm81oaGjgRywDSTXEsiw/lb5//35kZ2ejtLQUZWVlMBgMof9HRCiRGrdORtJHOI7z2GGlpaVFVMLbyTgcDjQ3N/Mr84KtosAwDLKysvhRx7S0NBp1FBkcx3nssNLT00Wb8JYgPOEt1jcjIyPm4tbDhVqtRnl5OcrLy/nqYxOnwT2Fe02HM1XRl19+idTUVJSVlaG0tBQpKSkx+7yYag2G2OPWxd06EdHX14exsTG3/ZEYh+NwONDQ0IDKykpUV1fDbDYHdT2NRoPCwkJ+1JEqI4ibnp4ej79zsU+fEMRkuru7YzpuXWgm1gtfsmQJWJZ1mQZva2vzexq8q6sLXV1dOHr0KBITE/mRyszMzJgylV1dXR5jUyPBY5CR9JFIjV1w4nA40NjYiOvXr4fEPGZmZvKjjhkZGTH1Bx/pRLqWCcIJaXlmkUgkyM7ORnZ2NtauXQuLxeIyDe5vNo/+/n588803+OabbxAXF4eSkhKUlZUhNzdX1KuWQ0Eka5mMpI9EYsJblmXR0NAQEvOoVqtRUFCAoqIiFBQUUCLaCMbbQhsxa5kgPOFNyxTrOzOoVCqXxOi9vb2orq5GVVUV2tvb/brWyMgIzp07h3PnzkGtVqO4uBhlZWXIz8+PyrCFSI5bJyPpAxzHobGx0W1/cnKy6AwVy7L8yGNVVVVQ5jE9PZ1fKJOZmUnlB6MAb1pOSUmh9BxEROHs6yaTlpZGi/pEgtFoxKpVq7Bq1SoMDQ2huroa1dXVaGpq8msK3Gw28yvA5XI5ioqKUFpaiqKioqj4XbMs69FIpqenR8QaDDKSPtDZ2YmRkRG3/WIZwZloHqurqz3GcvqCSqVCQUEBH++o0+lC3FJipmlvb8fo6KjbfrFomSB8pa2tLWri1mMBvV6PZcuWYdmyZTCZTLh58yaqqqpQX1/vVyU0m82GyspKVFZWQiKRID8/H6WlpSgtLRXdwI6vtLa2ehz0iRQtk5H0gRs3bnjcX1hYKHBL/obTPFZWVqKqqipg86jVavkVeTk5OTTqGOXcvHnT4/6ioiKBW0IQwUFajly0Wi0WLFiABQsWwGq1oqamBtXV1aipqfErGTrLsnwSdGdaIecK8ISEhPD9A0KMGD2GP5CR9AFPHZZUKkV+fr6g7XAOfzunrYMxj2VlZaioqCDzGGN46rBkMhlmzZo1A60hiMDxpGW5XI68vDzhG0MEjFKpxOzZszF79mzY7XbU19ejqqoKN27c8Cs0y5nWrLm5GYcOHUJ6ejpKS0tRUVGBpKSkMP4LgseTx1AoFBGjZTKS0zA8PIyOjg63/fn5+YLk3CPzSISKoaEhdHV1ue0vKCiIyuB1InoZGBhAT0+P2/6CggLR59wjvCOTyVBcXIzi4mI+r2JVVRWqq6sxPDzs17U6OjrQ0dGBI0eOICMjA3PmzEFFRYXoqur09/d7zN9cWFgYMSvV6S9uGqqrqwHcWj3FcRz/NZxDzk7z+P9v796j27jue9F/B+8BSYBPUSLFh16URFECcuw4aV527PilRHHkqkpiS5FjV36cuImdLp2ctjnL9VLa214ntu9pem8Su/FxnCat7eXYcWMnaRLLceK6dlZNSJRESZZESXyTIAmQIN4z9w8QEEXxNQPMYAB8P2tpRQIxM5vh18Mf9t6zd3rYOhQKqTpPunhsb29HS0sLi8cSl48sE2mBWS5+6W0VW1tbcdNNN6G/vz9TVCrdrre/vx/9/f345S9/iTVr1qCjowObN282xIM6x48fB1DYWWYhOYssy4hGo5mJvyaTCb/+9a8hCAJsNhui0ShsNhtisRjeeustXHXVVTm7dvrTV7rnUW3x6HQ6Mz2PLB5Ll5Isv/nmm7jyyivz3GKi+c2X5ddee23eLP/+97/HFVdckecWU64JgoDGxkY0Njbiuuuuw+joaKaonG/EcCGyLF+yVWNbWxu2bt2KDRs2LNiTHQqF4HQ6c7JW8nxZfv311+fN8htvvIH3ve99WV9TDyVfSCYSCYyPjyMYDCISicy7JIHNZsP27dvR1dWFjo4OvPLKK1kv6A2kQjUwMACfz4ejR4+yeKSsLJXl9M1KiywT5RKzTAsRBAF1dXWoq6vDxz72MUxMTGTWqlxoXdH5JJNJHD9+HMePH4fdbkd7ezu2bt162e/QQ4cOwW634xOf+ISq9qazHAgEEI1GizLLJVtIJpNJDA4OLrny/lVXXYXf/e536Orqwm233YYf/ehHiEaj2Lt3r+prB4NBHD58GD6fT/Xe1unisb29Ha2trSweS1gikcDg4CAmJiYWfZ8WWSbKJWaZlKqsrMQHP/hBfPCDH8TU1BROnDiB7u5unDlzBpIkLesc0WgU7777Lt59911UVFSgo6MDW7duRV1dHbq6uhCJROBwOPCRj3xk2e0qpSwLstKNMYvA5OQkent7l7V21csvvwxBEC77tAAAX/va15bd3R2LxdDd3Q2fz4czZ86oarcoipmeRxaPBKQ+lPT19emaZSItMMuUS+FwGMeOHUNXV9e8C9cvR0VFxSVrSG/fvh3vf//7lzwuEAigv7+/ZLJccj2So6OjGBwcXPb7bTYbQqEQXnnlFUSjUZw/fx6xWAxlZWW4cOECmpqaFvxBp3cROXz4MI4dO6Zofaw0Fo+0kFxmube3F6tXry6ImxYVn5GRkXlXFFgIs0xLEUURV1xxBa644goEAgF0dXWhq6tL0T1z7kYkr7zyCux2O7Zt27bgMbnMcl9fHxobGw2f5ZLqkfT7/Yom5gKph2D+/d//HbFYDFdddRXefvtt2Gw2XH/99TCZTHC73ZfdtPx+P3w+Hw4fPoxAIKC4neniMT1sXShLAJB+9MoykdaUfiACls5yZWVlQfwCJv2NjIzgyJEjOHLkyJLDzvMRBAGf/exnsXHjxsu+VqpZLplCMhwO4/Tp05qce9WqVXA6nTh69Ch8Ph96e3sVn8Nms6G9vR0dHR0sHmlR09PTqqdHLGXVqlWGX7yXioeWWW5oaEB1dbUm56bCJ8syent7ceTIERw9elTRGs1msxl79uy5ZMHwUCiEs2fPatBSoLGxEVVVVZqcOxc0LyTD4TC2b9+Om266CV/72te0vNSCJEnC6dOnEY1GNTm/IAj4j//4D/T19Sk+bt26ddi2bRs2bdrERaFpSektwdRMk1gOQRCwYcMGXRbbp9LGLJNRJJNJnDlzBl1dXTh+/Dji8fiSx9hsNuzbtw8NDQ2QJAmnTp1a1nFqCIKAtrY2w9YImheSjz32GHp6etDQ0JC3QlLNMKBSkUgEP/vZz5b13rq6Ong8Hmzbts1wq+yTsakZOlHK5XKhublZ02sQ6ZFlt9uNpqYmTa9BxSUWi+Htt9/Gr3/96yXf63Q68cUvfhGyLCuaF6mGkbOs6ZMbPT09OHPmDK6++motL7MoWZYVr4KvhsPhWHSVfKfTiQ984AO4++67cd999+HDH/4wi0jC2e/9E6YvLG8qhF5ZDgaDmn2ypuJ19jtPIty7vFEZvbIcCASQSCQ0vw4VD5vNhthPX4E9tPRQtyRJ+OUvf1nyWVb81LYkSdi+fTuuu+46HDhwIPP6G2+8gfvuuw+PPPIIbr75ZgDA3//93+N//I//gXfffTd3LVZoenpas6GTubxeL956663Mv81mM9ra2uDxeApq30zSz9DPfoGe/+9JrNyxHS37vwhn0+oF3xsKhXQr8CYmJlBXV6fLtag4DP7bq+j53j9h5Y5PonX/FyGublzwvXpmeXx8nFmmZZNlGbHfv4X3T4Uw2b4R8Ws+gvLmJrjdbrhcLlRUVGT+WK1WTE5O4ty5c7q0bWJiArW1tbpcSwnFhaTJZMI999yDgwcP4u6774bb7UZ3dze+8pWv4MEHH8wUkb/61a/Q2tqKNWvW5LWQVLtbjBoNDQ0AUhNjPdu2YcuWLRBFMfN1eZmLo1IJkWXIySQGXnwZgy+/smhBqWeWp6amUMuHbkgJWYacSGLgJz/F4Ms/W7SgZJbJqGRZhquiApHJKbiOdkM4cQorP/0ptP7pHcbIsgELSVVzJBOJBG688Ubs3LkTu3btwu7du3HdddfhoYceyrznW9/6Fn7605/CbDYjFAohkUjgi1/8Iu6///6cfgNLOXfu3GVrQWlFEASc3X8/nFP6BYuKj2A2z1tQ9vT0YGpqSpc2mEwmDH7uDl2uRcVLsJjnLSjPnj2r2y9gZplyQbCY5y0o9cyy2WzG5s2bdbmWEqoftvmXf/kXPPbYY1ixYgUaGxvxj//4jwsO3b7wwgs4depUXh62OXHihK7zvUa/9FVI4xO6XY+K19yCsru7W9c5MqP3PQApENTtelS85haUemd55J4vQ57U50MYFbe5BeXx48eXtYNNrmzevNlw0+RUP2yzY8eOzKbijz76qOG+sbTl7rWZK4Ldruv1qHilh7z/89N/guMPfYNZpoKVHvJ+a8cuHP/rv4Gk4y9egFmm3JETSQy88BLe+vQudB/8v3S/L+t9veVQvUXiwYMHAaQmMi9VRN56661qL1NwOA+SckoQUHf9dWj+wm04H9NmHdSFMMuUU4KAFTeksnwuEgb03AtD58KVipzJhBU3Xo+mPZ/HufA0SmRflwWpKiQff/xxHDp0CM8++yzuuOMOPP/887j99ttz3bacsFqtunY7y0F95mNSkRMErLjxeqy5506UrVsLALCcOqVrliWd5hZTkRME1N90PVrvuQtla9cAACwnT+q2mgYASDrNLaYiZzKh/uYb0Hr3nShb0wpA/ywbcfRXcSH53HPP4amnnsLTTz+NTZs2Yd++fXjyySexe/duQ666LooiIpGILteyWq1YvfuPdbkWFYeBF19GIjhrHuI8BWSaKIqa7c40l81qRdNn/0SXa1FxGPjJS0jMnocoCBd/6c4UkGlOp1O3X742qxVNn/+sLtei4tD/wotIzn5o1mS6+GFopoBME0VRtyzb7XaYTJou/62KoodtXn/9dXzpS1/Co48+ihtuuAFA6nH0j3/84/ja176GXbt2adZQtcbGxtDf36/LtYy88jwZ01u37Mb02Z5FC8g0PXZoSqusrMTq1QuvaUk013986o8RPn9h0QIyjVkmI3vz5s8g0tc/bw/kXHrs0JRWVVWFxsaF12fNl2X3SHZ1deGBBx7AgQMHMkUkAJSXl2PPnj144oknsHPnTsN1u7pcLl0LSSIlBJOAFTfdsGgBmeZyuXT75cssk1KCIKB++42LFpBpzDIZmWA2o/6TNy1aQKa5XC7dCkmjZlnzvbaN4MKFCwgEAppew2KxYOPGjRAEQdPrUHGJDg3DXr9i2e8/f/48gkFtl+SxWq1oa2tjlkkRZpmKhdIs67FetZGzbLzBdg3osRJ8XV2dIX/AZGxKblaAPlmura1llkkxI2aZ92VSg1lWpiQKSVEUNf1Bi6KI6upqzc5PlOZ0OlGj4XZvzDLpRessO51OVFVVaXZ+orSysjJN75tGz3JJFJIAsGLFCtg1WJRWEASsXr3asJ8UqPjU19fDZrPl/LzMMumNWaZiUV9fr8nKNSaTyfBZLplC0mQyobW1Nac/aEEQ0NLSokmBSrSQdJYtFtX7CVxGEAS0trYyy6QrLbOsRYFKtBCz2Yw1a9aUZJZL4mGb2eLxOHp6erJej89sNqO5uRllZWU5ahmRMswyFYtYLIZz587lJMstLS1wOp05ahmRMrFYDD09PVmvLVlIWS65QhJI7VU5OjqK4eFhVce7XC40NDTk9JMHkRqSJGFkZAQjIyOqjmeWySiyzbLb7caqVauYZco7SZIwPDyM0dFRVccXWpZLspBMi0aj8Pv9GB8fX9ZemRUVFaipqUF5ebkOrSNavkgkgrGxsWVn2eVyobq6mlkmw1GT5ZqaGvaok+FEIhH4/X5MTEwUdZZLupBMSyaTCIVCCIfDCIfDSCaTkGUZZrMZDocDoiiirKzMkFtAEs02N8uJRAIAmGUqOMwyFYtizzILSSIiIiJSpWSe2iYiIiKi3GIhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVS74bYBTxeBzhcBjhcBjJZBIAYDKZ4HA4IIoibDYbBEHIcyuJlsYsUzGQZRmJRIJZpoInyzLi8TgikUhRZrmkC0lJkhAIBOD3+xGJRBZ9r8ViQXV1Naqrq2GxlPT/bWRASrNcU1ODqqoqZpkMR5IkTExMwO/3IxqNLvpeZpmMTEmWrVYrqqurCzLLgizLcr4bkQ+Tk5Po7e3NfDJQor6+HrW1tQX76YGKSzAYRF9fn+IsC4KA+vp61NTUMMtkCMwyFYtAIID+/v6SyHLJFZKSJKG/vx8TExNZnUcURTQ1NcFms+WmYUQKSZKEvr4+BAKBrM4jiiKam5thtVpz1DIiZZhlKhalmOWSKiQlScK5c+cQCoVycj6LxYK1a9eymCTdSZKEnp4eTE9P5+R8zDLlC7NMxSKZTKKnpwfhcDgn57NarVizZo3hs1wyT23Lsozz58/nrIgEgEQigbNnzyKRSOTsnERLSWc5V794AWaZ8kPLLKuZtkSkVjrLuSoigdSDkz09PYbPcskUkuPj45iamsr5eePxOAYGBnJ+XqKFjI2NMctUFJhlKhZ+vz+nHVVpsVjM8FnW9NGg9vZ2bNiwAQDQ0dGBv/mbv9HycgvS+gcRCATgdrvhcrk0uwYRkMry4OCgZudnlkkvWmd5YmICbrcbFRUVml2DCACi0SiGhoY0O7/Rs6xpIVlRUYGXXnpJy0ssi9/vh9ZTQYeGhlBRUVEwT1mRMciSBMG0/IGB0dFRzbM8PDzMQpIUM2KW0/dlIiWMmOXh4WHDZrnoh7YlScL4+Ljm14lGozmdG0GlIfrbf0X8xNuQE7El35tMJnXJciQSyemcNSoN0UM/RvzkO5AT8SXfq2eWeV8mpVJZ/gPk5PKynO0qMMuRXpjfiBQXkpIk4aabbsIjjzxyyetvvPEGOjo68Oqrr2ZeC4VCuPXWW/H5z38eb7/9dvatVSEYDEKSJF2upUeYqLjIkRDihw8h/Mr3liwoJycnNf/Um8Ysk1JyNIS47zWEX/nukgVlMBhklsmw5MgU4r7fpO7LSxSUzLLK5X9+8pOf4ODBg3jttdfgdrvR3d2N2267DV/60pdw1113Zd43NDSE+vp6nDx5Evfccw9efvlllJeX5/QbWMrAwAD8fr8u17Lb7Vhj51OvtHxx328gR2ZN0LY7Yd14FSzrvBAsly750N/fj7GxMV3a5bDb0coskwKxzt8A0TlZ3vQBWNZ6IVguXQtP1yw7HGi1Ld2zRJQW6/w1EJ01KmMvg3XTVfNmua+vT5fedSC1tuS6det0uZYSqgrJRCKBG2+8ETt37sSuXbuwe/duXHfddXjooYcWPOZP//RP8ZWvfAVbt27NqsFKnT59Wtfu4DXdP4cg69MDSkVsnoJS9ywffxUCSmaZWdLKPAXle++9t+RWnrkiCAJaj73CLFP2HGWwbvwALOs8EMz5yXJ7e7vhnsVQNUfSYrFg//79eOaZZ7B//360t7fj61//+iXvCQQCiMVSw3SDg4M4ffo0mpqasm+xQvG4vp9EExa7rtejIhWdvmzIO/3fk16SzDLlQnT6siFvPe/LsiwjaTH2gs5UICKhy4a89bwvy7JsyDUlVT+1vWPHDvzt3/4tAODRRx+F2Wy+5OunT5/GQw89BEEQIAgC/uqv/gqVlZVZNVYNvTfukYWif36J9DRTUCaHeiDXduh6aWaZcmqmoEwO9kCu3aLrpWUYqweHCtxMQZkc6oFcvVnXSxtxM0LVheTBgwcBpBb6nltEAsB/+2//DS+//LL6luWI3l3AgmS8TwtUuAR3HaztH4a5cQOE7m59r80pGpRDgnsFrFs+BHMDs0yFTahckbovN6yHcPy4rsWd0Ya1AZVD248//jgOHTqEZ599FolEAs8//3yu25Uzdrt+w3OCIECyibpdj4qX4K6D7Y8+A8f1d8Cyug2CIOie5SSzTDkguFfA9qHPwHH9PlgaU1nWc+9gQRCQtJfpdj0qXkLlCtg+tBOOT+yDpXGD7vdlk8k0b8ddvinukXzuuefw1FNP4emnn8amTZuwb98+PPnkk9i9ezesVuvSJ9CZKIqabFs0H6vNhoErPgs7knCbEqgwxWEx3ocHMghJkhD7z5chhCczr13SAznnk6coirqt72i12VNZFpJwC8wyLU6SJMTe+imEyMXtDlM9kDO9NnOy7HQ6dXtwzGpPZdkhJOESEnCZ4jAzy7QASZIQ+48XIcx6avuSHsh57st6ZdnhcBiyR1JRIfn666/j4YcfxqOPPgqv1wsA2Lt3L77//e/jpZdewq5du7RoY1acTqdu1xKsDgBAFGYMS2YMS3ZU2C1wi1ZU2C2GDADlj5xI4Nu+EVggo9xug7t2BVxl9XD1DMM1FoHL5UJFRQVcLhesViucTqduS1nBmvqUHZXNGJYvZrlStKKcWaY5pHgc3/aNwiLMzvIKuM4OweUP5zXLgiV1X47IZkSYZVpCMhbDPxweg1WQUeGwwVWzAu7yelTMynI6z+ks67WUVVmZMXvWl11IdnV14YEHHsCBAwdwww03ZF4vLy/Hnj178MQTT2Dnzp2G63YtLy+H2WzW5UmnuOXyocDJaAKT0QTMggC3aIVbtMJhMfHmRbBYLNi0shqdvX5MJWIYDPUC53rnfa/D4cDatWuxZcsWXRbYj1uclz2ekMmySYDbwSzTRVarFW0rq3C4b2xZWV63bh3a29t1yXLMKi6Z5UrRCofVWL+7KD9sNhva6itxpH8cU1MxDEwtnOX0uo6bN2/WJcv5eGB5OVStI1lohoaGMDIyouk1rHYHkhUrlvVeu8WEStEKt8MKi5lPxpaqWCyGN954A7/73e+WfG9FRQXuvPNORKNRzbNsc4hIlNct673MMgGpLP/2t7/F73//+yXf63K5cOeddyIcDmN0dFTTdjHLpFQsFsPrr7+ON998c8n3ut1u3HnnnQiFQpr3sJeXl6O1tVXTa6il+qntQlJTU4OxsTFNeyUt5VVY7tmjCQlDk1EMTUZRPmuIxcSenaInyzLOnTsHn8+HY8eOLWsNMlEUsXfvXlRWViKRSGieZVNZ5bLfyyyXLlmW0dPTk8nyctaGdDqd2Lt3L9xuN8rKyjA2NqZpTw6zTMuhJstlZWXYu3cvXC4XRFHE+Pi4plmur6/X7NzZKokeSSC1H+b58+c1OXdNTQ1W1K/EZCSOiXAc03Hlv+RNAjLDhaLVzOHCIjM2Ngafz4fDhw8r2i/VZrNh3759aGhoyLwWCARw4cIFDVp5McvBSByBbLIsWlHpsMFh5dB3sUln2efzIRAILPs4u92Offv2YdWqVZnXtMxybW0t6lbUIzhzXw6ryLJZEOASLagUbZzGUYTGxsbQ2dmJw4cPK87yHXfcgZUrV2Zem5iYQG/v/EPg2aqrq2MhaRRa7ImZnrtmMl0cCoklJEyEYwhE4ognlf/fazPPDLGIVlg5xFKwotEojh49Cp/Pp+pDjNlsxp49e+YdzmCWSU+RSATHjh1TnWWLxYI9e/agpaXlktdlWUZfX5+iD1fLIYoi1qxZM3+Ww3HEJWa5VEUikcx9Wc2HGIvFgr1796K5ufmS1/XMstGUVCEpyzJ6e3sVffJYjN1ux5o1a2CxzD9DQJZlTMeTCITjCEbiUHHvQpnNjErRhgoHh1gKgSRJOHv2LHw+H44fP45EIqHqPIIg4LOf/Sw2btw479dznWWHw4HW1tbFsxxLYiKSyrKauwazXFhylWWTyYTPfe5z2LBhw7xfl2UZFy5cQDAYzKa5GQ6HA2vWrFnwwc9MlsNxBKPMcimQJAlnzpyBz+dDd3d3Vln+/Oc/j/Xr18/7db2zbBQlVUgCqR/0yMgIhoeHszqPy+VCY2Pjsn/AkiQjGE0NF4Zi6oYLXTND304OfRvO6OhoZuhazU2koqICk5MX15PcuXMntm3btugxsixjeHg464dv1GZ5IhzHdBZZruQ0DkMaHR3NDPfNzuRyzc3yH//xH6OjY/HtPXOVZbfbjYaGBmaZAAAjIyOZ+3Iusrxr1y5s2bL49p75ynI+lVwhmRYOh9Hb24toNKroOLPZjIaGBrjdbtXXjiclTIRTRWUsqXxyrtUsoFK0we2wwmYxbnd3sQuHw5khEjVzY5xOJ7Zu3QqPx4Oamhp861vfQiwWw/bt2/H+979fUTvyleVYUkIgiyzbzKbMslg2DhfmTTgcRldXF3w+H/r6+hQfn86y1+tFdXU1vvnNbyIej+OTn/wkrrzySkXtUJvlxsZGuFwupU3PiCUkBCJxTIRjqqdxuMVUUcmh7/zJNstlZWWZ+3JVVRW++c1vIpFI4FOf+hSuuOKKZZ9nenoafX19irNssVjQ0NCQVZb1VrKFJDAzxDE9Db/fj8nJyUX3y3Q6naiurobL5crZXAVZlhGOzwyxqBz6dtrMqHRY4XJYYTLx07DWJEnC6dOnM0MkSp+eNplMaGtrg8fjwYYNGy75tPnSSy+hqqoKH/vYxxS3S5ZlhEIhjI2NLSvLNTU1qKioMF6WRStcdmZZD5Ik4b333oPP58OJEydUZXnjxo3weDxYv379JVl+8cUXUVtbi4985COK26Umyy6XK2e9gcxy4Ukmk5n7sposm83mzH15bpZfeOEF1NfX48Mf/rDidqWz7Pf7MTU1pXuW9VLSheRssiwjGo0iHA5nQmgymeBwOOBwODSf6CrJMiYjCUyEY6qGvgUBcNlTn4adNg6x5Nrw8DA6Oztx5MgRTE1NLX3AHKtWrYLH48HWrVsX3G1penoaoihm/bNjlmkxQ0NDmeE+NdvHNjQ0wOPxoKOjg1legkkAKmaGvjklKfeGhoYy92Uts5yLHfLynWUtsZA0oHgyNcQSCMcRTagY+jYJM0MsNg59Z2F6ehpHjhyBz+fDwMCA4uPLysqwbds2eL1erFixvMXqi018Zuh7Qu00DmY5J0KhELq6utDZ2YnBwUHFx5eXl2Pbtm3weDzMchZTklK76DDL2QiFQpn7sposV1RUZKZh1NUtb7F6WhwLSQOTZRmRmSUrguEEkip+VKJ1ZojFYYWZQyxLSiaTOHXqFHw+H06ePKl4gVmz2YxNmzbB4/Fg3bp1Bf0pM5dmZzkQVjdcyCwrk85yZ2cnTp06xSzniCzLiMQvLoulaujbaoabWV62ZDKJkydPwufzqcqyxWLJZHnuEmeUPRaSBUKSZUxFE5gIxzEVVb50gQCgwpFaWLeMw4WXkGUZg4OD8Pl8OHLkCKanpxWfY/Xq1fB4PNiyZQtE8fI91+kiZlk76Sx3dnaiq6srqyx3dHTA4XBo0MrikYssp1fjYJYvJcsyBgYGMlkOh8OKz9HU1JS5LzPL2mEhWYASyfTTheqGvi2Z4UIr7BbjLy2glampKRw5cgSdnZ2qloNyuVyZ4b7a2loNWlj8mOXcmJqawuHDh+Hz+ZjlPGGWc2NycjKTZTVL6LhcLng8nsxqGKQ9FpIFLjLzdGEgEkdSxRiLw2rKLCVUCkMsiUQCJ0+eRGdnJ957771Fn6Kbj8ViwebNm+HxeAy/20ChyTbL4sxwYSll+cSJE/D5fKqz3N7eDo/Hg9bWVmY5R9LTONLLYqmdklRqWe7u7obP58Pp06cVZ9lqtWLz5s3wer1obW1lz67OWEgWCXnWEMukyiGWcrsFlaIV5XZLUf2HKMsy+vv7M0MkkUhE8Tmam5szQyR2u12DVlJaLrJc4bDA7SjOLPf19aGzsxNHjx5VleWWlhZ4PB60t7czyxqTZRmT0QQC2WZZtKLcVnxZ7u3thc/nyyrLXq8XmzdvZpbziIVkEUpIEoLh1JIVERVDLGZT+ulCKxzWwh1iCQaDmSGS0dFRxcdXVlZmhvuqq6s1aCEthVlOCQaD8Pl88Pl88Pv9io+vrKzMDPdVVVVp0EJaSrZZtsxk2V3gWQ4EApn7sposV1VVwePxYNu2bcyyQbCQLHLRRDKzi05CzdC35eLOI5YCGPqKx+OZIZIzZ86oGiLZsmULPB4PWlpaiqoHoNBF4snMslilkuXjx49nsqyUzWZDe3s7vF4vmpubmWUDyXpKkiU1JcklWphlyjsWkiVClmWEYklMhGOYjCSg5oeeHvquMNhwoSzLuHDhAjo7O3Hs2DHFW1IBwJo1a+DxeLB582bYbDYNWkm5IssypmIzw4Uqs1xhTw0XGjnLR48eRSwWU3wOZrlw5CrLRpySJMsyzp8/n7kvq8ny2rVrM1m2Wq0atJJygYVkCUpKMoIzTxeG48p3azALAlxiavkVh8WUt5vXxMREZoeOsbExxcdXV1dnhkgqKytz30DSXC6ynO6lNEKWfT4fxsfHFR9fXV0Nr9eLbdu2ZbV3OuVPUpIzPe6FnOXx8fFMlicmJhQfX1NTk7kvM8uFgYVkiYsmkpndGtQMF9rTw4UOK6xm7YdYYrEYjh07Bp/Ph56eHsXH2+32zNB1U1OToT7BU3ZykeXKmSxbCijLXq8Xq1evZpaLSLZTkvTOcjQazWT53Llzio+32+3o6OiA1+tFY2Mjs1xgWEgSgItD34FwHMFoHGpSUW6bGS50WGDK4Y1AlmX09PTA5/Ph2LFjiMfjio4XBAFr166F1+vFxo0bOURS5C7JciSe1TSOcrvxsrxu3Tp4PB5muQTkckqSFlk+e/YsfD4fjh8/rjrL6fuyxWLJWdtIXywk6TJJScbkzHDhtIohFpOAzNOFolX9bg1jY2OZIZJAIKD4+Nra2swQicvlUtUGKmzpoe+AAbLc2dmJw4cPq8pyXV1dJssVFRWq2kCFLdtpHCYBqQXPHTY4rOqHvv1+f+a+HAwGFR9fV1cHr9eLrVu3MstFgoUkLSqWSO/WEEM8qTwqNrMps1vDcoa+I5FIZojk/Pnziq/ncDgyQyQNDQ0cIqGMWOLi/shqs1w5MwdtuVk+evQofD4fLly4oPh6oihmsrxq1SpmmTJis/atj6sY+lab5c7OTvT29iq+niiK2Lp1KzweD7NchFhI0rLIsozp+MXhQhX3LpTZUrs1uOxWmGbt1iBJ0iVDJImEsoV7BUHAhg0b4PF40NbWxiESWlSuslwp2i6bxiFJEs6cOQOfz4fu7m7FWTaZTJksb9iwgVmmRcmyjOlYaj6l2ilJS2W5s7MT3d3dSCaV9YLOznJbWxvM5sJd+5IWx0KSFJMkGcFoargwFFM3xOJyWJGYDuDk0S4cPnwYk5OTis9TX18Pj8eDrVu3ory8XPHxRLnKcjIUQPfRIzhy5IiqLK9cuTKT5bKyMsXHE6WzPBGOYzqb+/LURCbLU1NTis/DLJceFpKUlXhSyjxdGEsuvVtDLBrBhfdOoOfkMYwNDyq+ntPpxNatW+H1erFy5Uo1TSaal+IsRyI4f/oEek4cxfjIkOLrOZ3OzM5JzDLlUiwpzaxgsLwpSdFIOHNfVpPlsrKyzH25vr5eTZOpgLGQpJyQZRnhmeHCwJzhQkmSMHShBz0nj6G/5wwkSfkQSVtbG7xeL9avX88hEtJUOssT8wx9S8kkBnvPoefEUQycO6s4y2azGW1tbfB4PMwyaW7JLKfvy+fOQJaUbdtoNpuxceNGeDwerFu3jlkuYSwkKeckWcZkJIGzF/px/OhhnDvVjWh4WvF56leuxPtmnu5zOp0atJRoceksn7nQh+NdR3D+PXVZXrlqFd7n9aKjo4NZprzIZPl8L44fPYLz751QmeUGvM+bGroWRVGDllKh4UxuyqlQKISuri50dnZicFD50LXD6UTLhs1oaWuHu7oWFpOAqaQJ5kQSdgs/8ZJ+QqEQjhw5Ap/PpzLLZWjZsBmtG9vhqqqBdSbLloQEm8X4+yNT8QiFQjh8+DB8Ph+GhpQPXTucZWhp24zWtotZnkwIMDPLBPZIUg4kk0mcOnUKnZ2dOHXqFCSFQyQmsxkNLevQurEd9atbYDLNf2MSrWZUila4HFaYTVw+gnIvmUzi5MmT8Pl8qrPc2LoerW3tWLG6mVmmvEkkEpdkWemvepPZjMY1qSzXNzZDYJZpASwkSRVZljE4OIjOzk4cOXIE4XBY8TnqVq5C04Z2NK1rg83uWPZxAoAKR2qv7zKb+kWiiYBUlgcGBtDZ2Ymuri5VWV6xqgFN6zdjNbNMeSTLMvr7++Hz+bLIciOaNmxG09o2WO32ZR/HLJcuFpKkyNTUVGaIZHh4WPHxLpcr86RqbW0tEsnUgueBcByRhLLeHwCwmITMXt8OK4e+afkmJyczWR4ZGVF8vMvlgsfjgcfjQU1NTSbLE+E4ollkuVK0choHKZJtlt1ud+a+zCyTUiwkaUmJRAInTpyAz+fDe++9p3iIxGKxYPPmzfB4PFizZs2Cw32RmacLA5E4kipWiXZYTKgUbXCJFlgWuAaVtkQige7ubvh8Ppw+fVpxlq1W6yVZXqjXJessW1NZdnO4kBYQj8cz92W1WW5vb4fH40Fra+u8WZZlGZFEaimhQDiOpIpygVkufiwkaV6yLKOvrw+dnZ04evQoIpGI4nM0NzfD6/Wivb0ddgVDJLIsYyqawEQ4jqloAmoCWmG3oFK0otxu4RBLiZNlGb29vfD5fKqz3NLSAo/Hk1WWJ6PKdrkBUsOF5cwyzUhnOX1fjkajis/R2toKj8eDzZs3657lCocFbgezXGxYSNIlgsEgfD4ffD4f/H6/4uMrKyszw31VVVVZtycpyZm9viNx5UMsZpMAtyM1xMKh79ISCAQyw31qslxVVQWPx4Nt27blJMsJSUIwnEhlWcVwIbNcugKBQOa+PDY2pvh4Zpm0xEKSEI/Hcfz4cfh8Ppw5c0bx8TabLTNE0tLSotknzWgimdl5JKFiuNBuMaFyZj6lxcyh72IUi8UyQ9fZZNnr9aK5udmwWXZYTKm5waKV0ziKVCwWy9yXz549q/h4m82GLVu2wOPxaJrlyKyNKJjl0sRCskTJsozz589nhvtisZjic6xZsyYzRGKz2TRo5fxkWUYolsREOIbJiLqh79nDhSYOsRS0dJY7Oztx7NgxVVleu3ZtJstWq1WDVs4vl1mu4HBhwZNlGefOnYPP5yvILE/FEgiE46qzXGG3wM0sFxwWkiVmYmIiM0QyPj6u+Pjq6urM0LXb7daghcokJRnBmacLw3Fl29UBgFkQ4BJTS1Y4LCbevArI+Ph4JssTExOKj6+pqckM9zHLlE/M8qXMgpDppWSWjY+FZAmIxWI4duwYfD4fenp6FB9vt9uxZcsWeL1erF692rD/UUcTyczThXGVQ99uR+rmZeXQtyFFo9FMls+dO6f4eLvdjo6ODni9XjQ2Nho+yxNZTONIL4vFLBtTOsudnZ04f/684uMdDkfmvmz0LGc7JcktWlHJKUmGxUKySMmyjJ6enswQSTweV3S8IAhYt24dPB4PNm7cqOsQSbZkWcZ0LHXzCkbjUJPwMpsZlaINFQ4OfeebLMs4e/YsfD4fjh8/rirL69evz2TZYimcnWHTQ9+BLLJcbpsZLmSW806SJPT09KCzsxPHjx9HIqHs6eeiyXIkzilJRYSFZJEZGxtDZ2cnDh8+jEAgoPj4urq6zBBJRUWFBi3UV1KSMRmJYyISx3RM+RCLSQBcM08Xilbu1qAnv9+fGe4LBoOKj6+rq4PX68XWrVuLK8vhOKZVDBeaBGR63Jllffn9/sx9WU2WV6xYkbkvl5eXa9BCfaWHvgPMclFgIVkEIpEIjh49Cp/PhwsXLig+XhTFzHDfqlWrivY/ylhCyiwlFE8qj73NbMrs1sDhQm2ks9zZ2Yne3l7Fx4uiiK1bt8Lj8TDLi2CWtRcOhzP35Wyy7PV6sXLlyqLO8kQ4hkAkrjrLlSKnJOUTC8kCJUkSzpw5A5/Ph+7ublVDJBs2bIDH40FbW1tBDZFkS5ZlTMcvDrGomLYDp82MStEKl90KE3dryEo6y52dneju7kYyqayHwmQyXZJls7l01qXLRZbLbGa4meWckCQJp0+fztyX1WbZ6/Viw4YNzLJCnJKUHywkC8zIyAh8Ph8OHz6MyclJxcfX19fD4/Fg69atRTFEki1JljEZSS2sG8pi6NstWuHkEIsiw8PDmSxPTU0pPn7lypWZLJeVlWnQwsLCLOfP8PAwOjs7ceTIEVVZXrVqFTweDzo6OphlAJIkIxhNDX0zy8bHQrIAhMNhHDlyBD6fD/39/YqPdzqdlwyR0PziSSnzpGwsqXy3Bqs5vVuDDTYLh1jmMz09ja6uLtVZLisry2S5vr5egxYWh3hSyjwpqzbL6f2RmeX5TU9PZ+7LAwMDio8vKyvDtm3b4PF4mOVFMMvGx0LSoJLJJE6fPo3Ozk6cPHlS1RDJxo0b4fF4sH79+pIaIsmWLMsIz9qtQdXQt3VmuNBhhbnEhwuTySTee+89+Hw+nDhxApKk7JeB2WzOZHndunXMsgK5ynKlaEUFs4xkMolTp07B5/Ph5MmTWWV5/fr1MHEnl2VLZ3ki2ylJjtR9mdM4coeFpMEMDQ1lhkhCoZDi4xsaGjJDJE6nU4MWlhZJljEVTWAiHMdUVNk8VAAQcHGIpcxWWkMsg4ODmSxPT08rPr6xsTGTZVEUNWhhaUkPfQfCcUzFVGRZAFx2ZplZzr9sp3Gks1wpWuEssSxrgYUkUgt2B4NBhMNhhMNhJJNJyLIMk8kEURQhiiLKy8shiqImgQuFQpkhksHBQcXHl5eXY9u2bfB6vairq8t5+yglkUw/KRtHNKF8iMViEjJPytotl/eqBSNxiFZzVk8eRqNRTE5OXpZls9kMh8OhS5YPHz4Mn8+HoaEhxcdXVFRkhvuYZe3EZ7IcyCLL6SdlizXLU1NTmftyNln2er2ora3NefsoJT0lKRBRl2Vr5r48/5QkPbNcqJ0/JV1ITk1NYXR0dNmTo+12O2pqalBVVZX1jSuZTOLkyZPw+Xw4deqUqiGSzZs3w+PxYO3atRwi0ZEsy4gkpMwuOkkV/wmJM0Pf7lnDhWf8U5AkoLXGCYvCn+fU1BRGRkaW3Yttt9tRW1uLysrKrLOcSCQuybLSW4rFYsGmTZuY5TxIZ3kiHEMwnFCd5co50zjOjE5BBtBSrTzLk5OTGB0dLdgsp+/La9asYZZ1pFWWT4+m6oPW6jLFUzuUZtnhcKCmpiYnWdZTSRaSyWQS/f39qhbsBlLre61evRp2u13RcbIsY2BgAJ2dnejq6kI4HFZ87dWrV8Pr9WLLli1wOByKj6fckmUZk9HUcOGkyqHvCocFZTYLBoIRAIDDYkLLMm9aiUQCAwMDeclyf38/fD6f6iw3NTXB4/EwywaRznI20zgqHBY4bRYMprNsNaGlqjCynL4vRyIRxddubm6Gx+NBe3s7s2wAuZiSNDfLotWMlirnsuZWJhIJ9Pf3q1p8HlCf5XwpuUIyEomgp6dH8bqL82lqaoLb7V7yfZOTk5nhvpGREcXXcblc8Hg88Hg8qKmpUdNU0kFCuthLGVExxDKb02ZGc5Vz0bXQIpEIzp49q/hBrLkEQcDq1at1ybLb7c4MXTPLxpWYNfStR5bD4TB6enp0zXIwGMxkeXR0VPG13G535r5cXV2tprmkg2ynJM1WZjOjSccsNzU1weVyZXUePZRUIRmJRHDmzBnFw8iLWb16NSorKy97PZFIoLu7Gz6fD6dPn1Y8RGK1Wi8ZIimkbm4CIjNPFwYicSTVPF6I1L6yTZXzz//SIssLfTCKx+M4ceJEVllub2+Hx+NBa2srs1xgcpHlCrsFqxfIcjgcxtmzZ3XLcvq+fObMGdVZ9nq9aGlpYZYLjB5ZVpOrxSy3wyqfSqaQTC/bkIueyLnWrl0Lp9MJWZbR29sLn8+Ho0ePqhoiaWlpyQyRFEq3Ni1MnhliCUTimIwkoPQ/NrfDiga345KbViKRwKlTp7L+xDufdevWQRTFTJY7Oztx9OhRRKNRxedqbW2Fx+PB5s2bmeUiIM8ZLiykLF+4cCFzX1abZa/Xi82bN8Nms+W8raSv2VlWMyWpUrRilUv/LBtVyRSSvb29mJiY0OTcNpsN4+PjeOedd+D3+xUfX1lZmRkiqaqq0qCFZARJSc4MF4bjy7/ZVDmtWFlx8aZ14cIF1fPIlmKz2TA2NoZ33nkHY2Njio+vqqrKZHm+nnoqDuksT4RjiMSX35NY7bShvsKuS5btdjtGR0fxhz/8gVmmBSUkCcFwaikhJdM45mb5/PnzqudELsVut2P9+vWG7QHXdIPlCxcu4C//8i/h9/thNpvxr//6r3l5vH1qakqzIhJILR8Ui8UUFZE2my0zRNLc3GzYgFDumE0Cqp02VDttiCaSmd0aEksMsYxPx2EWBKyocGBqakqzX7zAxSwr+cVrs9mwZcsWeL1eNDU1McslQG2Wx6ZjMAnAigoHJicnNc1yNBpFNBpVlGW73Y4tW7bA4/EwyyXCYjKhusyG6jLlWTabBNSV2xEMBjUrIoFUlkdHRw27JJqmheRf/MVf4IEHHsCVV16JiYmJvA0JqJlIrVRVVRVMJtOS83zWrl0Lj8eDTZs2cYikhNktZtRXmGEPDqHfVJlaIXcRo6HUTSuo4gEXpaqrqyEIwpLzfNauXQuv14tNmzbBarVq3i4ypnSWbcEhDCjJsg735dra2mVled26dZn7MrNcujJZDgxhwFy5ZJZHpqIwCwImdMiy3+/P5NloNCskT506BYvFgiuvvBIA8jY0EIvFlr1OZLba2trQ3d192es1NTXweDzYtm2b4SfNks7eeRn1ZisCaz6ISE1r5sZlMQmwmAVYTSZYzSZYTAKQTKja7UiNtrY2nDhx4rLXa2pq4PV6sW3btoJ4mpD0I7z9EuotDgTWfhCR6pZFsywn9Mvy+vXrcerUqcter62tzdyXmWWazfT2i6i3OVP35ermRbMsJWKqdjtSKpFIIBgMGrKGUFxISpKE7du347rrrsOBAwcyr7/xxhu477778Mgjj+Dmm2/GuXPn4HQ6ce+992JoaAg33ngj7r333pw2fjkmJyd1u9b69eszhaTdbkdHRwe8Xi8aGxsN+SmC8kuSZJgEwBIcwgrfS0B1AywdH4Otcf49eEdH9c1yupB0OBzo6OiAx+NhlmleyUyWB7Gi80WguhHWrR+DtWFd3rPc1taWKSTTWfZ6vWhoaGCW6TJJSYYgAPbAAFZ0/gSoWZ3K8qr5N0sYGdFuSHuuyclJQxaSqh62+clPfoKDBw/itddeg9vtRnd3N2677TZ86Utfwl133QUAePXVV/HXf/3XePHFF1FTU4O77roL9957Lz784Q/n/JtYjJYP2cxlNplgfvdVuN1uVFRU8CZFS0oOnAESsUteM9U0wLb1apgbLp1creWDCXMxy6RUsv80kIxf8pqptjGV5VXrLsmQlg8mzJXOcqW7EuUV5cwyLWn+LK+GbdvVMK9cm7cs22w2tLW16XItJVQNbe/YsQPf/va38cwzz2DXrl24++67ccstt2SKSABYuXIlOjo6sGrVKgDA1VdfjePHj+teSOrR5ZyWlCQ0mGMwB/oh6fP7noqQ5O9H5NCPLyso1eweo1ZSktBoisLELFMWpNE+RF770WUFpf5ZjsAU6GOWSTVptBeR3/zzZQWlnlmOxWKQJMlwW2+qKiQtFgv279+Pxx57DL/4xS/Q3t6Or3/965e8Z+vWrfD7/QgEAqioqMAf/vAHfPazn81Jo5XQYk2nxUhmG8xzPskQqTG3oNQ7y0mzDSYp9+uuUumZW1Dm475skvT7hU/Fa25Bqft9OZk0XCGpujU7duzIVOKPPvoozGbzJV+3WCx48MEHsWfPHnz6059GS0sLPv7xj2fXWqISJE2OQRobyOluCcvCEUDKMSnohzQ2kO9mEGVNCvoh+fNwXzYg1U9tHzx4EAAwPj5+WRGZdvXVV+Pqq69We4mcMJlMun5iYA8O5YzNAdumD8K68SoINgfMJ07kdBu5pZiSzDLliM0B2+Y/SmXZaoepu1vXLAvMMuWKTZzJ8vshWO0wd3drsmPeQozWGwmoLCQff/xxHDp0CM8++yzuuOMOPP/887j99ttz3bacEEUR8bg+Q81mkwlmmwOCzaHL9ajwyeEgMPcT7ZwCMs3hcOiXZbMZFrsIwLjbcpGxzJ9lEbbNH8wUkGmiKOq2oobFbIbFof9GGFS4Fs7yxQIyzeFw6LbEoMViWbDjLp8UF5LPPfccnnrqKTz99NPYtGkT9u3bhyeffBK7d+825EKuoijq9kSV6HSifOdXdLkWFYfQy/8v5ODMYrYLFJBpev7ydTqdKGOWSYHQT78NeXJmF5kFCsg0PbMsMsukUOil/w15aiL1jwUKyDRRFHUrJPOxM+ByKOojff311/Hwww/jkUcegdfrBQDs3bsXU1NTeOmll7RoX9bKy8uL8lpURGwO2LZdg7Jbvgzb1o8t2KPNLJPh2UTYPB9H2We+DFvHR+f9xQswy1QAbCJsnmtnsvwRZnkRy+6R7OrqwgMPPIADBw7ghhtuyLxeXl6OPXv24IknnsDOnTsN1+0qiiIcDgcikYim1xEEAVVVVZpeg4qPrf1DsDRtWtZ0CD2znK+dqKhw2bZ8BJbmzQv+wp1NFEXY7XZEo1FN28Qskxq2jo/C0ty+rCw7nU7dsmzExcgBlQuSF5qJiQn09vZqeo3KykqsXr1a02sQjY+Po6+vT9NrVFVVobGxUdNrEI2NjaG/v1/Ta1RXV6OhoUHTaxDpkeWamprMutxGY7zHfzTgdrs1nVtgMpmwcuVKzc5PlFZZWQlR1O4BGLPZjPr6es3OT5RWVVWleZZXrFih2fmJ0qqqquBwaPeQrdGzXBKFpCAImu4R3NjYCItF9UpKRMsmCAJWr16tWZYbGhqYZdKF1lnmfZn0ks6yVhobGw03bXC2kigkAcBut6OpqSnn562trTXsvAUqTna7XZObVl1dHbNMutIyyy6XK+fnJVqIw+HQpMZYsWKF4bNcMoUkALhcLjQ3N+fsfLW1tRwGpLxwu905vWnV1dUZeuiEihezTMXC7Xbn9IPRihUrUFdXl7PzaaUkHraZKxwOo7e3V/VTVmazGQ0NDey9obxjlqlY5CLLjY2Nhu+9oeI3PT2N3t5exGIxVccXWpZLspAEAEmS4Pf7MTo6uuwtFNNLSdTX13PuDRmG2ixXVVVhxYoVzDIZhiRJGB0dhd/vZ5apoJVSlku2kEyTZRnBYBDBYBDhcPiyTxAWiwWiKKK8vByVlZWGnvBKpY1ZpmIhSRImJycRDAYxPT192dagzDIVCkmSEAwGMTk5WbRZLvlCci5JkpBMJiHLMsxmc0H+UIkAZpmKRzKZhCRJzDIVvGLMMgtJIiIiIlKlpJ7aJiIiIqLcYSFJRERERKqwkCQiIiIiVVhIEhEREZEqLCSJiIiISBUWkkRERESkCgtJIiIiIlKFhSQRERERqcJCkoiIiIhUYSFJRERERKqwkCQiIiIiVVhIEhEREZEqLCSJiIiISBUWkkRERESkCgtJIiIiIlKFhSQRERERqcJCkoiIiIhUYSFJRERERKqwkCQiIiIiVVhIEhEREZEqLCSJiIiISBUWkkRERESkCgtJIiIiIlKFhSQRERERqcJCkoiIiIhUYSFJRERERKqwkCQiIiIiVVhIEhEREZEqLCSJiIiISBUWkkRERESkCgtJIiIiIlKFhSQRERERqcJCkoiIiIhUYSFJRERERKqwkCQiIiIiVVhIEhEREZEqlnw3wEhkWUY0GkUymQQAmEwm2O12mEyst6mwMMtULJhlKhbFmuWSLyQTiQTGx8cRDAYRiUQgy/Jl77HZbKioqEB1dTXsdnseWkm0NGaZisVysmy321FRUYGqqipmmQwrneVAIIBoNFqUWRbk+b6rEpBMJjE4OIjx8XFFx5WVlaGhoaEgf9hUnBKJBAYHBzExMaHoOGaZjEZtlsvLy7Fq1SpmmQyjlLJckoXk5OQkent7M93LatTX16O2thaCIOSwZUTKBINB9PX1qc6yIAior69HTU0Ns0x5xSxTsQgEAujv7y+ZLJdcITk6OorBwcGcnMvlcqGpqakgftBUfHKZZbfbjdWrVzPLlBcjIyMYGhrKybmYZcqnXGa5srISjY2Nhs9yYc/wVMjv9+fsFy+Q+gTd29s775wHIi3lOsuBQIBZprwYHR3N2S9eIJXlvr4+Zpl0l+ssT0xMFESWS6aQDIfDGBgYyPl5A4EAxsbGcn5eooVMT08zy1QUpqenc/qBKG1iYkLx/HeibIRCIc2yrHSepd40KyTPnDmDW265JfNn27Zt+NWvfqXV5RYlSRJ6e3s1O//g4CBisZhm5ydKY5apWGid5YGBAWaZdKF1lvv7+xGPxzU7f7Z0mSMZCoVw7bXX4rXXXoPT6dT6cpfx+/2a9ODM5nK50NzcrOk1iHI5L3IhzDLpQY8su91uNDU1aXoNolzOi1yIkbOsy9D2b37zG/zRH/1RXopIWZbh9/s1v04wGDT0JwYypr4fPYNIf9+y3sssk5H1/fMPEB1c3gd2vbIcCASQSCQ0vw4Vl74fPo3oMj/kyLKsy5QgI2dZcSEpSRJuuukmPPLII5e8/sYbb6CjowOvvvrqZce8+uqr2L59u/pWZmF6elq34Q3OySGl/K/9Gof/dB/OPPp/L1lQhkIh3Qo8o8/JIeMZ/fW/4/BdX8DZx765ZEGpZ5Z5XyalRn/1Sxy+ay/O/j/fWrKgnJqaKvn7suKdbUwmE+655x4cPHgQd999N9xuN7q7u/GVr3wFDz74IG6++eZL3j81NYX/+q//wmOPPZazRisRCoV0u9bU1BTqamt1ux4VAVkGJAmjv/w5Rn/1S9R+4gY0fO52OBoaL3ur3lmuranR7XpUBGQZcjKJkV+8gtFf/QK1n7gRqz53GxyrGi57K7NMRians/zqzzD6y5+j9vob0fC522Ffueqy9+qeZQPWGKrmSCYSCdx4443YuXMndu3ahd27d+O6667DQw89dNl7X3zxRfzud7/DN7/5zZw0WKlz585hcnJSl2uZBAFTf/5nulyLipjJNG9B2dPTg6mpKZ2aYMLUV+/X5VpUvASzed6C8uzZs7r9AmaWKRcEs3neglLPLJvNZmzevFmXaymhao6kxWLB/v378cwzz2D//v1ob2/H17/+9Xnf+/Of/zxvw9oAEIlEdLuWJMsQXG7drkdFaqaHcu6Qt65ZliSgokK361FxSvdQHvnTfTj72DcRGegHAESjUd3aIEkS4CzT7XpUnORkEiM/fyU1fePxi9M39LwvJ5PJrHbk04rqh2127NiBcDgMAHj00UdhNpsve8/k5CQOHz6Mj3zkI+pbmCVJkvS9oM2m7/WoeM0qKM8+9k3dsyzYCmevVzK2dEGZ/iWse5btvC9TblxSUP7vx3TPsu41zTKoLiQPHjwIIDWReb4iEgAqKirw5ptvwlZKxZUBf8hUwAQB1R+9Gitv3aX/NlmS8T75UgETBFR/7BqsvPVP9L92kvdlyiGTCdUf+zhW7vxjw29fqAfFD9sAwOOPP45Dhw7h2WefxR133IHnn38et99+e67blhNWq1XXrmA5pM8cNipyM790G2/bC7GlFQBgOXVK5yzrN4mcipggoPrqj6ey3NwCALCcPKnrYuHyNLNMOWAyoebqa9Fw2x6ITam1dvXO8kIdd/mkuJB87rnn8NRTT+Hpp5/Gpk2bsG/fPjz55JPYvXs3rFarFm3MiiiKus1hsFqtWPmpT+tyLSoOI7/4OZJTsx4Gm6eATBNFUbe5ZTarFa4dt+hyLSoOIz9/BcnZHz7mKSDTnE6nbr98bTYbXLfs1OVaVBxGXn0FydkfPkwm1Fz9cTTctjdTQKaJoqhblu12O0wm4+1sraiQfP311/Hwww/j0UcfhdfrBQDs3bsX3//+9/HSSy9h165dWrQxK6Io6raOmNPpRNP++3S5FhWHibf/M1VILlJApomiqNs6Ys6yMqxmlkmB8f94M1VICgJqrrkWDZ/fc1kBmaZrlp1OZpkUGf/971KF5Dw9kHOJoohAIKBLu/KxqctyLLuQ7OrqwgMPPIADBw7ghhtuyLxeXl6OPXv24IknnsDOnTsN1+3qcrnQ39+vy7Xcbj6xTcoIs3ttFigg01wul+ZbfaYxy6SUIAio+fh1ixaQacwyGZrJhJqPf2LRAjLN5XJpvtVnmlGzrMte2/l24cIFzT8xWCwWbNy4kRNvSZHY6AhstXXLfv/58+cRDAY1bFFqikZbWxuzTIowy1QslGZZj/WqjZxl4w22a0CPleDr6uoM+QMmY1NyswL0yXJtbS2zTIoZMcu8L5MazLIyJVFIiqKo6Q9aFEVUV1drdn6iNKfTiRoNt3tjlkkvWmfZ6XSiqqpKs/MTpZWVlWl63zR6lkuikASAFStWwG7P/QLLgiBg9erVhv2kQMWnvr5ek7VZmWXSG7NMxaK+vl6TlWtMJpPhs1wyhaTJZEJra2tOf9CCIKClpUWTApVoIeksWyyqloGdlyAIaG1tZZZJV1pmuaQ2wqC8M5vNWLNmTUlmuSQetpktHo+jp6cn6/X4zGYzmpubUVbGPVwpP5hlKhaxWAznzp3LSZZbWloMu0wKFb9YLIaenp6s15YspCyXXCEJpPaqHB0dxfDwsKrjXS4XGhoacvrJg0gNSZIwMjKCkZERVcczy2QU2WbZ7XZj1apVzDLlnSRJGB4exujoqKrjCy3LJVlIpkWjUfj9foyPj2M5/zdUVFSgpqYG5eXlOrSOaPkikQjGxsaWnWWXy4Xq6mpmmQxHTZZramrYo06GE4lE4Pf7MTExUdRZLulCMi2ZTCIUCiEcDiMcDiORSEAGYDGb4XA4IIoiysrKDLkFJNFszDIVC2aZikWxZ5mF5DxC0Tgi8SRqyh35bgpRVqaicUSZZSoCU5E4oskkasqYZSpsk5E44kkJ1WXF8XBjyTy1vVyyLGN8OoZgJI5EUsp3c4hUk2UZE9MxTEbiSEjMMhUuWZYxEY5iMhxHklmmApa6L0cRDMeKJsssJOeYjiUQnykgA+HsnroiyqfQTJZlAIFpZpkKVyiaQDwpQwYwwfsyFbCpaAIJKZXlQDie7+bkBAvJWdK9kWnslaRCle6NTGOvJBWqdG9kGnslqVCleyPTiqVXkoXkLLN7I9PYK0mFKDQny+yVpEKV7o1MY68kFap0b2RasfRKspCcMbc3Mo29klRo5vZGprFXkgrN3N7INPZKUqGZ2xuZVgy9kiwkZ8zXG5nGXkkqJHN7I9PYK0mFZm5vZBp7JanQzO2NTCuGXkkWkli4NzKNvZJUKBbqjUxjryQVioV6I9PYK0mFYqHeyLRC75VkIYnFeyPT2CtJhWCh3sg09kpSoVioNzKNvZJUKBbqjUwr9F7Jki8kl+qNTGOvJBndUr2RaeyVJKNbqjcyjb2SZHRL9UamFXKvZMkXkrGkBItJgGg1wywI875HtJohWs2IxJM6t45o+ZbKsoBUlh3MMhlcKsumZWU5zCyTgcUSEqzmVJZNRXpf5haJswwHwwjFEpe8ZhYENNeU56lFROoMBcOYnptlk4DmamaZCst8WbaYBDQxy1RgijXLJd8jSURERETqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlUs+W6AUcTjcUjxMBAOQ5IkQAYEkwCT3Y5o1AqbzQZBEPLdTKIlxeNxyDFmmQqbLMtIJBLzZlmwO5hlKhiyLC94Xy6GLAuyLMv5bkS+SJKEQCAAv9+PSCSy6HstFguqq6tRXV0Ni4X1NxmL0izX1NSgqqqKWSbDkSQJExMT8Pv9iEaji76XWSYjU5Jlq9WK6urqgsxyyRaSk5OT6O3tRTKZVHxsfX09amtrC/bTAxWXYDCIvr4+xVkWBAH19fWoqalhlskQmGUqFoFAAP39/SWR5ZIrJCVJQn9/PyYmJrI6jyiKaGpqgs1my03DiBSSJAl9fX0IBAJZnUcURTQ3N8NqteaoZUTKMMtULEoxyyVVSEqShHPnziEUCuXkfBaLBWvXrmUxSbqTJAk9PT2Ynp7OyfmYZcoXZpmKRTKZRE9PD8LhcE7OZ7VasWbNGsNnuWSe2pZlGefPn89ZEQkAiUQCZ8+eRSKRyNk5iZaSznKufvECzDLlh5ZZVjNtiUitdJZzVUQCqQcne3p6DJ/lkikkx8fHMTU1lfPzxuNxDAwM5Py8RAsZGxtjlqkoMMtULPx+f047qtJisZjhs6xpIfl//s//wSc/+Uls374d3/jGN5CvUXStfxCBQADBYFCz8xOlxWIxDA4OanZ+Zpn0onWWJyYmMDk5qdn5idKi0SiGhoY0O7/Rs6xZITk2NoYf/vCHeOGFF/Dyyy+jq6sLnZ2dWl1uUX6/X/MidmhoKG+FMpWO0dFRzXM2PDys6fmJAH2yrOUvd6K0Ur8va9ojmUwmEY1GkUgkkEgkUFNTo+Xl5iVJEsbHxzW/TjQazencCKK5ksmkLlmORCI5nbNGNJeeWeZ9mbSUTCazXgVmOcLhsGGzrLiQlCQJN910Ex555JFLXn/jjTfQ0dGBV199FQBQXV2NO++8E9dccw0++tGP4kMf+hCam5tz02oFgsFgahV5HegRJipdk5OTuvV6M8ukpWAwyCxTUWCWVRSSJpMJ99xzD3784x9n1knq7u7GV77yFTz44IO4+eabAaTmWh06dAi/+c1v8Nvf/hbvvvsu3nnnndy2fhn0rOC1mGhLlKZnLyF7JElLet6XmWXSEu/LKoe2d+zYgaqqKjzzzDMYHBzE3XffjVtuuQV33XVX5j1vvvkmmpubUVlZCYfDgauvvjovcyT1/D8+Go3q1vtJpUfPX76RSIRZJs3oeV+ORCKcv06a0fu+bMQsqyokLRYL9u/fj2eeeQb79+9He3s7vv71r1/ynlWrVuHdd99FNBpFMpnE22+/jTVr1uSk0UrE43Fdr8d1+EgrsVhM1+sxy6QVPe/Lsiwzy6QZPe/Lsiwbck1J1Q/b7NixI1OJP/roozCbzZd83ev14uqrr8ZnPvMZfPrTn0ZzczOuu+667Fqrgt7VuxE/LVBxYJapWDDLVCyYZcCi9sCDBw8CSC30PbeITHvwwQfx4IMPqr1ETui96XmhbLJOhUfvbJlMJbNfAemM92UqFoIg6FrcGTHLqn5TPP744zh06BCeffZZJBIJPP/887luV87Y7XbdriUIQkFssE6FSe8sWyyqP2cSLUrPvYOZZdKSnvdlk8m0YMddPikuJJ977jk89dRT+M53voNNmzZh3759ePLJJ3Wfi7hcoijqdi2Hw2HITwtUHJhlKhZOp1O3a4miyCyTZnhfVlhIvv7663j44YfxyCOPwOv1AgD27t2LqakpvPTSS1q0L2t63rD0vBaVHj3zVVZWptu1qPTwvkzFgvdlBYVkV1cXHnjgARw4cAA33HBD5vXy8nLs2bMHTzzxhCGfJiovL9etK7iqqkqX61Bpqqio0G3eIrNMWmKWqVi4XC7dslxZWanLdZQSZCM+ApRjQ0NDGBkZ0fQaTqcTa9eu1fQaRMwyFYvBwUGMjo5qeo2ysrK8LDtHpWVgYAB+v1/Ta5SXl6O1tVXTa6hVEo9l1tTUaN4rWV9fr+n5iQB9srxy5UpNz08EALW1tZr35PC+THoo9SyXRCFpsVjQ2Nio2flramoMO3eBiovFYkFDQ4Nm56+pqeGcMtKF1vfl2tpaZpl0YbVaNb0v19XV6fpQj1IlUUgCqXkMWsyVcTgchv6kQMXH7XYzy1QUXC6XJvO+RFHEihUrcn5eooW43W7NslxXV5fz8+ZSyRSSANDQ0AC3252z89ntdrS2tnLhZtJdrrPscDiYZdKdIAhobGyEy+XK2TmZZcqHUs5ySTxsM5ssyxgZGcHw8HBW53G5XGhsbDTk4qBUGmRZxvDwcNYP3zDLlG+5yrLb7UZDQwOzTHlTilkuuUIyLRwOo7e3F9FoVNFxZrM5571BRNlglqlYZJPlXPcGEWVjenoafX19irOcngdfSFku2UISSH1ymJ6eht/vx+Tk5KL7ZTqdTlRXV+u6ZhTRcsmyjFAohLGxsWVluaamRte1/IiWS02WXS6XIXf8oNKWzrLf78fU1FTRZrmkC8nZZFlGNBpFOBzOLKxuMpngcDjgcDj4C5cKBrNMxYJZpmJRzFlmIUlEREREqhRuCUxEREREecVCkoiIiIhUYSFJRERERKqwkCQiIiIiVVhIEhEREZEqLCSJiIiISBUWkkRERESkCgtJIiIiIlKFhSQRERERqcJCkoiIiIhUYSFJRERERKqwkCQiIiIiVVhIEhEREZEqLCSJiIiISBUWkkRERESkCgtJIiIiIlKFhSQRERERqcJCkoiIiIhUYSFJRERERKqwkCQiIiIiVVhIEhEREZEqLCSJiIiISBUWkkRERESkCgtJIiIiIlKFhSQRERERqcJCkoiIiIhUseS7AURElB3b++6EYDJDMJlhstggmM0wzfw79cc06+9mCOaLf7/0ffO/J/U+AYJJgCAIF/9uEmASLv7dbFn866aZ4wUT5j2feZ4/lkv+bbr068LFv9stpnmPv+yPkGqTWUi1Ye6/M3+fOffl/8bFY0yp160mU+bvggCYMPO/Ai7/N2a/D6nvP/O+Wf9G+njhsuMEWQZkCYIsAVLy4t9n/5FmvybP+x5h5n3zfl1KQpZmzi8lISeTqfdKSchS6u9yIpb632Ty4vtmH7PI8bIkQU7O/Jn1dyl5+dek5OXvTcYTM68l5xw/+3zyzL9lSEl51mupv0uSfPE9SXnx90vSzGsykrFk6nVZRlKWkZSxyP8u9jUZEhb/elIGviP35PHusjT2SBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVQZZlOd+NICIiY4rFYvjud7+Le+65BzabLd/NUY3fh3EUw/cAFM/3kS32SBIR0YJisRi+/e1vIxaL5bspWeH3YRzF8D0AxfN9ZIuFJBERERGpwkKSiIiIiFRhIUlEREREqrCQJCKiBdlsNtx///0F/zABvw/jKIbvASie7yNbfGqbiIiIiFRhjyQRERERqcJCkoiIiIhUYSFJRERERKqwkCQiosuMj4/jC1/4Aj73uc/h0KFDl3xtZGQEe/bswZ/8yZ/g9ddfz08Dl2Gx7yHtf/7P/4n//M//1LdhyxSLxXDffffhc5/7HJ599tklXzeqpdr7D//wD3jhhRfy0LLlW+h7iEQiuOuuu7B79248//zzeWxh/rCQJCKiy/zoRz/CXXfdhaeffhpPP/30JV/72c9+ht27d+MHP/gBvv/97+ephUtb7HsAgPfeew+/+tWv8tCy5XnllVfw0Y9+FD/60Y/w85//HNFodNHXjWqx9o6NjRVEMbzQ9/DGG2/gyiuvxL/8y7+wkCQiIko7evQorrjiCtjtdpSXlyMQCGS+tnHjRkxPTyMSicDhcOSxlYtb7HsAgH/6p3/CZz7zmfw0bhnS7TeZTGhra8Pp06cXfd2oFmvvk08+aeifQdpC38PatWsRj8eRSCRgtVrz3Mr8YCFJRESXCYVCKCsrAwCIoojp6enM15xOJ77zne/gM5/5DD71qU/lq4lLWux7OHz4MBobG+F2u/PVvCWFQiE4nU4Al7Z/odeNaqH2DgwMIBQKYc2aNfls3rIs9D1YrVb87Gc/w0033YQPf/jD+Wxi3ljy3QAiIsq/H/zgB3j11Vcz/z5y5Aimp6dRVlaGcDicKcgA4Hvf+x6+/e1vo62tDXfeeSeuv/56Q/RMKvkevv/97+Mb3/gGnnrqqXw0dVmcTifC4TAAIBwOo7y8fNHXjWqh9n73u9/F/v378fbbb+ezecuy0Pfwwx/+EF/96ldx/fXX4/7770dfXx8aGxvz2VTdsUeSiIjwhS98AT/+8Y8zf+6991784Q9/QDQaxcTEBFwuV+a9oiiirKwMNpsNgiAgkUjkseUXLfd7CIVCOHnyJO677z785Cc/wd/+7d8iFArlufWX27JlC9555x3Isozjx49neu4Wet2oFmrvkSNH8Bd/8Rf43ve+h+9973s4d+5cnlu6sIW+h/R/CyaTCeXl5YbvHdYCd7YhIqLLjI2N4c///M8RCATw3//7f8cnPvEJ/N3f/R3uvfdejI2N4X/9r/+FRCKBG264AXfddVe+mzuvxb6HyspKAKknhq+66ip84AMfyG9j5xGNRvHVr34Vg4ODuPXWWxEOh3HNNdegqanpktdvv/32fDd1UQt9H+vXrweAzBPbt956az6buaiFvofKykocOHAA0WgUHR0d+Mu//Mt8N1V3LCSJiIiISBUObRMRERGRKiwkiYiIiEgVPrVNRERkENdeey1GRkZgsaR+PcuyjPLycuzYsQMHDhyAyWRCLBbDU089hX/7t39Df38/7HY7PB4P7r//fmzZsiXP3wGVGhaSREREBvLwww9f8uDJiRMncMcdd0AURdxzzz3Yu3cvbDYb/v7v/x6bNm1COBzGD37wA+zZswdPP/00tm3blsfWU6lhIUlERGRgGzduxPvf/34cO3YMzzzzDHp7e/HLX/4ys5ZhWVkZ7rvvPgSDQZw8eZKFJOmKhSQREZFBxeNx/Nd//Rfeeust/Nmf/RleffVVXHPNNfMuQv61r30tDy2kUsflf4iIiAzi2muvhd/vv2Tf5pUrV+KTn/wk7r33Xtx888248cYb8eCDD+axlUQXsUeSiIjIQB566KEFF+euq6vD8PDwvF8LBAIQRRE2m03L5hFdgsv/EBERFYhrr70Wv/3tbzE1NXXZ1/7qr/4K9913Xx5aRaWMhSQREVGBuO2221BbW4v77rsP3d3dkGUZ4+Pj+Na3voXf//73+PKXv5zvJlKJ4dA2ERFRgbDb7fjnf/5n/OM//iO+/OUvY3R0FA6HA16vFz/84Q+5jiTpjg/bEBEREZEqHNomIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpAoLSSIiIiJShYUkEREREanCQpKIiIiIVGEhSURERESqsJAkIiIiIlVYSBIRERGRKiwkiYiIiEgVFpJEREREpMr/DyNDK3tU432XAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -4315,7 +4317,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]

From bce2352c9f76a7fe5709a675a1213b6702f67475 Mon Sep 17 00:00:00 2001
From: jakobrunge <jakobrunge@gmail.com>
Date: Tue, 27 Jun 2023 16:36:43 +0200
Subject: [PATCH 4/5] made significance=fixed_thres more coherent, now assumes
 pc_alpha / alpha_level is threshold

---
 setup.py                                      |   2 +-
 tests/test_independence_tests.py              |  21 +-
 tests/test_models.py                          |   3 +-
 tigramite/independence_tests/cmiknn.py        | 106 +++++-
 .../independence_tests_base.py                | 163 ++++++---
 .../oracle_conditional_independence.py        |  11 +-
 tigramite/independence_tests/parcorr.py       |   1 +
 tigramite/lpcmci.py                           |  88 +++--
 tigramite/pcmci.py                            | 129 ++++---
 tigramite/rpcmci.py                           |   4 +-
 .../biogeoscience_case_study.ipynb            |  28 +-
 .../case_studies/climate_case_study.ipynb     |   4 +-
 .../tigramite_tutorial_assumptions.ipynb      |  31 +-
 ...e_tutorial_causal_discovery_overview.ipynb |  28 +-
 ...orial_conditional_independence_tests.ipynb |  29 +-
 ..._tutorial_heteroskedastic_ParCorrWLS.ipynb |  89 ++---
 .../tigramite_tutorial_latent-pcmci.ipynb     |  48 +--
 .../tigramite_tutorial_pcmciplus.ipynb        |  83 +++--
 .../tigramite_tutorial_regime_pcmci.ipynb     | 332 +-----------------
 ...ite_tutorial_sliding_window_analysis.ipynb |  16 +-
 ...orial_general_causal_effect_analysis.ipynb |  46 +--
 ...rial_linear_causal_effects_mediation.ipynb |  72 ++--
 .../tigramite_tutorial_prediction.ipynb       |  28 +-
 .../tigramite_tutorial_missing_masking.ipynb  | 155 ++++----
 ...tigramite_tutorial_multiple_datasets.ipynb |  26 +-
 25 files changed, 721 insertions(+), 822 deletions(-)

diff --git a/setup.py b/setup.py
index 7f973b97..b856e83c 100644
--- a/setup.py
+++ b/setup.py
@@ -62,7 +62,7 @@ def run(self):
 # Run the setup
 setup(
     name="tigramite",
-    version="5.2.1.22",
+    version="5.2.1.23",
     packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
     license="GNU General Public License v3.0",
     description="Tigramite causal inference for time series",
diff --git a/tests/test_independence_tests.py b/tests/test_independence_tests.py
index 4a3ae15c..ccc85001 100644
--- a/tests/test_independence_tests.py
+++ b/tests/test_independence_tests.py
@@ -120,14 +120,20 @@ def check_run_test(ind_test, sample):
     x_nds = true_parents[0]
     z_nds = true_parents[1]
     tau_max = 3
+    alpha_or_thres = 0.1
     # Run the test
-    val, pval = ind_test.run_test(x_nds, y_nds, z_nds, tau_max)
+    val, pval, dependent = ind_test.run_test(X=x_nds, Y=y_nds, Z=z_nds, 
+        tau_max=tau_max, alpha_or_thres=alpha_or_thres)
+
     # Get the array the test is running on
     array, xyz, _, _ = ind_test._get_array(x_nds, y_nds, z_nds, tau_max)
     dim, T = array.shape
     # Get the correct dependence measure
     val_expt = ind_test.get_dependence_measure(array, xyz)
-    pval_expt = ind_test.get_significance(val, array, xyz, T, dim)
+    pval_expt = ind_test._get_p_value(val, array, xyz, T, dim)
+    if ind_test.significance == 'fixed_thres':
+        dependent = val_expt >= alpha_or_thres
+        pval_expt = 0. if dependent else 1.
     # Check the values are close
     np.testing.assert_allclose(np.array(val), np.array(val_expt), atol=1e-2)
     np.testing.assert_allclose(np.array(pval), np.array(pval_expt), atol=1e-2)
@@ -232,14 +238,14 @@ def check_std_approximation(ind_test, sample, xlag, ylag):
     ('analytic', False, 'analytic'),
     ('analytic', False, 'bootstrap'),
     ('shuffle_test', False, 'analytic'),
-    ('fixed_thres', False, 'analytic')])
+    ('fixed_thres', False, 'analytic'),
+    ])
 def par_corr(request):
     # Unpack the parameters
     sig, recycle, conf = request.param
     # Generate the par_corr independence test
     return ParCorr(mask_type=None,
                    significance=sig,
-                   fixed_thres=0.1,
                    sig_samples=10000,
                    sig_blocklength=3,
                    confidence=conf,
@@ -359,7 +365,6 @@ def par_corr_wls(request):
                       window_size=100,
                       mask_type=None,
                       significance=sig,
-                      fixed_thres=0.1,
                       sig_samples=10000,
                       sig_blocklength=3,
                       confidence=conf,
@@ -385,7 +390,6 @@ def par_corr_wls_expert(request):
                       window_size=50,
                       mask_type=None,
                       significance=sig,
-                      fixed_thres=0.1,
                       sig_samples=10000,
                       sig_blocklength=3,
                       confidence=conf,
@@ -414,7 +418,6 @@ def par_corr_wls_expert_time(request):
                       window_size=50,
                       mask_type=None,
                       significance=sig,
-                      fixed_thres=0.1,
                       sig_samples=10000,
                       sig_blocklength=3,
                       confidence=conf,
@@ -570,7 +573,6 @@ def test_std_approximation(par_corr_wls_expert_time, data_sample_hs_time, x_lag,
 def gpdc(request):
     return GPDC(mask_type=None,
                 significance='analytic',
-                fixed_thres=0.1,
                 sig_samples=1000,
                 sig_blocklength=1,
                 confidence='bootstrap',
@@ -676,7 +678,6 @@ def test_trafo2uniform(gpdc, data_sample_a):
 def gpdc_torch(request):
     return GPDCtorch(mask_type=None,
                      significance='analytic',
-                     fixed_thres=0.1,
                      sig_samples=1000,
                      sig_blocklength=1,
                      confidence='bootstrap',
@@ -785,7 +786,6 @@ def test_trafo2uniform_torch(gpdc_torch, data_sample_a):
 def cmi_knn(request):
     return CMIknn(mask_type=None,
                   significance='shuffle_test',
-                  fixed_thres=None,
                   sig_samples=10000,
                   sig_blocklength=3,
                   knn=10,
@@ -862,7 +862,6 @@ def test_cmi_knn(cmi_knn, data_sample_c):
 def cmi_symb(request):
     return CMIsymb(mask_type=None,
                    significance='shuffle_test',
-                   fixed_thres=0.1,
                    sig_samples=10000,
                    sig_blocklength=3,
                    confidence='bootstrap',
diff --git a/tests/test_models.py b/tests/test_models.py
index 7ccfc9c8..325111fa 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -62,8 +62,7 @@ def test_predictions(data_frame_a):
     (dataframe, true_parents), links_coeffs = data_frame_a
     T = dataframe.T[0]
     # Build the prediction
-    a_cond_ind_test = ParCorr(significance='analytic',
-                              fixed_thres=0.01)
+    a_cond_ind_test = ParCorr(significance='analytic')
     pred = Prediction(dataframe=dataframe,
                       cond_ind_test=a_cond_ind_test,
                       prediction_model=sklearn.linear_model.LinearRegression(),
diff --git a/tigramite/independence_tests/cmiknn.py b/tigramite/independence_tests/cmiknn.py
index f778893e..66192c8b 100644
--- a/tigramite/independence_tests/cmiknn.py
+++ b/tigramite/independence_tests/cmiknn.py
@@ -82,6 +82,9 @@ class CMIknn(CondIndTest):
         Number of workers to use for parallel processing. If -1 is given
         all processors are used. Default: -1.
 
+    model_selection_folds : int
+        Number of folds in cross-validation used in model selection.
+
     significance : str, optional (default: 'shuffle_test')
         Type of significance test to use. For CMIknn only 'fixed_thres' and
         'shuffle_test' are available.
@@ -102,6 +105,7 @@ def __init__(self,
                  significance='shuffle_test',
                  transform='ranks',
                  workers=-1,
+                 model_selection_folds=3,
                  **kwargs):
         # Set the member variables
         self.knn = knn
@@ -112,6 +116,7 @@ def __init__(self,
         self.residual_based = False
         self.recycle_residuals = False
         self.workers = workers
+        self.model_selection_folds = model_selection_folds
         # Call the parent constructor
         CondIndTest.__init__(self, significance=significance, **kwargs)
         # Print some information about construction
@@ -453,6 +458,78 @@ def get_restricted_permutation(self, T, shuffle_neighbors, neighbors, order):
 
         return restricted_permutation
 
+    def get_model_selection_criterion(self, j, parents, tau_max=0):
+        """Returns a cross-validation-based score for nearest-neighbor estimates.
+
+        Fits a nearest-neighbor model of the parents to variable j and returns
+        the score. The lower, the better the fit. Here used to determine
+        optimal hyperparameters in PCMCI(pc_alpha or fixed thres).
+
+        Parameters
+        ----------
+        j : int
+            Index of target variable in data array.
+
+        parents : list
+            List of form [(0, -1), (3, -2), ...] containing parents.
+
+        tau_max : int, optional (default: 0)
+            Maximum time lag. This may be used to make sure that estimates for
+            different lags in X, Z, all have the same sample size.
+
+        Returns:
+        score : float
+            Model score.
+        """
+
+        import sklearn
+        from sklearn.neighbors import KNeighborsRegressor
+        from sklearn.model_selection import cross_val_score
+
+        Y = [(j, 0)]
+        X = [(j, 0)]   # dummy variable here
+        Z = parents
+        array, xyz, _ = self.dataframe.construct_array(X=X, Y=Y, Z=Z,
+                                                    tau_max=tau_max,
+                                                    mask_type=self.mask_type,
+                                                    return_cleaned_xyz=False,
+                                                    do_checks=True,
+                                                    verbosity=self.verbosity)
+        dim, T = array.shape
+
+        # Standardize
+        array = array.astype(np.float64)
+        array -= array.mean(axis=1).reshape(dim, 1)
+        std = array.std(axis=1)
+        for i in range(dim):
+            if std[i] != 0.:
+                array[i] /= std[i]
+        if np.any(std == 0.) and self.verbosity > 0:
+            warnings.warn("Possibly constant array!")
+            # raise ValueError("nans after standardizing, "
+            #                  "possibly constant array!")
+
+        predictor_indices =  list(np.where(xyz==2)[0])
+        predictor_array = array[predictor_indices, :].T
+        # Target is only first entry of Y, ie [y]
+        target_array = array[np.where(xyz==1)[0][0], :]
+
+        if predictor_array.size == 0:
+            # Regressing on ones if empty parents
+            predictor_array = np.ones(T).reshape(T, 1)
+
+        if self.knn < 1:
+            knn_here = max(1, int(self.knn*T))
+        else:
+            knn_here = max(1, int(self.knn))
+
+        knn_model = KNeighborsRegressor(n_neighbors=knn_here)
+        
+        scores = cross_val_score(estimator=knn_model, 
+            X=predictor_array, y=target_array, cv=self.model_selection_folds, n_jobs=self.workers)
+        
+        # print(scores)
+        return -scores.mean()
 
 if __name__ == '__main__':
     
@@ -463,8 +540,8 @@ def get_restricted_permutation(self, T, shuffle_neighbors, neighbors, order):
 
     random_state = np.random.default_rng(seed=42)
     cmi = CMIknn(mask_type=None,
-                   significance='shuffle_test',
-                   fixed_thres=None,
+                   significance='fixed_thres',
+                   fixed_thres=0.01,
                    sig_samples=1000,
                    sig_blocklength=1,
                    transform='none',
@@ -474,12 +551,25 @@ def get_restricted_permutation(self, T, shuffle_neighbors, neighbors, order):
     T = 1000
     dimz = 1
 
-    # Continuous data
-    z = random_state.standard_normal((T, dimz))
-    x = (0.8*z[:,0] + random_state.standard_normal(T)).reshape(T, 1)
-    y = (0.8*z[:,0] + random_state.standard_normal(T)).reshape(T, 1)
+    # # Continuous data
+    # z = random_state.standard_normal((T, dimz))
+    # x = (1.*z[:,0] + random_state.standard_normal(T)).reshape(T, 1)
+    # y = (1.*z[:,0] + random_state.standard_normal(T)).reshape(T, 1)
 
     # print('X _|_ Y')
     # print(cmi.run_test_raw(x, y, z=None))
-    print('X _|_ Y | Z')
-    print(cmi.run_test_raw(x, y, z=z))
+    # print('X _|_ Y | Z')
+    # print(cmi.run_test_raw(x, y, z=z))
+
+    # Continuous data
+    z = random_state.standard_normal((T, dimz))
+    x = random_state.standard_normal(T).reshape(T, 1)
+    y = (0.*z[:,0] + 1.*x[:,0] + random_state.standard_normal(T)).reshape(T, 1)
+
+    data = np.hstack((x, y, z))
+    print (data.shape)
+    dataframe = DataFrame(data=data)
+    cmi.set_dataframe(dataframe)
+    print(cmi.get_model_selection_criterion(j=1, parents=[], tau_max=0, folds=5))
+    print(cmi.get_model_selection_criterion(j=1, parents=[(0, 0)], tau_max=0, folds=5))
+    print(cmi.get_model_selection_criterion(j=1, parents=[(0, 0), (2, 0)], tau_max=0, folds=5))
diff --git a/tigramite/independence_tests/independence_tests_base.py b/tigramite/independence_tests/independence_tests_base.py
index 3a547197..ba56b051 100644
--- a/tigramite/independence_tests/independence_tests_base.py
+++ b/tigramite/independence_tests/independence_tests_base.py
@@ -37,8 +37,7 @@ class CondIndTest():
         'fixed_thres' and 'shuffle_test' are available.
 
     fixed_thres : float, optional (default: 0.1)
-        If significance is 'fixed_thres', this specifies the threshold for the
-        absolute value of the dependence measure.
+        Deprecated.
 
     sig_samples : int, optional (default: 500)
         Number of samples for shuffle significance test.
@@ -88,7 +87,7 @@ def __init__(self,
                  seed=42,
                  mask_type=None,
                  significance='analytic',
-                 fixed_thres=0.1,
+                 fixed_thres=None,
                  sig_samples=500,
                  sig_blocklength=None,
                  confidence=None,
@@ -104,7 +103,8 @@ def __init__(self,
         self.significance = significance
         self.sig_samples = sig_samples
         self.sig_blocklength = sig_blocklength
-        self.fixed_thres = fixed_thres
+        if fixed_thres is not None:
+            raise ValueError("fixed_thres is replaced by providing alpha_or_thres in run_test")
         self.verbosity = verbosity
         self.cached_ci_results = {}
         self.ci_results = {}
@@ -159,9 +159,9 @@ def print_info(self):
         if self.significance == 'shuffle_test':
             info_str += "\nsig_samples = %s" % self.sig_samples
             info_str += "\nsig_blocklength = %s" % self.sig_blocklength
-        # Check if we are using a fixed threshold
-        elif self.significance == 'fixed_thres':
-            info_str += "\nfixed_thres = %s" % self.fixed_thres
+        # # Check if we are using a fixed threshold
+        # elif self.significance == 'fixed_thres':
+        #     info_str += "\nfixed_thres = %s" % self.fixed_thres
         # Check if we have a confidence type
         if self.confidence:
             info_str += "\nconfidence = %s" % self.confidence
@@ -324,7 +324,7 @@ def _get_array_hash(self, array, xyz, XYZ):
         return combined_hash
 
 
-    def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max'):
+    def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max', alpha_or_thres=None):
         """Perform conditional independence test.
 
         Calls the dependence measure and signficicance test functions. The child
@@ -338,11 +338,9 @@ def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max'):
         X, Y, Z : list of tuples
             X,Y,Z are of the form [(var, -tau)], where var specifies the
             variable index and tau the time lag.
-
         tau_max : int, optional (default: 0)
             Maximum time lag. This may be used to make sure that estimates for
             different lags in X, Z, all have the same sample size.
-
         cut_off : {'2xtau_max', 'max_lag', 'max_lag_or_tau_max'}
             How many samples to cutoff at the beginning. The default is
             '2xtau_max', which guarantees that MCI tests are all conducted on
@@ -350,11 +348,16 @@ def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max'):
             which uses the maximum of tau_max and the conditions, which is
             useful to compare multiple models on the same sample.  Last,
             'max_lag' uses as much samples as possible.
+        alpha_or_thres : float (optional)
+            Significance level (if significance='analytic' or 'shuffle_test') or
+            threshold (if significance='fixed_thres'). If given, run_test returns
+            the test decision dependent=True/False.
 
         Returns
         -------
-        val, pval : Tuple of floats
-            The test statistic value and the p-value.
+        val, pval, [dependent] : Tuple of floats and bool
+            The test statistic value and the p-value. If alpha_or_thres is
+            given, run_test also returns the test decision dependent=True/False.      
         """
 
         # Get the array to test on
@@ -363,13 +366,14 @@ def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max'):
         
         # Record the dimensions
         dim, T = array.shape
-        
+
         # Ensure it is a valid array
         if np.any(np.isnan(array)):
             raise ValueError("nans in the array!")
 
         combined_hash = self._get_array_hash(array, xyz, XYZ)
 
+        # Get test statistic value and p-value [cached if possible]
         if combined_hash in self.cached_ci_results.keys():
             cached = True
             val, pval = self.cached_ci_results[combined_hash]
@@ -377,18 +381,39 @@ def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max'):
             cached = False
             # Get the dependence measure, reycling residuals if need be
             val = self._get_dependence_measure_recycle(X, Y, Z, xyz, array, data_type)
-            # Get the p-value
-            pval = self.get_significance(val, array, xyz, T, dim)
+            # Get the p-value (None if significance = 'fixed_thres')
+            pval = self._get_p_value(val=val, array=array, xyz=xyz, T=T, dim=dim)
             self.cached_ci_results[combined_hash] = (val, pval)
-            # self.ci_results[(X, Y, Z)] = (val, pval)
 
+        # Make test decision
+        if self.significance == 'fixed_thres':
+            if alpha_or_thres is None:
+                raise ValueError("significance == 'fixed_thres' requires setting alpha_or_thres")
+            if self.two_sided:
+                dependent = np.abs(val) >= np.abs(alpha_or_thres)
+            else:
+                dependent = val >= alpha_or_thres
+            pval = 0. if dependent else 1.
+        else:
+            if alpha_or_thres is None:
+                dependent = None
+            else:
+                dependent = pval <= alpha_or_thres
+                
+        self.ci_results[(tuple(X), tuple(Y),tuple(Z))] = (val, pval, dependent)
+
+        # Return the calculated value(s)
         if self.verbosity > 1:
-            self._print_cond_ind_results(val=val, pval=pval, cached=cached,
+            self._print_cond_ind_results(val=val, pval=pval, cached=cached, dependent=dependent,
                                          conf=None)
-        # Return the value and the pvalue
-        return val, pval
 
-    def run_test_raw(self, x, y, z=None, x_type=None, y_type=None, z_type=None):
+        if alpha_or_thres is None:
+            return val, pval
+        else:              
+            return val, pval, dependent
+
+
+    def run_test_raw(self, x, y, z=None, x_type=None, y_type=None, z_type=None, alpha_or_thres=None):
         """Perform conditional independence test directly on input arrays x, y, z.
 
         Calls the dependence measure and signficicance test functions. The child
@@ -405,11 +430,16 @@ def run_test_raw(self, x, y, z=None, x_type=None, y_type=None, z_type=None):
             are continuous or discrete: 0s for continuous variables and
             1s for discrete variables
 
+        alpha_or_thres : float (optional)
+            Significance level (if significance='analytic' or 'shuffle_test') or
+            threshold (if significance='fixed_thres'). If given, run_test returns
+            the test decision dependent=True/False.
+        
         Returns
         -------
-        val, pval : Tuple of floats
-
-            The test statistic value and the p-value.
+        val, pval, [dependent] : Tuple of floats and bool
+            The test statistic value and the p-value. If alpha_or_thres is
+            given, run_test also returns the test decision dependent=True/False.
         """
 
         if np.ndim(x) != 2 or np.ndim(y) != 2:
@@ -467,13 +497,30 @@ def run_test_raw(self, x, y, z=None, x_type=None, y_type=None, z_type=None):
 
         # Get the p-value
         if has_data_type:
-            pval = self.get_significance(val=val, array=array, xyz=xyz, 
+            pval = self._get_p_value(val=val, array=array, xyz=xyz, 
                     T=T, dim=dim, data_type=data_type)
         else:
-            pval = self.get_significance(val=val, array=array, xyz=xyz, 
-                    T=T, dim=dim)            
+            pval = self._get_p_value(val=val, array=array, xyz=xyz, 
+                    T=T, dim=dim)    
+
+        # Make test decision
+        if self.significance == 'fixed_thres':
+            if self.two_sided:
+                dependent = np.abs(val) >= np.abs(alpha_or_thres)
+            else:
+                dependent = val >= alpha_or_thres
+            pval = 0. if dependent else 1.
+        else:
+            if alpha_or_thres is None:
+                dependent = None
+            else:
+                dependent = pval <= alpha_or_thres
+
         # Return the value and the pvalue
-        return val, pval
+        if alpha_or_thres is None:
+            return val, pval
+        else:              
+            return val, pval, dependent
 
     def _get_dependence_measure_recycle(self, X, Y, Z, xyz, array, data_type=None):
         """Get the dependence_measure, optionally recycling residuals
@@ -558,12 +605,12 @@ def _get_cached_residuals(self, x_nodes, z_nodes, array, target_var):
         # Return these residuals
         return x_resid
 
-    def get_significance(self, val, array, xyz, T, dim,
+    def _get_p_value(self, val, array, xyz, T, dim,
                          data_type=None,
                          sig_override=None):
         """
         Returns the p-value from whichever significance function is specified
-        for this test.  If an override is used, then it will call a different
+        for this test. If an override is used, then it will call a different
         function then specified by self.significance
 
         Parameters
@@ -610,12 +657,25 @@ def get_significance(self, val, array, xyz, T, dim,
                                                  value=val)
         # Check if we are using the fixed_thres significance
         elif use_sig == 'fixed_thres':
-            pval = self.get_fixed_thres_significance(
-                    value=val,
-                    fixed_thres=self.fixed_thres)
+            # Determined outside then
+            pval = None
+            # if self.two_sided:
+            #     dependent = np.abs(val) >= np.abs(alpha_or_thres)
+            # else:
+            #     dependent = val >= alpha_or_thres
+            # pval = 0. if dependent else 1.
+            # # pval = self.get_fixed_thres_significance(
+            # #         value=val,
+            # #         fixed_thres=self.fixed_thres)
         else:
             raise ValueError("%s not known." % self.significance)
-        # Return the calculated value
+
+        # # Return the calculated value(s)
+        # if alpha_or_thres is not None:
+        #     if use_sig != 'fixed_thres': 
+        #         dependent = pval <= alpha_or_thres 
+        #     return pval, dependent 
+        # else:
         return pval
 
     def get_measure(self, X, Y, Z=None, tau_max=0, 
@@ -729,7 +789,7 @@ def get_confidence(self, X, Y, Z=None, tau_max=0,
         # Return the confidence interval
         return (conf_lower, conf_upper)
 
-    def _print_cond_ind_results(self, val, pval=None, cached=None, conf=None):
+    def _print_cond_ind_results(self, val, pval=None, cached=None, dependent=None, conf=None):
         """Print results from conditional independence test.
 
         Parameters
@@ -740,12 +800,17 @@ def _print_cond_ind_results(self, val, pval=None, cached=None, conf=None):
         pval : float, optional (default: None)
             p-value
 
+        dependent : bool
+            Test decision.
+
         conf : tuple of floats, optional (default: None)
             Confidence bounds.
         """
         printstr = "        val = % .3f" % (val)      
         if pval is not None:
             printstr += " | pval = %.5f" % (pval)
+        if dependent is not None:
+            printstr += " | dependent = %s" % (dependent)
         if conf is not None:
             printstr += " | conf bounds = (%.3f, %.3f)" % (
                 conf[0], conf[1])
@@ -1038,31 +1103,15 @@ def _get_shuffle_dist(self, array, xyz, dependence_measure,
         return null_dist
 
     def get_fixed_thres_significance(self, value, fixed_thres):
-        """Returns signficance for thresholding test.
-
-        Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1 else.
-
-        Parameters
-        ----------
-        value : number
-            Value of test statistic for unshuffled estimate.
-
-        fixed_thres : number
-            Fixed threshold, is made positive.
-
-        Returns
-        -------
-        pval : bool
-            Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1
-            else.
-
+        """DEPRECATED Returns signficance for thresholding test.
         """
-        if np.abs(value) < np.abs(fixed_thres):
-            pval = 1.
-        else:
-            pval = 0.
+        raise ValueError("fixed_thres is replaced by alpha_or_thres in run_test.")
+        # if np.abs(value) < np.abs(fixed_thres):
+        #     pval = 1.
+        # else:
+        #     pval = 0.
 
-        return pval
+        # return pval
 
     def _trafo2uniform(self, x):
         """Transforms input array to uniform marginals.
diff --git a/tigramite/independence_tests/oracle_conditional_independence.py b/tigramite/independence_tests/oracle_conditional_independence.py
index 7ccc148e..0ba2569a 100644
--- a/tigramite/independence_tests/oracle_conditional_independence.py
+++ b/tigramite/independence_tests/oracle_conditional_independence.py
@@ -1049,7 +1049,7 @@ def check_shortest_path(self, X, Y, Z,
 
         return any_path_observed
 
-    def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max',
+    def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max', alpha_or_thres=None,
                  verbosity=0):
         """Perform oracle conditional independence test.
 
@@ -1064,6 +1064,8 @@ def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max',
             Not used here.
         cut_off : {'2xtau_max', 'max_lag', 'max_lag_or_tau_max'}
             Not used here.
+        alpha_or_thres : float
+            Not used here.
 
         Returns
         -------
@@ -1088,15 +1090,20 @@ def run_test(self, X, Y, Z=None, tau_max=0, cut_off='2xtau_max',
         if self.dsepsets[str((X, Y, Z))]:
             val = 0.
             pval = 1.
+            dependent = False
         else:
             val = 1.
             pval = 0.
+            dependent = True
 
         if verbosity > 1:
             self._print_cond_ind_results(val=val, pval=pval, cached=False,
                                          conf=None)
         # Return the value and the pvalue
-        return val, pval
+        if alpha_or_thres is None:
+            return val, pval
+        else:
+            return val, pval, dependent
 
     def get_measure(self, X, Y, Z=None, tau_max=0):
         """Returns dependence measure.
diff --git a/tigramite/independence_tests/parcorr.py b/tigramite/independence_tests/parcorr.py
index 5aa0a510..1a77008e 100644
--- a/tigramite/independence_tests/parcorr.py
+++ b/tigramite/independence_tests/parcorr.py
@@ -308,4 +308,5 @@ def get_model_selection_criterion(self, j, parents, tau_max=0, corrected_aic=Fal
             score = T * np.log(rss) + 2. * p + (2.*p**2 + 2.*p)/(T - p - 1)
         else:
             score = T * np.log(rss) + 2. * p
+
         return score
diff --git a/tigramite/lpcmci.py b/tigramite/lpcmci.py
index 8e9db741..5439e087 100644
--- a/tigramite/lpcmci.py
+++ b/tigramite/lpcmci.py
@@ -341,6 +341,8 @@ def _initialize(self, link_assumptions, tau_min, tau_max, pc_alpha, n_preliminar
         self.remember_only_parents = remember_only_parents
         self.no_apr = no_apr
 
+        if isinstance(pc_alpha, (list, tuple, np.ndarray)):
+                raise ValueError("pc_alpha must be single float in LPCMCI.")
         if pc_alpha < 0. or pc_alpha > 1:
             raise ValueError("Choose 0 <= pc_alpha <= 1")
             
@@ -828,7 +830,8 @@ def _run_ancestral_removal_phase(self, prelim = False):
                             Z = Z.union(S_default_YX)
 
                             # Test conditional independence of X and Y given Z
-                            val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                            val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), 
+                                tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                             if self.verbosity >= 2:
                                 print("ANC(Y):    %s _|_ %s  |  S_def = %s, S_pc = %s: val = %.2f / pval = % .4f" %
@@ -839,7 +842,7 @@ def _run_ancestral_removal_phase(self, prelim = False):
                             self._update_pval_val_card_dicts(X, Y, pval, val, len(Z))
 
                             # Check whether test result was significant
-                            if pval > self.pc_alpha:
+                            if not dependent: #pval > self.pc_alpha:
 
                                 # Mark the edge from X to Y for removal and save sepset
                                 to_remove[Y[0]][X] = True
@@ -867,7 +870,8 @@ def _run_ancestral_removal_phase(self, prelim = False):
                             Z = Z.union(S_default_XY)
 
                             # Test conditional independence of X and Y given Z
-                            val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                            val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), 
+                                tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                             if self.verbosity >= 2:
                                 print("ANC(X):    %s _|_ %s  |  S_def = %s, S_pc = %s: val = %.2f / pval = % .4f" %
@@ -878,7 +882,7 @@ def _run_ancestral_removal_phase(self, prelim = False):
                             self._update_pval_val_card_dicts(X, Y, pval, val, len(Z))
 
                             # Check whether test result was significant
-                            if pval > self.pc_alpha:
+                            if not dependent: # pval > self.pc_alpha:
 
                                 # Mark the edge from X to Y for removal and save sepset
                                 to_remove[Y[0]][X] = True
@@ -1154,7 +1158,9 @@ def _run_non_ancestral_removal_phase(self):
                         Z = Z.union(S_default_YX)
 
                         # Test conditional independence of X and Y given Z
-                        val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                        # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                        val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), 
+                            tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                         if self.verbosity >= 2:
                             print("Non-ANC(Y):    %s _|_ %s  |  S_def = %s, S_pc = %s: val = %.2f / pval = % .4f" %
@@ -1165,7 +1171,7 @@ def _run_non_ancestral_removal_phase(self):
                         self._update_pval_val_card_dicts(X, Y, pval, val, len(Z))
 
                         # Check whether test result was significant
-                        if pval > self.pc_alpha:
+                        if not dependent: # pval > self.pc_alpha:
 
                             # Mark the edge from X to Y for removal and save sepset
                             to_remove[Y[0]][X] = True
@@ -1197,7 +1203,9 @@ def _run_non_ancestral_removal_phase(self):
                             Z = Z.union(S_default_XY)
 
                             # Test conditional independence of X and Y given Z
-                            val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                            # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                            val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), 
+                                tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                             if self.verbosity >= 2:
                                 print("Non-ANC(X):    %s _|_ %s  |  S_def = %s, S_pc = %s: val = %.2f / pval = % .4f" %
@@ -1208,7 +1216,7 @@ def _run_non_ancestral_removal_phase(self):
                             self._update_pval_val_card_dicts(X, Y, pval, val, len(Z))
 
                             # Check whether test result was significant
-                            if pval > self.pc_alpha:
+                            if not dependent: # pval > self.pc_alpha:
 
                                 # Mark the edge from X to Y for removal and save sepset
                                 to_remove[Y[0]][X] = True
@@ -1876,7 +1884,9 @@ def _make_sepset_weakly_minimal(self, X, Y, Z_list, ancs):
                 Z_A = [node for node in Z if node != A]
 
                 # Run the conditional independence test
-                val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = Z_A, tau_max = self.tau_max)
+                # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = Z_A, tau_max = self.tau_max)
+                val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = Z_A, 
+                    tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                 if self.verbosity >= 2:
                     print("MakeMin:    %s _|_ %s  |  Z_A = %s: val = %.2f / pval = % .4f" %
@@ -1887,7 +1897,7 @@ def _make_sepset_weakly_minimal(self, X, Y, Z_list, ancs):
                 self._update_pval_val_card_dicts(X, Y, pval, val, len(Z_A))
 
                 # Check whether the test result was significant
-                if pval > self.pc_alpha:
+                if not dependent: # pval > self.pc_alpha:
                     new_sepsets.append(frozenset(Z_A))
                     val_values.append(val)
 
@@ -1972,7 +1982,9 @@ def _B_not_in_SepSet_AC(self, A, B, C):
                 Z = Z.union(Z_add)
 
                 # Test conditional independence of X and Y given Z
-                val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), 
+                    tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                 if self.verbosity >= 2:
                     print("BnotinSepSetAC(A):    %s _|_ %s  |  Z_add = %s, Z = %s: val = %.2f / pval = % .4f" %
@@ -1983,7 +1995,7 @@ def _B_not_in_SepSet_AC(self, A, B, C):
                 self._update_pval_val_card_dicts(X, Y, pval, val, len(Z))
 
                 # Check whether test result was significant
-                if pval > self.pc_alpha:
+                if not dependent: # pval > self.pc_alpha:
                     all_sepsets.add(frozenset(Z))
 
         # Test for independence given all subsets of non-future adjacencies of C
@@ -2001,7 +2013,9 @@ def _B_not_in_SepSet_AC(self, A, B, C):
                 Z = Z.union(Z_add)
 
                 # Test conditional independence of X and Y given Z
-                val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), 
+                    tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                 if self.verbosity >= 2:
                     # print("BnotinSepSetAC(C):    %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f" %
@@ -2014,7 +2028,7 @@ def _B_not_in_SepSet_AC(self, A, B, C):
                 self._update_pval_val_card_dicts(X, Y, pval, val, len(Z))
 
                 # Check whether test result was significant
-                if pval > self.pc_alpha:
+                if not dependent: # pval > self.pc_alpha:
                     all_sepsets.add(frozenset(Z))
 
         # Append the already known sepset
@@ -2098,8 +2112,10 @@ def _B_in_SepSet_AC(self, A, B, C):
                     Z = Z.union(Z_add)
 
                     # Test conditional independence of X and Y given Z
-                    val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
-
+                    # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                    val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), 
+                        tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
+                    
                     if self.verbosity >= 2:
                         # print("BinSepSetAC(A):    %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f" %
                         #     (X, Y, ' '.join([str(z) for z in list(Z)]), val, pval))
@@ -2111,7 +2127,7 @@ def _B_in_SepSet_AC(self, A, B, C):
                     self._update_pval_val_card_dicts(X, Y, pval, val, len(Z))
 
                     # Check whether test result was significant
-                    if pval > self.pc_alpha:
+                    if not dependent: # pval > self.pc_alpha:
                         all_sepsets.add(frozenset(Z))
 
             # Test for independence given all subsets of non-future adjacencies of C
@@ -2129,8 +2145,10 @@ def _B_in_SepSet_AC(self, A, B, C):
                     Z = Z.union(Z_add)
 
                     # Test conditional independence of X and Y given Z
-                    val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
-
+                    # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)
+                    val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), 
+                        tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
+                    
                     if self.verbosity >= 2:
                         # print("BinSepSetAC(C):     %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f" %
                         #     (X, Y, ' '.join([str(z) for z in list(Z)]), val, pval))
@@ -2142,7 +2160,7 @@ def _B_in_SepSet_AC(self, A, B, C):
                     self._update_pval_val_card_dicts(X, Y, pval, val, len(Z))
 
                     # Check whether test result was significant
-                    if pval > self.pc_alpha:
+                    if not dependent: # pval > self.pc_alpha:
                         all_sepsets.add(frozenset(Z))
 
             # Append the already known sepset
@@ -2764,7 +2782,9 @@ def _apply_ER00a(self, only_lagged):
                     Z_add2 = {(var, lag - delta_lag) for (var, lag) in Z_add.difference({A, B}) if lag - delta_lag <= 0 and lag - delta_lag >= -self.tau_max}
 
                     # Test conditional independence of X and Y given Z
-                    val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)
+                    # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)
+                    val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), 
+                        tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                     if self.verbosity >= 2:
                         # print("ER00a(part1):    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f" %
@@ -2777,7 +2797,7 @@ def _apply_ER00a(self, only_lagged):
                     self._update_pval_val_card_dicts(X, Y, pval, val, len(Z_test))
 
                     # Check whether test result was significant
-                    if pval > self.pc_alpha:
+                    if not dependent: # pval > self.pc_alpha:
 
                         # Mark the edge from X to Y for removal and save sepset
                         remove_AB = True
@@ -2821,7 +2841,9 @@ def _apply_ER00a(self, only_lagged):
                     Z_add2 = {(var, lag - delta_lag) for (var, lag) in Z_add.difference({A, B}) if lag - delta_lag <= 0 and lag - delta_lag >= -self.tau_max}
 
                     # Test conditional independence of X and Y given Z
-                    val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)
+                    # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)
+                    val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), 
+                        tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                     if self.verbosity >= 2:
                         # print("ER00a(part2):    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f" %
@@ -2834,7 +2856,7 @@ def _apply_ER00a(self, only_lagged):
                     self._update_pval_val_card_dicts(X, Y, pval, val, len(Z_test))
 
                     # Check whether test result was significant
-                    if pval > self.pc_alpha:
+                    if not dependent: # pval > self.pc_alpha:
                         
                         # Mark the edge from X to Y for removal and save sepset
                         remove_CB = True
@@ -2929,7 +2951,9 @@ def _apply_ER00b(self, only_lagged):
                     Z_add2 = {(var, lag - delta_lag) for (var, lag) in Z_add.difference({A, B}) if lag - delta_lag <= 0 and lag - delta_lag >= -self.tau_max}
 
                     # Test conditional independence of X and Y given Z
-                    val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)
+                    # val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)
+                    val, pval, dependent = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), 
+                        tau_max = self.tau_max, alpha_or_thres=self.pc_alpha)
 
                     if self.verbosity >= 2:
                         # print("ER00b:    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f" %
@@ -2942,7 +2966,7 @@ def _apply_ER00b(self, only_lagged):
                     self._update_pval_val_card_dicts(X, Y, pval, val, len(Z_test))
 
                     # Check whether test result was significant
-                    if pval > self.pc_alpha:
+                    if not dependent: # pval > self.pc_alpha:
 
                         # Mark the edge from X to Y for removal and save sepset
                         remove_AB = True
@@ -3552,7 +3576,7 @@ def _delete_sepsets(self, X, Y):
 
 if __name__ == '__main__':
 
-    from tigramite.independence_tests import ParCorr
+    from tigramite.independence_tests.parcorr import ParCorr
     import tigramite.data_processing as pp
     from tigramite.toymodels import structural_causal_processes as toys
     import tigramite.plotting as tp
@@ -3579,15 +3603,15 @@ def nonlin_f(x): return (x + 5. * x ** 2 * np.exp(-x ** 2 / 20.))
     # Data must be array of shape (time, variables)
     print(data.shape)
     dataframe = pp.DataFrame(data)
-    cond_ind_test = ParCorr()
+    cond_ind_test = ParCorr(significance='fixed_thres')
     lpcmci = LPCMCI(dataframe=dataframe, cond_ind_test=cond_ind_test)
-    # results = pcmci.run_lpcmci(tau_max=2, pc_alpha=0.01)
+    results = lpcmci.run_lpcmci(tau_max=2, pc_alpha=0.01)
 
     # # For a proper causal interpretation of the graph see the paper!
     # print(results['graph'])
     # tp.plot_graph(graph=results['graph'], val_matrix=results['val_matrix'])
     # plt.show()
 
-    results = lpcmci.run_sliding_window_of(
-        window_step=499, window_length=500,
-        method='run_lpcmci', method_args={'tau_max':1})
+    # results = lpcmci.run_sliding_window_of(
+    #     window_step=499, window_length=500,
+    #     method='run_lpcmci', method_args={'tau_max':1})
diff --git a/tigramite/pcmci.py b/tigramite/pcmci.py
index 27b51796..926cb9b8 100644
--- a/tigramite/pcmci.py
+++ b/tigramite/pcmci.py
@@ -414,12 +414,13 @@ def _run_pc_stable_single(self, j,
                     if link_assumptions_j[parent] == '-->':
                         val = 1.
                         pval = 0.
+                        dependent = True
                     else:
-                        val, pval = self.cond_ind_test.run_test(X=[parent],
+                        val, pval, dependent = self.cond_ind_test.run_test(X=[parent],
                                                     Y=[(j, 0)],
                                                     Z=Z,
                                                     tau_max=tau_max,
-                                                    # verbosity=self.verbosity
+                                                    alpha_or_thres=pc_alpha,
                                                     )
                     # Print some information if needed
                     if self.verbosity > 1:
@@ -441,7 +442,7 @@ def _run_pc_stable_single(self, j,
                         a_iter[comb_index]['val'] = val
                         a_iter[comb_index]['pval'] = pval
                     # Delete link later and break while-loop if non-significant
-                    if pval > pc_alpha:
+                    if not dependent: #pval > pc_alpha:
                         nonsig_parents.append((j, parent))
                         nonsig = True
                         break
@@ -1081,10 +1082,9 @@ def _run_mci_or_variants(self,
                     val = 1. 
                     pval = 0.
                 else:
-                    val, pval = self.cond_ind_test.run_test(X, Y, Z=Z,
+                    val, pval, _ = self.cond_ind_test.run_test(X, Y, Z=Z,
                                                         tau_max=tau_max,
-                                                        # verbosity=
-                                                        # self.verbosity
+                                                        alpha_or_thres=alpha_level,
                                                         )
                 val_matrix[i, j, abs(tau)] = val
                 p_matrix[i, j, abs(tau)] = pval
@@ -1107,13 +1107,21 @@ def _run_mci_or_variants(self,
 
         # Correct the p_matrix if there is a fdr_method
         if fdr_method != 'none':
+            if self.cond_ind_test.significance == 'fixed_thres':
+                raise ValueError("FDR-correction not compatible with significance == 'fixed_thres'")
             p_matrix = self.get_corrected_pvalues(p_matrix=p_matrix, tau_min=tau_min, 
                                                   tau_max=tau_max, 
                                                   link_assumptions=_int_link_assumptions,
                                                   fdr_method=fdr_method)
 
-        # Threshold p_matrix to get graph
-        final_graph = p_matrix <= alpha_level
+        # Threshold p_matrix to get graph (or val_matrix for significance == 'fixed_thres')
+        if self.cond_ind_test.significance == 'fixed_thres':
+            if self.cond_ind_test.two_sided:
+                final_graph = np.abs(val_matrix) >= np.abs(alpha_level)
+            else:
+                final_graph = val_matrix >= alpha_level
+        else:
+            final_graph = p_matrix <= alpha_level
 
         # Convert to string graph representation
         graph = self.convert_to_string_graph(final_graph)
@@ -2438,7 +2446,7 @@ def _pcmciplus_mci_skeleton_phase(self,
 
         # Update all entries computed in the MCI step 
         # (these are in links_for_pc); values for entries
-        # that were removed in the lagged-condition phase are kept
+        # that were removed in the lagged-condition phase are kept from before
         for j in range(self.N):
             for link in links_for_pc[j]:
                 i, tau = link
@@ -2770,7 +2778,7 @@ def run_pcalg_non_timeseries_data(self, pc_alpha=0.01,
 
 
     def _run_pcalg_test(self, graph, i, abstau, j, S, lagged_parents, max_conds_py,
-                        max_conds_px, max_conds_px_lagged, tau_max):
+                        max_conds_px, max_conds_px_lagged, tau_max, alpha_or_thres=None):
         """MCI conditional independence tests within PCMCIplus or PC algorithm.
 
         Parameters
@@ -2796,15 +2804,16 @@ def _run_pcalg_test(self, graph, i, abstau, j, S, lagged_parents, max_conds_py,
             tests. If None is passed, this number is equal to max_conds_px.
         tau_max : int
             Maximum time lag.
+        alpha_or_thres : float
+            Significance level (if significance='analytic' or 'shuffle_test') or
+            threshold (if significance='fixed_thres'). If given, run_test returns
+            the test decision dependent=True/False.
 
         Returns
         -------
-        val : float
-            Test statistic value.
-        pval : float
-            Test statistic p-value.
-        Z : list
-            List of conditions.
+        val, pval, Z, [dependent] : Tuple of floats, list, and bool
+            The test statistic value and the p-value and list of conditions. If alpha_or_thres is
+            given, run_test also returns the test decision dependent=True/False.             
         """
 
         # Perform independence test adding lagged parents
@@ -2834,13 +2843,15 @@ def _run_pcalg_test(self, graph, i, abstau, j, S, lagged_parents, max_conds_py,
         if graph[i,j,abstau] != "" and graph[i,j,abstau][1] == '-':
             val = 1. 
             pval = 0.
+            dependent = True
         else:
-            val, pval = self.cond_ind_test.run_test(X=[(i, -abstau)], Y=[(j, 0)],
+            val, pval, dependent = self.cond_ind_test.run_test(X=[(i, -abstau)], Y=[(j, 0)],
                                                 Z=Z, tau_max=tau_max,
+                                                alpha_or_thres=alpha_or_thres,
                                                 # verbosity=self.verbosity
                                                 )
 
-        return val, pval, Z
+        return val, pval, Z, dependent
 
     def _print_triple_info(self, triple, index, n_triples):
         """Print info about the current triple being tested.
@@ -3043,9 +3054,11 @@ def _pcalg_skeleton(self,
                             break
 
                         # Run MCI test
-                        val, pval, Z = self._run_pcalg_test(graph,
-                            i, abstau, j, S, lagged_parents, max_conds_py,
-                            max_conds_px, max_conds_px_lagged, tau_max)
+                        val, pval, Z, dependent = self._run_pcalg_test(graph=graph,
+                            i=i, abstau=abstau, j=j, S=S, lagged_parents=lagged_parents, 
+                            max_conds_py=max_conds_py,
+                            max_conds_px=max_conds_px, max_conds_px_lagged=max_conds_px_lagged,
+                            tau_max=tau_max, alpha_or_thres=pc_alpha)
 
                         # Store minimum test statistic value for sorting adjt
                         # (only internally used)
@@ -3068,7 +3081,7 @@ def _pcalg_skeleton(self,
 
                         # If conditional independence is found, remove link
                         # from graph and store sepsets
-                        if pval > pc_alpha:
+                        if not dependent: # pval > pc_alpha:
                             nonsig = True
                             if abstau == 0:
                                 graph[i, j, 0] = graph[j, i, 0] = ""
@@ -3330,15 +3343,17 @@ def subsets(s):
                 # Test which neighbor subsets separate i and j
                 neighbor_sepsets = []
                 for iss, S in enumerate(neighbor_subsets):
-                    val, pval, Z = self._run_pcalg_test(graph,
-                        i, abs(tau), j, S, lagged_parents, max_conds_py,
-                        max_conds_px, max_conds_px_lagged, tau_max)
+                    val, pval, Z, dependent = self._run_pcalg_test(graph=graph,
+                            i=i, abstau=abs(tau), j=j, S=S, lagged_parents=lagged_parents, 
+                            max_conds_py=max_conds_py,
+                            max_conds_px=max_conds_px, max_conds_px_lagged=max_conds_px_lagged,
+                            tau_max=tau_max, alpha_or_thres=pc_alpha)
 
                     if self.verbosity > 1:
                         self._print_cond_info(Z=S, comb_index=iss, pval=pval,
                                               val=val)
 
-                    if pval > pc_alpha:
+                    if not dependent: #pval > pc_alpha:
                         neighbor_sepsets += [S]
 
                 if len(neighbor_sepsets) > 0:
@@ -3877,9 +3892,9 @@ def _optimize_pcmciplus_alpha(self,
                 parents = []
                 for i, tau in zip(*np.where(dag[:,j,:] == "-->")):
                     parents.append((i, -tau))
-                score[iscore] += \
-                    self.cond_ind_test.get_model_selection_criterion(
+                score_j = self.cond_ind_test.get_model_selection_criterion(
                         j, parents, tau_max)
+                score[iscore] += score_j
             score[iscore] /= float(self.N)
 
         # Record the optimal alpha value
@@ -3903,6 +3918,8 @@ def _optimize_pcmciplus_alpha(self,
 
 if __name__ == '__main__':
     from tigramite.independence_tests.parcorr import ParCorr
+    from tigramite.independence_tests.cmiknn import CMIknn
+
     import tigramite.data_processing as pp
     from tigramite.toymodels import structural_causal_processes as toys
     import tigramite.plotting as tp
@@ -3915,10 +3932,10 @@ def _optimize_pcmciplus_alpha(self,
     def lin_f(x): return x
     def nonlin_f(x): return (x + 5. * x ** 2 * np.exp(-x ** 2 / 20.))
 
-    T = 20000
+    T = 1000
     data = random_state.standard_normal((T, 4))
     # Simple sun
-    data[:,3] = np.sin(np.arange(T)*20/np.pi) + 0.1*random_state.standard_normal((T))
+    data[:,3] = random_state.standard_normal((T)) # np.sin(np.arange(T)*20/np.pi) + 0.1*random_state.standard_normal((T))
     c = 0.8
     for t in range(1, T):
         data[t, 0] += 0.4*data[t-1, 0] + 0.4*data[t-1, 1] + c*data[t-1,3]
@@ -3927,40 +3944,42 @@ def nonlin_f(x): return (x + 5. * x ** 2 * np.exp(-x ** 2 / 20.))
     dataframe = pp.DataFrame(data, var_names=[r'$X^0$', r'$X^1$', r'$X^2$', 'Sun'])
     # tp.plot_timeseries(dataframe); plt.show()
 
-    parcorr = ParCorr()
+    ci_test = CMIknn(significance="fixed_thres", verbosity=3)   #
+    # ci_test = ParCorr() #significance="fixed_thres")   #
     # dataframe_nosun = pp.DataFrame(data[:,[0,1,2]], var_names=[r'$X^0$', r'$X^1$', r'$X^2$'])
     # pcmci_parcorr = PCMCI(
     #     dataframe=dataframe_nosun, 
     #     cond_ind_test=parcorr,
     #     verbosity=0)
-    tau_max = 2
+    tau_max = 1  #2
     # results = pcmci_parcorr.run_pcmci(tau_max=tau_max, pc_alpha=0.2, alpha_level = 0.01)
     # Remove parents of variable 3
     # Only estimate parents of variables 0, 1, 2
-    link_assumptions = {}
-    for j in range(4):
-        if j in [0, 1, 2]:
-            # Directed lagged links
-            link_assumptions[j] = {(var, -lag): '-?>' for var in [0, 1, 2]
-                             for lag in range(1, tau_max + 1)}
-            # Unoriented contemporaneous links
-            link_assumptions[j].update({(var, 0): 'o?o' for var in [0, 1, 2] if var != j})
-            # Directed lagged and contemporaneous links from the sun (3)
-            link_assumptions[j].update({(var, -lag): '-?>' for var in [3]
-                             for lag in range(0, tau_max + 1)})
-        else:
-            link_assumptions[j] = {}
-
-    for j in link_assumptions:
-        print(link_assumptions[j])
+    link_assumptions = None #{}
+    # for j in range(4):
+    #     if j in [0, 1, 2]:
+    #         # Directed lagged links
+    #         link_assumptions[j] = {(var, -lag): '-?>' for var in [0, 1, 2]
+    #                          for lag in range(1, tau_max + 1)}
+    #         # Unoriented contemporaneous links
+    #         link_assumptions[j].update({(var, 0): 'o?o' for var in [0, 1, 2] if var != j})
+    #         # Directed lagged and contemporaneous links from the sun (3)
+    #         link_assumptions[j].update({(var, -lag): '-?>' for var in [3]
+    #                          for lag in range(0, tau_max + 1)})
+    #     else:
+    #         link_assumptions[j] = {}
+
+    # for j in link_assumptions:
+    #     print(link_assumptions[j])
     pcmci_parcorr = PCMCI(
         dataframe=dataframe, 
-        cond_ind_test=parcorr,
-        verbosity=0)
-    results = pcmci_parcorr.run_pcmciplus(tau_max=tau_max, pc_alpha=0.01, 
-        reset_lagged_links=True,
-                                      link_assumptions=link_assumptions
-                                      ) #, alpha_level = 0.01)
+        cond_ind_test=ci_test,
+        verbosity=1)
+    results = pcmci_parcorr.run_pcmciplus(tau_max=tau_max, 
+                    pc_alpha=[0.001, 0.01, 0.05, 0.8], 
+                    reset_lagged_links=False,
+                    link_assumptions=link_assumptions
+                    ) #, alpha_level = 0.01)
     print(results['graph'].shape)
     # print(results['graph'][:,3,:])
     print(np.round(results['p_matrix'][:,:,0], 2))
@@ -3968,7 +3987,7 @@ def nonlin_f(x): return (x + 5. * x ** 2 * np.exp(-x ** 2 / 20.))
     print(results['graph'][:,:,0])
 
     # Plot time series graph
-    # tp.plot_time_series_graph(
+    # tp.plot_graph(
     #     val_matrix=results['val_matrix'],
     #     graph=results['graph'],
     #     var_names=[r'$X^0$', r'$X^1$', r'$X^2$', 'Sun'],
diff --git a/tigramite/rpcmci.py b/tigramite/rpcmci.py
index a452a291..e402e80c 100644
--- a/tigramite/rpcmci.py
+++ b/tigramite/rpcmci.py
@@ -80,8 +80,8 @@ def __init__(self, dataframe, cond_ind_test=None,
         if dataframe.analysis_mode != 'single':
             raise ValueError("Only single time series data allowed for RPCMCI.")
    
-        if dataframe.vector_vars is not None:
-            raise ValueError("Only single time series data allowed for RPCMCI.")
+        if dataframe.has_vector_data:
+            raise ValueError("Only scalar data allowed for RPCMCI.")
         
                
         # Masking is not available in RPCMCI, but missing values can be specified
diff --git a/tutorials/case_studies/biogeoscience_case_study.ipynb b/tutorials/case_studies/biogeoscience_case_study.ipynb
index 26c1e3f4..a9ee79d2 100644
--- a/tutorials/case_studies/biogeoscience_case_study.ipynb
+++ b/tutorials/case_studies/biogeoscience_case_study.ipynb
@@ -346,7 +346,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x500 with 9 Axes>"
       ]
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -557,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -578,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -813,7 +813,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f14d26357c0>"
+       "<matplotlib.legend.Legend at 0x7f69155ab910>"
       ]
      },
      "execution_count": 14,
@@ -892,7 +892,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f14d21113a0>"
+       "<matplotlib.legend.Legend at 0x7f69151102b0>"
       ]
      },
      "execution_count": 15,
@@ -901,7 +901,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 2 Axes>"
       ]
@@ -984,7 +984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -997,7 +997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1011,7 +1011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1088,7 +1088,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f14d1f0b070>"
+       "<matplotlib.legend.Legend at 0x7f6914edb790>"
       ]
      },
      "execution_count": 19,
@@ -1169,16 +1169,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f14d1fe2850>"
+       "<matplotlib.legend.Legend at 0x7f6915d8edf0>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
diff --git a/tutorials/case_studies/climate_case_study.ipynb b/tutorials/case_studies/climate_case_study.ipynb
index abaccc5a..985f430b 100644
--- a/tutorials/case_studies/climate_case_study.ipynb
+++ b/tutorials/case_studies/climate_case_study.ipynb
@@ -666,7 +666,7 @@
    "source": [
     "All these effects are in units of the seasonally-standardized data.\n",
     "\n",
-    "As a note, because there is no hidden confounding here, you can also use the faster ``LinearMediation`` class for linear causal effect and mediation estimation  (see [tutorial on linear mediation](https://github.com/jakobrunge/tigramite/blob/master/tutorials/causal_effect_estimation/tigramite_tutorial_linear_causal_effects_mediation.ipynb)). To this end, you need to provide the class with the parents of all nodes (read-off from the graph):"
+    "As a note, because there is no hidden confounding here, you can also use the (often) faster ``LinearMediation`` class for linear causal effect and mediation estimation  (see [tutorial on linear mediation](https://github.com/jakobrunge/tigramite/blob/master/tutorials/causal_effect_estimation/tigramite_tutorial_linear_causal_effects_mediation.ipynb)). To this end, you need to provide the class with the parents of all nodes (read-off from the graph):"
    ]
   },
   {
@@ -721,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
diff --git a/tutorials/causal_discovery/tigramite_tutorial_assumptions.ipynb b/tutorials/causal_discovery/tigramite_tutorial_assumptions.ipynb
index 888a3f0c..63c569b4 100644
--- a/tutorials/causal_discovery/tigramite_tutorial_assumptions.ipynb
+++ b/tutorials/causal_discovery/tigramite_tutorial_assumptions.ipynb
@@ -18,8 +18,7 @@
     "This tutorial explains the causal assumptions and gives walk-through examples. See the following paper for theoretical background:\n",
     "Runge, Jakob. 2018. “Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.” Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310.\n",
     "\n",
-    "Last, the following Nature Communications Perspective paper provides an overview of causal inference methods in general, identifies promising applications, and discusses methodological challenges (exemplified in Earth system sciences): \n",
-    "https://www.nature.com/articles/s41467-019-10105-3"
+    "Last, the following Nature Review Earth and Environment paper provides an overview of causal inference for time series in general: https://github.com/jakobrunge/tigramite/blob/master/tutorials/Runge_Causal_Inference_for_Time_Series_NREE.pdf"
    ]
   },
   {
@@ -259,7 +258,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -597,7 +596,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -677,7 +676,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -687,7 +686,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -738,7 +737,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
@@ -778,7 +777,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -820,7 +819,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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rBy8LpnwN86Y9UBq7TY/puo6JiQlMTU1lVJ/A7XajoaHB1mJ/hKwnpYA+3APt+nuQ85tNKzLw8kpwrz/lvZTuu8GD5gtRE4kERkdHMy79UFZWhoaGhrzOFlLgsYqUEvPz85iensbi4mLKD0/OOcrKylBZWQmfz0cBB8kbKVPBZjgHD9Zsnh5WnVAPPma+/mMVXdcxOzuLmZmZTfsjKYqCQCCAyspKWhNF8ooUOvTB69BuvAe5GNr8AYoDSmUtmJq6fALzVUDZc/+m3xmapmFmZgYzMzObbl13OBwIBoOoqKgoiIw7BR5JSCkRj8cRiUSg6/pKZTpVVeHxeOBwOCjYIHkl7VTwam4vlIo6MGz+McBb9kGp31qjQyklotEootHoSjaRMQan0wmPx0MLR0nekboGfeAqtJ7TkOH01lywsiAUfwWQxvtI2XUPeKBmS2MSQqy8j5anYTjncLlccLvdBTctSYEHIQVua6ngJYoDalM3kIggnQ9LOFxGtiPVvDUhBUxqCej9l5DoOQNE09xh4vJBbWgHYundn/mroOy6t+QvWulyg5ACteVUMGAEEO0HwZiEnE/V7I2B13cai1GFDt6wk4IOUpSkFod2+wK0nrNAPPXU4DLm8UPpOAhE54FIiqwIV8Fr2yFGe4y/Nu0u+aADoMCDkIJjpIKvQOs5k3YqGE431B1HodS0QO87D5nqA9bhhrLjGLi/CswTgD50FbymNTuDJyRPyEQMWu8H0HrPAYn0djUyXznU7uNgvnKI2+cBkWIXlzcAdcdxMHcZAAkZngX3V2Vl7IWOploIKRCZpoIdXUfBW/dBTg9CDFxKWeqcBWqgdB4Fc9xZES/Dc2BeatBGioOMRaD1noN26wNAS6/fEPNXQu2+C7xhB+TgFYjxWynvz2vawFv3r2QJpRBAPLwUhBAKPAjJc1KLQ7t1AdrNraWC1a5jUFr3AoxBv30ecmog5WN4027whm5KBZOiJKOL0G6ehXb7AqCnV2+GldfAsRRwQItDv3k69ToqrkBpPwRe1ZylURcnmmohJE9lngq+C0rzLjCuQMYi0HvegwyHkj/I4YLSeQw8UJ2VcROST0R4HtrNM9D7LqXXgRkAq6iDY+cJ8Np2MMYgF0PQet5LHfh7/MbUiid1DQ9CgQchecdIBb8P7db5LaeClcZuMG40nRbzU9B73kv5HMxfDWXHUTCHOytjJyRfiMUQtBtnoA9cudNnaBO8qgnqzhPg1c0rmT8xOQD99gcpn4NVt0Jp3b9pjRtioH8lQvLEdlPByx+URgGx2xD9F1Ou5+CNO8Ebd9HUCikqYn7aaIA4eB1pbRUHwGtaoe68C0pV08ptUgqIgcsQY70pHqhAaTsAXk2Lr7eCAg9CbCbC89B6zkDvzzwVvEwKHXrfBcjJ/uQPVlQonUfBg/XbHToheUPMThgBx3BP2o/h9R1wdJ8Ar1hbvlwmYtBvnkm95dzphdp9F5i3PNMhlywKPAixSbZSwctkPGqs51icSf4E7jKoXSfAPLS6nhQHMTNqdFweS73TZDWlsRtq93Hw8o0VRGV4FtqNUynXcxhTlMfW7P4i6aPAgxCLZSsVvP459ZvvAYlY0udgwXoonUfAlNS9JAgpBPrUkNGPaCJFdm81xqA07TICDn+l6V3E1KCxniNF5pHXdYK37AVjPJNhE1DgQYhlMksFdxprOCrMO1kCgJjog953ntZzkKJnrF8agHbjFMTUcHoPYhxK6x6oXcfAfcHkzzt4GWL0ZurnaT8EXt2y9YGTNSjwICTHMk8F3wVennyLqxQCov8CxERf8ifiirGeo6JhK0MmJK9IKSHGbiNx4xTkzFh6D+IKlLb9ULuOgqfY4iq1uLGeY24i+XM5PVC77gJLEriQraHAg5AcyUUqeJlMRKH3nIZcmE5+J5cPavcJqitACpaUEmKkx+i4PJeqt9AqigNq+wGoO46AuX2pnz88B63nFBALJ70P81dB2XGc1nNkEQUehGTR9lLBx8F9m6+QFwszRn2OFEXFWHktlM5jYCqt5yCFRwoBffi60XF5IcVi6dVUJ9TOQ1A7DoO5PJveXUwPQ7/1fur1HLUd4C37VmrjkOygwIOQLMhlKng1MdG/tJ4j+S4Y3tBNXTBJQZJChz5wFdqN05Dh2fQe5HBD3XEYasehtLISUkqIoasQIzeS34lxKO0HqT5HjlDgQcg25DoVvHIeISAGLqVuTsUVKB1HwCsb0xsHIXlC6hr0/ktGx+XIQnoPcnmh7jgKtX0/mOpM7zxaAnrvGcjZ8eR3crihdN0FXlaR3jjIllHgQUgGpBDQh65Du5G7VPDKubQE9JubBDYur1Gfg7rIkgIitQS0vgvQes6mXGexGnOXGQ0Q2/ZtqUS5jC1Cu/4uEE0e2LCySihdx6mFQI5R4EHIFliRCl5zvngE2vV3gMh80vuwQI1RzCjNqz5C7CYTMWi3zkPrfR+Ip9kA0RuA2n0cSvPuLfdEEYsh6NffSdm3iNe0L7Wyp/UcuUaBByFpsCoVvOac4VnjCi3FIlJe3wXevIfWc5CCIONRaL3noN36IGWxu9VYWYURcDTtBOPKls8pQqPQb55JvoiUMShtB8Fr2rb83CQzFHgQkkLGqeDuY1Bat5YKXk3MjkPvOQ2IJM3iuAKl/TB4kkqmhOQTGQtDu7nUcVlPpPUY5q8yqvU2dmVcJVQfvwXRdyH5HRyupfUcqbevk+yiwIMQE9tKBbfsyejKbJmY6DfKNicrp+5wQe0+CZbG1ltC7CQjC0jcPAu972L6HZeDtUYDxLqOjDN5aVUi9ZYbdW6c6a+3ItlBgQchq2wvFbxrW/PDUkqI4WsQw9eT38njh9p9N5jLm/F5CMk1EZ6DduM09IHLgEizAWJlg9EAsaZ1W1OHUujQe9+HnEleR4eV1xpFwTLMSJLtoX91QmBfKnjl/EJAv30OcmowxfmqoXTdRUXBSN4SCzNGwDF4NWXvoNV4dQvUnXeBVzVte62S1OLQb5xKWdGX1bRBaTtATd5sRIEHKWl2pYLXjEFLGO3s55Nvl2VVzVDaD9OKe5KXxNyU0QBx6AbS7rhc1w61+y4oldnpIySji9BuvANEF5Ofs2kPeEMXLca2GQUepCTZmQpeTcYixodliu2y1FmW5CsRGjcaIKZaS7EOb9gBx867wMtrszeOhRnoN95Nvl2WMaO4XlVz1s5JMkeBBykpdqeCV9t0uyxjUNoOgddQ2WaSX/TpEWjXT0GMp+iMvAaD0rTTaIAYqMrqWMTMCPTes8m3yyoOY+dKIHmnZ2ItCjxISciHVPCa8cyOG43ekn1YcsX4sMziVSEh2yGlhJgaNDouTyZfi7QG41CadxsBR1kw62PSx25B9KfYLuv0QN15kjo05xkKPEhRy5dU8JoxTfRBv30eybfLuqHuvBvMS9tlif2klBDjfUbAMTOS3oM4h9K6D2rXMfAclPGXUkIMXIYY22S77M67qfx5HqLAgxSlfEoFLzO6Yl6DGNlsu+zJLfVyISQXpJQQo71GA8RUTdVWU1Qobfvh6DoK5i7LzbjS2i5bZ7QRoO2yeYl+K6RoZJwKbtkNtSs3qeCVsaWzXTZQDWUHbZcl9pJSQB/ugXb9Pcj5qfQepDigdhw0Oi7nsMZMOttleU0bOG2XzWsUeJCCl4+p4DXj0zVju+zcRNL7sKoWKO2HaLsssY0UOvTBpY7Li6H0HuRwQe1caoDozO2UhoxHoF17O2V3Wd68B7yetsvmOwo8SMHK11TwalJLQL/xbuortMZd4I076cOS2ELqGvSBK0YDxPBceg9yupcaIB7YcsflTMjoohF0xJP0S2IcSsdh2i5bICjwIAUno1Sw6oDacQhq52HLyo1LLQ792tuQ4VnzOzAGpf0QeDVtlyXWk1oCev8lJHrOpCy6tYbLB0fXUSht+y2bEpSReSPoSLbtXHFA6T4B7s/N2iySfRR4kIKR76ng1WQianxYJisMxlUoXcdpuyyxnNTi0G5dgHbzLBCPpPUY5vFD7ToGpXWvpQs2ZXjWeB8lKwzm9Bo7V2i7bEGhwIPkvcxSwR6oO45YlgpeTcbCxodlLMlVpOqEuuse2i5LLCXjUWi3PoDWey79Boi+cqOWTfOubXVczoRYmIZ+/d3kvZM8fuN9RNtlCw4FHiRvFUoqeDUZXViai05yJelwGx+WdIVGLCJj4aWOy+eTZw7WYf5KI+Bo7LZlwbOYmzRKoCcpsMe8QSi7ToKpTotHRrKBAg+SdzJOBXcfh9Kyx7a9+zI8t5QWTnI16fIaQYfLZ+3ASEmS0UVoPWeh9V1IvwFieQ0c3XeBN+ywbbGzCI0ZVX2leQ8lVlYJZefdYAptOy9UFHiQvCETMePKrEBSwauJxRD0a28nTwu7y4ygw0mFwUhuifA8tJ4z0PsvJS/Jvw6rqDc6Lte22bq7SkwPQ+89k7SPEgvUQOm6iwqDFTj67RHbFWIqeDUxP2XMRYskV5XeANSd91i+1oSUFrEQgtZzGvrA1aTZgvV4VZPRALG6xfbt3GKyH/qtc0mPs2C9UY3UxgsMkh0UeBDbFGoqeLXNmr0xXwWUnSepGinJGTE/bTRAHLyOtBsg1rYZgXtVY24HlyZ9/BZEX/Jmb6yyCUrHEdsvMkh2UOBBLFfIqeDVxMwI9Jtnks9F+6uhdJ+gtDDJCTE7YTRAHOlJ+zG8vtMI3CvqcjiyrdFHbkAMXkl6nNW0QWk7mDfve7J99IlILCMWQ9BuZJIKPgFe3ZxXHzxiahB67/tIdoXJyuugdB2ntDDJOjEzagQcY7fSfozS2A21+y7w8uocjmxr0mmayOs6wVv25dV7n2wfBR4k54ohFbya0db+g6THWUUjlM6jlBYmWaVPDRkdlycG0nsAY1Cadhkdl/2VuR3cFhlt7S9BjPUmvQ9v3AneuIuCjiJEgQfJmYxTwTvvAg/mTyp4NX30JsTApaTHWVULlI5D1BmTZIWUEmJiwAg4ppO3gV+DcSite40GiL78K1InpYR++wPIyf6k9+HNe6E0dFk4KmIlCjxI1hVLKng9ffg6xNDVpMd5bTt46wG6QiPbJqWEGLtlNEAMjaX3IK5AadsPtesoeJ4WqJNSQO99H3J6KOl9eNsBKLUdFo6KWI0CD5I1xZQKXk8fuZE66KjvAm/eQ0EH2RYpJcRIjxFwzE2m9yDFAbX9ANQdR8Dc+VucTkq5adChdBwBr26xcFTEDhR4kG0pxlTwevrozZSr7nnTbiiNOy0cESk2UgjoQ0sNEBdm0nuQ6jQaIHYeyvvCdCvTK8mCDsagdB4Dr8y/NV0k+yjwIBnZTirY0XW0YHqV6OO3U67p4C37odR3WjgiUkyk0KEPXIV24zRkeDa9BzndUDuPQO04WBBF6aSUEP0Xkq/pYBxKV/6u6yLZR4EH2ZJiTgWvJyb7IfrOJz2utB8Cr2mzcESkWEhdg95/yei4HFlI70EuL9QdR6G27y+Y5mhSSojByxDjt83vwBUo3XeDB/J3bRfJPgo8SFqKPRW8npgaSlm+mbcdpKCDbJnUEtD6LkDrOQvEwmk9hrnLoHYfg9K6r+CK0YnhaxCjN80PMg6l+wQFHSWosF7FxHKlkApeT8yMQO89m/Q4b9kHpbbdugGRgicTMWi3zkPrfR+IR9N6DPMG7nRcLsBCdPrwDYjhJMXBGFsKOmqsHRTJCxR4EFOlkgpeT8yOQ795GskKnfGmPVDqd1g7KFKwZDy61ADxg/Q7LpdVGAFH066CLUKnj96EGEq2IJtB2XEcvLzW0jGR/EGBB1mj1FLBq4m5Ceg3TiVtyc0bd0Jp7LZ4VKQQyWgYWu/7RsdlPZHWY1ig2iie17CjoAvQbbYgW+k8Cl7RYOGISL4p3G8JklWlmApeTcxPLQUd5j1keP0O8MZdFo+KFBoZmUei5yz0vovpN0AM1hkBR11HwdeB2XRBdsdh8KomC0dE8hEFHiVOxiPQej+A1nsO0OJpPcZIBd8FpWlnwaaCVxMLM9Cvv5v0i4LXdoA37y34LwWSO2Jx1ui4PHAZEGk2QKxshLrzLvCa1qJ4baW1ILu61boBkbxFgUeJKuVU8GoyPAv9+juA0EyPs+pW8Nb9RfHFQLJPLMwYHZcHryadoluPV7dA3XkXlOrmHI/OOrQgm2wFBR4lptRTwavJyDy0a28nDbxYZROU9kNF9f9MskPMTRoBx1Dylu7r8bp2OLrvAq8srvUNIjRGC7LJllDgUSIoFbyWjC5Au/ZW0uklVtEApfNI0f1/k+0RoXEkrp+CGE3ezn093rDDCNyLcBeHmJuA3vMeLcgmW0KBR5HLKBVc0wJ15wkoRboITMbCRqYjyfZGVl4HpfNY0Uwnke3Tp0eMfkTjfWk+gkFp2mk0QAxU5XRsdqEF2SRTFHgUKUoFm5PxiJHpiEdMj7NADZSu40WxaJZsj5QSYnIQ2o33ICYH03sQ41BadkPtOg5eFszp+OxEC7LJdlDgUWQySwV3wbGz+Av6SC0B7fo7SeuTMH8VlK67Cn5rMNkeKSXEeB+06+9BzIyk9yCu3Om47A3kdoA2k9EF6DfepQXZJGMUeBSJjFPBO4+D+4szFbyaFDr0nlNAZN70OPNVQOm+u6ALoJHtkVJCjPYicf0U5OxEeg9S1Dsdl91luR1gHpCJqBG8J1sbRQuySRroU7aAUSo4PVJK6LfOQc5Pmd/BWw5l50kKOkqUlAL6cA+06+8lf42spzqgdhyC2nkYzOXN7QDzhNQ16NdPJc8Y0oJskib6pC1AlAreGjF4BXJ6yPyg2w9150kw1WHtoIjtpNChD14zGiAuhtJ7kMNldFzuOATmdOd0fPlESgH95hnIcMj0OCuvpQXZJG0UeBSQTFPBatsBqF1HSiIVvJ4+dgtitMf8oMNtBB0F2EGXZE7qGvSBK0YDxPBceg9yeqDuOAK1/UDJvV6klND7LkDOjpkeZ74glB20IJukjwKPAkCp4MyImRGI/gvmB7kKdefdYC6PtYMitpFaAnr/JSR6zgDRxfQe5PbB0bXUALFEs2Ji5AbkRJK1Yy4vrY0iW0avljxGqeDMiYVp6DfPmB9kDEr3XWDecmsHRWwhtTi0Wxeg3TybdBv1eszjv9MAsYS/VMVkP8TQVfODqpMyhiQjpfuOymOZp4KPQu04AKY6czvAPGds90te2EjpOAIeqLF4VMRqMh6FdmupAWKSYnHrMV/QCDiad5X8tmoxOw799gfmB7liZDpKcPqWbB8FHiaklIjFYohGo4hEItB1HVJKMMbgcDjgdrvh8XjgcDiyuoKbUsHbt9l2P968B7yqeJpz5TMhBGKxGCKRCKLRKHTdKDbFGIPT6YTH44Hb7YbDkd3XrYyFofWeMxogpttx2V+51HG5mxZIYql5YopS6ErnMfCyCotHVZp0XUc0Gl35Tyy1vOCcw+VyrXwfKUrhBMoUeKwSj8cxPT2NmZmZlQ/JVBwOByorK1FRUQFVzfyfMqNUsDcAtetYyaeCV9tsux+vaQev77J4VKUnEolgenoaoVAIMo0y/W63G5WVlSgvL9/Wh6eMLkLrOQut7wKgmxe3Wo+V18Cx8wR4fSdtA10iY2EjeE9WlbTtIHhFvcWjKi1SSiwuLmJ6ehpzc+llvX0+HyorKxEIBPL+tcxkOp8MRU7TNIyMjGB2djbj56iqqkJdXR34FlZ2Uyo4e6QU0G+8l3zlfbDeqEqa52/IQhaLxTA0NIRw2Dzw2wxjDHV1daiqqtrS70mE540GiP2X0u+4XFFvBBy1bfSaWEVqcWhX3gCiC6bHeUM3lOY9Fo+qtCwuLmJoaAjxeHrZuvVUVUVDQwPKy/N3DVtJBx5SSszOzmJ4eHglfbUdqqqiubkZZWWp5z0zTgXvPAGlsYtSwesY2/3OJ115z3wVUHbdQ5mhHJFSYnJyEmNj5kHfVrndbjQ3N8PtTr04WiyEoPWchj5wNel6nvV4dTPU7rvAq5sp4FhHCh369XeS7pxjVc1QOqhAWK7ouo7R0VHMzMxk5fn8fj8aGxuzPpWZDSUbeEgpMTo6iqmpNLenbkFDQwOqqjaWIadUcG7ow9eTr7x3+aDuuZ9W3ueIEAL9/f1YWDC/Qs4UYwytra3w+/0bzzk/tdRx+TqANDsu17YZaziqGrM6zmIhpTQKhM0Mmx5ngWoo3SepVkeOJBIJ3L59G7FYepnvdKmqivb29k2DeKuVZOAhpcTIyAimp6dzdo76+npUV1cDyCwVzCvqoVIqeFNish/6rXPmB1WnEXTQyvucEEKgr68Pi4tpLoTOQFtb20rwIWYnkLj+HsRIkoJwJnh9Jxw77wIP1uVqiEVB778EMXbT/KAnAHX3fbR4PUcSiQR6e3uRSCRy8vycc3R2duZV8FGSgcfk5CRGR0dzfp7W+hq4By9QKjhHxOy40SXT7CXMFSi77qWV9zk0ODiIUCiU03MwxtBZVwl+8zTE2K20H6c07YTafRw8UJ3D0RUHfbQXYuCi+UGHG+reB8CcVGgvF6SUuHnzJqLRaE7Po6oquru782bnS8lNekejUUuCDgAYGR1D63BPWkGHnalgKaUxRqGv+U+u/CyMFthCrLvd7L76yv02PAcArA6mVn5mAFv6M8XtbNXtUupJF8ABAHwVkFOD0EOjAFcBRTXWeCz9vPbvCqBkd2t0sZubm8t50AEYr83x0RHUphN0MAalebcRcFDAmRYxM5I86FBUo0AYBR05MzExkfOgAzA2UAwPD6OlpSXn50pHSQUeUkoMDqbZxTULEuBYrGqHb+x60vsYqeAT4MHarJxTSgFoCSARg9TixuJVLQ6ZiBl/anEgsXSbZtyW7noTu63JazC2NohZTQhgdhxidnxrJ+CK8d9yYLIqSFnzd4cLzOFe8ycUtWQCF03TMDSUpOleDszDicqKRqhJ1h+A8TsNEH35u5I/38jwHPTes+YHGYfSdQKsxBpKWikSiWB8fIufUdswOzuL8vJyBAL2/05tDTw+9alP4dVXX8Wjjz6Kv/mbv8n5+ebn5y2JLlcbd9eig/caGYNV0kkFSykBPQEkVgUJiaXgQYttDCISceP+xS5V0LGdmcPl7MzSTqP1z5TymbmyMSBxrgpMHG5jgavDXfAL9Kanp9Oqc5NNY95GNK0PPLgCpW0/HF1HwTwbF6GS5KQWh9ZzKumaM6XjME1T5djERJqNPrNobGwMfr/f9oskWwOPL3/5y3jmmWfwwgsvWHK+XOxg2YxQnIjWdMA9dmNNKph5A0A8CjE3CcQjkPEIEI8u/RmBjEeBhLVBUkHYLOiwa8mS0IFYGHKpeFnKUaiOtYHI6j+dbiNYcXkt2/4rYhHwNJvlSSlteR9FXQHowXoooVFAcUDtOAi18wiYuzQbIG6H0eL+dPJCe817qbrvFslEHOA87fdsPB5PuzBYNsViMYTDYfh8PsvPvZqtgccjjzyCV1991ZJzxWKxnK6+T2XMXYfORgnm9QN6Avq1t9Ou30HWycegY6u0BKAlICPzAFIEKQ43mNsHuH1gLh+Yu8z4u8uX1R0Gc3/5LbgO3gfXwfs3DUDm5uYsz3Ysmwy0oam2FWrnIVp3sA1i4DLk3KTpMVbTBl6/w+IRFT4xN435F/8nPEcfgXPP8U0DkGzV6sjE9PR08QUeQgjs3bsXTz31FL71rW+t3P7SSy/h4x//OP7X//pfePrpp7N92k3ZFXQAgKY4IeECS7elfcFhd9ZHcL7yM1u5bel2YOlbdumrdiVQWPf31bcv3V/qGhCdT35+t8/4U2jGmhVdS3snUd5KRCETUWB+amNwojrBXEtByVJgYvxcZhzbQipVxqOInn4ZsfNvbhqA2Pk+WlB9YF27wbbRnqDUickBiLFe02OsrAJK6wHb0/CFSi7OIvz6jxA5+4tNAxBb30dZrrmTiay/gznn+NrXvobnnnsOv/mbv4mKigp88MEHePrpp/H7v//7tgQdgLGQx06aLpAPPWONLzC+lDlgkGArP6/sKlm1m0Qu/91sx8mG25ZOoC/9B7H03zbXnUgBLhZh9nEoAQjuBbTlbWJOgDmNV7aUuJNPkKv+Lo3nWvV3rPo7W75tJUha9Zh8sbxQeHEmyVoUBWAcknHjT3CAKQCST1WlE4BkWg49WxZvnIcrYn2KuihIPcX7iEEPJ6Cd+6XlwyoGInznomizAERKaev3ka7rSCQStlY0zcmlw2c/+1n83u/9Hr797W/j137t1/Dkk0/ic5/7HL7yla/k4nRpsTvw0CPhrP9rS12H1DRIPWH8qWmQ2tqfsfo2XSuc6YhljMGzax9YmflK7FjvdWjT5mnjnFAUMIcDXHWCORzGf2t+XvWnTVeOxll1QOpg637dUgiIWBQiGoGILEKEw2AOpzFHvXyfJAGIlNLyxdnrabcuQfRdsnUMhYipDnj2HgRzbqzgK4VA5OpFiLD9V8LFJFkAEovF0mqemEvRaLT4Ag9VVfHVr34VX//61/GDH/wAR48exbe//e1cnCptmmbvllFdpPdCE4k4ZCIOGY9DJhJLQYN5YFHwUwlpcLV2QEkSdMRHh60NOgBA1yF1HTo2/wJeCUIczlU/G3/nq/9uYQ8ZxjkUjxeKxwtU3CnrL7UE9HDYCEYiYeO/aGRNAOLYd49l40xGSInC3hNkA8bg3rEL3CToAIBY300KOnJofQCitdrfZM/u78OcVS5dWFhATU0Nurq68M4775guZvnIRz6Cs2fPYnFxEZWVlfjbv/1b3HXXXbkYDq5cuWLbojgAqJvqQYDHlgKKOMTSn+t/LriMRA6pNXVwt5kvdNNmQ4jeuGzxiHKE8VVBiQPc6QZ3ucBcbnCXG8zpsiV7IoVYyowYgQir70CfvxWC27fGoq7vLNT+Ivm9W8TV2glHrXkb+/jYMOIDt60dUClzOMGPfAgDLnu3Kjc2NqKystK28+fsE+RLX/oSAKM8ebIyrS+99FKuTr8B59zWwCMhGMKX37ft/IWGlwXgaukwPSaiUUR7kxdlKzhSQMZjkHGjQdSGVyljYE4XuMtlBCJLAclKUJKjuiCMcyheHxTv8kWDQHfkNhJMRYw7EeUuxLgLMe5EgjmS7zjK5phyfobiolbXJQ06tLkQBR1WcTjh3n8vXIceQFQyoNd8ga9VuM21hHISePz2b/82XnzxRbzzzjt47LHH8Gd/9md49tlnc3GqtDmdzpw14UmHQivx08acTrh37DL9QpW6jmjP1YKptpoVUkLGotBjUeiY3XCYOZzrApJV2ZIc9GZwSA0OXUOZfmehqQBDjLsQ5c6lP92IcWfWgxGeJ70mCgEv88PVmiR4jxVZ8J6vVgUc3GME8E6bpzkA4/vQTlmfavnOd76DL3/5y3jllVdw8uRJfOMb38Cf/umfoqenx9bFLGNjY7ZUilvWVFeLch8VO9qMFDrErbNJt87ylv1ggeyUly92UkqjXkw8AhkPA/HIUpGzRSC6iHRbymdKgCHC3YgoHkQUNyLcbeyy2YbOlma4HRTEb0YmohC9p83rBTEO3nkMzE3VXrNFnxnDwt/9f+7cYBJwrHb16lVb11ns3bvX1qxHVt/BP/nJT/Dss8/ie9/7Hk6ePAkAeO655/CHf/iH+O53v4tnnnkmm6fbEo/H3oJDHn8API/aEucjKSVEb4qgo2EnlPpOi0dVnKQQQGQeMjyLyNsvgnt9UMqyW0qZQ8InIvCJCJAwwpwYdyHC3QgrHkS4G/oW14u4/QHb08T5biV4T1KkUOk8Al7ZYPGoipuMLC3O3STgWObxeDA/n6wuUW65XC7b30NZCzzOnDmDp59+Gs8//zw+/elPr9weCATw3HPP4Zvf/CY+//nP29aW1+fzgTFmyzYmh8MBl8t8RTm5Q4zdhJw2bz7GgnXgTbssHlHxYpwDvnIwXzngr4Hz2CPg/kojOxKegwzPrvxnZEeycE4AbhGDW8RQoRlTRnGmIsKXMiKKG3GWfHomEKCgYzNSSui3P4AMh0yP84Zu8MomawdVChQV7iMPbxpwLCsvL7ct8Cgvt7+RYs52teSj4eFhTE9PW37ehoYGVFVVbX7HEiZmx6Fff8f8oLsM6t4HwBT7puqKWeLyW0b5dsaNCrNLRcfAORhXjOkRPbHUpDCGuK7DwQR4DqZqdPCVaZmI4kGUu1amZzo6Omwv9Zzv9NGbEAPmdU5YeS2U7rupMmkOSKEjce4VY1ciXyrQuOr9xBR1pfM14wokV9A7ryPBrL8Q37Vrl63LHgCbe7VYraqqyvLAgzGGYDBo6TkLjYwuQr95xvygokLtPkFBRy45PdAuvZHWXVl5DfRjH8ft4RE4ZAJuEYNLxOESMbhEDA65vZ1jCgTK9LCxcHVpeibKXYg7y+COVUI6VaOZHtlAzE1ADCTZauz2Qek8RkFHjjCuAFJA6/0grfsrTd0Idt6DiUlr6xCVl5fbHnQAJRZ4uFwuVFVVWdpds76+3rbppUIghQ7t5mnjitqE0nnM6D9CckbtOIDE5TeTditd4XDDde+n4PaVwzc3j8XFRSS4E6sTxorU4daj8IgoPHoEbhHbVmaEAfCIGDzRGMSNKQgAcJeBB6rByuvAyqspKIWxmNQI3k3+rbkKtetEVhsLko3UXSfSCjxYoArOuz6Kaq4iNDtr2W5Lzjnq6uosOddmSirwAIC6ujrMz88jHs99d1ifz2drkZZCIAavAOGNW0QBgDftAQ/mxxulWOkT/UhcemvzoAMMrpMfBy8LAgCamprQ09MDIdZWz9WZgkXVh0UsTYlICbeIwSMi8OhGMKJimxV3owsQ0QVg/LZR46SsCixYC15eB3gDJXdVL6WE3ptiMemOo2Ae2sGSK1JK6IPXkLj81uZ3drjguvdTYA4XFADNzc24detWzscIGFP+dm+jXVZSazyWRSIR9Pb25nShqaIo6Orqyou0Vr4SoVHoN06ZHmMVjVB2UGo4V/TxfiQuvwkxMZDW/R0HHoJj991rbpudncXAQHqPXyElHDJhBCEiAq8ehVNm8YrP4QIrrwMP1oIFasEc+fFBm0v68HWIoaumx3jTbiiNOy0eUWmQUqwEHHIuvSy66/7PQGlYW43ZilIP5eXlaG5uzpvP05IMPACjpHtfX19Ogg9FUdDR0QE3bZ9NSsYj0C6+aj7F4i6DuvdBS3uYlAIpJcREPxKX3oSYHEz7cUrzLjhPfsL0Q2t6ehrDw8PbGpcitaVA5M70TLY+HllZBVh5rTEtU1aRNx+82SLmp6BffQtmUyysvA5K94mi+3+2m5QC+sBVJC6/DTmf/rS9Y9/9cOy91+T5JEZHR3O2BMDv96O1tTWvXgclG3gARovvvr6+rJZSdzqdaG9vz5uUVj6SUkC/+hbkgslCX8aNoMNr3hiOZEaf6Efi4htbCjgAgPkr4X70cykXdM7OzmJwcDBrQTyTAkEnQ53PASzOGB/u2ahUqzrAArVGNqS8DsxZ2BcGUotDu/SaURhuPYcb6v6HwVT6HMoWKeVSwPEm5PzWNinwhk647vtM0i9/KSUmJiYwPj6ejaGuqKioQGNjY14FHUCJBx4AoOs6RkZGEAqFtv1c1dXVqK2tpVoDm9CHrkIMm5dr5m0HodS2WzugIiZCY4hf+CXEaJrzyIzdaVSoOOB+7HPggc0bWsXjcQwNDWFxcXs1PxhjaGhoQEXFneyElBKIzEPMT0HOT0HOTgBabFvnAQB4A+DldWDBWmOdSAG9b6WU0HvegwyNmh5Xdt8H7qct/NkgpYQYu434hdcgQ2kGBqveR8xbDvfj/xzMuXkRy3A4jMHBwW2vQVRVFU1NTfD783NtT8kHHssWFhYwMTGR0QdnIBBATU2N7dVRC4GYm4R+zXwRFqtogLLjeN5F54VILMwgcfEN6ANX0nuA0wPHrrugtOxB9Gd/DmgJOO/+GNTWvWmfU0qJUCiEyclJxGJbCwyWt53X1NRsmi2UUgLhWYjQOOTs2NLV5zY/xrgKFqg2CtWV14K587teiD52C6L/gukx3rQLSiMV28sGfWoYiQuvpb0WivmCcOy5ByxQhdgrfwlwBe4PfRa8wrxRnxkhBKampjA1NbXlsuqcc1RWVqKmpiavd1NS4LFOLBbDzMwMFhcXEY1GTdPHnHO43W6UlZWhoqKCFpCmSSZi0C69CiRMvpScXqj7HqItf9skowtIXH7b2NYn09g94vTAsesE1K4jK2n5+PlXAV2D88hjmY1BSkQiEYRCISwuLiYNQhRFgcfjgd/vRzAYzPiDUmoJyLkJyNlxiNCY+dTDVnnLwSsbwaua8m47t1ychXblddPfL/NXQ9l1DwXv2yTmppC4+Dr0ofQa6TFfEI6990Bp3WvU9AAQff37UJt2Qu08lNEYpJRYWFhAKBRCOBxOuu1WVVV4PB4Eg0H4/f6CyLhT4JGClBKxWAy6rkNKCcYYVFWF0+mkN/YWSSmh33gXctYkVcmYkRouo63HmZKJGBLXTkG7nrwmyhomAcfKc8UjgOpc+QDdLiHEyvsIMLIbTqczJwG7lNLYbhsag5wdh5ybTC8AS8UbAK9sAq9stH1bqtQ1Y11HzCQzqzqh7nu44Neu2EmE55G4/Cb0WxeQThaNlQXh2HPvUsCx9gtfRhcAly9r3xW6riMWi61sYeecw+l0Qi3AzucUeBBL6CM9EIPmVRV58x4oDd0Wj6g4SF2DdvN9JK68k96VvtMNx+67oe7YGHAUI6lrS+tCxiBC40B0YXtP6PHfCUJsWACt9Z6FnDJfIKx03011bzIk4xEkrr4L7cZZQGw+vWFkOMwDDrI5CjxIzomFGehX37izaHEVFqiBsvMkZZC2SEoBve8SEpfehAzPbf4AxQF153E4dp0o6ZLjMroIMbu0NmR2Mq0vmaTcfvCqRvDKRsCT+8JlYrIf+q1zpsd4/Q4oLftyev5iJLUEtBtnkLj2rvkU8HouLxx774XaeShrGcFSRIEHySmpJZa2/JlUxnS4jNRwCX8RbpWUEvpIDxIXXjemETbDONTOg3DsuRfMk19rFewmhYBcmIIMjUPMjietoJsWd5mxJqSyKSfVU2VkAdrl1wCxces/8wWh7L6frry3QAod2q0L0C6/CZlO92XVaUxN7jxeEpnCXKPAg+SMlBL6zTOQM+YFppRd94AHaiweVeHSp0eQOPcKxNRQWvdXWnbDsf8B8LKKHI+sOMh4FDI0CjE9bGzZzXSnjNu3Mh0Db/m2gxApdGiXXwciJpktRYW696G834WTL4zA/SYSH/wCcmFm8wdwBeqOw8ZOFZc39wMsERR4kJwR47eh9503PcYbdkJp3m3xiAqTjC4ifvGXSwveNsfr2uE88OCWtvCRtWQiDjkzbAQhcxOm04RpcXnBK5vAKhvBfMGMghC97zzE+G3TY8qO40aAQzYl5qcQP/dK2jVtlLb9cOy7D9xXnuORlR4KPEhOyPActMu/NN/yV1YJZfe9YIxSw6lIoUPred/oHJvG/DOvqIfj4ENQatssGF3pkFoccmZkKRMyvs0gpBG8qhnMF0zrIWJ6GPrN06bHeE0blPbMtmqWEpmIIXH5LWg3zqS1w0lp2AHHgQfByykbmysUeJCsk7pmBB1mOwgUh7Guw0XF1lLRx/oQP/fztJpPsbIKOA48CKVpJy3SzTEjCBldFYRkuFXXGwCvaQOvaknayE7Gwsb6KLPt0R6/0VqAFjgmJaWE3ncJ8QuvAWms4+BVTUbgXt1swehKGwUeJOu02x9ATvSZHlO6TtAUQApicRaJD36RVuEi5i6DY999UNoP0MJCG0gtsbQmZMgopZ1JEMIYWEUDeE2b0cxuVZl4/eob5usQuGIEHdTqPikxM4r4+z+HmNq8gSELVBuBe8MOCtwtQoEHySoRGoN+413TY7y2A0rbAYtHVBiknoB29RQSV9/dfIunohq1OHaeoEqveULqCciZsaUgZCyzIMThBq9uAa9pgwiNQAyal7tX2g+B19B0mhkZCyN+4ZfQb5mvLVvD5YXzwINQ2vfTtK/FKPAgWSO1BLSLvwAS0Y0HveVQ99xPqeF1pJTQh64bq+zTqMehNO+C4+DDtOAtj0ldu7M7JjRmugV2U0kyWKyyCUrnUboyX0cKYRTSu/TG5uuhGIPadRSOvfdRlVebFF6tVZK39IGL5kEHV6DuOEZBxzpidhLxcy9DjJtPS63GAtVwHnmUFo4WAKaoYFXN4FXNS0HI2FIQMppeEJIsqHB5obQfpKBjHX28D/H3X06rrg2vbYPzyKNpdVwmuUMZD5IVKadYqNX9GjIRQ+LSG9B6zm6+Q8LhgmPf/UaJc1rHUdCknoCcGoaY6INcmE5+R7Pfs5SA6gKv6wCvbqXF2Vjqq/LBK9AHr216X+YNwHHoQ1CauilwywMUeJBtk1p8aYplY4qTBaqh7KRumcv0kZuIn/kZZGR+0/uqnYfg2P8AFS4qQjKyADHZDzHRvzZLyJh5xkPKNUEqK681FqRW1JdcJlFKCa33HBLnXwO0eOo7cxWOPXdD3XUCTKH1UPmCAg+ybUkbV3EF6v5H6IsTS0XAzr0MfeDqpvflVY1wHnmMdv+UACkF5OwE9PHbQGg0raBjDcUBXt1sLDbNQpXUfCfmpxA//RLEpHmjvNVoPVT+osCDbIuYGYXec8r0mNJ2ELzEp1iklND7LyN+7pVNu8cytw+Ogw8bHS+L/AuErKUPX4cYMglKUwUd63kD4DXt4DUtRXd1L4UO7dopJC6/tek6GVoPlf8o8CAZSz3FQl1nxeIs4md+BjG2SYlmxo3OsXvuoYZ5JUiGZ5eq/Jp9FLOt74pRVKM4WV1nUfRw0adHED/906X+OSmsrIc6XHLTT4WGdrWQjOl9F8y3rnEVSvuhkg06pBRGqfMLvzSvOrkKr2mF89jj4P4qi0ZH8okUAtqtc+ZBh7sMyp77gdkJiIl+yNmx9J5U1yBGb0KM3gQL1oPX7wALVBfc+1FqCWMR9vXT2Kxhn9K2D86DDxdFoFUKKPAgGREzI5DT5l1SldZ9JbuuQ8xOIn76pxDTm1RMdLjgPPgwlA7aHlnKxMgNIDxrekzpOAyuOoGqJvCqJsh4BGJiAGKiD4il0codgAyNQg+NAp4AlPpOsKpmMCX/P/b1sT7Ez7wEuRhKeT/mDcB57CNQ6jusGRjJCppqIVsmEzFjisVkRTkrr4XSfXfJfZlKoUO78g4SV97etGql0tQNx5HHwKnkdUlLNcXC67ugtOw1f5yUkAvTxrbcqaGtTcWoDmMdSF1HXl4cyHgU8Q9+Af325p2Y1e5jxq4v1bzXDclfFHiQLdNunjHPdiiqsYvFWVo1BvSpYWMOerMCRm4fnEceg9q8y5qBkbwlhTCCjohJtVp3GdR9D6W1TkHqGuT0EMT4bfO+LkkxsMoGYxqmrDIvLhS0wWuIv//zTRu6sUA1nMefgFLVaNHISLZR4EG2JFWbbqXjMHh1q8Ujso/UNSQu/BLaDfN/j9WUjgNwHnyESjQTAIA+dBVi2LwRoLLnAfCyii0/p1iYgRi9aVwUbOVj3VsOpX4HWFWTLYsyZSyM+Jmfbd4YkXE49twDdc9JWjxa4CjwIGlLPcVSB6X7RF5cOVlBhMYRe/fHm2Y5mC8I5/GP0NY+skIuhqBdfh1mCyZ5QzeU5j3be/54FGL8FsTYbUDbpG/JaqoLvK4dvLbDsgBZH+1F7L2fbJrl4JWNcB5/ArycSp0XAwo8SNq0ntOQMyaLJhUH1P0Pl8QUi5QS2o3Txo6VlHPrzNgiu+9+6iBLVkihL02xmFSu9fiNdvdZupqXQoecGoI+ejPpAlZTjIFVNoHX78go85LW2PQEEudfM9oGpKI44DjwINSuI9RBtohQ4EHSknqK5Qh4dYvFI7KeCM8j/t6LEOP9Ke/HymuMOejKBotGRgqFPnjF2MmyAYOy9wFwXzDr51xZjDp6E3J6BJttTV0zqrJK8PpOsIrGrPUKEjNjRrZwfirl/Xh9B5xHP0yVR4tQ/u+rIraTiRj0vvOmx1iwDqyq2eIRWU8buIr4mZ+Zd99dxhU49t5r9IWgOWiyjlwMQYz0mB7jDd05CToAgDEG5q8C91dBxsIQY7cgxm9vWmMGAOTCNPSeacDhNjIgde0ZV0WVUkC79h4SF19PvfPL6YHz8Ieogm8Ro4wH2VTSXiyKY2kXS/EumJSJGOLv/xx636WU92PltXDd/TGagyampJTGFIvZlIfHD3XvQ5Z2H5a6Bjk1aEzDpNGwcIXqWApAOre0jVWE5xA/9SLExEDK+/H6TrjuegLMXZb+mEjBocCDpCTmp6BffdP0mNJ5FLyIsx365CDi774Iucn8uLrrhLGWowAKMxF76GO3IPpNalMwBnXPA2A5ynZsRkoJOTdpTMOERtN/oKKC13Ua23E3KfOv9V9B/OzPzKscL+MqHIceMcqdU5aj6FHgQZIyag28ZnpFxIL1ULruKsoPCSl0JC6/Be3KO0g1H848fjhPfJR2rJCUZCIK7cIrgK5tOMYbd0Jp2m3DqDaS0UWIsV6jMqrJWE1xBby2A7yha0PmUyZiiJ/9P9D7L6d8ChasM7KFAWobUCoo8CBJ6aM9EAMmHxpcgXrgQ0W5i0XMTyP+7o8hZlJf/Sktu+E8+uGinmYi2ZF0qtLlM3aD5dl6IKknjNLsYzc33ea6gnHw2jbwhm4wlxf6xADip16EDJsUSFtF3X0Sjn335d2/AcktCjyIKRmPGFdpJltGefNeKA1dNowqd6SU0G+dN9rXp1p0pzrhPPo4LXwjaRFzk9CvvWV6TNl5Ery81uIRpU9KCTk7BjF8Y9MdKCuPASDicYjR2ynvx7wBOE88CaWm+HfDkY1oUpqY0vsvmdep8PjB6zqtH1AOSS1uVE7cJCXMq5vhPPEkbe8jaZFCGB2cTbCKxrwOOoCl3TDBevBgPcTcJMTwdcjZ8aT3l7oGfXIEMhZJ+bxK6144jz6+6doQUrwo8CAbiNlx80JhAJS2g5auvs81MT+N2Fs/Sl2BlHE49t9vbJOlIkYkTWKsF4ia7BjhCpTWfdYPaBt4oBo8UA2xMA0xdH3DQlQRDUOfHEldVM/hgvPoh6G2bq8yKyl8FHiQNaTQk1+lVTWD+4tnAZg2eA3x935iWgJ+GfNXGgvfKuotHBkpdDIWgRi+ZnqMN+0q2PVRvKwSfNdJyMUQ9OHrEFNDEPMzEKFNWge4PFA7DoBX0vuIUOBB1hGjPUDMZEGZoiZt011opNCNcs2bNHdTdxyB4+DDVPKcbJk+cDH5VGVt4U9VMl8QSttBaCO3Nw06eLAa3F8BhEPQL70GUV4L3rgTPEA1b0oVBR5khYwuQgyblXMGeNMeMEfh7+AQkXnE3/47iKmh5HdyuOC866NQm7qtGxgpGsZU5YjpsWKZqhShMcTe+t+Qi6Hkd1JUqDWNG7fZzo5Dnx2H8FcZ2Z9ADS3ULjEUeBAAS7s6+i+alzL2loPXtls+pmzTx/sQe+fvgVg46X1YeS1c9z6Vs+ZYpLgZU5VJ2gtUtRTFVKV26zziZ38OiOS1PpjHB6WyHkxJvk1Wzk9Bv/oWWFkFePNe8PKaXAyX5CEKPAgAQIZGIWfHTI8pbQcL+opESgnt6jtIXHwDqQqCKe0H4Dz6WMa9KAgRIz3mga3iKPipSqknED/7c+i3zdeAGRgc+++H0nkYcvQmxPitTbo4A3JhBvrVNyHKa6G07AOjXWNFjwIPYmyD679oeozXtBX01b+MRxE79SLEyM3kd+IKnEcfh9px0LqBkaIjo4tJOs8CvHl3QW8fFQsziL39vyFDybfTwuWF6+6PQ6lbquTbth+8sRti9Kaxw2eTaqhydhza7DhYVTOUlj1gLl8W/w9IPqECYiR5q27VaVQo3UIzqHwiZkaND8vF5L1WmC8I1z1PgVfUWTgyUmyklNBvvGta54J5g1D2PlCwWUNt6Abi7/1Dyl4rvKoJzns+Ae7xmx6XWtwoxz56E9A274oLxsHrOsAbdxZ0wEbMUeBR4mRkHtqlVwGTl4HSfhi8ptX6QWWBdvuC0cY+RZpXaeyC866PUtlzsm1iZgR6z3umx5Q9DxRk1lBKicTF16FdfSfl/dTu43AcfCitsudST0CM3TampLQUTeOWKSp4Q7fRjI6aMBYNCjxKmJQS+rW3Iec3bodjZRVQdt9fcFdpUkokLrwG7dqpFPdicBx4cKkgWGH9/5H8I3UN2sVfAPGNFTt5TRuU9kM2jGp7pBZH/NSL0IfMp44AAKoDzuP/CGrL1pvcSV2DGL8NMXw9ZR2dFQ4XeNNu8No2KuJXBCjwKGFiahB671mTIwzqvgfBvIW1yEtqccTf/TH04Z7kd3J54Tr5ceooS7JGH7hs1L9Zr0CnKkV4HrE3f5ByPQcLVBu7v7a5S0dqCYiRG8YUzCaLUAEA7jIoLXvBKhrooqGAUeBRoqSuQbvwsum8La/rhNK634ZRZU6E5xB744cpe0nw6mY4T3486Tw0IVuVcqqy4zB4dWFNVerTI4i/+UPIFF1plda9cB77cFYDKhmPQAxdgxjvQ6qdZ8uYrwK8dR8VIStQFHiUKH3oqpHmXM/hMq7SCmhLqT49gtibP0zZwlvdeRyOA+nNQxOSLu3GqQ19SwCAlVVC2X1fQV2Va4PXED/1YvLdJ4zDceRRqJ2Hc/b/JSPz0AcuJy3AtmFIwTojA1Jg2dlSR6t1SpCMR43UpgmlZX9BBR3awFXET/1D8mJGjMN5/CNQ2w9YOzBS9MT8lGnQAbCCqn0jpYR25W0kLr2R/E5ON1z3fhJKjhebM48f6s67IeanIQYuQc5Ppby/DI1BC42BVbdAad4D5vLmdHwkOyjwKEH68DXT+VTmrwKrbLRhRFtnfFi+hcSlN5PfyelZ+rBssW5gpCRIKSEGLpke47XtYN6AxSPKjNQ1xE//FHr/5aT3Yf5KuO7/jKU7c7i/EmzP/ZChMegDl4HIXMr7y8kBaFND4HWdS1twC2tdTamhwKPEyMgc5ESf6TGlZV9BXKVJPYH4ez+FPnAl6X2Yv2rpwzJo3cBIyZAzw+Z9ShQVvGmX5ePJhIwuIvbW30JMDSe9D69rh+vkJ2zZcs4YA6uoBwvWQU4OQB+8YrpzaIUUEKM9EBO3jSZ09TtoajVPUeBRYpJ9WbPKJjBf0NrBZEBGFxB7828hppPPAfO6drjueYoKD5GckEIYX4ImeEN3QexiEbMTiL3xA8hw8kyCuuMIHIcftb2pHWMMrKYVrKrJKEI2dB3QUxQh0zWIgcsQo73G+o/qloK4oColFHiUEDE3ad6PhXEozVvfi281ERo3Piwj80nvo3YdhePQh2z/sCTFS0zcNu/H4vSA1+V/y3t95CZi7/xdigqiDI4jj8LRddTScW2GcQVKQzd4TRvE8NIWXLOmlssSUei9Z8HGb0NpP1gQF1algna1lAgpJfTLr0OGQxuOFcL22U0/LBmD43D+fViS4iK1hLEN3aToldJxBLw6v9cTJW6cRuLcL5B0y6rqhOueT0Cpz/8ASsYi0IeuJp06Xo/XdoC37CmIjFSxo4xHiZDTw6ZBBxQHeONOy8ezFdrti4if/olprQQASx+WT0Gp77B2YKTkiJEb5pU2PQGwqmbrB5Qmo6LvL6FdezfpfZiv3FgXVSC1MZjLA7XzCGT9DuiDlyFnzHYY3SHGb0FMDxnTLzVtNP1iI8p4lAApdGgXfgHEN6aHefNeKA1dNowqPYlrp5A4/2rS48wXXPqw3F4FRUI2I2MRI9thkt5Xdp4EL6+1YVSbk0IgfuallO3seXUzXPd+sqC3o4r5KYj+S5AL05vel/mC4O2HCrKHTjGgwKME6KM3zbf+OT1GsbA8XPmdTs+VYviwJIVD630fcmpgw+0sUAN11z02jGhzUtcQf+fvoQ8n77mitO03KpEWQRM2KSXkzAj0/ovm63DWYTVtRgaEFqJbqvBfaSQlqcXNK5QCRsGdfAw60rhCK6YPS5L/ZHjWNOgAAKVlr8WjSY9MxBB784cQE+bjBrDULPHuopl2YIyBVTaCBesghq9DDN9IuQBVTvRBmx4Gb94DXtdODegsQp/aRU6M3DDfeuYtB6tssn5Am5B6YukKLXmjN3XPPXDsK7zOuaRw6QPmBbZYVUteluuW0UVEX/9+8kZvjMN51z+C2rbP2oFZhHEFSvMe8OpW6H0XklSYXaInIPrOQ0zchtJ+aNuN78jmaKqliMlYGNqFV8znpHfdAx6osWFUyclEDLE3fgAxOZj0Po7DH4Kj+7iFoyKlTsyOQ7/+zsYDjEM98CiYy2P9oFIQCyHEfvl/mxc4AwBFheueT0JpyP+dK9kiQqNGBjWWvJ/TMlbVDKV1vy1F00oFZTyKmD501TToYOW1+Rd0lPgVGslPUsqk2Q5e15l/QUdoHNHXv5+8YaLDBdf9vwKlOv+ynbnEg/VgB2sgRm5CJGkZsUxODUKbGQVv3gVet4NqAuUABR5FSi7OQk6ZZw6U5vyak07vCu0pKA07LB0XIXJq0LxPiOoEb+i2fkAp6JODiL3xAyARMz3O3GVwPfg0eHl+XXRYhXEFStNO8Opm6P0XIaeTl4qH0CD6L0GM90NpP1iy/2a5QoFHEZJSQh80b2DFqlvzqoEVXaGRfCWFbmQNTfCGnWBq/nRx1od7EHv775J2aWZlFXA9+I/Bffm3HsVqzOWF2n0CYnYC+u3zQDR5JWRE56FffROistGYfqEddFlBgUcRknMTkHOTGw9wBUoeNbDSJwYQe/OHdIVG8pIY6zVvSubygte2Wz6eZDYrsMeCdXA/8Ctgbp/FI8tvvLwG7MAjEGM3IQavJQ3aAKMAoxYaM5rPNXTl5W7AQkKBR5Ex5qSTtOuu6wRz5sectD7ai9ibP6IrNJKXpBY3doSZMLah58e8f+LGGSTOvZz0OK9theveT1GdiiQY50b/l6oWY/olyfQ0AEDoEINXICb6jd0vwfwsGFcI8uPdQ7JGTg8DZk3UVCd4nlQoNYKOv00edATr4H7kVynoILYRozcBfePrk/mCYBWNNoxoI6PvSvKgQ2naCdf9v0JBRxqY0w216ziUPfcDm01FxxahX3sLWs9pyCTZWpIaBR5FREoJPUmxMN64C0yxf076TtBhvqqc17bC/fD/g9LCxDZSixvTLCZ4y768qB9jBB2vJD2udh6C855PUIG9LeKBaqj7HwZvOwhs8m8npwahnX8ZYqIfVJVia+hVWUTkzIj5QimXF7ymzfoBrbNZ0KE07YTz7o/RhyWxlRjrNX2NsvK6vCgutWnQQQX2toUxDqW+E7yqCfrAJciJ/uR31uLQe8+CTQ1CaT9EF0xpooxHkUiV7VAadto+J71p0NF+gK7QiO2klkie7ciDhdmJ66mDDsfBh+Hc/wAFHVnAHC6onUeh7HsQzBdMeV85Ow7twivQR25ApijRTgwUeBQJGRo1rzfg9Nrerlsf2STo6DgA5/EnqE8CsZ0Y6zVf21FeC77Jl0+uJa6fRuKDFEHHoQ/BseuEhSMqDbysEsq+h8DbD6WefhE6RP8laBdf21CTSEwOQqYoWlZq6JO+CKTMdjR225rt0Ed6EXtrk6Dj2BN0hUZsJ/UU2Y7GnRaPZq20go6d1EogVxhjUOo6oB58FKyiIfWdw7PQLr5m7JLRNcjIAvTes8aCZQKA1ngUBTk7BoRnNx5wesCqWqwf0BIKOkghEWO3TBsqskANeFmlDSMyJK6/h8QHv0h6nIIO6zCnB+rOuyGmh43iY4lokntKiJEeiOlhQHEAUkAMXQOvbsmbkgZ2ooxHgZNSJm17bxS6sedXTEEHKSRS15JekdqZ7aCgIz/xykaoBx/dvJBcLHznolDoSfv+lBrKeJhIJBJYXFxENBpFJBKBpmmQUoIxBofDAY/HA4/HA6/XC1W1959Qzk2Y9zhxuMGrWy0fD0BBBzHEYjGEw2FEIhFEIhHouvF6YIzB5XLB7XbD4/HA5/OB27z4WYwnyXb4q23byUJBR35jqgNKx2Gw6hbove8D0YVNHyMnByBqO8D96WXQpJQr75/l7yMhjMWrnHO4XK4130eF8pnKJG1ABmD8gufn5zE9PY2Fhc1fQMvKy8tRWVlpyy9dSgn9yhuQizMbjvHW/VDqrG97TUFHaRNCYHZ2FlNTU4hGk6Wh12KMoaKiApWVlXC7rW9FLnUN2vmfA1p8wzFl9322BB4UdBQWKXSI4etG9nmTr1TmC0LZ91DKz0BN0xAKhTA1NYVEYmNAbEZRFFRWVqKiogJOp3NL47caBR4AIpEIBgcHEYtlXoXO6/WiqakJLpd1VQLF7AT0629vPOBwQT34mOX9BDbdMktBR1Gbm5vD0NDQSmYjE+Xl5WhoaLA0k6iP3oQwaTPA/FVQd99n2TiWbVang4KO/CXDc9BvnYNcmE55P6XzKHjNxoy0lBLT09MYHR3dVlGympoa1NTU2J5JTKakAw8pJcbGxjA5adJQLQOMMdTV1aGqqirnX65SSuhX3zR9gfOWfVDqrW0hr08OIfbLvzbdighQ0FHMdF3H0NAQ5uZMtnNnQFEUNDU1IRDIfRdlI9vxMqBtvOhQdt0DHrC2QaF2+yLi7/1D0uMUdOQ/GY9AO/d/gFT1PBxuqIceW1O3KBaLYXBwEJGISWPCDDidTrS0tMDjyb/FrPkZDllACIG+vr6sBR2AEQyMjo5ieHg45yV05fyUeVStOi2vUipmJxB7428o6ChBiUQCN2/ezFrQARiBTH9/f1bfm8mIiX7ToIOVVYL5q3N+/tW04R6jy2wSFHQUBv32+dRBBwAkomuaEEYiEdy8eTNrQQcAxONx9Pb2Yn7epJq1zUoy8FgOOraylmMrZmZmMDQ0lNPgQwxfM72d13dZWv1TLM4i9svvJ21tT0FH8UokEujt7UU8vnFtRDaMjo7mNPiQQocYNe9Ayxt3Wvqa1ScGEH/775KuD6CgozDIeARQVLBANeDyASmKIorhG5AxY/H1rVu3VhaNZnU8UqKvry/vgo+S3NUyOjqKxcXFnJ4jFArB7Xajujr7V01ifgpyfmrjAdW5+fauLJLRRcR++deQSVZzK+37KegoUlJKDAwMpL3wLVOjo6Nwu90oKyvL+nOLiX7TgJn5KsAsnGIRoXHE3vhB0m7NjkOPUNBRIJjTA3XHsZW/SykBLQYZiwCxMGQsvPKnjC1CG7yCvoQvJ0HHav39/eju7s6bRaclF3gsLCxgejr1wp9sGRsbg9/vz/qC06TZjrodlmU7ZCKG6Ovfh1wImR5XGrsp6ChiU1NTCIfDlpxrcHAQ3d3dUJTsLZaWQl+T6l7NymyHWJhB9JffN91RAwDq7pNw7LzLkrGQ7GOMAQ43mMMNlFVsOD44MAAtYlL8McuklBgcHERHR0defCaX1FSLEAKDg4OWnW/5l53NKRcxPw05Z5J+Vhzgde1ZO08qUtcQe/OHkKFx0+O8pgXOkx+3vTEdyY14PI6xsTHLzqdpGkZHR7P6nGJywLTqJPOWg5XXZvVcycjIgjFNGTPPvqqdh+DY/4AlYyHWm5+fR2g290HHsnA4bNlF92Zs+2YYGBjAww8/jL179+LgwYP4/ve/n/NzhkIhaJp5OjNXIpFIVq8MxUiSKqX1O8AUR9bOk4wUAvF3/x5iYsD0OAvWwnXfp6nLbBGbmprK+eLp9WZmZrL23pVCpMh27LLkilDGo0bG0Kz4HwClaSccRx/Pi6tTkhsTExO2nDMfNrLaFnioqoo//uM/xuXLl/Hzn/8c/+7f/bucrruQUmJqymRdhAWydV6xMAM5a5JlUFTw2o6snCMVKSXiZ1+CPmT+oc3KgnA/8DSYw7paJmT74sP9ad9X13XbrpqydV45NQDETXYPeANgwbqsnCPl+fWEkTGcNf/i4bVtcN79MerWXED08AK0mfQXQkejUcumKlfTNC0vFpra9spuaGjA4cOHAQC1tbWorKzM6QdaNBrdVoGw7Zibm8vK1VrSXhJ1nWBq7rMdiYu/hH7rgukx5vbB9eA/AXP7cj4Okl2j/+//Jya++3+lFYDMzc3ZdsU0PT297XNLKaEneR8pFmQ7pBCIv/13EJPmU768oh6uez9JGcMCo8/NYPiPfhPTf/tCWgHIzMzGatNWyYfplqwHHkII7N69G1/96lfX3P7SSy/B6XSaTqmcPn0aQgi0tOSuk6od0eVq292fLWNhyJmRjQe4Cm5BafTE9fegXX3X/KDDBdeD/xjcV57zcZAckBKRy2cx+n/97qYBSK53g6Wiadq2A3g5N2HeU8PjBwvWb+u5Nz23lIif/in0EfPAh/kr4XrgVyhjWKh0HQunXk0rALHzfRQOh22fbslJ5dIXXngBzz33HPr6+lBRUYEPPvgADzzwAH7nd34HX/nKV9bcd2pqCg888AC+853v4N577832UFYMDAxg1sKFPOtVLU7D0Zt5Z0J3VQCuio1bCmMzC4hOZa94kxmFxeFSzPtuSAnEdC9E6W2QKhrh8xsDSs/eoyh/9Ck4G9eWdb5+/XrO6nako26yH2wk/amh9bwNlXD4NvaDCY/OILGQveJNG0k4eAwObv5vJyRDTPdBltZ6/6IhomFEr6/LBisKyo49gMDDT0KtuFNWQQiBy5ft7VK7c+dOW7fW5iTw0DQNO3fuxOc//3n82q/9Gk6ePImnnnoKf/Inf7LmfrFYDI8//jh+/dd/HZ/73OeyPYw17P7A9N+6CPbWSxk9likKav/RE+DrXihSCIy/9DOILFa7W88R8CGwo8U0BS2lxNzNASTm7IveSW6tDkCklLh0aWNPEysF338V4vKZjB6r+v2oefyxDbfrkQjGf/rSps29tsNdW4myZvP1I0LTMHu9D3rUvs8nkkPrApDlKqV2amlpQXm5fRnqnFymqqqKr371q/j617+OH/zgBzh69Ci+/e1vr7mPlBJf+MIX8KEPfSjnQQeAnBdo2fz8EplWIfC0tW4IOgAgOjyc06BDcTvh72hKOu+9cHuYgo4iF7l8FpHLZ+HZexRlj3zM7uFgO6GBt8u8f9Fib29Ogw5neRl8TeZbdKUuMNczQEFHMVuaglk48zrKjj0A9cSH7B6R7d+HOcvrffazn12ZS/re9763ofjPm2++ib/+67/Gj370Ixw+fBiHDx/GhQvmCxezwe45re3w7egyvX3xRk/OzslUBYEdLeBJijYtDIwiNpPbKR6SP+IDvYjfvpHTL+hcYk4nvK0m3UB1HeFbt3N2XsXjgr/dPHiXQmKudxBa2HwakxQZXUf09nVoI312j8T278OcTcx/6UtfAgBMTk6aVhy8//77LY26OOfbate9XZmulnfV10P1b1zbEZ+eRiJXK6MZQ6CzGYrLfA4wPDKJ6IR9q7KJdRR/EIGHPgrfiYcARQVsnptmyCzr4e1oBzP5HAr390PmaAp2OXhnysbrOykl5vuGkZinjGEpUGsbUf7oJ+Ddfxci0SjQ22vreLjNxR1zEnj89m//Nl588UW88847eOyxx/Bnf/ZnePbZZ3NxqrS53e6c95VIxeHxQPq3Pqfm27XL9Pbw4DB4Bs+X1jnrgnCUeU2PxebCiC7Ec3ZuYj0xv3HR9eqAgzvuBKCqqlpehG/NuDw+YKuvPcbg6zLPGkZGxnLzWmZAoLkaitN8m3tkcg6axuh9VCx0HSK8cbfU6oBjuZJztltoZMLuMWR9cel3vvMdfPnLX8Yrr7yCkydP4hvf+Ab+9E//FD09PXA4cl9rIpnx8XGMj5uX+LZCJot5ZHgW2qXXNh5weqAefDQnBYYSV95G4uLrpsd4VRNcD/0TqjFQZPq/9i9Wfk4WcKzct78fc3P2TbF1dXXB7d64KyUVMTUIvffshttZoAbqrnuyNbQVUkrE3/176ANXTY8r7QfgPE59jIpJfHQAo//1d1b+bhZwrHbt2jXbLoQZY9i7d6+tr7+sfnP95Cc/wbPPPou//Mu/xMmTJwEAzz33HObm5vDd7343m6faMo/HU3Dn18fM03G8tj0nQYc2eC1p0MG85XDd9ykKOoqU4g+i4mO/iob/8C3473vcNOgAAK/XPBNmBcbYlq/UpJTJC+/Vmy823a7E5TeTBh28pgXOYx+moKNIqbWNqPqn/woN/+b/Bd/Bu5P2q/L57Cu06Ha7bX/9Ze1b5MyZM3j66afx/PPP49Of/vTK7YFAAM899xy++c1v4vOf/3xWO0xuRVlZGRRFsWWdh8fj2fKeaZmIQU4NbTzAFfCatiyN7A59egTxUy+aH1SdcN3/GTCXfV86JHcqPvarSTMc6wUCgaw3bEtXMBjc8gemXJiGDJvU73GXgQVqsjSyO7T+y9Auv2V6jPmCcN3zSTBuz2cgyR3u9qLqn/6rpBmO9crLyxEKhXI/MBPBYNCW866Wkzoe+cqu6Zbm5uYt/7L1oWsQw9c23M5r26G0HczSyAwiPI/Yy/9fyKjZQjcG1wOfgVKf++qoxB5SCgAs7S91u6ZbMplm0XreM634q7QdBK9tz9LIDPrUMGKvfg8QJhc3DhfcH/pn4IGqrJ6T5I+tvI+klLh+/brl0y2MMezevdu2BMCyksqbV1RUWN6dT1VVBAKBLT1GCh1i/JbpsWyXR5daHLE3f5Ak6AAchz9EQUeRE8M9SFx41fgLYwDjxp9gS39fdRtjqG7agzlYW/XQ5/NtOeiQsUXzNgOKA6yqOUsjM4jFWcTe/KF50MEYXPc8RUFHkdOunYJ++7zxlzTeR7WtRzBk8TKPyspK24MOwMYmcXZwOByor89tP4b1mpqatrx1SU4PAdrGLX6svA7MvXFrbaaMRXAvQobMs0DqjsNQu45m7XwkP/GGLjCP3/iLlMaXp64BesJ4HSZiRjfXWBjcG4Bnx2FL07WMMTQ2Nm75cWIsSfBe257VtUoyETOCjph5PyjHkceg1LVn7XwkP6kdB4HlabRN3kdKbRuCHXssXXuoqipqa80L2VmtpAIPwIj4rFogFwwG4ff7t/QYo3tmkkWlWc48JC78EvqweYt7XtsGx+FHbV+ERCwgNLB0rsZdXjgOPQrGORoaGqCq1iRM6+vrt76oVE9ATJj0dGEsq1MsUgrE3v37pC3u1a5jcOw4krXzkTwmJZgvuOndWHkN1N33gDGG5uZmyz5jm5ub8yLbAZRg4MEYQ0tLS84/NN1uNxoaGrb8ODk/BURM5s89fjB/9cbbM6Tdvgjtmnm3WeavhOuep2gRXJGT8SgSN04j9ur/D2Lsduo7MwbnoUdXFhgrioLW1tacf2gGAgFUVlZu+XFiYgAQG+uNsIpGMGf2rjIT51+DGEl+oeA49EjWzkXyk4zMI3HpDcRe+57x+Z2KwwXn4cdWPltdLheamppyPsba2lqUlWUvW75dJbXGY5nD4UBHRwdu3bqVk2JILpcL7e3tGUWXYsx8659StyNrH/IiNI74mZ+ZH3S6jR0szq3Np5PCIWMRaLcvQO+/ZKSB06DuPAFeuTaQ9nq9aGtrQ19fX07WTfn9/oyuCKWUEOPmwYCSxS202uA1aNffMz3GAtVwnfx4WjscSGESi7PQe983ssZpvv4dBz90Z1pzSTAYhBACw8PDuRgmampqUFOT/R1c21GSgQdgBAc7duxAf38/IllstLb8YZlJ0CGjC5ChsY0HVCdYVXaiYhmPIvbWj0yvBsE4XPd+ErysIivnIvlFRheh3foA+sAV80WQSfDadijt5jupysrK0NnZif7+/qyu0K+qqkJ9fX1GwbYMjZqut2BllWmlwtMh5qcQf+8fzA+6vHDd/2kwh/0VKkn2iflpaL3nIEZuYisF/JUdR6HUtJgeq6yshKqqGBwczForEcYY6uvrUVWVf4uaSzbwAIzMR2dnJ6ampjA2NratqzbOOZqamhAIBDLOTKRcDJeFaQ8pJeLv/QPkYsj0uPPYR6DUbGykRQqbCM9Dv/UB9MGrgNzahxrzBuA48FDK17TH40F3dzfGxsYwNbVJqnkTTqcTTU1N2yqwJJIV3svSjjCpxRF7638DmkmgxRUjeM9SgEPyh5idhNZ7dvNpSRO8qnnThfqBQADd3d0YGRnZ9nZ1r9eLpqYm20ujJ1PSgQdgRIXV1dUIBAKYmprCzMzMliJOVVVRWVm5ErFmSuoaxKTZYjietcVw2rVT0IfNO9qqXUehdhzIynlIfsgkFcwrGyHmp4wV+FyB4/DjaV2586UFp8FgEFNTU5idnd1SIO90OlFVVYWKioptNbCS4TnzeXanF6xi62uuNjy/lIifeQlybtL0uPPIY1Cqs7tVl9hLhMag3XzffLGyKQZe2wYxftv4q9sHx6EPpVVt2uFwoLW1FfPz85iamsLCwsb+L6l4vV5UVVVt6wLYCiUfeCxzOp1oaGhAXV0d5ubmEA6HEQ6HEY2ubVnNGIPb7YbX64XP54Pf78/KL1hOD5umv1lVE5hj++st9PF+JC780vQYr2qkRXBFJJNUMK9pgdp5BLyiHtrNs9BunIa69/4t157weDxobm5GQ0MDZmdnV95H8XUdYDnn8Hg88Hg88Pv98Hq9WXkfmQbvAHhdR1aeX7t5Dnr/FdNjSvsBqJ2Htn0OYj8pJcT0CPTe9yHMKkibYQxKYzeUzsPgviDi534OMXbLWEy6xTVzfr8ffr8f8Xgcs7OziEQiiEQiG6YzVVVdeR8FAoEt17qxCwUe63DOEQwGV+oUSCkhhICUEowxcM5zEkmKyT7T25Xaju0/d2QesXf+HqZfQk4PnCdpB0sxyCQVzOvajYCj/M7iM6V1P2QiDrXZvDNyOhRFWckEAlh5D+XyfSSFDjE5sPEAV8Crtz+FqE+PIHHuZdNjrLwWzqOPbfscxF5SSojJQWi970POpNkagHEozbugdBwC994pFqnuOAJRUQ8erMt4PE6nc83CUCHESkaec257e/tMUeCxCcZYzvc+y8g85MLMxgPe8m0vhpNCR/ydvwdi5pVJXSc/Du7dWq0Rkl/EzBi03i2mghs6jYDDv3GrKnM44dh9MqtjtOIDUs6Mmu7SYRWNYOr2OmPLWBjxt35kvkbG4YLr3qfAFPu6b5PtMXZC9UG7+T7knHlNlg24AqVlD9SOg6aFHbm/Ctyf3YWdhRxsrEaBRx4QE+bZjmxcpSUu/BJictD0mGP/A1RRsUBlIxVcbJJlDfk2F0wbRcJ+DBmZNz3uPPFR2glWoKQUEKO3jAzH/HR6D1IcUFr3Qm0/QI0zM0SBh82kEBBTJoEB4+Db3EKbqs4Ab+iEmuWrWpJ720sFHy7a7JaMLZov+HSXgZVtvQDZaonLbyWdvlJ33w21sXtbz0+sJ4WAGOkx3keLJt2LzahOKG37obbtpzpH20SBh81kaNS8L0tlI5iaeSMuMT+N+Hs/MT3GvOVwnXgyr1c9k7W2lwo+BObOfHtqITBd2wEja7id17k+2pu0zT2vaYVj3wMZPzexnhQ69KHr0HvPJc1gbeBwQ20/AKV1H5jD2uaIxYoCD5vlYprFqDPwI9OAxqgz8FRWy0aT3KFU8OaklMn7smxja6tYnEXs3R+bHmNuH1UmLSBS16APXoV26wMgSSfuDVweqO2HoLTs2fYaIbIWBR42krGw+dWryweW4aIko87Az1LWGeAV1nboJVtHqeD0ydlxIBHdcDsL1me8FV3qGuJv/28gvvF5wTic9zxV9FmkYiC1BPSBy9BunTc6w6bD7YPaeRhK066sdjEmd9C/qo2S1hyoyTw9rN++AL3/sukxpX0/lA7z0tckP1AqeOtykTVMnH8NIskaGsfBh6lIWJ6TiRj0/kvQbl8wiuGlgXkDUDoPQ2nspvICOUaBh02MRYJm89IMvMq8nv9mxPw04u8nqzNQA+eRx2ldR57KLBXshdpxEEpz6aaCZSIKOWvS38jhBiuvzeg59dFeaD1nTI8pzbugdh/L6HlJ7sl4FFrfBeh9F81L2ptgviDUHUfA63fQ1JlFKPCwiZybME39sWBdRmlyKXTE3/2xebdR1QnXvZ8s2S+nfJZZKrgMauchSgUDxlZxk9LsmWYNZSyM2Kkki7L9lXAef4KC9zwkY2Fot85DH7gM6Ol1HGf+KiPgyFJVW5K+0v7UslHS9HCGNQcSl99KmhqmOgP5h1LB22dkDbM3zSKlRPz0T82L7XEVrnueoo6zeUZGFoyOy4NX0+64zMprjYBjG1PaZHso8LCBTMSMbbTrOVwZpYf1yUFoV94xPaZ2HoLatHPLz0lyg1LB2SMXpk2npVigJqPdPPqtD5I2UXQcemRNWXliLxGeg957DvrQ9bQ7LrOKBuN9VNVEAYfNKPCwgZhKkh6ubk2rg+FqMhFD/N0XYdaHhZVVUPO3PEGp4OxLViI+k2yHmJ9G/NwvzJ+voRPqjsNbfk6SfWIhZLQHGOlJv+NyVbPxPqrcfndikh0UeFjMqDmQvfRw/P2fQ4ZNtlsyDufdH9tWETKyfZmngo+C17RQwJGE1BKQM8MbDygOsC1uF0+5Psrlhev4P6Lfg83E/JTRmn60N+3H8No2ox9RMLNFxiR3KPCwmFyYAaILG25n/uot1wXQBq5C77tkesyx7z4oFOHbhlLBuSWmh0wDOV7dsuX1L6nWR7mOP0H1OmwkZseNgGPc/GLNDK9faoAYyG6DNpI9FHhYLFuNrER4DvEzL5k/V3Uz1N13b3lsZPsySgVXNxsflBQopk1mKWuYcn3UjsNQGru2PDayfWJmFNrNs0kbXG7AGHhDF9TOw7SQvgBQ4GEhqScgp5Olh9P/0pFSIn7qH8x3Q6hOOE88ueW1ImR7KBVsHRmeNZ1eZL4KMG8g/edJxIwpFrP1Uf5KOA7S+igrSSkhpoaMfkQzI+k9iHEoTTuNjstb+N0Te1HgYSE5PWyeHq5q3lJ6WLt+KunCOufRx8F95RmPkWwNpYKtl3RR6RazhvGz/wcyPLfxAONw3f0xqntjEWPd2wC0m2eN8vfp4AqU5t1GA0RPWW4HSLKOAg8LJe2guYUPTDEzhsSF102PKS17oLbty2hsZGsyTwUfAS8L5nRsxUwKYazvWI8rYJVNaT+P1n8laWsBx/4HqJ+RBaSUEGO3jAzH/FR6D1JUKC17obYfBHMXfwPEYkWBh0VkPGLUHViHecvBvOllKKSWMLplmixWZN4AnEcf3/Y4SXKUCrafnJ807brMKpvSruIqwnOIn/2Z6TFe0wJ1113bGiNJTQoBMdprNEBcmEnvQaoDSut+o+NyCTVALFYUeFhEmK3tAMC20JclceGXSa8MnCc+Sm/IHKFUcP4QUybZDhjTlelIuT7K4aL1UTkkhYA+vNQA0WyKy4zDtaoBIlWNLRYUeFhEmqWHAfDKxrQer08OJm1cpe6+G0qGpdZJcttKBXcczKh6JklOCh0yZJJpcrjA/Omtl9FvfZBifdSHKSuVA1LXoA9dh9Z7zrSUgCmnx2iA2LKX1toUIQo8LCCji5CLoQ23M39VWlkKqWtGDwkTLFgHx777tztEskrGqeC2/VDbKBWcK3J23LTqK69oTKvuiYjMI/7Bq6bHlNa9UFv3bHeIZBWpJaAPXjEaIMbC6T3I5TMaIDbvLvkGiMWMfrMWEGYVFoG0F8MlrrwNOb9xfQi4aqy+p4ZhWUGp4PyWfLpy8/eRlBKJMz8zXx9C66OySmpx6P2Xod0+D8SjaT2GefxGA8SmnfR5VgIo8LCA+bw0A0+jdocIjUO7+q7pMce++2hLZhZQKjj/SV0zb6zo9ID5Ni8YpQ9ehT5y0/SY89iHKWjMAqMB4sWlBogbAzwzzFdu7PRq6KIGiCWEAo8ck5F5ILLx6pkFqjf9sJNCGFMsZrtYgnVQd9Lq++3IKBXs9kHtoFSw1eTsmHkNnMrNp1lkLIL4+y+bHlPa9kGp78zKGEuVjEWg3T5vbE8263djgpVVLnVc7qDFvCWIPjlzLFl6mKcxzaLdOG3eQ4IxuO56gq4QMkSp4MKznfdR/INXzANLlxfOQx/a7tBKlowuGg0QB66k3wAxUGMEHLVt1I+ohFHgkUNSSvNiR4xt2kFTLMwgcekN02PqrrvBg3XZGGJJoVRwYZJ6AjI0tvGAywdsUgNHH+1N2kjReeRRMJcnG0MsKSI8D/3WOeiD19JvgBisMzouVzdTwEEo8MipyJx5J9ry2pTt6qWUiJ9+yXQFPyurgGPvvVkdZrEzUsEXoPdfolRwAZIzo6ZfcLwydRdfmYghfsa8UJjSsANK8+6sjbEUiMVZ6L3vQx++kX4DxKomKEsNECngIMso8MihTNPD+q3zyWsNHH+C1hakKeNUcNcR8BpKBeeL5O+j1DVwEhdfN9+dpDrhOPph+v2mScxPL3Vc7oVZQz0zvKbVyBRWUGaWbETfYDmSfJqFgwWTT7MYtQZ+YXpM3XEESk36lU5LlZEK/gD64FVKBRc4qcUh50yqxXr8KTvRGgX3zpoecxx8GNzrz9YQi5aYnYTWexZi7Hbaj+F17VA7j4KXV+duYKTgUeCRI3IxZLqgjQXrkmYspJRInP0/5rUGPH44DjyY7WEWlUxTweqOI2AVlArOR3JmxPR3mSrbkargHq9pgdp5KGvjK0ZiZswIOCbMm1puxMAbdkDtPAzur8zp2EhxoMAjR2QG0yz64DXowz2mx6jWQHKUCi5epllDpH4fpSq45zz2EQowTRgZ2hHove8n7YezAWNQGruNBoi+YE7HR4oLBR45kHSahStgSXajGLUGfm56TGndC6VhRzaHWBQySwV3GItGA5QKzncyEYWcm9x4wFsO5jZvvLdpwT26Il9DSgkxOWg0QDTbOWSGcSjNu6F0HKIpK5IRCjxyQC5MA4mN9SFYsD5pDYj4hdfMaw04PXAeploDq4nQGLSblAoudmLGpCEckk+zSCmNXSxUcG9TUkqI8T6jAeLcRHoP4sqdBohuX24HSIoaBR45kHSaJUlPCX16BPqt86bHjFoD1OU081TwzqVUcOp6DyT/yCS/52TTLHrfRfMdMFRwb4WUAmJ0qePygsl0lBnFAaV1H9T2A1T3hGQFBR5ZZnxBmnz4KQ6wQI3p/RNJplh4QyeUltLumLmdVLDaeQjMQ6ngQiTjEdMvRuarMA3EZSKG+PnXTJ+LCu4tdVwe6TE6Li/Opvcg1XmnASJ1XCZZRIFHlsn5SUCLbbidVTSYTrMYV2kmKWWuwnnk8ZJdCEep4NKWtBNtkmxH4tKb5rvIvOVw7L0nq2MrJFLo0IeWOi5H5tN7kMNtNEBs3Zuy0CEhmaLAI8uS72bZOC+d6irNsefukpweyDgV3LYPahulgovFVt5HYm4yec2Owx8CU0qve7DUNeiDV6Hd+gCILqb3IJf3TgNE6rhMcogCjyySUkKYTQeoTjCTXRSprtLUXSdyMcS8RalgskwmYpCLMxtuZ/6qDb9nKaXRedaspHpdO5TGrpyNMx9JLQ59YKnjcjyS3oPcZVCXGyBSVWRiAXqVZVNkLvlulnX9PsTsJLSeM6ZPU0pXaRmlgp1uqO2UCi5WctZ8LQ+raNhwmz50HWK8z+TO3FiYXSJTlTIRg953CVrfBSCxcarXDPMGoHQegdLYTQtviaUo8Mgi02wHsGFhm5QS8XM/N6/IWNdREldplAomySR9H5Wvex9pCSSStRfYeRzcX5X1seUbGY8uNUC8CGjpNkCsMIrn1XdSwEFsQYFHFpnuumB8w24W4yrNpAkc43Ae+VBRX6VRKpikIoWAnDVZTOz2b1gwnLj6jmkTOOb2wbGnuBeUylgY2q3z0Acum3axNsMCVUYflbr2ov6MIfmPPsWzRCai5vPSgeo1X5ZSSyBx7hXT5yjmqzRKBZN0yIUpQGz8Il2fNRQLIWjXTpk+h+Pgw0XbXkBGFoyOy4NX0++4XF4LtesoeHULBRwkL1DgkSUyZNJBEwBblx5OXH3HdC1DsV6lUSqYbEWyWi3rWw0kPnjF9IuXVzdDad2bk7HZSYTnoPeegz50Pe2Oy7yyAcqOo+CVjRRwkLxCgUeWiCQL4lZfqZXSVRqlgslWGbvCRjceUBxgZRUrf9VHe5M0U2RFt6BULMxA6z0HMdKTfsfl6maoO46CV9TneHSEZIYCjyyQQoecNcl4ePxrqiyWwlUapYJJxqKL5tvLy2tXdoVJoRvbZ02oOw4VTYVSMT8F7eb7EKO9aT+G17YZDRDLa3M4MkK2jwKPLJDzU+YBxapplmK/SqNUMNkuMWuS7QDAg3eu3LXrpyEXNq6lgtMNx74HcjU0y4jQOLTe9823CCfB6zuNgKNI14eR4kOBRxZsNi8thUA82YLSHYcL+iots1Rwi/FBSalgsor5+4iBlRu7wmR0EYkrb5k+1rH/wYKuWitmRqH1nIWYGkzvAYyBN3QZa6HKgjkdGyHZRoHHNkkpzdd3KA6wMqMFu377AuS8SflvpweO/ffneIS5Qalgkk1Si5u+R5i/cqVIXOLK26YLlFmwDmrnwZyPMduklBBTQ0Z7gBmTfk1mGIfStNRx2RvI7QAJyREKPLYrumA+Lx2sA2PM2D576Q3Thzr2PwDmLKyrNEoFk1wwandszJgt7woTCzPQbp4zfazzyGMbKgPnMyklxES/EXCYrQ0zwxWj43LHITBPWW4HSEiOUeCxTZtVWdRunIE0qczJAtUFdZVGqWCSS6a7WXBnV1ji4uuma4eUlt1Qqs071uYbKSXE2FIDxPmp9B6kqHc6Lq9aqE5IIaPAY5tM+0owBlZeCxmLIHH1HdPHOQ88mPdXaZQKJlaQUphf+bu8gLsMYmYU+sDVjccZh2N//i8olUJAjN6E1nvOfGGsGdUBpW2/0XGZGiCSIkOBxzYknZcuqwJTHYhfeh3Q4huO8+pm8IYdVgwxI5QKJlaSCzOAvnHtBg/WgzGG2PnXTB+ndh4CX1XfI99IoUMfvmE0QDQp7W7K4brTcbmI6voQshoFHttgfCmbzEsH6yAWZ6H1vG/6OMeBh/Jy+2jGqeDWvVDbKRVMMpN0V1h5HfSx2+briRQHHHvzs9Kv1DXoQ9eg9X5grAFLh9MDteMglJa91ACRFD0KPLYhVTfa+Plfmtb2UBq7825OOrNUsHMpFbyfUsFkW0x3hXEVKKtE/NQ/mD5G3XUXmDu/MmtSS0AfXGqAaLLg3JTbd6fjMjVAJCWCXukZSj4v7YOMRaD3XTR5FIPjwIM5H1u6KBVM7CZji4BZ76LyGoih6+bZEJcXjp13WTC69EgtbjRAvH0BSETTegzz+KEsd1zmSo5HSEh+ocAjQ8nnpesQv2A+J610HAAP2L+lNPNU8CEoLXsoFUyyRiRrrhioRvz0/zE95thzT14EvTIehdZ30bjIMFnLZYb5yo2dXg1d1ACRlCwKPDIkk2z/k0JCjJgU1eIqHPvuy/GoUqNUMMk3yd5HYmYCcjG04XbmK4e643BuB7UJGYtAu30eev9l04sPM6ys0qhlU9+R97vZCMk1+ibJkDCZZpFcSVrkSO0+Bu7x53hU5jJPBR+B0tRNqWCSE1LXzBcxuwNIJOvivP8B216PMrpoNEAcuJJ+A8RADdSuI+A1bXm5oJwQO1DgkQEZj5rOS0MyyGmTehcONxy77879wNYPh1LBJI/JhWnTomBicTZJNeBaKC17rBja2vGE56HfOgd98FraDRBZsM5oTV/dTAEHIetQ4JEBOTex8TYpoY+ZlxF37Dlp6c6PjFLB/krjg7KunVLBxBKm7yNdgz5s/j5yWrwNXSzOQu99H/rwjfQbIFY1Qd1xBKyigQIOQpKgwCMDYm5yw21yYRbSbHW+xw+166gVw6JUMCkoZu8jMZtk0XZtG3hduwWjAsT8tNGPaKQXZnV6zPCaViNTWFG4naYJsQoFHlskpdxwpSalgD5rXnDLse/+nC/KzCgVXFFvZDiqmijgIJaTiRgQnl17m5aAMOviDMBx4MGcv07F7CS03rMQY7fTfgyv6zAWjQaqczcwQooMBR5bFV3csEBTzM+at+sOVENp35ezoYjFEPTecxmlgnllY87GRchm5LxJtiM0Zfo6Vpp3QalsyNlYxMyYEXBMDKT5CAbesANq52Fwf2XOxkVIsaLAY4uESbZDJMt27H8gJ+slMk4F7ziy0u2TEDvJddMsUktALIQ23pGxnDSCk1JCTI9Av3kWYno4vQcxBqVxqQGirzzrYyKkVFDgsUXrp1nE/Cygaxvux4J1UBq7snpuMTthBByUCiYFbn0AL0LmwbvSti+rWQUpJcTkILSbZ5P2iNmAcaMBYuchMJu2xBNSTCjw2AIp5ZoUccpsx777sjYnTalgUkxkdHHNdtmU2Y492WkEJ6WEGO8zGiCa7KYxxRUoLXuhdhwEc/uyMg5CCAUeWyIXQ2uyG8myHbyiDso2295TKpgUq/XrO5JnO/Zvu+29lAJidKnj8oL5wtWNJ3ZAad0Htf0AmMuzrfMTQjaiwCMNcmnB2+orJSlSZDv2Zp7tyDgV3LIbagelgkn+Wn4frd5GmzrbcTLzcwkBMdJjBBzrds8kpTrvNECkjsskTy2/jwp5NyIFHutIKbGwsIDFxUVEIhFEo1Hour58ED5vCwIsAU9kBuDKhowHr6gHzyDbcScVfHbDwrukKBVM8pQQAvPz8wiHwyvvIyGMrd5MOuD3NsPPEnDNjgCKuuF9lGm2Qwod+tB1o+OyWXVhMw431I6DUFr3gqnOLZ+TkFzRNA1zc3OIRCKIRCKIxWIrgQcAuFwueDweeDweBAIBOByF0cCTSZnmPswip2kapqenMT09DU3bOH1iSghUhkcRmOkHX0rjuu7/zJamWTJOBbftg9pGqWCSX2KxGKanpzEzM7MSaGyG6wlUh0fhm+wFiy4CjMH9xK+DlwXTPq/UNegDV6Hd+gCILab3IJf3TgNE6rhM8kg4HMb09DRmZ2exla9ov9+PyspKlJWV5XVGpOQDDyklQqEQRkZG0v6gNHkS+CNTqNLm4Lvn42n9wjNKBTtcUNv2Q2nbnxdtwQlZJqXExMQExsfN29yn9yQClYtjCLpVeA8/kt5DtDj0gaWOy/FIeudxl0HtPAylaSd1XCZ5Rdd1jIyMIBQKbet5ysrK0NTUlLcZkJIOPDRNw8DAABYX07xCSkMwGERjYyN4kgZrGaWCnW6o7ZQKJvkpFouhv78fsVgsa89ZW1uLmpqapEG8TMSMjst9F4BEeudl3oDRcbmxmxogkryzsLCAgYGBO1P728Q5R2NjI4LBYFaeL5tKNvBIJBK4desW4vH0urZuhdfrRVtbGxTlTvvubaWCW/bQlRnJS5FIBLdu3co8W5hCMBhEU9Pakv4yHoV2+wL0/oum1YLNsLIKo49KfScFHCQvzc7OYmAg3XIJW1NfX4/q6vyq41SSgYemaejt7c1J0LHM6/Wivb0dTGiUCiZFKRqNore3NydBx7KKigo0NjYCyx2XBy6bbmE3wwJVUDuXOy7n73w3KW1zc3Po7+/P6TkaGhpQVVWV03NsRckFHlJK9PX1YWFhIefnqilzofzmW5QKJkVHCIGenp6cBu/LWnwcrhtvp99xubwWatdR8OoWCjhIXovH47hx48aWFpBmqrOzE16vN+fnSUfJXU6HQiFLgg4AmJiPIuBwg20SeKykghs6c9LbhZBsGxsbsyToAIDhhQQ6GAeQOvDglQ1QdhwFr2ykgIPkPSklBgcHLQk6AGBwcBBdXV1J1x9aqaQCD03TMDycZhXQbGAMk75G1CTZtUKpYFKIIpEIpqbMi+flgs4ULASbUTZ1y/Q4r24x+hFV1Fs2JkK2KxQKIRwOb37HLInH45iYmEBdnf2NQksq8JiZmbEsulw26wyiyu0Hj97ZwUKpYFLIJifTLHCXRePOapTxAUDcWd/Ba9uMgKO81vLxELIdUkpb3kdTU1OoqamxPetRMoGHlNLSq7QVjGG2vAUV0cuUCiYFL5FIYHY2zbozWSQUB6JVrXBP9ILXdxoBhz9/FssRshXhcDir28/TJYTA7OwsKiq21wNpu7Yc9oyPj+Nf/st/idbWVrhcLtTX1+MjH/kI3n777VyML2sWFxfTr0iaZVNqAOzYR+E88XEoVU0UdJCCZUfQsWzUWQ3HfU/DefgxCjpIQZuZmSnJcy/bcsbjM5/5DBKJBF544QV0dnZibGwML7/8Mqan0yz3bZNIJM2trLnAOKJOP6jWKCl0Vs5Jr6cxFZrTC2XzuxKS1+x8H0UiEUgpbb0A3lLGIxQK4Y033sC3vvUtPPLII2hra8OJEyfwta99DU8++SRu374NxhjOnTu35jGMMbz66qsAgFdffRWMMbz88ss4fvw4vF4v7r33Xly7di2b/18b2Bp45MH5CckGu1/Hdp+fkO3Sdd2yHWFmpJS2TPOstqXAo6ysDGVlZfjRj3607YH/x//4H/FHf/RHOH36NFRVxTPPPLOt59uM3R9Y0WjU1vMTsl1CCCQS6VULzRV6H5FCZ2fQsaygAg9VVfEXf/EXeOGFFxAMBnHffffh61//Os6fP7/lE3/jG9/AQw89hL179+I3f/M38dZbb+X0QyWX1RUL4fyEbFc+1BrMhzEQsh3Z6sWyHXZ/H215celnPvMZDA8P4+/+7u/wkY98BK+++iqOHj2Kv/iLv9jS8xw8eHDl54aGBgDYXmfLTdCCTkIIIXaj76IMAg8AcLvdePzxx/E7v/M7eOutt/CFL3wBv/u7v7uyN3j1VUmy1Ozqdr3Lv4hcRmF271te3TCOkEKUDx+Ydr+PCdmufHgN2z2GrJx97969WFxcRE1NDQBgZGRk5djqhaZ28ng8tp7f7Xbben5CtotzDqfTaesY6H1ECp3LZf/+RrvfR1vaTjs1NYWnn34azzzzDA4ePAi/34/Tp0/j+eefx1NPPQWPx4OTJ0/im9/8Jtrb2zE5OYnf+q3fytXYt8Tj8dhag8DuwIeQbPB6vbYujqP3ESl0nHO4XC7bFnjmwwXElgKPsrIy3H333fgv/+W/4ObNm0gkEmhpacGv//qv4+tf/zoA4M///M/xzDPP4Pjx49i1axeef/55fPjDH87J4LfC7q589IFJioHX60UoFLLl3IqirJmiJaRQ+Xw+2wIPj8dj+7QpkyWyTFxKiRs3bthytVZWVob29nbLz0tItum6jqtXr9qyu6SmpiYvGlwRsl2RSAQ3b9605dwtLS0oLy+35dzL7F/lYhHGGKqq7CmzbNd5Cck2RVEQDAZtOXdlZaUt5yUk2zwejy1ZcEVREAgELD/veiUTeABAMBi0fHeJy+VCWVmZpeckJJfsCKTLy8tpmoUUlerqasvPWVNTY/s0C1BigYeiKGhqarL0nM3NzXnxiyYkW9xuN2prrWtFryjKSq0fQopFIBCwNPvgdrvzJvteUoEHYPyyrUoV19bW0qJSUpRqamos25LX1NQEVd1yP0tC8hpjDI2NjZZk4RljaGlpyZuL4JILPACjUmquA4JAILBS14SQYsMYQ2tra84DgpqamryYkyYkF1RVRWtra84Dgubm5ryoH7KsJAMPRVHQ3t6es+AjEAjQFAspek6nEx0dHTkLPmpqaiyd0iHEDj6fD+3t7Tn7vmhubrZ9F8t6JbOd1owQAiMjI5iZmcnacy5/WFLQQUpFIpHA4OAgFhcXs/J8yynoioqKrDwfIYUgGo1iYGAga/U9VFVFc3NzXm5uKOnAY9nCwgKGhoa21fLb5XKhubmZ1nSQkiSlRCgUwsjIyLZ6LpWVlaGpqYl2sJCSJITA5OTkthumVlZWoq6uLm97hFHgsUQIgdnZWUxPTyMSiaT9OJ/Ph6qqKvj9fspykJKnaRpCoRCmpqa2FMiXl5ejsrISXq+X3kek5MXjcczMzGB6ehq6rqf1GM45gsEgKisrbe/FshkKPExEo1EsLi4iGo0iHA5D13VIKcEYg8PhWCn+4vP5bK95T0g+klIiEokgHA4jEokgEomsfIAyxuB0OuH1euF2u1FWVka7VggxIaXEwsLCynsoGo2uZBSXe74sfx+VlZXZ3nU2XRR4EEIIIcQyhREeEUIIIaQoUOBBCCGEEMtQ4EEIIYQQy1DgQQghhBDLUOBBCCGEEMtQ4EEIIYQQy1DgQQghhBDLUOBBCCGEEMtQ4EEIIYQQy1DgQQghhBDLUOBBCCGEEMtQ4EEIIYQQy1DgQQghhBDLUOBBCCGEEMtQ4EEIIYQQy6h2D4AQQrLJeeQZMK6Aq04wroApCpTlnzm/c0xRwPjqY8qGY1x1gnMGxhkYY1BUDsYZOGPgKl86huTHGIOi3nk85wyqyqFwBoUzOFUOdeln4+/KnZ8V437qqvsqJj8rnMHBORQG8A0/G+NRlv508OWfYdyPwzjOGByKcR/GAIUZ92EM4GBQOMCwfF/j/3f5+Mp9gZXnXv6ZSQlIASYFIPRVP2uAFIBIfowtHYfQjZ+lAPQE5NJtEDpkImH8KXRILbF0u4BMxJduN+5759i6xwgBEdcghYDUBfR4YuVnqQvoCW3lZ7H0sxDLf0+Y3E9CT+gQurzz97gOIeTKz1KXELqAFKv/bjxOLj1O6BK6JqBLCV0CcSFXftalRFzA9FhCrr/fnZ//RNy29425CmU8CCGEEGIZCjwIIYQQYhkKPAghhBBiGQo8CCGEEGIZCjwIIYQQYhkKPAghhBBiGQo8CCGEEGIZCjwIIYQQYhkKPAghhBBiGQo8CCGEEGIZCjwIIYQQYhkKPAghhBBiGQo8CCGEEGIZCjwIIYQQYhkKPAghhBBiGQo8CCGEEGIZCjwIIYQQYhkKPAghhBBiGQo8CCGEEGIZCjwIIYQQYhkKPAghhBBiGQo8CCGEEGIZCjwIIYQQYhkKPAghhBBiGQo8CCGEEGIZJqWUdg+CEELyUSwWwx/8wR/ga1/7Glwul93D2SCfx5fPYwNofHaiwIMQQpKYm5tDeXk5ZmdnEQgE7B7OBvk8vnweG0DjsxNNtRBCCCHEMhR4EEIIIcQyFHgQQgghxDIUeBBCSBIulwu/+7u/m7eL+/J5fPk8NoDGZydaXEoIIYQQy1DGgxBCCCGWocCDEEIIIZahwIMQQgghlqHAgxBCTHzlK1/BAw88gM9+9rOIx+NrjkUiEXzsYx/DQw89hMcffxzT09N5M7Zlf/AHf4Djx4/bPiZN0/CFL3wBDzzwAP7Nv/k3lo0n3fEts/rfa71k47P7tZYLFHgQQsg677//PkZHR/H6669j7969+Ju/+Zs1x3/yk59g//79eO211/CP//E/xne/+928GRsAzM/P4+LFi3kxpr//+79Hc3MzXn/9dYTDYbz11luWjSud8QHW/3utl2p8dr7WcoUCD0IIWeftt9/Ghz/8YQDAE088seHLsru7G+FwGAAQCoVQU1OTN2MDgP/6X/8rnn322bwYUzrjtXN8gPX/XuulGp+dr7VcUe0eACGE5JtQKITGxkYAQHl5+Yb09o4dO3Dx4kXs378fjDG8++67eTO22dlZXLhwAb/1W7+VF2MKhUIrvUbMxmv3+Oz491ov1fjsfK3lCmU8CCEla3R0FPfff/+G/6SUmJubA2B8KVRWVq553AsvvICHH34YFy9exO/93u/hP/2n/5Q3Y/vjP/5jfOlLX8r6eFKpqKhIOqZUx/JhfHb8e62XanxWvNasRoEHIaRk1dfX44033tjw30c/+lH87Gc/AwC89NJLuO+++zY8dvnLIRgMIhQK5c3Yenp68I1vfANPPPEEbty4gW9+85tZH9t6J0+eTDqmVMeskmoMdvx7bWV8QO5fa5aThBBCNviN3/gNef/998tf/dVflbFYTEop5Re/+EUppZSzs7Pyox/9qHzooYfkfffdJ69du5Y3Y1vt2LFjto1peTyJREL+83/+z+X9998vn3vuOcvGk+74VrPy32u9ZOOz+7WWC1QynRBCCCGWoakWQgghhFiGAg9CCCGEWIYCD0IIIYRYhgIPQgghhFiGAg9CCCFp+8IXvgDGGP7Vv/pXG47963/9r8EYwxe+8IWV20ZHR/Hcc8+hs7MTLpcLLS0t+PjHP46XX3555T7t7e344z/+YwtGT/IBBR6EEEK2pKWlBX/1V3+FSCSycls0GsX3vvc9tLa2rtx2+/ZtHDt2DK+88gqef/55XLhwAT/96U/xyCOP2FqinNiLSqYTQgjZkqNHj6K3txc//OEP8dnPfhYA8MMf/hAtLS3o7Oxcud9yBuTUqVPw+Xwrt+/btw/PPPOM5eMm+YEyHoQQQrbsX/yLf4H/+T//58rf//zP/3xNMDE9PY2f/vSnePbZZ9cEHcuCwaAVwyR5iAIPQgghW/a5z30Ob7zxBm7fvo2+vj68+eab+Gf/7J+tHO/p6YGUErt377ZxlCQf0VQLIYSQLauursaTTz6JF154AVJKPPnkk6iurl45vlwUmzFm1xBJnqKMByGEkIw888wz+Iu/+Au88MILG9ZsdHd3gzGGK1eu2DQ6kq8o8CCEEJKRJ554AvF4HPF4HB/5yEfWHKusrMRHPvIR/Mmf/AkWFxc3PLYouqySjFDgQQghJCOKouDKlSu4cuUKFEXZcPy//bf/Bl3XceLECfzgBz/AjRs3cOXKFXz729/GPffcY8OIST6gNR6EEEIyFggEkh7r6OjA2bNn8Y1vfAO/8Ru/gZGREdTU1ODYsWP47//9v1s4SpJPmFxeAUQIIYQQkmM01UIIIYQQy1DgQQghhBDLUOBBCCGEEMtQ4EEIIYQQy1DgQQghhBDLUOBBCCGEEMtQ4EEIIYQQy1DgQQghhBDLUOBBCCGEEMtQ4EEIIYQQy1DgQQghhBDL/P8BqH7q88ArsM4AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -877,7 +876,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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BhdQRUXJyL3bbooIA3Uu2gwDbZtyQOqoDO1JHyW4PHDjg+B7kr+d2xZ6YmIiWLVsGfS8idYFAABMnTsQ777wDIK/e+vTpg6NHjxr3Z8mCyFzBEmgKrnKb7Pe+++7DPffcg507dyqrnCtWrJBum6RC6vJDtQ8EAli5cqVlXxk1ahRuuOEGAGZyRfXJkjoic1b9VKbUWfU/ntQVhAXjgw8+MLWjUHwau3btigcffBCAuezFLaVJbm6u4Vdp1e/djqXFldQdOXLEUcJ4K1J39OhR/P3333jhhReM70q8+bW4QIWIEGQkymkjcxIuL0OrVq1cb+3Fd+aoqCjTZ7edRkW9oQGBJuriQOryS6mjOrAjdf/++y+AvDRBPL744gssXbpUei6ROrdBSKdOnRLmdxJFBqalpeHJJ5/EvffeCyBv54358+fj9ddfN6VBYOtt7969+Prrr41N0VkCHSqpo2euX7++sO4IbBvctGmTdINzt+bX3bt325Lql19+Wdpvvv32W3To0EHJT2vcuHGmd03KIxsoQXuWihTTUHzq+N9CNc8dP34cY8eOtexb7733Xkj3+Pvvv+Hz+XDgwAFTvRX11C1WYMtu9Q5mzJgR9F0gELAdk90EzWRmZoYsFoSKwYMH45JLLlE+nq87at9JSUm4/vrrg8pW4s2vRQWnTp2SKmyAM18Yr3zqcnJyQiZ1mzZtcn0uP/jypCJUUmc1kFDDHzZsmON75eTkFMrqUPaunCp1/DGqpO6ff/4BICZ13bp1Q5cuXaTnEqmzc/J1ChGBtzJd0nG1atXCoUOHAADfffcdatasia5du+K2224DEDqpY9uT6mSr2gatSL7M/BoIBFCnTh1bp3UyE4vqkPZrpXqzwoQJE7BgwQLjs4jUkZrFb4E3e/Zso669ML+GSuoee+wxTJo0Cbt27ZIewy9IneKrr74CkLfjANWVCrEBioYvtagMbL2zfy9btsy0AHzggQeCzu3Tpw8eeOAB+Hw+TJo0CXFxcRgzZozpGJ7gr1q1Cpdddpm0zrKyshAVFWWYtp2C7xOF5VNHdfn1118be5az9V/iza8FhVdffdVysEtMTERiYiKAvMlBNvCoDEBekjq3fkLlypUTdkYn4FdYvEoZKqmzMhPyQRReKHXJyclo3bq1q0ACFagodSrPwZedJlCrienrr782lLrzzjvP9h48KOVMYZM6Qnp6uol0iH4nOCV133//PcLCwhwHXKlGOlqNFbLFIREomgREmD17Nn777TcAwUQLMEfWvvbaa46UId6nLjs7G+PHjwcAk5/lH3/8gf79+xt+tl4ESuTm5mLhwoWutnUCzimtVs/rltSJlCOqo9zc3KAxPS0tLWgcYNXqrKwsS/KZXxCNhzKlrnPnzpYLQCDPKrBmzRoAefn1zpw5g8mTJ5uO4dvCU089hW3btkktWfQ97dDjFHy9F6VACdkiUpM6l0hOTsZDDz2EunXrKh3fokULw9+HUFikzq1Sd/r0abzxxhuuziXwjdMr8wFN6r169cLnn38uvK8bUpecnIzdu3dLSd3KlSuxZs0afPDBB8LzV69ejdq1a7tW+bwyv8pInUyp27NnD7p27YrRo0cDcBcwEKr5VQZRX7Ayx7Bt7O+//5YeR3WdkpJiEAwRqVu3bl0Qefvuu+8AnIv6DKVdyyZ8QDy4y5Q62qC+Zs2a0nv179/f+FtE6ljTNSl6duMVEWJqM1Q+Mr0C5xRAILiN8+ZXNmedDKIF86233orOnTtbllUGeu9WpF7Wd/j3kJ2dbQQN5ObmIioqClOnTjUdR3WQnZ0d5FNXpkyZoH1pWSJPWR5k/rf5hejo6KBnlSl1KkhLS7PtNyzZB84Ra5mLAb0/UVozHosWLUKDBg1M34VC6kKBCqmTmV+dQJM6BjRgqQYMHD58OEhFopdi1fizs7NNK3UeTkmdFz51hJycHGzcuNGROsE3Tqf5pWTPyz4Tr0ykp6cjIiIiyAdGpYN27NgRderUQU5OjpA4kNIoK9ekSZPw77//uiY2XplfeVJmR+rovtS+3fqWAd4rdSKfOv7dsL+xf1spGjQp1q5d26gv0XO3bNkyaJKl+qJJJhTzK/8sdqROptQRqatatapSWVj17LbbbsMvv/xiInUs0bICkXne/Epo166diUDydcUnH2ZNsTLIrCBu+x21e6vxXabU8WUZO3YsGjVqhNTUVOO3Rx99FCNGjDCOYQmwqD+TYk6gdpCTk2O858JIy8W/k9zcXON9OiV1Z8+etR1n+DZI45csRRa9P5VdpUaOHGnsHEHwaq4kqM7XTkmd21yLmtQxcKpcZGdnB52jsgKNiIjAuHHjioRSxyMlJQVXXnmlrQPo77//jq5duyInJydoUuUHBTuiJRvY2VUqP0nwHdUJNm7cCEBsfn388ccNJ3u796O6kpo1a5YpqsmNUhcIBIJSRshInWxi4svrRgWge+aX+VWWDuS7777DH3/8YRzDtgerSZ6ekVWRZJMMrwzQ/WmSCUWp44mEW6WOnkNVJWaJ1ieffIIBAwYYz7F161Zs3boVgP3YR7nqZKSuWrVqpmvwdeVGqeN/ozp0u2MKvXc3pI4vC5kUMzMzhePE2bNnTQt8lehX1spDLj7k/1qQEJE6guhZX3rpJSGJB/LqwS6oh28LdkodvT8VpY76j0hB5Y9RgehY1m/0ySefFKb2SU5Oxg8//GD6TrSQtVLqbr75ZqUyalLHwGkkpBWps1vRzJs3T0renMrBXpI6miBTUlKwefNm6XGjRo3C119/jc2bN+OOO+4w/cbW46lTp6QpHUTHs2CfiW/gslBypz51/GD9/PPP48MPPwRgT+pUyfc999xj2qtRRqasSN2LL76IcuXKmQgJ3/boujKljh943Sh19E68NL8GAgHDP4atA7ZdXHfdddIoUiuI6pp97nXr1kmJBdUvrZhDIXV8XdOzRUdHO/Kpo7LSxBYIBNC6dWv8/PPP2L9/f9B1+HyCrMsC615gN/aRL6Vs/+LY2FjTd1akjlWunJhfabJktxJ0AnoHViRD1nf456VrZGZmCp/h7rvvNv4my4wdWKWuXLlyAICdO3fanuc1rBK5i9rqtGnTTJ8rVKiA8PBwvPXWW0hLS7MldXxb8FKpo/Ky46nX5ld6vt9//x0TJ07Ek08+GXRMjx49sGXLFtN3TpW6JUuWKJVHkzoGTkkdG6BAEU4q5lc6XnbME088gR49ergqB4vx48fj2WefVboGDcKsAkMO0CJQQxYFMbBOw7fddpulzxMdL4KVUkeqDQ+n0a9W71xG2lQmJCvICDjt6QgA/fr1w1tvvWV8Xr58OYBzdbJ06VKT+gTYm1/551FR6o4dO4ZBgwYZ7yg/lLqvvvoKX3zxRVAZZe9m4sSJytsmieqa6uno0aNo2bIlnnnmGctznSZGFrVBmVIXExPjSKmj8+gZsrOzsWbNGrRt2xY1atQIUgl4nzqRHypgr9TZmV9jY2NN1xC5ZLAmVzc+ddTmnCp1rGoEWCt1MqLAl5MIh0yp489VWQCKlDrW7cTuPvfdd59BBkOBU1LHjjeXXXaZMS599913OHv2rK07k1NSR3OPilInci9wK4CcOnVKWCdUHkoJJvJjpfGbhTa/FgBCMb/edtttCAsLsx2s6KVZkbrjx49j0aJFyuWQ+dQ9/fTTeOKJJ4yyZGdn46effhJeg1a/7KRgNXFTR5U9Jz0bOZtbwY1SJ9uNw8s8dXakzm2KBdmgQmZhIG/f0qFDhxqf2bD/rKwsdOnSBVdccYXpfDvzK/+uVEjds88+i3fffddIeUNlZ1ffO3bskO4mAgBTpkxBs2bNpG2FJf1snXqx76boGb/88kv4fD4jyl1myqdnpfftpVJHdSEjdTKljtor9T8+6TjfLyj9El2HjTxlEar5tUyZMpYmZZn59fPPP5f2Wa9IHZ/024pkyMYD/nlYpc4pqZM9L6vUxcXFATDv4GAnOkyfPt2TvY7ZqF0C+f/ZkToqN5Dnx5qVlWVrEeD9K71U6qiurUidypyRkZGBxMRErFy5Mug3Pum+6juwI3Wud+hwdVYJBfviVfPM0SDH+2HJOjq7Ag817xJwrhFYrT5I1ZoyZQratWuH3bt3Bx1DpI5N50ImNhG5oY4lG2hUJuRAIIAJEyZIU4ewz8SvWrwwXdspdXbmV7fvLz093ZgkVUEDhszcA5wjDitWrBD+zp+nYn6lFBq05R69V3bAbdCgAdq0aSO9xqOPPooNGzZI2yhrIlRR6pxAROrmzp0L4JzKbGd+zU+lTmZ+tVPq0tLSsHXrVmPXBwKvEvA7rbCJf0XXlUHF/Mr2ef533vxKz7xu3TqEhYXhs88+C7qnzPy6du1a/Pzzz5blZUHl4gOFRJC1Uf55QlHq7HaUycnJMd4Xq9R5scjhsW3btiCfaLqPXc46AkuuWFKnaiaXRb/akTonSh3bx/l3vH79elsXIav5ld3dBlC3YtiROrd5CzWpY2DnvMyCSBn/su0mfPYeXpA6uoao0dHgTSrKf//9B0DcWagjsQSLSJ2onNSQZY3dagCiAevkyZMYN24cvv32W+FxVuZXWd2F6lPH/y6Ck7Q1Ihw6dMgwr6iC3Q7NjtTJBhW+vCpKHZE6mmjonJSUFLzzzjvGZ8qLZgVZW2HNyDKfOreweka798iTOlVYOVMTVM2vfBtkSZ3oPfN1zKuNMlLn1PzKl5n3qRM524vMrwSyTLC+mvw92DbWtm1by/KyoDKrKHWyerAyv9rNFTypk5EVtj3S8eyx+bH7zWWXXYaLLrrI9J1IqePLyNYT+y5ZUqdKbmTuLE6UOrt91LOystCtWzdcdNFFQX1k4cKFtv66VvMKzYVUD16ROq3UuURERISxvYnVoMRD1LgBe38r9nsvSZ1oMKJGr7JPJ5Wb3S2DTRzKgzqWrOOpKGBUFzLTnZX51Uqp++STT4yUAFawM7+qKnXr16/Htm3bcNtttym901WrViEtLQ2BQAA33XST5bEUnUjPb6UMiNoAq9Dwg5kTUvfCCy8gLCwMixcvBgD88ssvuPfee00+m3bEQHY/mVLnhTKRkZFha+6S9VU7nzrZda2UbYIdqbMLlDh79qySGZVX6mTPamelIGV5zZo12LBhQ1C/iYmJsRw/ZUodISIiAp9++ikSEhKMNB5Ox0fZOMKnsrEaD1VJHTvu2pWTj36VjZlsexRds6C2NMzKysLp06eFgTcff/wx2rdvb2rPbN2Qog+4J3VW2+ddccUVQbup/PPPPyhXrpwwlylL6r744gvs2rVL2adu9+7dRpuxesd2Sp3sXFEQh1bqQkQgEEB2djaee+45ABCaDzIzM/H666/j2LFjpnNlZKoomV95UmdlPqKysKRORamTDVBWEzLfiWXXsCJ1Vh3ltttuw5VXXim9P3tsqKQuNzcXzZs3x2WXXYZPPvlEaesl4JyZzM4ZtkmTJgDUlDrRs9AzxMTEoGXLlqbfVCYkInVz5swxfU+DHRudZ7V9HqBG6vJDqZNN4qy5i7Bo0aKgPiUjdSoLN0JaWhqWLVtm7HsbaqCETG2yU+qSk5OFYwXbX0WTCZnSnn32WTRr1iwo0jU6OtoU5anqU0eIiIgw/JVYdVgVy5cvNxEK/tlYVxlZ3W3ZskUagGPlZqJifmXfI413vHlSpNQBQPny5aVlmDVrltICVgTZM2VlZaF+/fqoV69e0G/Lly/Hjz/+aKpD9jpulDrep47+F/VbdjtLep+UCYH1PySomF9lqFOnDvr27Wsqowi8Urdv3z6DU7Dl5CFaVGpSFyLo5RL5YRvn7bffjkAggGeeeQYPPvigsacfgV5EKKTOi73+rMyvMqXOKqqJnZjT09OFA/D8+fONCdoNqVNV6liTlar5la6pqk6GGv0a6m4aqrnuWJ86u2dnYdceZQMcvVdRJBcLlsTK2gJ/L76O2AkiP3zq7BQctm569OiB2bNnC3/ny62Sb5Bw9uxZdO7cGS1btsS6devw66+/AnCe0oQldaK2ZkfqAKB3795B51lFrlI5ReUA8ggZ+TfJfO5kyYfZa/B5AZ2Quu+//176W2ZmpmlBwZO6I0eO4Pjx43j33Xel17Dyu3RqfqXn5P1q2UUGe/wFF1wAILhOMzMzcc899wjfpwrIp5T3TcvKyjKItQwypc4L8yvVD5+OhwfVB5leRZG/oUa/UiCS1Tum8YV9Z6NHj8ahQ4dw6tQp16ROm19dgLfNsy/+66+/RkZGhjFp8R1KRup401xWVpbJTy1U82tOTo4wWtCqodJzOlXqgLwGy5eTdVKWTZgiMkjgO7HsGuzAoWp+lXWg4cOHB+X5IaVWBtavha1f9h3zz+iUqKuSOhWlzorUySBTz2hwtiNWe/fuNf62ioAF5Nv7sOTda6UuIyPDdtHA1xsp1KzD+IQJE/Dpp58GXVsE9hkoGz+7yGjZsqUR2RyKUueW1IlA1929e7dwL11eVeJJHRGx3bt3Iy0tzdL8KlPqqKw7d+7EL7/8Ytt2WXMbn96HRWZmpqmN8aSuSpUqqFSpkuXOJKEqdVbqJ0Gm1BGp48c2Uqic+ucSqO9Wr17d9L2K2wPbp9jnZ8vC+7nVqFFDeC161szMTIwaNcqItJ07d66SqZzuk5CQgH///Rf/+9//gkybTpU6Ol/F/EpzJv+Oq1WrZtrJhgdvbuavoZU6F6AGQyYwvvIzMzONChftZUjHsAMvScD0woYPH27qNKGaX5999lnUq1cvyDTKlo+P9uI7hqix8KseQnJyclA5jx8/buynx6szlKuHVD4ReLldhdTt2LHDlLdNVneypLivvPKKMCO3lS8R3SMuLg5XX3218b0VqXNKRFRIXSAQUPKpsyIHMoSyVRhgXo3bKXWsc/l5550nLMPixYsNk65XKU1k5aL78u+MFnms+fX5558POl9WPvb9+Hw+1KlTRxpI4jalSXZ2tnByckvq6Fk6dOiAO++8M+j3yMhIU1tl68zv9xtE/eKLL8aNN97o2Pzq9/uNsvbs2RNXXXWVsF5YNaZ79+4A8rZOmzlzpvTZ+K2qRAuZQCBgmU9TthjPyMiQ9sfLL7/cOFdU/3w+SVapY68pU+ooOTy/t6kqiIwkJCSYvlcZw1gVja1bVtHllTo2sIHa9bfffmvsH3zw4EFMnToVq1evRoUKFXDq1CnLnYOonPQcfr8fa9euxYcffogPPvgAn3zyiVCpk80RbO5TaiMqOUnJmiFqB6dPn3ak1LH9XZM6FyCyI1LqgLyBjidIBHoRGRkZwkmDXjBtDcKbHPft2xeUYVoFFOLOrw5YyToqKgqzZ8+WkjqRCiBKVQGISd2hQ4dw4YUXCo8nAnvs2DHpikjVp47Fq6++iqFDhxpllg2kdn5dPOycuwlr164N+r6gSF1qaqpJqZM9u5VPnQyhqmHsImD//v2IiYmRtmt2MGUHfF456dSpkydlA/IGZ5kaSZMR304zMzPx33//mcyvogFW1fzasWNHYfJRQGx+PXz4sDD69ZtvvjFNEKKIv1CVOpkyEh4ebiIhMqUOyEupI7Ns0N9W5leCqO3SopHFq6++KiwzIS0tzWgDUVFR0gmasgOIIFN6rBZZZF6VkTqZj6aq+ZUCSmR5KVn89ddfuOqqq0x9gf4WmV/tcPjwYeNvti+z4xlP6tj70L2vv/56w1+XHQfIn4+1BPDghYjs7GzjugMGDMBtt91m1GOjRo2M82Sq7qWXXmqczxLVM2fOWI6jROpkfcwu96FswaDNry6gQuqoA8tIXWZmpjDPGr0cauSiZL3sfqCqoEGNiBCv1JFfCEVNAnJSxzZCNqqORXJyctAgeOjQIdSqVctUDkLNmjUB5K267JQMO/OrCHYdyGnyTStSx5f/iSeeMN1bROqcqksqWcOPHTtmUupkofBOzK804bDPLFKjnGD16tVIT0838sAB5oFJtkLm2xwNul6QutzcXFuljid9I0aMQK1atYxy5OTkCAdYVVLXuHFjqQpEAQaEGTNmoGrVqkbkId133bp1uOGGG7BkyRLDbCfyd+SfxalSx/pEsQgPDzdNyuyzsz51BP7d8f3MSqkjiNpu2bJlTZ99Pp/QQZ5Famqq8a7Lli0rJXVWdcSWn63jvXv34rLLLhOeQ2O1rP0AeWlaMjMzcejQIVvzK1+ntIBVMSdOnToVv/zyi2FipsAuwB2pY3cSomcbOnSolNSFhYWZfhNZCNjxoVq1aoiOjsaePXukZeDNryyp48vGgg96ZEE5XNnrXH/99UrmV9kxVkrdkSNHTOlU6L2npKQI26MK0XNM6k6fPo1PPvkEL730EqZNm4aPP/7YsTpSVMCTOpH5VabUseZXUbJXGjisSJ0b0KDGpxuhSV0UfckrIbIcWKJjRT51hw4dMsgbP2EmJCSgXLlyOHjwYJCvBsHKp85OuaLJzk6pU/VV4xUEFnx7oPQdLKnjz+EHREpKLYNKOY8ePWqr1InKAuQRLVG0H79DAAA8/vjjpmOckioiQawJhh28VUkdffYq2arMYZvqRZZhnl04iQZTFfMrIA+GAILNr2+//TaAcxMVXxbgnN+SiNSFan6VRZD6/X6TGsfem/8NCO5LfA5QvjxsdCr7HQ+RUmeXiJgldfHx8a4WC+zzsBM+72fJwk6pO3bsGBo3boxy5cqhWrVqpvaWm5uLypUr49133zVI45YtW0xtmcY6kV/3M888g1mzZhnfscJFIBDAtm3bpFstqtQPn+7krrvuwhtvvGFapLLvLzw83KRMnj17Nuj9ss8WFRWFCy64QEmpYxeBPKkTtSEr/0tafLFj0s8//6xkfnXq552dnY2JEyeavsvNzcW6desQHx8v5FUqaagckbqZM2eiefPmWLt2rRHNtHbtWrRs2dLSp6Eo4pNPPsGUKVMA5L0UkXNvRkaGlNRRvq7MzEzDxMqCV+qIQIZK6mhQe+655/DKK68E+XfQ9dmGy6c0yc7OxuTJk4WOqzwB4M2vJ0+eRHJyMmrWrAm/3x+kskVGRqJatWrYs2ePpU/d/fffbyhf7ITF+3fwoBWirAMRcWAHEKs6t1LqRCvg3NxcU9oGO/Nrnz59pPcG1EjdyZMnbZW67OxsYX13794dLVq0CPpepNTxcOpvRytgltSx71Zk2gsEAtIUE17l5WJVaxYy8yuP5cuXCyeHo0ePYsOGDTh06BCaNWsmXbFbbWsVExOD9PR0XHvttdi+fbuxVRyVjdo5204oxYUoOjBU86uVUscSN3Z8cavUde3a1fSdilKnuksBi7S0NKM+4+LiXI3BbPnZfsG6ZfCgsVpG6misovZP7ZTGlZiYGAwYMMCo9yFDhuD66683zpeRupycHHz88cfGvMRmWkhKSjKIAZEovp5ffvll6TMRFixYYIrejYqKgs/nk45n/KLowIEDQe+bJTGRkZGoWbOmJamj5yazuUipk5FpGUjJZN9x7dq1802p4+srOzvbMvLYbh9dwCGpe+GFF7Bp0yZMmzYNI0eOxMiRI/Hyyy9jw4YNIZtuChpLly7Fl19+CSDPCb9Dhw6OfOqGDRtmHLN161bUr1/f9PvEiRPxwAMPGOTCa6Xu888/x6JFi0wN6aOPPsL7778PwJ7UjRkzxnBQZcFP5KdPnzZNxhs2bACQ5wQcFRUVpNQRqbNyOs7NzcWbb76JpUuXAjArdawDvQh2Sh3BTuonsO+cXxllZGQEDQr79u2z9KlTVZcoRY4qqaN3J1PqsrKypG2L3WqIIFLqeIgGkOjoaBw8eBBDhw4NSpxMfmMsiWEHWRGpsyJUXpE6NkEyC2p/du/s1VdfFdZtly5d0KxZM3z88cfYsGGDkVbDKalLSUnB999/jwkTJhjf85G5rJN5fpA6p+ZX9t5OSd1rr72G3bt3m56JdXUhiOpcZWsoHqxSFxcXJ21XZH0QQabUWZnD7JQ6HhRMQ0odjQ3sM69Zs8b4m9Rcvv2SxYa+f/rpp40E+6dPnzb6tSwPqWybQRb83EF+faqb0Ldo0SLoPnybqlKlisl3jwepjmQyFSl1ovdjlSpl9erV8Pl8RsohIC9qNz+UupycnKDx3yprBKC20HZE6nw+n9A/5cyZMyFtdl0Y4J1Lf/31V0vz6y+//CLcmub06dPYtWtX0Obq27ZtwxtvvGHUC5GsUEkdu1I9deqUqSENHjzYmDjpfmXLlpX61InAN5ohQ4aYfEZ+/fVXnHfeeahbty6ioqKkSp1VegC+0bKDuZ1Sd/z4caxevVq4sTILtj1aSdY0wAcCgaDtcjIyMoLq4++//1aOfrUa8GnPThVSd+rUKZO5X6bUOWlbKkqdaD/Odu3aoWrVqnjzzTfx5JNPCs9jSQz7bkWkzs2WTV6Bomyd5K1iQfVNky69A34isnJkZ+uKnRD5yFz2NyJ1IvMrb1KzI3Vz585FWFiYcR8r8ytLLr744gvTb3bmV/Zzamoqtm3bZnomdqwliCYw/j5239P9aAywUupUSN3ff/9tmROPhZ1SJ4MVqWMhU+pSUlJw+vRp492/8847xm8sqZNFf6uC7btU/6pKHZC3E4/smMjISFSuXNnoS/v27cNTTz1lOj4zMxPHjh0zWcFUlDqZGwhwLrCR0uW0b98e6enpISl1VoESfH2J+oFq2QmOSN3UqVPRvn173HrrrRg2bBiGDRuGHj164Oqrr8aLL77o5FKuMHLkSLRt2xZ33HFHyAO+aBDgK58Nhf/111+Fvhtnz55FIBAwwtd50Evr0KEDVq9e7WjitXOUPH36tLQh0WokMTExiIjzm4A7wY4dO3DxxRcjLCzMUqmzks2tBjjeEZpHdnY22rRpY7rvrbfeankOOzn8+OOPpt/INCHyX8jIyAgiHcePHzeROpEicezYMfzxxx9KHVBlZXvq1ClTah3RO3dL6qz60QMPPGB5DRkBiIiIwJNPPomlS5eaCJNoQUjvhtJTsPB6W6QjR46gdevWQd+7JXUE1l9pwoQJQW3JTqnjrwOYlTpSAgmU1kNE6vgcc1abs9P9IyMjjeNkZeV9olg4VerYa7K/8+/hkUceCTpHRt6sxg0r8ytbLj4ZMAs67qKLLsKIESOkx7FglTonkYxE6qh+nJI6iooVZTQ4depU0Jim4qdlB1q4OCF1VveNiIhA5cqVDUVw0KBBeOaZZ0zHZGVl4Z9//jF9ViF1VkmRqa7oOpUrVxZmgCCQsJGbmyvtYzK/6uzs7KDxn02jxmL8+PEAzr1bKzgidTfeeCO2b9+OkSNHon379mjXrh1GjRqF7du348Ybb3RyKcfYvHkzDh8+jJ9++gkNGza0dFBVgWj1zFdm27Ztpf44PCpXrgwAOP/8803fswPh9OnTHU28W7duxZkzZ3DJJZcYahnbuFgFhwcpdRdccIHQzOoW+/btQ7Vq1QBASOqysrJQrVo1S+Jm9ZvIEZqF6HnZyZDAphpgO3r79u2Djs3KyhJGWR04cCAosnn48OGGaXnPnj1BW+lkZmaicePGuPTSS22zsgPqSh0bbe3Ep04GFaXODiypu/fee01lmThxIrp06YIBAwYY31uRuuHDh6NVq1bG96tWrfKc1FWqVEk4QYY6qbGkThQJb0Xq2PKwAzxNvFlZWejbty8eeugh47fIyEjExcXZZtwH7JW6+Ph4UzoRWZ2Hh4dLryFS6uwiyemalL5GpFCI6tINqbMyv/I51gKBgHDRK0sGbQXqY7KUODKoKnX0/mWkjt492+/eeecdw5ec4Lb99+vXz/ib3otskeokepyuU7lyZaSkpAiDKs477zxkZmbizz//hM/nQ9myZZW3abRKocXPGZUqVUJKSop03k5MTEQgELBMe/L6668Lv8/Ozg5aKMlI3YgRIxAWFmYZDUxwHP0aHh6OVq1a4dZbb0XPnj3RqlUrZTt6KFizZg2uvfZaAHm+LL/88kvQMRkZGUhOTjb9k0E0OISi/hEZ8fv9pomaTbGhsqUMi8svvxzdunXD9u3bDb8o9vyUlBRpmem+tWrVwsGDB3H8+HHhhsdOsW/fPlStWhVAXh3ynXXDhg0G6ZPBSsp2Q+pk7Y9keTs/BBmpO3HiRBAJZE1rIhNzVlaWMbB4RepOnjxZKEqdHVhSx/qtsYMS65tiZX7lo0Bff/11ZGZmKkcxq0K0AHCTBFyErKws1K1bN+h7mfn11KlTprYrUupEE19ERATi4uJst3Bjz5eRiri4OERGRpqSGovg9/ulxMlOqStXrpzwuuHh4fj+++/RsWNHpZ0ZAHl/sSN169evR3x8vJGn7ttvv0V6erppbCDVVDSeiCZaK5Mv4NynjkBuHXakThYlzit17Bi9Y8eOoKBGuo7V4kOE2bNnG2W0M7+KYOV6cfLkSVSpUgVAXoJiIj/x8fFo2bIlBg8ejKysLPz555+48MILUaZMGWzevFm4G4ob8KRO1jZJNU9OTnacLFhmfhW1taioKFsLGMH1iCnyt8lPnD592phEEhIShAPa5MmTkZCQYPyTbUsCyJU6pw2bQINKWFiYaVBgJ3anpA4IdlrlG5fdwH7hhRciKSkJN910kym3kAj8djOiCZAldWwdklLbtGnTAid1onIC5yY0t6Ru3LhxllK96F2yHdLKyZegqtTRdQvCp45VhVSuAZjbgkztsVLqYmJijPM6deqEDRs2ICsry2SedBP5yMONo70d+GjoChUqmH6XjSkxMTGmtitS6kQqe0REBOLj4wtcqeODmMaNG2eUx8qnjn23/DUBGKRSRQWTHcOSuvj4eNSuXdv4nJqaipkzZ2LEiBHw+/3YvHkzrr/+esycOdNELOg9ifokPQ+rrNi1pfz2qQOAihUrWpI6lTZCBGbevHlSlwoZqP3KzK/0jp3mizt8+LAxj/To0cNoP2fOnEHz5s0RFRWFzMxMbNu2DRdffLFnCzMC1UnFihWRkpIi7RfUJ9yQOpnLj0zVvuCCC/JHqSPcfvvtmDZtmuUxbjMii1CuXDlDeTt9+rRwv7vRo0cjKSnJ+Gdlf5aROiu/CitQBw4PDzcNzmwggRtSx4M/367T0s4PvFMqjwYNGhgO2ARRJFxWVpZB6mgQrFevHr744gucPHkSb775pi2psyJZ7OAsGsxkK34RqCPamReysrKE9WgXiSsaSNgOaRUBTOAHwYcffjjoGNb8WhBKnZ1fI4Gd4FRIHUWbEr7++mu0bNnSKA+Vv3Xr1vjvv/9w5MiRoMCgUaNGKZVNBtkCIBSweRdzcnKC2qOM1EVGRprKI1LqRIiIiEDZsmWVfAGJUFgpdceOHcNTTz1lacLnt3br2bOnMQ7wQRSAuQ2Eh4cL2xmr8ojGxvLly2P69OmmhbFsTmEXg/PmzTP5OB89ehTZ2dmoX7++qZx+v1+o1IlI3dq1a+Hz+Uz3t+tvoSh1rE+dlSWsUqVKluZX3odYBHZnCaeLHmqzMvNrpUqVpOeKSB2NPYcPH0bDhg0NPzoKjKN6iYiIwL59+/Ddd9+hc+fOrrfUkuHs2bOIiopCQkKCZQJz4iFJSUm2xHLdunWmDQe2bNmC5557znSMTKkLDw9Hhw4djPncCq5J3ZIlSzB+/HgMGzZMuGfm+++/j4svvtjt5YPQsmVLfPfddwDyJoerrroq6JioqCjEx8eb/snAryx9Ph8yMzNDJnW8UsciP0idVSJFAMbOD3YNrkmTJkHlltUfyeL0zDQQlCtXzghFZ3Hs2DF06dLF+GxlFmcJBbvPI8GJUkeDnZ1SJxt07VatdkodRVdaga9z1geNkJycbKvUnTlzxjOfOp7UqZA89h2olmPkyJHG39HR0cZ5FIW4c+dO06QQFRUlTbmhivwgdazKJSJ1MvOrz+ezVepE8Pv9SltDEUSpeQjx8fFGn9q3b590fEpLSzP1x6ioKJMjv4zUXXDBBfD7/VL1gc4X+dTVqlULQ4YMQcWKFY3vZM9htRgk37yEhARTfbO+doC1+XXRokVB39kRCbeBEjQeEbm0UvMrV64cRO7JRJeZmYk1a9YYO1LIQO+KfY925xB4UseX9dJLLzX+5v3HRKSOFgpXXXUVfD5fUEJ0ugctlKKionD//fc7VursLHLp6emIiYkxxhvZBgtOzK+XX355kHDCIxAICOcrn8+HiRMnmtIeyeCa1F133XX48ccfsWjRIvTo0QNnz55FZmYmpk+fjrp16+Lhhx+2TbzqBE2bNkWVKlXQtm1bbN++3Tbi0Q78oOj3+z1T6qxIRqikjm+8dkqdbFcHHpUqVQoqN9shWdCzUoPnB9HIyEjTQEwRdgTRtmDlypXDuHHjTGljRGlLnPjUOVHqRNe1S68iepfsAGuV1oXAD4Ii80VWVpbpWURl3bJliylC0g40qNkpdY8//rgSqfP5fDh8+LDRj1TAtgOW1NH9/vjjDzRs2NB0jhMyI0J+kDo2NYQTpQ4wt10+4z4Lttwic6cVrEhdmTJlcMkllwCQqwRAHgFilbrIyEiTksSXJyMjA02aNMGePXtQoUIFoSsCb37l26LIFcMpqStbtqzh48qTuuTkZFM9W+VaE/W5d955R+hDSYiOjobP5wvZp84KlStXtjS/7tmzB3Xr1sWVV15pey2W1NnNg0Rm6HhZ3bEWNZ7YikhdVFQUjh8/jpdeegmAuL+Gh4cbglFsbCwiIiIckzo7N5+MjAzExMQYx8ksBFQPu3fvNvnPN2/e3HBPYMutApW90K0Qkhdy48aNsXbtWuzevRstW7ZErVq18NRTT2HQoEHYs2cPnn766ZAKx2Pq1Kn46aefMHfuXEeDmgiiaK3k5GTbly2DzPzKoqDMryyhoqhcOyQmJgaVu3///sJjqe5oIBW9C9YEy5uZaANnFuXLl8fTTz9tXKtfv35CpVdFqaPJUVWpmzZtWpAMDgSTOrtcXIA5OSglxbSCHamLjY017RRy9uxZ4X0HDBhgUm1vu+02y/umpqaazGKsjyw7QXbt2tWSCA0ePNj4u3LlyqhZs6ZBbO184NhBkN0Dle6fmZkZ1AZEBEllwiLkJ6mTmV+tiChbHrZty7b2A5yTuhMnThikgichYWFhxnZS6enplkpdhw4djM9RUVFG2X0+X9DCLjk52fiuWrVqQdtKAWZSJyKUIhcbFZ+6iIgIYwyIi4sz/BITEhJM5UxKSjKNDVQeEZkSkbK77rrLcuFGdRRq9KsVrEhdRkYG9u3bhxo1amD9+vWmaFURWDO6Vd8dNGgQVq9ebZwDyJU6q+uI/MH9fj/Kly9vlMPn8wW19bCwMCMvLPUTp6TOjrSmpaUhKirKGG9kFjFa6Nx333245557TGXk1U7VXL6igDInCInUJSUl4b333sOBAwewa9cunD59GsuXL8eYMWNCNpPkN0QD7ezZs4WThkrwhBWpe+655zB06FCsW7dOSpRU8Ndffykpdaw6p6pssKSuR48eeOedd9CrV68gPyjg3OpMptQBZlLn9/tNk9fHH38cdDzvkyEbvFWUOrqWqlL3yiuvCL/nza8NGjQwfRZNgLNnzzb+PnjwoNCEbAURqWMnvLS0tKA64B3zAUjzJhJOnz6NiIgI47o9e/Y0fmMnyOjoaEs/mzfffNMUTBIREWEMtHZjALsijYqKClLqAASROlF7Xr9+Pa6++mrLexFUSZ2TZOps+hEVpW7kyJHGNoNseVgiZ2V+VSV1VI/79+83SIXI/EPlS09Plyp1aWlpuPnmm42dUFjza1hYWFAbmTdvnkE2qlevLvRvtjK/Tp48GdOnTw86RzYuxMbGGu+MLQvbBkVKHesKYmXu5OtFRbCgxWyogRJWSExMFKrt0dHRyMzMxN69e42AQbt5jG1XVqTnvvvuM/qlXaBETEwMvvrqK+GYL3seUblYhIWFGWZayqXpVCxRJXV28ydFovMIBAJo3ry5ozIRCo3UjR49GjVr1sT777+PSZMm4dixY+jVqxc6depk65RfFCB7WaIJTCXqjpWf+YmDNT+GkkLi4osvNva5I4hWECyhUnV8ZUldfHw8Bg0aBJ/PF2T+As6RJlVSx/sOiUDXsDtORamja6kqdTLwSh2ROvJTtHuXgUDA1vzND0b8oFimTBkTqRMpdbwPI2BP5k+dOmVKOsuCJ3Vs/fJBMOHh4SbyKyJ1Km2QJj/+/qzqTOURQXWlrkrqnCh6bPoREaljn//aa6/F6NGjcfPNNwfdhx3M09PTpaRYlBdOBGp7Bw4cMEiFqF1Qnf76668mpZkFlY1V50mlYPcmZkE5PtnN6lnwSh3brh9//HGTlWHIkCGoU6eOlNRFR0cLk/XypI5X6lgTIB+YwI5zbPnvvPPOINOaCKTUhRooYXcPUR+uXLkyUlNTcejQIUekjo6xmvPYticLlKAx4Y8//sANN9xgcsWaMWNG0LaadF/RooNv63SPrKwsY3tSp4ESKm5WkZGRtuNoWFiYtJ9edtllroJFC43ULV68GK+++ip27tyJwYMHo0yZMnj//fcxePBgdOjQwZOcaPkJaigtWrQw5SITDZYqnYsGizp16ghX6qGaiwm8ciZS6tjB0MoczCIxMTGogwLizk3PSgO8aNK++eabMWjQIHz99dcA7IMOeFIn6gyUY4qHbBKlwc4tkebLTAMR+RqqJO10mt6FV4h48+tXX30VlHhb9H5lgxENQKxSxw+IrAsCq9R16tQJr732muXzREREGOY8qr+oqCgsW7bM8jzgnBrCtjl+8OWf69tvvwXgPalzknuTSCz5O/Lnsu906dKlJjXCKjhCpFoAzpS6uLg49O/f39gFQXQe1amVEzYpoUQQ2rVrh6ZNmwI4t2cpvwii8UHWB1jyZDeRTZ8+HX///bd0kkxISLAkdZSgllfq2EUxr9TRdfgE46oLZVapk5VbNL7ygRIylClTxiDE/MK1UqVKSE1NRW5urpHeRUTq2PYXERFhlIftd/z2mGwflJlfKTCOotuBc/2gX79+Qe4hNOaokDq6h9/vN67J9n8VlV3FzYo1v8oQHh4uPCaUzB+F5lO3fft29O/fP2gAmzBhAl5++WX06dNHmkm5KIAaZvny5dG7d2/je1GHVZHBw8LCsHLlSsycOTOoTvhAgVDAEonw8HCsW7cu6Bh6NiJ3KibYiy++OGiFCohXNLxSJ5p8brrpJrzzzju4/vrrAQRHsj744IPCa1qRuujoaEdKHRsxyn6vCv7ZL7zwQgwcOBBTpkxBeHi46V3IzKx2pI7KOGXKFOzevdtWqQOCt4IaNWpU0MAiG4xI+WInBN60xE40rFLXvXt326AJ9r1R+1AxYwDmKDwC/w7Y54qPj8d1110HwHtS5ySJKk2olPneCSFky8P7GMkCdZz41FWvXh05OTlYvnw5APNY8MknnwA4V6e84/pvv/2Go0eP4syZM7j//vsB5KnUKSkpuPHGG422TQEvGzZsMKXkoZxabdq0EZaNnj0iIkJZneDHhebNm+OLL77A/fffb7oegdprfHw8wsLCTPX9119/GUoPEEzqqI75PaFVxxEVpY5XeXw+n2HGt2uDsbGxiIyMxJkzZ4L6CY394eHhaNGihVEeHmzwi8z8KnNvoXPYa7OEKz09Xeir7Pf7g9o2jaVOlDoZ+F2dROeqKHUq41Z4eLij4C0inI8++mjQbzTuFppSZ8WGBw4ciEWLFmHMmDFuL5/vYFcWbIeLiIgI2q1CdZBu37590GoQ8FapA851HFlj8vv9+Oabb7B27VoA9pnPf/jhB1x44YVCUiciB7xSZ9WJCHwn5ld/dE3ep47dskdG6lSUuipVqpj2CVQB38ajoqIM0wFP6mTbyYlMoyxIgbvkkktQq1YtW586Ee644w689dZbpu+scqMtXrwYc+bMMZQ6q2uzpE5lMmNVUZqwIiMjlfxSRaSOP49t8+wELyN1bCAHkL/mV1ap69Spk0GEVO/z008/mX6z2mtVdTzhVSY6r169esZiVvZuqlWrhooVK6JMmTKmsrDEfsaMGcYG93Xr1sVjjz1m/EaLvXr16gm35yMlV0Wp48tPqFq1Km688UbEx8eblDp2BwJA7Aqwb98+kyrCm1/pWLekjiKEnZC6xMREI1rZjtRZzStE6ho3bmzcQ/Se2fEpIiLCaI+qpE5mfvX5fIiKihK2Yd5lA8izcAEwdo1iIfKpswJdiwVZV2hbOhWlTsX8KlPq2MwRovQwdevWRbt27YzP559/PlatWgWgkAMlrHD99dcLU1IUFbArC7ZjR0ZGmvagpGOcID+VOiAvOeumTZukg77f70eXLl2MRIWie7OO5bQ6onKzDVl0D57UiRz1efCkju8sMqWOHLMBa1K3bNkyY6sqKl9aWhoGDhyI//77D1FRUZY7jPCgaxw/fhxPPvmk6Tu6J0vqZCTAzuxMREbmaExJee1MyPzKU+YT4/f7cfPNN6NSpUqGUsdfmzVxR0dHm1bfdmCvRc8eERGhtJqlY9h65s06TknduHHj8PnnnxtJWK2eYciQIcbfTtQ2UqRYUvf9998HWSpEkehW5XnjjTfw008/Yfjw4abvExISlMeTGTNmmD6LUk/ISJ3K+x44cKAxUQLy4BjRxEZjghNS99Zbb5lUfrZ/WZlf2W0cZaA2xkfBUt5EQn4qdeXKlcPRo0fx22+/KZE6UXoo4FzSX9ZSIHrPbJtkU5qw44cTUmcVbEL91efzBc0HDRs2RGZmponoiO4nKg8PntStWrUKmzdvRiAQQKNGjQDkr1K3efNmvPHGG8bnTZs2Gdt8UvR53bp1jee65ZZbsGvXLuMzy0fGjBljupYK8o3UAfYReIUJehE+n89UiaIO63TrFDufuri4OIMouIHf70fTpk2lA6FVJyS8/fbbQceLfOpEoN8pdYXdzgtAcB3yHUHFp05G6qKjo3HNNdcYEZx0rTVr1mDmzJl47733lCdBv9+PU6dOGb42bHg9n42eN4WLYOffQQRKlhKAJiO7jap5EmdF6giy/GAs+WXfk8pkxubpYycsFVK3Zs0avPfee0JSR/XLTkwqpC4sLAzdunUzlGGrSX3y5MnG305I3aZNmwAAy5cvx+nTp4XnrlmzBps3bw763uo+9evXR5s2bYxIP0L58uWNeqldu7bh4iBC5cqVMXr0aOOzqJ2xypZq2WSQEUSeGAHnxg0necYqV66MV1991dhTmG1jVuZXmsT5aE0WPKkD8vovTwDc+NRZ5QlkUa5cORw7dgx//PGHbf1HRUVJ3SGIrLHkyYlS55TU8YtSUXt6+umnDfcPntSJIqhF92PvIQOfticuLs4oJ6nHIlIn8t8V1dmiRYsMUURE6po0aWI6r3z58kYuyLZt22LPnj3o0KGDcV5CQoKJI/zxxx/GuY0aNcJ9991n+bw88pXUFWWwgxv5HADnOuxff/2FI0eO4M0331TeC5Ngp9RFREQYPlhOUifw15dtXsxfU0Ro2O9kUV8yUB0RIbHaCobAEz9VpY6FjNTRAET/07VYHyEVUteiRQtkZWXhvPPOM5FQKouVUicbgO0GIDtSxw40FDEpAk/iZJMr7xjNK3ULFy40RezKtrGSQWR+DQQCSubXSy65BHfffbclqWPbDTtRWpE6q88sZLs7iPDee+8Zq2/C6dOnsXLlSuG5LVu2DCJn/D150BjB9xWW1FWvXh2XXXaZZVnZtiHazsnn8wnfj5ucfrLxjHwfWbBKnVMQaWGva6XUUT+i39j+TalTOnbsCMCsNkVFRQWRDSdKnd2+tjyRYMtlN3ZERUXhnnvuwZ9//hn0m4jUid6nTKnzwvzKo1evXsaWbyJSJ0OoSh37vqgPiq7BE+zIyMigd12jRg10797d2NHKqU8dcE61pvPof1E/cGolBEoxqWNXFjfddBPuvfdeAOcqtn79+qhUqRKGDh3qOOeeVUoTIG8iF6k/Tq/P5hZjwQ8gosbCExT2f/54Pq0E/f6///0PU6dOtSQbBDvzK+9TJ4IdqaOJia71/vvvB5XZCrIORPXJls1rUmdlfgXy3gFNOiJ4pdSJts8juFXqAoGAo4HPa6VOVj4AaNasmfG3E1IXFxcnJGkypU4Gqy3zREQWMJM6Vl2RwY7Uie4hOiYUtGnTBikpKejevbvxXSik7oILLkBqaiq6detmfCfyqeNJHdUVkaeGDRtiyJAhCAQCaNKkCQCz2hQZGRlUPlWyS6QgMzNTWaljyRTbdkUkidK48OlBgHOLaHYxLboGq9SxyYetSB2/uwmgptSx4C03Vm3NqVLXqVMn3HnnncLy0vOKzNYiH2r+O9qCjX1eN8QLOPdcPLmzKpMKSi2p45URGmBEExdf2VOnTrW0yduZX7Oysoz7uBnQnK6gRY3FSqnjCQG7Rydg7siPPPKIUnn4KFC35ldRShNeqRO9Q5V6tpvE2A4mInW8OTw8PDxkpU7kZyaCqomIH5B5pc6qDkJR6uzaIAu27OyuBIC53dx+++3G36qkjn9HrMnVCakLCwsTlj8jI8MRGbriiivQpk0bSxcGvu4SExOVSB31IbY/yxYPLFm+4YYb0L9/f8933yhbtiwWLFhgBE1Yjbkq4Ns8S+ro2e2UOtE4w74/kVrjxPwqSxskewYZqSM88cQThrLIuhDxZaL0OHZbHbpR6thxUKbU2Y17TpQ6J4ESAwcORLly5fDhhx+a+giBFmK0mLr88sule3RbLUSttpRThYpS5walltRRw+TD11UIwXXXXWfZsUXmV/Z4ltS5Yfl2DcmtUseWl8WoUaNM+fDclLlWrVqmTbH50PVQzK80IdH/otUNdZwZM2ZIzel2z2VF6vx+f9AAHRkZaXtNCpSwI3V2e6ry70w2IdspdfT7Sy+9ZPjWOZl82WtRmSpXriw074mULv4+FK37ww8/ADj3jjt16oR33nnHOM4pqatVqxYeeughKZGze2/h4eFB9UH+TU4G+vj4ePz000+WLgx8/2WjX2NiYmzfNVv3snbGHlO/fn188MEHrlQCAqleojJR/RCRZZ/vpptuEqbBUIGKTx3dm11w8GBNiG5Jnd/vN4h/Zmam1PzKK3UU3MaWg8oC5NUPbYvHvjO+f9EuLyx5Yt/nZZddhtq1a5usUKo+dfxzAsHtyq7tVKlSBdOmTTMWZl6ZX9nAINGCmJQ62qIwKirKIPgipU4G1rLktp/Q9UX9gOA0qTJQikkddQJKzGhlDuVfbmpqqrJvDt2LH2jcrk5F1weASZMmmSRnFqo+daLkr0BeY1cJhrADG1XMp0GRpTRhYWd+9fv9eOKJJ4R7y9LzDhw4UJpkVTZYyAZ+O/NrZGSkcqCETEFhFxtWW+Hw70z2LHy6kNTUVNMkSueNGDHCMDVQeL7K4MUSz0svvRQ1atTAzJkzhW1QRmR4JeDmm2822gs7ULPP6JTUffnll3j55ZdN9cGTdiuIHLtpgnSzeueDVVgHeCuVk026y2LKlClGHjqRUse3aZYUhLLrDZCX4kWUP5P9HRCbX5999llTWhQnYC0O/JZh5MdEz20V/DZy5EjceeedaNWqlWtSx7ZTq/okslmvXj3s2bPHtC2eaI5hSTHbLtj35/f7DTWUkgDzuPXWW/HPP/8EKfei6FeVuY5vo3Zjhc/nw/Dhww2S5YTUqYoKvDsOcC5TQ9WqVREREYGwsDATiWe38bRSzui5QyF1dH3ynRXdz+metkApJnVVqlTBypUrjT38qEJFFUsNlraHql27Nlq3bi29Nt/AY2JiTINoYmJiSKRONIiPGDFCuom76F7sd9RJZKSOPSYU8D5pLKjerTpIVFSUJakD8pJf87m52OuL7k1w8owq0a8qSh1vfpWtFvkIQf6d8iqh7L7seQkJCdi0aZMx+cueo3HjxgDy9rJ1gho1amDv3r1o0qSJsDyiRKM8ZEE/PMF1SuqIUKgomiKITOtekbrdu3fj8OHDxmd2PKGgLqqH+Ph4YVlHjhxpqD4inzpeAWBJgZUirAK7vJykTolyx4VigiL1lG0zF110ET766COMHz8ewLl2YmV+rVSpEj788EMjua8bUsfOJ+THKUoVRe8mEAjgggsuUEqhIfIvZd9xzZo1Ubt2bQQCAVPUMVsvvI8hYF6osG3bqi/wSh3Vp+pYypIjGejaTpVwkVIXFhaG1atXY8aMGcbCkMrg8/nwwQcfoF+/fqbzRaBr5ubmuiZ11M80qfMQ7du3D1LorJS6Zs2aIRAIoGLFivjkk0/w119/Ca/LN4bo6GjTIFquXDnPlTqROYhgtwsDdRKaWFQiFd1AxW+COohosPX7/cJVr9U+hRTeHgqpY/Mrsddgyyg6NyoqKuRACdb8euONN0qvw9eBKqkjNY4galu33347HnnkEfTq1Ut6f7t7idChQwdH1wPOPSebGw04t7E3DztSJyujilLHwytSV65cOZNZjm27fELxuLg4W3IsUupEpI6uGSqps8Ps2bMxe/ZsYWBWqOOiyP+qb9++xphGz60S/EaRjaEqdbT4Y+cFnnDQGM0eI9r6il1MsMey73jmzJnCMolIHd9WqTzs2GZHbti9val+nZI6lbmBiLjoWHY7MoJIqQPy8rzGx8cHKXUEPoDBqtyh9BXyeyRSJxo3NKkLASqkjp3wypQpI4w6AsRZ8NmXo0rqZMEYopfPrjh4QsTvC3jgwAGhqclKqfMCfLlfeeUVI+M/r1TJSB0fvQhYl5fehQqpcxooIfuN4KVPXUREBBo1aoT58+cDCK4fkclcBJ7U8RDVQVRUFKZOnWrrdG11Lx6DBw/GqFGjHF0PyKun06dPB5372GOPCdsMX58NGzYEYJ+M1q4tiH5341NHYNs1X2+iyYW+i42NFSrTLFSVOgpGCdX8aocqVaoYaghbJv5vpxAtbPl3oaLUEaKjo4WJ490qdex12EVc9erVDZcQWcoe9nn4hOXssd98841w9w4gz+RKc5ZIqQPOqfKsa8SoUaMwduxY4TX9fr/JzUS0ALaCCqlTUepWrFgRtM2eXZAZkTpWqQPOWRCs2iJdMysry7VSR6SOXJtE19E+dSFAxfyqGg0m8i9gX05iYqLBzvnNe7/44gskJSXhlltuwYoVK3D06FHs27cPlStXNgZvUTmskjfyEX98JCqv1OUXqeM77rBhwzB16lQAwbtUsDtesOVkTZ4EK2XRCamTDSyU3JUIgdU1WERFRQUlwuRhF/3KB/DwigNb9rVr1xopI7wkdU7AbstlNSjWqVPH9WCYkJCgfC5fn2+88Qa2bdsW5HPFo2/fvo6uC3in1KmQCLY98NtY8RCZ6XiFoW3btujatSsAd+pAKPCS1PETNP+eeFJnNWmOGjUKb775ZshKnShJO9vf9+/fjzvuuMN0Hl82VqkTmXMHDRqEq6++WrrPLpAnJlDAkUypu/vuu7Fz5040aNDA+C4hIQETJ04UXpNIHcEtqXNifhX1vejo6KD9t2VKHXtv1pxNID8/FfOrlZ+zHXilTgSt1IUAPqUGC9W0Evy1WNDLCQ8Px0svvWQkeOUHlcTERMTHx2PhwoVo3rw5KlasiPPPPx+HDx82HNbtSAm/+qTGA4j3aS1opY4tP1+3ZcuWxcmTJzFs2DDh+U7NryJSJxtwZPXasmVLBAIBU1JelYm7bt266Ny5szRkHgDefPNN9OzZU5oKgF9Q0POIFIYWLVoYdeGW1IWaxuLtt982nJGt+gtbPi+CcGQQRXlSdndA/rzDhg2znPBF798rUseXSVSP1Fejo6ONtBTsc7Fg+4dsMfnMM8/gtddew9ixY/HSSy85Ln8oEKWwcQOZ+ZWFE/PrhRdeiCuuuMKW1IksKuxijEjYww8/bASzybbTUlHq6HosWR81ahRWrFgh3WGCkJiYiMsvv9xoK3xb8/l8qFevXpCvHQAMHz48KNG13+8XltlL82uoPnWyPi5T6ojUWZlWvVTqWFJ34sQJ7N69G9OmTQOgSV1IsFohOlXqRMdRiP/atWtRvXr1ILVMpRx82g/APMDIGhc1ni+++ALr168P+r2glTp+EKfUAYRy5cpJty3ywvxqVz4V2A0sS5cuxezZswEEb6rO4oorrjDtDMKXgZQ+XqmTmY3sTAf5rdQB4vcM5LVDqhN6v2vXrsXvv/8e8j3tyiKDlfnVarCm61LqCCA0UvfFF19g4MCBCAQCQfcVER1SrGNiYuDz+bBkyRJ8+eWXwmuz/YO2SRIlPfb5fJg4caI01Ux+IT/MrzKljgIHSIkSLXJ52JG6HTt2GH9/++23qF69uin3GI1ZiYmJhqoqK5/Mp47g9/vRokUL9O7dGyNGjLAtO4/o6Ghs3LjRViAQkbpp06bht99+CzqOfWdE6pwqdVYkm65PLhNOol/9fr/lApdNHsyTulOnTtmWOzs72zWpo/fLLmoTExNRq1Yto5+6Mb96m12yGMNqhegFqWvatCkyMzODJmcn5RBlg6dcYyz4CZ/Mr9dcc42QABUUqaP7sPXj8/mEzsiy870mdZQclC2fCuzawrXXXqt8LRbsgLVnz56gCEG+3fADynPPPYerrrrKpCqyYOtZlNbBC1LHJoFlwfonUbnZLfryA3YTgNtACVEEYig+dV27djXMnyplJD9Zuv9NN90kvTbbP4j0W+1kUdDwKlCCJXWyKMyrr74af/31F+rXr4+vvvoKzZs3t72uHak7//zzUa1aNRw8eBDXXXcdmjdvbuzVzAZK+Hy+IIWObysqgRLR0dGmiPVQoBL9bRfEwL4/WsDKMjHwoLqxclPhXXOcKHVW7YlPi0R1TJYG8tH77bff0KhRI2GZQgmU+OCDDzB//nxhGanONakLASryr+qAQx3iueeewxVXXGF878TB1uo3t0odTwhos+mCMr/KFJxRo0ahc+fOtufLSJ2KT53MPyIqKsp4bidKnVfZv3mw77BSpUrGwE5KLz1PuXLlhCvJMmXKWA6obNsRmY282EVAJQFpKIltncDuPm4DJURt2Yss8yJYJSXlk9eKwLZ9UgCKEvLT/CpStClY4IYbbnBcPtFnHhQ1C5iVurCwMKM9qih1MvOrl1AhdVZ9iDe/JiYmWgaf8Pjvv/8AWFszIiMjER4eLvU7lkGF1ImuRUodWdNEeyt7YX6tXbs2Hn/8ceFvVC435ldN6jiIGqSVUrdu3bqggZKOu+iii4JSL7C45557kJuba9qjVEWpY8vRtGlTrFixwvYZgODOmZiYiKNHjwaRuvxOacLX4zPPPKN0viz6VVZn5cqVs1XqoqKiDP8iJ6Quv+qIj7CNjo7G1q1bjaSkVHfdunVzlfWfV3mtfneLhIQEHDhwIOTrFAREeblEn3mwCUsJIiXdC4ja9+DBg5GUlCRV92Tnq5DAgoaKv6sKROZXL+CU1NWvX9/YL5v1qWNNfU596lilzkuouGlY9YV69eo5zl/JYv/+/cZ1ZIiMjERsbKy0zmTgk/7z4Ekd7dRRqVIl/Prrr0YkMABDiSXQnJ+YmIhu3brh888/VyqTKug9l1hSt3HjRgwfPhxhYWGoXLky5s6dG9KKTgSrQcBKqRPJ9zTx2qkeM2fOxKlTpxyTOrZjL1q0CLt27bK8zwsvvIAPP/ww6Pvy5cvj6NGjRsPO7zx1dhGHdpBFv4pw5MgRREZGGqkvrEgde31VyOpoypQpJj+rUEDth5X+69evj8mTJ+O+++7DBx984Pia7CDWrl07fP311ybFwotJ46uvvsL3338v/M3JKr4gIOurdqRANLEUJKmLjY3FU089FfT96tWrg/oIe77XZfMCXqneopQmXrQ3vnwiP7wnn3zSCO5iI0UjIyON9yHy35JFuwMFQ+poRyUebDmsSNRDDz0k3XZRBS+//DLefvttSwW5Ro0auPDCC6UmaxmcKHXbt283JWpu1qyZ6dg//vjDlEWiWbNm+Pzzz3HDDTfA7/ejf//+nnISesYSm9KkevXqWLp0KVatWoW6deti8eLFnt/DqvNTuhCnPnUqja9cuXLYv3+/YX50GrCRkJBgrDDInMYnIx01apTQGX3hwoUm+ZdInRe7R1jBrYkvPDzctHIZMmSIdIVUqVIlnHfeeUpKHcHJ6l5G6kaOHCndhswpROUJCwvD448/Lt2v0A68DwmlaxH97hYXXnghBg0aJPzNacqD/IZV1LsVZPn8ZL+FAieTRevWrdGxY0fp+fndt93Aq8lQZH71Avw12XQfhCFDhggj83mljto9KaZW+S7tksZ7AZmrDVuO/GwzF198MV5++WXL8eCee+7Bpk2bPFfq2C3XLr74YmleWCAvmIH3U+7WrZuygOMUJd78SjZuAI7IlZeIiopyTOpUj69evbrByJ0qdSwaNWqETz75xMhVZocGDRpg8uTJxuf8ziRPcKoETp06Fb/++mvQc1etWhXdunVTupesXu225bG7bnGD22hQr1DUSJ1oE3gWPp9PuOATDbYFqdS5Pb8okrr8VOq8ANsn/vzzT0fnsqTO5/MZ7Z6UKb6tkJM+UDA+dSoo7DZDO1Y4VSvd+tQVBVx33XXo0aMH7rnnHsfnFs0nkmDv3r1YtmyZdLukjIwMJCcnm/6pwm6SOe+885RyGwHnBgEnExdNEk596nj07t3b9SB51VVXWf4+adIkaRJKVbzwwgvS1AsyPPLII/jkk0+COrOKaYWiUI8ePSr83S05KyqkLhSljrBo0SLL371EUSV1sj4lG0NEvp1FQakTgVddxowZg1dffTXUYnmG/CB1XrYv9loilc4KkZGRJgtISkoKgHPmPX5XooiICMOqUlCk7s8//zS2nxOhqBAfp0rdFVdcYTmn3XHHHejRo4cnZfMaZcqUwWeffWYi+aooUkrd4cOH0bNnz6DvlyxZAr/fjzvvvBOzZs2SDnKTJ0/G008/nS9l++6775RyGgHmHDaq4HcWEOGCCy5Qvp4bfP755zh27Jj099GjR4d8DzdbQxHckLpOnTph+vTpQQ7lMTExOHv2bIEpdTLFJ1Q4nbxEA2L37t3RtGlTbN68ucCUgKJC6qgcMlInS+YqMrXxm5p7BS8jrcPCwvDss896dj0v4JW6Fhsba1gbHnnkEaxdu1Z5zM4v8Coppcm46aabMHz4cJMfF3+OLKWJ17AjqkWN1KnWQf/+/dG/f3/p7zIXkeKOIkXqqlSpgp9//jno+5ycHHTv3h3jxo2z3BJn9OjRePjhh43PycnJqFGjhtK9qSPJBhiKPlQBTRBO7OEqSl3//v2RkJAg3LzYC5QpU6ZIRscR3JA6n8+HIUOGBH0fFxeHs2fPup4wnZK67du3Y/v27bj11ltd3U8GL5Q69jpFxfx6/PjxAiV+Tp/bSqnzepstL02KRWWCZsHnLnSLZ5991lgcX3755fjnn39CLluo4AMOTpw4ASDPzCoidMC5910QgRIqKCptxqlSV1pRpEidDPPnz8cvv/yClJQUTJgwAUOHDkWfPn2CjouKinKtvNx4442YNGkS7r333lCL60qpe+eddzBt2jTLTuvz+ZT95Uoi+Ik3FEWEyKsXSp3KQNugQQNHphsV0vjcc88FOcXbQTYgFuakIQIf7JOf8Pv9ePLJJ22PO3HiBDIzMzFq1CjT3sSxsbFo3769MYGHsh+kCF5OYkVxQrRLyK6KCy+80IPSeAs+jygpdVZmNStRoDDeX1FpM06VutKKYkHq+vbta7vBdqjw+/2emBcBd5v9XnrppZg5c6Yn9y+pcKPUyXDJJZfg33//NQ26Tq5HE9D48eOFqSVCxRNPPGF7zGOPPeb4unZKXUH51BUl2AUIrV69Gnv37jWc22mrMwKlOiBfUa+VOi9RVCZoFlSmouKn6iVkSp3VokWk1BEKw22hqLlKFMU2XJRQLEhdcQNNjF6v2Es7vCR1c+fOxfr16/Haa6+5Op8moPwwV+cn8bFT6oqK+bUooXXr1mjdurXtcfml1HmJolzvJZHU8T51M2bMwJtvvmlpIaA+6CZHWX6gqLQZbX5Vg66dfAA55dtFk2o4A7+naCjkJz4+Htdcc41rfyWagKxyGxVFyJS4gjJtFEdSpwqaqIsyqSuq+N///od58+YVdjE8B0W7Anl9rHHjxnj77bctz7EKlCjN0OZXNWhSlw+oW7cuAoGAcpCGhhqoM9P2LV4oWm5JHQ0wTpW6Dz74AJs2bXJ1T6cQrWhlq9xJkyahYcOG+b4KLsmkrjgodUUV77//Ptq3b1/YxfAchw8fNv5W7Vsipa4k9hen0EqdGnTtaBQbEKmjXRB69+4d8jXdkjqVaGUR+vfvj6ZNm7q6p1P8+eefQTkBZavcjh074o8//iiIYgEomZMUkbqi7FOnUbCgPWAB9TYvUuo0zpG5ouiXW5SgSZ1GscGZM2cA5AWVBAIBNGnSJORruiV1tIouyqvGiy66KCg/X2GbLkrygKzNryUXq1atwo4dOxyf99BDDxnpsFTHCqtAidIMIsW6XqxRdGckDQ0OtCtE5cqVPbtmqEpdYZMkpyjKJLS4Qyt1JRft2rWzzJEqQ3h4ONq2bQtAve/RcSx5GT9+fIkMJHGCoq7UXX/99UoBVfkNPcJrFBscOXIEgLekjk054GSwoIm7uJGkwiah5cqVA1CweegKClqp0xCBgqmcKkzs8SNHjsTZs2c9LVdxQ1FX6r7++musXr26sIuhSZ1G8UGLFi0AwNOtf9wqdVSGKlWqeFaWgkBhk9Bu3bph8eLF6NWrV6GWIz+glToNESiYKi0tzdF5uh2ZIVIwNYKhSZ1GscF9992HpKQk6X6cbuCW1N1+++34+eefi13amsJW6nw+H26++eYSGSihlToNEYjUUZJqVVxzzTX5UZxiC03q1KCTD2sUG/h8PsTHx3t6Tbckx+fzFTtCB1hvT1SSEBUVJdyfNT+hlToNEdyQukOHDhk7mGjkQZM6NWhSp1GqUdjKVUHiyy+/xA033FDYxSgQ/PXXX9izZ4/j8+bOnYvdu3e7uqdW6jRE6NmzJ1555RV06tRJ+Zzi5tZRECjqgRJFBZrUaZRqFLaPWUGCT29SUtG0aVNceOGFrjZ4v/32213fl5LG9uvXz/U1NEoeqlWrhn/++aewi+EYPp+vSBGoBx54ADt37kS7du0KuyhFGprUaZRqlBaljnJllXRkZGQUGlEvapOghkYoaNu2LX788cfCLoaBChUq4KOPPirsYhR5aFKnUarBkrqSOiFv37691Jhz2BQ1JQlbt24tkcElGkUXX375pWmbM43iAU3qNEo1SoNSV1pUupKMRo0aFXYRNEoZ4uLiEBcXV9jF0HCI0uNQpKEhQGnyqdPQ0NDQKNnQM5pGqYYmdRoaGhoaJQV6RtMo1SgN5lcNDQ0NjdIBTeo0SjU0qdMoDahevXphF0FDQ6MAoAMlNEo1NKnTKA3YsGEDDhw4UNjF0NDQyGdoUqdRqsH61JXUlCYaGlWqVCk1aW00NEoztPlVo1RDB0poaGhoaJQUFKsZbd68eahYsWJhF0OjBEGbXzU0NDQ0SgqKDanLzc3Fp59+iho1ahR2UTRKELRSp6GhoaFRUlBsZrSPPvoIPXv21JOwhqfQSp2GhoaGRklBsWBIOTk5mD9/Pvr06WN5XEZGBpKTk03/NDSsEBERUdhF0NDQ0NDQ8ARFKvr18OHD6NmzZ9D3gwYNQu/evW1VusmTJ+Ppp5/Or+JplED06NEDo0ePxvHjxzFu3LjCLo6GhoaGhoZr+ALFII/DY489hs2bNyMsLAxr1qzBPffcg2nTpgUdl5GRgYyMDONzcnIyatSogaSkJMTHxxdkkTU0NDQ0NDQ0ChTFgtSxuPLKK7FhwwalY5OTk5GQkKBJnYaGhoaGhkaJR7HwqWOhSug0NDQ0NDQ0NEoTih2p09DQ0NDQ0NDQCIYmdRoaGhoaGhoaJQCa1GloaGhoaGholAAUu0AJJwgEAkhJSUFcXBx8Pl9hF0dDQ0NDQ0NDI99QokmdhoaGhoaGhkZpgTa/amhoaGhoaGiUAGhSp6GhoaGhoaFRAqBJnYaGhoaGhoZGCYAmdRoaGhoaGhoaJQCa1GloaGhoaGholABoUqehoaGhoaGhUQKgSZ2GhoaGhoaGRgmAJnUaGhoaGhoaGiUAmtRpaGhoaGhoaJQAaFKnoaGhoaGhoVECoEmdhoaGhoaGhkYJgCZ1GhoaGhoaGholACWa1AUCASQnJyMQCBR2UTQ0NDQ0NDQ08hUlmtSlpKQgISEBKSkphV0UDQ0NDQ0NDY18RYkmdRoaGhoaGhoapQWa1GloaGhoaGholABoUqehoaGhoaGhUQKgSZ2GhoaGhoaGRglAsSN18+bNQ8WKFQu7GBoaGhoaRRB16tTBN998U9jF0NAoFBQrUpebm4tPP/0UNWrUKOyiaGhoaGgUMeTm5mL37t3Yu3dvYRdFQ6NQUKxI3UcffYSePXsiLKxYFVtDQ0NDowCQk5MDII/caWiURhQbdpSTk4P58+ejT58+0mMyMjKQnJxs+qehoaGhUTqgSZ1GaUexIXVz5sxB7969LVW6yZMnIyEhwfinzbQaGhoapQfZ2dkANKnTKL0oNqRu+/bt+PDDD9GlSxfs2rULI0aMCDpm9OjRSEpKMv7t27evEEqqoaFR0Fi1ahX69+9f2MXQKGRopU6jtMMXKIYbo1555ZXYsGGD7XHJyclISEhAUlIS4uPjC6BkGhoahYFq1arh0KFDxW6f58zMTIwbNw5PPfUUYmJiCrs4xR4nT55E+fLl8dJLLwkX/hoaJR3FRqljoULoNDQ0Sg+KG5kjLFy4EM8//zzee++9wi5KiQCZX0mx09AobSiWpK6o44cffsDmzZsLuxgaGqUGxZXUEQnR8Aba/KpR2uEv7AKURFxzzTUAiu9Eo6FR3FBcJ3EaI3w+XyGXpGRAkzqN0g6t1GloaBR7hLqAyszMxEcffeRRadShSZ230KROo7RDkzoNDY1ij1BJ3XPPPYc77rijwN0mNKnzFjqliUZphyZ1GhoaxR6hTuKnTp0CAKSnp3tRHGVQufUuOd5AK3UapR16JNHQ0Cj2IMXLrWJXWP6vWqnzFprUaZR2aFKnoaFR7EHkKNTJvKDJlSZ13kKbXzVKOzSp0ygxOHHiBHbs2FHYxdAoBNAkXtwmc03qvIVW6jRKOzSp0zDw2WefITU1tbCL4RpNmzZFgwYNCrsYGoUAr5S6gjbDap86b6FJnUZphx5JNAAAx48fR8+ePfHwww8XdlFMqFevHho2bKh0rN7rt/TCK1JX0NBKnbfQ5leN0g5N6jRMOHjwYGEXwYS///4bf/75p/C3VatWYcCAAQVcIo2iCO1TF4yvvvoKu3fv9vy6RRlaqdMo7dCkTgPAOfNPVlZWIZdEHb169SpRe2aeOnWqyJHq4gLtUxeMG2+8EU2bNvX8ukUZmtRpFGUcPXrUSJ9kh+zsbDRo0ABr1651dA9N6jQAnBsMMzMzC7kk6ihpA3e9evVQvXr1wi5GsYRXSt306dMLdGFD5c0vhTA5OTlfrltUUZCkbtmyZfj999/z/T4aJQeVK1dG1apVlY49deoUduzYgUmTJjm6hyZ1GgDODYKa1BUeTpw4UdhFKLbwitR9+OGHePvtt70okhKo3DpQwhsUpE9d586d0ahRo3y/D4/MzEzcc889OH78eIHfWyN0ZGRkKB1HCz2nwVt6JNEAcG4QLE7m15JG6koq9u/fj9WrV+frPUI1v7ID5+nTp70okqP76kAJb1AazK8//fQTZs2ahZdeeqmwi6KRj+DHhKeffhpPP/207Xma1GkA0KROI//QuHFjtGnTJl/v4WX0q+pK2gtoUuctijqp++WXX3DrrbeGdA1SdQtrFxQRTpw4UaysPMUJ9J5//fVXbNmyxfZ4Teo0ABRP82tRGtQ05Dh58mS+XDctLc1or16SuoLsA0WVfOQXTpw4ka/9tqinNBk8eDAWLlwY0jWI1BWlZ6xQoQJ69+5d2MUoUeC3PszKykJERITteZrUaQAonqRONqhpslc6UKZMGbRq1QqAt9GvBdkHimt+PTc4evQoKlSokK8R67xSl18LCrdwMjbFx8fjjjvuCPqeVN2i1maWLFlS2EUoUaD3q0mdhiuUJPNrURvsiiIOHz6MwYMHG8pGccWmTZvwww8/GJ+Lq/m1qLbZ1NRUz4gRXSc//StZUvfzzz+jfPny+PXXX/Ptfk7h5D2npKTgo48+CvrerQN9fqOolae4Q5O6Yo7Nmzejd+/ehdYxSpJSV5yIqQiPP/441q9fn6/3eOaZZzBjxgxs27YtX+9TELjmmmuMv7VS5y2uvPJKlC9f3pNrFYTCxJpft2/fDgD4448/8u1+TsGb1NygKPrU8Zg9ezaOHDlS2MUo1tCkrpjjvvvuw4IFC/D222/j/fffL/D7lyRSV9zVp+effx7dunXL13vkd360wkJxU+qKetLkv/76y7NrFYTCxCp14eHhpu+KAujZQylTUfSpYxEIBNC/f3+h6VhDHTypy8zM1KSuOGLo0KG4++67C/y+NMh4rXL98ssvpl0SVqxY4VnKCL7RE4o7qQPyP29ZSd1IXit13sFLQgd4o1LZgSV1fr8fQNEaD7x430WN1PHvk96B6s4JXqNp06a46667CuXehNTU1JDbHU/8s7KyEBkZaXteyRrRNVwjv5S6q666Ck2aNDE+d+zY0bMoKdkAWdzNr0D+K2ia1JnBTkwFSeryKwWH6HrJycn46aeflK9x8cUXe1mkAolMLepKnRfKbFHzqRORD6DwSOeWLVvwwQcfFMq9CWXLlkXfvn2lv6vMUSXe/Lpx40a0bdsW7du3R+/evUvExF2UkJ/m12PHjpk+//vvv8LjnO57Klv5F6WVuVvkN6krqfnRior59fDhw/juu++wcuVKy+Pyi9SJiEy/fv3Qrl07T+9jhWeffRaNGzc2PlO/DIWMZGZmYuPGjdLfWeJYUpW6glA8nYCfiwub1OU32rdvj8cff9z2uE8//VT6W1pamu35JZ7UVa9eHUuXLsWqVatQt25dLF68uLCLVGSxZMkSXHnllY7OoQYkmgx8Pp9SI1aFaDCaM2cOqlevjv/++8/x9fjBoygN4m7hlmylpqYKI+Z4WL1vL3D06FGMGTOmwAf2omJ+rVq1Kq677jp06NABl156qfS4/FKvRO91586dnt7DDk888QR+++0343MopO7pp59Gly5d8Nhjj+HKK6/EmTNnhMeVBqWuqPlhykhdUSGdXuPHH3/E888/H9I1nJA6QokjdVWqVEFsbCwAICIiwliFlRR4qZjs3LnTcVSj3QDx5ptvhlIkW6xZswaAu/1Pi7P5NTc3V+h74rY9PPbYY7jjjjuwf/9+2/sC+TfhPf7445g8eTL+/vvvfLm+DEVFqWNhFX1ZkEpdKO9aNEEfOHAAY8eOVZ683Uz2gUAAf/75J8aPH4+lS5cafn4y4l0afOqKOqkr6gmgiwLOnj1re0yJV+oIe/fuxbJly3DjjTcG/ZaRkYHk5GTTv9KIrKwsxwOZXQfkJ4RAIOB4JWZlNqBBOioqytE1RdcrSoO4DIFAANOnT8fw4cORmJgY9LtbUke5wOyILdVZftUV+eoVNMGWtePU1FTEx8cr5SwrTj51/fr1E7YVEYELhQyI2snAgQMxadIkZYd4N0rdnDlz0LBhQ+OznT8Z3ePMmTOeRJp6DTeLKb4P0blFRQlTMb9u3boVc+fOLdBy5TfmzJnj+lxtfv1/JCcn484778SsWbOEDzd58mQkJCQY/2rUqJGv5VmzZg0uvPDCkCauw4cP459//vGwVHmTUk5OjqNO75TUhYWFYdiwYabv5s2bhz///NP4zN7/zJkzlpMKqSNkMnGCwlbqsrOzHSuMJ06cwH333YfXXnsNQPAA7ZbUiSa9tWvX4tChQ6bj8lupKyyVRNaO//33X6SkpODtt9+2vUZxInWyidJKqXNzr/T09KDvyFdWtQ258bXasWOH8HvZNagsX331Ffr06eOofKEiKysLa9eutTzGjVKXkpJi+iyL+i8sqJhfmzRpgn79+hVoufIbd955p/Kxc+fORUxMjPHZiVJHKHGkLicnB3fccQfGjRuHiy66SHjM6NGjkZSUZPzbt29fvpbphRdewJ49e1yZDAl16tRB3bp1PSzVuUnJycDhlNQBwPTp002fb7/9dtOqmj0nLS3NcjCiMrsZgL3wqdu4caNrAjJkyBBUqFDB0Tn8So1/bi9JXatWrdCyZUvTcVRn+UW6aPApKkodQaVeCzJPXUH61IVC5EWTEKnCqiTYjVIne1+vv/668Drss9H9CorUjR8/Hq1atcLRo0elx7ghdfyOHkXd/OpFoEQgEPBc7ChMTJ482bQwchL9unLlShw/frzkkbr58+fjl19+wYQJE3D11Vfjk08+CTomKioK8fHxpn/5CS/yBdHk7qVPHQ2yTiZsu4HPjY8OO9hnZ2dbkjqaSN2QjFDNrydOnMCVV16Jp59+2vG9AeCbb75xfA6vfPBtyG2qEVnW/r179wrvV9RJ3R9//OGojPkdKHH69GnbVbYTElEYPnWiex09etRycSpS6gqT1D3zzDNYvny59B5238ngxq2EsHv3bgCQBnEA7ghZvXr1TDvMFHVS58VCZe7cuahbt67J8pMfuP322y0DmbwCHwOg0mfY99yhQ4eSR+r69u2LEydOYOXKlVi5cqUhrRcmilq+IIIbUmfXAZ08I6UmYRvuiRMnjBWslVLnhmTk5uaaJjGnRIImabfJVt0Qcn6S9Fqp498nP6jkt/lVROpEvoNWSE1NxaWXXoonnnhC+RxZO3bSfq3aT7ly5dC0aVPL81NTU6X3T0pKgs/nw1dffQUgdFJH5J9/j06VunvvvRejRo2S3kdE6pKSkgCoK5tOFJy0tDQEAgHLfiC6b6gBImFhYRg7dqzy8SxUXA6cKHVsn92zZ4/xd34tBGTYsmULfD6fUIE8duxYUFCe1XtW7YcUXMS7jXiNefPm5ds2cmzb5ZMGOyF1ALBt2zbk5OSULFJXFEEvzQu1w0s/nvwgdVY4duyYiRDNnj3bVA4AaNSoES644AIA6kqd6gAwcOBAPPfcc6Zrbd261fKciIgII0FlqHtSulHVeLWHv7dbUscHKNB1+QCU/A6UEJE6pxnm6dzNmzcH/SZrG27Nr05In8zPi8AqNZUrVwZw7llokqIcVuxOLirO0zwoIwBPJJ0qdceOHTNImggiUkfwWqk7ePAgypQpgwULFgT1Lfb9qQaIOG3j7777rqPjCUTCRGUgMn/48GHpMTzYeipTpozxd0H71FFb/f3334N+a9SoEXr27Gn6zirKWXUBUNR2zXADtn3yZMwpqZNdRwRN6kKAFxF+xOB5v4lQUNCkrlGjRqbs8z///LOpHDxUSZ3qCvuzzz4zqTnPPPMMmjRpIvVtOXv2LLKzszFt2jQAoQ8gXih1XpE6fqFB9+FJXUEpdazCALhTzETtSPau7L5XqddQJ0uW1MXFxQEInsyoHFT/Y8eONU3cqiDna97k51SpO3v2rGVb8ILUqaY0+fjjjwHkEWCn/cALUud2PKcgL9H9+AWNyliTk5NjLITZOi6sQAnRuyCSysLqPasuXEoaqQvF/MpCkzoX+Pnnn/Hkk08qHUsvLRRSV65cOQDA8ePHbY/duXMnZsyYYXucV6SO9YNjwXdWtmNXqFDBUDJCJXVuVSTKOC9bFRLZo7qnAcTtIOnGDM8rdV6bX6lN0iAaHR1tOi6/fepoEBswYIDwviqg9pOSkqIcDGMXFTljxgzMnDlTuQwyWCkOIlJHz6Li/1m3bl3b5OGLFi3C+vXrDaXOitTxCp2ojs6ePWv5bgpSqduyZQuAPHM93w/slLpQfOqonlSeR/QMVuZX3vxm1w/o9+HDhweVqaj41PFRuQQrnzqnpK44pKfiwabSoWBNrdQVIjp06ICJEycqHSsidU6JwXnnnQcASjn1rr76agwePNj2OCeRpPv370ePHj2CSMauXbsQERGBFStWBJ1jNZhcdtll2L17N7KysgpMqeNBzyJLUE2kjny8Qt12h1ddVMBPkvzCwCuljsxybsyvCxYsgM/nc/UeZIOPk0Ga6mTjxo3G5EaQlUnWNtn6ffjhhy3vq9IOrCLrWYJFwVp2yXJZ/PPPP5ZbYQFAjx490Lx5cyWljjfFi+6Znp5u+Z6tgkNUTWqqpI4tp1U/8Pl8WLZsmenZRc+guuimd6RyvOg+NN6I6oNvl6rZBuj9smUqaJ862fsSRfx3797d2IqOLR+pmE5JnRuXhMIG+9yktKoode+99x7atm0rvA6BXxyIoEkdByeTqYjUOe1oCQkJyseqNnAnSt3WrVuxaNEi0yQVCASwfft2AMDq1auDzrG67qWXXoqcnBz8999/0sFeJVAiLS3NtZ+hTBUhHDlyBMA5pS7Ula8bUsdPkjLznNuyqCp1VmWmlDVuUnx4SeqAPIKpch3ZO2SP9yIpeWZmJhYtWoT27dsH/cb63PHmV758bgjz6dOnAeTVsYpSxy/yZEpdQZlfrYj3ggULLEkd+3nPnj3o3LkzhgwZYnwnegaq+xUrVgS5A4ieI1RSp+Iu4JTUFbT5NTc3F3fccYdpa7kXXnjBNG6JnvPzzz83/mbL55TUOT2eLbcKzpw5g1deecXRtUMpgyxQYvny5UbqlgEDBhiuS4C4jWmlzmM8+OCDeOedd4zPbkndQw89hA0bNgBwl2zXDlQelQmUGg470eXm5loOHOx1e/fubfqtWrVqAPImnlCUujJlymDo0KG25WfBRyXKJilS6kglDXWQ9EKp4z/zk1lYWBimTJmiXBaWHAOhmV/dkF0vSB3bfvjyh0Lq7KDSDnJycjBw4ED8+OOPQb+xQTu8+ZX6pps2QyCH9YsvvtgRqQvFp85KqXNqfhWRpqSkJLz44ovo3bs31q1bZ5TTKgiJyBybrkf0nql8HTt2xOWXXy69npNFpKidWSl1fN2qkrrIyEj4fL4CJ3VHjx7FRx99hEcffdS4z9KlS40xSBThzSMUpY6ePy0tDQcPHlROG6Wqyo4bNy5I/XdSLqfH8OMhtZFOnToJU6ocPXpU6BOuSZ0LWCkkq1atMm0zRMeKOpwVXn311aCIIRWodmJe9frtt9+MzPOffPIJvv76a+NYEamzyynHDpy8gkLql5XSppp8WJSL0ApE0gh2pI5PL5Cbm4tHHnnEcNRWhRdKHfkR8dckBAIBJbcAfqEhM7/aKXUHDhwwTO90zNy5cw2VSLUcPNwqdXz5nZI6J36vKv0sOztbes2MjAzD7Er/0yAuy+kl+ywCqU0XXHCBEqnjk/C68amjNEUiOFXqRMefd955GD16NADzThVOA1uslDrAOiDNCakT3YeIi4pSJ+t3e/fuxe+//278Hh4ejsjIyALxqfP5fMaChK7Nk2rq/yrpn9jyOTWn0iI3NTUV48aNww033GCb0QBQH1+scgmGen0rFZfAvk+RCl65cmXceuutQd9rUucxsrKyTC8gFJ86mtSdbm6tcg5P6ho3bmxs0XLbbbeha9euxrHUANl0Bjk5OcqkjgcRKytSJxqMqByhOMby0YOyQY8ILK9gBAIBvPTSS+jbt6+j+3qh1PXq1cv0WaRQqKRO4R2MZUqdnU9d48aNjb9zcnKQmpqKfv36mUxdVvBCMWP7FV9+pz51/H1DVTmsSF1ubq5RXmqTMtMe/xwqxIKOCQ8PN+6jQupkRD4nJ8fYWpDA188jjzyC9957z/SMqmXeu3cvUlNThUqdKHqSnsUNaRG1i08//TRo3P7vv/+CjguV1NHEnZqaGvSeVc2vNWvWRKNGjQqF1AHAm2++CeDc8/FjDo0n//77r+21REqdncL33nvvoVmzZsb7SktLQ/ny5QEAzz//PHw+n+E+I4Lq+OK27txaNvj+5Na1SJM6F7BaGfKkTpTSRFVWP3r0KG6++WZX/kp293DiU0fH8Eqd1aRnRV4o+CA1NdWy4Z48eRJNmjQxfPlYs6HbAAlSLezKyeYGA0I3Z7iJ1LLyUQLM7ZDKpaJayMyvvNJlR6LZ3QWWLVuGlStXAsh7b3ZlB5yTuoyMjKBVfH6aX/lJ1+m7tyN11Cao3vk+uXr1anTp0sUVqROljLAii/SbTKmj9ykigixon2L+d7sxrGbNmujVq5dxDj3j2rVrUbVq1aBgLDZ6UGXyZetB1i4+++wz02fWV4wgqvtDhw6Zrp+bm4vWrVvjl19+CTqWSF2fPn2CfKXd+tSJSB395qTNvvnmmyYrEw9+jKHyhYeHm+5DYoTKFpzHjh0zlD1V8+uDDz6IDRs2mEgd3Z8sKL/99pv0fFVFPj9JnWje4duWJnX5AEoGydvqrdJTZGVlmQYwO1InuwZhyZIlwoSOdhA1rN69exublatGv0ZFRRk+giypy8nJsRw4rBq2qvn1119/xdatW40ys2TESV4yFk5JHa/Uuenox44dMyaIUMyvPFjTPrUzFaVO1fxqtS8m/8779OmDG2+8EQDw/fffIyYmRprOgOCU1F1xxRVBSquVUhcqqQt1f1d28SEyrdG7Igdp3vy6Y8cOLF26NKhcKht90zWysrKkaTicKHV0T9muLERWatasGXRN0b1F2LRpU5D5lcY+CsrikZOTE1Q/dgsbWR/k2w//mS0XIS0tDdWqVTP5SKanp2PNmjWGqZgFa2KzSy4+ceLEoL2zWagodU5I3f33348WLVpIf+d9PVnzK3sfImX8toMykNVDldSRuwKNW2lpaUbfoXJYEZv8VOrWr1+vtNuIJnWFBPIJYP3LWIheuor51Y7UufGh4a/Hr0a2b9+OBQsWYNiwYQDUlbrMzExjlcwrdXxHUi1z2bJlER4ebkvqqPPSfol2pE6FMEVFRZkCT+wmeF6pc9PRL7nkEkdlJKgqdaL2pnJednY2Pv/8c2OlzPt0WAXT2BE2QKx0sHBK6kRb9XhB6nw+H4YNGxbUZ6xIHbX5rVu3Ss09ojxwbBmoHfJKnWzzc4LKrhts/6bntVLq+Lri60hE6thzXnzxRfTr18/kV8feT2WCqlOnTlC/o/vK2nVOTo5jRVXWB/nriMosInVA3kKGh6j9WS26+HLNnz8f9913n+3xROo+++yzILO03Xi1du1a/PDDD5bHEKg/8NvO8YF8VCcypY5fPB44cADAufoXmb1Z0LxAfS41NTWor8pSVQH5S+quueYavPHGG7bHqaTVyczMlLZlqzauSZ0FyJejSpUqpu+ttv5ySupCyZlkBb5stOLt0KEDADVSx5MKXqmj30XltbpuVFQUYmNjbUkdXV9E6lTyPIkQERFh6vCqSp2T/Rh5kFO31f2APEfzNWvWGJ9VlTqRszF7zKRJk4TnbdmyBd27dzeCK2RmPlnOMjvYOUrL6rJRo0bo1q2b7fUB8yTLD2ayuhblW5s+fbojpY7K/sUXXwCA0GzFJ8keM2aMsagSmV8zMjJQtWpVXH/99ZblUCF1IqWO7adr1qwxqUB2fnzUFtl3RufccMMNGDp0KOrUqWOayJ0qdRdeeGGQ+VWF1NkRUsA+UAIIVohUSB29G9YPTBQ1rpIeyOnYwpKq9PR0/Pbbb6b2pXLNVq1a4ZprrjHqxyrTAj0rP//JfOqIrPHg79GwYUPT9alPyUDR4rSdHqvUEWSkLjc313KrOxZuXG1Ut4MUmdr5tvXCCy9g6dKlAILbv9V71aTOAvv37wdwzlxIsCN1bAOzI3UytS9UyNQ+up8KqeOj2XiljgZcEflgr8uvzCIjI5VIHdUjrchYx33Vjbp5REZGmhq9U586t758KmVs3LgxWrdujc2bN+PIkSNBxIkfqETtkO38VOaXX35ZeB7VK/nG8WWzUupUSJ3MZMaXj0daWprtwE6wUoNkbbtHjx7o1KmTcS8gr4/zx/PXExEDUg1Eee14Ujd58mS89tpriI2NNSl1ZH7NzMwUBgWwiwLATOo++OADNG/ePOgc9t2JlLrWrVsb0e58WQFnPnVDhgxBREQEzj//fBw+fFjYblRN2XzuRBpbZCY5VVLHQtYueAd90TjMtons7GxTBCZ/f1GSdFHZypUrhyeeeCIkUkdlJZOn0+TDVL85OTmmPGgs6NlFpI7tG1QXZ8+eDZo72fOAvJylpEilp6ejUaNG2Lp1KyZNmiQlVdTniNSdPXs2qK/KFgEjR44UpgcRwc0CXpXU8WOtbC6khRc/9lvN2wVK6uwyoBc1kAzMrhrZyYzPyA+cU+oCgQDKlCmDhQsXGt8T7FaNoUR3yiIW6T68AmV1L36lJVPq7EgdJcckkFJnFSiRm5trXJ+PHFUxv+bk5Ai3couMjDR1EFnHlfnU8Sos5cs6ePAgOnXqZBu5ZUXqaBu4yy+/HFdccYWwXllTrojUHTlyxCAGdD2KDCNQG6H3SaSCLduTTz5ppFBxS+oWLlxoudr1IjKP7Vd8mazaNgV1EGEqV66crfmVd4b/8MMP8dBDDwEQm6N5UkegfG+y6Fcef/75p+kzS+qGDh2K9evXB53DmnJFSh0PVaVO5FNHkwg5/hNBcKrUpaenG+dQfdJ9ZepKbm6udKxjoaLU8dHBdkrdmTNnlEmdlX/q6dOn8eyzz0r7A0/qCbVq1QKQR+p4071TdxG2/bI7FrDgza90L5lPXVZWlkHAWLB1eMkllyApKQnLli0DcK4NjR07VpqehK5J4xsvpNA9/H4/vvzyS9P3NB+roCBJnWwupPfCq5tWfZkXUUTwjNTdcsstXl2qQEANhTpu2bJlUbt2beP37Oxs/P777yhbtizmzJmDTz/91GhgWVlZSEtLMwbgjz/+GHPmzAFQMEqdbJBmzTL0DDLw6gOv1IlI3aWXXopLLrnEltSVKVNG2fzKb9ysYn79+eefhTnbeFLnVKnjFQMKonn77bexfPlyYcSb6Lp2OHDggJCksFnHZYpx/fr1AZxTWitUqIB169YZK3A+RQ1NyvT9yZMnMXHiRFOEIQ9V8+vvv/+O33//HZMnTw763QtSR+2nb9++QW3Cqm1T3bF7/NqZX3liwO6xrKLUscjNzcXdd9+NiRMnGo7iqrursKRONonIlLrt27cLfZ3c+NTRPag/URCSiNSNHz8ehw4dgs/nkzr/p6enG9c8c+YMAoGAcS0ZqRMpdXZ9TPR7WFhY0ILMjtSlpKSYIjD564uUOquyyX6rVKmSMOiCYEXqVAUClZxsTs2vMlLHIj4+Hj/88AOuvfZaAOdMq4A8tQl/TRGpS05ORk5ODqZNm2b63sq8nJWVhSlTpkh3NWnTpo3lswDuSd2ZM2csSZ0TpU6F1Mk9DgXgdw8gBAIBy6SORRGvvvoqPvvsM9P2PYcOHTJW1tnZ2Vi+fDkA4M477zTOE/nsfPfdd/juu+/Qr18/W586kpVDgcz8ykeXWQ0yfKdiP7Okjp3gyZmdvT/vwM6aX5OTkxETEyNUpWSOuapKnQgq5tfXX3/d2NCdV+r4cqpkTZeV0Q6iOmE7rCxNChEMltS1bNkSQF4/pOOJHPCkjg9IoONPnjyJZcuWoXfv3rakrmnTpti8eTNef/11g/zwExPVably5ZT8xESg9hwXF4eMjAycOnXKMPlY1fWFF14IwBmp4/stS7ZECZftAhFiYmIwcuRIBAIB+Hw+SxNlWFiYcX8aR/1+vxKpYxcorNIrOp5w3XXXYf/+/cYiQuRTR89E7ceK1AHnFMfZs2cLd4LJyMgwzgkEAkhNTTXashWps0vWTNez+t3v94dE6twqdbz6K8Nzzz2HyZMnCxf8XpA6lcAnusfOnTvx/vvvG4tHvg1SXWRlZQUle+fBkjggTzgh8Avo3NxczJ49W7jxPb/woXN5M6wVqZszZw4effRRnH/++ejbt2/Q+xBth8lDhdStXLkySDE8ffq08N0S2XZC6lT2fnVE6pYtW4bZs2ebXg6Q13hF2+UUdURHR0u3Z8rOzsauXbuCzrHb+NpKqTt27BhatWrlurx25leaOFTMr1art8zMTHz11VcA7M2vfINkSd2JEydQpUqVoESVrFLH+7Pl5OTY+tTJzH4q5le289opdU5Jnai+d+7caQyQLEQpAVhSZ+XbCZwjdbxfC9UTmS/4+uVT6ND3d955J77++mv06tXLltSdf/75OH78uEnNysjIMJU/NzcX1atXR0JCQsikrkyZMtiyZQsSExOxc+dO1KtXz7Jt0yba7HZwMp+6LVu24LLLLgvqt+xnNmcfwU6pownA5/MZ/UGGqlWrGu4QNAFHREQYk1ROTo5pwlJJaSIrK5A3Dr311ls4//zz0aNHDyXzqx2p4xdpPFjzKz0nkWXZXrxeBUqISN0999yDNm3aoF69esZ3bkmdTKlzun2kaExm3ztPHq36AOs7p0Lq2DH37rvvxqpVq4LuT/c8cOAAzpw5g+rVq1tek1fd2M/8+/j+++9x1113BV0jKysraLyneuLbmhWpozZml8D/+eefR9myZXH//fcH/SZr28OGDUP16tXx+OOPo0uXLvj2229Nv//333/C/snn8CMUKKm7+uqrUbZsWeEm1vy+m8UBdqTu77//DjonIyNDmdTxxzlRM19//XX07ds3yGcKkJtfnZA6K8Ly5ptvGoqOHanjiS9NYvv378fu3btRuXJlIanjy8oOlnZ56qxIHa/ULVq0CCtWrMCrr74KwKw82il1/ETsJp0CkWMeohQeKuZXAvni8ISJJ3X0nul7nkzS9YlUsP6OMpQtWxaXXnqpydSXnJyMihUrGp+J2NiZC4YPHy41fWRmZiIiIsKkBu/bt8+W1BEBYcmCyKfu+PHjaNq0KcaPH2+p1FFdslAldUCeYmE1sbJ1xE5YNNhnZGQYz7RmzRrjuaxSmsjKSiB/wb59+xqKikh9pEUSuVmkpqZi9uzZQQ7pixcvBiCfWFnzK5BHNKhOnJhfZc955MgR9OnTRzh2RERECMe7cePGYd68ecZnntTxWzlFR0cLCRUfqEZgxw8VUidqIyIyrxLYxfrOOSV1gDxQIhAI4PzzzweQF8lOuOGGG4LSg/GkjlXu+DLx2yQS2IULgSd1pIZbpTqhd0ljrOh9ZGRk4PHHHwcAR6SOTcpN/rws/v33X2G7JP/oQg2UWLhwoZDQAQhip8UBUVFRUrNIdna2cENdK6UuKSnJ9JsoZ5YqHnzwQWPgJciUOvqckZFhci52q9SxZNaK1MmcbsuUKYMff/wRa9euRaVKlYJ+Z5U6Ptt9qNGvvE9djx49TJ2OvbadUqe6T6FVGa3IM98eWFJnZ2KhshE5pOem4+l33hQvG7zpuD179hgTtAxxcXGoVq2a6btt27YhOjoaO3bsMMpvReqoLb/yyitBW6QRsrKyEBkZaboGld+qbfOm66ysLKGyRO9/8+bNQUodO5GJBmQrUpeTk+OI1LHvXUbqgLxFYevWrfH+++8bzyUKlOAniX/++Ud673nz5hkJyFWUukWLFqF///7GlmE0+dE1ZKSOzK/k3pKSkmLcw0mghGhsCAQC+OCDD7Bq1SpT2iCC3+8XjndWCWHvvfdePPLII8bnmJgYZGZmOlLqRL54VhApllakzkvzqyqpY4+j3YNuu+02zJ49O+iaVqSOd2kQBQQBYp86epc+nw+rV69GWFgYpk2bhm3btgmvAQSPgyJSJ1OMCSrmV9GCePfu3ZZK+pEjR0ypqVTGNiuU2pQmgL1SJ1oVZmRkCNUzIM/Mw2Yfv+GGG0wqlVPncSvCSTh+/LjxmV8Nu1XqWHOTiNTNmjULgDgrO2COyOR3CQDy6sFKqXNrfuXz1MlWYwR+kJQlH1WFaOC2Is/8oMdO7lZRjT6fz1gR0rsilwi74BC+bnnzXZ06dSwz3dO9WFUOyDP3ZGRk4PnnnwdgT+pU6paUOvYapHarTJJWpK5r167GtdLT04NytFHkM3svme+WzDmf4Eap8/l8xjX4nI5sGURKHb+af+CBB6SpLFiI8tTxgRJUL1RmPu+elfk1KyvLcBdISUkx6tCJT52T/aQJfr9f2N74vsB+3r17d1AuxjNnzjjyqXOi1K1fv16oVrGkjkhHqIESTz/9dNAxfL1SvcuSDwN5efA+/fRTzJ4927QtWr169XDttdcGLS5Yty2e1IlcHKgcfNnYPkIk/uGHHxaeT2BzDt54442GeZmFiNStXLnSWKiqBkrwsCN1AEw7VYSSIQMIgdTxe+kVR0RFRWHnzp2YMmWK8R2bYsNNpCorv27cuNHEwJ1uDSKzn7Nyf8WKFfHEE08AyGu47D127twpzG0GWJMNdjATkTpSCmQTNpvQWeRkbqfUud0mTCVQgh24eULJg4ivqsLqVKnjHY3Z+rTzm6FJmiZE1v/KqmyyfG9O2mZcXFwQqSPSsnbtWgDnSJ2sDauoB1lZWUHmVxr8rQY+vu5kCzQyRZ89e9ayfYmUGNreTlYWdjIM1fy6YcMGHDx4MMjVQabUiUw0e/bskd6fIDK/8kod7btJfYJftFmZX7Ozs02kzk6pE5lfRfkRA4GAJckX+dQBwaQuMzMTsbGxUjNeSkqKpfk1FFLXvHlzU0Aega1PPp+gap/lCe348eODjpEpdQcOHMCLL74ovG5ERARuvfVW+P1+hIeHG23lsccew9KlS9GzZ0988MEHxvFWSp3oWaKjo22VOis/OhbUxk6cOIGvvvpKOC+JSF2HDh3QoEEDAO5J3f79+03987nnnkPnzp2lxxcaqbv99tuDQop5uN0gvaAQHR2N1atX49FHHw36TTYR2IEkaYKIRKiCOklGRgbuvvtug2DxUa7U2XlSx/s4sIOOFdk4e/YsmjVrhvj4eMudD2RKHUvqRE7yLKnjZXEVpU42QIaHh9umNGGVWapHWTtduXJlkD+gFXJycvDxxx8bSiZgXc/8pt8ipc6ug9NxqqROptQ52Qs1JibGMKuT+Y38Q9gAGCulLiUlxXZ8IFInUuqckDqRUkdlBNRJHXsMay4SXdut+ZVtL/ROu3XrhurVqwdtzSZT6kSkxMrfiEDPuXnzZqOeeVJH/ZkmVn6/ZdnEt3//fixYsMAYH1mlzkmghF3ZRZD51GVmZmLAgAEG8cjMzERUVFRQ1CaBVer4aPx9+/ZZKv1uU/ywpOX06dOYO3euca1du3YpzSkqdSgjdZ9++qn0HL5NUVuh/8uUKWOk9AHMpI6fF0TPERcXZ0nqwsLClNo1ez+RnzyB76N80mVR27YbwxISEnDgwAHTM9xwww2WW8QVGqlbsmQJxo8fj2HDhgU9WE5ODt5//31cfPHFIRUuvyEiJXZ7kNqBj0ZkX6bTTcSpc6xYscJQx6hs7P/s9dkyW2Xht4vsvP3229GpUydLp3nZhF25cmXjb34bK7asgNhPhMq9fft2Izw8JycHhw8fxnPPPScl27yzrJ351U6pA8zRsiqBEn379sU999xjfGeliIai1PGgwV92vKpPnQoiIyONyZlMKm5InV1/yMzMRGRkpEkpdaPUyUgdmwTXqVIHAO3atZOWhSd1Vu2AnbjpOD7iFQhWWGTRryKlzgmpu/zyy3HzzTebzouIiDA9E5WTV+rs1Iz4+HiEhYWZlDpZO1AldSpKnaj+MzIy8N577xlRl9TeZKQuJSVF2E5OnjyJCy64wPAzJDj1qROBbwP9+vUzuQ289tprtnno7NxwRLs2qAgafDvjSR1gbndsvZ45cwb//vuvMaaKxp/4+PiQlLo1a9YY9a5C6ngTLks8d+zYIWzbdu+1Ro0aOH36tOm48PDwoAU9i1Bz2bomdddddx1+/PFHLFq0yAiLz8zMxPTp01G3bl08/PDD6NOnT0iFy2+IJpxQSZ1s/0DRb3bgc4wR2MmKv7fIZ0x0fztSV7VqVURGRloqdXz9UXlZFYf2o2XBpzTJzs4WbhPWoEEDw8yXm5uLAQMGYPTo0dIoYp/P58j8KktEyWLv3r0mPzEriAbP/DK/8nCr1LkldaQukYmVSB1NZETqZGouJfDmceWVV5qOiYiIQOfOnTFs2DA0a9ZMyacuPT3dlBdNRupocjh79qwlYZc5V9OiNVSljj2WjuODLURglTq2nbkldaL2TdeiqHaCTKmzM4dFRESgSpUq2L9/v5IKrTrBeWF+TU1NRWxsrKVSJ7rP5s2bhce7UepE9blq1SpTgGJqaioSExNRq1YtjBw50hifZLCqw7vuugvDhg1zlNybwLczevfs9+yCjK3XPXv2oHbt2pg6dapQ5aTjRT518+fPN65t1d5at25tbKNIEexWpI7fFYvddem///4T9ke7eqL0SizsSF2hKXVA3n6Wa9euxe7du9GyZUvUqlULTz31FAYNGoQ9e/YIHTJDwciRI9G2bVvccccdrggXD9mEA7gndbwpITMzE2fOnMHMmTMdK3WiLZ6A4J0jWLCrNn4iYe9vR+qqVKkCv9+v7AvEfiayIop8BcwpTQCzSZTqnRQalqxQ3creC9/JQ/Gpow48duxYo3yqplAWTkidk+hXHlY7RLDf2wVKqCAyMtJIZ3DLLbcgISHBIHUUAU6kjp+k2PuKSB07sBKpq1OnDl555RXUqFHDUOqsFORffvkF8fHxJlIn6iuhKnWUfzBUnzp2smDzwNkRJFapY8cdEanr3r275bUAeY43gojU8feyU7P9fj8uuugi7Ny505awff/99zh48CBuueUWy/yeGzZswLPPPmt5TxVSd/z4cVSoUMGxUrdhwwbh8S+99JLxtyqp40lAeHg42rVrh4EDBxrfJScnIyoqCps3b0aNGjWEEb8srMaQw4cP4++///aE1FFbkfnRsvVKe1M/+uijuOCCC4RzY1xcXJBQwWLJkiUYMmSIZRmJxJH/rCg/qAy0PzyQR+pEbdtuTq9Ro4bxN/WfIk3qkpKS8N577+HAgQPYtWsXTp8+jeXLl2PMmDHSjuEWmzdvxuHDh/HTTz+hYcOGlrZ+VVjl0MrOzrZUqWQQkbr7778fAwcODIpeswN1Gn5AkCl1AHDZZZcZf/P7CrIN0E6yr1q1qm15RbtJADDez2OPPSY8j8+FxpO6jIwMkxIEmJMSy94LH9AgGkhFPnXscaxPpJP8QVRGHlb1zCfxZgfDXbt2YeHChcod3G5rOKtAiZdfftnRQBIZGYlKlSohEAigWbNmKFOmjCl6LSkpySB1ouhnuq/dwoLIPaF8+fLGfUS54/hzvTK/yvbapChvL5U6dvN1kTLARkyzSh17fVU/Ix6pqalBfYiduEWkjr+X3SQXERGBevXqYdeuXbZt7t9//8Uff/yBsmXLGoqLG8h86viFwbFjx1CxYsWgfklgfeoINWvWxKZNm4THr1ixwvhbdUHPkgDg3OKAnauSkpIQFhaGhIQEDBo0CBs3brQk01b1nJGRgaNHjwaVT0UhtfOp48FyAn4MF43pMvOrE+Tm5iIzM9PVTk5EPKOjo/HII48Y+8Wz+Pjjjy2vUbVqVeNvmltUSJ1qAIgIrknd6NGjUbNmTbz//vuYNGkSjh07hl69eqFTp07SnDOhYM2aNcYecl26dBHuw5mRkYHk5GTTPyvwk3BYWJgxqPH+aaoQqWM//PADAPuJiAe/2T1BJQ8dEBz15FSpe/PNNy2P4UkxOwHfeuutljnKZErdjBkz8MgjjxjnUuNm06BYkTp20O3Zs2fQMXZKXevWrY2/QyF1AwYMwG+//WZZz/zgx69wb731VmWyJYvA48vGD5C7du3CiBEjlO5B4MsdHR1tqsNTp065VupYpKenm9pQYmIiTp48iX///VcakcdC1fzKpzQRlZX9n0CTf36ROtHAzvqr5ubmGpMve32VBKUiiCZy9lrsPs/Urvk+kp6ejv3790tTVPj9foPUsffjfZH5MoQyyfn9fiHp4ecHkVLHm8b5NkCmZDvQ++E3oechI3Xs2JCcnGx8X79+fZw+fRoHDx6UtjGrMSQzMxNHjx4NmusGDx5sWU4guJ2R5UHW/liyzNd9ampq0HwRFxdn2zftkJOTgwMHDiAQCDjuF2lpaYiIiEDXrl0BiK0Zdkoha42hNk6EXIbs7GzXCzMgBFK3ePFivPrqq9i5cycGDx6MMmXK4P3338fgwYPRoUMHfP75564LJcLp06eNVWpCQoLQr2ry5MlISEgw/vEdhAelXyBER0cbREplE2QR+MaakZFhdHpRMmMryHy+rMyvVmDJk93zxcXFoXHjxpbHkFI3aNAgAGqbDQPnfOpIxRGZ0nhSp6rU2e08wZIa8uVjz6lQoQKAvDrnBwE7gsW++/feew/Dhw+XpmsAggc/Uf3RPUVbjbHgU8PwkJE6N3s28+STV2xZUmel1MlIXW5urrEVETvBklJ32223mfxdZGD7iRfmV/4YVVJnt02Yk1QJdE8iWDTReEHqRHBqfk1PT0eNGjWMXQcAc/uIiIjAeeedhzNnzpjeiSwBNZUhVFInAks8c3NzcezYMVSoUMEo7/nnn4+LLrrIOEZkfk1ISAiyiIhA4wC/EwcPkfkVCCZ11Gaof51//vnG/Chz1xEhIyMDJ06ccLwlIhD87mnslLU/th3wY/6ZM2eCFoCk1IWC3NxcI52PVeCmqMxnz55FTEwMZs+ebSx8e/ToYQQRqYBtt0TqsrOzLftooZG67du3o3///kGdbcKECXj55ZfRp08fvP76664LxqNcuXLGpHn69Omg1CFAnnqYlJRk/GO3MRLhtttuM32uUKGCQepU8miJwJM6ljypdH4Wb7zxBnw+nzSK1WmDlwVKiAZMldxsNNBQZ1QldUBeh6FBSETqaPJyYn4F5L4ru3fvRlJSknENeuasrCzTOeygKlLq5s2bhxYtWgjvwb/fGjVqWJI60b65POhdszI+gR0YsrKycOjQIfz000/Ce4lIXVxcnLBNTpkyxWTG5yEjdTRosaSOVXf48rBt8JZbbjH+7tSpE84//3zs3r3bROoSExORlJSkPOBRLipeqXvllVcAmPuqG6WOymZH6tjtpUSQkTrRooTvc/Q+2fHKyc41dnBqfqW+zPbp+Ph49O/f3zieTdU0YsQI7Nq1y/Kd5hepYzFo0CDs3LkTFStWNI5/4YUX8N133xnH7NmzJ6i/8AnEZaC2aFeWevXqYcmSJcY4JCJ1ZH4FIOxf/LxgZ34NBALC/H924IkJBbXJCIvVvqWBQCCI1HnhwpWTk2NYyGrXri09jr031TmRupiYGGOP4FatWqFZs2bK92f7NkvqgLy6Z3ctIRSa+dVq4Bg4cCAWLVqEMWPGuL18EFq2bGl0sKVLl+Kqq64KOiYqKgrx8fGmf1Z44oknTIM5S0rckjp+IGYThrpV6vhVlFtSJzO/ul0V0Hm0WlTZbJhw9uxZQ4IWkTTq0NS4k5KSDJ8GmerRtGlT6eRcp04dtGzZ0ng/RER4Usequ/zglJWVhcGDB+PXX38FkOeoy4If8KtXr26p0PDt00qpE038rMkqKysryNwsytnHEvty5coJzWQjRowwJdflwb9nKjf5mJHyZRX9yit1bF2QL9K+fftMJhu6vpXpggX50Zw9e9ZEMh544AEA5gWXHal78skng4i1FaljB2Unix0WTkgdW36qV6tIP1Wwz2FH6oYOHSpcoLGphlhSl5aWhgsuuAB169a1HIPi4+NDmuRUlEtKR0ImNyBvYcm2v+nTp+O6664LKpsKKD2GXVkqVaqEm266KWj8E/nUAcHRsj6fz5QnExCbDV955RX8888/RvvhIz9VwD8L9U/Zu7KbH/KD1LFR9nzCdNm9acwiUgecmxdEeVetwNYF5aajcSQyMlJYV4Wm1Nnh+uuvF25u6xZNmzZFlSpV0LZtW2zfvh233nqrJ9dlyWlOTk7ISh0PdsB3SuoIvPon8qlr0qSJ0KTapUsX42+W1LETaqikjjqEE1KXlpZmTM5WSh01ejZ6T0QC//zzT9StW9fSYZjdKYM662OPPWaqR9ZsJDK/ssfyMjz/nmSErm3btnj55ZdN+ewA8Xug+4kWUay/Rk5OTpAplVWzadIX7d3Ig93NQASZUkfXu/nmm/Hpp586InUionbixIkgpY5/BisQqTt06BAWLFhgfE9pWNg+zitpDRs2NP1Ge5uycKLUEUaMGBHkryRT8USLNhmpY3HmzBk8+OCDqFOnjvC6IqgQH3bioz7IttkKFSoI+3JYWJjJiZ4NALNzrgfyFkf5rdTReNCrVy/jXqTSWMGO1NH7ckLq2DLb+dSJfFbZHZIAcRsZPnw4unbtatrz2Sn4eiXzq8yX3e7Z+bpm+75VpgoetAMEkDePnz17FmFhYZZ+m2w90twjInUUPKEKdhy49tprEQgETG1G1DazsrIKR6lTweWXX+7p9aZOnYqffvoJc+fOdUQg7HDw4EEMHz7cROrsgiycgF6sG4lbVBYyI7KDfiAQMPkvjRs3DqtWrcKFF15ofCdLA+HEB+eSSy4x/mYHPwDCbW5kEJlfWZJCnYzqjlUWRaSOOoeqUy0NEm+88YZpn0+WXIjMr1bqKF+/Mr+vV199FQ899FBQlF2FChXw0ksvmSZ9IgzVqlULug4/SPHlFeW9Ywd42R7Gdvmf7MyvQJ6vnhWp482voskxJydHqNRR4JHdHrVWi6ioqCgTqePfHUtEcnJyhKqBKqlj30OTJk2CdoaQkTrRZEzXov4hi7h2ulBTUZxEBIcnrzKljuqSVeroM/u/CNWqVTP9Xr16dcty8m1XpS7Onj2LBx98EJdddplxfExMjK3KaldvNKYRqbMrC5kIrUhdVlaWpfmV2iO5F8ncVU6dOhVSZCk/b3Tr1g0ApIsJO99RK6XOjlwTrrvuOkyePNn4nJSUhLS0NMTGxkr9ewFzEm2qa5bUUZuLiYlxtFOW3TPLlDr2PNHYb3lPR0eXUFStWtWU1wpwFyghM0mrNkgZeL+sESNGoHXr1iaCwZOZ4cOHo127dqZJVdaBnZC6bdu2oWbNmgDM2eZzc3Px0EMPKV+HNb/SREArPeBcY69atWpQPjcvSB07WLOkmX1XfL0cOXJE6jTPPgdBRurouvyE4ff7MWLECNPglp2dDZ/Ph9dffx1XX3216XielPETBr+VEaCm1AHWSWRF0a9AsNpmtaMEr9TJBlx2YGcdnS+66CIMGTLEcoC1Mn1HR0eb+jjv4hAWFobMzEw8+uijQeSSQG3FiVIXHh6O+++/3xSEJFsosO1pzJgxOHHiRJBSBwS3o9TUVMekrmnTprbH8OOY3+83jXllypQRWjhYguz3+00Ehf1eBl6ps8pRCORtWM+2C/7a5B/Fg/oTO67Z+SfakTrqEyo+dR988IHRznhSx79jmfkVODd3Ud9JT0/HVVddheHDh5uOS0tL85TUNW/eHIFAwNJ3zQr8GMDWLd/2ZEQnEAiY2gqRupiYGGmqGsBcjzRWsqSuQoUK+PLLL02EUQVhYWFo2bKl9HfROPv333+brC4qQWGmezo6ugQjPDzcNAlSkIUTGVSUPRoIPRpNpBpu2rTJktTRwKlC6pw6Vq9YsQIffvihaRXp8/kcXUdE6kQ+D/Hx8Xj33XdN34kma6ekjjdnENjOzQ/AzzzzjPG3aCKeO3eu6bMs1YFsIqPPfC49v9+P+Pj4oAhBGamjwbFt27bGb16SOplSxw+8dkod+x5lyjs7EIeFhWHkyJEA1NQX/h1ddtllmDNnjlFmloDwizhqyzQuiCYEeo9OfOr8fj969uxp2n5OtLk4YG4HFSpUQGJiolFP7ITH17EsHYoVRD7KPGTJZgmNGjWSml9ZoiRS6qzGyGrVqpmeZ8CAAZbl5J+dv/bcuXNN6jyBJ3UqW3vZkTq/34+4uDglUsf+ZqXUAdakjlRB+i09PV2YFiYtLc3kb8dvc2YHL6OsgeCAO7bP8WOLrH3zpC45ORlnz561Ver4hTRgJnUA0LVrV0tiKEJYWBiWLVuGgwcPSn/nMXbs2JCifjWp+3/4/X6T+XXx4sVo0aKFISU/+eST+Prrr03nDB48GFdccYXxWZZCRdT43333XSOKxk6ilZmCnZK6RYsWmcpDg5jTPEC1atXCnXfeadSV3QQ7adIkzJgxw3ScyKeOVepY3HLLLRg3bpzx2QulTkbqYmJi0L59e9x0002Wg5bI+Zi/t4zUsfXA7rrBRl0RWKdZWQoB/rpVq1ZFIBAwpUERBUp4TepESp2VTx2rjqlkoQfUlB0ZrrjiCtxxxx0A8iYOlsjxpI76JJE6kfnVar9dK6WOLz+tynlzOkuQ6Dy6LttuRGqoSv1QXbZs2dKUukMGKoPoGYC8+hUt7KzMr/z77NixI2bNmmWKvq5SpYpxz0suucR2Wyw6duDAgXj22WeFiycRGaLxkPoNtec///wTbdq0Ed7LjtSFhYUhPj7eIFpW/Yot5wsvvICWLVtKd2ngXV9EoGf87bffhBHE/HjVrVs3pZx7hPwidXfeeScCgYDpmfn3JRsvZEqdHalj++vRo0exfft2Q+ELBZTWSZS9gMoLBO8uFNI9PbtSMQcN3qxs37ZtW6Phnnfeebj++utN54wZM8a0fQ3rZM8iMjIyiKkPGDDA2BfVzm9DlhaDnfz5Dsr6hRAo+zV1EJoY+BWp6iqf6sqOlI4ePRoDBw4M2peV96ljV0Hs5BAWFmbacs6K1Kn6O7ADKKuUxMbGYuXKlViyZInlxKiykgoEAsJ3y9bDH3/8EVQmfocNGamjrboI9B6ovbCDGG3dxRIQr0kdPzA5Uepkefj4lbGKD5YM7LvwQqkj30MRwZf51InKTxM+Dfx0PNvGeMLPjjWiyUqlfmgcWLRokdLx/P7C/JgTHx8vHANlgRJsOdk2ftdddxn1/8MPP5hUJjqX38Xhyy+/NEgxXWvGjBkYM2ZM0LPJkmITqRs8eDA2bNhgBMs0aNBAOulaOd/TvRISEox3LCK9ojbRunVrrFmzxjheptRZzR009h84cAB+v992nC5fvjyqV6+u7EYTSoSmCFReGsPZZ+bJ1TfffIOxY8cGXSM3NzdIqSNyZkXq+PdyySWXBCl1BC996qgP0WJThvnz5xtZF2zvqVa0kg8avFmCEx0dbbmfXXh4uGmAku11GhERIWTq1GDszJYypY6NCOUHWLqmaFKl+1J2ep7UyXYB4EGNW9XsypMSXqljyyq65qpVqwB4o9SxAxK70wf77E6VOhFE6i17XdaESoMRS+pOnTolbYMdO3Y0fSaSRO2FDTZh8/wRCpPUkU/dFVdcgUAgIF3Jeknq2HJHRUWZ2oqdUiczu/j9fqFbg51Sx+9UAJzrj1YBCfSO2QAoOo9VE52QOpUJny07TY60sfqPP/6IZcuWmX7jy66q1PHEkeqCVwd5H8AmTZoYzux2gRI+n09YTlK+fT6fyQLDlpNFXFyc1LpAIKXuzJkzUhcVah9W70zmU2c19rLtyC7Xn4ho20FVqVu1apXtThpA8Lwj280EyPOLpBQhLHiljvLU2Sl1IqSkpAj7oizATAQ7gYTmULt21KtXL+X8eJrU/T9EpC4mJkbq00DnsB3g/vvvF16bb/yk2lEjtiMIKpG4MjJjReqIhPLmI9V0MXTPUEkdkTQ7xZIIkojUsduJOS0Lm6tNNAGL4BWpY+tOpNRNnDhRaropV64cpk2bZqysiRw88cQTAM5NsNS2efJhlQfKSaAElZtXLcLCwtC4cWPceOONQdeg6FfqAyr7RbLHhUrq+H7BK6+8UiebEPx+v9SPjCBS6kR9hu4hmkjofZDJmoKVgDzzJN2HN5Fage6jug0XPdOHH36IvXv3GtsntW3bFtdcc41xLR68+ZV9D7wKzZM6/ntZO0lISAg6lsCf4/P5hAtX2cICEI//1atXtx2z2F1VZHVM17Bq0zKlzgr8AlV2zpIlS0yLGlWypnpcu3btjLbywAMPmHyTReUloiNLfC26P6lYgUDAeE7ycT98+LBjpY6uKeqL9957Lx5//HHptVjYvSd6Vpk/vhtoUvf/kCl1MvmbzqHfZ82aJfVLocZHkx4NHtTI7LafstqVgOCE1FmZX48fP44ZM2bY3o+9p+o2R/wgQErRY489BgDo3LmzcS3RgEGDnojUsbmvnJaFVerYzm11LVVHVlFnlQ2G9HxNmjQR/s6TsjJlymD48OHGbgwpKSn43//+Z5A6Nu1Fbm5uEPmwSgvkRKkTpaQBzuW7mzZtWtA1hg4dimXLlpnUIhFUlDoiOLfccotlGiW23CpmM0DslsEiPDxcSOrY+lNdKFC7cEvqsrOzjYUS1Y9sD1YAJkKtQuromMTERKn/sGx3GlmgBE/SecWd3pnMj49QpkwZ41hZABJbHlEdyywtbDlZVKtWDbVr10bHjh3x2muvCc+bM2eO8f5l4yS/JaIIfJ9TWUjzQV+y68fGxgqJth3c+NS99tprGDp0qLQcgJjUid4XW2aKhs3NzTUWwZR+6/Dhw4iNjbW0QPl8PgwYMCBIDRM9Y3h4OHr37i29FgtV86vqHsJK9/TkKiUA4eHhyM7ODiJ1RLhEK7Lw8HDjpVl1BGoY//33n4lAqDphskqdrCM5IXV0DRGpK1++vHKnDpXUlS9f3jQ4Va1aFdnZ2Rg3bhxeffXVoPPzi9TJCJpV9JvVVmUs2MlXdG8WNOhOmDABU6dODfpdlHaDvV5ycrKpTdGigeqEXxxcd911+OSTT4T+Ql6ROvZ/HocPHzbKyLc5eiYVpW7Tpk3YtWsXFi5caOmbwpab3yKQB6/UydpCcnKyUHmQmV9Voh9VSF2VKlWM5yFSl5WVZbwDupaVid2pUqeihouez4n5ld0WkP3dapytXLmySQ1UyVNH31155ZVBzyeCjNTFxMRg+fLlQeZaIG+R0bBhQ1tSJyOjVs8gW+izbU3V/MrPbV4rdTxkbZL3qRO1E9n9RUEVpB4fOnQIsbGxlvXr8/nw7rvvGvuYE2Rbjar6nauSuoiICFSvXh1333230nUt7xnyFUoIaPBmVbOYmBihEz97jgqpowYQHx9vssdT3jE7W7lKPi+vSJ0TqAZK8Pcl8B0tLCwMPp8PTz/9tDAPkRWpc2p+VSGuVvWydu1apfuI9lC1U+oiIiJMSZ4JsrxrdL309HTT++ZXpvxEEBkZid69ewtX/QVB6tgysu+jTJkyxgCvotQlJiaibt26wnLfeeedRhAGO3m1a9dOWia2zOHh4UhOTpbuqWt3Pn9fUb3u2LED69atc6TUxcXFGYSXdaPgSZ0MbASoU586q34hej52hxKe1FE5KWiK+jav1NE5fLAakLc4YO8tI3VXXXUVxo4dawRAfPPNN/j222+lz8JCRuoIIhWI6lRVqXPiUiBTYGX9PywszPb+BNVyuN1jWFYOvg7ZZ1EldYFAAJ07d8aWLVuMbRMpUEKFhNKzX3vttQDkW+15TerovjNnzjTtiOEGmtT9P8LDw5Gbmxuk1NmROj6th5MQ6Nq1ayMQCODSSy9VPkcmIcvIDD8Bf/TRR0ZGe1qlOonmEd3TrU9dbGys6d52HcWK1BFUn0XUwXnzptXkxa/oZGAjVPmtz3jITHYEWVJPdhAWKXUEivKlzO804IjqzIlqIesjTkgde0x8fLwx0fBKnV2gBF/uuLg4YzN5ti9QcIHsWqxSB5h3g5k5c6aRZ062IGOfx84J/aKLLkLz5s2VSB35Jvl8PqO+6ficnJwg86sMR44cMRZVdjuIEMiZ22rLJpn5lR0nRQoMmcPJKsErdVFRUUhKSjLyFIogW2DTNSpXroyJEycax3Xp0kXZ6V3kqsC2IdG4zAereUnqWIvPxx9/bIxHMlJnlbvQrVLnZOsuFfDmV3aRKKo7tr5YUufz+dC4cWNTiqXY2FjUr18fL774Itq3bx90Lb6/V6pUCXXq1BFG2LLH2UHVp46u5/P5bHdMsb1nSGeXIFAD4QMlnJI6pxE2dB1V0PX5vElEsF5++WVjlQGIs/9TmWW+W6pwq9RR542JiTGRUbvrqAx6opW8VVmsru9WwWTBbplDAQMyEszeX2Tuv+WWW/DPP/9g0aJFGD9+vPG9zPdEptS9/vrrppW+aEFg1Sb53+gZRXnqAGvSz5uIgTwiJto5AQg2x/EQ+VNRXbITs1XQBFtmUT20bt0arVu3BgBphnn2PPb5VfKUycYaIG9rNPIZYhcJzz77LH788UdjIrQbUxITE03qhcoYdP/992POnDnSnG3sM7BjEP+uRP5bZJKjYB/qe+yx8fHxlm2JVVdZ0PuXkZUFCxbYRmeKzmWTpauQulACJQikPrELsT59+hj7ULPjBtuOcnJyPFPq2rdvj6SkpJBzuPGgBZyI1IkWnmxboHbCzr/8NmM+nw8PP/ywZX9nF41///231E3Da1LHHueEDwjvGdLZJQiiihQpdQ888IDpHH4gUU0HYnfv1q1bm7YSItD1eb8EmhQfeughLF261PieH4yioqKwbt06rF+/PuTG40apCw8Px6pVq9CpUydUqVLFlVJnhZdfftnSOZwtCw/+OUQBLKo5nIYOHYrTp0+bnmnWrFn466+/pOewx8qCGGrXro3u3bvjqaeeMr6TkTqZUpeQkGBqP3ak7q233pKWGchLzL1x48agHUF4pS4xMRHPPvus6Rhqz2wSV1LqypYtGzQo2rUBkelNROpYiNqCjCDw38miH1VJp6gcPDlmr+f3+43JjsaksLAwI2emqvkVyKt7J6TO7/fjjjvusOzvdJ2HH34Y1113nansQN4kZqXU0cKD9TVShcz8Sv1Adq2ePXsaCqgMonPZNBRW5ldqI6EqdampqZg3b57wN1GyeXYs8FKp8/l8SnsFW+G1114L2raM3Ah49Yr9Tobw8HDMmDEDH3zwgek78jel/wFx5gKe1Nn1B9W50+440RyqSZ1HEFVkTEyMYeqjAZSNcmKVOj6HU6j3rl+/PrZs2RLUeWjw4KP3ZOZXfqCIjo7GRRddZHIQdgs3gRJhYWG48sor8f333yMqKspzUuf3+y2dw2XXEuWQEil1Mn8Hdl/Shx9+GBMmTAianKOjo6VJdvky2aVKkJ1npdQREeTbqBWpq1SpEu69917b+4uiTkWkbsyYMaZjqIxxcXE4cuSI8TeROh52E46I1FmlJZJd00qpkwVByI6xKh9fVkC8XZ7oPJE5n0/AawWnSp0K2Gcgf7WKFSsa9cmTOl6p482vVhHaw4cPN3wpgeBtvgh2pE4Fbkid1+ZXK2d/Ealjj83Ozpa+Y5mLA4F8EAmq470VHnjgAUybNs1kTrcy59I88dZbb0n3Qh04cKDJJA6cey/s+EykTpbRgv1fBiuzPbtHt1bqChEypY4agJ35leAVqaOOJVtFqZI6kVLnFciZv1atWkrHW+VKArwxv6qCv5bo3iJSxw8aBLaeX3zxRUcJKkVlYgecxYsXW57HEjl2EcC3xUOHDgEIbm9OfepUwZM6kcLDtmPqY6TUifLoUR3JVu78PchnjC0HgdJyhKLUOSV1KkqdKF+aqBy0aGDv5Vap82KiZu9L7XfBggXGTjYEkVJHz0LmVxWlbtq0adi1a5fxmSLNZUpdKG1a1HZZUiciCPnpUye7hozUicyvAwYMwPTp04PGKr7OWasA4D5AQoQpU6YYf1v1bWoPNWrUEAbRyUDXYhfjVnO6qlLHL9jHjx+PefPm4bXXXsNXX32Fe+65B4B6oARbp6HWr3ezZDGHTKkjiMgaq+5Q4xEdZycdW4Xc8x2MGolbpU5G6v7++2+lfHgsunbtir1790pzVvGwI3V2HYkcunNycjBkyBCMGTPGddJGvhyqpE6m1IWiAhBkZr1OnTpZnscSTXanAVVXACc+dXZtuWzZskYiU7tAiRdeeMG0OTtNjKTUWW2pJANfPqs29ddffyErK0voW1pQSh17Ppn7VUmdyH+OJhsVf9C4uDhbwuEUVBZqv+QDxkKk1NF5lF6ID5RQAZE63v1CNcm7FURtkVVURb/z0a9e+NTJwAaUENj7icyvEydONJklCXZ5/qzy+YUCK1JH3zmto65du+L11183zRMNGjTA+vXrTfO7yKfOCZ588skgNwNAXanzktRppe7/wZpoaOJmB1yZGYAndV751FGj4nOoOSV1fMSkbBKqU6eOZeJWGVQJHRA6qQPM5h2rDPB24AcOVVLHrhIXLlxo/G1lJlIFOyCzf9tdm603Ni9eeHg4OnbsaFoNi6BC6l577TUMHjzY8joAsG3bNoNs2JG6Pn36mJ6N7kmkTqTU0eQlI5f89+zgzP8WGxtr2omARag+dTJnfbY8K1asMEXVEulgVQA++S4LGgNESp3KAm3o0KGYM2cOAPsE6KqQmbrZcZJ9Fhlpo77nhGzSgoYUaQIttEMhdSLYWWXyK6XJt99+i6+//tr0HbVt0SKB/ubvL6t72Y4xQJ7yOn36dKVyOoUKqXOqtr700ks4cOBAkI/wxo0bhURKVanjwdetU1LHHqdJnUegl9i5c2fL7Xp48GkhRHu4qTh58qDGxWerpxcuC5TgkZCQgGPHjhmfnZpfvVrBA2JS17lzZ0f3onqJjY0NqfG7JXUiEgJ4o9TJzDlOVo28aWL58uW46aabLM8RtR2+XT/wwAN4++23be9fs2ZNI2WAnflVNPFHRkaaAiV42NUF/yys+VXWD52SOplS98033wiPAc69W/Z6V199tcl1gVLqsGSBCJ6VUsfWKxE9CoqxQrVq1XDVVVeZ7h0qZEqHrK+ydT979mxjuyc3keedOnXCqFGjgvYEpfpU3QVGBZMnTw56ppdeesmkTPLmV1kdqOwoweK6664LivKnds+O7yyJFfnU2eXLFH3u2bOnMJDHC4ien9xJ3Cp1ERERQWNibGxskIDBK/Mq72LixIm2x9jNaS1atABgVudDnXM1qft/sCtM+lslDw81BupUr7zyinA3AJV7s6DGK9pPU/S9VdJdKx+gK664wrJxeuVADYiTnLL+Yk6UutjYWFeNn57fDalr27atiYiw53jh7ycLlFAhr0TyReWwi1SzSxfgFLyfluxaIgUyIiICcXFxuPvuu3HXXXdJr+1GqbMrLws3PnXsO+Pbk8yJnwVNwiyps0pRQr+RHxpwrh1Y5XLs3r170HdekTo7X1L+/bDH9+vXz8j7p5pEnIXf78cLL7wQtODND6WOtjZkMWLECFPeQl6pk5FKlR0l7EACBGs5adiwIXr16gVA7FPnRqnLL/Tq1Uuo1P3999/4559/XJM6K4iUOupnKvcZO3asbXu3m6Nuv/12HD9+HOeff76wXG6gfer+H6yDL71YJ0odDULlypXDI488Ypkkk4cVqbv22mvx7rvvBt2PXaUHAgFlUscrdRs2bLAtm1crXNF2RLGxsYiLi0NKSooSSaNj3Cp1UVFRSE9Pd0Tq6tati+uuuw6vv/667Tlewak5d8uWLTh48KDwN5bUifYXdDOBWoHarp35VfSMI0aMwPXXXy91BXDqU8cqdTI4jX6V9ScVUme1cBGROvpbVIc0BrCqXKtWrfD0009LU++cOHFCqLR4RXimTJmCZs2aKSdQlbVzL3JEErzwqeMha1MioiAiddWrVzeiOL3wqbvsssuwaNEiNGvWDG+++SaAvLb2+uuvY8GCBUKfOlnd2/nUeQ0af/79918A5j5csWJFVKxY0bX51W1ZVO9jN7aoXIcnhnTNhx9+WJgo2Q5aqft/UOVHREQYkmh0dDSmT59uuXuAnWlHBaLORR3pxRdfxDPPPINjx45hz549Rnh57dq10aZNG2P7IrdKnR2mTJliDNChRs7KfOqc+DGQn4gb30Wre4g6J91r165dQYSOv1Yo718EpwNpjRo1jHbLg62rULOVq4BX6lTNr0DevrdWvp1uSJ1qeVmoBkqw57LqOX8eDdxWvmsi86tVOUSm1rCwMIwbN05qIouNjRVe6+qrr0ajRo1Cbh/x8fEYOHBg0PdkAlP1g5Vl8neD/PKpE8GK1NH9t23bhq1bt+Ktt97C2LFjPSF1QJ4CywsRdG2RT51sLBQpdZ999pnJh9hLUMAhlccq+lWUtYBNdO30vvzftJhQJXVvv/22ZTJuNwt/KkuLFi2M3X+cQCt1/w+W1L3wwgsYMmQI/H4/hgwZYnkeRQKFkoxRRJao8cbHx+PJJ580vn/uuefQt29fVKlSxbQfpVulzg4PPPAAHnjgAWzbtk0p/5sVvCB1NDC6JXWyrbFUferszvEKXqYN8NL3UAUypY4vhxs/RDfm1y5duiAyMlIaRRyK+ZV9JjYqkG8bjz/+ONauXStNiQOIlTorUkck3snALyMO5cqVw9atW3H69Gns379fuGdxKOjatStWr15t7MRhh3HjxmHcuHGe3LuwSB1vfqX7Uyooyv9IBNYLRYxfKNG9c3JylMcBUa7HHj16hFw2O1iVT2Z+DUXRFQkctOhSfRc9evQQ1o3T3ZZYyFIwqUIrdf8P1qcuIiLCMkksi//9739YsmQJrrnmGukxdhOjqHHJCE5kZKRwv0krUsc2DrdRmpdeeqmj/EAiyEgdPatKIyYThuoWNfyG3US+69ati6eeesrYm1V0bz7pJgs2qS0QmlLXqlWroFxQBYlZs2ZZrjadQqbU8XBDNu3y1PHflylTBhdccAEyMjKkkdosqSN/M1WljgW7sOOPueKKK7Bv3z7LdktKHRsgYlWOChUqIBAICHeekcFu4XTeeefh0ksvxZVXXumpWubz+ZQJndfIj0AJFViZX1l4pdQBwQsUGu9zc3OVxyg3areX8DL61QpspgBeqfPqmUMhdW4X41qp+3/YZZ2XISwszDa60A4i9cyJLf3xxx9Hly5dpL/LlIWChhdKHZFXFaUuMTExKBr5xIkT+PXXX3H55ZcbOe9+++03Ybm+/fZb7N69O+j7Zs2aYejQoZ4NML/88osn13GLu+66C/369RMqVo888ojj66n61LmBU6XOShnjr/nOO+/gpptuwuLFiy2VOtm92YnAzTPXqVMHK1euRGxsLL777jvs2bPH2KLNq7am2v/Xr1/vyf1kKF++PG6//fZ8vQchv/LU2R3HkzoZ8pPU+Xw+TJgwAX369MGaNWuUriGKIC8IWLky5UegBJvT06351Q6FodQVC1K3ceNGDB8+HGFhYahcuTLmzp3rSQoJEbzINwYAx44dw9GjR3HJJZfYrpB4UudU9ZFtKk7ITzOhE3ih1BHsSN2xY8cQERFhON8CQKNGjTB06FCT0ml178TERKHJmdIusNvVeO1TV9BwQl7soOpT5wZOU5o4SZQaGRmJChUqoE2bNhg/fjwAc7to3bo1rrjiCmGqFUBsenOCV199FYMGDYLf7zdS/VAamfx2EC9oHD9+vMDuFRYWhqZNm9qOk15AROpE+RZZeEnqRO3uiSeeAIAiT+qsIk9lPnWhQLQTEql3ZMFxi8I0vxYLUle9enUsXboUsbGxGDNmDBYvXmyEansFksa9InUVKlRQNhE6DV5wisJU51h4odQR7EgdKXTs/d5++220bNlSeG83Hah69erYv38/zj///CJP6r7//ntLv08v2whfp/lxbVWlToXU0TlRUVHw+/0mX1W2TdaqVQuvvvqqUjndkLDY2NigYJf8UDtLIzZt2hTS+W76N70zu7xuTvPUuYXqMxQWqatatSomTZokDLTJD/Nrjx49jL2oaYxq2rQpDhw4ELKrEUH71ElQpUoVYxKPiIiQNrKMjAwkJyeb/qmCpHkvFUDVa3m5H6sV3EYJeYWoqCjL9C1OOmydOnWUjmM7hpXTu+sOVEwm206dOqF58+YFci/VQAk3sOtTbkgdTWJWG3yLrm0Fr9qFlU+dRsHh9ttvVyI3ojyWdqTuhhtuwAsvvFBkFt988EF+WcV4+Hw+jB492rT9GiE/zK/169fHP//8Y9yb4BWhA9z121Kh1BH27t2LZcuWGXIyj8mTJ+Ppp592de3CJHX5rdQBwNq1a3HppZfm+32scM8996BVq1ZB3zshVl26dEGVKlWUFVW2U1mphG47kFUYfmmFLPlwQZhfBwwYgJ07dxq5ulTaCb07O1InQ0xMTFC79prsa1JXuGjQoAEyMzNt3+uAAQOwf/9+/P7773j00UcB2Ad11axZE6NGjfKsrDJQO586dSpuu+026XG8WbKgAyVEyA9SB+SPNQHQ5lcDhw8fFm4AvWTJEvj9ftx5552YNWuWlCyNHj0aDz/8sPE5OTnZ0d6kgP1+fk5AL8cqMhYoGKVOlsOsIFGvXj3Uq1cv6HsnSh27FZMK7JQ6urfbTs1fUxSZXNoQKlG2gl2gRNmyZfHGG29gz549+Oqrr5SuaUXqRJt080hNTbU8LxR4odRt377dMpJbQw0qY0R0dHSQ715RUeAIZcqUscxH2KhRIxw9ehTNmzfHf//9V6RIndeLGy8XnCKUelJXpUoV/Pzzz0Hf5+TkoHv37hg3bhwuuugi6flRUVGuCdL111+PZ599FkOHDnV1vgxHjhyxze9WUObXoor8JAEFZX4NBALIyMgoNubY/ASv1HkJ1Qlm4cKFyltfsT51PFhTlIzUiSYEr6NVQ7nexRdf7ElZNIoHLr/8ctxzzz0hXYPdxaEokTqvy5LfhLvUkzoZ5s+fj19++QUpKSmYMGEChg4dij59+nh6D7/fbzhN2mHRokX47bfflI5V8ekpCPNrUUabNm2wadOmfCEB7GRopdSFan4FvAuyKe6Q1engwYM9u7aduTsyMlL5fVgpdSypc7Kdmja/ahQWNm7cKPzeqYuI1znbQoHT7buKCtyQxlJB6vr27Yu+ffsWdjEMdO/eXbgptluUdqVu6tSpGDFiRL6smgpSqdPIAyl17Pv0qn7yY1C3CpRgiZwOlNAoCVAdZ/MjjYhbhOKjpnLdomQiDzXivfDflkapJ3URERGmRJBeQlWp8+L6XmPfvn3F0pzboEEDADAiy3jMmjULBw8edHVtel9e7mGrqtQVJqkrju1Ao2jB6cKqKJE6iogtLqQulEVsqVDqSjpY8+uuXbsKsSQlD6pKndtOmJ+T7fnnn59v185PtG3bFgDw+++/C3+/6667XF87JiYG33zzjafbmuUHqfNqkvBKqYuKilL2MdTwHjfffLMpWXlhojgqdW+++SZuueUWaeJvt8hvUheK+VVvE1aMQUpd8+bNUbdu3UIuTfEDvxUYC9Xo1/wkdUuWLMHJkyddXb84omzZspg2bRquuuqqfLm+1ZZ4bmAVKMFGwxeGid0rUvf7779j586dXhRJwwUWL15c2EVwjKJE6hISEoSZMUIF5b/Nr3lX+9SVUtht+Kwhx4IFC9C0aVPp76rmVydO8KLr33LLLdJjQt0buDhi+PDhhV0EZVgpdW3atMG4cePwzDPPKJG66OhopKene1Y2r0idLJ2QRumBW/NrSfbnrFy5MtasWYMrr7yysItiQJO6EgBSCELZcLq0wm71xnYMq31nQ1HqMjMzi8RqVsMdrEidz+dDnz59lEldVFSUp6SOUJInVo2ChVPza0n35+S3jixsaFJXAkDRgprUeQ9V8yu/NY4TFNQ2Ohr5A6voV8CZid7roCcd/arhFcjXlf63g1vrhUZoKBV7v5Z00EvUpM572Jlf6Ts9gJVeWPnUAfa7WLC4++67vSsYNKnT8A4XXXQRAoGAEZ1uB52myT0KM/pVk7oigqpVq+KZZ54p7GKUOOR3oIRG8Qc5S8sUVydtZPLkyZ76xub3NkYazvDMM894HqhTVKEXuu4xYcIE9OzZExdccIHjc3X0awmB27xdGtZQJXV6ACu9mDNnDhYvXixdGTshdT6fz1P/yltvvRUrVqzQJv4igieffLKwi1Bg0GOie9SuXRsLFixwdW6oCzit1GmUaKiaX7VSV3pRrVo13HfffdLfC1PNve+++5Cdna0DcTQKHDVq1CjsIpRKEKlzO97okUKjRKMgAiVKCmrXru3p9nclBYVJ/H0+n/an0ygUrFixAtu3by/sYpQ6aFKnoWEB1R0lypUrV2BlKqqQbetV2hHqIKuhURxRrVo1VKtWrbCLUeoQ6nijza8aJRqqyYf14KUhA/mzFddt2zQ0NIoPSIjQSp2GhgCs06nIAZWIXtWqVQusTBrFC2XLlsV3332Xb9ueaWhoaBC0+VVDIwRQ9n+t1GlYoXPnzoVdBA0NjVIAInVuo481qdMo8ShXrpzU2Zykbqv9YzU0NDQ0NAoCWqnT0LDB8ePHkZ2dLfztqquuwo8//og2bdoUcKk0NDQ0NDTM0KROQ8MGYWFh0n09AfW9EDU0NDQ0NPITlB8wPj7e1fm+QAmO009OTkZCQgKSkpJcV5CGhoaGhoaGRkEgJycHq1atQseOHV2dr0mdhoaGhoaGhkYJgM5Tp6GhoaGhoaFRAqBJnYaGhoaGhoZGCYAmdRoaGhoaGhoaJQCa1GloaGhoaGholABoUqehoaGhoaGhUQJQoqNfA4EAUlJSEBcXJ9z3U0NDQ0NDQ0OjpKBEkzoNDQ0NDQ0NjdICbX7V0NDQ0NDQ0CgB0KROQ0NDQ0NDQ6MEQJM6DQ0NDQ0NDY0SAE3qNDQ0NDQ0NDT+r70zD4+i2Nr4O5mZrCRhC4vIIqKCCwIXFQQUFRW8biCLyod49YJXBcQFEOEC4u51ARVFroq7wEVRVGRxARUQBBGRRRAFFRAMIQshZJ3vjzynqa6pqq6eJZNJ6vc8PJqZ6e7qru3tc06dqgEYUWcwGAwGg8FQAzCizmAwGAwGg6EGYESdwWAwGAwGQw3AiDqDwWAwGAyGGoARdQaDwWAwGAw1ACPqDAaDwWAwGGoARtQZDAaDwWAw1ABqtKgLBALIz8+H2d7WYDAYDAZDTadGi7qCggJkZmaioKAg1kUxGAwGg8FgiCo1WtQZDAaDwWAw1BaMqDMYDAaDwWCoARhRZzAYDAaDwVADMKLOYDAYDAaDoQYQd6LunXfeQVZWVqyLYTCETCAQwBdffGFWZRsMBoMhosSVqKuoqMD8+fPRvHnzWBfFYAiZJUuW4MILL8SCBQtiXRSDwZE5c+YgLy8v1sUwGAwaxJWoe/vtt9G/f38kJIiLXVxcjPz8fNs/g6G6ceDAAdt/DYbqSm5uLq677jqMGDEi1kUxGAwaxI2oKy8vx7x58zBo0CDpbx555BFkZmZa/4xFz1AdMW5XQ7xQXl4OAMjJyYlxSQyG2sMff/wBj8eDH374wfWxcSPq3nzzTQwcOFBqpQOA8ePHIy8vz/r3+++/V2EJDQaDwWAwGMJj/fr1AIBFixa5PjZuRN2WLVvw+uuvo3fv3tixYwfuvPPOoN8kJSUhIyPD9s9gqK54PJ5YF8FgUGKsygZD1UNzQ0VFhetjfZEuTLR47LHHrP/v3Lkznn766RiWxmAIHTNRGuKFUCYVg8EQO+LGUseybt26WBfBYAgZEnXGUmeo7lBMncFgqDoozCwUA0BcijqDIZ4xos4QL5ClzliXDYaqg+YGI+oMhjjCiDpDdcdY6gyGqseIOoMhjjBWD0O8QKLOvIAYDFVHOAsljKgzGAwGgxBjqTPEI3/99VesixAWxlJnMMQRJqbOEC+YmDpDvPH555+jUaNGcb2g0oi6GsKVV16JadOmxboYhihjRF3s+OmnnzB58uRYFyNuMJY6Q7yxadMmAMD27dtjXJLQMaKuhvDhhx8KkyobaiZG1FU9/fv3x9SpU2NdjLjBiDpDvBFOOpDqghF1BkMcEc+DTbxTEwb8qsQslDDEG+EsMqgumDx1BoPBoAEN+LXBAhUIBHDw4MGwzmFi6gzxRjhWruqCsdQZ4pI///wTv/zyS6yLUeWYmLrYQW/AtUHUzZo1Cw0bNkR2dnbI59B5TvE8eRpqHjVpXDWizhBXNGvWDCeeeGKsi1HlUEc1k2HVQwN+WVlZjEsSfVauXAkAyMnJCfkcTqJu1apVSEhIwE8//RTyNQyGSFLdLXU6Y4+x1BnikniOeQgH6qi1wVpU3agq92vLli1xxRVXRPUaTkQitsjpOa1evRrAsRWHBkOsqc4xdb///jvS09Oxa9curd8bUWcwxAE0URpRV/VUlaXut99+w0cffRTVazgRiUUhNDHKXFperxdA7bB8GuSsXLkS6enpOHLkSKyLUq0tdfv378fRo0exb98+5e/CiWU1os5gqGJIzFXHN8maTm2KqYukpU42ufh8PtvvagLfffcdPB5P2ItMahOvvfYaDh8+jK1bt8a6KNVa1NHLT0lJifJ3VHazTZjBEAfQBPif//wHp5xySoxLEx+UlpbizTffDHugrk0xdZGw1DmJtZpoqZs7dy4AYPPmzTEtR05ODkaMGCEUAKtWrcLrr78eg1KJadGiBQBg9+7dMS5J9RZ11J+cRJ2x1BkMcQR17F9//TWus55XJTNnzsSQIUOwYsWKsM5TGy11kXC/yiBRVxueZ1Xz9NNPY8aMGcI2361bNwwdOjQGpRJTr149AMDOnTtjXJLqLercWuqMqDPEJdWx80UTMwG65/DhwwCAvLw84Xf79+/XOk9tstRVxUIJcr9Wp+d56NAhfPnll7EuRtgkJSUBAI4ePRrjkjhTXFwMANUiRZVbUffSSy8hPz8/mkWyoH4ybtw4NGjQQPo76rMHDx7E2rVrXV3DiDpDzHF6a6lpVKcJMF7w+/0AKt2wPN26dUOTJk20zlObkg9Hwiqp636tTs/zrbfewpVXXhny8dXF0pOcnAzgmGCqzlAZi4qKYlwSd/X322+/YdiwYRg7dmy0iwXgWD/ZvHmzMtUQlf21117DOeec4+oaRtQZYg6tmJoxYwZeeeWVGJcm+lSnCbC6kJubq1wRRqJO9ALwww8/aF+nNlrqwrlXXUtddWrTR44ciQsh5EQ8WeqojNWhX7mJJaWXxMLCQtfXWbp0qet2r/t8wrGuG1FniDn0djdixAjcfPPNWsdMnz4dAwcOjGaxokZ1mgCrCyeffDKOO+446feJiYkAxJY6N9SmmDq613AmWt3JpTpM5kRpaWmNqF+y1MWDqCMRXR3agRtLnVPKHhkbNmzApZdeihkzZrg6Trdd8mX3eDzaz9bnqkQGQxQIxWQ/evToyBekiqgJE06k+euvv5Tfk6gLx1VfVlZWKy110XS/Vseci+GKuurifiVLXTxYHcMRdTt37kT9+vWtxRbh4qb+Qt2ykWLw9u7d6+q4cCx1xcXFlmVchbHUGWwUFhZW+QBdHeIwqpJoP99Dhw7hmmuuQUFBQVSvU5Wo3K86ZGdnw+/3WysJdepg7dq1EU3RkJ+fj379+iE3Nzdi51QRCUudrqirDiK5oKAAeXl5KC0trVE5IOPJUldaWoonn3zSWtikQ5s2bXDWWWdFvExuRB31FV1CFf66/UR0Xt1rxY2oW79+PXr06IHzzz8fAwcODNsNYxBTp04d3HHHHa6OmTVrFq677jrX16KOUR2ykFcl0RZ1mzdvxnvvvVct0gtEChp0Q+33Bw4csP2tM7iec845Ed2b+P3338eCBQvw9ttvR+ycKqoips5J1Hk8HkybNi3k67uhWbNmqFu3rtVGQhV21WVDeHqm8SDqqIxr167FPffcg6lTp7o6PpJjlZt0IKG6X2k8ctvGdMd+0Xl1rxU3oq5Zs2ZYsmQJVqxYgTZt2uD999+PdZEixgcffIAbb7wx1sWwWLBggavf33LLLZgzZ47r65BLrbZZ6qJt1aABNtLisbS0FIcOHZJ+P2HCBHg8HjRr1sz6bN++fRGxdukm7ZTBv4nrPptIPsOqDnyPhKhzmkjo3CIXIbuC76+//kKnTp2CxPXKlSsdJ9+9e/c6bqsEwLJMk6iLhUv47rvvlsZZ7dixAx9//LH2uejZxsP4SPVP/ZNfeLB48WKMGjWqSsriJnEv/datpS5UUReO+1W3PcdNTB2bssDv9wt9y8XFxbbBpapyz4TKBx98gNTUVFx99dWxLooNtw08VPx+P4qLi+Ni0Iok0Z5sorUS7aabblLu6vDwww8DsMeZ0OKHcOOTdJN2yuDfxGPhLuRTVOTl5SE5OdkSe5Emkpa6Tz75BBMmTMBDDz0k/F4k6lgryK5du7Bhwwb88ssvaNSoEQDg888/x0UXXYT58+fjmmuukZaBXhJ021C4ljoilDb71FNPAQBuv/32oO/OOOMMFBcXa5+X6s2NK7OqqaiosNLaAMeeGT/GXXXVVSgpKcGTTz5phVJEs0xsWXR+69ZSF6r7NdSFEm6OjRtLHfHbb7/h008/xeWXXx703SOPPILMzEzrX/PmzWNQQn2uvvpqXHLJJbEuhkWoDTxUqHOHI+oWL16M33//3dUxP/30U0xjbvjOGemA7GhZ6hYuXBjR87lBxxXVt29fqcWYr+9wns3VV18d0kIdfjVj3bp1heMYlS9SK31Foi4QCODRRx9V5sqichDPPfdc0PcqsU3feTwe6zxsX6dr79q1S1kGt8gsdX/++adWqEe0xj+3Cx7oPpYtW4YdO3ZEo0hhw9c73SPf30499VQAwJYtW6JeJjeLd0JdKKFjqcvJyUGfPn1sMbRVYamLK1GXn5+PIUOGYPbs2UK1P378eOTl5Vn/3E72tR0aRKrKUkcdKZzJq0+fPujevbv27/ft24e2bdtab9RVyfz583HgwIGgzhlpgRktURfLWCMaDKdPny6dHL/88kvpfp38YBqq9erAgQP44IMPMH36dNfHilJUfPrpp8LfTp48GX379g2pjITKUrd9+3aMHz8eEydODPruhx9+wLXXXotAIGBrQ6L2pLLUsdelNs6KukisaBYhE3VNmzatVi/RTtDz27JlC4YMGRLj0ojhx24Szfyzb9u2LQDg+++/j3qZqK25EXWhLpRQjd1z5szB4sWL8cEHH1ifhbNQosaJuvLycgwePBiTJk3CySefLPxNUlISMjIybP8M+tDgWlWijhpuuKJmz5492r8ll/ymTZvCumYoDBgwAAMGDKgyURdpF6NM1F111VXWJujhIrNa0jM7fPiwdCPzw4cPS++Zn3xCFbxbt24FABx//PEhHQ/oxdT9/vvvrtMl8KgsddQPWNcZcfvtt2Pu3LlBq0hF7VQVUyey1LH3LhN1paWlyMjIwBdffKG4Ozkq9+vKlSu1zxPrFbRsvVXXxWSyF/I9e/bgf//7n/U3GWGcUhfpUFhYqDwP1ZvO+BdqTJ3OYgzqW3SNo0ePBrV1UT66J554onZY6ubNm4dVq1bhgQceQM+ePSM2iRiOEa6ocysiVG9U3377LS688MKID6yxyj1F1z148GDQ/UbaolbVlrqFCxeGtPpZREJCgtLNB1S6LVevXh00sJeUlEjvmZ98QhW8buJ1eETCRkZxcXHY9aey1FEge1pamvS4SFrqRO5XmagrKChAQUEBnnjiCdFtORKphRKxzr1XVlaGRo0aYeTIkdL2FutcejIr65IlS2zJ4el3kUizdPbZZ1txmSLcWOroN27nPDpONT/xLtqUlJSgxSKi45955pnaYam77rrrcPDgQSxfvhzLly/HoEGDYl2kGofM/VpSUoINGzY4Hu82Nk5lqRs3bhy++OILrTiUUAa2qnYlss+Wn2SjJeqitRgglBxKP//8s1WX7G/XrFmDP/74w/bbRx55BECli/Crr74CYL8Xv9+Pc889F7169Qq6TrQtdW6sAID9XunY4uJiR5fj0aNHoyrqKPi+Tp060uPLy8tDFnXff/+9VXcej8dmrSBonOGfBZWbF7//+te/pGVlcRJ1uuNFVVvqdu7caVspXlZWBp/PB6/XK70X9vMVK1ZUuRB1Cp3hLbkffvghfvvtt7Cu6RSXF4qo050PsrOzUVJSYl3DjahTXZ8/TmUVdyJuRJ0hunzzzTdWx+NF3ZgxY9CpUydHF4COBWLBggVIT09HIBCQrpQCjpmudeJt3Ii6aL/ZLl++HIsWLQr6nO6DdUURzz33HL7++musW7cuIuWLtqUulMnupJNOwq233oo//vgDaWlpVnB8ly5drCBq/jp9+vTBeeedF2S1ome5ffv2oOtE21KnarMiRKLo6NGjlqWMrFU8kbDUicpAkKhTWep4UVdRUYFAIIB58+ahrKwM+/btw7///W+rvCwdO3a0YgJlCyVkaWpkFs0XX3xRq+2RNUj224SEBKxfv156fCR24giFNm3aoFWrVgAqjRiLFy+Gz+eTTvKAPe6uZ8+eeOaZZ6qquACcx2dq59Q+NmzYgPbt20e1TG4WSri11GVlZeHGG2+0jlON1zpbEvL9C6hsf7XCUmeIHitWrEDXrl2tpKh8A6c3I9lESOlldETdc889h8OHD+PgwYNKSx2dM1pb5ETLUnfBBRfg73//e9DnrGub75z33nsvevTogbPOOgvz5s0LuwzRFnWhCqI1a9Zg//79KCoqssVB8i4Zuk7dunUBAJ999hnKysq0hL5spSefe1FmdXJ6Zm6sAIBdTLLWKprsZOkdVJa6pUuXWvF2M2bMwIABA5RlpWdSUVFhvZjl5eUBOLZ4g4UVNWzfDAQCWLNmDQYNGoTp06fbXElOVnqRqJOtnBVZ9QidJLV0b6o6Wr16tfS7WIk6ljlz5uCbb77RttTRPfNW72jjVtQBx8qqori4GLNnzw7pJdeNNd2tpQ6ozLig437lY+pU1wfs3pxaEVNniB6UEJQGTL6BOzV4N6lJKMB89+7dygmSOoRI1PFWl1Asdfv378drr72mfVy4qCx1LNu2bQv7WlQP0Voo8ccff6BHjx7Izc11PfGpYrD465DV4sCBAygrK0Pjxo0dj+XLs3fvXjRt2hTPPvus7XPRs2nUqJG1Sk+GzoTBDsgyUUeWMpmoKy4ulgrUSy+91LKCjRgxAvPnz9cq6/33329Z5mhiFU0eMksdlR2odEOxqFKjyNyvbi11ALBx40bpdQgdUcdfk0+WC8RuoQT7AuIk6qhuYxVb5+R+pXbu9uV85syZuOmmm6xt/dwQTfcrUNln3bpfddznrKgzlroo8tdff9Wo7ZZE8DEsMlO0bOAgAaZjqaPcgbt27bIGJLeiLhLJij/55BPceOONVTZwqyx1LKLJxS3RttS9+eab+Prrr7Fo0SJX6SjYwHtVW+EtguSKTExMhN/vV16Tv+f77rsP+/fvd/wdUClMfv75Z6ussnuQHS86NzvpsYLWyf0qs9TR4hCdPkDH03P85JNPrHNT7izRNVSijnUpsQngnUSdG0udSvjrJOIlUffYY49JVymy9fLDDz+gTp06+PLLL4XlqGr69etn/T+JOif3a6xg607Ulqmd83XsNFdQOwsl/i5aCyXoWft8PtfuV9n9isaKn3/+GTfddJPytyqMqHPgpJNOQps2bVwdU1paiqeffjooT9Orr75apW9/OTk56N27t+MG4tT4aBAVpTkA5I2KBnfZRJOdnW2lUKhXrx6ASksd6xbicSvqdLde4q9F7qjRo0dHdfEE+xam6pyRSF0Qqqjr2rUrHnjgAen3vFs0Ozvb1Rt4IBCwLRZwug7dB1mtvF4vEhMTrWOPHj0aVO/8JCerU6fJUPbsdCYMUfwfe2w4MXW//vorAKBFixaq4tuuR/dKiyLy8/Mt4SN6DipRR/2SF3UHDx5UlkUVU8e3BZXwV9UblZvubcaMGfjll1+Ex7H1QtZxirMLJ3Y00vj9fuWYodvHi4uLo7K1JiuORfGZIvcrEGzp5aF5gt9WTgddUdekSRNrsZXO2E/tMTEx0bX7VTY37t69G/369UNJSYnjC7IRdRFCx//P8/bbb+Ouu+6y5en53//+h3/84x9Vmopl7ty5WLJkiS35oQgSdTJLndMgRy4kOv7ZZ5+1VjBWVFQgKyvLijOjc+zbty9k96uog3Ts2NE6l6rO+HugQSeUZLI6HDp0CJdffrllLfJ4PMqJiY013Ldvn9DK5ESoq1+/+eYbTJo0Sfo9tYOUlBSrrNG01FHdk6jz+XxITEy0XZPPWcm3Jdlg7TRAyp6djvvVyVLHi7qffvoJa9assZ1DZqkjkaKzWw5vqaNJNz8/33rJchJ1fH+RibqCggJpWxC5X9977z388MMPANxZ6srKyjBu3Di8+uqrQd9RfCA7PtDz592E7N/00stbXSK5UOWVV16x/j5w4ID2y5uT+7Vx48YYO3as9fdTTz0ljBecMmUK+vbta70URAq27tyIOqf7p3YZyvinu1CCPbeOpY7abijuV5moW7duHRYsWIADBw44urKNqIshNDiwe8/S/1dlEkndxIr8JOp2IqTBfc2aNTjrrLMwatQo3HfffSgpKcGbb74J4NhkJHpjd7tQQlQOetseNWqUZUnSuYdIuDtVvPfee/j4449ti1BUnXPOnDlWrN9xxx1n2/NYF11LXV5entCdNWPGDKHo4i1ooVjq3MTU8ZY6n8+HpKQk20TC7xrDixRZ23cSvLLvddyvTjF1ZWVltoUSbdu2RZcuXWznkFnqaMLmJ1DRhMALUFbUqeIu2Zc4lfuVjweUuWBF7tdrrrkGU6ZMASAXdaKJsKysDI8//jj+8Y9/BH0n2kP3559/xk8//RT0fOiab7zxBm655RYAlfd78OBBa8yKlKibN28ebr75Zuvvxo0ba+9s4STqAGD27Nm2v0W7Nuzbtw9A9HbvACIn6ubMmWPVbygJuJ1evN544w3XceOAXdTpuF/ZfiS7X/q8rKzMiLrqjGg7IDf7qn777bcRCXzV3QKFzyUk+73srYQE2OTJk7Fu3Trr89tuuw1Dhw4FALRr1852DlHANIvKUqd6O3r++eel34muFe3NsmmiIVHvJOoAYNWqVWFdU9dSV7duXaHFZ8SIEcKUD9R2aSCqSksdWYZY96uISFjq0tLSpLsZ6Ljk2HPfdttt1j6xrMvGaaGEyFJXUlKCl156CUCwiBO9nPCWOtb9qrOYRuR+Za0grKUOqIx/+vHHH6XnovvicbP6VVVe0bO84oor0LZtW6mljt2mraKiAgMGDLCsWZFyv4rEqe7OFk4xdQBQv35929+qMTPSLmW27kQ5Dz/99FNs3rw5qEyql2l2DCdR17VrV/znP//RKpOT+1V0Hp15meqRFXWi59mvXz/bHF5eXi611NFYWlpaatyv0eTaa68V7slYVlaGOXPmOAoukaijYyZOnIj09HTl8WeffbZ0Y3I3yCx1/fv3xymnnILdu3fjiSeesE2eot8TTpY6fsBlxQnrtgGCLXX8M1WJOp3GLRv8ZZY63Xi6nJwcfPPNN1q/BY61BVbUOYktWuUpY/PmzVi8eLH0ezcxdbJ4S9FbIy/qcnJyohJTR4hi6pKSkpTH0rP9/vvvMWfOnJAsdUeOHMHLL78s/M6tqFu6dKnl2mcnVqeFDiJL3d69e4PEGPUT1epNt5Y69j74MrCLm3hRd8kll+CMM84IOg9rqVu8eDGmTp1q+15mqRO1X52YOhEySx2bTqeiosK2ujZSljr+Obk91ulFkOLPCOof7Ep6lVAOlSlTpuD++++3/haJuhdeeAGTJ09GSUkJHnzwQetlUWWpY/ssWRi/+eYbjB07VsvYIRN1P/74o3TsjpT7taysDAsWLMDIkSNtwk/W39l+aCx1EWDmzJm4/vrrgz6fO3eucAPlmTNn4rrrrsNXX30VtIUOi8pSt2/fPi3rECVnDQeZqHv33Xexfft2jB49GmPGjLE6DjWwXbt22VwpTnmbZKKOfXPm92PkLXWy2J3CwsKgxq4zscom/nBF3dChQ9G1a1ftDkaB8CTqPB6P44TeoEED5fenn346+vTpI/3eSdS1b9/eMc5SNXlS+cvLy5Vvl6LUMzqWOn7hzssvv4zt27dbljrVsYWFhZg2bRo6duyI6667LuSYOpnb262oI9h7r6iokK78JI4ePRpUB5SDLDU11fqO2pdoklTF1Om4X0X9kupEJOp0Ylnz8vIwefJk23cyUSciUqKO/uZFHTvulZeXIzs7O2yviZOoe/jhh6W7JOi4X+vXr2+rp5KSEnz88cdo164dvv76awDHjAqisaeoqAj5+fkoLCy0rQD+z3/+ozRA3H///Vi7dq31t8j9ClSG3hQXFyMlJcXyDKhEHbtQj+YmQkeUykTdGWecga5duwqPiZT7lV1sqCPqWPersdSFSSAQwK233op33nkHANC3b1/ccMMNViWJOiKt8CoqKsL48eNtvwkEAvB4PHj55ZeDFh7Q927LFy5OLl+KPaO3UxI4Bw4cwJlnnhn0+507d1qxce+++y48Ho+yc7Ir+9jJAAheBccO1h6Px8rVdM0116Bly5bC++Jhn1k4oq5Tp07o0aOH8HgSZ/QcnKDJg4S8jqgL10Lg5H7dtGkT7rnnHuU5VJM96zKQPeeKioqglZ2hxtTl5OTg888/t0Sd6vktWbIEd955Z9C5eJyspbxLi70HJ0T1d+TIEavdbt++3UrcKxrIqT/w56GEza1atQoSdSpLHZ2HXrLciDpeDFHdi0QdIdqkXNWm3Wzhxpb38ccft73chGKpY1+w+XHl8OHDyMrKwpNPPik9rw4yFzsxYcIEYcJyQF/Usc+luLgYP/30EwCgR48e+PDDD617E/Wds846C5mZmZg7dy4uuugi67eTJk2SGiBE/Ve25dyvv/6K4uJiJCUlWcJvxIgR0n7MGiFIbBJsHTm9sLlZKObWUidzv9Jv2DrTcb8aS10EYDcDr6iowPvvv4833ngjyK3Bwlq+SAwGAgFkZ2dbD3zmzJlCa0QsRZ2scVNaBAqsZd9aRZnJL7roIpx44okAYK3spcSwItjBjAYBkaVOFJDNugX5tzWd5f06eYEAsajbsGGD9Ya7f/9+2wbzlJxWFjvEQ/fNul+dFsuEu4uGjvtVFitF6Ig61R6mIuHNul/dxNQR5H6NRJ5CpwFS577cpJnIyckRPhOR+5t/ASL27NmDtLQ02yROfYwXdQ888IC1jRqfOigvL88mznjo+e/cuRNPPPGE7Tu6jmihBCFKK6N63rKUJkRWVhYmTZqEZs2a2c49btw4WxiCamLWcb/yYy7VzZIlS6TnVfHggw9i586dSkudalcdQC+mrk6dOrZnxvfLJUuWKPvd5s2bAVSu1GfFhex5bty4UbjCVmapy83NRWlpKZKSkiwv1h9//CENZeLnXnb81xE2bnd9AY61+TVr1gj3Od+9e7cVB6lyv1Jb9vl8tt/Ixnzjfo0g7GDKug2o0Ys6Ij1Uj8eD1NRUAMBHH32ErKwsywzNChSR+1WXcEXd+vXrrRw/TiJh69atAIK3ayJEb0TUgQsKCqSNkbXU0CCjY6lzQvYs2XJEyv16yy23YOTIkdazIdcovQk7QffNijqVKElNTdUWdbI2ohJ1sngl/poLFy7EDTfcYPuMF3Wq3EpOLkgdNwpfJh1LHY/M0uDU3mTXkK1sZZGJOtHnIlEnq789e/agWbNm8Pv9QZa67t27Wy8fgUAAkyZNsiZePsm3rqVOFAIis9Sx/YdvE19//bXNesojWyhBlJeXIyUlBT6fL+oxdSxUN7K2WlZWhtdff13YDw8fPox///vfuPXWW5WizmkVpU5MXVlZWZCljn2m7LZTqr5DfYWeFYkrSh/zyiuvYPTo0ejQoQMuuOCCoONloo5ISkqyCUWfzye8b15MsgYG0XO46aabrB1WgPBEXZcuXdCpU6eg71u1aoXx48cDULtfRZY6HferzkKJPn36aIVu1VpRxwo5No5CJepYSx01YAq83LRpk/UbkXDhK98ppilUUbdmzRq8/PLL6Ny5s7XKRxW3Axwb2N1MlmRqz83NlQ60KvcrL3gjLepCtdTxz52eHf2XXynsBP2OJggnS11aWpr2uWX1pXK/ynbx4K/53//+F2+88YbtM5GlTlc8A/ruV1H4AnBM1LlZscymFXIqH4vs2bLtg9pbTk6OzVLmxlIngrVqs9c7fPgwMjIy4PP5rGuzfWzkyJHC6/N1/ueff1qhJPRdYWEhrr32Whw6dMiqZ9G4wYpBdoyk3IWy42QvjOz9EiIrstfrdRR1KmQxdWxb4stBfVbWVl944QUMHTpUuJUVpdpp0KCB0oJI55aN936/39H9yrvqeVHHWvpUYzwlBOYtdf/4xz9QVFSEm2++WZnPU0fUsbAWLxbeUsf2Yf45BAIBzJ4925ZY2UnUieYGHfcrW24n9ysrxMvKyiKyUAKA5UFSEfqynDiHfUNmRR1rPuURiTr6PeuW0LHUXX311cryhSrq+HxXgLOoc4J/A87Ly7NE3fr16/Hnn38GHXPcccdpu19Fq+xUyH4bCVHH1xPdA5WfX/DhBB1HndfJUudG1BUUFFgWYxZ2IQOPrPyya1KsKBAZ96uOpY4SNPPl9/l8KCkpwdKlS6XH8siC9yNpqWvQoAFat25tbSfoxlIngh83aJIrKSlBYmKiTdyIdqTgJwde1LGJe+m7jz76CHPnzkWXLl2sehZZEam/FBUV2XKIpaSk2Cy4btARdT6fTyrqtm/fHpSEmkfHUse/LDiJOtrCSnS/JOqOO+445VjBvzTy6MTUlZaWOlrqVP3O7/ejtLTUSuXCW+oAWKl0VNCcwCcIJ/i2ysaZsvACS/XCJGoPTmFHTjssOcGKUdnLMet+LS0tlY53bhZK0O+ccG2pKyoqsgJ2WcgvHy/IRJ3KUkdCi3W/0u9pQIiU+zWSyAalUJe3//HHH5aolblV/H6/TdSp3K+RstQ1bNjQ+v9Q3a87duywfc/vlqHar5Zl3759+PDDD4PKEQgElM89LS3Ntjenim3btuHWW2/Fd999Z/ucLev+/fttz1bXUkewVkVe1P3yyy/SN0fZ82FTmqheXETl8Xq9jtsL8UTaUidzv7ILZ0TnPnjwoOsXAf5cIlHHTrz8nrl8OUXXp9+y+1ry222xUN1/+OGHmDlzpvW5k6VOxf79+20xWqKJmyx1ImvGKaecAkA9xspEHXstmaiT9VcShKK+SqKucePG0ra2cuXKoHRSPCTqAKBbt27W/r0s/AsQHxbBegfYdj1gwAD897//tRYFURsWxdTRwh4VNCfIVu+feuqptr8LCgq0RB1bL3zbErU1Ouf777+P9u3bB31/6NChoM/cGFFYkc33NZH7VUfUHT161MpnqSLiom7+/Pk4+eSTcdlll6F9+/a2rW1EKUCqM+yAxVYyNXqVpa68vNxqwCQKnERdJBY+AMC5554b5BZzIlxLHc9nn32Gp556Svhd586dMWTIEJSVlUXNUqczObLn/+qrr6zJ3clCRUmSCboHPgbJqby33HILrrzyyqAs+06uw9TUVMycOTMo9xQLtc33338fM2fOtCXTZEVjaWkpmjRpgokTJ1rf04CtK+qWLFliDfa8qAMgXRmoY6mT1aPH47EJBsLn80VM1Dm5OkSibuPGjbYJXHaOhQsX2v7OzMwM2VLHDuIiUceWgeqHL5cqxlIl6uhely1bZiUVl4UOsBbjUHYtOPfcc63/Z8tJbYZEnWzMqqiocCXq6D7YcZl3EdMcIbsmubCzs7Px119/2RZUkKhT5aXs3r27tW2kylJHImfVqlXW9ossIvcr25+9Xq9Q1H333Xf48ccfrbFGZanTgSx1MlHHW1Nloo5/FuyYyfdL2epxYtOmTVLLNYub+XnTpk1WnJ9M1LHWUVWWALqft956SyujQsRF3YMPPojvvvsOGzduxCuvvIKbbrrJ2v4oUqKlqsjNzUVGRgb8fr9tM2r6fydRR4MYraKlAWHr1q1WvJzTVlgq6Hnu3bvXNjGtXr0aw4YNc3WucC11vPv1jjvukG7g3a1bN5x88skoKysTphihhs5Ojm4tdTqTI3tv5513nvXSIVv1KQuy5i11MlEkg092SXUpij/xeDw2i4cMcmOQxZwVjqWlpdZzp70N2d0h3FrqrrnmGmvFMz0jlfuYUkyIns/vv/9uTRyybbCAygFRlHLF5/O53gtS5n51Etci8dKhQwfccccd1t8yUTdu3Djb340aNXKMqWPbH1sXmzdvtsrqJOoIvi+xoSGy39J3Xq/XFgeamZmJXr16WW2OXw1LhGOp42HL+dJLL6G0tNQSdTJRWVhY6CptCj0TdoziXwDoZV/W3km4ZWdno2/fvujdu7f1HfVJpxdWmsh1LHUAhHnWnNyvXq/X5jYnysrKcPToUWss4sc4N3FmwDFhz4u6Dz74QLhDDS/qZHF/rPtVJerKysrw22+/BYlz8r6oFqxUVFRot9tt27bhwQcftK7JwrpfdUQdtedFixZpXTvioq60tBRZWVkAKi0yX375JV588UVMnTpVO3lrdSE3Nxd169ZFenq6rdHQilG+AbDpGMrKyqyOQJMMO7HSbhBsBYSa0qRZs2bo0KGD7Ts3Vi2gsuGL3F2RSA3BU6dOHXi93qAYAd5SR3/Xq1cvYqtfWfhOtHXrVixZssS1qKPJTOV+FdUt5dbjg6hp4sjIyAg6hlJ2OEFlopgmarNsOYFjq8bYHSrcxtSJ0MlNKKujSZMmWeV0+6ITSqC8TLyJXDAs7D22atVK+BZdUlKCwYMHO5ahfv36jqKOnbjZOjz77LNx3XXXWddLTEy0rX7VEXX0DETjxocffoi//vrLOs/w4cOxbNkyAJVjJCWfdWqXkRR17HMaPnw4AFiiTjZmyaw+xLRp02x/i9oF70alv2XXpDH/r7/+CgpJYlcJq8Zr+p1TShNCtvhJtVCCdb/ef//9tjiuo0ePBs11oYo6gs/xePrpp9tWlK5fvx7JyclBdUb3xhsb2Lrixx523MrLy0PLli3x+uuv235DixhV+QIDgYByMY8MmVWcXZzCizpK6wIca1u6cX46iylc1VqjRo3www8/WH83aNAAy5Ytw9atW22fxwN5eXmoW7cuUlNTbW/zJNJ403N5eblN1FFHoAmVzXvHHkOEk9KErBui8+pQUlKC5ORk25YugHpFWqjUqVPHmnzZhlxWVhaUj87j8VgxJ073xD4PN5Y6Om7Hjh3o3bu3lbuLP5dsAJNZ6qg+A4EAEhISgiYN/voEPfOmTZsGXYs2rHeCF3Ws9Uok6ho1amR95tb9yiJyv8rK5lRHKkud7HOv1ytMN6BCZo2WbT5PsBP57t27hdv2lZaWWp4KFSTqVM+EHW/4uiDXp46lbsmSJVKrlGgMKigoQN++fYVigRV1ogUZLJEQdexemTy8qONfwvLz85XPl49FEyVr5oU+zQtFRUXCFzeaiLOzs4MEA/VzXVEngxd1on4qstTx7lf2b3rRJFHH31uooo5dOARUxtDdcccdQcnjO3XqhLPOOitI1NHxVMcNGjQIWu3OPy+2rcmEES1oURmeKioqpKEaKkKJqWNDFZzqn0fHu+aq1t544w3bBAFUdvZ33nlHuKy7OkOuhdTUVJsrUWap40UdVRj9XhTr42TNUSH6PSsk3ECd5L333rN97tSInXakEJGenm7Fvhw6dAinnHKK5bIpKSmxPRPqtDruV7bzuomp4wdU3m3sZKmjdtCrVy/s3r07yFJH5eIXjMgsRPTMjzvuOOG1dEQdDbbsC4UoVpHcQ40aNbLaDJ+IlnCaiO+9917rniIh6kQb1hOlpaVo0aIF7r33XmvXE6Cy3avGGXahjBNOljreOiN6y+f3MAXEfTMcSx1wrA2yoo4mQFbAVVRUoHfv3kH7VqssdUBlkmHRd4cOHapSUaeK/eNFXb9+/WzfO1nqeHQsdazIFG15R6Lv0KFDQc+HRN0LL7yAb7/9VloOJ29JWlqaTVyJJnXRQglWtPJxfdSnSNSJFtZ8//33rkUdlY1EXePGjTFt2jRhbF56errSUnfJJZfgxx9/RFJSkq2u+DoqKSmxRJJM1NFLr2reZEUd3fewYcMcU4joijpWVLNjiVtRp/N7V7V2/PHHS/dE7Natm5tTxZzZs2dj0aJFSE1Ntb2102DPJ0YkSxP9P78YQiTq2IEgEqIu1MGSBhg3ezUC7hsccMxSV1xcjG+//RZdu3a19vsrLi62deLGjRtbjV9HBBBu3K/8b/lBVHWuQCBgG6yXLl0qFXU8MlFH1xOJOl33K7UrGrjLysrw5ptvWoM0QYPcggULkJCQgJycnJAtdY899pg1OPK/Zd27VH6nOuLbAn9/hw8fRmZmpu03P//8s3QrolGjRlluSh0iIepoVxUW0aSrY6lj+6Yo6TKgH1PH91tRTF1CQgL+9re/WdeTWeooTKAq3K+qVDyU0oTujY/bcrLU8RQWFkp3kFCVjbjvvvtsuSt5UUcCYd++fXj22We1z8uTmZkZkqWOF3Xss9m/fz8CgYA1XvBt6Ouvv0bHjh2xe/duZdl4KN7vwgsvBKAeA0SijrXUnXXWWWjSpEmQpY5PiF1SUmK1UZn1XTZusbAivU6dOqioqMBLL73kGF7BPzs276DM/UohbHSvboi4qGN59913Qz00ZO655x706NEDgwcPDjtuw+fzoU6dOkGWOqpYfvl8eXm5NQgMHTo0KGBb5Mp0a11iCQQCQbmBVI3ylltuEa6MAo4NMDt27MB5550X9LkM2hNWZsVq2bIlXnvtNdtnJOqIpKQka0LgLXWNGze2sp27yRvmxv3K/5a/Z5WlrrS01DaZ79y5M0gUsXXMJ4pVrWAVvRz5fD5bvIWIIUOG2NorvaUOHToUjzzyiFBU0G4nbKJo9rmsX78+rK3J2E2/I2GpKysrQ2FhIerUqYPly5dbcXh86haWDh06OC4yefHFF63/j4SoEyETdU4pTVSWOrq2rqjjyyoSdYsWLbKel0zUHT58WMtSN23aNNvzCnVsnjlzJtauXatlqeM3mi8oKHAl6gKBQJALVhWvxC96e/TRRwFUxsaWlpZKRZ0TTpN0RkaGUNSx45Uopo69/vr165Gbm2uN/X379sXLL78sFXXkrnTDwIEDceaZZyIQCOC0004DoB4D0tPTsW/fPiu3I1DZ3tLS0rB7925rHExKSrLVEx+KVFJSYr1UyspNW1mq+h9rqUtLS7MEPuspEMEbc9jYa7r/tWvX2ur5oosuUp5TRVRF3fXXX4+nn35a+ZtIrojdsGED/vzzT3z11Vc49dRTMX/+/IicVybqEhISbFtBsY2iqKgoaEcIkWUmHEtddnZ20CpX1cQ7a9Ys3HfffcLvqLEePnwYX331VdDnMrp3727L1M0TCASCRAgtlCD8fr814PHWmYYNG8Lr9eLFF1+0rAYy3K4klu2fyU/mKlFXXFxsE6jbtm1TWurY+iksLMTxxx8vLJvH4xF+p3K/rlixAmeccQbefPNN2+esONywYYNt02mexMREoaWuc+fOrkUde36v14vMzEzrGoCepU426BcVFaG4uBh16tRBx44drVjQ888/X3o+NjBZtAgFsAdwHzp0SFnGUEWd6M27Xr16jpY61tUVrqVu1qxZtr/ZdEtEnTp1rOscOXIkaMUuQRYxWbts06YN7rjjDtvqxlBF3YQJE3DOOedoiTpewOfn57t+cXYS9sCxZ3/kyBHk5eWhXbt21h6gQOUYxqdvAvTjlZ0mad5SR/2bHa9KS0uDRB1rdXz//fdx9OhRdOzY0fpsxYoVUver7mIkuudevXpZqVmAYwsBnCx1W7dutSXL37lzp/U8WFHHzq38gqWSkhKrjnjBR+zfv99xfAsEAjZRRzHyTqLujz/+EC5UY+tk7969ePfdd9G0aVP4/X70799feU4VOpa9kEXdwoULMWXKFIwaNSpIsJSXl+PVV18NyvkVDqtXr8Yll1wCAOjduzdWrVoV9Bt6Q2H/OSETdWVlZbbEhXv27HFtKmUHN7eLGz777DPl+dwgew46E7kqd05FRUXQ4Mpb6srLy60JgZ/I09PTteM23Io6maWON9GrRN2mTZts2ffz8/ODFkqwdcIO0IcPH5bGeP3rX/8KCh4G1O7XO+64Az/++GPQ56zrc8GCBdYAI3JTqiyibkUdaylJSEiwBj83ljq+Hl944QWMGjXKmmzZezh06BA++ugj6fl0RB1NQpQRXjXx8mIpHFFHCaVVkyX7LHhLHfURVtT98ccfOHTokPBlkd+QnCxxbJ2kpKRojSckhGX3T9dnc5BFcvUrwac0oXFnwoQJ8Pv9GDp0qNbKQBadFYd0/0VFRVizZg22bduGyZMnW99nZWUp3a9O0CIYGRkZGUGCf8+ePUGxaLz7VRRaw47VlHFAZKkTLSIRQZ4Ifg7QCcHgLa2AfbEhuzsFK+q2bNliex6fffaZ1a5Zqx/LgQMHHMc31lKXmJhoE3X//Oc/lceuW7cOe/bsQU5OjnWdFStWBMXcdu3aFSUlJcJFcgBw+eWXK68DRNlSd+mll+LLL7/EggUL0K9fPxQVFaGkpAQvvPAC2rRpg7vuuguDBg0K9fRBsPEdlMyT55FHHkFmZqb1j2K5VKSmpto2DGZFHUv79u2Dlko7wbtv3fDzzz8HfRaqiyyUVT1ESkqK1P2qI+qOHDlic7+yHT0tLU07wSWfrFiFz+cTWicAuaVORLdu3WypCgoLC5WWOnYwPHz4sFSgnXrqqcJBTWWpk7kW+IVLDz30EAAItw9T7S/odiLmRR07CAPOwpt1v9Jm3ImJiWjVqpUlFFhRV7duXaVrmhV1ZDXkIWFCbZbayAknnOC4ilV3sRCf3PXPP/+0rscPyOw5Kyoq8PHHH6N58+ZBoo7aFSvqcnNzrZ0UdODzuHm9Xq06J0ud7P6prhYuXGjtKuSmLYn6v8pSR8+GXHw9evSQinig8kWBOOOMM2zf6VjqSLgUFRVZZWXFQ8OGDYV9Kpwxl4V3v65bt85m5T/77LOFljqRqGP7D3meRKLulVde0SpbOKJOVGfstnPU93n36+7duzFw4EDr7ylTpljinM9sQOTk5DiuGq2oqLCeWXl5uU3UvfzykEM/zQAAOP9JREFUy8pj9+zZg+OPPx4nn3yydR1RHld6LrJQhvvuuw9333238lpRFXUAcOaZZ+Kbb77BL7/8gi5duuCEE07A5MmTMWzYMOzevTsohUY41KtXz+ooubm5QblwAGD8+PHIy8uz/tHqPxX85CcTdaHAJ0YMF1bUUS4pHUQCURfVRFpRUYETTjgBQGU+PUAs6lj3Kzv48Cu7VLix1B133HFWpwzH/crjRtQVFhZKO2/Dhg2Fg5pK1MkmIF7Usa52HlU+QLftkxVcIlHnJLzz8vIslx17DNt2ZIsiWEh0sMk+ZaKOrkNtjiazXbt2YezYscrr6L5Qse00KysLjRs3tiY93gLC9q2KigqMHTs2yJ0DHKtTNk8dIE6jJIMXdQ0bNtS6J9E4y0Kirm7dupa17vrrr9cul6i967hf27Rpg5KSElx66aVSywcAtG3bFkBlzCUfkynqU/x4R/d/5MgRq92wL1jkfuUttJEY74Fg9ytrzHj++efRs2dPbN261bYfsszVz4ovqnsnC7IKCv3gnxn14SuvvFJ6rOilln2BZkUdP5bx7ku6F5moKykpwfTp06VlAezuV1bUicrJkpycbBNyfDtgvS9Ooo6fO0VEXdTl5eXhlVdewZ49e7Bjxw7k5ubis88+w3333ef4MNzSpUsXq+EuWbJEuNo2KSkJGRkZtn9O8G8Z1NEj0SnZN6BIi0Ra8u0En7g4nGvyVFRU4MQTT8S+ffusHRtUoi4cSx3b4cMRdfyE6ZSnjqWwsDAoJo2dGPnNwZOSkjBq1ChL+BJZWVnWdnssXq9XaGFTwS7GaNasmbU6TDRhi1IzsN+5gY3PSUhIsJJx64o6ALjqqqsAHLOglZeX29x8OqKOVpJ5PB5t9yu1zy+++MKKjXWyLoUi6qht85ZBghU0FRUV1tjDJ7LNy8tDIBCwWerccvjwYVRUVOCyyy7Db7/9hmbNmmndk2zLJ4J1/+r05RdffNG2m4rIrasSdTSpseJWJuLZ8zdr1izouYlEHb/TC+t+Ze+vUaNGWLp0KerXr4/S0tKoJHIHgi11LAkJCdY9saJONj6ycx0JEZGlThca1/g51OPxoKioSBrjDeiLOt79CgSLyKNHjyIrK0v4IpuamopAIIDHH3/cOp8I1v1Ke2YDYosbXxZRxgEiISHBGtOpv2dlZWHkyJFB59KZD3XmqZBF3fjx49GyZUu8+uqrePjhh/HXX39hwIAB6NWrlzIvT6h07NgRTZo0QY8ePbBlyxZcc801ETkvDSDvvPMO0tLSLEtdaWmpNNBdF3aiYDvOP/7xD/Ts2dP1+fhBWEcoinJpsTittjxy5IjS/QpUvrFRgxWJOhpYeTeBG1FHrh3AWTCoRJ3sHnQsdYcPH1Za6liXS0lJCZKSkjB9+nSMGTPGdp6GDRvC7/cH5Q30+XxCUacadFkBc9JJJ1n1Jcs8L2szbkXdLbfcYv2/SNS5CVpnhSA7yYu2UuMhUccGyjuJOmpzN910E66++moA7kSdrM0GAgHbYg5e1PGWOlbUlZeXW1YYNhyEvqNN2kMVdWSp83q9thRDQPCenCy6ljpArw8NHz7cFnzO34vf71emNGF/R6hWPVPbEj0zkajj+x/rfmUn1KysLFx88cXW7h7RFHWyidzr9WrHegL2sZ611OmKOt7zRn1P9PyTk5OV7SEcS51I1LVp00brOrJnyVvqaJGkzPrHloWtez5Mpry8PEjUJSQk2PbrJlJTUx37tigJOk/Iou7999/HM888g+3bt2P48OFIS0vDq6++iuHDh+OCCy4IWh0aCZ544gl89dVXeOuttxyTYepCFckKE+BYWgWd4EUZMvfr3/72N9eWTH6HBkAvuzSbE0cEdR5ZY1IFzbITd2JiorV3KTvpsaKOTzvgxv26ZcsW4XVFsKLO6be67teEhASbpU60UIKPo6H2xA+8tICCFwc+n08oZETxo/Tc2AG1devWACoHMmpv7AQaSUsdXxZe1LmxTLOijnUns4tAZFD7ZpP7OsXUiUSZ08TG9j2Zi5w/B/0ts9SxkxNt5QcEW+qAY8IuMTExpO2bDh8+bIk69pqAOseoG0udLmwZ+P6ZlJQkXSjB3jfbp0T7ofK/E41vooUyMkvdkSNHbH2EHTerg6VOB7bdhuJ+Pfvss21/d+/eHYDazSpDNP+x4ydrqePbGN//VKKO+h6NCRdccIHwd6ylrry83DIiOO2UlZKSYpuHP//886Dzkqhj+7uoTtPS0kJ6YeMJWdRt2bIFN9xwQ1DhHnjgAUybNg2DBg3Cc889F3YBow1VZNOmTYWiTmXad4K2xdq9e7ftrdDv97uuvCNHjgRZE9iJRiZKnCxx1Hmo4fXs2dMWCMz78Nk3WbazNWjQAFlZWUEDze23324NrH369LFl6OZF3e233y4so9frdSXqmjVrJrTUUXwNi0jMUF43onPnzpg1a5a1GIg9TiXqSKzoijqv1ysUdWwAMVCZCLhFixZB56YVteyAyVpaVDF1bkTdbbfdZhtEvV6v7c0acBcs36dPH3To0AEDBgyw7guQW9xYKJ6KtQI7iTqRKHIj6mQvlHxCaRLUOpY6tk3v2bMnqM2UlpZaoi6UBVNkqROlTlGlbXAa/1Si7qWXXhKmKmLHB37cSkpKkrpf2cmTrYPJkycLV5MD9hXPPCJ3Hd//WEsd20aovUfTUrds2TIkJSUFjRNUX24tdaIE1+zLhJvjgcrQnkAgIBVKKkSijp1r+PGEhZ/TAoGAdFEk2+fvuusu6UsKm3y4pKQE27ZtA+D8csq7X0XwljpALOrcht7ICFnUqSwb//znP7FgwQKlT726ILPUHT161JatWoWqMkpLS9GqVSu888471meJiYnabkfiyJEjSkudbPB1SsjKi7rk5GS0bt3aygOYl5dnsxSNHTvWigdgJ6KhQ4dizZo1AI51/ssuuwxDhgyxDTxszEFKSop1jldffVX6EtC+fXts3749SFDJaNq0KQoKCoIS3IqSAfOWusTERHTu3Nn23AKBgDXYs29zgJ6ljp5Hu3btcOedd1qf831IZqnj9ztNSUmxzskOtGTZYs/BTn7s6tdTTz3Vdk528JLtGgNUrq597rnn0KRJE8ui5PF4rGvSPbmJ08nKysKGDRvQtGlTm6jT4YILLsD06dMxatQopfu1devWQQslWETlZZ/tgw8+KPychY2/KS0ttZ6JzFLHl6NBgwZW+hO+HVBfT0xMtOUqW7lypdb2aBRTJ7LUqdzcTuOHStSlpaVJXalEWVmZLTY4MTFRKurYSZ/Pk8jGeLK4tdTxY3lqaiqSkpJQVFRk6yP0zCJtqWNTdZFY4ucKWpRGidt1YZ8BO3foWlt566hOdgkZonhZXVEn+kwmbqnPHz58GImJidK5mrXUZWdna3nBgNBFncfjCRr/+TYeKmEtlFDRp08fLF++PFqnjxhTp05Fx44dkZGRYXvopNp1LHXsoMi/gcgyvocj6hYsWADAbj3g314SEhLw8MMPK+Nl2PJSw6NyXXPNNTj77LPx/PPP26xrbdu2xVNPPQXALuqSk5PRqlUrAMcGD5q0ZB2OfStXbUPUvn17lJWVWat4RQMZG3NHbsj33nsP33zzjfW5jqijzsaKi4qKCquOSZS6EXV0/6effrr17ETIRB1PcnKyVU/sQE2uSH47KPZeaWLir3PkyBE0bNgQ+/fvV7YZv99vPSs2RoTOx1oAdGHL6JTsk8fn82HUqFFITU212gXfB6+44gps3rxZ6X4VIZu0dGLH2HqhvuWU/ys9Pd2qQ37yIdGQmJho/f8XX3yBc889V6s8L774IpYtW2a7dyqj6qWUFXWUeoaFFwS8BddJ1OXn51vpSYDKFb0i97NK1PHnZKHr89+LAvCB4H6RnJyMlJQUHDlyRGipo0TQ4Yi6m2++2fp/tpzUL3jxTxZhr9fr6rqqreh0YJ/5Y4895mpPcNW5CJGoE1nFRd4n/hnRvdLxtDuQ7CXl22+/xffff28JZrYMKviYOhEiUScqM6CfI1BF1EQdEGxhqI6cf/75+O677+DxeGwPnSZvHUsdW/kUZ0CIJrdQgp3ZmLpzzjkHgDp3W1ZWFsaPH+/Y8XhLHTvoiwZ7NmBZ9pZI39O5VG9RJDJUMZKUBJpcsKLrshP5mWeeCQAYPHiwbe8+XVEHwObOCQQC1nOigUcUU8e/+fOizqkunEQdm0KAzsU+W7LYsAM2L+poYuLrtrCwEF6vF40aNVK2TbZ90ODKijoa4ELNVUb3pbuQSBSfxQ/6GRkZSE5OVlptRPB5zQi3MW2spY4dY3hBlJCQYL1EikQ3YLfUUZt3ald+vx+ffvopAPvzWrduHV5++WVlm2OfJb+wR8Rbb71l/b/P5xP2VVHKk86dOwOoHOdE2x2yAmbcuHFBZZYJdRpf+DFIFIAPiEVdcnIyiouLpe5XPt6OvYYOtFgHELum+XsjUZeQkFCloo49PtzYr1atWgV5ZlhRx6cBYRGJOv4ZieKZExMTpaJuw4YNOOGEEzBq1CjrMxpPVS7ulJSUkEWdqM3I9gx3Q1RFXbzBu18B95Y6vnH9+eefQb8PxVJXVlZmTZQkNNmOyTcQ3YmHJgZq7GxnFQ327PcyUUf3FilR17hxYyQnJ1tv8KLOwN5vRkYGTjrppKDfiOI4ZKKOrI6A3VLHH0d1kp6e7hhT5zT5ymLqCJpIkpOTLUHA1gcNQkePHrWSf7PtjCx1CQkJQYPbkSNHhNY/URkJGlx79uyJQYMGISMjA+eeey6A8BLQ5ubmYvHixa6PpfbItyW+Her2PZlLz6lvffHFF7a/2Zg61UTPurF1RJ2OVZf/HVv29u3b46abblK6WJ2eFS9M2bYjs9Tdd999Qe1j0aJFtphEUbwpPYNbb7016JyyNnvqqadi5MiReOCBB2yfJyUlCd2vvGBITk5GYmIiSkpKpJY6mftNtfczC9teRc+b/4zCLAKBQMiiji/zww8/7LifO+/yDgePxxMUQ822Cd4bwCKaK/jysOE07HGqPnP99dfb6p+s5irvQXJyctC4z8duk1BjrYAyjKiLMKIG5NZSxzeu008/Pej3ocTUFRcX48YbbwRwTPlTx8zNzZVuLeSEW0sd27HDtdQlJSVZok412fl8PjRu3NjKHSS6Li+YRM9dNHnJUprwgde8KZ4XdfXr17esu3TffEydSNR9+umn1vJ2mahr1KgRZsyYYYlSVtSxz5YGoeLiYjz55JOYM2eOrR1QTJ3P5wsaGFlRp2qb7Hderxd79+7F/fffjxNPPBF5eXm2hQu68G01MzNT28qhI+ro+bsVdaywZ3ES5/z1aaIoLCxUvvWzFk/+ZZIVdbL9T0XccMMNyvEJgHD3AV2cRN20adOCvBcejwd+vx8XX3yxtb91VlaWzQ0pCoanZyB6OZPVqdfrxTPPPBO0klpmqePPk5ycjKSkJHz00Ue27ddYS50M3VACtr2IxClbpqZNm1riwG0sH3se3lLXtm1b9OvXT3l8JEWdLqLFZnRtNvaXLw+1Fd5Sp3Kp0l7khK6oY/vPZZddFrSTFqU54cWe6BkaURdhaCJhH7aOpY7Pru9EKKtfd+zYYf0/uYpJyNWrVy8owDJUUcdOWDL3K12TDR4X/UYWU0cWpRNOOEHLUufz+dCkSRNL1Ine/vnjTzzxxKDfiCbBr7/+Gp999pl131Rm3v3KtwNW1NGG9iTq6P503K8XXXSR9XvWUsPSsWNH3HbbbdbgIrPUkWVAllONLHV+vz9INLGiTiVa+IGoadOmts/o+T366KPSczid0w28exmQizr63Ol6d955JzZu3Cjto059i3+2Ho/H5qomeEHE1j8/kdAWhSkpKVbMI7VJWX2VlpZi9uzZSk8CoLdJuAz+HvjJ99JLL8VXX30lPHbp0qWYNWuW9Tf73PhnyMatybbYc4NM1PH3k5KSgsTERHz//fd47LHHrM+dUkEB+qsZnVZFUpvp1KkT9u7da523uLhYWXd8bKLK/aqzipYX7NHg9ttvt1lQ6dmwz5LKoXpuohc8J1Hn9Xpt56FxWWXY4UVdVlZWUJsgUcenXRG1HVWqM50YP8CIOhvUSNgJ3ClPE6B2v4pQ+fZl8LtHUJwHC7sXpKwcor1agWOdhh3UKDaNhRpiTk4O7rnnHuE1eEsdPwleeeWVCAQCaNSokXShxOHDh3HbbbdZ52ncuLHlyuYtdZ999lnQCkDR25UsvUuvXr2UMXUVFRVB23Gxoi4xMREZGRlW2hoKdqVjnNyvoqB6Fj5zPnsfojgX9nmLYupElrr//e9/jrGS/PVEUJ3zm8rrHKML+xzZYydOnIju3btb+bQofxZNfKqUJiz16tVD+/btlfnBVIheUKjfqQSzylK3dOlSnHLKKejatSumTJmCjRs3Wr+R5ejy+Xy2LdwA8bMeNWpUkHtSFydLnRtEedSIsrIyofUlnGuJ3K88ZKnjEVnqQlk4cMIJJ2hb6qjdsTv0iMZo4rjjjrO1VfbcvCVdR9RVhaUuNTXV1l5Foo73hKjKw1vqVO5XNl48LS3Nes5Oq8NZUSdKM0O7LcleNllmzpxpZZ4AKmP+N2zYgM2bN9sMOyqMqGPgRV1aWprWsm2nQZPH7/dj6tSpuOKKK7TLtmnTJgDAhRdeaJX1oYcesiV+HDlyJF577TUA9omHDUjlGyiVXSQyR48ejTvuuCOo7E6IxAUL7xIEghs8m4iRt9TxooOeCYsoP5ZTzj5AvlDC7/fbcr5RGYqLi5GYmIj09HRL1I0cORL/+te/rD0KnUQd22ZE9UDHiyx1fr8ff//73y0X14wZM7By5UrhucvKyvD222+jrKxMGZei2lPUyT0QSmJct8fIRN3pp5+Or776Cq1atUJubi7uuusuAMe2htPN6cVbbVXXFyESASJRp2Opoz6Qm5uLc889F36/H36/31o8BAAffPCBMhUM2+dFZc/MzMTEiROV9yRDJepCsZ4R/CrA8vLyoJ1ZWEIRddSWv/jiC2sbKf5+0tPThX2FLCoiSzmhk27kl19+0Y6p4+PESkpKMGbMmKDtBlnY+1HVh05dVYWo4/sO3Wuooi5US116err14q46hrfU8TufAJX5Gp1SJhH16tWz7Za1fPlydOjQAaeeeqoy1RSLEXUM1GjJ3HrKKadoNXaRqFPFBCUmJiIzMzNo2xUVO3bsQIcOHfDZZ58BqGxMubm5+PDDD23X7tWrl60cQKVJmwLYZaJO5H71eDxBWcR1ngdNhqo4F0LlfmVXf7ExdTqJcnv37o2BAwfaPlNZR+ncdM1mzZpZq4xpcGbjclauXInRo0fbLHWUz+/qq6/GCy+8ELTwQLXdD11b9Bt6NvSyUVZWZnO/fvTRR5aL67bbbrOtOmfPt3fvXmzYsAGFhYVW2hdROchdIMp/xqaOUd0L8eijjwqvxSZUjpSljiUzM9OK76O6ZfeY1UHHUidapStqy9TP2rZtG/SiRIhEHbXZgoIC6eTSoEEDq3+LYI8LZ+cQEZG01KnKlpaWhjFjxkhzqoXjfu3atSvOOussAPb7WbVqFc4++2zhWE5xbazQ0Y2h418u3Frq2ATfCQkJwkVhhK6oozKpdoKqCvcr/6xV7le277Hlady4sVWfbix1rKirU6eONP0TS3Jyss3i6/V6g/qpx+NR1uuYMWPw8ccfS6/hFiPqGHi3WTiiTmUVcptaAajM5yQyS7N4vV6pgKDOzTdQPk+dapDWLTOJIB1Rx7tfn3vuuaDNjn0+H+rUqWPVj+wNmBegrDsaUNcJvUmxVprp06cDOPZMqF3Qs50+fTomTpyIv/76C+np6UELJQhd9yvVwZNPPik8ngaq9PR04UIJEWw7YBM/33vvvejTp4/tt7yljrX+NGvWDA0aNLBcCTrXA4AuXbo4xo6EI+pUVj4SdWSpo+OchI3OlnGE6PmL+iYJZJ/PhyeeeML6/JdffsG0adOs8/LbGrGTmSre5rrrrpN+x/Z5N9u3iTj11FPRtm1b7Nq1C4BzTJ0bRCumJ02ahEWLFlkpT2SI2pgqdCYpKcm6nt/vt8YG9n66du2KhIQEYX3SvuB0XUp9AgCnnXYarr32Wtu52HOoRJ0qpo6PUSYXte48oiPqVFt+VRdLXWJiIn7//Xc89NBDtvLQ/W3evNnaI5VNS+RkqePdrzqijjcU+Hw+x/2S2TIDlam3VNZWt4SXbKaGcdddd1kdedmyZdIJiYcmv7/97W+2PTllq8p0fPU8+fn5tgYkEig+n08aN0QDDB+zReeUBfXyA5COC8tJ1Kncr+wyd9ZS5/f7g/Zd5Vm+fLnNbcOXVSeOUTRh07Oj+szIyLAJJPqMyse3GV33K71xDxs2DHfffXfQ8ddffz06deqEdu3aCRdKqM4N2Lc/8/l86N27Nz755BPhb9nrApX1w28073Q9+ltURrYuIuV+5SERRKIOqBR6U6ZMCbLiipClqmCvL7LKiUQAvRCw1oBAIIATTjjBcvV7PB6rXqnsbdq0sSyNqgmJ4lTr1q0bNO6wY0W4oo4stdQWImmp4+Po3Owry7exxYsXB73UAZUvJ23atLHqLSkpCQkJCUJRR4jqmEQdG+9K7WLGjBk4//zz0aFDB+v3zZs3t5Kn889FZKkTGQqon5BFUGcFLouqPpxCU959992YiDr6mx+7jz/+eFtbpn5FC8Fat26NjRs3ok2bNlZogY6ljrWG6rpfWXw+n3YqG34BV6QwljqGCy+8EBMmTLAGlyuvvFKr8aalpaG4uBjLly93TOMBBLvTdCgoKLAJL1kCRidLHd9AqZy6ok5H5FJncHI3AnruVxKrJJpklpaUlBSby5Avu5uYOroucOzZDR48GC1bthRm1ldZnpzaEC0AoUlIZelr166d7Tun+mDrgJLH0g4hsnJ+/PHHePbZZ0OKjxOd0ymwPRruV6JXr1546aWXrL/37t2LAQMGaJ1ftrKQfS6i64vaMqVHSEhICBL3dD5W1DVr1gyffPIJnn32Wet3Oqvffv/996DP+D2tIwHfN/jPgchY6nThr3XppZcKU9L88ccfWL58ufVMKDRAJepE7ZfP65mRkRFkUWPPddxxx1nWTb5fiSx1bCw3L+q6deuGt956y/Jo6Io61VghW+HZqVMnFBQUoF+/fjEVdTpjCJ+6qH379rbz6cTUsUJLR9SRuGfP4dZSp5u+SRcj6gSMGTMGK1asQKtWrbREDK0mrFOnjrSx33PPPVZwM7+aUYeKigrb24rM/SrLxSVzvzqJulDcr5REtHfv3sLvRZY60f2wk53f70dJSQmys7ODkj3KCEXUiY4ny+A999yDXbt2BXXyKVOm2AZF/lnSs5eJJKpXcjGp3uT5czoN6KL2SBMGXx767WWXXYYRI0aEtJpPtF0PtZmdO3dql1GFG1G3bNkyDB061PGcrCin88v2YWTvceLEiWjZsqUtZMDJUkd069bNdj6Px2O1tdTUVPTu3dtWLh1RJ3LRRkPUsdZGFrY9uo1zC2WXg1CvRc+ErOP0t0jUqSyGdN3MzEylqPN6vZYFR9W/6Tt2b2Ze1Hk8Hlx//fVBYTxt2rTB3LlzHcsqQibqTjvtNKFFsKrdryL4MAyRQYUtp87qV/o9mxhfdMy6deuwc+dOXHvttUHncGupM6KuCkhLS8N5550HQM9lJ3o7bdeunRUrAwCPP/641dioobLHiToVP0DzcQU87JuGrqjj47l4QnG/Nm7cGOXl5bbtb/hyErLcYoC90/r9fgQCAWRlZQm3ERIRivtVZakj2PLfcMMNmDx5sq2u+GdMx8tE0v/93/9h/fr11g4G/OCrEnVOk5nomlQ+miREgkPnPCJkljqPxyNcMMGWQxc3ok4X0cAtE3Xs9c866yzs2rULzzzzjPWZqE74eMxdu3bhxRdftH2WkJAQ9ALA3p9unio+iWy8WOqGDx8u3ZrNCXbVog68qBMJMYL/TBTiwbpfRe2Zdbvz37P1k5mZiRdffNFmXZbtAcuXoWvXrsqwglBEnSx+1K2I1oUf6+jZyMYfNkRHNvex59Zd/comxheljTn55JPRunVrJCUlBY1Hbi11xv1axfCN6cwzzwzqAKI3A6/XizvuuAMXX3wxxo0bB4/Ho3TN8oNRixYtsGjRInz55ZfWZ6woEb3Vso2Sz6DOx9Sdd9556Nq1Kxo3boykpCThSkcguPPqDpqqiVonpQlw7NlTShG3iCx1bHCtCF5I0vVZ2EGDXzENBAtk0ZY1LAkJCdIVq6L7YMsUynOhgc3J/B8JSx0Ntqr2EK2FEm5gy+CU0sTpmqLnRu5XEgQtW7a0njtrgeEncLb/6Yq6+fPn20IUommp42HL69YC0aZNG/zwww8hlYeu+/bbb2ulEqHnS6KOrPiirZz4/i9aialjqZNlBeD78PDhw20radnV8SKoDE4vrSohJqsrtq1H42XKqRxOoo59pk7PKTExUdkmefcr9ZUOHTpYISvnnHMOPv74Y9s8yMZu+nw+7TkyWu5Xs1DCJS+//DL27t1rWyUkejul/y5dutT6jk8eycI3hN27dwOw5wVjB5M+ffrY8pFROTweD959912cf/75tu94S93NN9+MG264AUBlTNf3338vvF9+wNFtsCpEok4kTlSiLiEhwXHwFlnq7rvvPuzZswfPP/+88Bi3ljqqS/a58KKubdu2mDp1KkaMGKEsr6gMovtgcXpjVgV+86vpRHnTVOcRwQ/2FA+pmgSqg6WOfY50/lGjRiEpKQl33nmn7behCEkSC/v27Qv6jhUD9957L4DKVcN8uXT7nsfjkS7mcCPqVHk0dfoGLzJ03VKhQNctLy/XehmhlDq0O0fjxo3xySefoGfPnvjb3/5mfQ6o2z5rqVNZ1FSWOqc2LDuOL4OTqAulr+jERUcSN+5XKkd5ebl0QRZ/Lo/HgzfeeEO4ip99+UxMTLRewFJSUmy5a/mVqrfeequVpogV706YhRIxhN1cPDk5WbiMmVDlaFNZ6kTJcvlzs9edMGEC1qxZIzx/v379pMv56W2fDUquW7eutEOEG5cmgr02WRRUA7FI1LlNlglAuFUTj8jdwItH9jfUIVlLHV/3Ho8H//73v0Oe1ESucV33q2hC4i1ROqLu119/1SprVVvqIjW5iHYGSElJwejRo5XX14Vcz/SyxsJaGOrWrYtHH33Ulnib0LXU8YRiqWvbtq0yZ5kspo6FbbfZ2dnWQoFowIo6HSgXI5vjrXfv3khOTsbgwYOt9EGAfkwd3690LXVO0PjjRtSddtpp0rK6IdqiTuWKZv/WsdQ988wzthXHPDRW/9///Z/we979Sm0pJSVFaQUcNWqU9eJHz1i2hSZ/PbZckcKIOg0uvfRSKz7MSdTxljoWlaj773//i9WrVwd9zp6bn9z5v1UdjV3BOWTIkKBNh3XcKUBoExoPmypCNcmwg2MosX0yUUf3KhrkdCx1IlEXCQumiPvvv1+YrFbX/aqakHjLMS9eQ6lrWUydqm1WB1EnstTJCMVSR4uHRHF6rPtVVS43KZBY2InSTfJh1XOQ9Q0Wdnxq0KCBcg9NFj7fpA7kmWAXGKggUUf1oiJUS12kRB2NW7IYZd79umHDBqxYsUL4uxEjRrh6OYi2qOPPIxN1Mthn2r9/f+X2hKx42r59O958882gsrCLF4YNGwag8vk7CWKy5NH9TJgwAQCEK7CJWbNmoW/fvhELISGM+1UTNkkuL+pkMXU8CQkJtlU6LCkpKZbLhUXlznAj6mjCrlu3rrU5uOhY1Wq2SMGmilCJOif3qxOyhRKsmOGvL4qp48UO+7copi6STJo0Sfl9KJY6gn/JkFnq5syZoz0R8EJAZanz+XwoKysLy/0a6oB4xRVXYMuWLdi5cyf8fr8rK0akt0JzctsRuhvE87ATo2i7IhFO7nbVwgIiVKv+ypUrteLiWLp06eIqr90nn3yCRYsWadVlqKtfWVhR57b9JCcno6SkxNGbQqJFZq3y+Xx49tlncf311yt3IGGpClHHtkmZ+1XHUieDdiNiRd1JJ52ELVu22H7Hu1+vu+46K6m3U7yeyFCQl5enHFe6dOlipZmKJEbUaUKDjEjUubHU8abWt99+W5nUlRZYlJeXBw3q/Ju7jqVOJtJ03a+RgLXUqQZv2qarRYsWQa4binfQ2bmD4C11ontjkx/LrBGstSPaljoZkbTUyUTdvffei40bN6J///6uBvGioiL07dsXixcvtlKaiI5PTU1Ffn5+TCx1CxcuxKpVq9CtWzekpKS4stSpArFVudauvPJKYUJcXUtdqCJJN6Zu27Zt+P7774N2QxDBvnDJCFVwR2tlJcuFF14o3DNahGqMUq1+5S11ov/XRSeu1um5hZJCI54sdTI6deqETz75JOg3fF/wer3SRW26cY/s76L1ou+EEXWaUMf2+Xwhi7qEhISgzqna3oc9f3l5uaOlTtWpnUSA7E0kGgMsa6l76aWX8Nprrwl/d9VVVyEnJwf16tXDnj17bN9RfbgRdbyljq+jBx980DKbs8dXZ1HnVD+qCclJ1PXu3RuHDh1yXbbk5GRbvkS/3y+cHGIp6oBj9827V5zEk2yi27ZtmzD5LyGLUVNZcCIh6nTdr6eccoplkdW1lLmxjsUrkcpTR0Tc3cYl3ZUhWm3ZunVr5UbxsRZ1OgslnMoze/ZsTJs2LSgjBG+1Zq2GfDl0VyBXxQuJE3ERU7d+/Xr06NED559/PgYOHKjtQogkzz33HEaMGIG6deuGtVAilKBIWS45N+5XwknUVYX7lbXU3Xzzzba0LTy0uEBWDtWbnGyPRVkHFSXOBYKfCfuGR9eI9LJ0J0JdKMFuIeXkfg0HPqeVaHLg8+Xp4rSjgy5sLBKVd8CAARg+fLj0evQ3m/GfOOGEE6z8lm5QTRiRFnVOCyXcPs/aLurq16+PlJQUtG7dWinqqB6HDRsWFMtFsCtu3UB1GoqlbufOnUFZFL799ltcddVVAOTtIRqphPiysX87iSkn9+sjjzwSVGZRzlZKFRYJS12siAtR16xZMyxZsgQrVqxAmzZt8P7771d5GVq3bo1nn30WHo9Hy1InSw4cjqjjrysKSpfhJAKq0v0q235Jhawcupa6k08+2RoYaEJm08UAwQMV/Z63WogsdVVNqO5X1iXA50KL5ARNz6W0tNS2UGLJkiXWJEKC3e0EweYSC2fhDmupo/LdddddQXXKr/z1eDzYuHGj9opgJ1Tu10gI2FBEnW5bqO2irl69ejh06BA6duyodL9SHtBZs2bhrLPOwsKFC/H5559b3//6669B2Qx0UaWFYtF1v3bu3NnKxhBpq6KsTLKcbU6ijoRZKH3j8ssvx5w5c3DiiSda53ASdW5i6mJFXIi6Jk2aWFYpVVBzcXEx8vPzbf+iQWpqKtLS0qxOJFooIcoZJHK/6kDndAqUDiemrirdr7KN0lWEK+rYoNhLLrkEQHACZ9kApuN+rWrCSWlCOLlfw2Hw4MEAKlc9spa6Sy65xArSfvfdd/Hkk0+6PvfSpUsj8kZMz451v4raQIsWLWx/JyQkoF69esqVbW4INYBeF3ai/Oc//6n8rRF1wVx++eUAKncB4cNAgGPPV1WPlHyauOKKK3DBBRdYf7dq1cqWcNgNupY6KpeOxZfGhGiLOt5bIHN7yqCwl1DK6fF4bFkgvF6vbZs+N+Vw+7toEheijvjtt9/w6aefWp2M55FHHkFmZqb1T+QiiQQ+nw9bt261EhCLLHUiURdpS53o/DJ0LXUq9ysbb+aWc845x4rXiaSlTvVM2GPYTn/iiSfirrvuCtpOSfb8VJa6aFgydaB6chpEdCZd2SrfcLj88ssRCASQnp4uTWnSokUL3HXXXa7P3bRpU/Tp0yfsMrL5vZwmMfZlJxJpfVhUlrpIQGPO6NGjMWrUKOVvjagLZuTIkSgqKkLLli2VKVBU7lfaJi4aUDiSaGxfsGBB0Ge8CBWhesmJJE6ijsRVz549hceTqAtHTLHx8r1798a///1v3HbbbcJyOmEsdRx//vknunfvHvQvJycH+fn5GDJkCGbPni2dSMePH4+8vDzrnypoOVyaN28uNLmqrGo6lrq1a9fiu+++s30WiW1gKEjUyXzMQ+Vt1aqVVkJFGatXr7Ysp7J8SyrCtdTx9/3kk08G7TOpGxQss9TddtttePrpp6XliQZOQkA16fLW22hN0E7Jh0OB+kKk3K9Ok1gou2voUlWWOh3R7lbU0a40NRmPx6Nl3VKJOt5SF0lUlrqrr74aCxYssMXrUXtw2twekIekRAodUXfgwAHcfffdwuP5LQ9DgX1B9vv9mDp1atBcq/viVR0sdbGXlQxNmjSx9lhjoc3hJ02apAwmTUpKqtKAdZH1iyo/VEsdm8mckC2UAOxpFFQNau7cuUEBsSyyCYWu7SZpqQjqDMXFxRHZxxWofMbjx493dQyLaqcIYvr06ejVq5ftMzYuia3PGTNmKK8XSSJhSalKURfpwe7555/HueeeG9Zkyb4sOQVcs9vShdsXeJzidcJFllxaVRadtlBcXFwtLBPVBdWewbK9tSOBk/v16quvtr1IU3tQhfPILNdJSUkhhc/I4EWdaMxW9fFIWupU56C6dUoAHm3Lpg5x0SPnzZuHVatWoaCgAA888ABuvfXWoB0RYgE1Bt2YOnoTcIvKUnfjjTdi1qxZtmuLaNiwobWiyQ2Rds2FGoMmem6i7PxOx7DoiDqRu6o6uV8j8bto30P9+vWtjOuRomHDhsItvEKBtcLoWOoi6aZmrxktUUfnj7Soi1U8aXVFZakT5SeMFMOGDcPPP/9s249cBbUzkQGBkFnq/v73v+Pdd98NsaTBsKIuKSnJdR+ItPtVdZ0XXngB/fv3F35PdR2pPrx06dKQx+W4EHVsZufqCNugqFJl7tdIpjQBKi0WP/74I1atWhVWw3bK2B3picwtkbLusfD3pPv8WFEXrYn4wQcfDHIPs0ycOFErHk3HUheN1a8sd955J26++eaonDscqD9deOGFVlodmahjP490SqVou1+jJeoMdmT1WFFREbVxAqhc0f7CCy+4Ombt2rVo27at9HvZ2PnWW2/hmWeecXUtFbyoU3HSSSfhoosusn1G7lfdPY1F6FjqAOBf//pXyNdwy8UXXxzysXEh6qorNPCJBnyRVa19+/aoX7++6+uoLHVer9daNRVNf36kXU5uCUXUOQloHUudiKoQdU6LUu644w7hnrA8OqKOnlO0JvK0tLSQ9y2NJnXr1sWBAwfQsGFDa99lHUtdOBOIiKqy1OnUr5vfyti2bVu1rO9ow7tfzz33XOzcuTOqgi5UVFY64Nicw4+RSUlJWvvl6uJG1G3fvj3oM7LUFRQUhFwG3UVnOueoDhhRFwYikyvFt4kE2Lhx40K6DsUkyYQNXT+cRkk5wwYOHCj8Ph4tdbqijrZhC0XUVXeqg6irzlC8Dk1isgm4KkRdtCx1OglaCZ3tv5yIpquxOsOL8//+97+4//77Y1mkkJGJukjjRtSJ6NixI4Bj81co6Lhf44macRcxQiTqKPeZ00pVN3i9XqSkpDhOOOE0ytTUVOVAHo+izinvE4mzxMREFBUVaU+qkZ7Uo4mbhRKxruNY4rQgKJ4tdaeddhoeeOAB277GTtRGgR8uokUFJ5xwQoxKEx40JkT7BbZJkybYtGkTvF5vSKJuwIAB2Lp1q9KV7ISu+1WH6mCVjf1SjTjm3nvvxYknnogGDRpYn5Goi+QqXJ/Pp1yppLOpcbiMHTs2aufWIRRR51QH/EIXXVEny5NYHVEJtaqKqYsH6BnI4uWmTJli/X+kRV20V796PB5MnDgxLGuGwRnZDjTxSFVZ6t5++23MmzcvZFEHICxBB0TG/VqdMKIuDM455xz8/PPPNsFB7tdIizqV5S8S7lcVgUAgZNdxpIjGvfGiTvca//znPzF16tSIl6eqqaqUJvEATWIywTZmzBisXbtW+ZtQibalLhRqc1sIlUjEI1YXqspS17BhQwwYMCAsURcukbDURXr1azgY92uEke0dFw66ljqDO8hykZ6ejoMHD2o/R4/HY626ilVKE11osHn22Wdx6qmnCn9jRJ19T1wZ7H62kSTaMXWhUBuSCkcayqFaE1K9VJWljvB6vTGzlJmYOoMSstRFWtSpLHXVJe1IVdG6dWsrQDYcxo8fj7Zt22LOnDnYtWuXq0n1tttuQ0VFBXr37h12OaIJCbUBAwZYu4rw3xn3a2WKmMaNG1sbmYugPh1pS50qaS0ALFu2DM2aNYvoNVWUl5dXC4tDvDF16lRcddVVUd09oqqIVNJ5Xbxeb8xElXG/GpTEIqaOLDCxMl9XNfPnz8f8+fPDPk9SUhKuvfZaqzO7EXVJSUm4++67q/1AoNrz17hfj5GWlobRo0crxUy0RZ3s2r169UK7du0iek0VCQkJRtSFgN/vR5cuXWJdjIhQ1Yun4t39SlSHfmNEXYShIPomTZpE7JxOlrpJkybhu+++sy3YqMlE2uV54oknRvR81QmdAcusftWDnlOkRV11iscxGIDYuF9jLerCCX+oTi/Exv0aYQYOHCjN9RYqxx13nHLA9/l8EXFHxgtuzPRPPPEEFi5cqPzNQw89hObNm4eVxbu6orLUUZvp3r277bcGMdGy1NGkYkSdobpQ1S96ycnJMUtYHcn+Vx36sBF1ccDMmTNjXYRqhRtL3d133427777b8XwjR44Mt1jVElW8SLt27WxCzog6NdESdaKdaQyGWFLVlronn3wyZovOInGPN9xwAxYvXlwtDANG1MUBNWVVTrgkJSWhuLjYPA8XqCx1st8axJCoi3TweJMmTdCmTRuMGTMmouc1GEKlqhdKyFbmVwWREHVZWVlYtmxZBEoTPmZ2NMQN6enpRtS5RHdl18CBA6t0w+p4JFqWhMTEROzYsSMq5zYYQqE2xdnWtJdZMzsa4oY6deogOzu72ueGq07ouvbmzp1bFcWJa0y7M9QWqtpSF0tqmqgzQRyGuCE9PR2AcUe7oaYNWLHExLwZagtk2a8Nlrqado9mlDLEDSTqjMVEn86dO8e6CAaDIc6oTQnta9qLrxF1hriBdm8wok6fl156Cdu2bYt1MQwGQxxBoq42uF9rGkbUGeKGCRMmYO/evUhOTo51UeKG5ORknHLKKbEuhsFgiCNqk/t17NixsS5CRDGizhA3JCQkoGnTprEuhsFgMNRoapOl7rHHHqtRLlgj6gwGg8FgMFg0bNgQLVu2rHFWrNqAWUZoMBgMBoPBwu/3Y9euXbEuhiEEjKXOYDAYDAaDoQYQV6LunXfeQVZWVqyLYTAYailerxdXXnllrIthMBgMQuLG/VpRUYH58+ejefPmsS6KwWCopZSVlcW6CAaDwSAlbix1b7/9Nvr376/M6l5cXIz8/HzbP4PBYDAYDIbaQFyIuvLycsybNw+DBg1S/u6RRx5BZmam9c9Y9QwGg8FgMNQWPIFqlKDlzz//RP/+/YM+HzZsGLxeL/7v//4PnTt3xrp164THFxcXo7i42Po7Pz8fzZs3R15eHjIyMqJWboPBYDAYDIZYU61EnYxx48Zhw4YNSEhIwOrVq3HTTTfh6aefdjwuPz8fmZmZRtQZDAaDwWCo8cSFqGNRWep4jKgzGAwGg8FQW4g7UeeGQCCAgoICpKenw+PxxLo4BoPBYDAYDFGjRos6g8FgMBgMhtpCXKx+NRgMBoPBYDCoMaLOYDAYDAaDoQZgRJ3BYDAYDAZDDSButgmLNLSIwmAwGAwGgyEecFr4WWtFXXZ2Nho1ahTrYhgMBoPBYDBo4ZSirdaKusTERADA77//bnLYxSG0W4ipv/jD1F18Y+ovfjF1F/+kp6crv6+1oo7MlxkZGaZxxzGm/uIXU3fxjam/+MXUXc3FLJQwGAwGg8FgqAEYUWcwGAwGg8FQA6i1oi4pKQmTJ09GUlJSrItiCAFTf/GLqbv4xtRf/GLqruZjtgkzGAwGg8FgqAHUWkudwWAwGAwGQ03CiDqDwWAwGAyGGoARdQaDwWAwGAw1gFor6u655x706NEDgwcPRklJSayLYxCwfv169OjRA+effz4GDhyI0tJSzJ07F127dsWFF16I33//HQCwZcsWdO/eHV27dsWnn34a41IbeN555x1kZWUBgKm/OGL58uW46KKLcP755+ODDz4wdRdHVFRUYOjQoejRowfOO+887Ny509RfbSFQC/nuu+8CgwcPDgQCgcCDDz4YeOutt2JcIoOIffv2BQoLCwOBQCAwfvz4wLx58wJnn312oLi4OPD1118Hhg0bFggEAoGrrroqsH379kBeXl6ga9eusSyygaO8vDzQr1+/QMeOHQMlJSWm/uKEoqKiwOWXXx4oLi4OBAIBU3dxxvr16wODBg0KBAKBwNKlSwOjR4829VdLqJWWutWrV+OSSy4BAPTu3RurVq2KcYkMIpo0aYLU1FQAgN/vx/bt23HaaachMTER3bp1w6ZNmwAA+/btw0knnYSMjAw0aNAA2dnZsSy2geHtt99G//79kZCQgB07dpj6ixNWrVqFlJQUXHHFFejbty++/fZbU3dxxPHHHw8ACAQCyM3NRVZWlqm/WkKtFHW5ubnWFimZmZnIycmJcYkMKn777Td8+umn6N69u21rm/LycgCVAxdh6rP6UF5ejnnz5mHQoEEA7P2OvgdM/VVH9u/fj19//RUffvghhg8fjilTppi6iyMaNmyIhIQEtGvXDmPHjkXPnj1N/dUSaqWoq1evHvLz8wFUTjT169ePcYkMMvLz8zFkyBDMnj0bjRo1suoNALxeLwAgIeFYMzb1WX148803MXDgQKt+2H4HmPqrztStWxfdu3dHYmIiLrzwQmzYsMHUXRyxZMkSpKSkYNu2bXj33Xcxffp0U3+1hFop6rp06YKlS5cCqGz83bp1i3GJDCLKy8sxePBgTJo0CSeffDLatGmDLVu2oKSkBCtXrkT79u0BVLppd+zYgfz8fOTk5KBhw4YxLrkBqAzCfv3119G7d2/s2LEDs2bNMvUXJ5x99tnYsmULAGDDhg245JJLTN3FGfXq1QNQKdCzs7NN/dUSau2OEvfccw/WrFmDFi1aYPbs2UhMTIx1kQwc77zzDkaMGIEzzjgDAHDrrbciEAhg+vTpSE5Oxuuvv47mzZtjy5YtGD58OMrLyzF16lRcfPHFMS65gadz585Yt24d5syZY+ovTpgxYwbmzp2LhIQEvPLKK1i7dq2puzihvLwcQ4YMwZ49e1BcXIynnnoKv/32m6m/WkCtFXUGg8FgMBgMNYla6X41GAwGg8FgqGkYUWcwGAwGg8FQAzCizmAwGAwGg6EGYESdwWAwGAwGQw3AiDqDwWAwGAyGGoARdQaDwWAwGAw1ACPqDAaDwWAwGGoARtQZDAaDwWAw1ACMqDMYDAaDwWCoARhRZzAYDAaDwVADMKLOYDAYDAaDoQbw/8dDFFGyhax6AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -923,7 +922,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n"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+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -956,7 +955,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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QSqVgmubcfaSqKhKJBGKx2JLvIyklrOunYVz8CJDuQQuvbUFszxfA4tFaXyaFR+hpiKFbizpXTgxBXbsbTFv8Dg0pJTKZDNLpNCzLmruPNE1DMpmEqqqRDybuVtSBRa7I1CT0U/8IMXzb/SRFhbb9Maitm4IbGCkqi1n6uJucHIW24f4cj2rxxNgA9JO/hpwadT8pXoLY3ieg1DQHNi5SXKzJYYjxwUV9sUshwIQFtW1LACOLLgosckRKAfPKcZiXj3mep6zZBm3LQ2ARqJZGCoOUEmK0DyI14flhKKfGAC0OFktATo9DaVwfucRIaRowzr4Dq+ey+0mMQd18CGrHrrx6qiPRJqWANXgLmN1J5XbexBBQUgmmqJDjg1DX7QNTtYBGGU0UWOSY1d8F/dSvvZdGKhsQ2/sEeEl0k3FIfpCWCWvghl2nwu0cKSHHBqA0bQBTVVhj/WCpKajr9wY30CWQUsLqOg/j/HuARz4Hb+ywC2otYQqaECfCyEAM3PRO0LRMYGIYavt2iKkxiMwUGBjUFpqFpsAiACI1Af3EG5Cj/e4naXHEdn8eSsOa4AZGCopIT0IM9XhuJ5WmDqSnoLbvnPsCNu9ch1JRB5YsD2qoyyJG70A/8aZd5MsFK61EbO8XwSsof4ksjzU+CDE5BMY87qPMNBgYlNVbwbg922x2X7SDdSV62z+DRoFFQKRlzewaOet5nrphH9SN93v+UhNyL2u4FyK9wNLH9DhYSQWUxvV5u2Qg9bSdv+S1a4Sr0HY8CrV1c3ADI3lPSglr4KZdmdbrvIlh8Lo2yuvxQIFFwMyeyzDOvP1pPXkHvK4NsT2fpx4JZEFCCIi+a4BHnCClhJwcgdK8EUoB9K+RUs7kLx31PE9ZvQXa1ofpCZIsSJi6fR95/K5IYQFT41Dbt9Nn8wIosAiBmBiCfvwNz2x3liiz8y6qG4MbGMkrIjMFq/+GdwVAUwczDSirtxXcF6w1cMvOX9Ld6wqwynrE9j5J+UvElZgYgjV2B0xxT7iUmRSYGoPSsilvZ/uCRIFFSKSpwzj9Nqzeq+4nMQ5ty4NQ2rfTLzOZxxq+DTE96v1hmJoEK6+DWl+4Dbzs/KU3IUfvuJ+kxe1qnV69fUjRkVLCutMJaRlzeRKO502NgTe0Q6mIRkvyfECBRYiklLBunIFx4UPPQkBK8wZoOx6jap0EUghYPZ9AKop3ctn0OJTmTeDJsgBHFw4pLBgXPoR144zneer6vVA37af8JWLv+uj5BPAorialAEtP2wma9Nm7JBRYRIA10gf9xBtA2r3BEyurtqd0yxdfFZEUFpGagNV7xXP3xmyBHqVlc9GVuzZvX4Fx+q0F8pdaEdv9BbA4rZEXK2t8AGKoByxR6nqONA0wLQFl1VqaLV4GCiwiQmZS0E/9GmKw2/0kRUNsz+ehrKIGTMXGHOiCnBxe4MNQBy+rgVJdvF10xcSwvbV7csT1HJYoQ+z+p8C92rWTgiOlhNVzCVKYnrVOpJ6GUtsKXgCJzmGhwCJCpBQwLx+DeeW453nqfQ9QlcEiIS0T1s0zkFrcO0nT0KGsWgtOfTPs/KUz78C6fcX9JEW168Y0dgQ3MBIamUnBvHkGKK30zKeAaYA3bwQvsETnoFFgEUHWnRvQP/6NZ7VOpe0+aNsf9b5JSF4TU6Mwu86DVdQtuKzBV61bsANvMZFSwrp5Dsb59z3zl9TNh6Cu201BegGzhm9DDNwE80i+lFKCa3Eo9VSg0A8UWESUmB63p3THBlzP4TXNiO17kvZUFxgpJay+q5Djgwt8GArwkkooVbQl2Y0Y6bN3jaQnXc9RWjdD2/4Y9espMFJYsG6ehRQWmMd2YyksKJWN4GVVwQ2uwFFgEWHSMmGc+x2sWxddz2ElFYjd/zQldRYIaWRgXjsBxBLeJbaFAK9tBffIuSA2qaegn3jTLnfuglc3Ibbvi5TUWSBkahzmtZNARS2YlvA4UYI3rAUv8qZhfqPAIuKklDCvn4Z54QP3k9QYYnu+QH1G8pwY64d5/TRYTTOY5rG9jXEoDe20DLYEUlh2kN51wfUcliy3g3TqM5K3pJQQAzdh9V0Fq231vkfUGJS6Ntp+nAMUWOQJ684N6Cd/7VHHnkHb+hAV08pDUghY3RcgRvvAats88ylYvBS8uon+jZdBSgnr+hkYFz4A4PKxp2qI7f4CFdPKQ9I0YHWehDB1z4rFUkrw8lrwshq6j3KEAos8IsaHoB97zbO7o7Jmq90fgZ5m84JMT8G8chRSUb3Lt0sJVrkKSmllcIMrUNadG3YpcNMjSN/yAJS1O+mLJ0+IiSGYV46CldeBLZArwWtaaPdUjlFgkWdkZhr68dchRvpcz+G1LYjtfRIs5rG2SEJnDd6C1XkSrKoRzCtHhjEota3ea8VkScTEEPRjv4KcHnc9R1m9Bdq2RyhIjzApJcTtS7C6L4E1rAZLeFSaVVT7PvIog0/8QYFFHpKWae/T77nkeg4rrUTs/i9RpnMEScuEdf00xGAXWMMa7yRNNQ6lprngGohFgcykoJ94HWK41/UcCtKjS+opmFePQU6NgjWs9fw3Yoky8KpVlE8REAos8pSUEua1kzA/Oex+khZHbO8TUOoKtwlVvhFTozAvHwGMDNiqds+twqykEryinqbjc0haFoyz78Dq/sT1HFZSidj+p6kSY4SIkV6YV48DXLHvI49ZCF5RD1ZSSfdRgCiwyHNWbyf0j//RvT8CY9C2PQJ1zbZgB0bmkVJC9F2DdfMsoMbsD0OPSpq8sgG8hPIpgiClhNl5CubFj9xPUmOI7X0SSgF3is0Hdm2KcxB9V4FkOVj9GvdkZ8bAq5spnyIEFFgUADE2YK8XexUBat8BbcuDRdeYKgrs2hTHIUf6gESZvfzhtm7PGHhVE9WnCIHV1wn91AJB+taHobZvD3ZgBAAgUxMwLx+BnB4DymvtbdlusxBctZcQPXqCkNyhwKJAyPQUMsd/BTna73oOr1+N2J4v0M0WIDHWD/PKMcBIA2XV9t561w9DZebDkNbzwyLGB6Effc07SF+zHdrWhyhID8hcbYrrHwPCAqtuBKtscP8ByksKHQUWBURaJozTv4V1+6rrOay8FvEDz3h2ySQrZ9emuAjRY6/ds6pVYFWr3H9Ajc18GFLGetjsIP11yNE7rufwVe2I7XmCvrxyzK5NcQpi6BbAGFhdG1hplev5LF4CXtVEQV/IKLAoMFJKmFeOwbx8zPUcVlKB2P5naMdIjszVppgcBsDA6lrAyty3k7JY0i56RdsaI8MO0t/y7JDKa5oQ2/cU7RjJETExDPPKESAzbSdpNrR7PhCxZAV4ZQMlaUYABRYFyrx9BcbHvwWE5XxCLIn4gWfAK+uDHViBE0Pddo8CywAYX3A7KUuWg1euog/DCJJSwrx6AualI67nsPIaxA88SzOAPrJrU1yGdes8IOWnyc4eS4S8vBastJruo4igwKKAidE7yBz7lR3xO1E1xPY9BaWuNdiBFSBpmbBunIbov2G/oGgLbyctq6GywnnAun0V+se/BYRzUidLliN24FmaAfSBXZviOOTYTK5YLLnwdtKqVeBJ9+6lJHgUWBQ4kZqAfuRVyMkR5xM4R2zX56E0rw92YAVETI3CvHIUmC21HkvYBXs8OibSdtL8Ikb6kDn6mp2E6ySWQHz/M+BVHkmFxJMY6bNrU5gZ+4VkBVj9ao/tpBy8uom2k0YQBRZFQOopZI6+5pmMpm1/lGpdLNG82hRS2C8uZjtpdRN4nKbO842YHIF++BfuO0YUDbF9X6RaF0skhQWr6xxE711J5wttJ1VUKNW0nTSqKLAoEtI0oJ94A2Kgy/UcdeP9UDfcT1Pzi2DXpjgBOXJXOehFbSdtoQ/DPCZTk8gc+YX7DCDj0HZ/DmrzhmAHlqdkasJOdJ4anXuNVTeBeeV+0XbSyKPAoohIYcH4+Leeme7Kmm3Qtj1MNfU9iLEBmFePAXrq0xcr68Grm9x/iLaTFgypp6Efe82zEaC27REqpOXBrk3RBev6qXkJ5qyuDcyjdDptJ80PFFgUGSkljAvvw7p+xvUcpWkdtF2fB1No++PdpBSwbn1am2IWq2oE81hbp+2khUdaBvQTb0L033Q9R92wD+rG/TQDeA9pGrCun4IYvHXXq8zOpyh1zztiJRXgFbSdNB9QYFGEFtPAjNe1Irbvi579LIqJzEzBvHIMcmJo3uusphmsos7152g7aeGSwoJx+m3PLsPK6q3Qtj9CM4Az7NoUR4HM1KcvMmbXqPDYlk3bSfMLBRZFzOy6AOPMOwCcfwVYZT3i+78EVuRZ1/NqU9yF1baClXsUvqLtpAVPSgnz4ocwOz92PYc3rkNs9+eKOicgqzbFLMbt7aSJMpefZOBVDbSdNM9QYFHkrL5O6Cd/7VpIi5VW2nv0S4rvxrZrU5yB6L9+zxEGVu9dWpi2kxYX49pJz+6ovLbFrtKpFd8MoNTTMK8e+7Q2xSyugK1a6/7gQt1J8xYFFgTW0G3ox14DTN35hHiJXaXTY8q/0IipMbuc8GxtilmM2a2aPQItKthTnMxbF2GceXv+E/ldWEWd3aeniL4oxUgfzGvHASMz/4Ci2kGFWwE5xu1kZ48CcyS6KLAgAOyujpkjr3pU6Ywhdv/TUGqbgx1YwKSUEHc6Yd0482ltilmMz6wFe0zbVjeCu07rkkJn3bkO/cSb7jOAJRX2DKBHkmIhkELM1KZw2IGmaGCNHe7brqnLb96jwILMEdPjdgGg6THnE7hiFwBqWBPswALiWJtiFud2NU3XnhAMvIYKXxHAGr4N/divsp/SZ8WTiB98DtwjPyefOdWmmKPG7KDCLSmcK1BqWotyyaiQUGBB5pGZaWSOvAo5Puh8AueI7f0ilFXtgY4r1xxrU8ziCtiqDrA4TduSxRHjQzMzgFPOJ8SSiB/6Mnh5bbADyzFr4Caszo+d+6pocfs+cit1r6h2UOFRCp/kBwosSBZp6NCP/wpiqMf5hAIKLqQUsLo/gei+6HyCotofhm6tsRm3q2lS62xyDzE9bvfpcXpyB+zg4uCXwSvyP7iQlgGr82OIQZfKvrGknVPhtjNG0aDUtlABuQJBgQVxJC0L+sf/CNF7zfkExhHb9ySUVWuDHZiPZGbanrK9pzbFHFWzgwrPtWAq0U3cyUwKmaO/zN4RMSuWsJdF8ji4EJMjMC8fcZ+diZfYQYVbgTg1Zt9HRbwdt9BQYEFcSSlgnHkH1i2Xp3nGEdv7JJTG/AsuxFAPzGsnsmpTzFHjYE0d7k9QXLWfsKiAGFmANHXoR1+DGL7tfIKWsJdF8mzXlZQSovcKrK5zrjthkCiz61S4FQjT4nZQQVVpCwoFFsSTlHImuLjgfALjiO19AkpjR7ADWyZpWbBunoa4c29tirtoCbCmde4fdopmfxjSWjBZJGkadn8Rt+XFPAsu7NoUxyHH3DsmI1kB3rAGcCkQZ5e6b6a+HwWIAguyICkljLPvwOryCC72PAGlKdrBhZgeg3X5KGRq3P2keAl44zrXD0OatiXLtXBwEbdzLrw6e0aAGL1jJzq77XoBgLJa8LoW18MsXmL3z6FS5wWJAguyKFJKGOfehXXzvPMJjCO25wtQmtYFO7BF8KxNcbfyWvBa9w9DqHF7+YOmbckyScuwl0XyMLiQQsC6dR7i9mXP8xbsUJooA69qpFL3BYwCC7JodnDxO1g3zzmfwBhiu78ApXl9sAPzIA0dZucJSLf17Rm8oR0oqYRb3xRoCXtLKQUVZIWkZUA/9iuIwW7nE7Q44geeBffomBs0mZqcqU0x4nkeb90CqO6zedSUrzhQYEGWREoJ4/x7sG6cdT4hQsGFGB+0Oyk61aaYxRXwtTsBIVxnM2gtmPhNWuZMcHHL+YQIBRfWQBeszlPOtSlmaQkoa3dB6i6VewGwkkrwinoKKooABRZkyezg4n17acEJY9B2fx5q84ZgBzZDSgHR/Qkst9oUM1hpFZSO3RCTI+5BRbwUvLqR1oKJ76Rl2vViBlyCCzWG+MFnwatWBTuwGdIyYF3/GGLApTbFDFbdBKX1PogJl6J6AFhptd36nIKKokCBBVkWKSWMC+/Duu4SXIBB2/05qC0bgx1XZhrmlWOQHh9yAMCbNoA3b4AY6XXv60BrwSTH7ODidfcvbzVmNwCsbgx0XGJyxJ7tS0+6n8Q4lDXbwaobIUb64LaMyMtqwMpq6D4qIhRYkGWzg4sPYF0/7XJGsMHFgrUpAECNQ12/F6y8FtZQt3tQES+dyVqnD0OSW1EKLhZVmwIAkuVQN+wH1NhMfQ6Xjq5l1VDK82MLLfEPBRZkRaSUMC9+CLPzY5czGLRdn4Xauil3Y7AsWDfPQNzp9DyPVTZAXb8P4Cqs4W7Acl4ztrfCNVNQQQIjLQv6idch+m86n6BqiB14FkoOgwtpzNSmGPWoTQE70Vlp3wlYBqzhHvc28aVV4OV1dB8VIQosyIrZwcVHMDtPuZ5jBxebfb+2mB6HdfmId20KxqC0bQVv3ggIy56pcJnVYLEkeE0z5VSQwNnBxRsQ/TecT1A1xPY/A6WmyfdrL6o2haJB6dgDpa4VUk/PBBUuuUmUqFnUKLAgvpBSwvzkMMxrJ13P0XZ/AWqLPwmdUkqI/uv2MoxXbYp4CdQNB8DLayAt0zOoQCwBpbqFdn+Q0EjLgn7yDYg7N5xPUDTEDz3n226RRdemKKuBumE/WKIU0sjY95FbUJGsAK9soKCiiFFgQXwjpYR56TDMqy7BBeOI3f8UlIY1K7uOqcO8dhJy2KXI0Axe2walYzeYqkHOzlSYuvPJVKeCRIQUFvQTb7qXnY8lEH/gK+AeRagWdZ30TG2KyQVqU7RsgtK6BYxzO6gY7vFIeC4Hr6I6FcWOAgviKzu4OALz6gnnE7iK2MFnlz2du9jaFMraXeD1a8AYmwkqegDTZZqXKmqSiJHCgn7y1xB9znlDLFGG2INfAU+WL+v9rcFbsDpPuuYZAQC0BNQN94NX2rMj0tS9E54TpeBVlPBMKLAgOSClhHn5KMwrx51P0OKIH3p+Sa2ipZQztSlc+pXMYCWVUDceAJv5wJVC2E9YRtr5B9QYlNpWCipI5NjBxT9C9F1zPM5KqxB/4Ctg8eTi39MyZ2pTuCSJzr53dSPUdfvAtLj9c6ZBCc9k0SiwIDmx4G6ReIk9nVtaufB7LbY2ReN6KGu2zQUJUgpYw7fdZzcUzQ4qqKEYiSgpLLtCp8tWVFZZj/ih58DU2ILvJaZGYV4+sqjaFLxx3VyQsFBuEiU8k3tRYEFyxm65/hasW584HmclFfYTV6LU9T3E8G27NoVbbgQAqDGo6/eBV3+6vCKlgBjudS8xrKgzQQW1PifRJi0D+uFX7WJuDnhtC2L7v+QaIEspIfqu2j1+vBKdE2VQNx4AL62669qU8EyWjgILklNSCHsLnUsiGquoRfzQ83NTrnM/t9jaFBX1UDfcDxb7dDpYSgkx0guZmXL+IT4TVKgUVJD8IPU0Mh/9DHJiyPE4X7UWsb1PZn3BSyMzU5uiz/P9eX07lLU75wUni0t4pqCCZKPAguSctEzoR3/p2iqa1zQhduCZudkDOT0O88oRyGmP2hRgUFbbtSnuXteVUkKM9kG6TfdyZSaoWHjqmJAokekpZD78iet9obTdB23H43P3gxjrh3nlmHt+EWDP3HXsgVLXNv9alPBMVoACCxIIaejIHP4Z5NiA43HesAba3ichB2/BunHaNfMcwExtiv3g5fOTP6WUEOP97gEJV+wnrHtmRwjJF2JqDJkPfwJknJf41I7dUDcfhNV9AaLnkud73V2b4m4L5iZRwjNZAAUWJDAyk7KfuKZGsw8yBqWuxbs1MwBe2wqlY4/jMoaYHIZwmSoG4/aHIQUVJM+J8UFkPvqZc5VMrkCpb3WfaZg97a7aFHdbcMaPEp7JIlBgQQIlUhPQP/jJ/A8uVQMvqwJTPJ6A7qlNkfW+0+MQYy49Dhi3ZypiiRWOnpBosIZ7oR/+xbxAnMUSYKUV3jkP99SmyHrf8UHIKZeCWZTwTBaJAgsSODExjMyHPwWMNFiyFCxZ5rn/3a5NsR8sWeH8fpmpmQ6Ljj9trwXHFr/Xn5B8YPXfhH7sV4CUYKXl4IkSz/NZVSPU9ftcZ+3E1CjEuPNSJSU8k6WgwIKEwhrognnxAzDNO4mSN66Dsma763ruQn0LeHUTeKJsxeMlJIqM66chej7xXppgbKY2xXrXAF6kJ123s9IyIlkq2idEAieGb8O6cdozqJBCgNWtgbp2l3tQMde22SWoqKinoIIUJCklrN6rkHc6PYMKaZlQ1uyE0rTBNaiQegpixH07Kq9uoqCCLAll4JDASGHBunnWtUTx3HlGBmJyDGJ0ELyy3rFpmRSWnbnu1regtHpeoR9CCsVia1OI9DTk1AT0M+8gXlYNXl6T/V6mbt9HcJ645lWrwOPeSyyE3IuWQkgg7NoURyGnx9zPkRIyNQmZuquwFVcRO/RlKNWNd53nvR2OOiySQrWY2hRSCMipcUj9rnMSpYg/+E/mNS2zq2recu3/wctrwcuygxFCFkKBBckpKSVE/42Fa1NwFdbIHcB0KB0cSyL+0O+Bl1QsuB3O7lvQQkEFKShSiEXVpgDjsIbvON5rrKLWLqGvxmaa83U7b1mFnTDNK+rpPiLLQjkWJGekqcO6ctRuz+wRVPDaFmh7vwh19TbnE/QU9GOv2UskE4Pue+zVmL0eTB+GpIDI9BTM8+8uGFTw5o1Q930JvK7V+X3Gh6Cf+kcIYUGM9roHFfFSCirIitCMBckJMTEE88pR1wqBAOzaFO07wRvawRibaVr2NqxbFx1PV9buBK9Z5fJeKpQ62mNPCos12A2r84TrcgUAuzbF+n3gVfa9IS0D+pFXIYadd3mo2x52b7WuJezt2dSplKwABRbEV1JKiJ5LsG5dgFtCGDBTm2LDfrCS+bUppBDQj/8Kov/m/POrGqB07HR+iqLtcKTASMuEdeM0RP8Nz/NY1aqZ2hTzi79JI4PMB/8AOTm/2BVv7IDSst75zRQNSl0bleomK0aBBfGNzKRgXj0G6VZkZ8aCtSlMHZkPfjLXyZGVVkHZuNf1fF7TQpnrpGCIqVF7ti814X4SY1BWbwdv8qhNMTWGzAd/D8wkcbLaZqjtLsuN1JyP+IgCC+ILMdIL8+px9xbLAKDGoK7bC17TvPD7pSaQef/HABjUzftdP/B41Spwl4qchOQTKSVE3zVYN8+61mYBACRKoW44AF5WveB7WsO3oR/+OVhZNZT1u52XOBiDUtNKJe+JbyiwICuy2NoUrKIO6vr7wZYws2AN90KMD7iuBzMtCcUlUY2QfCKNDMxrJyDdql/O4PVroKzduaRcIrP7EiSEYyEtKSWUivpFBSmELBYVyCLLJlPjMC9716YAGJS2+8BbNi8py1xKASkM16DC6u+CHOwBf+j3lhSsEBI1i6lNAa5C6dgNpX71kt5bWgagaWAuu7KsrgsQQiJ28FnKrSC+oRkLsmRSSoiBm7Cuf+xdmyJWYndSrKhb+vt71KoQo/2wrn0MAODVjYgd/DK1cSZ5R0oB69ZFiJ5PPM9jpdV2E74llqeXUsAa7HZtoW71XoO4bc80Kq2boe38DG0xJb6gwIIsiTQNWJ0nIYa6Pc9jNS1Q1+1ZVjKYmByGmEnczD42Cuvy8Xlr0ErzBmi7P08fiiRvyPSUXYl2ctjzPN68EUrbVu9W6E7vLyXE6B3ItHMCqBi6DevGuXmvqZsPQVu/Z0nXIcQJPeaRRVtObYolXyM95RpUSD0D6+qprMQ26/YVsLJqaBvvX/L1CAmaGOqGee0kYDlUmZ2lxaGuv3+uNsVSyalR96BichTWzfNZr5uffAReWgmlad2yrknILAosyIKklBC3L8HqWqg2RQXUDQeyalMs+jqmDuHWWIlxKPWrYcVPQU5nfyCbl4+ClVZBbdmwrGsTkmuLrk1ROVObYpm7NERmCmJi0PmgGoPS0G4H6A6BjX7qN4gny8GrGpZ1bUIAWgohC5D6TG2KsQVqU6zqgLJmB5iyvAQwKSxYQ92u21V5TTN4vBRiYhiZD/7B+TyuIH7oOfC7GpYREgViagzmlSOLqE2xDdyjxflCpKnDGrzlvF2VcbsAlhqDdecG9GO/guODQrwEiYdeAEsuLaeDkFkUWBBXi6pNoWhQ1+8Fr2lZ9nWklBAjvZCZKcfjvLxu3nY4q78L+rFfAk6/unc1LCMkbFJKiDudsG6c8a02heu1hLC7lboF59XN4InSuf82O0/DuPC+47msog7xB56ngllkWSiwIFmksGB1nYPovep5Hiuvg7phabUpnFgTQ65JbCxRBl7VmPUEZ944C+Pc71zGVYP4A/8ETKMPRRKeRdemqFsNpWPXivrcLBycZ7dAl1LCOPsurK7sfAsA4KvWIrbvi5QUTZaMAgsyj0xNwLx8ZIHaFIDStmXJtSmciNSk3WnRiRq3G4u5NETSz71nPwk64A1rELv/afpQJKEQYwMwrx4D9JT7SVyF0rELSv2alV9vYghiicE5YD9E6Ed/CTHovMtLXbcH2n2HVjw+UlwosCAAllKbIgl1w/4l16ZwvKaRsadunX4FuQKltg1MdX+Kk0JAP/YaxECX43F1437aKUICtfjaFFV2orMPeQwiPQnhNiuixuz7yGO7qlvDslmxvU/SThGyJBRYkJnaFKcghm55nsdqmqF27PVliUEKy04yc9lyp9S0LGqJRRo6Mh/+A+SE89NabP8zUBqWVq2QkOWQmSmYV47NNc9zw5s2QFm9bcm1KRyvaegzwblbsuZqz+B8lpgaQ+b9v3eu/qnGEH/oBfCyqhWPlxQHCiyKnJgYtrPVvWpTMA5l7U7whrW+LC1IKSGGb0PqztfkFfXgpVWLfj8xPW5/KDpNO2sJxB/5p+DJ8mWOlpCFiaEemNdOLKI2xT7wKn92LS0UnC+16681ZDcscwpSWHkt4g/9kxXlgZDiQYFFkbJrU1yGdeu881LEDJasgLLxgK+7LKzxAcipUdfr8cqGJQcw9ofizxz/f2FVDYgf+sqyt8IS4sauTXEGov+653msssFuwudTB1E7WfM2pMsDwVKD81nmzXMwzr7reExp2QRt12cpb4ksaOVzcSTvSD0F8+L7sLrOeQYVfFUH1O2f8TWoEKlx16ACWgK8sn5ZH1xKbTPUzc5JZnK033VbHSHLJabHYJ592zuomKlNod73kK9tycXEkGtQwZLlYCWVy3pfZfVWKK2bHI9ZPZdcd5AQcjeqvFlkxEjfTG0K58ZEAOzaFOv2gtcuvzaFE2mkIUb7nQ9yBUp1k+sOkMVQO3ZBjPRB9HVmHbNunoNZ3QjV5UOTkMVadG2KeCnUjfuztnmulEhNQE45J1pCiy9rxm8WYwza9kchxgYdc0WM8++BVzZQZU7iiWYsioQUFswbp2F+8oFnUMHK66Dt/Jz/QYVlwhruhXNJcAalunnFHUoZY4jt/AxYqfPTmnH2HYhx78Q6QrxIQ4d56bC9e8ojqOB1bdB2fNb3oEIaGYjROy4XVez7aAXBOQAwRUNs75OAU3EsIaCfeANS92jxTooeBRZFQKYmYJ57Z8GCV0rrfVC3PrzigldZ15cS1mgfIEzH47yy3rdpYqbFEdv7RYA7BCmWaX8oGh6VRAlxIcYHYJz5DeTIbfeTuAJl/T6oG/YvajfGUkhhwRq5Dbd+PUp104qD81m8rAqxnZ91HkdqAvqpfwSl5xE3FFgUMCklrIGbMM781j2vAbBrU2x9BErblhU/7TgRE0OuhYJYSSX4MteD3fCKWmg7HnM8JqdGoZ95iz4UyaJJKWDeugDz/O88C16x0ipoOz7rS8Gr7DFIu0Gf5RacN4DFkr5eU2nqgNqxy/GYGOiCeeW4r9cjhYMCiwIlTQPW1WOwrh73LHjFapqh7fgceEV9TsYhMlOu68EslszZddXWTVDWbHUeU+81WNdP5+S6pLDIzDTM8+9BdF/0PI83bYC67TGwHG1rllOj7smaOQjOZ6mbD4HXNDseMy8fheVSnI4UNwosCpCYHIZx5rcQgx4FrxiHsnYX1I0Hc9ZTQ1qm+3qworqWGfaLtuVhsErnJDPj4oewhjymtEnRE0M9ME7/BtKtBTkAqHGomx+E2r4DjOdmO7PU0+5t0GOJnAXnAMA4R2zPFwCX5VH95D9CeHVsJUWJAosCIqWE1XMJ5rl3AJdmRIC9HU3d/hkojety9sU+N3XrMlvi53qwG6YoiO19AtDiTgOEfvJNyLT73xMpTtKyYHaegnn5sGfBK1bZAG3nZ8Gr/Sl45TgWYdn5SU64AqWqKed1JViiFLE9TwBO1zHSdt6S5dEGgBQdCiwKhNTTi6xNsdauTeGyc8K38UyNQLqsR/OKOjDNvz39XnhJBWK7P+98MDNtJ6EJjy2DpKjYtSnegriTvWV5zrzaFP7mNdxNSgkx1u9eWbOqMefB+ayF68R8EMg4SH6gwKIAiJE+O1t9zKVGBGDXpth4EGrHnpx/GEk9ZSdsOmDxUrCSqpxe/15KwxqoG5ybkYmhHpiXjgQ6HhI9UkpYfZ0wz7wFmRp3PzFeCnXrY1BaNuV8pkCmxiHTk47HWGn1ksp1+0Ht2AXe2OF4zLp5Fmb3pUDHQ6KLCmTlMSkErK5zEL1XPM9j5bX29rcAPojsLXFuU7cqeNWqUEoCqxv3QYz2QQxk552Y106CV6+C4vKhSQqbNHSYnScgh71zbnhtG5SO3b5vI3UeUwZibMD5oJYAL6/N+RjuNVsnJjM+BDk9lnXcOPsOeGVdKGMj0UIzFnnKrk3x9oJBBW+9D+rWR4IJKqS0kzVd6lUo1Y05S3BbCGMcsd2fB0s4t6nWP/4thNeWXFKQxPigPdvnFVRwBcq6vVA23B9MUCHFTF6Fw5Im4/Z9FFK/DqbFEdv3pHudmONUJ4ZQYJGX7NoUby1cm2LLI1BzVJvCiZweg3RJGuVlNTldj14MFkvaFQWd/j5MHfrJX0N6bM0lhUNKCevWRZjn311cbYqG9sC+zMXYAGA6fznzqlWhdxjlFXWedWKMc78LdkAkciiwyCPSMmBema1N4TwrAACsuskuJ1yZu21oWWMz0hDjzlviWCwJ5nNp4+Xi1augbX3I8ZgcG4B5+VjAIyJBs2tT/A5W9wXP83jT+pzWpnAiUhOuOR6spBLcZcYtaGrrJiirnevEWD2XYN72nkklhY1yLPKEmByBeeUI4LU9knEo7TvAV3UEOlUqhZjJq3CYuuVKzutVLJWyZhvESB+snstZx8yrJ8EbVkNxKQpE8psY7oF57aTrjAAAQI1BXb8PvLopuIEBkKZu7wJxHFMcvKIu0PEsRNv6EMTYgGPSuHH2XXtLeTIagRAJFs1YRJyUEtbtyzDPve0dVARQm8KNGPfYEle5KrAtcYtld3B8DKy0yuGohHHqN7ROXGDmalNcOuwZVLDKersJX9BBxWw/HafGZozN5FVE6+OaKapdJ8apWZmRgf7xb6h0fpGK1m8qmceuTfEBrJtnvWtTNKyFFkBtCidiehzSpfIeK60GT5QGPKLFYapm17dw+LCWqQkY598LYVQkF+T0OMxzC9SmwGxtiodDyQUSE4OA4dx1mFc0gDl9eUcAL6mAtu0Rx2NiqAcmlc4vShRYRJQYvTNTm8KlJDYwU5viANR1ua9N4USauj1b4USLR37bGa9qgLrRub6F1f0JrNve3WBJtEkpYd3phHH2Lchpr9oUJVC3PRpIbQonIj3lmojNkuXgJRXBDmiJlJaNUJrXOx4zP/nINfeKFC4KLCJGCgHz5lmYF993fYIB7NoU2s7Pgte2Bji6T0kpYI30Os+kMB5IqWE/qOv3uE5762ffgUw5Fygi0SZNHeblI7A6T3k24eO1rXYTvpCCYLufjkvdF0UDr3DudRMljDFo2x513sothF3d1qUrKylMFFhEiExNwjz3DsTt7KTCu/GWzTO1KcJbZhDjg+5b4iobAtnv7wfGOLTdnwOcxmtkoJ/+La0T5xkxPgjj9G8hh3vcT5qrTbE/tN9Vz7wKMDv5kefHRzSLJaDt+qzjMTkxDOOTwwGPiIQpP35ri4A10AXjzG9dW4wDALSEXZti9dZQE7lEetKx8h4AsJIK8AC35/nBc514sBvW9TMBj4gsh5QSVvdsbQrnFuOAvW1T2/GZQGtTOJGTw641NOx+Og7N8yJMqWuF2rHL8Zh1/TQsh6q3pDBRYBEyaRkwrx6DdfXYwrUpdn4u0NoUTqRlemyJi+W0hXMuKS2boDStczxmfPIRxLhz7xMSDTIzDfPC72DdWqA2ReN6qNsfB0uGm7cgjTTE5LDjMZYoBSsJPhHbD+qmg2Auy0r6x7+F1NMBj4iEgQKLEInJERhn3oIY6HI/iXEo7TuhbjoUiScYMT7gsmbNoFRFb0vcYs1uQYXTLhZh0TpxhInh2/Zsn1eSoBqDuvkBqGt3hlZWfpa9BOKSlK2o9hbtPMhPcsIUxd5t5fR3nJmy85ZoabHg5ee3QJ6bX5vCIzkwWQ51++NQmtZH4oNGpCZcuy3yyvpIBD4rwWIJxFzXiYeoC2rESGHBvP4xzEsfedemqAinNoUbMTnsOl6lKrx+On7hFbXQXFqsi95rsHqoC2qho8AiYNJIw/xkMbUp2mdqU1QFNzgP0jLt2QoHLF4a+tSyX5S6NqgdOx2PmZ0fwxqkdeIokNPjMM++DdF3zeMsBqVtK9Qt4dSmcCKNtJ1b4YCVVUdmnCulrN0BXtfmeMw49zsIr+2/JO9RYBEgMXoHxunfQLpNgwKAokHZcADqur2RqVgppXRfAmHc3gUSgRkVv9A6cXTZtSmuz9SmcE4gBvBpbYrWzZH53bS7lrrc+2oMPCL9dPzAGLNn/5xmMU3Drm7ruBuGFAIKLAKw6NoUZTV2J8W6cGpTuJHpSe8lkIgEQH5hijqzTuxwe6SnYJx9l9aJQyBNHdaVI7A6Ty6iNsVnI1egTUx4LYGsytv8JDcsUYrY9sccj4mRXphXTwY7IBKYwvpNjiCZnoR5fjG1KTZB3fooWMRKYHvtAmHxUrBEfm0tXSyvdWKr96pjAzOSO2JiCMaZ30IMLVCbomPPTG2KaJXAlnradSs5K6sB0xIBjygYSvN6KK2bHY+Zl49BjLrsMCN5jQKLHLIGb9nZ6pML1aZ4GOrqbZErhiOltIMKx8ZIhbcEci9l7U5wl9kjWicOhl2b4hOY594FMgvUptj+GSir1kbud1JKAcutNH+BLYE40bY+DOZUllzOVOU0nRsYkvwVrW+yAiEtE+bV47CuHAU8tiiy6saZ2hTRLNsr0xOQGeeOqryyoeCWQO7FGENsp9s6sQ7jzNu0JJJDMpOCeeE9WLfOA/BIdG5cZ9emiGhPDe8lkMbIBUJ+Y1oM2q7PAcj+/1NOjcKg3VYFhwILn4mpUXvKduCm+0lztSkeiOwWTXsJxGUXSKLUuS9AAWLJMvd14sFuWN2fBDugImHXpvgNpMtOJAB2bYpNh6Cu3RXZLZpeSyC8rCay97/flJomqBv2Oh6zrp+B8EpoJ3mnsB85XUgpkU6nkUqlkE6noes6hBBgjEFRFCQSCSSTSSSTSajq4v6KpJQQfVdh3TznUvt/RqIM6sYDkdlG6mTBJZCKwl4CuZfSvB5K/w1Y3dn7740LH0BpWAMWLwlhZOESQszdR6lUCoZhQEoJxhhUVZ27hxKJBBRlcV/8Uliwbp5dYBupXZtCXX8/WDy62zPtXSAuDcbUOFiBL4HcS92wD1Z/F2RWzpaEfvptxB9+IbIBYi5ZljV3D6XTaZimCSklOOfQNG3uHkokEuARWy53w2QRzeUahoGRkREMDw/DNBdXRbG8vBy1tbUoLS11/TKVRgbm1eOQbh8iM3h9O5S1OyO/hCCmxyFc1oR5VWPe9QLxgzQySL/zI8BhaUhp3oDYni+EMKpwpNNpDA8PY3R0FEIsvGWQMYaqqirU1NQgmXQPBGRqHOblo97bSMGgtG0BD6nF+VJY4wOu7dCVutVFM1txNzExjMx7fwc4/N6omw9CW+88q1FopJSYnp7G8PAwxsa8ft8/pSgKqqurUVNTg1gsWsnJ9yqKwEIIgTt37mBoaPn9HuLxOFpaWlBSMv/JVIz1w7xyDDA8ahsoqp2t7lIwJkqkZcIauOk4W8ESZVAiUr0wDFZvJ/QTrzsei93/NJRV7cEOKGCGYeD27duYmJhY9nuUlpaipaVl3gejlBKi/wasG6c9t5EiXgJ1w/7IbSN1IvUUrKFux2O8rBa8vLhmK+5mXD4K8/Kx7ANcQfyR/y94WVXgYwpSOp1GT08PUinnBnSLUVVVhaampkXPBAat4AOLyclJ9PT0wDD8yTyuq6tDQ0MDGADr1gWI297laVlZDdQN+yO3jdSJlBJi5DakU/Y9V+ynrIjPtuRa5vjrEH2dWa+zRBnij/1fkdvm6JeRkRH09vYuaoZiIYwxNDY2oqamBrBMWJ0nIVy+hOd+pqYF6ro9efH3K6WANdAFWA6fOWocSl1b5GdbcklaFjLv/Z3jbjle24LYwS8X5N+PlBIDAwPo7/dni62iKGhpaUFFRfSSlgs6sBgZGUFPj8e+92UqKSlBa/o24LaFbAZv2QSldUvktpG6oSWQhcn0FNLv/P8cs/yV9h2IbXs4hFHljpQSd+7cweCgR4OvZaqqqEDD0AUg7bzzCIAd0LbvBA+5xflS0BLIwqzhXugf/sTxmLbjcairtwQ8otwSQqCrqwuTkx69oZapsbERdXV1vr/vSuTHN94y5CqoAIDp6WkMSo+ksQjXpnDj2QskUUZBxQyWKIV23wOOx6wbZyBGvPNs8omUEn19fTkJKgBgdHwck5p7e3BWUhHZ2hRupJ5yDSp4eS0FFTOUmiYoa7Y7HjMufgjpFWzmGSEEbt68mZOgAgD6+vowMOCxeyoE+fGtt0RTU1M5CypmDcs4pkvrs15nVdGuTeHE3gVyx3kXCFfy6v+XICirt4DXOOea6GfehvTKE8gjIyMjK8pLWozbsgxmInsql6/qgLr9M5GtTeHEsxeIFgcrrQ52QBGnbT4IOC0RGxkY598LfkA50tfXh6mp3AZKd+7cwfh4dAr2FVxgYVkWuru912v90sOqIOIzNwZjUNp3QN0c3doUbmR60jmvAjOFsIpwC5gXxhi0HY879hKRE8Mwr50KYVT+0nUdvb29ub8QY+hS6iAVzf7v2doUHbvz7vdOTAw751WAQalclTezLkFhWgyxbY86HrN6r8Hqux7wiPw3OTmJ4WHnbrZ+6+npWfRux1wruMDizp07viVqLkQyjm6tAUhWQN32OJSmDXn34SGF5bEEUg5eJIWwloqXVUPdcL/jMfPKMQivMu4RJ6VEd3d3YFVFTa5hoKQZrLzObh5W0xzIdf0kTd29EFZ58RTCWiqlcS2UpvWOx/Rz70IazhVL80GQD7mz17t9+3Zg1/OS08Di+eefR3V1NX7v934vl5eZY5pmYNHhrDSLYbRpB3hZfk5zislh5y1+XAGvzF7qIZ9S1+0Gc9o2KASMM+/kbbnv6elpTE+79+XIhVEkkGrblZeFxuYKyjmhJZAFaVsfci6bn56C8cnh4Afkk9HR0cBnEMbHx5HJuHfQDkpOA4s/+qM/wv/5P/8nl5eYJ+igYtbQ8LAv2/CCJo2Me6JZRX3eTUUHjXEFsR2POx4Tw7dhdV0IeET+yHVehZvBkK67UjI9Cak71ySgJZCFeSZE3zwLaziAJTmfSSlDu4/C+h68W04Di8cffxzl5cHsJpBShvYXalnWiooGhUFKCcutHXosWTS9QFaKVzdCad/heCwfs9tN0wwtCWx6ejoST1tL4bmUWFJFSyCLpLTdB17b4njMOPM2pJVfCdHT09PQ9XCWcYYj8KC75MBCCIHNmzfjW9/61rzX33zzTcRiMfz4xz/2bXBLYRhGqIkrQU8dr5RMTbhWCy22XiArpW0+AJZ0CMRMHfq53wU/oBUI+/c47OsvledSYhFX11wqxhi07Y8BDrOkcnIE5rUTwQ9qBXK9C8SLlDL0AH3JgQXnHN/+9rfx/e9/HyMjdrLS6dOn8cILL+A73/kOXnjhBd8HuRgrKY/qh3z6QJTCgphwrk3ASqvBtOhXN4wSpsaguWS3i75OWL3ZlTqjKuz7KOzrLwUtJfqLl1VB3eiWEH3C3nWTJ8L+PQ77+staCnnxxRdRV1eHV155Bd3d3Xj66afx1a9+Fd/85jf9Ht+ipdMevToCun6+JOuJiSHnpyxFBS+yjot+UVa1Q2ne4HjMzm7Pjyn+sD+Q8iVAp6XE3FA7doFVOPSCkcJeEsmTz9iw76Owr7+swEJVVXzrW9/CK6+8gqeeegp79uzBK6+84vfYlsQKeQ1OSpkXv/RST7t2j7SfsgpuB3JgXLPbM9N5k90e9n0U9vUXS6bGaSkxB+yE6M8AyP77EyN9sLrOBz+oZQj79zjs6y/7W+TFF1/E9PQ0pJT40Y9+5Nhl7YknnsALL7yAX/3qV2htbcWxYw4d7XySD1/qYZNSwhp3ecqKl4DFo98oLcpYvATaloccj1k3z0OM56Y0tp/Cvo/Cvv5i2EuJzhn/tJS4cryqAWqHS0L0J0cg9XBnp/NB2PfRsgOLb3zjGwCAwcFB19atb775JgYGBjA9PY3u7m7cf7/z+pkfeASetKP+lCKnxwDHKXlGT1k+UVo3gde1OhyRMM6/H/oNv5Cw76Owr78YYmKQlhJzTN14AMypP5GRhuHUcj1iwv4sDfs+WtbV//RP/xSvvfYaDh8+DNM08Vd/9Vd+j2vJYrFwnxI0TQv9l8mLtEzXpyxeVg2magGPqDB9mt2efWuJoR7HlutREo+Huz0y7OsvxF5KdN6OS0uJ/mGqBm27S7nvm2cjn8gZ9u9x2Ndf8l3wgx/8AN/73vfw6quvYufOnfjjP/5jvPzyy4GV0XaTTHp0Gw1ASUm0KwaKiUHnJmOKBpanVUOjipdWQu3Y5XjMuPABpBWNev5O6D5y55mwSUuJvlMa1oA3rMk+IKM/+xf2fRT29ZcUWLz++uv4+te/jr/5m7/BwYMHAQAvvfQSxsfH8cMf/jAnA1ysRCIR6vXD/of0IjMpu26FA15ZD8boKctv6vq9gEN5apmagNn5cfADWqSwf4/Dvr4XOT0GmLSUGCRty0OAw+eTGLwF0X8j+AEtUti/x2Fff9HfKCdOnMALL7yAl19+GV/5ylfmXq+oqMBLL72E7373u6FmonLOUVERXovlMK/txTNhM1EGTk9ZOcHUGLT7DjkeM6+egExNBjyixUkkEqEtKyqKEtkZC1pKDAcvq4K61iWR8/wHka3IWV5eHlqgWVJSAlVVQ7n2LCajPJ+0RFNTU7h+PfhWu+Xl5VizxmHKLgLE5IhzMSzGoNSvAVPoAzFXpJTIfPD3kKPZgZ3SshGx3Z8PYVQLGxoaCqZl+j3q6+uxatWqwK+7GNZon/Osn6JBqV9Ns345JI0M0m//v4BDPxb1vkPQ1u0JYVQL6+npmSsiGaS2tjZUVlYGft27FdTdUFJSEkrSSm2tQ0GXCJCWATHp9pRVS0FFjjHGENv6sOMxq+cyrJG+gEe0OFVVVaFkldfURHNHBS0lhotpcWibDzoeM68cj2w/njC+F1RVjcTseUHdEYwxtLQ4N7LJlcrKSpSVRbPKnhgfBJwmpNQYWGlV4OMpRry6EUrLJsdjxrn3IpmApigKmpqaAr3mqlWroGnRC3RpKTEalLbNYBV12QdMA8alI8EPaBESiUTgwUVLS0skcn0KKrAA7FmL+vr6QK6lqiqam5sDudZSST0FmXZex1co0SxQ2uaDgJK95inH+mF1XwphRN6kZaKCm6gK6Hs+mUyirs7hSyMCZGocMB26VDIG7vRFR3KCMQ7Nbfbv1kUIh+XGsEkjg/o4kOTBdBqtrq4OrJv4QsLN8MiR+vp6TE1N5bzvQGtrq2txsDDZT1kuTcaS5WDx6GbeFyKWLIO6fi9Mhycr45OPoDR1gKnBJ0xKy4ScGoWcHIGYHJ378+y0/6r9z2LqznBOt5IrioK2trZIBrpSCI+ETVpKDJpS2wyleT2s21ezjhnn30fsgedD+T2Shg45NQIxOQI5OTrz51EgMw2oMTTf/yw6u7pzOjsZj8fR2NiYs/dfqoIMLDjnWLNmDW7cuJGzZixr1qyJ7BKIzEw59zFgDLycnrLCoHbsgtV1IXutPjMN8+oJaJudd5D4SYwNwOq/YX/43RVAOOGNHdCq6rG2pBKdnZ0wTf9rbyiKgrVr14Ze3M6NnBpxqbCp0VJiSNT7HoDVdz3r30WM9MK6fRVqi3MjQD9ZA7cghnrmBxAu1LU7oJZXor1dxY0bN3ISXMTjcaxduzZSD7kFtxQya/ZDy++poVy9r1+klK49KXhpDZjDlDzJPaao0LY86HjM7PwYYsq5MZyvY4iXwOq+DDHQ5RlUgDGoM5n2sVgM69at871OTCwWQ0dHR+j1Z9xIy4SYcs7o5xV1kZxhKQY8WQ513W7HY+bFDyGt3BdqZFoc1q0LEEO3PYMKxBJQ2rYAAEpLS9HR0eH7NtBcve9KFWxgAdgzF6tXr0ZbW5sv0VxVVRU2bNiA0tLoJmzJ6THA6ebiCj1lhYw3doDXOiQXCwHj4oc5vz5LlELb5rxOfTeleQN46afb1TRNw7p167Bq1SpfvlDr6+uxfv360MsOexGTw86Jz7EkVdgMmbpuj2NbepmehHn1VM6vz6sa5gJvL+ranfPqmySTSWzYsMGX3U+MMTQ3N6O9vT1SMxWzCjqwAOx/gMrKSmzYsAF1dXXL+kcoKytDe3s7WltbIxcZ3k0KYX8gOuDltdTHIGSMMbu1ulNL6L5OWIO3cnp9mUnB6rrofRLjUBzKkTPG5gKC6urqJQcYs/fh+vXrsWrVqtCbJHmRpm4H6A6UcpqtCBtTNahuxeeunYTwmo3zgZgcgbVQz594KZTWzVkvK4qC5uZmdHR0LGtbKOcctbW12LhxI2pqaiL7u1hQBbIWQwiBiYkJTExMYHp6GrqenfGtKAqSySRKSkpQVVUV2TXge1kTQ5BOgYUag1K3OrK/hMVGP/sOrJvns15n5TWIP/z/yUkAaA12wzj3O8ciQ3dT2u6Ddt8DC7+fZWFsbAyTk5NIpVKOCZ6qqiKZTKKsrAyVlZWRDsrvZg3ftvOU7sESZVCqg92GS5xJKaF/+FOIkexCbkrzesT2PJGTa1o9l2B+ctg59+Yu6n0PQm3LDizuZRgGRkdHMT09jenpacfq1bFYbN59FOWgfFbRBRb3EkLAMAxIKcEYA+ccqqrm3ZewtExYAzccp295dTN4gqZvo0JmUki/8/86trDXtj0CtX27f9cSFsyrJ2DdOLvwyVxB/KEXwJbxu2JZFkzTnLuPFEXJm0DiblJPwRrqdjym1K8JZfcOcSZG+5F5/8eOx2KHnodS618pAGlkYFx4H+LOjQXPZclyxB78vWU9IJimCcuy5u4jTdPyIpC4V/6N2Gecc8TjcSQSCcTj8ci3P3cjJoYcgwoWS4I5NMMi4WHxJLQN9zseMy4dgdQddvQsg5geh370l4sLKgAobVuWFVQA9izf3fdRXgYVXtu0S6ooqIgYXtUApe0+x2PGef+Kz4mRO8h89NNFBRUAoK7bvexZR1VV591H+RhUABRYFARpZOxCPg4ogz2alPZtzu3qjYwv3U+t3qvQP/oZpMsXJQAgXgJeM/NUp2iuzZ6KhUxPumzT5uDl0Sw3Xuy0TQcAhwZwcnwQVm92vYulkFLAvHYK+rHXAI+y4ay8BqzM/v1gpVXgTetWdN1CQIFFAXAr4sMS5WBaNLfzFTvGFbsltAPz+mlIr21sHqRpQD/7Loyz7zrvDprB61cjfuh5aDs/C5Ysh7JmK1iseH9XpJTe3Ut59DLvib3TSd2wz/GYeekopFhe1UuZnoJ+/HWY104CcJ/5UFZvRezAs9B2Pg4oqr1jhXrHFGaBrGIiMtOOiWYAAy+PZnM0YlMaVoPXr4YY6Jp/wDJhXD2J2FbnwMONGB+EceZtyGnn2SsAAFegbtwPpe2+uZksbcfjYCXhNy4Kk/s2bZW2aUec2r4T1o1zWbVZ5NQorJ5LUF2WS9xY/TdhnH/PMQdqjhaHtu0RKPWrAdgzFdquz4PXUHIvQDMWec1+ynJZEy6tnLeHmkSTtumA4+vWzXOQKedeL/eSUsK8eQ76kVc9gwpWWonYgWegrt4yb3mMV9aDadGtKZFrUljusxXltfQEGnFMUaBudM5ZMi8fg3TYaeFEWiaMix/B+Pg3nkEFr25C/NDzc0HFLKW2mZadZ9Adk8fsNWGHG4Bx8DJaE84HvKoBvLEj+4CwYFw5tuDPy0wKxqlf231IpPu0r9KyEbEDX6ZZLAdicsT5706NgSWjWWGXzKe0bHKcWZKpCVi3Liz482JyFPqRX3ifyxjU9fug7Xty2UnOxYICizwlpXCdreBlNbQmnEe0TfsdX7dufQIxNer6c9ZQj52tPui8PRIAoMag7Xgc2taHaQbLgbQMSJe/Y0p8zh+Mc6gu95Fx5bhrqW8pJczuS9AP/wxy0rmEOwAgUYbY/U9D7dhJM1iLQH9DeUpOjQGWQ2MoRQW7qxwziT5eXgulZWP2ASlgXs6etZBCwLh8DMaJNzwLXrHKesQOPQfFaUaEAJhNfHbapl0CTqW784rStB6swmFGLjMN83r2lmtpZGCceRvmhfc9C17xVWsRP/QceNUqP4db0CiwyENSWN6luymizjvqxv2Aw7+b1XMZYvzT9f9Pa1Oc8Xw/Ze1OxO7/EjhN5buyt2k7l3/mFdQFON8wxqBtOuh4zLx2EvKuZWMxegeZj34Gcee6+xtyFeqWh+zk5iLOQVoO+gbKQ+5rwnGwBH2R5CNeWule7OfyUQCA1XttpjbFgPsbxUug7f0itA37qDfMAlwTn5Pl9EWSp3jDGvDqxuwDRgZm52m7NkXnxzO1KdyTo1lZDWIHvwy1dRMthy1D0Zf0zjfSMmH134DT9C2vaQGnKpt5S6YmkX77bxynZZXmDRALNCnjdW3Qtj1S1PUoFsu9dDeD0rAGTKF8lHxlDXZDP/zz7AOqBl7TDDl6x/PnldVboG64H0yhagzLRY80eUZMjcBxTTheQkFFnmPJMihrtt3zIgO0mHdQwTjUzQeh7f48BRWLJCaclxJZaRUFFXlOqWsFr2ud/yLjAOPeQYUWh7br89A2H6KgYoXoby+PSMt0befMy2lNuBBo6/fC6rpgF2viip2M6zEVy0oqoe14HNwpaY04knoKUneobMo4uFOZdZJ3tE0HkJndLaWoCwYKvLoJ2vZHaRupTyiwyCNiatS50ViijNaECwSLJ6Gs2Qqr6/yCW4aVlo1QNx2kbaRL5Jr4XFpF27QLBK9uBKtrhRzt9841Ygzquj1Q1u6gpHcfUWCRJ6SwIKdHHY9RMazCYQ3dhui/6f0Fp2rQ7nsQCjU7WjKpp537sDBOpbsLhJQSVs9lYHLEO6hIlCG24zHaRpoDFFjkCdfZingpzVYUACkEzGsnYV0/7X0i44gdfA68yHt7LJfbbAWj2YqCIA0dxsX3Ifo8tpECQLwE8UPP0WdnjlBgkQeksNyrA1I757wnpidgnH0bcsx9G6mU0t4tYpn2eRRYLJk00s4N+xgHp9mKvCdG++0mfB7bSKWUdmFBfQQyM02BRY7QolIekFOjjnUrWLyE2qLnOauvE/rhny4cVJjGXKVV49IRSI++IMSZ606Qkkqarchjdm2K09CP/dI7qBACMPSZ7dzS7q9DcoJmLCJOCsu1XwQvo50A+UqaBsxLh+21YE8MMNLzf3ZyBFbPFaitm3I3wAIjjYzLbAWjnSB5TKanYJx7F2K41/s8YdnB+V2s3msQYwPglfW5HGJRohmLiJNTY+6zFVSzIC+J8SHoh3/uHVQwDnXTASgubdXNy8do1mIJXHMrSii3Il9ZA112WW6voEKLQ9v1OSitmx0PGw69eMjK0YxFhEkhZgpiZaOdIPlHSgmr6wLMy0c9W5zbtSkeA6+ogxQWrGunsuqXyOkxiN5OKM3rcz3svCeNjMsUOaPcijwkhQXz8jFYXec9z2PVjYhtfwwsUQpeUQer6yIg5jduFHeuQ0yO0KyVz2jGIsLk9KjzbEUsCRZLBj8gsmxST8M49Y8wLx32DCqU5g2IHfzyXBMsxhX3dtDXToEq8i/MfSdIJVVYzDNiahT6kV8sEFTYtSli+744V/CKJUqhrt3heLZ57VQORlrcKLCIKHu2YtTxGC+n3Ip8Yg3dRuajn3qX5VY0aNsfs3t93FPwSmlaD+awC0SO9UMM9fg93IIiTd1jtoKeUvOFlBJmz2XoH/0c0iUJFwCQKEXs/qehrtudVfBK7dhpV7O9h9V9CTLlnvRJlo4Ci4iS02OOzahotiJ/SCFgXDkO48TrgFNRphmsoh6xQ8+5FrxinEPt2O14zLx60pexFir33AqarcgX0tBhnH0H5vn3spYy7sYb2hE/9Dx4tXPBKxYvgdLmkGshBcyF6seQJaHAIoKkdM+tYJRbkRdEagL6sdcWLHiltO9AbP+XFix4pbRtBhwCSjF4C8Jjq2oxk6YOmZpwOEI7QfKFGO2HfvhnEH2d7idxBeqWB6Ht/MyCdSnsAD2794558zykns7+AbIsFFhEkNtsBbQEzVbkAauvE/pHP4Mc63c/KZaEtvdJaBvv9y47PIMpqscaMc1aOHGfraig2YqIk1LCvD5Tm8IxOLSxsmrEDn4Zautmz2Z9s3hppfPMoGXAvHluJUMmd6HAImKkFBCTLjtBymsWdfOQcEjTgHH+PRhn3gZM3fU8XtuK+KHnodS2LOn91TXbAIeW3tbtaxBTzl1vi5U0DdcvJJqtiDaZmYZx4g2YV447tjGYpbTdh9iBZ5f876mu3+P4unn9DKTlvtRCFo8Ci4iR0+MesxUlwQ+ILIqYGIJ+ZHG1KbQ9XwCLL33micUSUNdsdTgiYXZSZvvdvGcrqBtsVFkDt5D58KcQw7fdT1Jj0HZ+Ftp9Dyxr5olX1oPXtWUf0FOwbn2y5Pcj2SiwiBAppftsRRnNVkSRlBJm1wXoR161i5m5YCUViB14BuqabSv6d1TX7gQc2jtbtz5x7tpZhKRlQKbGHY/xUspRiiIpLBiXDsM49eusSrN3Y1WrEH/geSir2ld0PXW9SzJ05ym79DdZEQosIkSmJpyznrU4WJxmK6JG6mkYH/8G5icfOc8yzeD31KZYCZYsg+JUyltYlNk+w22bNktWZG3lJeETU2PQj7wK6+ZCtSl2I3b/U2CJshVfk9e2glU2ZL0up8dh9V1b8fsXOwosIkJK6dEThGYrosYa7rVrUwx0uZ+kaNC2P4rYtkfA1Jhv11bXuTxt3TgHabjndhQDKYS9nOiAciuiRUoJq+cK9MM/g5wYcj8xUYrY/U9BXbcnqzbFcjHGoLndR1dPUuG5FaLU6KjQU4CZyX5djYHFS4MfD3EkhYDZeQpW58ee57GKOmg7Hl9wG+ly8LJq8FVrIe5cn3/A1GF2nXf9wCwGMjXuXK02UeZrcEdWRpo6jAsfQiwwO8Ab1kDb+nBO2pvzpg6w0sqsJUw5PggxeAtK/Wrfr1ksaMYiIlxnK0qraLYiImRqAvrx1xYMKpT27YuqTbESmltme+fHkJb7skwh85z1oyqbkSHGBqB/9DPvoIIrUO97ANrOz+YkqAAAxqjwXK7QjEUESFN3bunMFbBkefADIlmsvuswLrzvuY0UsSS0bY9AqWvN+Xh4dSN4TXN29nxmGlbPJairt+R8DFEjM1OAZWQf0BLUCTgCpJSwbpyFedV7GykrrbJn+8pzn2irtG6CcfloVmVcMdQDMXoHvMq5iifxRjMWEeBWg4CVVPq2pkiWR1omjAvvwzjz1gK1KVrs2hQBBBWzXPfjXztVlC3VpcesHwmXzEzDOPkGzCvHvGtTtG62E50DCCqA2cJzOx2PGVdpC/dy0YxFyKSwIFPOgQUvqQx4NORuYmIYxpm3Xb+wANi1KTbsg7LCbaTLwetXg5XXZiW+yalRiL7rrr1HCpE0MpB6KvuAovqyi4AsnzXYDePsu57bSKHGoG19eMXbSJdDXbMV5tUTWQ8Oou8axOQoeFlV4GPKd/Q4HDI5Pe4YwbNkOZUdDsmntSl+4RlUsJIKxPZ/CWr79lDyYBhjrrMWxrXiymx3663DSyhHKSx2bYojME6+uXBtikMrr02xXEyL21VtHVDhueWhwCJEUkqI6VHHYzR9G45F16ZoWm9P2VbWBzi6bErTesc8HDnaDzHkUb2wgEjLdG57zZhju3mSe2JqDPrRX8Ly7L/BoHTsRmzfU2DJcGeV1LU7AIeePVb3J5Bph/w34okCixDJ9CTgVJs+lgTTKNksaGK4F5mPfrZwbYptjyC2/dFIbF+0W6rvcjxWLE9bYnoMgNOsXwUYV4IfUJGzbl+BfvjnkOOD7ifFSxHb90Vo6/csqglfrrFEKZRWh5bqQsC8cTb4AeW58P9Fi5jXFlMSHCkEjKsnoR9/HXDanTODVdQhdvDLUJo3BDi6hSmr7wMcdj2I/psQLsWiCoWUwu4G7IDuo2BJU4d+9h0Y537nvDtnBq9fjfih58BrmoIb3CK4bj3tulC0W7iXiwKLkEg97bzuqGhUECtAMjUJ/fivYHWegtNT7yxlzUxtitLoJdQyRYPavt3xmHeZ5Pxnl8HP/tBn8dJIzCgVi7naFL0L1KbYfAjars9FcvsvL6sCb+zIPqCnqMz3ElFgERL32YpKSjYLiHXnOjIf/RRy9I77SbEEtD1PQNu0P9LT6urqrY7NycxbFwq2FbRXQSxGsxWBkFLCvHEW+tFXXdvUA/a/R+zAs1BXb4n055t7gO6VK0LuRdsOQiAtEzLtcBMyDpaM3hNxoZGWCfPSEVjd3i2SeW0ztG2P5kUDOJYohdLYAav36vwDehpW7zWoTo3L8pzUU861RdQYWGzpbenJ0shMCsa5dyGGejzPU1o3Q910IC92ufHaFrCyash7ukyL4V6I8UFfGgkWA5qxCIHrU1ZJRSQSmQqZmBiGfvjn3kEFY1A33A9tz5N5EVTMUtqdt8wV6tOWdNtiWlod6afiQmANdiPz0U+8gwo1Bm3HZ6BteTAvggpgZgu329bTG4V5H+UCfYsFzDPZrKQq2MEUESklzFsXF65NkSxHbP8zUNfuyLsvJ17TDOZQsVCM9EGMDYQwotyxy+BPZx/gSuhbFwuZXZviqF2bQl+oNsVzUBrXBjg6fyitmwCHQMjquQRpODSKJFkosAiYnJ7w6L6ohTCiwieNDIzTv4V58cMFalOsQ+zQc6HXpliuYnracs1RojL4OSOmx2dqU3hvv1Q6ds3UpsjPPkdMi0Np2Zh9wDJhdV8KfkB5iO7AANnJZm7Tt1XBDqZIiOFeZD78KUT/TfeTFBXatkfsfIo830mgtGwClOwA1eq5XDBPW3YZfKdttAyMyuDnhNV7FfpHP1ugNkUJtH1PQVu/N++XdN2SOM2b54qqou1y5cfCV4GQmWmX7otxgApi+UoKAbPz45kW5x6dFMtr7U6KEdxGuhxMi0Fp3ZSdVyFMWLc+gdrh3HApn8jpMSqDHxBp6jAufgRxb1LwPXj9amhbH47kNtLl4BV14NVNECO9816XkyMQQ7eh1LWENLL8QHdhgLwK+eTben6UyfQk9DPveG8jBaCs2QZ1w75IbyNdDnXNNseETfPmOSh5mDtyN7sMPhXECoIYH7Sb8HkVWeMK1I37obTdl9e/V06U9m1ZgQUAmDfPUmCxAAosAiItE9KpqiNXwBL5uRYZRdadGzDOv+fZ4hxaAtq2R6DUtwU3sADxilrwmiaI4XuetqZGIYa6odTl7//fUp92LIPPYkkwLR7CiAqPlBLWzXMwrxx3zAebxUor7dm+8toARxccpXEdjNj7wD1dc0VfJ2R6CixBhQzd5PdCWB5xXhOeTTYrrEg/DNIyYVz4AMbp33oGFbymGfEHni/YoGKW4rZGnOdJnG5Pz1QQyx8yk4Jx8tcwLx/1DCqUlo2IHfhywQYVAMAUBerq+7IPSAmzq7Ar2q4UzVgEwJ6+dflApO6LKyYmR2CcfstzGykYg7p+L5T2/F4KWCylsQNGPAlk7nnaunMdMjWZl1sypbCcO01yhcrg+8Aa6oFx9t2sJ/R51Bi0LQ9CcSp9XYCU1VthXj2Z9bp58wLU9XsLbhnVLzRjEQCppxyTNlm8BMwhg58sjl2b4hO7k+JCtSnu/xLUtTuLIqgAAMYVu8z3vfL4acue9XPpYlok/665IIWAcfkojBNveAYVrLIBsUPPFU1QAQC8pAJ8VXv2gcwUxJ0bQQ8nb1BgEQC3pE0q3718dm2Kt2Be/MC7NkVjB2IHnwOvaghwdNGgrN4CIPsL1+y6AOnxdxZFXrN+nGb9lm2uNsUCrcGVtTsRu/9p8DytTbES7rVhqJ26G1oKyTHP6VtK/lkWMdIH/ew7gNPf6yxFhbr5EJTmDUX7NMuT5eCr2iHuXJ9/IDMNq68TasTav3syMs65M7Fk3tceCYvVew3GhQ88W5wjXgJt+6NQapqDG1jE8PrVYCUVWfk9YqgHYmIY3KHabbGjGYscszv+0fStH6QUMK+dgn7sV55BBSuvRezgl6G2bCz6v2PXbo15lsTpusU0SbMVSyVNA8a538E4+45nUMHrVyN+6PmiDioAu6Kt4jZrUaB9eFaKZixyyHPPPU3fLolMT0E/+w7kSJ/necrqrVA33k9JVTN4XStYaVVWDooYvg0xMZQXWf1SCI9uwPmXhBqmRdWmYBzqpv1Q2qLd4jxIatt9MC8dyVp2tbovQW4+SLNm96AZi1xym77VEvSLuARW/w1kPvqpd1ChJaDt/jy0zQcpqLhLIfQPkekJ90qb1BdkUaSUMG+eg37kVc+ggpVUInbgWairt1JQcRcWS0BxWjo0dVg9l4MfUMTRXZlDIuU2W0FJm4shLRPGxQ9hfPxbO0hzwWua7E6K9asDHF3+UFo3AdyhW+PtK5AOxaaihpI2V0ZmUjBO/dp+4l6oNsXBL4NXRH8WKwxqu0uAfuuTgEcSfbQUkiNSCMjUZPYBxsESNH27EDE5Yk/ZTjo3bQNwV22K7fTk6oHFElBaNsC6dXH+ASMDcecGlOb14QxsEaSRAQyH9txqHIz66yzIGrpt51J41qbQoG15qKi2kS4Hr1oFVtkAOdY/73U5egdicgS8rDqkkUUPfRrniExPOrdHT5bnfee/XJJSwuyeqU3hEVSwRNldtSno73MhSptDBUEAZk+020AL14q1NFvhRQoB48oxGCdeX6A2RT1iB5+noGKRVJf7iJZD5qMZixyhpM2lk0YGxoX3Fyw8wxvXQrvvITCN8lQWi1c3Om+Z6++CzKTA4smQRuZOSuGSD8DAirCewmKJ6QkYZ9+GHBvwPE9ZuxPquj30oLMESvN6uxfRPQ+NVvclqBv3U17KDPqNygFp6i7TtzFApUZJTsToHWQ++ql3UMFVqFsfhrb9cQoqlogxBqV1c/YBKWDdvhL8gBZBpqdcZv3KKEHXhdV7Dfrhn3oHFfESaHu/CG3DPgoqlojFEo6VOGVqAmL4dvADiij6rcoB99kKajh2LykFzM5T0I+9tkBtihrEDlFtipVQWjY6vm52R3M5xG33AqeKtVnm1aYwPWpT1LXZic61xV2bYiXU1k2Or1sRvY/CQEshPpNSzhTFuhdN395LpqdgnH0XYqTX8zxl9VaoG/aBKfTruhK8tNK5nfpYf+QqCErTsFuk30vRgBglbd5NjA/N1KZwfqABYNem2Hg/FNpGumK8YQ2gxbN2qlm9VyG3PUKfU6AZC9/J9KRj7wqWoOnbu1n9N+2lD6+gQotD2zVTm4JuVl8oLS5PWxFL4vRK2qQvRptdm+I89CO/8Awq7NoUz0Bds43+7nzAuAKl2WH2zzRg9V3Pfr0IUWDhM+nygUjt0W12bYqPYHz8G+/aFNVNdjnhBqpN4SeleT3gEOBa3ZchHYpQhUFK6boMwqiENwC7Y7Jx6h9hXjrsXZuiecNMbYq6AEdX+FyXQyIWoIeFHgN9JC0DMuM8fcti0cu6D5qYHJ2pTTHsfhJjUNftgbJ2B20jzQGmxaGsaofVe23e6zI9CTHUA6WuNaSR3TWWzDQgsgt3sXgpzVxhpjbFuXcBp8+aWYoGbcuDUJrWBTewIsKqGpxL5Q90QWamweIl4QwsIuiT20fOuRU0fWvXprg0U5vCI6hIlCF2/9NQO3ZRUJFDjrtDEJ2nLbf7iBV5xVq7NsVxuzaFR1DBKuoRO/QcBRU5ZO+ycpi1kBJWTzR3WQWJPr19JJwqbaK4p2+lkYFx5m2YF953fAqdxVetRfzQc+BVqwIcXXHi9W2AwwyadfsapFcL7QBIKSAzDvcRV4r6KVBMT0A/9ktY1097nqes3YHY/i9RvZwAuO+yohLfNK/oE2nqgJmdM8BiJUU7fStG70A/8w6Qdg64AABcgbr5EBTaRhoYO/lsA6wbZ+YfsOzkM9XlAzMIdu0Kt4Zjxfn7YfV1wrjwvuc2UsSS0LY/CqW2JbiBFTleUgFe2wIx1DPvdTk+CDE+VNQ9V2jGwieu07dF2NbZrk3x8UxtCveggpXVIHbwOaitm4r2SyMsUd2LL11+X3gRbtWWpgHj/Hswzry9QG2KVjvRmYKKwLnNWkRlWTEsFFj4QEoJ4fKBWGwNx2R6CsbxN2BePeH45DlLWb0FsQPPgJdVBTc4ModV1oM5NE0SA7fsWYMQSCGcr61oRVexVkwMQT/yc+8eFIxD3XQA2u4vRLIkezFQmpx3WZndlyE9dusUOgos/GDq9v/uweKlRVW7whroWmRtis9B23yoaJeIosA1+QwSZkgNlezcCqdlkLKimdGSUsLsOg/98C8gp7xqU1RQbYoIYFrMuYFbZgpisCf79SJBgYUPRJEvg0jLhPHJRzBO/eMia1OsCXB0xE3UpnGlS/IzTxTHMojU0zA+/g3MT7xrU/DmDYgdfI5qU0SEc4Ae/rJimOiRcYWklC7rwgwsXviBhZiaqU0xsVBtit1QqMV5pPBkuUvy2RDE+GCgX1xSWJAZh2UQNQamFf4yiDV8G8bZxdSmeMCefieRwevagHgSyMxvT2/1XYM0HwFTi69hIn3Kr5SRARy26LFEaUF3DpTSnjLXP/q5d1CRKEVs39NQO3ZTUBFBrjUtAn7ack3aLPAcJSkEjKsnYBxfqDZF3UxtCgoqooZx7ryTyjJh9XUGP6AIoE/6FRJpl2WQAv5AlIYO4+zbMM+/t0BtinbEDz0PXk21KaJKaeoAHHJdzJ7LkCK45DO3ZZBCbtwnUhPQj70Gq/Njz/OU9u1UmyLiohKgRwUthayA3cnU4QORMbBEafADCoAY7beXPhasTXEQSgttI406ptrJZ1m7DzLTEMO9UOpyv4VRWqZzJ1M1XrDTyFbf9ZnaFNlJ33NiSWjbHolEmXXijVfUgZXXQk4MzXtdDPYUZYlvmrFYCSPt0tOgrOCm/aWUMDtPQz/2S8+ggpVVI3bwy1BbN1NQkSdcO572XXN83W/utSsKb9ZPWiaM8+/DOPOWZ1DBa2dqU1BQkTeUVqdkaAnrzo2ghxK6wvr2C1ix7AaR6SkYJ96AefW4d22KtvsQO/AsuEN9BBJdvK4FcEiQtPo6A+l46l4DprCWQcTEMPTDP/fedcM41I37oe2h2hT5xi3/5d6Gf8WAlkKWyXU3COMFNe1lDdyCce539uyMGy0ObevDtI00TzGuQFm1Fta9PQ7SU5Cj/WA5zJGRlgnoqewDWgJM1XJ23SBJKWHdugjz8lFAWK7nsZIKaNsfA6+sD3B0xC+8pAKssh5ybGDe62KwG9LIFMXuplkUWCyT1FOOHxIsURjLIFJYMC8fg9V13vM8Vt2I2PbHCjanpFgoTR3ZgQXsWYtcJt9Kl+TnQlkGkXoaxvn3IAa6PM/jTeuh3XeoYHNKioXS2AHznsACUsDqvxlqD56g5f83YEgKuTeImBqDfuTVBYIKBnXdHsT2fZGCigLA69ocd4dYvddyuhzi2hG4AJZBxHCvXYnWK6hQNGjbHkVs+6MUVBQAxyqcAKze4tp2SjMWy+C6DMIVsFj+LoNIKWHdvgLzk48Ay30bKRKliG1/DLy6MbjBkZxiigqloR1W79V5r8vpMciJYbAcdGqUpuG8xBZL5nW5dynsJnz2NlL3oIxV1EHb8ThtIy0gvLwGrKwacnJk3uti4CakZYAphbG8t5D8vXtDJDPTjiV37WWQ/NwJIU0dxoUPIBYo6MIb2qFtfaio1guLBW/syAosgJnlkFwEFm7LIHlcA0amJqCffRdy9I7necqa7VA37C2qXkLFQmlcC/Pq/MAClgkxcMt1RqPQUGCxDG4fiPlaFEuM9sM4+47r8g4AuzbFpoNQqMV5wVIa1sDgHLinMJbV1wlt4/2+X899GSQ/7yPrznUY5xeqTZGAtu1R2kZawJTGdTCvnsx63errpMCCOJPSpbUzV8Bi+bU9TEoJ68aZBVucs7Jqe8qWtpEWNKbFwOvaIPpvzntdjg9CTI2Bl1b6di1p6oCZ3bCOxUrybhlEWibMS4cXrLLIa1ugbXuUtpEWOFZZD5Yoy1out+7cgBRWUcxSUfLmErkvg5Tn1ZO8zEzbtSmuUG0K8inX5DOfex64l/DOr9mKudoUXkEFYzO1KZ6goKIIMMbAmxzuIyMDMXQ7+AGFgAKLJXKcrUB+bY+zBm4h8+FPIYY9fsnVGLSdn4V23wN59wRJlk9pXAsgO0BeKPdmqYRTJ1PkzzKIlBLmrYvQj/wCcmrU9TyWLEds/zNQ27fn1YMHWRmlcZ3j60FVsw0bfWMsgZTSubUzVwAtEfyAlkgKC+aVY7BuLlCbomqVXZsij4Il4g8WS4LXNme1UhcjfZDpKV+2FkvLdNwNwuIleTFNLI2MXZviniWje/GmdXZgTttIiw6vaQRiyazib1ZfJ+S2Rwqi1pEXCiyWwsw4F8WKl0b+aURMjdnNw+5pkjMfg7puF5S1uwq65TvxpjR2ZAUWgN04S23ftuL3ly7twVk8+vVQxHAv9LPvAi4zLgAARYV23wNQmjcENzASKYxxKI1rYXVdmH8gk4IYuQOlpimcgQWEvj2WwG0ZJMoFomZrU+iHf+YdVMRLEbv/Kajr9lBQUeTc8yz8mcZ1nPVDtAMLKQSMqyehH3/dM6hg5bWIHXyOggrieh+JIlgOoRmLJXBeF2aRLYolTR3GxQ8hFmiCwxvWQNv6MNWmIADsBEpW1QA52j/vdTHUA6mnwWLLX/ZzXU5UY5HtDSLTk9DPvLOI2hTboG7YlxfLOST3eG0roMayth9bvZ1Q73sw8rPcK0GBxSLZ68IO2+PiyUg+4YuxAXvpY8HaFAegUItzcg+lcR3MewILSAmr/wbU1s3Lfl+ppxx3IUV1tsK6cwPG+fe8a1NoCWjbHoFS3xbcwEjkMUWB0rAG1u0r816XqQnI8UGwAm42F71vxIjKl+lbKSXM62egH33VM6hgpVWIHXgWatt9FFSQLLnqeeC6qypiy4nSMmFc+ADG6d96BhW8thnxB56noII4UprcdocUdu8QmrFYJNf8iggFFjIzDePc7xwT7+6mtG6GuukAbSMlrnhZFVh5DeTE8LzXxUAXpKkva6eD6zII45HaVSUmR2CcfstzGykYg7p+HxTaRko88PrV9q7Be5L+rb5OaJsOhDSq3KNvlkWQUkDqDpnsEVoXtga7YZx7F9AdmjrNUmPQtj4EZdXa4AZG8pbS2AHznsACwoLo74LSvH7pb2gZ9v/uwRLR2FUlpYTVfQnmpcOOu79msWS5XYm2gKeyiT+YqoHXr4a4c33e63JiGGJypGALD9JSyCJEeV1YCgvGpaMwTr7pGVSwqlWIH3qeggqyaH5X4YzyrJ80MjBOvwXz4geeQQVv7EDs0HMUVJBFU5yqcKKwl0NoxmIRorouLKbG7OZh44MeZzEoHbugdlBtCrI0rKIOrKQCcnp83uvWQBekEEv+fXKtthkPd1eVGOmDfvYdwOU+B2DXptj8AHjz+kjMrpD8oTS0w2A8qxWE6L8JrN8b0qhyiwKLBUS12qZ1+wqMix85Ti3PiZcgtv0x8AIvxkJygzEGvmotrOun5x8wMhCjSyvyI4WVVYUQsCt9hrU9U0oBq/M0zGunAHg04SuvtZc+fGzCRooHiyXAa5qcq9kamYLc5k+BxUJMHbDMrJdZvCSUJxe7NsVHEL1XPc/j9avt2hQrqDlAiNKwJjuwgJ3EuaTAImLVNmV6CvrZdyBH+jzPU9ZshbrhfqpNQVaEN6zJTqqXEmLg1vLylSKO5sYXEKVtpmJsAPrhn3sHFVyBuvkQtF2fo6CCrBivaQYcdg9ZC/TJuFeUqtZa/TeQ+ein3kGFloC2+wvQNh2koIKsmNKwxvH1pd5H+YJmLBYgXBPOglsXllLCunlupsV5dsv2uTGVVtlTtuU1gY2NFDamKOB1rRB3bsx7XY4NLLopmetyoqIF2qBLWibMy0dh3broeR6vaYa2/dHQcz9I4WBl1WDJ8qzaQtZAF6SUBZe3Q4GFByks5y6MAa4Ly0xqpjZFt+d5SusmqJsOUm0K4julfk1WYAEA1sAtqG2LqMJppB0D4iBnK8TkiF2JdnLE/STGoK7fC6V9R8F90JNwMcbAG1Znd5bOTBdkFU76FvIQ9jKIXZvid45Jb3PUGLQtD0FppG2kJDd4w2rH10X/TWARgYX7rF/u7yMpJayeSzA/WURtiu2PgVc15HxMpDgp9WuyAwvYyyGFtn2ZAgsPYa0LS2HBvHoC1o2znuexqgbEtj8GlizP6XhIceMlFWBl1VlP+9bgrUVtO3WrtsliST+HmX1dIwPjwvuOsy13440d0O57EEwLblmGFB9e1wJwDoh7t512ARv2hTSq3KDAwoW9LuyQyZ7jdWExPW5P2XrWpsBMbYrdVJuCBEJpWA3z3mWERWw7labh2Gsj17uqxOgd6Gfe9q5NwVWo9x2C0ryBlj5IzjE1Bl7TDDE4f1lbjPStuGtw1FBg4UZPBb4ubPVehXHhwwVrU2jbH1vSVj9CVoo3rAE6Hbad9t8EL62ENXALvKIOvKJ23vGglxOlFLCuz9SmcKiWO3f98pqZ2hRVORkHIU6UhjVZgQUgYQ3eAq9aZW/jblqX89m8XKPAwoUIcN+9NA0YFz+k2hQksnj1zLbTe2q6mJ0fw7x6AgAQf/T/yvo598DC/x0XMj0F4+y7ECO9nucpq7dC3Ui1KUjweMMa4MIHWa8bH78FCBPgCpS2LSGMzF8UWLhwXAbJwbqwGB+0lz7uKZs8D1egbtwPhVqckxBIKSFTE3Z5b4emZAAARQUrq7rn54TdZ+deWsL33UtW/00Y598DjIz7SVoC2raHodQ7J6MSkktSSsA0AEXLnpUWdsDOKuoKYnmbAgsHUliAmf0BxWJJ377YpZSwus7DvHxsgdoUlTO1KWpdzyEkV6SeQubDn3pv0wTAK+vB2D0fiHrapXmff7MVdm2KY7BuXfAeX3WTXZsi5P4+pDiJ8UFkDv/Ce4cfUDC7QyiwcOBeftifD0SZScE4/zuHtbb5lJaNdm2KiLRmJ8WHxZLQNh2AfuIN7/McPhCFy4co9+k+EpOjM7Upht1PYgzqur1Q1m7PDnwICQivqIPavh3m5aPe51FgUbiknrvAwhrqgXH23UXUpnjQtW01IUFSmtZB2/aIXVPFBa/Mrv/gvJzIVty8z65NcRnmJx9516ZIlNmzfVSbgkSAumEfZHoKVld2LYtZhfK7SoGFA5lx+NLnqr02ttz3FGKmNsUZz/NYZb39YUi1KUiEqO3bIdNTc4ma97r3Scuzau0KlhPt2hQfQNy57nkeX7UW2pYHC7JzJMlPjDFo2x+BzEw7//5yBaysOviB5QAFFveQpuG43ZPFl/+BaNemeAdyfMDzPGXtTqjr9hRE8g4pPOqmA5CZ6exeG6qW9YHomLQJgMWWP+tn16Z4B0hPup/EVaibD0Jp2UiJziRyGOOI7fkC9CO/gBiev3uJV9YXzE4lCizu4fcyiNV7DcaFDxZRm+JRKDXNy7oGIUGwn7gehUxPQQx0zb3OKxuyvsT9zFOya1OcgXnt5MK1KbY/Dn7P7hRCooQpKmL7nkLmg3+AnBr99PWqVeENymf0aHwP1w/EJT5pSdOAce53MM6+4xlU8Lo2xA89T0EFyQuMK4jtfQKsom7uNe7wgeg4Y8EVYIlVa2V6CsbxN+wlGI+gQlm9BbH9z1BQQfICiyUQ2/8l4K7yBYWSXwFQYDGPlNL5A1GNLWnfvRgfhH7457BuX3E/iXGomw9C2/15KnhF8gpTY4jv/xJYogxAdmAhLdO5jHdsaWW8rYEuZD76qXfBKy0ObdfnoW0+RJ19SV7hpZWI73/azt+Dc4Cer+hOnCGlhDQyjlnmi52tWHRtipKZ2hQVVJuC5CeWKEVs/5eQ+fAnc09acmZGwb1q7eKKy0nLhHnlGKwuqk1BChuvWmXnXJx5G6ykAsCn9xGAvM0TYlJ6zC8WqHQ6jYmJCaRSKaRSKRjGp0sVcZWjIhlDaUxBDAIcAry6CXzm6cyN1FMwzr0HMXjL8zyqTUEKgZQS09PTSPXdxJRaiunpaVjWp0F5SUxBRTKGEpVDgwUGCaWhHWyBnVViaqY2xb0VPu/GGNR1e6Cs3UG1KUhek1JiYmICmf5uTCpJpFIpiLu6n6qqimQyiWQyifLyciST+dFDpGgCCyklRkdHMTw8jFTKu/rZ3RIqR1llNWpqaxGLOa8PW0O3YZx7F3B5UgMAqBq0+x6E0rRuqUMnJDIsy8LIyAiGh4eh69nLHW5K4xrKqmpQXV0NVc2eKJVSwrp9BebFj+bKGztKlCG247GCmjYmxccwDAwPD2N4eHheQL6QeDyO2tpaVFVVgUd492BRBBapVArd3d3IZDz6CCzCqlWrUFdXNzc9JYWAee0krOvZXR/vxirr7Wz1EqpNQfLX+Pg4bt++DdP0+OJfAOcczc3NqKys/PQ+MnQYF9+H6KPaFKSwSSkxPDyMvr4+rOSrV9M0tLa2orQ0msuABR1YSCnR39+PgQHv+hFLEY/HsXr1amiWDuPs25BjVJuCFDYhBHp6ejA2Nubbe5aVlaGtrQ1sYshe+vCsTaFA3XyIalOQvGYYBrq6upY0Y76QmpoaNDU1Re6+KNjAQkqJ7u5uXz8MZymKgvaxK2BeBa/iJdC2PQqllraRkvxlWRZu3Ljh64fhrISmorX3hN2szAUrq7ETnWkbKcljmUwG169fX9Fsn5uysjKsXr06UksjBRlY5DKomBW30mgbOOe4i4TXtULb9ojvLdYJCZIQAtevX89JUDGr0pxAfb/z7g+lbQvUjffTNlKS13RdR2dnZ06CilllZWVYs2ZNZGYuohPi+GhkZCSnQQUAZJQEhqvvaRLGONRNB6Dt/gIFFSTv9fb25jSoAIAxtRzTVW3zX9Ti0HZ9Dtp9VJuC5DcpJW7dupXToAIAJicnfV3yX6mCCywymQx6ez0K6vhoOFYNvbIJgF2bInbgGahrtkUmaiRkuSYmJjAyMhLItXqTjRDJSgAAq260K9E2rAnk2oTk0tDQUM6D81n9/f2BXWshBbcUcv36dUxNTQV2PS5MdKjTiG+8n2pTkIIghMDly5dz/pR1t7iVxppSDm3dLqpNQQqCruu4fPlyoNeMx+NYv3596A+3ObuDb926hcceewxbtmzBjh078OMf/zhXl5qTTqcDDSoAQHAVgxWrKaggBWN8fDzQoAKwlxbHq1ZTUEEKxvCwR5G3HMlkMoF/BzrJ2V2sqir+8i//EhcuXMBvfvMb/Jt/829y/v/w0NBQTt/fzdjYWOAfxITkSlj30dDQ0Ir29hMSFUKIUAILILz79245Cyyampqwa9cuAEBDQwNqampy+hc9W1kzDFJKjI+Ph3JtQvyUyWRCW6c1DCMya8SErMTExMS80txBX3sp1TxzYcmBhRACmzdvxre+9a15r7/55puIxWKOSx7Hjx+HEAJtbW1Zx/ySyWRCfdqZnvYo501Ingj7i53uI1IIwr6Pwr7+kgMLzjm+/e1v4/vf//5c1vjp06fxwgsv4Dvf+Q5eeOGFeecPDQ3h93//9/E//sf/8GfELsL+i6QPRFIIwr6Pwr4+IX4I+/sg7PtoWUshL774Iurq6vDKK6+gu7sbTz/9NL761a/im9/85rzzMpkMnn/+eXz729/GAw884MuA3aTT7tX7gqDrOq0Pk7wX9n0U9vUJ8cNK+1Ll+/WXFVioqopvfetbeOWVV/DUU09hz549eOWVV+adI6XE1772NXzmM5/BV7/6VV8G6yWs9ay7UWBB8l3Ya7NRuI8JWamwf4/Dvv6ykzdffPFFTE9PQ0qJH/3oR1AUZd7xDz74AH/3d3+Hn/3sZ9i1axd27dqFs2fPrnjAhJDcCXv/OyEk/y27Xu43vvENAMDg4GBWUAEADz30UKBRk9MYgkYfyiTfhd3IKOzrE+IHznmos39h30fLuvqf/umf4rXXXsPhw4dhmib+6q/+yu9xLVkikQj9+hRYkHyXTIbb4ybs6xPihyh8H4VpyYHFD37wA3zve9/Dq6++ip07d+KP//iP8fLLL8MwjFyMb9HC/kAK+/qE+CHs3+Owr0+IH0pKSkK9ftj30ZICi9dffx1f//rX8Td/8zc4ePAgAOCll17C+Pg4fvjDH+ZkgIsVi8VCnf4J+xeJED+E/YFE9xEpBGHfR3kzY3HixAm88MILePnll/GVr3xl7vWKigq89NJL+O53vxvqmhJjDNXV1aFdu6KiIpRrE+KnWCyG0tLSUK4dj8dD/0AkxA/l5eVQ1WWnMK5IZWVl6DmHBdXdNJPJ4MqVK4Fft7a2Fk1NTYFfl5BcGB8fR1dXV+DXbWlpCe3hgBC/9ff3o7+/P/Drrlu3LvQZk4JKwY7H4ygvLw/8ujU1NYFfk5BcKS8vRywWC/SaqqqisrIy0GsSkks1NTWBJ/SXlJSEHlQABRZYAEBzc3OguRaNjY2Ix+OBXY+QXGOMobW1NdBrtrS0hL5FjhA/qaqK5ubmwK7HGENLS0tg1/NScHeypmmB/eUmk0nU1tYGci1CglRSUoL6+vpArlVdXR3KTCMhuVZVVYWysrJArhWlh9yCCywAO3mlrq4up9fQNA2rV6+m2hWkYDU0NOT8QzGZTFJ+EilYs7N/uf7Cr6qqitSSfEEGFgCwatWqnAUXmqZh7dq10DQtJ+9PSBQwxrB69eqczSaUlJSgvb2dlkBIQVNVFWvXrs1ZcFFVVYWWlpZIPeQW1K4QJ8PDw+jt7fWtQVhZWRlaW1tD20pESNCklLhz5w4GBwd9e8/q6mo0NTVRUEGKhmVZuH37NsbGxnx7z9kH6CgFFUARBBaA3dK8p6cHU1NTy34Pzjmam5tRWVkZuX9EQoKQSqXQ3d29opbMmqahtbU1tFoZhIRtfHwcPT09K6r7lEwmA1liWa6iCCwA+6lramoKw8PDGB8fX/TPxWIx1NbWoqqqKvSiI4SETUqJ8fFxDA8PLylQn010rqiooFkKUvQsy8LY2BiGhoaWFKiXl5ejpqYGZWVlkX7ALZrA4m6maWJychKpVAqpVAq6rkNKCcYYOOcoKSlBIpGY2xMc5X9AQsKSyWQwNTWFdDqN6elpmKY5dx+pqopkMolkMjl3PxFC5pNSzt0/s99HlmXN3Ueaps3dP2VlZXmT11eUgQUhhBBCcoPmJAkhhBDiGwosCCGEEOIbCiwIIYQQ4hsKLAghhBDiGwosCCGEEOIbCiwIIYQQ4hsKLAghhBDiGwosCCGEEOIbCiwIIYQQ4hsKLAghhBDiGwosCCGEEOIbCiwIIYQQ4hsKLAghhBDiGwosCCGEEOIbCiwIIYQQ4hs17AEQQshSxHb/ARhXwLgCrsbAFAV87r+1T49psU//PHuuwzFFVcE4A2MMnDNwlYMzBsYZFJWDcbgfYwyMwz6mcHBuH4upHApnc/+Lz/03//QYs89T7zrv7p+b9x6MQeP2GO7+M2cMCgM0hd/zZ0Bh9nFNYfP+zBjAwaBwzP2ZMUBhsP9/m/vZ2eOAwhkYYL/v3J/tc5mwwKQApADu/rMUgGU6HxMzrwsBSAkmzLmfkaYBCAsQFqRlAaYBKSxACEhTt/+vNXP87nNn/mwfs8+VQkBaAsIwIS37z5ZhzP1ZCgFLN+f+LHQTYuZnpGXN/ZyYew8JYQlIIWHp1sx/S1iG9ekxa+aYsI8J3YKw5My1ZsZjSUhLwhAClgQsKaELOfdnS2Lef9/9Z4HZY3efZ//5/5E3Qr03Z9GMBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8w6SUMuxBEEJIGDKZDP7iL/4C3/72txGPx8MeThYa3/JFeWxA9Me3EhRYEEKK1vj4OCorKzE2NoaKioqwh5OFxrd8UR4bEP3xrQQthRBCCCHENxRYEEIIIcQ3FFgQQgghxDcUWBBCilY8Hsef/dmfRTZ5jsa3fFEeGxD98a0EJW8SQgghxDc0Y0EIIYQQ31BgQQghhBDfUGBBCCGEEN9QYEEIKUrf/OY38fDDD+PFF1+EruvzjqVSKXzpS1/Co48+is9//vMYHh6O1Phm/cVf/AX27dsX6nhM08TXvvY1PPzww/jX//pfBzKWpYxvVpB/V07cxheF3zW/UWBBCCk6p06dQl9fH9577z1s2bIFf//3fz/v+Ouvv45t27bh3XffxT/9p/8UP/zhDyM1PgCYmJjAuXPnQh/Pq6++itbWVrz33nuYnp7Ghx9+GMiYFjs+INi/Kyde4wv7dy0XKLAghBSdjz76CF/4whcAAE8++WTWl+GGDRswPT0NABgdHUV9fX2kxgcA//W//ld8/etfD308ixlrmOMDgv27cuI1vrB/13JBDXsAhBAStNHRUTQ3NwMAKisrs6af161bh3PnzmHbtm1gjOHIkSORGt/Y2BjOnj2L//Af/kPo4xkdHZ3rdeE01rDHF/TflROv8YX9u5YLNGNBCClYfX19eOihh7L+J6XE+Pg4APtDv6amZt7P/fVf/zUee+wxnDt3Dv/xP/5H/Kf/9J8iNb6//Mu/xDe+8Y2cjMlJdXW163i8jkVhfEH/XTnxGl9Qv2tBosCCEFKwGhsb8f7772f976mnnsKvf/1rAMCbb76JBx98MOtnZz/8q6qqMDo6GqnxXb16FX/+53+OJ598EleuXMF3v/vdnIxv1sGDB13H43UsKF5jCPrvaqnjA4L5XQuUJISQIvQnf/In8qGHHpL/7J/9M5nJZKSUUv7hH/6hlFLKsbEx+dRTT8lHH31UPvjgg/LSpUuRGt/d9u7dG8p4ZsdiGIb8/d//ffnQQw/Jl156KZCxLGV8dwvq78qJ2/ii8LvmNyrpTQghhBDf0FIIIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhJA5X/va18AYw7/8l/8y69i/+lf/CowxfO1rX5t7ra+vDy+99BI6OjoQj8fR1taGZ555Br/97W/nzmlvb8df/uVfBjB6EgUUWBBCCJmnra0Nf/u3f4tUKjX3Wjqdxo9+9COsXr167rUbN25g7969eOutt/Dyyy/j7NmzeOONN/D444+HWkKbhItKehNCCJlnz5496OzsxE9+8hO8+OKLAICf/OQnaGtrQ0dHx9x5szMYR48eRWlp6dzrW7duxR/8wR8EPm4SDTRjQQghJMu/+Bf/Av/rf/2vuf/+n//zf84LFoaHh/HGG2/g61//+rygYlZVVVUQwyQRRIEFIYSQLF/96lfx/vvv48aNG7h58yY++OAD/PN//s/njl+9ehVSSmzevDnEUZIooqUQQgghWerq6vD000/jr//6ryGlxNNPP426urq547NFmxljYQ2RRBTNWBBCCHH0B3/wB/jf//t/46//+q+zciY2bNgAxhguXrwY0uhIVFFgQQghxNGTTz4JXdeh6zqeeOKJecdqamrwxBNP4L/9t/+GqamprJ8tiC6dZFkosCCEEOJIURRcvHgRFy9ehKIoWcf/+3//77AsC/v378c//MM/4MqVK7h48SJeeeUVHDp0KIQRkyigHAtCCCGuKioqXI+tXbsWJ0+exJ//+Z/jT/7kT9Db24v6+nrs3bsX3//+9wMcJYkSaptOCCGEEN/QUgghhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ8Q4EFIYQQQnxDgQUhhBBCfEOBBSGEEEJ88/8H1xSw96J/sWwAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1002,7 +1001,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -1166,7 +1165,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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CwaDo59RfnAZKZGZmwu12G8YJ1W8yYVVPcv+uqqoC0DLmot2L3iGeFE6pBJlav//+e1GvKqJM8zavbyJdKvIVD2mVU32lyvxK7RYPnI7NSCSCm2++Gb/++qvhO2effTaOP/54x2v4Kaecgn/961+Oj2DsNKRu6tSp+O6773D77bfjgAMOMDmrtidUSl2spE4mL8kgdcnIlccHrixpq5Q6IDFSBwB77bWX8nMVeJk6AqlLhk9dU1MTIpFITASLrnVC6l5++WXss88+mDdvnvjM5XLB6/Xik08+SVqaAOpbsZC69evX4+STT477mZzUySko4gUtgjxRLl+s/H5/ypU6WhDiWcyCwSAqKioAqM1WMql74YUXlPfh5lfqy9TX7IIo6L5Aq1LHSV1bKnXy2KCTJuzmISulLl1IHZUjIyND1KtKqaO/8XmIcg6qyFc8bhNy1DkFLtkhHqWOyh2t74RCIeE+QXBK6v73v//htttuw0MPPWRY/x566CEMGTLE8RpOvsVOTdydhtSdfvrp2LJlC2bMmIEZM2akVWLHaGk8/ve//8HlcmHdunWGa1JJ6n777TfssMMO+O9//+vsJSzAy2GXV41fJ0+YTnauqsk+Ly8vJqWuI0TAJkOpo7qKJYKT2qRLly5RSR1NvPzM10gkAq/Xi/feey9paQLiUepuuOGGqBGadkil+ZWTOp6AV6XUpcrhO54cm1xVjKbUWZ2Wwa8LBoOCDNHcaFfvW7duNfnUtSWpKy4uFj/LY0NW31Sg+pOvTRfzK40zl8tlS+ro3WWVGUgeqaOTQgjPP/98VIXKqU/drFmzxN9pLYjWdx566CHsuuuuhnNonZK6zz77DACw/fbbG/pNTk4OsrOzY7ZqOD0to9OQunRGNFJH6VfWr19vuIaTOrmzOiUo4XBYkDo+EZJKZ3XWolPwewaDQcNEJZO6goICAPEpdar3zc3NRXNzM6677jqDesfx0EMPiZ/TXan79ttv8cgjjwBoIawAcPHFF4u/J0rq7EhzdXU1AKCioiLqZKNymg+HwwZfm2QgnkAJnrU/HnDiQUjUpExK3aJFi7D77rsDMJKrVJM63k7xKHV83KiICK8rWsgA8zvwupWVOitS9+abb6JLly5ivsrMzERWVpahb6cigIiTOkp4zMtLoHJ0BqWOkzoV2eHRyzJUpC7WYIS6ujqsWLFC5G8jNyqam6zgxPy6ceNGjBgxAuPGjUMwGBR9Ohqpo6TtfG22I3U+nw+XXnop/H6/UBmbmpoMZczOzkZ2drZpTdtjjz0wdepUy3vTaSrRoEldGyAaqaOdj+wbkgxSFwqFxPEkvFNQJ0vUH4UTNLnzyse3lJWVmb5D34vlOYScnByEQiHcf//9+PHHH01/b2pqwmOPPWYoQzpj//33x5w5c4R/GgDD0TKJkjqrhWfcuHFC2S4tLY3aHvR3vnOMRCJKP6pEEI9Sl4gDNKAmF4meJ0lkd/Xq1Rg6dCiys7MNJ3z4/X4xnlPhU/fKK6+In+MhdXzsRSIRrF27FkOGDMGmTZsAWLeP3P/onXjOzGik7qWXXgLQqgpnZmaipKTEQC5TER3NfUZLSkrEz7GSumAwKOYmp4ESkUhEOZ+lCpxcOlHqVFAFSsTaLuSetPfeewNozUEabd52Yn6lvvfqq6/i4osvdkzqaP7lxNJuPnrqqafwxBNP4OOPPxZjPBgMGsrocrkEqeN1//PPP+Pyyy833I8/9+abb7b13SRoUhcnnHRYGiBOSZ08aOxIXSwpTbp164aDDjoIH3zwgen7iaorfOKWSZ2s1HXp0gVA8kgdKXVWkBf4ZJG6NWvWYPbs2Um5lwq5ubli4ufBPLH41AHm9/2///s/5fVTpkwRP8vRxSrQffmEk0qlLhZSl2jSYJVvV6Kkjm+c9tlnH/Ts2dPgK5RKpW7NmjU499xzxe8bN26M+R587IVCITz99NP45Zdf8P333wOwLqvVOA8Gg+JnmdTJcyWZ44j0ZmZminlEvm+ysHz5ckPeTbsTU6jdrMrAx5asPFuRutdeew177bWXwWc1laBy8AAWt9uNrVu34oADDhDk3W6joRojsSp19OzddtsNAMQmMdqYdqLUcfI0Y8YMS1InvyOROr6W8GtktZXq0uVyWZI6oGXdjUQipvLK9+YbCrv349CkLk5EI1UfffQR8vLysHLlyrhJHS3i77//Pu655x7D32JR6jIzM7HvvvsqJ6dElTrZ/Gql1HFSZ+VTZweV43JbkzqauPr27Yvhw4cndC87lJaWivfipM6pUkf1Lk+GkydPjvrdzMzMqJMx1aus1HFSFw6HEzYvxUrq4jk1Zf78+YZkqVTv3F8wmaRu6NCh6NWrl+FoKwqUcLlcwlUiWURFJg7xKIByP5o/fz6A1rMrnSp1nDDLJI7Gt3wvUm8aGhqQkZEBl8uVclIn11EwGMSee+4JwFqpa2xsVJI06kcnnHCCY/MrpdrigXWpBJXD7/cbSN0777yDmTNnCvcgq75TUlJiWI/iVero2TS3ko95NFLnxKeOXxMKhZRrwQcffACv12uod5rT+GaI30t+R3r3mpoa0acDgYCp3xBhpXpTRX+rOIaT+U2TujgRjVSRb08ipI4acObMmXjwwQdjej6/B0WM8XLQMxM1XSSi1B1wwAE4+uijU6bUyUf8JELqXnvtNRQXF2P9+vUx+1iFw2FHiyktkllZWeK9kml+dQK32x21T9DOnSt1kUhEmIyBFnL48MMPx/x8jljNr/GYXnfbbTcMHjxY/E7vft5554nPkmV+BVr8hHr16qVU6nJycoRykCzzq9yW8SRdlcce+bdR/0pUqQuHw2IBtyOI1L9kUudkDvv9998dma5U92tqasK8efOwyy67WJI6n8+nrNtgMIhu3boJVxEguvmVNgFtlVeTnhMIBAykjsY3BYpYlad379625/k6BT2b/KNpPkym+RVoqXe6J7+e3JO4vyvd24rUyf2B5ix+D5VSR+Ocni9HgQPq+dvJGqZJXZyIVrnc+VQl6apInUzU7AZFPKROZVKKh+gEg0GRtkJW6vhErgqUyMzMFGWfOXMmPvzwwzYzvyYS/Tpz5kwAxsHt9LD5a6+91pFpkgJJuAmUR97Fan51SurouYAzpY76q6zUyX3ptddec/R8K8RK6uIxLcpQPStRUsfJrtfrRdeuXQ3RdKTUZWdni8k+WeqTPPfYjZfGxkb07dvXdL6yVT+ycy9RfY+TOr6YbdiwIWrUsR2pc1JXu+66q1DbosHKJOb1em1JnaocgUAAWVlZhnEVzfyainOUCapn0rjlSl1GRoYgdTR3ye08ffp0TJkyBVlZWcp3j1epKy0txYcffijmDyfmV5obnSh1nNRt2rQJX331FYDWTSpfJ+hnPrf861//Mjybg+qX1L5u3bohGAya1h5yrbJyQwDU7+1kTtekLk5EIwi0IDU2Nho6VI8ePZCTk2NI40ETvFUHkZGRkeGIoEQiEYTDYQOpi0Qi2LBhg1io4iE6t99+O4499lg888wzePXVV8XnTU1NwrEZMJtfs7OzlRPjSSedFPWZPB0EoS2VOmrP119/XXzWp08fR2kJrPJ2ySDTG/fz4AtYMkhdtInWCamje8g+dXLSb6cpG3jSU9X3EyF1diZgVX9IBamT65Oi+3g5SKmjBT1ZpE6OurNr261bt2L58uWG5N6AeUNFinG8Sl1TU5NhMXOSv7OhoUEshPEodbGA5q1jjjnG8LsqDQUndapyBINBE6mLZn4lEpVsUjd//nxkZmYaIpQBNalramoykJxNmzaZ0l/tvvvuOPfcc02CASFWpY7KkZubiyOPPBLbb7+94XMrBAIBkS3AiVInm18PPPBARCIRZc49agN+/ddff236O4HalNTs7bbbzlapk0kdnzO1UtfGsCNDfr9fOBH7fD7RoWbNmoX58+cbBsGsWbOiml9lFBQUOCJj1EE4qXvwwQdRWVkpJlKnRGfGjBmiw5JCddFFF+Hll18W1zQ1NeHHH3/EoYceKt4nHA5jyZIltqSOsGzZMmHa4/jrr7+wdOlS0+e5ubkG3ycZqSB13IEaaDlrOBqcLtCc1JEqEY9PnR2pU5mgqJ9VVFQ4Mr/S/XmS4UgkgjvuuMNwnROfulAohH333ReXXXaZ6W+x+uaQMsvPdLb7LvdrI6jGVaK+mPI4lqOEKfqVm19TQeoKCwttF1qVqQkwkzPqV9FInSr6NScnx6DUBYNB4UOWmZlpq/rFq9TF6ttJ96PzrOl3O6Wuvr7eUA6KMrYjdVabHiKvyTa/kuP+7bffbvickzoyGwaDQQOpO/7440055GgDYkXq4lXqaHw4DZQIBoPIzs5GRkZGzEodYenSpeJ9+SaO2tuqLazW7JUrVyInJwddunSJyfzKwd97xowZADSpSzr4ILQjVf/+97+FT53P5xMNtvvuu6OkpMQwCL7//nvReZ2SusLCQkekjr6fkZEhJHI6QJt8WJzc57vvvsPo0aOFCmc3cOrr68WkGwwG8eijj6J///5Yvny5SLpo98xRo0aZPiPHbAKlRsnJyTEQN3nyTmaghNXCsGjRIgAt9Uk/y1BJ6yoQqTvyyCMxbtw4AEZVJxmkbp999lGa5K6//nosWLDAkVJH9+c72nA4jF133VWYqQFniyndS0XaY1Xqampq4HK5xOkHgH2d08LL0zeoJtdo7fbVV18JZ3IVYlHqku1Tx+9TWFho+y40LuWNldyPaNGLRamjSL/c3FyTT92mTZuQkZGBysrKmMyvw4cPx8CBA03fmTNnjoj0nzt3LnbZZRfLd1aB6oxSajgxv9bX1xvqtlu3bti0aRMCgQC8Xq/S/GoF+nuyk6VT2eVxyUkdbdSCwaBI+tvY2IiFCxea7mdF6uj+8fjUETkDWua7nJwcR0qd1+u1JJf0PgQVqZs7d64IbFApdVb3teoPK1euRGVlJbxeryB1e+yxhyDWsvlVlbSbl3HAgAGmpNtWMCej0bAEb1g7hYifDOHz+YScrhoES5cuxYABA/DLL7+kjNSRUhcKhcRES2TIyX1o8eO7OBWamprQ0NAgnPsDgYAhsjCaUgeokyHLySdpcidHWv58HmmYCqUOAB5++GFceeWVAFpI3RFHHIFevXopIz6bm5sNOblU+Z/4M4455hhMnDgRTU1NOOigg7DDDjuIvyfLp666uhpdu3YVv4dCIfTp0wddunSJidTJZQdakyYDzsyv1JdUhDVWnzqfz4fCwkKDj2BTU5OJRBGInPA+o+rb0RbhAw880FBeGVSft912GwCzUqcidalQ6goKCpKi1Dkldbz/1dbWIhwOo2vXrqipqTEoFE1NTfB4PCbfLPmIP9n8mpWVBY/HY2qfxx57DH/88QeOPvpojB071nKzZQXqA3IkciykDgC6du2KLl26oF+/fsjMzBTjIdr4oufzkxuSASqrPNZoXqytrcXvv/+O4uJiBINBQXL8fr8yuXAqlDp5Ts/NzXXkU5eVlWVrZYhG6rZt2yaeY6fUyWPcqj80NDSgvLwcWVlZ8Pl8CIfD2H777dGnTx8ARvOrz+cTqrDqXkDLvOqE4AJaqYsJvOP+9ttvuPjiizF37lzTdTy3TE1NjegQ1JB8ECxZsgQ77bQTvF6v40CJ4uJiZeCADJnUAa27ECKlTjoJlZUmVStSRnmO8vPzkZWVhWAwaJhArDJpc3BzI6Gmpsaw8FqROrlcXKlzu91JI3WcEBHhtCIwJ554ovg5GoGmSLmMjAx4vV6cdtpphvrjP0+fPj3qBGY1GcrH7lAwDdBST/GQOnp/p6Tur7/+QnFxMf766y8AySF1NTU1KCoqMpTB7rvUdtyHkfeh0047DSUlJQkTrFAohCFDhuDGG28EYCR1NO7r6urEuIlW7ljAFzMegamCU6WOriOTo4oIZ2dnizkqFAqJXHm9e/c2KXXNzc1wu90mciCfFy0rdR6PR0koamtrRTvKChO9/+23326prtL9qC3Gjx8PQE3qaE6Rza+ELVu2ICsrCxkZGSbzqxXkNC/JghUJl4/6IlIXLV2Laj0DElPq5Dm9rZS62tpag38kvzdgfTaxFakDWvoprYNkhidw86vVPMnvlZOT44jgAprUxQTeoD/88AOefvppnHPOOabrODEhn7qsrCyxcPHOt3z5cvTt21c5YVgNii5dujjaxTkhdU6UOior3UPuWFxKpoFJ7yOTOvrcToWUUV1dbYgCtSJ18uLClbqCgoKkkTru0xPNmZn7nTk5fssubyDV5Y8//ojjjjvOcFoGRzSljpM6mlCoDTMzMx2Zh8g0RaD6IXWDrlO9w6233or3338fNTU1+PTTTw3vxhGr+ZWUOl42u+/SjtwqDQ8RDav6WL9+veEYNytw0gwYSV1RUREaGxsFqUu2+ZW/T7S2tVPqunbtavKZvO+++5CVlaXs13l5eaL/LV68WBCo7bbbzhQoQaRu+fLluP/++wXR53OTyvzq8XiU6kxdXR2CwaByjqF586abbsIJJ5ygrAe+CY9EIrj11lsBtLSbPIfwEyWsxptV9KsVUq3UyXM+V+qAlrbjpM5qjaC+6na7HSt1zz//vOVpGY2NjXEpdcFgUJC6eAIlgJZ3p02InVJH73n++eeLvy9YsAAfffQRAKOqXVpaaknq+JpptRbKR+FppS4FoA7TvXt3EQGzePFiw5E/gHEQcFJH4KSuvr4eRUVFysiqVJA6CnKgd3HSSeRnyWZNUibJpy43N1c52XOlzkpp5KYzQnV1tSFfG03usllNXgi5Uuc0uMQKfDfFiYv8TPm9eLtHI3W047QCER+qfysXAOpbVm3LSR3vI/S/E6WuvLzc8BnVT7du3cRnKpMNALz44osm1SIZpI6UOl4GJ0odP66Ht5EVaSB89913ePrpp6OWKxqpo/HASZ2q3NXV1TFvTHj/jNa29O6yUldTU4O+ffvikEMOUX7vjz/+MLTfDTfcYFiMeT8lpY4HShCpo7JR9C0fr42NjaIO8/LyhOnVTqlTBTH5fD5HijlgTsxeWFho6tPkBwYYyQA/vcUqUCLa89uC1DU2Nor6pueR3yP1NflMcgK5gziNfl2wYAHOP/984bqiKp88p+fk5FiSOsqLys2vTpS6pqamuJU6us/OO+8MoMViMmjQIBx11FHiHQilpaXCp85KqVOROpqL6F50ZJ1W6lIAati+ffsazlCUUzlw2by6utqW1NHfnCp1H3/8MUpLSw0D/ssvv4TL5TKRLb5gWylATogOLX5EWGSzACkjslInEx6u1FmROlmpCwaDmDVrllKpk98pmlLnxGRtBT4J8IlHbjNO+AAYVKNElTpKUfPmm28CsD7iLRaljhbXWM2vFKxCJkWaiDh52bJli0HhJJKWkZERNVcXv2cspK6wsNAxqeOLsN/vRzgcNlzvdrsdRQNHg0zqeP8pLCxUKnWqcpeUlGDkyJExPZv63NixY6O2LTe/8nbbtm0bSkpKLM/2/fTTTw1/u+uuu5CXlyfGG58rS0tLTXnqiNQRqK7kfGF0DZ0qYafUBQIB/PHHH6ay+nw+A9lT1bNsfiUUFRWZ0tuQ7xRg9P3t3r27+DleUhdtvrr88svxxRdf2F4jlxUw1uvnn3+O6upq9O3bV8zrslL3zDPP2N7XqU/dJ598AgAYNGiQZflUSp3VRmb//fcXYkgs5tdAIIDGxkaD28XmzZtFn+drq5VSR+IDberI5cNKqSO/UQI3v1olCG9oaIDH4xFn4WqlLgXgpI5DHqR1dXUYPXo0xo0bhz///BNbt241Sa/cRk+kzolP3dChQ1FQUGDoeN999x0AGM525d/nSp2MeEidTE5IRfP7/SJnkJ35NRal7u6778bs2bMRCoXw3HPP4eabb8aTTz6JAw880LBQyuVqbm42TLIyESaMGzcOl156aZQaMPs3EFRmMl6OWEy10Uhdc3Mz3njjDZH3zmqRjYXUqZQ6JylNiGTTbp2Ts0mTJgFo6Q98EaS64v5F/KxEGfS3c845x5Fq4fP5UFRUZFhQnSh1gDmfJNCq1DlZKOS+yOFEqaurq0NeXp4pKk5GrOeBUp977rnnHJtfqTyEaKRu3bp1pr9xVYGTuqysLMO8RqSOz0+qJLCAsY7JX8lOqaNcYRw+n8+QNJwngSZQu8pBTdFIHf9bXl6eGBt20a9DhgzBcccdp3x+tHH42GOP4cgjj7S9Ri4rYNygrl69Gh6PB7vuuqul+RWw79+y2dMqFVG0ZO1WPnVW8xgpWJzUNTc3IxKJ4N///rfh1BZ5bNfV1RmepTpFAmjtg7JSR5t3qjPqv7xuqY9SMJCVUifXEz1DNkdrUpcCUMPuuOOOtteRKaVHjx745ZdfcM899xhUFZ4ImBi8U6XO4/GYSB2ZPyk3nvx9TupcLpchitJJJ+Gk7rvvvjMMAKBVXaNJjcyv3CEaaDW/2il1svpEUviKFStw3nnn4ZZbbsEhhxyCL774wjTp8oErR8yWlJQozYFTpkzBE088YfXqAry80Ugd+QQBRqXOikD/85//xGOPPRaV1IVCIUO7e71enHjiiXjvvfcM18VD6rhPnZPkw1w1AVp9TOh9yD3huuuuE2oC9W+u1FFZZVIXDocN40FOT/Prr7/C5XIZFmVS6vbbbz/xmYoc/etf/8I333xjyLHY2NhoGn/RfOqo7SdNmmQIzpBhR+pkpc7lcpmO9EsEwWAQHo8HGRkZjs2vgNEEu3XrVpSWllqSui1btpjMZlyp27BhA7p06YINGzYYyBsteNSfaBw6IXV77LEH+vXrpyR15FOnGu+1tbWG7ARWCag9Ho+pTxYVFYlIRoIVqeORzHZK3S+//CKOpyJQ28ttNX36dEPiW8B5gm8qK2Cs1/Xr14vUG1yp40eGAcaTbWQ4Nb8SybYaTz6fzzSO7JQ6AvnU0QZs7dq1+M9//oPrrrvOcI38LBWpKyoqUvrYWil1BKpT/hzZp473fTufOh7oxsuoza8pgB2p4wslTdA9evQQn/GfaaHgMr8VqevSpQseffRR8Znb7UZBQQHq6urEgKbByDv//PnzBQHgpM7tdhsGjpPzMmmSraurw3777YdwOIz7779fHK9CHZyu44ESMhnyer3Ct0LGiBEjTJMDpfSQSRq9CwcfUDJpLCkpSchHxUqpo10YB1cIeF1bKXUzZszA7NmzHZE6/uysrCxMmzYNJ598suG6aKSOL3ayUud2u4WZl7Bo0SKhEIXDYYTDYTEGdt99d0QiEdxyyy2GZ5AJ9LnnnhMO9jx9CU3sVhF+GzdutExvAbRuYPiCSIES3bt3F+WV2yYSieDOO+80mTFVSl008yvdOz8/31bxtiN1OTk5huhXQL1IxppAl8D9NJ2aXwFjsIRKqePWikAgEFWp69OnD7p27WrYtJWUlKCurk6QuksuuQRdu3a1JHV8vL/44ou49dZb4Xa7DemgIpGIUOpUfau5udkUgCFDXoAJRUVFiEQihvtamV/56SBE6mJNaSL3u+OOO86UxzNRUldVVSVIHc2ZeXl5CIVChnvHQuqiKXVWKvTSpUtNa6uVUiebU7lqS3lNuStPMBhEbm4u3n77bQAt5J7PpUTqyD2A35uXWVbqAAg/8UgkYknqrJQ6lfmVkzpexu7duyt9zmVoUhcDqPJ79+5t+hv5OQGtpI6bgSorK8XP1Pl4VKlVoERmZqbpu9Swso8bHyy77babUCw4qcvMzBQdsry8HH/++WfUSYZIACdKubm5QiEksxEndbQL5xMgKXUrVqwwRUBdd9116N69u2lhpYlFPtoGsCd18u6uuLjY0nEfiO6UbKfUyfflZoZgMIjhw4cDsCZ1pFZEC5SQfY+szsuUSV1GRobIpQYYFxXZp47+59cMHDhQ+HXQvQcPHozVq1fj+OOPV5aV91lKBqxS6qjeZVVEdnKXCQctMjwPYl1dneH8XP5+BLl/0TmOKqUuWqAELf7Z2dkIBoO2eeqsfOrIpGJF6pYvX46ZM2daEvSlS5fi4IMPNvnT8jLSYuLU/AoYlToVqRs2bJjhuzKp40rd0qVLRX4uvsnp2rUrtm7daujXxcXFpnOpyb1DZQIMhUL466+/RDuSv14wGERtba2pXORcT1CpQFabKyoHV+QaGxsdKXXc5cAqKpwSd1spdSpEI3WzZ88W11AfCgQCoq+SUpeVlSXaS0UceJouGdGUugsuuAC9evUSqrrq/f1+P1asWIEBAwYYPrdS6ugUEnofUuqam5vx+++/AzAHtGVnZwuiV1dXZxiHJG6UlJQ4Uupyc3PFnEUprmhNHzduHB588EGMHj3aUqmzC5SgZ8nrwQsvvIDnn3/eVBcyNKmzwEMPPYQpU6YYPuPK2saNGw0L7LfffgugJZChqqoK+fn5hp0CN3nyhqbfrZQ62R8uMzNT3JfIhEzqaMCSesgDJbhSt/POO6OpqQlr1qxBJBIxDBQOuj8nNryz0QTGza8UKMG/Q752Mi644ALcc889ol44mpubkZeXh4MPPtj0PZnU8fqTF0EynVhBjmCWYRcoYZWsdcuWLQgEAqK97PL70UIUTanj5MbKRMd3eqSscTWPT6oqnzr+uQy+EenZs6dlWfmEKvticlJHfUsmdXJf5O9dUVGB008/HYCRQFPkNZWPl1d1nz59+mDMmDEA7M2vdj51WVlZlifCEOyUuuzsbHEYvIrUDR48GAcccIDl+bP33XcfvvjiC5SXl5tOXqEy0ZiTza9z5swx9CG/3y/qj0id3+9HY2OjidTtv//+hudYKXWRSATz58/HrrvuKj4ndO3aFdu2bTOROlmpsyN1NMeR/5TsCyin3pFJnYosy6oKQSZ1kUgEDQ0NImiIfEkB5+ZXjnvvvRdAbKTODgsXLsTw4cPx5JNPAjC+K9UBV+roM55pgPqknUIULVDiueeew5o1a0z+aRxLly5FOBxWkjpVG3HLmBwoQXM5v0Yeq5TsW0Y0Uiev2UDrptXv96OpqQkVFRW46qqrhA+5Sqnj5lerjafsa+oUmtRZYPr06eJILQJf0MrLy4V6AbQ26JFHHonVq1cjPz8fo0aNEqYnPpmRT5xsflUFSsikzuVyiQFGKgdNZFZRU1bmVxpAK1aswPPPP4/ttttOSexUx6dkZ2ejb9++2HnnnXHllVciKytLTMZE3hobGw2L7oABA5Skrm/fvnC5XJakzuoUBvpcldtL3t0VFBQoSR0Rrs2bN6Ourg4///yz8lnUNr169UJmZiamT5+Oww47DMFgEA8//LDh2qamJixevBhlZWX49ttvRXtZmegoLD8cDisXk8GDBwNoqQv+XlYkgit1tDDwCUxF6rhPHf/c6t5OJptHHnkEO++8s+iP3PxKE7WdUsfLbFV3/HsNDQ2ib9P7jBgxwpBklt+nvLxcPIOCfIDWDQtX6hobG5WKKPnDAi35A++66y5TGaOZX0llowWUjwMae5zU/fnnn0Kh5Ist9xUj8I0CN78GAgEMGzYMl1xyibg2EAggPz8fubm5ol0oHUlpaamhzfv374/169cLcpeTk4NNmzaJDQ4pdevXr8fWrVstSZ3f70dtba0jUqfqc7Rwk3LLFfetW7eaFCYiddQeVkqdlfkVaG0LUlm4Kk2gdCuAc1JHZxEni9RRW9CcTonhgda6XbduHbp162aYdzipI1JsZ0Fw6lNnFwBCmwgeuQ5YBwdwUzf51AHA5MmTRWAOV695lgmCHJQBGEndH3/8IfLI0vtRf6MTUIBWpe6ee+4xbcyjKXX8xCFeVqClf9mdQGSFpJK66upqvPnmm3jggQfw4IMP4o033rA9TiudoVLO5AWNFLGSkhJxKDehV69ecLlcmDBhAm666Sbcc8894m9FRUUi1QndT/W8cDiMzMxMU8NSJ6LkszSRUflk8mJlfiWTyLp168RB73KOKqB1YeFt6fV6kZeXh0WLFmGXXXaBx+MRA438SaZOnSomlCuuuEKcQSuDFmb5mCDAGamj9+GkjkjDWWedBaCFvNXW1ppMZETq1qxZg1NOOQV77LGH8ll+vx///ve/RZqEY445BhUVFfD7/XjnnXcE8aIy811iNKWOOyWr6mfGjBk4/PDDEQqFDLtWu/xNGRkZaGhoEBMon1DslDqqUyszHX3uZLK5/PLLsf/++4v+w5W6aKSuqqrKMMHT2JLNTfQ7qZ2yUhcOh3HCCSegd+/ephxl5eXlYpInYg20EiXuU5ebm2sIwKBn8t3//vvvjwkTJpjqwYrU5efnIzs7W4w5rtRR25HCv2bNGvH9Pn36iP7GrQGqxN1WSh0tlJQ0lV/L1RGaSwoLCw1t5PV6UVlZKUh0dnY2ysrKhCmS7kEmt+233x6A0fxKCge3etDcyMtIJEP1fmQ2I1LHlbotW7aYSB25OdDnVv5aTpQ6+q6qXKTyAsbo12AwKNRhGVakLhQKmeblWM5VpnLU19cLkkY+pFVVVejVq5eB7HD/ObrezoLgNKWJXd5JqzQyVkod3+TU19cjKytL9CcKzOL+4vJYBcx5TgGjT91nn30Gj8eDsWPHCmJGuRr5WkXP/c9//mNKXRLtRAk7pU6+l1MkjdQ999xzGDp0qLDhh0IhzJ49G8OGDcNzzz2XrMfY4rrrrsOIESNw5plnJhw9piJZ8oJGA6u0tFTkviGMGDECQMsCduuttxoOGSe/kXjMr0DLLvmwww4TPhiyUif7h8mkjiZWWryi1ZVKqZN3bnxxKigoMPx95cqVQs1SqQn8pI1YlDpaKFXvQW1x3333oampCYWFhYhEIthtt90M70EkYOzYsfj4448BqFUhv9+PsrIyU6DC3LlzsWnTJhx77LGGMvOBmiipKyoqwoABA0yZ0K2CDOh9OanjdRirT518b8CZUgcYfas4qbPKeUiQTxFZs2YNNmzYYNq1y/6D1Lfl8q1atQqbN282tG3Xrl0N70vlo+fKPnVz587FXnvtJTYqNOnKpke57qxIXWVlJXJycgzzCNCy2Nx7773Yb7/9BJFQ5Vzj7wuoibhM6uTD4nm6Eb/fj+zsbMNCSu0j51+ke9L4sfKpozqlv8tKHWAkdXyxrKurg9vtdkTqZOsFYK/UURtbRb+qxiGVnb5DdaRSfLiSw5U6+Yg+Duo3sqI1YcIEw9GE/Fo78LEaiUTw559/ol+/fgBa2nrdunWIRCLYbrvtLJU6fs4uABxxxBGm58hpf6IdE6bqp1bzihOlrqamBl6vFw888ACA1j6gUupUpE4O3qE+29jYiLy8POTn56OpqcnwTI/HI67ja7vP5zO8A41pyjlHsIt+5enO2pXU3XPPPfjpp5/w4IMP4rrrrsN1112Hhx56CPPmzcPdd9+drMdY4ueff0ZVVRW++eYbDBw4UES5xAsnpI52Hl26dDEpdf3797e8N+1GnQZKqBp25MiRYkKWferslDpufqVOzQdkVVWVISUHv78c9MBRUFAgIoj4GZZlZWVilw607kY5aKKJ1/xK6sDGjRsxbdo0AK0Tb25uLtxut3jX33//HXPmzDHcX8ZFF11k2IhEIhGx4HFQeTMyMgxRaTKpy8nJgdvtdkTqrMwctCDzXStvD37vpqYmFBUVoaGhQbSt2+3G6tWrsd9++8XtU8cjtuMhdSrzq5VPXU1NDYqLi4WLw9ixY1FZWWlyLaDy0OeyUidfy8doWVmZQZmcPXs2gFZSp/Kp+/HHH4U5V2XSAczqj0zqsrKysP322+PBBx8U/TIvLw+77767KBfQkocrGqnjZfvyyy/x7LPPGv5uZX5VbVyIAPKIQ6rXaKROVj2IGNJzVKSOFsNNmzYpU+pQ8AgRM042CDQHy5kAgBbCx33qMjIyBKmjSPxYzK9c1QWMpE723eNKHSd1TlJSyEodD8IjOEmITeX0eDzYuHEjqqurhcLr9/tFMJITpc7r9WLTpk1ifuWgMRIMBg31qSojtYEMLnBwOFHqiNTxPlpYWGjKkSmTOvqZt11BQYEhX6PX6xXzPCd1WVlZos/x03XkpMY864V8fjllAaB6ohRLaeNT53K5lLvuuro6ZWLRZOP777/HoYceCgA4/PDDRUJejkAgAJ/PZ/hnBdUxV/ICyM2vPp8PZ5xxBgBg5syZtu9MCy5XZ+x86lSkprKyEps3b0YwGDSROpVSxyPgiNTxDks46qijDCQsEokI2V7OkcaRn58vSCYPiJAne0olsWzZMmzevBmLFi0SEZTxkrrtttsOAHDxxRfjxBNPNJAkej5fEPgzVBPPSy+9hAsuuMBwTTgcNpE6esf+/fsbJkJOfui9VJsEXh5qQyszB18UBg0ahP79+xvGGyc7ROq4H5jb7UbPnj2Rm5tr61NnZ34955xzhM+h08kmPz9faX6l8lqZX+nIL1KjCfIEz4NCAGulDjCTuoKCAvG+oVAIV199NYDWvmKV0uSZZ57B2rVrlQuFqowyqXO5XFi5ciWOPvpo8d19991XlJkvElQW1WYIMCpNt9xyCy688EIsXrxYfCZHv9qROq7U0X157jIOJ0rd5s2bxRxL18vRr0BLOgne/3gQTX5+vlCLVKTuzDPPBNDat2Sljo9LIh8q8sphpdRZkbqcnByR3omgInXhcDhqlH1TU5NIqkttpVL3nCh1NMY8Ho/oE0OGDAHQ0takOPfq1cvwvrzOqI2ysrJQVlam3HRSvY4YMQK5ubliXbz33nsxdepUw7Xy/MPfm+7FQW0km5s5waqvrzeRuh49epgCHrj/K90bMCaI5/M0jQdKQcbdj3g55TQsvC7lVGYcVG/Ulvy4TaqTeHzqYv+GBe677z6MGjUKgwYNEi+yZs0aLFiwAPfff3+yHmOJ6upqkUKkqKhIORAmTZokDmeOBrtkwCrzKx1MDlhn+ifQoCFzZazmV6DVoXTDhg0m8yv3g/H5fCaljvvuUMeyIqGNjY2IRCIoKyvDkiVLxOcqUheJREQmdfq7XBf33nsvbrnlFjFB8wEVL6nr3r07ysvLRX3W19eLHRNdc8ABB2DevHnYa6+9TCdPRAMtcCqlDmjJ1cbrQ1bqvF6vkrQDLX0oEAgIQkwqjepdyfxKiy4nckS86X5Uv9dffz0A4+HbfEGIRal7/fXXRfLTRJS6jIwME2mQUV1djW7dupnqPJlKHfk6AcZ+YGV+Jfz++++44oor0LNnT6X5VS6jTOo4aGHhCwM3GVL/tVLqVH1qwIABhlxh3PVCNr9ycFOtU6WOyi/XQa9evQAAJ510kuF6rtRRPTc0NNgqdaSkqMyvr7zyCn799Vcxpnl/CofDhnLT4sx9B2NR6jIzM5GRkaFU6nbddVf069cPS5cuxbhx4zBkyBADqYtEIgiFQrZCAtBCzImw8HqQ4WTe4t8jVY6C4xobG7FlyxZkZ2cjPz/fMH9x8kxrKicq48aNM2xaqV6JjFLf++2333DqqacaypSXl4eZM2fisMMOM6yZ3L+cg0giD4YAzHlLs7KyDH2re/fuWLx4MSKRCFwul5g3eT+lZ1mROsq/SO/Ozbm8PmRSx9+BB9HIGwUyW1NbUvnTxqfu6KOPxsKFC3Hddddh1KhRGDlyJK6//nosXLgQRx99dLIeYwl+YkB1dbVJDgdafBNqamrEP6sUHoC9+ZUmaG5+5VA5YHLQoKHoJB4o8d577+Hiiy8GYCZ1nHgRqauqqrIMlKBrrMyvWVlZSiLFQROD/I7yJE6TJ4/gU13HfWRkxErq6NquXbsanOoXLlyIlStXmtqBTOLRlDoZtADK96NJpnfv3ob3jEWpo2NtaIFQRdIBRvNrbm4uPB6PKQkqlTUSiYioxA8//BAAlEoIEHtKE+oHTneQeXl58Pv9Bp81l8tl8v+U24GUOnmz4VSpk00d9AxOZrKzsw1KHYErdbRgycjMzLQ0v8ZC6qjfcJ8p/s5EOlRHXtHfuRogg4+faOZX6lvc5MUT0gItaVAefPBBkc7Gyvx6wgkniFQa/D15PfBF2IlSpyJ1dG+VUgcYySgtotGUOrvUQvxMa9pEUvkownfy5MnweDziXbOzswVZjabUcbchOzXOiVLHFXKqH6rDYDBoIPFWPnXkq8j//vzzzxtOY5FdFFTBdgRqc7vMEhzUr+R2qqmpMUWTZmZmiut79uyJSCQi+lJ9fT3y8vIM8zS1j0zqKOckmV/pOfy9+LPlDY/sO2e10aS5hcpIZU8b8yvQUknDhw/HSSedhDFjxmD48OG2Z8YlE8OGDRMd5dNPPzVFqgEtDVZYWGj4ZwU+eGfNmoX6+nrTAkgTnZxtOxGl7vjjjxeHBMukjt+XFv/169ejpqbG4KtQW1uLrKwsseO3I3V2ebgAa1InL2Q0+Ol/K/OrHWIldVR/5eXlhkTPI0aMwH333WdyYKaJKValTvYLku8nmyVkpS4SiViSOvkz2SGaQIsC5VfiyUKB1sWXTlLYe++9MX78eGXKEl42eaOiIjm8HqkOYlHqgJZJmZtfqc/RxsiK1HHI7wy0pPdYsmSJSanjixC9i6zU0WJLfyNwJdvq7FcK6lEpddHMrxx0b25y5aYmHsiggt/vt53H+PiJZn4lUsed0+vq6kRiV6Dl7OmrrrpKEE8r8ytgPLhdZbbj9W+l1BUUFNjmqaN7q5Q6Xj6glXzQYm3lhG9lfpWftWrVKng8HjEXT5kyBf/73/9ExDKPpnZK6lauXInzzz8fZ599tu3cxP/m9/tx5ZVXmu5NvwcCAdFXuQmZk1f+vtxE7mS8ezwe2yTIfJNidZwe+SbLbSwHpxCqq6sNG+ALL7wQQOvYpY0O3/RRUnyCTOrITYnIIJlf6Ts8Hym/j9W6QKD1Xv5cNr/KpK7dlTqO//73v6m4rS2GDBmCyspKjBgxAgsXLhSyf7ygwRsKhTBixAhcdNFFJlL32muv4fHHHzcRl2hEhhqZK3VyoATJ9dynjnce6ohVVVWor69HWVmZwfxaUFAgBpBVShNS6lSLFpEDmiRls2C8Sp0dYo1+3WmnnQC0+CPJ+Y0Aczuo8tk1Nzcrc4txWJE6mqy4wz3dk9dpU1OTMhAGMJK6srIyy8XE7XZj/fr1mDZtGnw+H7Kyskw+dRs2bBA+i7m5uejevbtwFuZHRTnJU8ev4e9NwTCxkjo6jxNoIXVyO8t9UI5+BVoIr0yY6uvr0b9/f5NSB7QkNJWfYaXU8c9pISGfOpWaQ+MmKyvL1M9iUerIpMNJHb9269attv65jY2NSlLHza8qQs/fl/oA5fmTlTq7c23p2aoIUP6Z6v15hKidUsdzeqnAiZZMbFRnbqtSt3CQ/1W0Z61atQo9e/YUJK6goECkvKD70OeZmZmIRCJRza9NTU3YZ5990KNHD1s1jtfFu+++i0ceeQQvvPCC4Rqu1MmqMn1Gv8v1RKC+bZdCha63SlDshNRZEWl6/h133GFo25qaGkHqevXqJZ5N6w9t8qkNqB/zslCf5EqanOLITqm76KKLcPnll5vWBSu10cqnzs782u556ghnnHEGHnzwQdtr4j3L0A733XcfvvnmG7z66qu2eXWcgAIlaNAvXrzYtAD26tUL//d//2dq1GhEhjoj+TnwQAn67tq1a01KHV88PB4PiouLRfbssrIyg1JXWFhoSepkpU5lfqWoQ5qEoil1Mqmj94hHqeMTnx2p23fffdHQ0IDdd9/dEamjBMeyUhetjFakjsrJk9gC5txDNHmq1BFe91amV8C4KM6fP19J6vgClZeXZ6gT1fmf8+bNwzPPPGO4v8r8SoSjW7ducZM6nt4CMKfRkUlwQ0ODSanj/nkyZKUOaElqzfMO2vnUkRJw7bXXmpIPq9QV6qtZWVkmUhULqaPgLn4W7e2332441o1Mnbx/kArv9/uViyk/kiqa+ZX7Jcrm17q6OltSd+655+KZZ54RSgmHiuhx9OnTx9AvAaNSV1tbi/z8/Ki5E/mYrqurM2wG5Nxg3KfOSqkj/yurZ1HfXbVqlQjSUoHqOD8/X7S/6gxrGUVFRaIerBQwPj5pHpLbiZM6OVE2kRaVUsfHNs15dkocXV9bW4sTTzzR9Hf+XTtSp5pTqB2efPJJ3H777eJzrtRxVysVqdu4cSP+/PNP07MpT+sBBxyAiooK3HPPPYb6sVPqXC4XnnrqKTzyyCNxkzq32w2/3y+OuEtL8yth+vTpuOWWW3DFFVeYyFsoFMKUKVNMx4GkG2hHRpNbKBQymaoIcqNGIwkUAk9H23DzK3WqXr16mUidTKTKysrEPcrLyy2VOqClE44cORIPPfSQyafOLlDilVdeQZcuXbDzzjub6odDPk6GFuRYlDoKEy8qKsIvv/wCwJ7UAa11rVIrVBMzN6sD5t2QavGNRurKyspQVFSE5cuXY/jw4fj9999xzjnniOuI1EVT6qxMr3K5PvnkE3g8HpP5lU+eubm5lqSO+vHee+8tUrfYmV89Hg+uvPJKHH/88aIunE429Fx+PBOdkcjBf6fFSCYrTU1NlqROjnYm8HqzM7/S4j58+HCDq4Lsu0jw+Xx48803Ew6UGDRoECKRiKGtKioq8Pjjj4vfaUPF62Pbtm349NNPRS4tfgwh0NqvnJhfea4/Mr9ypU72GeIoLi7G+PHjDe4PhGikzu12iwVTDuT566+/8NVXXyE/Px+HHXYYjjvuOIwbN055H1mp41YFmazISp0VqbOaw/mzVq9eLQJCVJCVOsA5qaN6UFlRuK8YYJ1LkD73+/0xKXV8LaB6cELqABiUShV4n5A3vqo5hQcN8XHFlTouOOTl5aGoqMiQkL6iogLr1q0z9ccddtgBDQ0NOP7441FVVYXLL79c1EMwGDREvwKwPF9ZZVbloDpUXffpp5/it99+M1yXNnnqOA477DB8/fXXmDZtGk488USRvfrJJ59E3759cc0115iiYtINKlInm18JsSp1QAtpI5WNB0rwwWXnUwcYSZ2dUkfvMHPmTBxwwAFKpU6lnAYCAcyePRtjxowxBZ6o8tQBrRMLjyB0Ct7pFy5cCCA6qSOofHZURE+l1PH7q4hVNFJHE88OO+yArKwsQx48oGVwOiF1dosglTEjIwPDhg0zKXVjxowRyi/di5v0VAloOWSlTva7c7vdhj7g1CxAbUpZ1QGInFYcfPGyqm9S8FTw+XxKcsPLqSJ1LpfLEI2blZUlvuNyuSyVujfeeAOA2mE9Fp86K6hcLWQcfvjhJjWBwHf7KtOm6gxhMlHJKU3slDo7WPXnb775RkTzyuZX6p9XXHEFgBaClZ+fj3fffdfQnzlknzo5op7AAyWys7MtAyWI3EZ7llVAHiERUsdPoJARDAYN/Y76pzzXUhtzpY77FVv51FEZzjnnHNEP7UzB/Lm9evUSrjsq8L7EU4RYmV95Yl+O6upq4TdHaW2AlvWHu7Hw+ZWeTXOEx+MxkXf+PVqP6TOr85ddLpeBz8jvYWd+5XM4WQbSJqWJjN133x2zZ8/GkUceiWHDhmHz5s1oamrCVVddhcsvv9z2gOB0QCykTiYUTibwXr16ifMbaQcViUQM3w0EAsjIyBANK3fAsrIykWKCkzpS6s444wxMnjzZtItW+dSpdmKUgZ+OM+KQOy4NGLo3ER2r3GwqqJzbEyF1ql00V+rC4TAikYjh/sXFxVi/fr0oOzebynWwww47ADD6G6pILB1HFY3U2bkMUL/wer3CjCwvSFRuoGVB5Qu3KgGt6v4q8yu1ATf/OSXrnNRxpc6O1MmnEPBrrJS6TZs2KQm5nVLH1UsVqQuFQpakjqDykYpFqbOCitSp2o1UJY/HoyRqVkqdnKwaaCUzPMl1NKXODlbEiCKzAWMb8DLSgs1z7lmBj2k7UqdKacKPkiLYKXVZWVl4+OGH0adPH/h8PtsgFarj/Px8QSSi+dQBraSuublZSeoCgYBh00U5WeVreWoOInC0ntB4pPqR508in3Rvp0pdSUkJ9txzT8treZ/YunWrIOpWSp2qfsPhMHw+H7p37276HpE6rrgRuJjh9/uVz6Pvvf7669i6dSsqKysFcecpvWS88cYbmD9/PhYuXGi6L41lVUoT3h9IuU7LQAmghdU+//zzWLt2LZYuXYrq6mp88cUXmDhxYtoTOqB1oqAdIyd1shoQi4mRQH4yAJQmHKBlcomm1HG/Lp4morCwEKNHj0YkEjHt9Pv06YPc3Fx07dpVKHWqyWPz5s1iguPPvv766011QDsqOh6NlDqrg9hVSDap43XMn8FNU4BRzeGTDjdfAGZSfeONN+KXX36xVbA+/vhjXH311ZaBErzerU6ToHLza1QEkN+LzGjyvVV513i56X/aRa9ZswZr166F2+02LMaJkjo78ytdR2X+6KOPcPzxx5tIGcfGjRuVpE4OYOHmNp5mg0gMz21IPi12GxN5915SUpIyUhcOh/H222/jsMMOE3+rrq42KHU0DoPBIJYsWYIlS5bEbH4ln7qmpibMmzfP1tfT6TtYwSpQguaPp556Kuo9+IapsbHR4Ispp71wktLEzqeOLBovv/wyamtrbdcyqmOu1DnZ5EZT6mRSR1kf5Gvpd1mVo/ri5le7aF/AntTx9SVaX+FKHR87VkodNwXTWkdneBcVFZnmoZNOOgnnnHOOUqmT0+/YJZieOHEifvzxR2RnZ2PgwIGorKzEpk2bcMghhyiPugRaCWgsgRJ8HFL0bVr61E2YMAG9e/fGlClTcOedd2LTpk04+eSTcfDBB2Pu3LmpeGTSQQ3+5ZdfAmhNU0HHe3DEU/EyEaDOxNUWmdSRYyeBDyZS6tatW4fvvvvOdgdJpxF07dpVKHWqyWbSpElYtWoVsrOzDYSDiC7H8ccfLxJvAvGROk5CEiV1ubm5Br82fp18tqKVTx0pNFZKncfjEUc7EeSyHn744aJ9p0+fLvrTrFmzDERH9Q7y+wCtGwrVhMQVJTnRZqzmV/KN6dWrl+iHu+22m+HdnYCTOn7ySTAYNJ3CAQAPP/wwbrrpJkOZjzjiCOy///62pG7Dhg1RSV1TUxMWLFggfqf6sVLqnPQ9WXnJz89PuvmVlOBwOIyTTjrJENm/fv16odTR84GW+u7fv78g5PSeKvNrU1OTODmGzrqsra3FDz/8gCVLluDSSy+NqewEeeOngpVSFwgEsNNOO9mqPvwe/BQA+XxmgorUqXzqGhoaLJU6fvRWNKWO5pmcnBwDqRs2bJhlMmmghRyQos6JGs01lJXBqmzy8/1+v0H14aTOSqkj0HfsSB3Pk6jyreTg9crr3okqRT5tpCLK0fEAcNZZZ+H//u//DL6DBCKUdgnK5XmVLCOUIq24uFgZlAeoc/oB9j51fK4gH960VOooxHrJkiW48MILkZeXhylTpuDCCy/E6NGj8d5776XisUmF3MlJqbNzpI8FNBlkZWXB5XIpBxUtptnZ2Zg+fbohmSdg7EQFBQUIh8MYPHgwGhsbo6qhREztlDo6c1BW6lQdLSMjA3379hW/x2N+5c9IlNTV19eLlCcc0ZQ63r4Uwm51ooQKVmWl8h100EHYsGEDRowYAa/Xa0inYkfqaGKgelG1AffPyMnJiUmpk0md6r0yMjJw2223AYjuBE/gaWR4kuxgMGjwkSK16KqrrhJHC6nSUajMp0CL07rK54q/z+bNmw3plrhSR23MlTonPi2yUpebm5sUpY6/G5FVWlh5mbZt2ybyFgKtC9ePP/4ornGi1AWDQYTDYeTm5qKiogLNzc3iDOjevXvHVPZYYKXUyScI2MHr9cLn8+Hbb79FY2OjYZxmZWXhww8/xOzZs03Jh61SmtgpdXzuCIfDjqxOLpfLQOooX6oV8vLyRFvRuJgxY4bIKCErdQSaw8nnLppSZ5V8mCNaMnLA6PdGdc/93Dh43+Wkzsr8ykHzMY05qyT2QOv44esajR87UterVy/D5pDehwikXRBkNKVOJsY5OTmGuSIjI8Nkfk2blCYLFy7EOeecY5rIbr/9djz00EM49dRT8dhjj6Xi0UlDLKTO6qgjO8gdgD9v6NChAFpJHQAcc8wxpgmEOifP5UQd36lzs51SR5CVOie7h0SVOpq0EjG/Wl0nK3UejwdTpkzBaaedZmjftWvXAmjZufN8gXaIRuoAo7P0Bx98IH6286mjtqaJgdcV1TVX6lwul2lxo/KFQiHTCQVy9KsM+vzGG2+0Tc4qg65ramoSqhYtOnzypMWL11M0UsdVgb/++iuqUrd+/XpDQFA0nzonfU8mcKq0K/GQOm4NILIqp1Qi8Ag9GvfccdsJqeNHXpEJjQK5nBL4eKAKlCClLpYxvXLlSuy///7YtGmTqd8feeSR2GeffYRPnc/nQ35+vkGpe+6550R6EidKHdWfHTnj4KSO8h8SXnjhBcyYMUP8TiSQEzM5ctVOqfN6vTjzzDNNpC4epY7UVjulTjVGnnvuObz11luW9wPMSp3VnPLZZ5/h4IMPxi+//IK//vpLzJ92pE5lfqU2syN1ZWVlqKqqEnMq1QutvXZjga6R70v3koUT+V7kK52W5le7ZJkXXHABpk2bhokTJ6bi0UmDFalTdWDKMfX222/j9ddfd3R/mgyIWPDnUWflpE4F6hR5eXmmAeEkygqwV+oITpQ6GfR+sZA6/gz6Xjykjp8pKEMeNEDLpHTuuefi9ddfN0w6ROrIR9GuXxOckDorZ38n5leaXOnIMwAi2lZ26Of1yZWQRYsWiSAP+e+8v/GFg79XLLtHbn4lUkfvzw8L37JlC/7880/DIin3OfmYLzpOD2jZvUdT6mSneCulbvjw4QAgiIATjB49GkDLWEyG+ZWD3kul1AFQml85nJhf+XFgMqmLN/rVCWTzK5UxVlJHWLt2ra35tbq6Gg0NDaisrDRE+V5wwQVYvXq18CW0Wry5SROwJ3UTJkwwHNUItJAM7lIDtJhVyV91l112EdfzQAkKaqN72Cl1QIuVhZM6TprIWsFJnVUwTL9+/XD++ecbcsQ5gdfrxeGHH2763IrU2Sl1Bx98MN599114PB68++67YhxbnZNNzweMpE4+ucGKRLpcLtHnaY6g+olHqbvlllvwn//8x+Sqo+pjaR0oYYcjjjjCsDNJR8gTyrp16/DOO+8oJ+e+ffsiEongpJNOwmmnnebo/kTcqJNzBYE+c0rq+E6drqcksdHgVKmLldRlZGTgyCOPNJmM7cAHDC2MsZK6vn37ioSuVtdxh33A2vy6Zs0aAIjqO8MRq1JndY0MWcKncyb592TFWNVOVv1JZX7lk248ZgBATeqobYmsnHLKKcjJycHbb79teD6vD3p+XV0dRowYgQ0bNphyJ6rIBy83+eRQX+Z9+qOPPhLlHThwICKRCHbfffeo7z1r1iwALQEx27ZtS5r5lSMaqeOBEnZ1wJW6YDAoNinvvPMOZs+eDQDC/Aq0kDor15BkIRlKnbzQWs1VHo9HnPddUVEhAiX43EvzslOlzs78eueddwqneprTg8Gg8J3i5crMzMTUqVMxc+ZMAIiq1KlIXSAQMLwLJ6CyKief/WrlJ+Z2uzF58mTbfHxAi9ooK3Oq9uOKXyw+dXl5eejfvz8WLVqEzZs3w+VyGXLYyeDmV1L0zzvvPAD2Sh1/HtDal2JR6mSyWFhYiIkTJ5pEAVUfo/UY6GCkDoAh03s6QsXi582bl9DkzEEkgToPn6h5JJcTUudyuUxHtVx33XWOyuFUqYvV/Aq0HCbPM+NHA5+M4yV10U4qsVLqCFTfxcXFQqlLBqnjk5lVviMnSh0typzUUV8lpe7VV18FoFbMrcqnInWyE288UJE6qgsKFiosLERZWZk4w5ig6nO1tbUiclsuk6r+VKSOdt0qvxu5b1N5zj//fEQiEcOGYY899hAO1F6vF8XFxUkzv3KQomnlT8mVOqekjp+kcscdd+Dkk08W38/Pz0d+fj6WL1+O3NxcRwp1vFAFSsSq1MmnGKjcDgAjqausrEROTg6CwaChn8tHNsngud+A2M2vKlJHZTz55JNF8Jvb7UYkEjEEZkQzvwaDQbE54WV0EigBAJMnTxanG8SKsWPHYsyYMYbPVHMGLzeRutdee02ckmOHnXfeWZC60tJS2zElm19vueUWR4ESBLqW6tyJUmdlfrVCNKXO6dono91IXbpDdorv3bu3kM6TAZnUDR8+HA8//DAA56SOvku2eKBFBbrxxhvFMV/RQDsDO1JHiToJyaoDGclQ6ux8P+g6J4ES3bt3x9atWwEgauoCDquy8vq1UursJgPZ/KpK21BbW4vMzEycccYZMZdP5VOXDKVODpTgu2uaKEtLS5GTkwO/36/0eeP3qa2ttQxYUS0KcqAEf66qz8j3oGCB888/H0CLIkdRwKrnpYLUUZ05UeqimV9DoZA4V1pVj/Ssrl27Ys2aNQn707311lsi5YYKqkCJWJW6vn37YsGCBeJ6K/Or2+0WY4/Mr4DaqmG1ePN8eIC9Usdh51Nnp6hzX0++CaG5a9999xXf8fv9OProo8Xv9F4NDQ2WKU14/Zx//vm44447HL2PE6g2A5zUrVy5EpFIBGeeeSZ+/fXXqGRowIABWLx4MTZv3mxregWMGzZZ8XJySgaVnTae1M5249jK/GqFTmV+7QiQI75o5xTv4iZDJnVAq3PzXnvtBSA+pQ6wdyCVQUqdnfk1JycnpX41hHRQ6kiZycvLExNQMpQ6Xr/8DEEOuxQQslIHADfccIMh8rGuri7qQhgtEEKV0kX+PBZQ3ySljm829tprLzz99NOYOHEisrOz0djYaBndqiJ1slrhRKnzelsP6KZ34pO7PIn+4x//wBNPPCH87DIyMsRzVM9TRVQmSuqofZ341EVT6ug+Vmcek1M3/Z/ouB8zZozt0VEq82skEoHf74/J7Dtw4EAR7W6n1NH/JSUl4v2rqqpM94tGZolsOZ0X6NkNDQ1wu90GwhMrqeNKHY88lfOnUc4+OtGJK3UvvvgiNm3alFLTugp8zD7wwAOYMmWK+D2aUtelSxfU1NQ4InWZmZnIzMw0nKZBIBcpVUoUguz3S6TOLgo4VqVONf7SNqVJZwCPpDvuuOOUi14iIOLFJ4+KigqsXr1a5IFrampyROoA44CIhdSRUldTU4OrrrrKdMQV0HqcUqqRCKmj93dC6uyUOiJWOTk5YgBv27Yt5slbBlfq/vnPfyqvsWtrmgD4+911112or683EJ5ok3QsPnWkVALxK3VAK5H2+XyGBT4rKwsXXnihCMTx+/0GUqdSM+rq6uImdVu2bEFRURFee+01XHTRReJzfh95YenTpw8uueQS5T2dKnXxpiaQYafUxULqKOhLpdRRP1fNT6mAKlACaBn/sRIO2Q8KUJO6fv36weVyiXdTkTorywUvE6WbcgIiEFu2bDGNQVUd0zU0D1r51PH2JlVZfi4Ff1BdUC7U1atXO45iTxRTp07F8uXLTWOW562NRmBycnIQCASwYcOGqKQOUAeEAC3m+kgkEhOpo7pX+TISDj74YEycONH26DgOO6WOzvfV5tcU4d133006qaPJ85hjjjF83rNnT8NE4ZTU8cnGKQEBWpNorl69Grvttpuyo9n5ESQTvAPTZOZ0QYzF/Gqn1HGfllAohH/+85/46quvElbq7MzbBKdtzcFN705InVX5iLRbRYsmQko8Hg9qa2vR1NSEiooKZQJlSi9hFS3tRKmLZn4FWsjKXnvtZTipgN/Hyc6Y7umE1EUiEdtjp2IBKfkqpc6p+RVoTaQulyk/P19cQwteqkmdSqkDWhbVWEkdvY8VqaNnHHHEEYbrVcc/7bjjjspn/PTTT+J7TiPigVYzXmNjo6n9VOQimlKnInXz58833aeoqEhslqguyK8QcJ4KKlGUlJRghx12MI1Zbp1wQuqAlghnq/OQOWiuj0fxkkmdk3x9lZWV+M9//uO4T1iRukAggM8++wxAfAcbaFJngw8//FAk4E02qfN4PNi4cSP+/e9/m/7GJyInucvknGSxKnXLly8H0LKD3WWXXfDAAw8Y7hfPMWiJIlXmV6/XK+5NTu78/o888gieffZZdOvWDaFQSCTKdpqaxaqs8rFYKtj1LbvBnZmZiYyMjITMr6rnkA+ak+/ZISsrSwSH5Obmiv7Jn5WdnY2amhrL9lMpdXL0WzSlDlCPDTulTgU7pY7Mr99++604NSQSiSRM6urq6vD0008bnk+wUuqsCJOdUkdoK1JHPm7bb789gMSUOqpjXte8j/3+++8AgKOOOspw3YQJEwAAQ4YMwbPPPotIJCJy1skYOHCgSKwby3GXvK/K7adyu6BroplfVccaclA7+nw+wxgixEMa4gGVU05KzJWvaGOP2mvr1q2O+iVFNydC6kgRdELqYoVV9OsPP/wgjgLUpC7JOPLII3HKKacAMO92k4Hy8nLlgOad227i5aSOd5BY/GB4pyGzxNVXX21QpujeqcwsLyPePHXRlLqBAwdi4cKFqKurEwOX37+oqAgXXHCBiBQkp3jK2RUNVmVVnW4hw86nLtruj9SweM2vhJycHPz8888Akmt+pYUkKyvLcJoKf65dbkXuk0TveOCBBxpyEjpR6lSLgVU+Pis4Mb/uv//+GDFihFiUEyV1dMoAYB/9yt+P8mKplLpQKGQqE1/sifym2peWVLJBgwYBSK1SRymKaDMnH7v4/fff44ILLoj6HLp/LBYRfsKLkz5G9UDzYFZWlsFPTGVlUIFI3YIFC5T9Vc7dmCqQsjZs2DDDxo37GkcjMNS3OUGN9kwKFomVHJFlhb5H+QNpQ5AMWCl1PDuCJnUpRLKVOjvEQ+r4dbHsrulZhYWFBj9CTiLo3nPmzME333zj+N6JIF5SF02pGzVqFJqamvDGG2+Iz6wclUOhkCCy/DB7O1iV9Y477sCcOXNw2WWXWX43UWd6JxGDTuqSTE/cnJMoqSMCnZWVJQiD3M+3bdtmeQ9Z1SPwFCNOlDrVe/A+E0uCaStSxxcqSuMSD6m77777lCkmVEodLZq8nsgUS9fT73V1dZaBEoS2UuqofqjPcV+yWEmdKv8gH1Mffvgh3nrrLdFupaWlhshXp8+Tc5c5Bal1TsY5N7/yY8ZCoRAuueQSfPLJJwDMG8EbbrgBixcvFr9z0y7182nTpuHKK68EAMNZyKmElbmUTkACovc16q91dXWOyE5ZWZnwH4yVHD333HM46KCDxO+VlZWIRCKGaONEoeoHWVlZhkCreI4gTZ7s1MnRXqTOSe4ymdTFsoDQYcw9e/a0XNCoPBUVFYZoq1QiVlInRwhaYdCgQSgpKcH48ePFZ6r7Z2RkIBQKIRwOo6KiAnfeeaejcluVNSsrC0OHDsXQoUNxwgknGCYMgpO+ZZVYWXXcXCzl46D+Q/nuAGeHs1uBK3Uej0cZ+h8vqeOIl9TFimjmV4558+YBiI/UXXvttbbPJ+Tk5BhO5yAQ6ZD95Kqrq9PG/Pruu+/ihx9+MKXUSZZSx+e0IUOGYMiQIYbvqI6Wi4Z4lDqghUSuW7cuJqVuxYoVyMrKMs3NdHKRPGfccssthsAP7m5AUffHH388jjvuOHz22We4/vrrY3qHeGGVKNiqrCpYmdWtUFZWhlWrVjm+nuO8884TyYpTBdW5w/KcokldChHtwPNkP8vlciESidhOvNQBRo0aFTepo0nOKqXJc889F5OPXrIQK6lTncxhdZ08cFRtSkpdKBSKmuiSw0lZrRIyR3vGli1bLM1h9E5yf5k+fbohSENFznjQAACRHJX7ASbiSyIrdVbm10RJnRPza1uTOopITGawkUqpo80WXwTsSF1zczO8Xq/o5wcffDCuueYa8V1qb34cXSowcOBADBw4UPzO067ES+qc+K9ynHfeeQblPhriVeooWIK3X7SD71944QVl8AvN13L/ls/p5kodqVZAC9ltK5UOsJ7bOKmLRpLjIXWUzaGtonxjgcoHkpdz6tSpOP7442O+ryZ1DpEKnzorUERjIBCIGqSwatUqVFZWGnZyseyuyZfF6pgYOte2rcFJXSxEOhqpA4BHH30UzzzzDAYMGIBHH31USWg5qYvl+YmQ/mhqmF2ovNXB3HJ0tbzgWdVXbm6uwbcjUVLHfepok8CdpCmliRXSUamzylPHQealVJK6nJwcQeq4OZE2APyUFKBVqaMkuKFQCKeddpqICgWAww8/HB999BEuv/zypJXbCfi7xUrq6JSVWBW0yZMnY/LkyY6vp7aMldRR/dM72s1VfB6xO6NUZV1xQuraG3PmzMEZZ5yBP//8U3yWbKWuvLw8bvNrW+Css87CJ598gs8//1x8xst52GGHxVVuTeocoi3Nr4BzHynVmXyxLCDZ2dn473//i3322cfwOU0Wbb3DmT17Nh555BF88cUXAGLL8TVo0CDccMMNUa87+eSTcfLJJ6O+vh79+/dXqhGc1MVCBMhHKB4kYuJ0an4l4jRy5EiTKYojPz8fNTU16NevH5YuXZoQqfN4PAal7uabb0ZVVZWh7/I+O3XqVNMmoy2UOgoQiQY7pU4ee6kgdfJEn52dLdQucqegzzlo0SSljiwCqnsOHDjQsNi0FazO/nWCCy64AEOHDsWAAQNi+l6sOTjJaZ6yBjiFnJPPDk5IHSVqBoAHH3xQjGcrUkcpcdIBQ4cOxZgxY3D33XeLz6KROqu8rFYoKysTG8d0JHUVFRX47LPPsHHjRrFuOHW9soMOlHCItiZ19Lx40onEuoCceOKJhsWAo61J3T777IMhQ4YI8uH3+x2/z/z583HWWWc5flZeXh4uvfRS5aROpC5WpZDOi42Ge+65R/y8yy67oLCwEAcffLDj58iwUupkkCp51VVX4aGHHrK8jiZQmmiTpdR5PB70798fX375pWVA0D777GMKTOHXylGLBDulju5vt6D269cv2qsY7uHE/Ep+TKlU6jweD3bZZRcsWbIEp59+uiiDnLCa6uCcc87BsmXLDPdpCwuEEySi1LlcLhHxm0qzMfnDkpXDKeh9YgmUAOxJHWGXXXbBqFGjDM8BWn3Zdt11V0yaNCmm8iYDdqqpnJ8v2Uodt2ykI6kjdO3aFX379gXgPJ2ZHdJjJCeIH3/8EVdddRUyMjJQUVGBV199NemN2NakjhAPqUvmBN0evghkiqPjgtojT1685ldKmzBr1iylikq4/vrrcfzxx2OnnXZCv379RA6teBErqYvWrvTONCkn06fODuPHj1fmCOvevbv4eeedd1Z+147U5efnw+/3244NpySivZU62oQMHz4cF1xwgfidSGlBQQEaGhpsz7hcs2aNSJEBpA+pS0Sp45g1a5YYi8mG2+3G1q1blb5udki2UhcIBER0Pj9hQU7RBKQ+4MUKa9eutTQzp5rUcfN4OpM6DmprVXCMU3QKpa5Hjx749NNPMXPmTPTt2xfvvvtu0p/Rlj51HG19Nl86PD87O1sc7xIOh9vsRAuOzMxMhMPhmEndv//9b+y+++7Yd999LROYEmjSSUYdOzW/EqmLRpRpQkkWqeM+dSoQ+Tn99NOVf+fjzioC2878Kqf3UCHWYJhoSl1hYWFKSB2RtF69eikj9KhfqY6W43C73Za579oLySJ1ZWVlGDx4cBJKpEZJSUnMdRYLqePX2G2EzjvvPHz//feWrhRElK666qoYSpo85OfnW/oexkrqONFxUvecdKdL/44Gq4C3WJAe27MEUVlZKX72eDyWgyYQCChzSDlBWyt1NAG0h0oFtJ9PHdD6zuSo395KXSxE/oADDsAvv/zi6FqadJJB6mJV6qJdR/59ZMbiYyxWZGdnR1XqyExpp26+8sor+PXXXy13sKp2os/kSFAVnO6MaQ5QqR/8s+7du4tD1lPhU2fVJvSuNG6s0vzQSSRA+ih1iZhf0x3JVOrWrl2LuXPnwuVyYdiwYZb3KS4udhQ81h6QSV005ZPO6+XnXduB3y8do19VcDqP26FTKHWEVatW4fPPP8fRRx+t/PukSZNQVFQk/tktIDLamtSpci61B9pjsqd3phQX7aXUxWN+jQVEAJIx4ThV6i688EIMGjQoqopBi/2JJ56I2bNn48gjj4y7bLwPW03Gp512GoCWfIlWOPPMMw2+iE4Qi1LnFOTvqTpUnPfVoqIikf0+meOoe/fumDZtGu666y7l3ykKVKXULV26VPzcmZW6dEQspI4HTanmh+7du+O4446Lep/y8vIYSti2kEmd6gxcGdSnO7tSl0jfT4/tmUNUVVVhzJgxps+nT58Ot9uNs88+Gy+88IJlA06YMMGQi8nn8zkmdm1N6mghbO+JLV67fiKgd6djo9pTqYs1UCIWZGRkID8/PyltTPeIdq9+/fopD/5WlQ1oCSahaL94YXVsE8dxxx2XEkVBVuqSMblTv4xG6mhRScWmxC5/1ZNPPomzzz5b5ADjSl3fvn2x6667Yv78+Vqpa2PEEihBx8sBiW36Yk270pbg5tYtW7Y4SkXT2Umd0825HdJjJDtEZWUlZs2aZfo8FArh+OOPx0033WR7xqbX6427strap0515E1bYr/99sPbb7/dLs/+uyh1AJJG6ignWbIWQk7qEoUTUpcqyEdkJWP8EqlTqSBcYaFntvUYzs3NxcEHHyxOBJHJMp2iwJW6dCF1WqlrAbkrAIkRkvbYlDsFV+bscnByxELq+NzV0cyviZS3U5hfp06diu+++w633347DjjgALz55ptJf8bfzfz60ksvYdmyZe3ybFmpa29Sl8oFb9SoUSINQyJIpn8ekDpS19Y75lSYX+2UOo72InUEakPZp46fQZpu5te/g1LnpA9ydZy3TVVVFVavXp38wrUDnJhbZdBa4IT0cKUu2lhNF9B7JULG02N7liBOP/10y6i5ZOHvZn7NyckRh2y3NWSlrr0DJVLZ5rEcT2SHjqDUZWRkpKQuP//8c3z99dfKv9GCmApSZ3VIOSHZbRIr6J3lNuTkQit1bYdYSF337t0xZcoUjB071qD+ttXZ222B7OxsHHPMMbjsssscf4f8kJ1sQnj/SWczNAeRukQS0afHSO4AaMuzX4H4lLpp06bFfO5hOiKdlDo6IzPdkWyl7vTTT8fcuXOTUvex7K7jwUEHHSQSwso46qijMHnyZPzxxx8AkkvqotVNe2/MjjrqKDz33HM499xzDZ9z3y5N6toOREScLth0fTqbUBOBy+XC9OnTY/pOLObXjlhvyVDqOoX5tS3Q1j511Hljadzjjz8eJ598cqqK1GagxfC7774z/N6WaCvza7KQbFJ31VVXIRQKJWVipPZrD7+W8vJynH/++WIRULXlzz//jPfee8/xPZ999lkcdthhUa9zGrySKmRkZOC8884zbURVSp02v6YeVNdOcz4mQuo+++wzzJ07N+bvpTtiIXUdETRXJiLOpP9qlSZoa/Prddddh2+//TatQ9JThcrKSrhcLnzwwQcA2t+nrq1PEYkHyVaFrA4LjwdUtvaciO1I3eDBg2NKVEtnB1vh+eefx3bbbYcZM2YASD8nba7UpVv0K+8jmtTFP14SOXIwndHZSR2lI1q8eHHc99BKnUO0NanbZ599sG7dunbPU9ceyM/Px8SJE8Xv7a3UdQRSl4xQ+FQh3UldsjFu3DgcdNBB7W5+tQKRzHT0qeNpLdKt3hJFrKQuGaa4zoZYfOoA4KSTTmqXM2/jhV2eTqdIj5HcAdBeZ7/+XcEn9/YidYFAADNmzMDZZ5/d5s+PFTTJWZ0e0J6g9mvPzPZtSeoI7W1+tYJKqUsX5YOfyJFu9ZYo2tL82lkRq1LXXmm5EsGzzz4bNQjLDprUOYQmdW0LilbKyspKKBIoXvBnpouKYQea5Jqbm9u5JGYQqUvk/NhE0R5nNxMpSWfza7opdZzApFu9JQqq47Ywv3ZWpDroKh1wwQUXJPR9bX51CBqQyUjxoBEdTrKLpxKcvHcEIk/9Mx2jn6OdQdoW0EpdK9I5pQlHZ1OoqK6dbry0UmdGZ/epSwY0qXMImvQocadGakFKXXuodEDHI3U0yaUzqWtPpY7qpy0XAyJP7dWHrZDO5tfOjHh96jRaoUlddKTXbJPGIFIXTxZsjdhBpK69lNGORur22GMPw//pBCJ17VmPNH7bkmClgy+hCulsfu3MoLp2qlhr4mJGrIESf0fokewQJJlrUtc2IPNrOpC6jrDg7brrrggEAmm5uydyM2DAgHYrA5mw2pJgEXlKV1IHQJO6NkT//v0BAHvvvbej67X51Qyt1EWHHskOUVtbC0CbX9sKpNTxaLi2REdT6oD0NdeQuak9VcT2WBjTzZeOQASuubk57fLUdWbstNNOqKurc7xR1SlNzNCkLjq0+dUh6urqAGilrq2gSV3nweDBg3HNNdfgtttua7cytKdSl27gvl107mY6LZIjRoxo7yKkDLFYHtKpTdIFgwcPxv77768DFm2gSZ1DkFJXVFTUziX5e4BIXXuRaE3qkoesrCzcf//9abEhaktSl64+dTwK87TTTkMkEkmrPv7ZZ5+J83X/ztDmVzOGDBmCb775Jq36a7pBa+4OQaSOztjUSC3y8vJw2223YezYse3yfD1pdC5on7pWxJovra3h9XrTVuVsD2hSpxELtFLnEOTcmkimZw3ncLlcuPHGG9GrV692eT4ndem6+Gk4hza/tiLW1Boa7QPqs9rfUSMWaFLnENdccw2qqqqEWVCjc0OTus4FTepake5KnUYLunXrhltuuQWPPvpoexdFowNBbwEcIiMjAxUVFe1dDI02giZ1nQva/NqKWE820GgfuFwu3Hzzze1dDI0OBq3UaWgooEmdRqIgRSzdSJ1W6jQ0Oi80qdPQUECTOo1EQXnG0k3hLy8vBwD06NGjnUuioaGRbGjzq4aGApzUaTNVx8egQYMAtOS5aiuUlZXhnXfewaGHHtpmz3SC3XbbDd9++y2GDRvW3kXR0NBIMjSp09BQgJM6p2c1aqQvhgwZElM2/2ThhBNOaNPnOcW+++7b3kXQ0NBIATqV+fX1118XpgUNjUTAD37X5tfOAZ2FXkNDo7Oj05C6cDiMt99+u93ymml0LnDndm1+1dDQ0NDoCOg0pO61117DmDFjDAqLjEAgAJ/PZ/inoaECV+e0UqehoaGh0RHQKUhdKBTC1KlTceqpp9peN2nSJBQVFYl/WtXTsIImdRoaGhoaHQ0dKlCiqqoKY8aMMX0+fvx4nHLKKbYqHQBMmDAB11xzjfjd5/NpYqehBDe5alKnoaGhodER0KFIXWVlJWbNmmX6/IYbbsDPP/+MV155BUuXLsXVV1+NBx980HSdPihawyk4kSstLW3HkmhoaGhoaDiDK5Ju6c4TxF577YV58+Y5utbn86GoqAg1NTUoLCxMcck0OhJmzZqFESNGYL/99sPHH3+sz/zV0NDQ0Eh7dDpSFws0qdOwQiQSwVtvvYUTTzxRHKukoaGhoaGRztCkTpM6DQ0NDQ0NjU6AThH9qqGhoaGhoaHxd4cmdRoaGhoaGhoanQCa1GloaGhoaGhodAJoUqehoaGhoaGh0QmgSZ2GhoaGhoaGRifA3zr6NRKJoLa2FgUFBXC5XO1dHA0NDQ0NDQ2NuPG3JnUaGhoaGhoaGp0F2vyqoaGhoaGhodEJoEmdhoaGhoaGhkYngCZ1GhoaGhoaGhqdAJrUaWhoaGhoaGh0AmhSp6GhoaGhoaHRCaBJnYaGhoaGhoZGJ4AmdRoaGhoaGhoanQCa1GloaGhoaGhodAJoUqehoaGhoaGh0QmgSZ2GhoaGhoaGRieAJnUaGhoaGhoaGp0Af2tSF4lE4PP5oI+/1dDQ0NDQ0Ojo+FuTutraWhQVFaG2tra9i6KhoaGhoaGhkRA6FambMWMGDjroIIwaNQrvvfdeexdHQ0NDQ0NDQ6PN4G7vAiQLfr8f999/Pz7++GNkZWW1d3E0NDQ0NDQ0NNoUnYbUfffdd8jJycExxxyD3NxcPPnkk6isrDRcEwgEEAgExO8+n6+ti6mhoaGhoaGhkRJ0GvPrhg0bsHLlSrz//vu48MILccstt5iumTRpEoqKisS/Xr16tX1BNTQ0NDQ0NDRSgE5D6oqLi7H//vsjKysLBx54IBYuXGi6ZsKECaipqRH/Vq9e3Q4l1dBwjvr6erhcLnz66aftXRSNNkBtbS2+/fbb9i6GhoZGB0WnIXVDhw4VRO7nn3/GDjvsYLrG6/WisLDQ8E9DI52xYcMGAMCUKVPatyAabYI333wTBx54oE6zpKGhERc6jU9dly5dcOyxx2LkyJHIyMjA888/395F0tBIGJmZmQCAcDjcziXRaAs0NjYiGAwiEAggOzu7vYujoaHRwdBpSB0AXHrppbj00kvbuxgaGkkDKTahUKidS6LRFqB2bmxs1KROQ0MjZnQa86uGRmdEU1MTAK3U/V1ApK6hoaGdS6KhoZEKTJ8+HVu3bk3Z/TWp0+j0aG5uxuLFi9u7GHFBk7q/F5qbmwG0KHUaGhqdC+FwGMcddxwOPvjglD1DkzqNTo9XXnkFAwYMwM8//9zeRYkZmtT9vaCVOg2Nzgsa16lcizSp0+j0oIXyhRdeaOeSxA4iddqn7u8B7lOnoaHRuVBXV5fyZ2hSp/G3QSr9GFIFrdTFjwcffBBVVVXtXQwDrr76apx++umWfyfzq1bqNDQ6Hzipo7GebGhSp9HpEQwGAbQSpI4ETeriQ319Pa655hqMHz++vYtiwEMPPYQ33njD8u9aqdPQ6LzgpC5VY1yTOo1ODyJ1/NzfjgJtfo0PVG9+vz+h+3zzzTe47LLLklEkR0h3n7q6ujoxnjQ0NGIDJ3X19fUxfXfLli3Ytm1b1Os0qdPo9OgMpE4rdbEhWW09cuRIPP7440m5lxOkM6n75ZdfUFBQgDvvvLO9i6Kh0SHBiVysY3zcuHE499xzo16nSZ1Gh8Tq1asxfvx4R34JtMBrUvf3QUdsayC9U5p8//33AIAff/yxnUuiodExwZW6WEldU1MTPB5P1Os0qdPokLj55psxefJkLFmyJOq1nUGp0+bX2EBt7XK54r4HJ9LJJtVW90tnpY6I5pYtW9q5JBoaHROa1GloWKC4uBgAHPkYdAZSp5W62JCMtuZEOtlBNlb+NOkcKEFlWrt2bbuWY82aNW2SGkJDI9nQpK6TYsmSJViwYEF7F6ND4+9C6sgcp0ldbEh3Uufz+ZSfp3NKEyrTunXrlMrxkiVL8Mcff6S8HL169cIhhxyS8udoaCQbiZC6YDD49yN1r7/+OsrLy9u7GFHRv39/DBo0qL2L0aFRVFQEANi0aVPUa7VP3d8PyWhr7q+Z7IjP2tpa5ecdQalrbm5Wjrv+/ftj5513bpOyzJ49GwDw+++/4/3332+TZ2qY0djYiKeeegoul0ub5R2grq4OhYWFAGKPfv3bKXXhcBhvv/02evXq1d5F0WhDbNy4Meo1HVmpI1LXFjn2QqEQbrrpJksVqSMhHZU6Tgyt6rgj+NQB6VO+XXfdFccee2zKn1NXVxfzIvx3wBVXXIFLLrkEADB37tx2Lk36o6GhAV26dBE/x4KmpiZkZWVFva7TkLrXXnsNY8aMQUaG9SsFAgH4fD7Dv7ZGJBJp82d2RtACuWHDBsfXdmRS1xZl/+2333D77bfjxhtvjOv7CxYswIoVK5JcqviQjkodJwXRSF06K3WAff1WV1enrAztlSOva9euWjBQYP78+eJnJ1aTvzv8fj8KCwvhdru1T50dQqEQpk6dilNPPdX2ukmTJqGoqEj8a49BmsoJzwrNzc0YP3481qxZ0+bP5tiyZYuIVq2pqcG1116LefPmxXUvIjtOjv7SpM4ZvF4vgBaTVjwYNGgQdtxxx2QWKS7sueeeOOKIIwAkFv2abKXOCalLd5868mW1649OItLjhZXZOtVobGx05L/7dwMfI8ne0K1YsQIjR44UpvbOgEAggOzsbOTm5mpSZ4dXXnkFp5xyiq1KBwATJkxATU2N+Ld69eo2KmErVq1a1ebPXL58OSZPnox//vOfbf5sjpNPPhn9+/fHli1bMHv2bDzwwAO4/PLL47oXETUnikZnIHVtscjTsxYvXpzyZ6USP/30U1Luw5W6tiJ1qTS/1tTUJKR0NTY22pI6ItA1NTVxPyMa2ovUEXRqISNSSep+/vlnfPPNN7jtttuSet/2hN/vF6RO+9TZYOHChXjppZdw+OGHY+nSpbj66quV13m9XhQWFhr+tTXawy+DntmeE2IkEsHXX38NAHj33XfF8U3xHmpMi5OTxa8zBEqsX78+IdUplmetW7cu5u92RrcCvmB1BvNrcXExzjjjjLi/39jYiJKSEgDqsUR9IJXjjOawzMzMlD3DDu2xKU9n8DHixGoSC2hujybWdCQQqSsrK8PmzZtj+q5TUueOt3DphLvvvlv8vNdee+HBBx9sx9LYI14SkwhosLVnbqcVK1aICWDTpk3Iz88HEL95LB6lLhwOo7m5GW53x+n2skIUCoVStqAlokalq2kqkajhZCt13PWirc2vRLimTZsW9z2ikTpCe5C6SCQSdS654YYb8PXXX4uTMeLB+vXr0adPn7i/39nAx0iyfdTT0QUhUfj9fni9XnTv3j3mzfPfSqnjiNdHq63QHvJ9e5C6+fPnG96V8ld5vV5s2bJFKHVtRepoEUj0gPe2hkwmUqn08mfFqrzFo+61BRIhY8n2qSNS16VLlzZX6ijdREFBQdz3sPOp4/0llcEMVG/yxszJM++55564/bM66vyRavA1JdmWICJ1bRH531Ygn7pu3bppUtdZ0J6krq3UlM2bN2O33XYzHPz9xx9/ICcnB7vvvruB1MVbH7GYX4PBoFjMOpoJtr1IXawBPevXr09yaZKDRNo72dGvNP569erV5j511D6JuJzY+dQ5jYxNFFZKXarHNZkANakzgptcU6XUtVfEcypA5td4lLq/ZfLhjgC+ULSVHxINvLZKDkmDe9GiReKzJUuWYKeddkJ5ebmB1MU7Gcei1AUCAU3qYnxWrP4efHJPp4UvkfZOtlK3bds2FBYWoqSkpM2VOlpAElHquPlVbmNOQtuC1Ml+Vqnuc5rUmREOhw1KXapIXUebs+0gk7pYOIBW6tIUyXa+dgKu1Pn9/pQniaQFkO+mq6qq0KNHD3Tp0iWppO7vptSl0oTOnxVrzimu7KUy+jFWpJNSV11djeLiYhQWFra5Tx2Rury8vLjv0djYKE5ykeuVbzZSOcaoXwaDQXz44Yfi82hkK1GSTKQuHfMHthdoLnr55ZfxwAMPYNOmTRg9enTS7t8ZlLq//voLt99+uyBvROp22GEHNDU1YdmyZY7uE4lENKlLV3BS11YEg0hdJBLBSy+9hGHDhqV04aUJnvu91NbWoqCgQJC6RCNSY/Wpo8Woo+20m5qaDPWYrkod70/tkYvRCumm1JWUlNiSulSZX+l5iSyQdMRRZmamLalL5SJMuTYbGxtx9NFHi8+jjeuVK1cm9Fzy/e1o80cqQX2qrKwMpaWlAIAZM2Yk7f6dgdSdc845uOmmm+D3+zFz5kz8/vvv8Hq9GDlyJDweDz755BNH96F54W91okRHAd/9t9UE0dDQIDrDsmXLEA6HU5r9myZ4rtTV1dUhPz9fhHK3pfk1GAyKM4HTSUVygqamJmHyAtLXp45fX1VVlaQSJY5kKXXJCpQgpS7a2a9+v9+QrT9RUPnjdWZvamoSSp3X6203pc4qgbrVXOrz+fDkk08aXEHiAfUFTepaQaSusLAwIbO+FdKZ1AUCAUyZMiWq+ZTUzPr6ehxwwAEAgOzsbOTn52P33XfHL7/84uh5NH61UpeGaA+lLhgMonv37gBaJ8VU+tfZKXUVFRXYsmWL6OzJML9GG1gNDQ3o1q0bgPRSkZwgGAyKswKBtiN1sZqZqqur0b9/fwCJqyLJRLKUumQFSpSUlCA3N1f0/4ULFyI/P1+Mx1AoJCbu3XbbDUuXLk34uUDipI6+V1hYiOzsbFO9tpVP3Zo1a8QGjcOKbN166634v//7P5HKJScnJ+ZnRiKRmDaRfxfwPpGbmys+T5aLSCI+dcFgMKU+6/feey/GjRuHH3/8MWo5AOO4y87OBtDi3+q0P2lSl8bgC0Vb7foCgYAgdXSKRqyJIr/66iu8+OKLjq61U+oqKysRiUREEs946mDcuHH4+OOPARgnXLvy9OzZE0D65lOTUVNTA7/fD7/fbyB1beFTl5OTE/PiVVNTg8rKSnTr1g3jxo1Lm3MyEyHByVbqtm7dipKSEoPS9corr6C+vh6//vqreCYfN8lSluld4iV1XJVRKXU0jrOyspJK6ubNmweXyyWid9esWYNdd93VdJ3VPEILO+Wmi6cd+Xe0UtcK6hMFBQWGeSkZkfCvv/66OG4u1g1VOByG1+vFAw88kHA5rEDvHs1NgsrO64dIXW5uriZ1HQ11dXWmRYUvFG2l1AUCAaFUxavUHXjggRg7dqzp8/Xr15uOZaJ35hFqpNRVVlYCAP78809Rtlh2VKFQCFOmTAHQqgTaDYxIJIKGhgZ07doVbre7w5C64uJijBgxAn6/X6SRAFKv1LlcLuTl5cWl1BUXF4vErGvWrGmXUybk5N4NDQ1xJ/xOtk/dxo0bUVFRYSBFdF9yjwiFQjj99NPx2GOPJe25/D5+vz+u+ohG6qi/FBUVOV6EV6xYgdGjR+O3336zvOa9994D0Jr3csOGDdhll11M17300kumz9auXYt33nlHPAto6R+x9kv+PlqpawXvE0cccQRGjhwJIPGclZFIBGeccYY4gzpWUkfEm9o+FaCNV7SUXCqljvwzc3JyHPvOalKXJigrKxNkitBeSl1ubi7y8vKwYcMGAPGbX+UJsXv37thzzz0Nn6mIB1fqgFYTXSQSQXNzM5qbm0XZ7MB3PE4iBRsbGxGJRJCXl4fi4mK8/PLL7XKqRzyYN28e/H4/cnNzxfFOqSZ1Ho8npsmGUFNTg6KiIgwePFh8FmuwRTKg2ijFm2oh2dGvGzduRHl5uYEU0X1pog+FQsjJycHBBx8MIPmkDohP7aU6tPKpo7mssLDQ8Wb1tttuw4wZM2wTxlO9RCIRbN26FZFIBDvvvLP4+3HHHQcAeO6550zfPeOMM/DXX3+Z7hVrnfK210pdK7hSl5eXJ0hUoq49sotMvKQulUiE1NH4i8UiQvfRpK6dEQgETOaO9vKp83q9yM/PF50o3oHHzbachJ1++unYc889EQqFTL4QgUAATU1NKCgoQNeuXeFyuQwLbSAQwJNPPonKysqo/liqBdpuYBAJysvLw+bNmzF79my88cYbDt40PUAZyF999VV069atzUhdPEpdUVER7r//fnH2MqmxbQk+pijAJF5Sl0ylrqGhAfX19ejatauS1NHvZH4l5S4VpC4eEyyZgUmpkxdO+r24uNjxvEaKjt0izEkdBXdxUmeXTFkuB7kxxEoS+H3eeOONuEhDfX09brrppg6zoXSC2tpa5OTkCIsJmRUTXdf4upKRkZHWpM6p+ZW/E5G63NxcrdR1BvBB3VZSfiAQEKSOECupo++STx5g7NBvvPEGfvrpJ/h8PkE8aHBRJ87Pz4fH40FFRYWpfLTwPv/887blUC1IdiZVKgt/d+7Um+6gswKBlh1xshN8chCpi8XXg7BmzRp0794d2dnZuPHGGwG0T8AEn9DJjzQZSl2i5IoICSd1lHsKaJ0LQqEQ3G63mLyTFfmXLKXOzvyamZmJvLw820X9119/xW677YY33ngDa9euBWCvPnNSt3HjRgBAz549xRi2I3XyOCdSl4hSt27dOsNJOU7x1FNP4fbbbxe+wJ0BPp/PUP9E6hJd16idAaB3795irJDVJRro+al0/yDXomhjicYC3+BSf3e6eQ4EAuJ4O03q0hB8999WZ7Emg9SVlZUBMJI61eRYX18vOi11aCJiFPa+9957A4AhkSkpE9HKpVqgKehCBa7UEZwMjHQBJasEWpSnVPoExqvUbdu2DTU1NcKfrri4GJmZmTEH4yQDnFAkSur4vRIlV7RQEakjtwO6Lz82LzMzU/TRZCp1pC7Eo9T5fD5kZGQgNzfX0vyanZ0dNVDihx9+wPz58/HZZ58J/14npK6pqclAjCkvWl5eHg499FAAZlOYHOkaK6lbtGgRXn75ZWy//faGz+U5KhwO48ILL3QUqRwv4XnxxRdxyy23tIufqhVkUkf9lvryl19+GVdaHk7qBg0aJMZK79698f7771t+7/PPP8fcuXPF81NZV9Qv7fpuU1OTGGt83bzkkksAtPTPhQsXYuHChbbPGjZsGM466ywAmtSlDbiiRZM20HaRmESaEiF1RIr4Qq2aHBsaGgRZVSl1ALD//vsDMB6STYtbtAWY//2II45ATk6OwW9GBid19957r2W50xVkfgXSl9SRIrfDDjsAaJnwVGkv2gLct+u2224DEB+p27JlC0466SQALUEMVn3G5/PhzDPPxNq1a9HQ0GCZD438C8vKyoTySm4JgFGpSxWpI3N0vKSusLAQLpcLJSUlpjyXROq8Xq8tAab5o7q6WrSLvDDyIBtaPBsbG7Fp0yZ4PB4UFRUJgpaVlYWzzz5bvCOHrNTRxtQJQff7/Rg4cCDOOecc099oPBKWL1+OZ599FhMmTLC8H2/zeDB27FjceuutaZUDkoLfOLKzs8UYPOigg7DbbrvFfF9O6igoxufzYdOmTbaRtYcccgiGDh3aJuZXakc7Usffg37+/fffxTtR/1QF/nDwXHZ/K1L3448/YsSIERg1ahROOeWUtFq4uT29ublZqGZtlTON+9QRYlVRqD75Aqmq4zVr1ohINBpcslp22mmnGcqw44474u233wYQPYUDLUgLFy7Ef//7X/Tu3dsxqbvooossy51OkFMotCWpc7vdMQdKUGQhKXUAlH5XbQGabL/44gvDgmCHzZs3m1QWvunJzc21fJdFixbhtddew9FHH42zzjoLAwcOVF5HG5uCggLDAk/lpfs3NzcbzK/JJHWkbkWzEPz111945plnDJ81NjaKfrjHHntg3rx5BiWksbEROTk5ShWPg/ovV9d5ef7880/06tVLRLhzUrd582Z06dIFLpdLRIRnZWUJlV8ma4mYX+36jLzhIaXFLgEvtWeiYyKdom9lpQ4wkjrC448/HtN9eYDVgAEDALSuC07ari3Mr/QMq7EUDocNibKJ1HH1OJ6ciX8rUtejRw98+umnmDlzJvr27Yt3333XdA35bvF/bQHO2Mlnpri4uM1IXTLMr7TI812+aoAtXboUdXV16Nu3r2nBokVhu+22wyeffIKXX35ZfI92I06Vun79+iEnJwe9e/d2bH5Ntp9SqsAnRe5TV1JSklKTZrxK3bp16+D1eg359FSTe1uA+hz1dzkoR0Y4HEZ5eTl22mknw+f8OJ6ioiJLkksmv+XLlwu/FxWoPon4UFlpPLWFUkekLppSd+yxx4oNEIEnRd57772xefNmw2aKK3VOSB39n5OTY1A7SIn6+eefxXOBlvppbGwUG0MibB6Px1IFS4TU2dWRrFIuWLDAdM2CBQvwww8/iN9pLBA5Wbp0KSZOnBi1HABM5Lk9EQqFMHXqVEQiEcek7rLLLovpGY2NjaioqMD3338vyDvVm5NAk7Ywv8qChYzDDz8cw4YNAwCDss2JnFPfbq4M/62OCausrDQMdH6aAWHSpEkoKioS/1KZIJV3KD5BUHRbcXFxhzK/0qIWTamjdyoqKsIvv/yChoYGMQBo8gWAww47DLvvvrvp+9GUOp/PZ4i4KisrsyU6tJPipC7dlTo+cbeHT12sgRJbt24VCgqhvcyvPErT5XKhsLDQtk+RyiiDv0thYaHl5M3zv4XDYQDqNAcNDQ3IyMgwkRAaT6n2qWtubkZ+fj7cbndUUkdjhs9hpCACrWZ2rkT4/X7k5ORE9anj5lcAKC8vN9StbHalemlsbDT43tJcb6fUyUpILJs6VR0988wz8Hq9hk060FoP3DQ4aNAg7LPPPuJ3ekd6//PPPx+TJk1ypIjz+mnvlCrPP/88Tj31VHz55ZdK82tOTo5Imh4vAoEASkpKMGzYMNG20UhdWx+/SfOj1bzw2WefAWjpxzvssENCSh2fi+QgQxU6DakjrFq1Cp9//rnhsGfChAkTUFNTI/5x58Vkg09s1PAbN25EfX093G43SkpK2k2py8vLQ11dXUyKFXXiaEodTVperxc1NTW45pprRF3Ivihc2SFEU+pqa2sNu0PV4eiRSARvvvkmwuGwqPvc3FxkZmbC5XKlPanjkxL3qSstLU1Ln7qtW7cKFYjQXuZXGlPkP9alSxfbfHlWhI8vEnakjsZQU1OTIHMqUkPmSZfLZSB11J5U30Sekt1XqW3z8/Md+9Tx+YGfdEHjj9+HzLPRFFp6X6r3WEhdMBiMidQRySYQKXVSpyqz2qGHHopLLrnEpNRRX7FLukvkjd7faWAYYJwT21upI+Lq9/stlbrGxsaELAq0XgGt9UTj2orU8eeRyhuPUtfc3Iy77ror6oZU9he3QmlpKUpKSsRY4USOJ+e3A/XX0aNHG84Bt0KnInU+nw9nn302XnjhBaXt2ev1orCw0PAvETz++OMGFs3Bd2A0aVVUVOD2229PmVKnmiDoGC1O6kihdKrWhcNhMZlEU+po8C1fvhxAyy5WpdQBEImIOZyYX2VSJy9S77//Pk477TS89tprqK+vR3Z2tliQPB5P2pM6Wanj5te6urq4yv/000+jX79+ttfESupCoRDOPvtszJ4920TQ28v8Wl1djYyMDNHXu3btalqEOawIDlfbnCh1QCuJUL03kTrA6DRPi5Gs1AHJ7avUtvKRTnbgCxu5jQCtvmO87khRLi8vt61vmvNocS4rKzOUh4gZLXgyqaO6c0LqOAG44447cPzxxwOI3/yam5uL/Px8y1OCyHda1f6yUkfl72ikjvpENPOrFan75ptvouav5KSO/o9G6ng93nTTTaKMseLdd9/FhAkT8MILL9heF02pI+Tk5Ii5yOVyGcynTlRaivx98sknhfoXDZ2G1IVCIZx55pm46aabTP4x8cLOAR9odQC1MrcQ6uvrDdcQqUumUvfFF1+grKzMECkDtE5gXq9XTJRE6pySSj5JRVPq6J40Efl8PmGekJU61U6lpqbGdjDKkr8qdxvVa01NDerr603pTNKd1E2fPt3wO9UbpYChNpg/fz4efvhhR/e8+uqrsWzZMttrYiV1GzduxCuvvIK5c+ealLr2Mr9SEmTqW+Xl5SZzGYcTUpefn285AfO+FE2po4WcFiq/329S6tqC1DlV6vh7cPMrLVJ83JH5tbKyEhs2bLAcw/JiLyt1VA8qUsfNrzQm7Egd1d2cOXPwr3/9S9R/vOZXOrdXJm3c7w9o3dBy0DvSfWlOcnLqCleT29v8Sn2ipqYGPp/PMvrViqyOHDnSEFBl9QxZqYtmflU9T1ZqnUDleiBj6tSp4vi6aBskt9st6oiUeoKTRPLUt7xer+FMaDt0GlI3depUfPfdd7j99ttxwAEH4M0330zofjNmzMD222+Pb775xvIaGmDU0e+66y6cfPLJAIykbvbs2QZpPtFAiW+++QZz5swxfLZ48WIAMOUForJlZWWJhffiiy8G4Dy1Ab1LTk5OVKWOJu0PPvhAlPXaa68VZYiG5uZm24lLpdRZqXsulytmUhcKhZSOz22FSCSCf/zjH4bPaAGjiY4WpdGjR+Oqq65ydF+aTOyIFi38Xbt2xdq1a00k0OfziSOdmpubDcRPVuqszK+BQMBRPq94QWfQEmJR6vgiwBePvLw8R0od1YfqvRsaGkxK3ebNm03qXqpJXSzmV5nUUbkyMjKQl5enNL9WVFSgqalJuWEMBAImlxcrUhfN/EoEM5pSN2zYMAwdOhQAYvJTVNWR2+1Wblaor1BZVd+ld6QyUl9oa6WuubkZd955Z9wbLiIx33//PTZt2uRYqYtEIgaiZDfHc5cTp6RO1d/iqStqH9mqFAwGRb+ZNm2a+DwaMaMxB5h96I455hgA9gETsZwkQeg0pO7000/Hli1bMGPGDMyYMQOnnnpqQvejheePP/6wvEYmdRMmTBCpOTipe/7553HFFVeI3zMzM5UyPl171FFH2ZZt5MiRIrKGQB1HnlB4J73yyiuxdu1acVar0+hfGhwVFRWora0Vx3rJaQ+AVpVsyJAhIikoPV9lqh4+fLjpM7tFR94dFhYWwu/3GyZq7pcTK6n797//jUGDBtmWIZXRs6qdO01wsqN3LKH7pHzYBQ3Qwj9+/Hi43W5TBPkpp5wiEkePHz8eO+64o/ibSqlTTdwTJ07ETjvthOnTp9tGi8YLmdRFU+pktYnAlTqnpE5OJMyhMr9yx/rGxkbhA0pjORWkzuv1Or6nlVIHmN0eyPxKLhWqfGqLFi1CKBQSucs8Hg8KCwsNc6WdUsfNr1QWHniiInW8zImSOgDK6F4iGuRXaWd+pTJSudqa1E2bNg3/+te/RMoYFaqrq/HWW2+ZPn/nnXfw3XffAWi1UvHgO8Co1LlcLrz44oui3Jx4WZ3z7ff7sXnz5ph96lT1Ek9dcRGEY8cddxSbA640JkLqdtppJ9x4442muZOD3lcV+GmFTkPqkg2aVOwkXH62qTyQZXPNV199JX7OzMxEbm6uskP89ttv+P777x2VkU9i1HFkOZinePB4POjevbvS0dkONDi6du0Kn8+HTz/9FP/3f/+HDz/80HQtdwTmHVHe+RBmzpyJ//znP8oyq6AKlJDfJRFSR34LVhPC+vXr4fV6hfyebKj8TeQJjtqdiIcTkwwRaiuT+6ZNm7B161Z4PB7k5eUhPz/f1A6//fab+FkmfCqfOlU70vsdd9xxGD58OGbOnBm17LFg27ZtBmfiWJQ6OUk4wY7UqQi+XaAE0NqeRHyKi4vh9/uF6wFN8vGQuqqqKnz66aemz4nUud1ux+ePWvnUAWa3B25+BYCPPvrINBeRFWGPPfYA0KJQZGVlGepQHndW5leVUnf11VcbNjj0zgQrRU8FO1Ln9/sNz+F9pbGx0TAe6TqZ1NE1bW1+pfeyc9K/4oorcMopp5g2gCeddJLJEjRmzBjD7xT9unXrVhQXF4sNVm1trcFaZfUew4YNw1dffRWz+dVqzMWCFStWiDHJSd2mTZuwZs0a4drE+1Q08yu5PADqaFe7xObA31ypSzbkHGsq0N/8fr/JrCDLz3yAuN1u5OXlKf106uvrUV1drfTTk/H777+Ln2nRtiJ1vJNSJ3Oq1FHHKi0tRW1traXa43a7DdGvfKDJ/nQEyhAPtHb6WMyv9C4HHnigIB18wo2V1PHoLhWWLFkCAGLHmmyoSJ1siqCFgTYcThzfaRJ/9NFHlX/v2rUr/ve//xlUQbmeqF7D4TC22247w9+cRr/KRy4dcMABUcseC2SlrrS0FPX19ZYLuRNSZ7UBA9SqT6xKXc+ePfHmm2+KuuCkLhgMwuVy4bXXXlM+X8ahhx6Kww8/XFlOp6ROZaqXVS/ZN48CeijlwvXXX4/99tvPcN+1a9eitLRUnOxApI6fqkGbDpn8VFdXWyp1NC7mzJljaKdoSl04HLasi9raWsO8QaDxwdtdPs9bzjMJtJI6qlMiHFYuOFu3bsU+++yDNWvWwOfzITc3F3l5eQkrdTxfohWoTGvXrkVdXZ3lRvDMM89U+tQ1NjZiy5YtKC0tNQTVyMq0Cr/++isAmAIloiUfVo256urqmPzqdtxxR3GuLx//VCZyP6D59qyzzkpIqQNg2tTI0KQuiaCObCePc/Or3PEXLFhgmWGcK3Wy6ayurg6RSMSRvx03cVDHkHeYXKkjkG+Iz+fDihUr8M4779g+hzoWJWG12sV269YNNTU1cLlcyMzMNCwKVkod0DpRUuePRupk8yvQMvCuueYaw7XxKHV2pK6xsREXXHCB4bnJhiqRMk0GtHjxRQlwRupooX788cdtA4CIrNmRukAgYHLadRr9amdqSAY2bdpkeIaVaY4g+4UR4vGpIzgldeQHu/POOwNoVUKp/FlZWaJ8TgNirCIL6bSQREgdb3PZl1V1as1vv/0m+sxLL72ECRMmGPJF5uTkCOIKACeffLLwJ5VdW1asWGHwqaN7hMNhw4aVt4es1MnuC/vtt5/lCSD19fXo2bOn6XNVouNQKCTmMDlHG52eIyt1tIGwIjcfffQRfvjhB0ydOhU+nw9FRUVCBUsE3D/aCtSGc+fOxc4772w5ZlUpqbhPXZcuXWImdYRYza+BQAAejwd333234TM71ws78PmCTMVUpvr6euy5554YNGiQI1JHdaASNqKROm1+TSJIcbKTx2lRlUldKBTC/PnzMWjQIOX3SKmLRCKmQUoLtFVIOCeB3KxEk5m8wFs5fpJPzH777SfOuLQCdSzyf7EiEZSUNCsrCy6Xy9BZrZQ6/jcawPGYXwG1b5lM6qLJ3XQP1aTz0UcfieCBwsJCNDc3W571GS9qamrgdrsNARA0KciLEvU/Fcn++uuv4fF4lO/x5Zdf4vrrr8cJJ5xg+hspaarJhvd3ud86jX5NZeRxJBLBH3/8YYh+j3bmZm1trZgw7cyvTqJfCVaBEnL065dffonDDjtMkDoCV+qIOMltEQqFlGWiNpLHQnNzc9LNr7W1teI5RLhkv1lSWM4991wALXXJc83x8cjdObilxOVyYeXKlWhoaDCZX5ubmw2kzk5dlDdFs2fPxtKlS5VzfFNTk3LjxiOX+XOICMlKHUWyy6TOLqgGaN1cUr7PwsJCoYIlAvq+XdAa9dOxY8di7dq1ltfZkTqVUmflv6pCrOZXMv+PHz8eAMSJKPIG9tNPP3Xkx8jHGxHDQCCASCSCuro65Ofni3yvBJWFze12i76hUg2jiQxaqUsinJA6ghzt4/f7sXjxYstdICl1gNn3jga/Vcfjg4GTuliUOqDVJ8bJToYrdRTZxk1cBHIgpWfxa5wodU7OR5TNr/wZqgUt3pQmqjJwX61AIIDx48dj4MCBST1urra2Fv369RO5loBWUiebX+k9VST7ySefRHNzs1Bz+WTz0Ucf4b777lMepUfpblT1RPUbDAZNi4uV+bWpqcnQHqkMMlm3bh3q6uoMJCmaH1VtbS26du0KwJ7UNTc3IxgMmsZXU1OTUJwI0Xzq3G63MIcfdNBBpnyNnNTR8+R7nnPOOUrzILWRvPjF41MnExeZ1G3btg0ZGRl48sknDSoahzx/EpGjn6mfRSIRg58Xd23ZZZdd0NzcjGXLlpnMrzKpkxMm25lfaVzJaaDoGo/Hg//85z8YM2aMIGc0V8mE14rUEahvyeZXK5JG58lWVVWJOS/WpOAq8ChrK6iewccw1UE0pU4mdaqAGCvEo9R5vV6UlJSgqakJkyZNAmAkdU1NTTj88MMxbtw40/dlv1uVUhcKhVBXVydIXX5+viEqtkePHiIIiOB2u2192GnzbBXsRu+rSV0SQMqbE1InK3UNDQ2orq5WdnqgNSUA0EriZs2ahSlTpogdGpHEdevWGfz1uD+bSqmTCYZVNA8pdU58Dqhjke/bhg0bUF5ebrqOlDrqgC+++CIOOeQQALBM0gyYSV1dXR3++c9/mkzQTU1N8Pv9BvMrr2N6F26ejIXU8bpQTcz8e9dff72IIEtmQlC+CyTQpGBFUFSkjuo7HA4jHA6La7KysvDRRx9ZPp/a1Y7UBQIB0ztbmV+PPPJIdOvWTfTDVJI6Mmf2799ffOZEqSM/MDtSB7SY+AsLC/HJJ5+IvwWDQeTm5ho2cNHMry6XS2zqcnJyDEf/ZGVlGY47tFLqyMdOPlfTKgEykZTMzMy4lDqV+ZX8Sp9++mkDqeNjXZ4/VeZXuk41/vx+v2jPbdu2mZS6pqYmw4aRl1k2v9IpHVSXNJ+pToKg706cOBFvvfWWSD+h6k92Sh2hvr4excXFJvOr1QaWsi6sX78+qeZXfnKJFVSuP3wuIOKiIiJcqZPNrzytj9M5U/aps1PqaB2hlGEFBQUGUkf5UpctW4a77rrLUH7a2BFUpA4A/vnPf+K9995DXl6eaQ1vaGjAwoULDX3e5XIJ31JVJg3qz/Recp1SvWvzaxJApMpJhKiK1NHiLDsLAy0dkDoEDe4JEyZg3LhxYmEipa5Hjx4Gp3QrUkedUJ78rZQ6l8uFJ598Muq7AUalDmiRo1V+FkTq6JmVlZXCjGinZvHBCAD/+9//cPfdd+PBBx80XEdtwUkdn7S5KYj+j4XUyY7fMqwmIieZwWfPnu1og1BXV4eCggIDCafyy+YjVbkJtLDSIhOJRPDqq6/io48+Upa3S5cu2HPPPYWzvsr8yn3q5HuozK+NjY34/PPPsWHDBuHrlUrzK40ZmSRRmVXw+XzieiufOiJZ5DDNg2Ro8ecm32ikDmjt87LKV1paKtrOTqnjPpKRSARfffUVIpGIgXhzJBr9qlLqeFmcKnU8sIGrdrIvIDe/8gWXrh8zZgx23HFHHHTQQYZy2ZWZvk99kOpBZWKUCSFBZX4NhUKiPi699FK89NJLhu9EIhHU19ejpKTEZH61mlPoTOLnnnsOn332WdLNryol97LLLsOHH36Impoak0sAr9fRo0cDaFmbZPDo19LSUjF3EamjzV80ckrly8zMREZGhqPoV77GuVwulJSUGNbLlStXAmhJrTNhwgTMnDkTixcvVpJY2fzavXt3AMATTzwBAIZTa2TfeO7r7nK5UFRUhIcfflgZ7MTV448++gg9evQwbG60+TWJmD59Os4++2xL/zH5AGFufm1sbBSkbsaMGRg1ahQAiKgvnl2eWL5s+5d96i677DIsXbpUkKOePXsqlTp5MrfyqZN93OwUO+5TB7TsXFRBIGS643VGzsZ2aSVosaOOSyZhuYxEJKySNXLzINBSJ3V1dY5JHZ8AVJOn1UTkhNQNHz4cI0eOjHpdbW2tKfcTmaW4UseVpDFjxpjMKbTo19fXGyR82WRH/aW5uRmnnXaagVBYKXUjR440jQu5rbxer2GxtFPq4jnORwW6NycXTgIliNRxp2eVUqdyVaDF/x//+Idw0lYRSK4kAK3tk5OTI/JfAUbF006p4+/43//+FwceeCA+/vjjqEpdMn3qCHIQg0qpI5UvEomI6woKCsSYl+c/rtRxqwC1Z48ePbBs2TJ07drV8DxeTxQcwsH7NfVhO6VOhsr8ypW6n376SZw9yssUCoUMSp1M6mbPni3yftbV1WHDhg0YMGAAgBaFsrCwEJWVlfjuu+8M43L9+vW2Z87KoOfJY3v58uV4/PHHcfTRR6O6utpkieHvu99++2HFihXi2DUOHv3apUsXQX5qa2vR2NgoXFiikVNePr7BdKLUEeQAJ3njMHr0aAwYMEB5nirvR5s3bzYdsbhp0yaDUsf9Qc8++2zT/a644gqcfvrpps/5nL5y5UqsX7/e4HqVtubXH3/8sS0ek1QUFxdj++23t1TqeKekMxypgerr60USUZKCgdadDVePaNFdu3atwR5/1VVX4corrxS/P/7447j44osF8Rg4cKBhIqROKC8oVubX1157DQcffLD4nfxapk2bZikBk1JXVVVlIB40carMzaQy2hEf2fxKC4HsqEx1bkXq6O/xKnXR8kHRZ/Iu1gmpA+AoqII2AyrwQAkeSQaY1Tpa6BoaGgwSvlx3tHmQd7p20a/r16+PSsTk51gpyUB85uvPP/8cixcvxmeffSbKQuXlfT2aUldbWysWMF42InXfffed6D+0S1eZ+Pbaay/84x//sDSRqRYdoIXUdevWTUSgc8XTTqnjkzwtAkuXLk0KqeOKLEE2v3JS5/P54Pf7TXNMfn6+GMv0t3A4LMpeWloqPqd+OGXKFEOQjd/vN8wr0U6lkcssL4YUbUuuGUBspC6a+ZXAy0zEvLS0VHxPNr8OHz5cOPeTojR58mTRZwoLCzFhwgQsXLgQX3/9tbj3QQcdhB49ejiO8rRS6rhatXLlSpM5kr9vXl4e+vTpo3Spyc7ORkNDAwKBgOjLFFTT0NCA/Px8y3RHHDKpIzhV6qicnNTJacfswOeCuro6DBgwwPC+GzZsEPOCz+cT5nlA7aNpBU7qqE640pe25ldVlF1HgN05iXwhX7VqFd5++23sv//+AFpJCQ10anxSrXgkXENDA+bOnYtwOGw6XeGRRx4x/B4KhQTxOOigg7BgwQLRaanxnZpfe/fuLZKA0vfeeecdnHjiiYZjUACzUrdx40bDpE4JKFUmWSKCdpDNrzRByWH30cLxyQRO71xdXY36+nqT+pEIqaPTQDicnOHnFGR+VYFPAPIEJZu3uVLHJXwrUiebz+yiXwnPPfcctm7dqiR48u7XTqlzSop5OQ455BAMGDAAhx56qAj4CAaDyMjIMJAPJ0pdSUkJXC6XaaEGgH79+onxS8oj97Fpamoy1JtVKhfVokPXA61zhEzqrJQ6VVJd3haJkDqV6q86UUJ+PyoH/d+1a1cxF9K78xQk+fn54j1oDJ1wwgk499xzhcuA3+8XOdr4va3g1Pza2NgoVEN5g0R1YKfUyeZXeU6orKzEK6+8AqB1XiopKUFzczNCoRB8Ph/cbrdyQ0NpjXr37i3mgqKiIgwdOhRFRUWGk1hoo+iU1JE6KfcB2X1IVuF4vVptOgGjYk/zLil1tO45CfiIldQ5Uepimaf58+vq6lBWVmZwgyovLxf1EItSKoO71KhIXbsqdaeccory38knn2yZniPdkZ+fL/LGyeAd5Ntvv0VdXZ2IWKQBRo1OCymRusbGRoNSt++++wKAwQxD4B2aJgMAOPjggxEOh/HTTz8BiK7UqRYUvsAHg0ExAdfU1GDNmjV4/fXXAZiVumAwaBjYL774ItauXauUse0CJAg0GGnCp0lWXsiiJc6UE5fOnTsXkUgEgwcPFtckQurofEsrsmkFJ4mkCXZKHZ8AaOJ/6KGHAAAvv/wy/ve//4lryWTb0NBgyHWkInXNzc0Ih8NRlTqZ1HXp0kXZ5oCZ1NkpdbGSYjmrPTe7y5Ofk0AJ8mFUKXWUfohDzg/Jn0lKhQwrUkd9yYrU0abSjtTRnMDn2UR86vhJOQQr86uKQH/77bd44IEHkJubK8YslTcSiYif8/PzRZ/mgTxEjOmds7OzxfOsouivv/56AM7Nr9TnevbsqUymnqhS19zcLK6ldqExUVVVhebmZvTp08c0z9TX16Oqqgoulwtdu3YV9y0oKEBGRgaGDh2KH374wVQuJ7kqgVY3GHlsc6Wub9++Yk0ixEPqVEpdbm6uI99A3kd5myei1MVy3i3vR+QSQxuZs846C2+++abok3YKYLT1j1tfqC/Im0YgNqXO+ZVR8Pnnn+Pll182NXgkEjHIxR0JBQUF4iw/u4V88eLFBmdpGjhUF7QA05mrwWDQQOpyc3NRXFyMAw880PAMl8sFt9ttSFfywQcfIDc3V5y7KRMgefJX+RkRZFJHi2NGRgZGjx6NZcuW4fTTTzdFvwIwmUTIkVSFu+++W/jbqUATASXxpEVTnvCszK9TpkzBpZdeipqaGmzZskUscrNnz0ZWVpYhMtHj8ViSMPqey+WyVOpycnJiJnV8MrFyKCeofOp42ekeVVVVKCwsFCb0f//73wDM/mnRlLpt27Yp+whPCkuQ722XwFRWbZOp1Mk+SzRxqurWzvw6b948BAIBFBQUwOv1KkkdTz9EkH1Z+eJfVlam9B+1M78C1qROLg+/H4E2M1xt4X8Ph8PikPR4SZ1VoERJSYnJxLrHHntgjz32wKuvvmpy/wiHw6K9VEodneXKF7ns7GwxJqzGzrXXXot7773XsfmVSFCPHj2wfPly0/1iIXWhUMh0LR0nCLSSOmpbWg+23357kbqEsHnzZlRVVaGsrAwej8eUPmS77bYTJwlxYuR0Y0SEwU6p83q9pnrm72s37vnfqMyc1HXp0sVRFK+VUme1Iaf+zZGbm2sg7E5I3YknnoglS5aYzK/5+fnYbbfdMH/+fDzwwAMoLy8X6wUndb169YrJzOvU/NouSt0BBxyA/Px8jBo1yvDvgAMOwJAhQ5L1GFtcd911GDFiBM4888ykpE7g56n++eefhoHDF6JFixahd+/e4npZqaMJ++STTxbf4YtNfn4+LrnkEmy//faYOXOmICEej8d0XMk777wDj8djCg23CpSgTNuqHQNXIGRSR0l2+b05qZNNxXb4xz/+oXQSJcikjpedw8r8eu6552LKlCmIRCIoKysTh037/X706dPHRFY++eQTZR7AmpoaZGZmorS01DJQIjs72zTAok2ofAKLltPOzvzKSf7atWvRo0cPy5MteOoEO6UuEAgo1Vwn5lcr30bATOqSqdTJygot0LIpFLA3v+69994AWkyJWVlZpoUaaCF1fJxcdtllhvaUF//u3bubzHmhUAihUMgUnQe09mWaK3i9WSXsJsV+7NixAFpVFk4mVUdV5ebmJkTq5JQmcnlVdS/3rXA4LMYxj/Kur68XpnMiXipSZ6XUqci7Sqkj8ysndbEodSrzq8rM6/P5RFlpriGljpM6eZ7ZvHkz1q9fL3IXUr+lILuioiLR3nwOs1Pq/vvf/6K6uhqRSMSS1HGlzu12m96dv6+TRPKAUambM2cOampqoppfaTwcddRR4jOn5tdoSp2TdDB33nkncnNzDfNVU1MTCgoK8PTTT6Oqqkr44NK8wEncJ5984vj0FyDNza/vvPOOiPKUwfM6pQo///wzqqqq8M0332DgwIF4++23E74nz7HTp08fw8kLnNSFw2H06dMHubm5yMjIEPlwaCJ67rnnsHDhQgMpcrlcYiFpaGgQHWTkyJFiwreagP1+PzweDzIyMpTBARx8cpHBF+VAICAWbU4Am5ubTT51AIT/YDLASR0vq5VSp9opWpkBZYJEA/C2224zXUtBFVY7STK/yv4rVkpTdXU1Nm/e7JjU8WzlQEskHR0dRSCyRaqhFamjuuKBEh6PxzQ5NDU1WSp10cyvdjv2WHzqYiV1ch3ybP2xKHUE2fw6duxYYc7LzMw09Ek+4QNmItmtWzcTqbNzgbBT6uS+S0ST3p+c2WlB5vXC35ePm1hIHb+fnVJHUNU93Ysf60UERFbquE+eTOrkZLQyVOTdSqmTSR2PECfEqtRlZmaaykbzmmx+pTmod+/epnlm06ZNqKqqEqSOnkOkrri4WJBQbm63GkOBQABjxozBxRdfLM7Ppbrh4EqditRRkNAjjzyCbt26KZ/F3zkjI0Osd6tWrcKKFSswd+7cqOZXj8eDSZMmiQ0L4DxQIppPnROljtLsUD3xvpqXl2dIl0QqPid1O+64o+F4ufYwv3aalCbff/89Dj30UADA4YcfrjxwPRAIwOfzGf7ZgZM6wJifSh5EPXr0QGZmJvr164c5c+YAaCV1JSUlpugZoHUnK0do0nNdLpfSHysQCMDlchl2PCql7t1338XEiRMtd3GyUkcDht+DTgVwuVyGRSmZZ59aKXVOza+ANamTTZlk7lBF6gaDLedXqpzdX3/9dXz//ffIyckxOcZakbqePXuivLzcMIHZ9bmGhgaEw2HR/kOGDMGuu+5quIaUBlp0rEy19Ey+YLndblMfDAaDStKRbFJnp9Q59QciyMoKJ3Wx+tQB5on8xRdfFG0s1xlXnwDz4q8idZycyKA6LCoqQm5urjiVBTCTOroPvT8tMLQgW+VZ5KmAopG6UCgk2pkrQbJPHdWJHanjJm3y8bzsssvE/YuLiw1KHdUj9XFeb6QSRiN10QIlPB4PZsyYIfKHUkYCeVxakTqyesg5Dd1ut4m00+88UAJoITl5eXkoKytDY2OjYUxEI3VWSp0dqaP7qsgCobq6WtSxitS9/PLLAGCI8lSB+nhJSYloc54ehZQ6qzkzEAggLy/PMOaSpdQFAgH0798ft956q2X5c3JyLEmdCvn5+Ybzur1eryFnZTTQu73zzjuirdJGqeP473//m4rb2qK6uloQjaKiImVwxqRJk1BUVCT+2fl5Aa0NSQOedxrqlJSuhP4fPHiwMF3aOZTS/WpqahCJRAxE5c0338TQoUOjKhic1KkWzU8//dT2+7JPHb2TPBD4jnf//ffHiy++aHnPt956S6RncAoa/DvttFNU8yspnDKckjqaEK1IXVZWllKpO+OMMzBr1ixkZ2eLRfvRRx8FYD2h0udOlToqm9W7AK0qBj8hQAVqS1mpk2Gl1NklHyY4dZgGWttSPgEAAI4++mj861//sryXDLkO7cyv0Y4JA8wTOQc/ugowkzqZSBKp43WlIs2y+TU3Nxd//vmn2JgCZlJHY536iazUWQX6xELq+LvJ+bJ4X+vfvz/OPfdc3H///eIzO/NrU1MT/vGPf+Dss8/GRRddhHvuuQeHHXaYY6WOggPIL1kGJal1Yn7l6jf5AssbBStS53K5UFFRYSDupNRRnz/22GPx1FNPifYmMkXErKqqCqWlpcjJyUEkEjHcq66uDtXV1WIOUCl1fr8fgUDAkfmV6jAjI8NgYZD7wNatW8V6qFIdCXbHPQKt454rzm+99Zb4OTc3F7vtthvefvttAxkCWuYXVcCDXaDEl19+iVtuucWxUjdgwADceOONlgpadna2YS6gjZIdqVuzZg3cbrdI3TZo0CDHayDV83/+85/086njOOOMM0ynAchIVsJRQklJiZjsq6urlek1JkyYgJqaGvEvmkMjTaq0a+cdnSZK2lGR1Dxo0CBxjepsRg6v1yvIJ792u+22ww033GD7XQCWSp2ct8sKMqlTnTtLSh1Njt988w3OOeccAMD5559v8H0AWtKbxJPCZvbs2Xj55ZeVSt3MmTPhcrmwbNky5OTkKAekU1JHQTsq5YZInVUEI9Ay6IcNGwagRXXYYYcdojr6x0rqVOfqEsjfyGrRIVgpdTKcKnW8XxGipauprKzEfffdh8zMTMOmY+zYsbjuuusM195555229+Lw+Xw48MADMW/ePOy000625tfMzExkZmYq27tXr1444ogjsMceewgCwhWYjIwMU1+Tfe9UgRLcER+wN7/y/l5eXm54nqyGWyl11G/46Q3xml/pewUFBSZSx/uP2+3GlClTlOfs8t+52Z3+npOTg+uvv95AHrhSp/KpO/zwwwG0RGZaQQ52sTK/cpBS55TUAS0mNu5zTHVDbXnPPffgoosuEu29bt06ocQCLf03JydH/J0HyPn9foPlhupPFg9qamrEvFNSUmK5saQ6dLlc4hpVH9iwYYNIsKtS6ghOSR3fNJeUlIh+kpOTgzvvvBN+vx9fffWV4bs038jPsFPqDjroINx6662Ofeqys7PhcrmUqvmVV14pIrKdKnUUTNmjRw9DijC7fspBpzDl5uamt/l1+vTpuOWWW3DFFVeYFoJQKIQpU6aIbNnJwrBhw0RKh08//VR5PJfX60VhYaHhnx0qKyuRkZEhju6SSZ3b7TaROvqfHH5VIALi9XqFNC8TwGiEEIAhZQB1wkgkIky20XxnZPMrdWC+OLz++uuYOXOm8l0mT56MDz74IGo5nWCfffZBQUGBYfBQJyefzDlz5lg651u1pTwYR4wYgbKyMltSl5eXZ0nUcnJy8Mknn4hdpt218nsAzkidHVmSlToVGhoaDKqr3W4vGAw68qlTldsqoIOwfv16XHvttQbFhupYRVxlV4MXX3xR+KcSampq8PXXX6OoqAh77rmnYeK2iiyWF3tCU1OTCPiheuWnYKhU0GjmV5VvlxPzqwpWSh0REFLqaA5RBUcAaqUuEongxhtvNKkl9G7du3e3Nb8SuJJpZ37leew4nCp177zzTlQzvaptVOZXDlntJKhM+YS+ffsaImZlpY7mKOoL06dPR5cuXcT71dTUICcnR8xNdCQY0NLGnNQRcad6prmhurrakJUgmlLH8zDm5eWZ1ob169cLUldcXGxSqAnRSB31Z1lUoffp2rUriouLsfPOOxvy7QHWpx/xk0q2bdtmUP4IGzduNM2bNDfwZNp2vpm00VSROqu5Tg6GJFDfieZT53a7MWnSJIPLz+bNm01H2bW7UnfYYYfh66+/xrRp03DiiScKv4Enn3wSffv2xTXXXINTTz01qc8cMmQIKisrMWLECCxcuNAQ1BAvPB4PevbsKfJiyebX3Nxcg8kXaO3UtCOQsW7dOrHL40qdTFbsSN0VV1whniWTOsBo6rKDrNRRB+ZRdNdffz2mTZsWs99TvCAzA9A6IdHEvGjRIstF0GoSUu2w5MmfwEmd1ftSziwyVeTm5ipJHTcnJlOp4z51NDHNmDED55xzDrKzsxGJRJCXlyd8B2tray2VuuLiYjQ1NVkqdbxPqSIErepcVWau1GVlZSkJAq/HSCSCsWPH4thjjzVcc/7552P9+vVifFAuSX5v1fPt2puXkR8lxMt4yimn4LbbbjMRRNnkqzL32il1dnVo5VMnm1+5k7t8LaBW6urr63HHHXdgzJgxhgXeitSp/NNk2JlfyV/V6jsyqeMnPlDEebSNrmxCt0o+zMGVL454lDp6PyqnHGBDv1dXVyM7OxtHHHEEnnrqKXENLez8aMOZM2caNs5UXiJ1LpcLhYWFUZU6bprOz883rA2RSARVVVXo27cvnnjiCTz77LPKe8nvpIJKqQNa+yApo4MHD8aCBQsM11iNE57X8M8//8Qpp5yCbdu2Gd558+bNhhOSgJZ2CIVCho2FXfmp7LH61PHvEmJR1goLC8UxasXFxYhEImLsUTtZudmokLJAid133x2zZ8/GihUrMGzYMPTp0wc333wzxo8fj7/++svWWTFe3Hffffjmm2/w6quvRs087hR9+vQReYF4h6ivrzccxk2DzS7NA9Dic0O7GCvzq+p3wuDBg0XINHc45YOUOmQ0UsefQQEbgPkQbiC2BLqJgE8GNMjJ52Tz5s22ygaB56VTDcZoi7ws23PIgzU3Nxf19fW45557xJl/33//vcGcyMlKMkgdqWvU90aNGoWRI0eaziAGWkidlVJHBMXKpy6aUucUKqVONelxIk1lkoMOyN+E/udtZRex6JTUTZ8+XfydT6RvvvkmbrzxRni9XoTDYUGE5GfyFAUE1WJFp7DYgUgd3ZMrddnZ2WJDqdqAcDOySqmj+p07dy4uueQSU1m7d++Obdu2iaAG2adOBafmVw6qu7q6OkOgBNDqz2SXQoODt3MoFEJjY6NpvpD7B9WxXId2pK6srMyg7FHdyEodL3dDQ4N4LzK/ZmRk4OijjxbXdO/eHfX19Ybk9Ntvv73BxUVW6ig5thOfOiulrq6uDg0NDejWrRsuueQS0xFhHNHIisqnDmj1LyZSV1hYaCqznNOQQEob3+TU19eb5iQ5zRaND7qOm2itjjij51O9OTG/8u8SYlHWCgoK0NTUhJqaGiFq8JM/VAFudkgZqaupqcHzzz+PtWvXYunSpaiursYXX3yBiRMnRjXbpBP69OmDP/74A4BxUq6trUVhYaGY6GSlzgniMb/yziMrdbIfRrSFmL+PlVLX1uCkmAYW9320q18axG+99ZZQglX16ESpsyJ1p512muF3Mr/ecMMN4lggORs7P8s2GqnjOQhVsPKpo3qTD61WKXWffvopZsyYIUysTnzqqNx0PmUskBd3OtlABp/k6Wc5YIV8c8j8xRc0O6VOZX7l11Of+PLLL8XfrcyvAEzvw59FnxNU5teHH3446vik/kzO/FypKyoqMrSXPKfyulSROl6v/FhAeq9evXohHA6LucBOqeN1yKEi8zKszK9Aq3oWTR2Sn7ds2TJ88cUXiEQiJsVIXmyzs7ORkZGBuro67Lbbbpg8eTIAe1KXk5OD5uZmMT7INP3www9j9OjRorzyeiGbXwEY0oPk5+eLTZkViaBI0k2bNsVM6ujnnJwc/PHHH4Kw08aJXIkSgZVSx0/wAFreT55jrZQ6qiv5FBX+fbfbbeqftCZTP1IFU6jKzo8Hra2tRUZGhuX3aH2xUuqckDEa55s2bRL1Fm2jaoeUkLoJEyagd+/emDJlCu68805s2rQJJ598Mg4++GDMnTs3FY9MGXiKAT4p0cQqk7poSh1HPOZX3uHlQAmaCGhBUSXYtXoGj35tT1LHBxZNQuvXrxeLll0HpzagQ6PpZxlWpI78fmRSRyrls88+a0ggDVibXzko6qyiosLyyLxgMIjLLrtMpI+xgpVPHbUlHQYOtEzSKqXu0EMPxahRowyqH92bQOSR/kYToyq/XzRwk6VTpY5+lvNZUf+gqEu+ONj51MntHYlElEodP5dZRerkvHdWSl0082tmZmbUzS39nUgdV+qKi4vhdruF+ZbnzwKM6U1U5ldO6rgJmMpKPs/k72XlUwe0LuBWPnXhcFgZmcy/IwdKAMAXX3yBnj17Rs0iwO8VDAbRr18/HHbYYYayyc8juFwuFBQUYPHixZg/f77o33aLKfUPqldS6vbdd198+eWXYvzy9q6rqxO/19fXG/K5EXJycsScbTX/5+XlIS8vDxs2bBCkbrvttjOMew7Zp46EhE8++UQEM9J8b6fQHXTQQVF9tIHWY/XkE4aoLonAqDbOVqTuxhtvBGC0wPh8PsO8qxqrsmk9mvmV+ndRUZHYcFHeUKs52YrUxarUAWpSt3btWsf9n5ASUvfuu+/ikUcewZIlS3DhhRciLy8PU6ZMwYUXXojRo0fjvffeS8VjUwJO6niH4BMr0Nq4sSp1VuZXK3JoReqCwdbzWGlw8IldhaKiIpF7jzsmq8yvbQW+K+OZ7U8//XRceOGFYietAk0YBQUFYgKIxXHeSqkjUqTarZH5laCK6iZSN3z4cHz77bfKsqvyKqpAbW6l1PHJfbvttrP1qSPiZnWixObNm+H1erFx40YxyUWLeFVBZYZTTcIqUieTscbGRuyzzz4iwtppoISs+FGdyKQuEAiIQCaVv5us1MlkhScTJdj51NmB2ov6sqzU8XvyBTkjI8OgAjY0NAh1VEXq+IIlkzpSRO3Mr1akLisrC8uWLRNHGqren+rL7/eblLqpU6fi1FNPdWx6qqioMGU0iKbUAS0bAzK7U2R7NKUOaCV1VoSXf3baaacZ6ke1TmRnZ0cldUBLW2/cuFGQukGDBmHBggVKFxnZ/Or1ekXfIF9xJ24fXq/XkV+Xy+XC3LlzcdZZZxk+/+yzz/DMM8+ItlSROqtAid133x2hUMhgXv3111+x++67i99VY5WbqgEjqeO+26rvERG0O+EHUJvagdh86uj+oVDIROpeffXVmOMDUkLqFi5ciHPOOcfUCW6//XY89NBDOPXUU/HYY4+l4tFJByd1fJDTxEpRtrQQxErqiAQkan6tra0VHcIu0auMffbZR1xLE0Bb+c+pQOYFHuLd1NSE0tJSPP3004aBLIN25/n5+eIIKJXqGE2pk00DVr4egDn6VaXabdq0CV6vF4cffjh+++0304kUQGuqlalTp1q+H9BKIp2YX4nUWfnUkd+c6v1kU0dNTQ2ysrJiJiaAWqkj3HPPPSK6WUXqZMjnMHPTk9VCzHfeBFmdJOIZDAbFWFb5pMrRrU6UOrvoVzsMGDAAzz//vPDPXLRoEYYNGyZSZPDn8QSvXbp0MZE6qjO32204qgtQK3WlpaWoqKgwkDqrhYoWSKvEz9QnVeOHjgbjf+fXye4Odthjjz3w008/GT5zSupIkaRgIyekjurQivC6XC7U1dWhtrYWzz77rCWpu+666zB27FhkZ2eLDbUdqauoqDAodbvuuiv8fr/yDFtqT07qyOxK70cExm7DFsu4HzBggOn6XXbZBePHjxe/x6LUUfl5/5MjZ1X1L5tf+fnL1157LYCWCHt500BuMnT6iZ1SZqXUxeIDxzM3cFLX3NyMtWvXYujQoY7vBaSI1Nm90AUXXIBp06Zh4sSJqXh00sFJHSc7pNRdeOGFWLt2rfBHiNX8CrR0DKukqVbfoWdxkwxN7HZHMsmgwRIIBBydjZdqnHfeeXjiiSdw/PHHW5q4rHD33Xdj2bJl8Hg8OOCAAzBp0iSDEzhBFShx7rnnYvr06UqlzmoHCZjNr7JjP9AyAWVnZwvFRfZ7A4DffvsNBx98sMm8K4PKZmV+5ekRZFKnigTkZJ4vNPze4XAYPp9PTJL77bcfrrzySttyys+hPHeNjY2GMVJRUSHUEStS98UXX4if6ag2ghPza0lJiSlCVEXqZKVORfxVSl08gRJO4HK5MG7cOFGee+65B3PmzMHMmTOFqkL35Hkau3TpYjK/Up1TH+D9W0XqvF4vevfuLVKe2Jlfzz33XADmuc8u3xiHbHbl11klG1Zhjz32MJkho5lfAaM/YkNDg5jnnZhfw+EwIpGIZd3k5eUhPz/fdNwc78P33nsvXnjhBUfmV8BM6vbYYw9kZGRgxowZpmtVSp28ifP5fOLIKyskK/CQkJeXJ6L4CXabZ8DYHvLmWaXUEVlSmV8vvvhiLFiwAOeccw78fr8h4W9RUZE4sjEaqbNS6uh3noPQCrz/kY9lfX191PqwQrscE3bEEUcoO2A6olu3boIp8w5ISp3L5TL4D9DC6CS5shMpWIaVUldTUyNMMFwVueCCC6ImWaZFNxAIiMTC7QWPx4NLLrkEXq9XmMisFmsZbrdbmHoyMjLwz3/+U5mEWqXUvfTSSwBgIHU8vxH9TYZMAOW8agCwdOlSZGdniwErHzMGtLSfqqyq59EpEXySo0Wdk7revXujublZECRVItZgMIjGxkbT6RTyucBbtmwRpG7WrFl46KGHopaVQEqdijxSChaglcjNmDFDnDUJtByh8+uvv2Lu3LmmiEYn5tfi4uKopI7KGCupcxIoES+pI5DZlN6hubnZZH7lpK6srMyg1Pl8PrEwEfngpJm3Oy9rQUGBqFs7pW7s2LFobm42WSmoLoYMGYKsrCzLhKw86pD/D8SmeIwePdp0vRNfJ75oR8vrCBjNr0QAnZgmMzMzTSeJyGWNh9SVlpZi//33x4cffmi6lsYcP61Bns9qamrEWmaFWMi1E9D7qSwiVuOkf//+4mc5BY2q/j0eD3Jzc8WpTbJPHXfR4T6pXOGrra2Ni9QRQaczpO3AFdL9998fLpcLDQ0NtmKCHdrt7FeefTmdkZGRgTlz5mDcuHGmvF0qH4RYza+A+sgqu/IQ8vPzhUxcW1srSB1fcPr37284YNiqHLToUobr9obb7RaTazwRQHawMr8CraSOVCUgNqXOikBnZ2ejrKwMbrfboOYtWrQI27Ztg8/nc3SeLlfq+OJHKi0ndZRLj8iAPPGR+ZWbJQikngEt7z9v3jzstttuUcunAm0arM7upWz8RDROPPFEcczOqFGjsG7dOgwePBhDhw41kTrKU0cmMyulTpVclsrGy8jNr6qNmVOlTja/ut3umHJNyeDtEwwGTSmUZKXO5/NhwYIFWLJkCdauXStSSaiUOg6+sHJ/0WgpTawWVQC45JJLEAgETOcYE+gdZMUuVnTr1s2U1sKqTECLuADETuqovFzVc+JDxY84VK0T8mbFCmVlZdiyZYuBaO+6665KCwCROtqwyBHuQCups8LChQuFuTJZiIfUkasQYLaIWPVN8o+jhNtOCBLVxXbbbYdt27Y5InWqvlJaWupoU8Lbes899xTjLt7NYLuRuo4GOcUDd1bmiMf8Ggup4zuU8vJybN68WZhayATMc9c5UbhI8YsW8t2W8Hg8lrnAEoUdqeNJTrkCBKiVutzcXIOpSyZ1PI9RRkYGKisrsX79evzwww94+OGHMXDgQAwePNgxqbPyqcvJyRH5pyorK3H11VeL/rl161ZlriNufpXbne+K6+rq8MMPPyhPaXGC4uJi/P7776LvqhY0nkR4u+22E5/v8P/tnXd8FNX6/z+bTbakbSokkIh0sIEKCEogCqLYFQUF20UU27U3vl4rKnbwWlCv14KKgqBYroAChi4KUlSkSUtCKOl1s9lkf3/k9xzOzM7MzmzJ7obzfr14AZvN7OzMmXM+5/M85zndukk6cCWnrqWlhQkyPeHXefPmsbQKXtTR5/vaexfQt1Bi9+7dWL9+vc9Vd3qQXzO6t9S++BBORkYGampqcNJJJ6F3794oLi5mEzslp66wsJC1YX4g4R1rrfCrGiQofLVr6jPlTp3RzwOk6TJKe3vTsSdPnoxvv/0WgHf41YhTR32UXsFO7UCpn6XX+BqESlB75kWdWrkkvaJO6/P69u2ru9C4XpREnS9nqkOHDjj++OMBeEc71M4vJSUFVVVV7DoYEXUAsHDhQs2FEvQ9At32dOvWrfjoo48kz50QdSGGr3VFocpwOHW845CZmQmn08m2NqKOmzpovWFLEnVUnHHFihV48803dZ9TKAiXU9fS0uLV4Wg9XFS1nJCHX6mzpA47OzsbBw4cwBlnnIF77rkHALB//36/nDr5NSG3btiwYXj11VdZ51RaWqo4QNJERalIq8lkwqpVqwAAu3btgtPp1FykosUdd9yBv/76iy0GiY+P9+oEeVHHC47s7GzJVlZKCyWA1nultrVTSkqK5Lm555572D3jhQQ9N3ztMDl6F0o0NTWhe/fuGDhwYFBEnXxgob6Hvj8/mUxPT5cMekVFRV5OHV1j2qua2iIfmqO2Ron1RkWWr8KthJqo8+ea8aks8v2FAWlpDRJigYRftfZVVkLLqaNj3H///ZrjCO1z3tjYKBF1StUOtEQdnbsvpy4U0DXXu0cysWfPHpxxxhleFRq0nLrKykrF1A815M+aHqeOnhF/6du3L0t9EqKujeCdunXr1gGAZDNr/n168SXqMjIyvLY+kYs6AGzLGsrtq6mpYVXv9Yg62p6GHJu8vDzcfvvtACBZsdSW8JtO63Uc9WK1WrFnzx5JXhi5WC6Xi90P2ljZl1PHI99Lk4QaX2xUaTFFaWlpwKKOBjTqlOh7fPjhh4qLYLScOuCo00FhHXntKb3Iw8B8x0rXnQ+j8tdHfr2Uwq9A6+Cg5dTV1tay55evfack6kgAKeFvTl2gDri8bchXv/LXhO9PjjvuOBQVFak6dVR4mPoQuoYmk4m1NaNuFGFU1MkXSvgj6ui7T5s2jW2nyEOfodSGAKmoU+tz/A2/8sdUEheUrH/22WdrHoOcZH6yRk6dfLKkJeroZ+EQdVrhV199vVKRZDWnjsKvfK1GX2RnZ7PdgYC2EXU8QtS1ETQAVlVVYcyYMcjOzsaAAQO83keDlB47ljoYNVF35MgR/Pjjj5LX+DASiTraySA1NRUJCQmSFY96nToSgvzg09TUJNmbsC2h8GtzczNaWlqC6tRZLBaUlJTg3nvv9dow2eVysYRuKhGg9XDJz2vLli2S/9NgTO/r1KmToqhzuVy6OlbK4VNypag90Gf6coCpTctXlBLykhT+ijo6TwoPKaUoJCYmoqamhhUVfeaZZ7BgwQKvfNCamhpNp05N1AFHUxeURJ3dbmeDnFb41d+cukCdOnnbIKeOhBZ/Tfh+idoKuY+8qIuNjWV7cZOzxw/88fHx2LZtG2sbWtdFCWovvnYqUHPq/JnIUdtXc1upPfHCPS8vj51vXV2dz1BdIOFXEh9K4oJcfrUFJQTdhyNHjkhEHV+eiKDvQtElI6LujTfewDvvvKPrexkl2KJObUIsF3V6JlexsbGYNWsWWxyiNdmm7xHMMmAip66NoJWCf/zxB0pLSzFt2jTNPAOqmaYFuTrnn3++7vMYP348+zctjPjiiy8AtDY+2uKEBhU9YshutzMHkG9AfMV6f/JbAoHCr75CIf7Af0f58V0uF5KTk9GhQweJewEodzZ0XX788Ueceuqpqjl1JPYp/KqEXqeuublZUoGfoBVcdBxfDgm5z/KQJkHfd+/evbDb7brOTwm6RuSEaeXUkTs6dOhQXHrppV6uxcGDByUdM79yVi3MSWKXnBAlUccX79XqRHlR5/F44Ha7JfdBrfhwoE6dfNCl/9O15YUyv1hAXveM3k/7xwKtG9Tv3bsXbrdbcg3lqQU9e/Y0dM7//Oc/sWbNGkl+phJqCyX8uWbUttQG2YkTJ+Lw4cOSCMTll1+O3377DXfddRfq6+vZNVOrShATEwOLxeKXU0ciUOkZIEdbKReQR03UAd5bQ5IJoOXUFRcXK07Y7rjjDtxyyy2+v5QfqIk6com1kIu6mTNn4rvvvlN8rz9OHf+7gPduLTx0vGA7dfX19ce2qNuwYQPy8vIwfPhwjB071udG9v5AKwVp0L7ssstU33vgwAF89tlnPo/56KOP4o033vC5Yotobm6WbBQv73QyMzO9RJ3e8CuJOqWOtKqqyueWY8GGnLpQizq6TtQ50v979OiBnTt3Sl5Terguuugi/Prrrxg5cqRiOJ46W3Jus7OzFYsP8+/VgjpDpfBrr169AHgLSTX0OnX79u1Dp06dDJWX4KFr68upo7pQ9H+gtWN97LHHMGHCBMl5878HgDksWmFkpZXJdCx+oNDqROPj42E2m1FeXu61KwXQes35fFDA9/ZEepC3DfreSk6d1WrF6tWrWZ4c/3O6F2+99Ra7Vn369IHb7cYff/zhJep4+EUIejCbzbr6tmDm1NF30qq5mZmZ6dWWTz31VGRkZMDpdOLFF19k79M6Z3+cOhqolZ71//znP/jtt998LkrQI+q2bNmC3bt3s+LrvKijkHtjYyOam5uxf/9+dOnSRdf5Bwu1hRJ67jk9q71798ahQ4dw6623qlZtCETUUZ+tJero+gdT1FHq0TEt6jp37ozFixdj+fLl6NGjBxYsWBD0zyCnrqioCElJSZqhsuzsbF2zzN69e+OOO+7QfQ7yhz0uLo59zqxZs2CxWPwSdbxTp3TeycnJfrs0/iJ36oKZUycPMQPeDktWVhabsWuFBUwmEwt3kajikbtlnTp1Ug3NGxF1/DkTJCp97UVL8CVNtJy6wsJCn2VxtDDi1Cl1vk8//bSk3tO2bdvYv/U4dZ06dUJMTAwKCwu9Jnz0HfmO29f+kLTXptZOHcEOv8qvGQkh6hN4oRwTE4MzzzxTsrCF2jz//em1oUOHIi0tDXPmzEFDQ4Mk/Cr/XqGAPoeelUBEHQlPrcUualBbovFDq26k3W6XOHXBEHXJyck49dRTfR6DRN3hw4e99lSlUjb9+vVD9+7dFUXdZZddhgsvvBBOpxMlJSVwu91tLuosltatAuVOnRFR5/F4NPerBbxXvxoRdXSvtEQd3fdIEnVtG1MLEfwsm/Y4VILyCgilJeBq8E5dIANcsImLi4PT6WQPeqCiLtDBJ1iQU2ckjKwXvkOVH5/+b7PZ2L68eq+lUq4HDZy8U6fnvNTgB1r5+dCAplfU0URFzeHiBypaOOMPSk4diRFyTJKSklRFHSANj44dO5b9m89vUvsesbGx6NSpEwoLCzF16lTJz4w6dUDrdd69e7dq25SLumCEX+XOk3yHCKXBim9P9POhQ4ey1/iiv4MGDcK2bduQnJzMrgWJnEGDBuGRRx4J6Py1oPOgwTMQUZeXl4cNGzboEkdy5M+fVp9js9nw5JNPsiL6Wq4eD40/WmUyfJGUlASz2YympiYvp66oqAiXXnopey+JOnlOHdWFpHxZKhXSVvALcQgKv/qCtoDU01/S6ld/nDp6hrVEXc+ePXH77bfj0Ucf1X1cXwinjmP//v1YsmQJLrroIsWfT5s2DQ6Hg/3xlbvAwzt1kSbqAAQk6mw2G8u9iJQ6ddRZ0WAWTFHHz8DVwq82m409VHofLnr48/LyWO6j/He0ZsR6Oim+srv8mvTr1w+vvfYaHn74YfaaVpFvX+FXPkTlzyApP8/q6mrExMQgLi4O119/PR588EEm0LScOqBVDJeVlcHj8eDcc8+VfAegVchqFRfNzc3F/v37vfIZ6dzkOXWFhYXYsWOH4rG6detmyKmrq6sLeLLE5wECR0WdUviV4KMJfA00yifjz4nKGvGTVjrmBRdcgMsvvzyg89dC7oiYzWaMHDnS0K4lPKeddppfqQJGFoLQtSNR52txA0HOXiCRD5PJxBbKyEUdL+iA1pzK9PR0OJ1OL1HX2NjoVQ6rLVHaY1vPc9K5c2fs2rWL5ZJr4XA44HK5mGkRCqfuzTff9HsRmRLHlKg7ePAghg4d6vWnvLwc1dXVuO666/DBBx+oCoApU6agqqqK/fG1fRYPOXW1tbVtHoqcMWOG6sbW9FDLRZ3R1a+R5tTR9yLXKZiiju+86TrRIEC1gqxWKxOUH3/8MQDf15IefpfLxaqfy506rZCO3vDrfffdB8D7mphMJtx1112S+okbNmxQPRbV61MLvxo9NzV4py4+Ph4mkwl2ux0vvvgia29yUaeUd6d07eieUGhXbVKSnp6OiooKWCwWSQcsLw0CtF6XnJwc1YUBOTk5KC4uVn3GqK8gqqqqAn6u1Jw6cjy19r4EvHPuAO8tB51OJwoLC9lkl/oErUEtGJCDS59jMpnw448/4swzzwzp58rxR9QBrSJDawcIJQJx6oCj50rPllq/Ul1djTPOOAPl5eUoKipi5039W3l5Ocxmc5uPaYD3FotGck+7d++uy12k55sWSRkRdTTmBnqvjBIbG4vm5ubo2vvVX7KysrBq1SqvPw6HAxMmTMDjjz+umNdEWK1Wlh9mNE8sLi4OLS0tcDqdQRUYerj77rtVF17IRV1ycjKqqqoMh1+JSHHq6BqTqAtmPg/fefMFpadOncpyHKl2n9vtxpIlS3D22Wf7TGCmQcnpdDKRKO+ktBwEX6tV5Z+j1414++23FcOnNAFQc+p4Aun0yU2SlyPh8eXUaR07JiaGiTq1QYEGEKfTKRkAlXKhfLU1cjnUnLq4uDjJ88qvNPUXeb1IOt4VV1wBQLl8DX/P+M9XE3X19fUoKipioo76Ur0LufyFFseEWjz6gp8M3X333Zrv5duI1pijRrBFndbq2wsuuABAq2vH33sSdXq3swo2CQkJeOmll7B+/XoA+hdKGIGuM62qN3L8KVOmoKmpqc2vjdypa9eiTo25c+dizZo1mDp1KvLz8zFnzpygfwZd2Nra2jYXdVrIRV1OTg5+++03FoLTG35V+nc4CaVTpxR+ledjUadXVFSElpYWXTlFNCg1NDSwjsDI9dS7FQ+FCinnzxeTJ09W3CGECpaq5aLxBNIuYmJiEBMToxmGJFFXX18Ps9ls6H5bLBbm9qidJ1/Q05cj46vjJydOK/zKh26D4dRdcsklkgU21FbGjBkDj8ejuLsNH37lRTL1CXJRV1RUBKfTyURdXl4eqqur/d5JRC8kyMMt6vh24Sv0y99P2pXDCIH2Z3JRp0Xfvn1Z+1ASdUZ2NAomFIqmCWcwVonLoXZfUVEBm81mSKDRSva2hkQd1SI1ukVbuxB111xzDcrKylBQUICCggKMGzcu6J9Bir+8vDxkq8D8gUqrUAdNIaMffvgBgP46dUSkhF/pvMm5CXX4Vd6hUKdHicR6VoeR0zZy5Ej2ICpdz7Vr1+Khhx7y+/xJ1FEStL8kJSWhsbER27dv9+mMBTpbjY2NRUNDg+p9TEpKgsfjwZEjRwyFSADpbhB6nDq1UBktEPD1DFB+rdZCCZ7KysqwPFdGnToqis3nV7VF6Onxxx9H9+7d2zzMJcfIPeLfqzefLpgYEXUUoaJ/A633vqGhgTl14YCeWXp+9C6UMAK18YqKCsP9SrjgnTp/+o12IeraAqVl5JHAK6+8ItkHT54HZDT8Gq5ZmxzqrEIt6lwuFzwej6Koa2xsxL59+wBIN5nX4tChQ5gxYwa7D1RmhHdZBg8eHNDEY8SIEZg8eTJuu+02v48BSGvoqeVsBovY2Fg4nU7VQYjaXVFRkV+izpdTR0nZWq4k5ejocepcLpeqUyf//WCEX/Vw4oknSv5vt9thNpu9nE81UUcYWUAWDM477zzs2rUrLCFAf+HvcaidTCWURN3LL7+MmJgYXHvttZL3Wq1Wdq/pvMmlD6eoo2eWF3WhdOqiSdTV19fj/vvvl+yNq/v3Q3BO7RKyr+vr6yNK1MXGxkqEmD+ijh74tLS0iGn4ocypkxcfbmpqgsfj8XIznE4n/vzzT3Tq1En3dSEX7dxzz8XOnTuZwydP/A+k87JarUHZvo1EXWxsLE4++eSAj6dFXFwc6uvrVUUdFdL2V9Tpceqolh3/vXm++OILzJ492+fzTW2RJhzytikP77rd7jZx6n7++WdJ4rnJZEJycrJkJS6gHn4FWu9TuMOg0QDdz4kTJ2L48OG6fy8rK4sl7QeCkqi7//77cf/99wNo7cv//e9/s3OVizqHw4Hq6mqUlpaGxWkEjjp19B1CIeqi1anbtGmT378vnDqd8DkrkRR+lZOUlCTJn9JzrjRT05uo3xaEMqcOAD744AMAkOyXKHfqGhoasGDBAowePdqvz+jRowfi4uLw5ptvYtasWZKfRUIbInHTFi6Sr4RumpgUFhaGxKnjw69WqxWzZ8/G5s2bJe/JycnRFRane0cCSt42ldzuthB1iYmJXoLM4XB4XU9+BSRB7+ncubPhHJ72xNlnn4277rrL5/vonhtdRPLHH3+wlI5A8BV+la/mlt9zh8OB5uZmrFu3LmzRGT71BWgtSeZP0WgtqD8oLy+PKlEXCMfu02sQXtRFklOnBF9TTI946N+/PwCobl8VDuROXbCvOQk1qrQOeIs6j8eDnTt3eu0/apTbb7/dq7OKhNxFymFqi86OOiq1yvuBhl/1OHX19fVspe8111yDE044wdDn8J8HqIs6pYUY4VqAlJyc7PXZvkTdscyyZcvw2muv+Xyf2up2X6Snpwdl9wYjos5isXjl+NJ41tzcjCuvvDLg8/GHQYMGAQDKysrQ3NyMv/76yyuFIFCo7VdWVkaNqOP7SK3tSNUQok4nSUlJ7MGIdFHHr3rTI+pow22t/RLbmlDm1PHHc7lcTBDwidr8QKhUMy1Q+Pty7rnnYvLkyUH/DF/odeqCUZjUl1OXkJAAq9XqV+erN6cOaA3DBCqoqe2oiTqlZy5cIj45OdnretL58YMHvcdIrTZB+O6rUadOLkL5MaKt6wESP/74IyZNmoSSkhLMmTMHTqfTr5XEWlC7jiZRR/d01KhRbJ9eQ78f7BNqr1AV7/Ly8qgSdXrO1Ww24/nnn5fsVhBu5OHXYIcr6XhNTU2syKpSBX4gNCKe/z60Urmt0SPqamtrgxKO8yXqTCYT0tPTceDAAcOdb1xcHMrKygBoO3VAa62uQF0zX05dJIk62tBc6Vz4hQl0zcO9AjXaiHZRF46SHURycjJOP/10vPfee5gwYQKAowvLgkVcXBxMJpNXznQkQ/fE33FHOHUGIMs6EvKhtDAq6gDg4YcfxsiRI0N1SoYJZUkT4Og9dLlcbNDjQ+x8BxCK+x0J4VcSOgMGDNB8TzBmuHT/tAYRWixh1Bm1WCysHIdWTh3QmpwdLFH3999/IyYmhp03Qd+VNobXOi+jbNu2DTt37tT9fi2njhfr9J5w7CwQjdBq9mgQdRaLxUvUUV8XiiiEEeTPTjC32wJaJy707EWbU+fvuCOcOgOQWIp0p87oljWRSKgXSvDhV3LqeFHHd9ahdOrUcszaApPJhA0bNgR9dqyEnsr3OTk52LJli+EQoFK5Djn8MxHoQEz37vnnn0fXrl29BBstVuAnV8Ea/ClVQi+TJk3yypVVcuroOwinzhjRIOq0nLpwjxXyRRqh6A9pX2Mh6gRe0Kwm0kUdPcC33nprmM/Ef0K9UIKqhTc1NTGnri3Dr/TghnsQPe2009rkc/SIOqqP1rVrV0PHdrvdXp8jRy1f0h/49kATAp7bb78dGRkZGD58OCskGq7B/5xzzvF6TUnUkSMunDpjRLqoo35OLupIzPm7WChYyJ26UCCcOoEq0SLqgNZcqGhpxErwCyVMJlNIZnBURNblciE+Pl5yX0MdfqVONtirvSIVX6tfASAzMxOAcVFHq5cB9Z0v1ELr/sC3h08++cTr57GxsRg/fjyA1kHU7XZHVD4PP+ATtJDEn31Mj2XCJepo4Z4vUWc2m2EymbxEnclkwsKFC9kK1HDBi7pQtT0h6gSq0AMR6Tl1QPht9UDhnTpKdg3FZ7hcLtTX10tcOiD0Th0ALFy4UDOfrT2hx6lraWkBcHS7Lr2QqPvpp59U38NXzQ9W+BU4ulm6GjabTXPP23BAuXT8M5Wfn4+VK1firLPOCtdpRRWUUxcusU65nGqfLxfuSiVYzj///BCfpW/48Ouvv/4aks8gMXesiDqxUMIAga5KEeiHz6kL1fWmjdmrqqq8NkQPtVMHtHaqbRF+iAT0LJQ477zzABh3L0nUaYWy+Q49mE6dL+SV/CMBEiTyVc1Dhw6Nqq26wkm4F0oArQtxJk2apPgzPaIuEuCfpVCF/o81p65dibrPPvuMhXBCgRB1bUdbiDpy6iorK72culAvlDjW0OPUDRs2DB6Px/Deo3pEHXC0Uw+mU+cLGlAiKfxKjqgQcIETzqjNkCFDVMc7+XlFqqgDgH/+859YvHhxyI4fic+gFiL8+v9paWnBvHnzQroZNQ3u0RB+jXb4kiahut6UU1dcXOy140NbOHXHEnpEnb9Q0Wxfoo4WA9D+vP5iROQr7d4QbpQK0Ar8I1KFcbQ4dQDYHrWhQjh1Ucrs2bNx5ZVXahZKbWxsRHV1teSPEYRT13a0VfjV5XJh3759Xlv3tEVO3bFEKEUdOXV6wzd9+/YN6POiPfw6aNAgvPfee2zzd4Fxhg0bBiD8q9fViCZRF2qoX4gWUUeLyY7p4sPNzc2YO3cuxo0bp/m+adOmweFwsD9GXT0h6toO+UKJUGCz2fDKK69g+/btXqKO7/yEUxc4ela/+guJOr2FVANN0Yj28KvJZMJNN910TA7wweLOO+/EkSNHvHJxIwV5G6VnI5w7SIQLet6jRdQF2ldG1R0+ePCg4ubDN998M8aOHetzO6MpU6bgvvvuY/+vrq42JOyEqGs7qAp6VVUVK+YabPgVwvKNzPm2JO534ITSqXO5XAB8h8Ly8vKwatWqgD+PBkw93yUSnTpB4JhMpohe5ER9G6Um/Oc//8Enn3wSsc5iKCHhHW2izt/tGaNK1GVlZSl2yg8//DA2btyITz75BDt37sS9996L6dOne73ParUG1LkGGusW6MdkMiE5ORlVVVUhy5PknR2tFZfifgeOntWv/pKVlYWDBw/6fN+yZcvQ3Nwc8OcZ+S5C1AnCgbw/y8rKwgMPPBCmswkvVKg5nLv3GIH6FX/zNaNK1KnxwgsvsH8PGDBAUdAFA+HUtS20GXmorjfNZu+77z7NyurifgdOKJ26tWvX6toPNTY2NiifbyTnhcRcJIVfBe0fMYk4Cjl1tHNQpHNMOXV6WL9+fciOTRebSgIIQkuo99olUecrL+ZYzEMJNqEUdccffzyOP/74oB9XDZpB63FwhVMnCBfbt29nqQnHMrRfcrSs9qb+RYi6NoAGJH6vSUHoCLWoo/Crr903IrVsQTQRSlEXLmiw0EKIOkG4EFu+tXL22Wdj9erVGDJkSLhPRRdkGglR1wbQgBSMvByBb0jUhSqnjcQc7XspCB2hXP0aDn766SecfPLJPt9ns9kQFxfndwctEAgC58wzzwz3KehGiLo2hJR+Tk5OmM/k2CDUTh09NNG+T240QI5Ve3Hq8vPzdb0v0MVZAoHg2EKIujbk4osvRnl5OVtNIwgtoRZ1tH+jcOpCz7FaJ8tmswlRJxAIdBOoqBMxAYMIQdd2UCXwUIs64dSFHrrGx5qo69Kli1dha4FAIFBDiDpBu6WtnDoh6kLPsSrqJk+ejF9++SXcpyEQCKIEytkXok7Q7gj1QgkSdcea0AgHx6qoM5lM7WZxiEAgCD3k1PnbbwhRJ4hY2sqpE4QeyqkTNR4FAoFAnby8PADA8OHD/fr9Y2vaLIgqQi3q7r//fmzdulVXaQpBYJBT19jYGOYzEQgEgsilZ8+eARkOQtQJIpZQi7quXbti2bJlqj8vKCjA3r17Q/LZxxpC1AkEAkHoEaJOELGEOqfOF8OHD/fbAhdIIVEnti0SCASC0CFy6gQRS6hLmgjaDsqpE06dQCAQhA4h6gQRS6jDr4K2gwrwClEnEAgEoaPdiLqCggKMGDECw4cPx9dffx3u0xEEAeHUtR9omzAh6gQCgSB0tIucOqfTiVdeeQULFy4MW/6VIPiYzWYkJiYKUdcOEE6dQCAQhJ52IerWrFkDu92Oiy++GPHx8Zg5cyaysrK83tfY2CgZVKqrq9vyNAV+8Morr2DYsGHhPg1BgOTk5GDcuHF45plnwn0qAoFA0G4xedpBBdbPPvsMr776KlavXo2lS5fi66+/xttvv+31vieffBJPPfWU1+tVVVUs1CcQCAQCgUAQjUSVqDt48CCuvPJKr9dvu+02rF+/HtOnT0djYyPOPfdcrFixwut9Sk5dbm6uEHUCgUAgEAiinqgKv2ZlZWHVqlVer5eVlWHWrFkAgI0bN6Jbt26Kv2+1Wlluj0AgEAgEAkF7IqpEnRrp6em45JJLMGzYMMTExOD9998P9ykJBAKBQCAQtClRFX4NNtXV1XA4HCL8KhAIBAKBIOppN3XqBAKBQCAQCI5ljmmnzuPxoKamBklJSTCZTOE+HYFAIBAIBAK/OaZFnUAgEAgEAkF7QYRfBQKBQCAQCNoBQtQJBAKBQCAQtAOEqBMIBAKBQCBoBwhRJxAIBAKBQNAOEKJOIBAIBAKBoB0gRJ1AIBAIBAJBO0CIOoFAIBAIBIJ2gBB1AoFAIBAIBO0AIeoEAoFAIBAI2gFC1AkEAoFAIBC0A4SoEwgEAoFAIGgHHNOizuPxoLq6GmL7W4FAIBAIBNFO1Ii6X375Bfn5+cjPz0fv3r1x7733sp8VFBQgNzcX+fn5GDFihO5j1tTUwOFwoKamJhSnLBAIBAKBQNBmxIb7BPQyaNAgFBQUAAAmTZqEyy67TPLzcePG4eWXX277ExMIBAKBQCCIAKLGqSPcbjd+/vln5OXlSV6fP38+8vLy8Nprr6n+bmNjI6qrqyV/BAKBQCAQCNoDUSfqli1bhuHDhyMm5uipDxgwANu3b8fSpUuxaNEibNiwQfF3p02bBofDwf7k5ua21WkLBAKBQCAQhJSoE3VffPEFrrrqKslriYmJsFgssFgsuOSSS7B582bF350yZQqqqqrYn8LCwrY4ZYFAIBAIBIKQE1Wizu12Y+3atRg2bJjkdT6MunLlSvTo0UPx961WK5KTkyV/BAKBQCAQCNqKAwcOYMqUKSGpvBFVou6nn37CsGHDWOh18uTJAIC5c+di0KBBOPPMM9G5c2cv0ScQCAQCgUAQCdx55514/vnncfDgwaAf2+Q5hou0VVdXw+FwoKqqSrh2AoFAoIPNmzfDarWiT58+4T4VgSAqueCCC7Bw4UKUlJQgKysrqMeOmpImAoFAIAg//fv3BwBRtF0g8JOmpiYArRU5gk1UhV8FAoFAIBAIohkh6gQCgUAgEEQ0TU1NqKurC/dpRDwul0vydzARok4gEAgEuli2bFm4T0EQwbz00ks455xzwn0aEY/b7QYgnDqBQCAQhBFfe2vfeOON6NKlSxudjSDS+PPPP1FSUhLu04h4Qhl+FQslBAKBQBAUPvroo3CfgiCMFBcXh0SotDdI1Inwq0AgEAgiHhq0BMcWxcXFcDqd4T6NiEcslBAIBAJB1BCKoqqCyMbj8aCoqEg4dT6YPXs2duzYAUCIOoFAIBBEKJT8DbQ6NoJji4qKCjidTjQ2Nooahhq89dZb7N9C1AkEAoEgbGRmZqJbt24AgJaWFsnPDh06xP5dVFTUpuclCD/8PQ9Frlh74fjjj2f/Fjl1AoFAIAgbbrcbSUlJAOCVO8XXJwtlrTKPx4PCwsKQHV/gH7w7K0Kw6pSVlaFjx44AhFMnEAgEgjDidrvZPtn19fVePyNCOahPmzYNxx13HCoqKkL2GQJjOJ1O7N69m/1fiDp1SkpKMGbMGMTGxoqSJgKBQCAIH7xT19DQIPlZc3Mz+3coB/Vnn30WAFBeXo7U1NSQfY5AP9nZ2aisrGT/Fytg1Tlw4ACys7NhsViObadu7969yMzMRH5+PvLz83HkyBH2M7fbjRtvvBF5eXm4++67w3iWAoFA0H5pbm7W5dSFclCnz62urg7ZZwiMwQs6QDh1ang8HpSWliIzMxNWq1Xk1A0fPhwFBQUoKChAZmYme/3bb79FTk4OVq5cifr6eqxZsyaMZykQCATtE96pC1f4lRCiLvLo3r07ACHq1HC5XPB4PIiPj4fVaj22nToAWL16NfLy8vB///d/kiXTa9euxahRowAA559/vqqoa2xsRHV1teSP4NigsrISY8aMweuvvx7uUxEIopKWlha0tLSoLpRoq/ArUVNTE/LPEBjjhBNOABA8p9btduM///mPpG1FM3RdbDabEHXZ2dnYtWsXVqxYgcOHD+Orr75iP6usrGQhAYfDgfLycsVjTJs2DQ6Hg/3Jzc1tk3MXhJ/Nmzfjyy+/xCuvvBLuUxG0ERUVFVi/fn24T6PdQANrQkICAG/h1hZOHT+ZF5PyyOO4444DELz7//bbb+OWW27BkiVLgnK8cMOLumM+p85qtSIhIQEmkwljxozBpk2b2M9SU1PZA15ZWYm0tDTFY0yZMgVVVVXsj1gWf+xAD0+0zvi+//57lJWVhfs0oooLLrgAAwcODPdptBtItMXHxwNQF3V2uz1koo7ffkyIusghMTERCQkJuO222wAET9T9+uuvAACLxRKU44UbuVN3TOfU8Vb7ihUr0KNHD/b/wYMH44cffgAALF68GGeddZbiMaxWK5KTkyV/BMcG1MnwbkK0UFBQgAsvvBAzZswI96nowuPxYPLkydi4cWNYz+Pnn38G4F0kV+Af9OwkJiYC8B64acIUHx8fsoUS/IpbEX6NHNxuN5577jmkpKQACF74devWrQC88zejFboudrtdhF9XrVqF008/HXl5eSguLsb48eMxefJkAMDFF1+MwsJC5OXlwW63Y8iQIWE+W0GkQTOiaBzgv//+ewCtHUE04HK58O677+Kqq64K96kAAKqqqsJ9Cu0CefhVPnCT6EtISAiZU8eLOuHURQYtLS1wOp0s+R8InlNHoi6UxazbEmq/NpsNNpstJJOfqKlTN3r0aIwePVry2jvvvAMAiI2NxUcffRSO0xIYoLm5md2r66+/HnV1dbjzzjvx6quvhrzeVDSHX2trawFET+0nmlXv27cvzGfSSkVFhahnFgR40Qaoh19DKep4x0aIusiA+qVgi7ri4mJ2v9uLqOPDr3a73avWYzCIGqdOEP1QuOT9998HAHzzzTf48MMP8cEHH4T8s9uDqAtFBxBMZsyYAZPJhH79+gEIb6ibH/wjbeeB+vp6HDhwINynYRg+Zw5QD7+2lVMnwq+RAT1r8fHxsNlsAIIzAd2yZQv7txB1+hGiTtBmUBgsNrbVIKak57i4uJB/Ng0y0Vg/iURdpOeV3HvvvQAQEQuQfv/9d/bvSBN1ubm56Ny5c7hPwzAk6uLi4mC1WsMafu3YsaNw6iIEXtTFxsbCZDIFRdTRBgPx8fHtUtTZbDYh6gTRx2effcZmXCTqzGaz5P9t4Z5RTl1DQ0PU5dVFi1OnBL9asS3ZvHkz+7daiaNwcODAgYg6HyOQaIuNjVVM8uZFXahSBUhA8KJu+fLlET/hCQYPPPAA3nrrrXCfhhe8qDOZTEhISAjK/SgvL0d8fDzS0tLapaiz2+0heU6EqBOElPHjx7NwHG0lQ6KupKQEAFipDpfLhR9//DEk58EPQNGSm0ZEi1OnRFVVFZxOZ5s7pJs2bcIJJ5yAmJiYiHLqiouLw30KfsOLOpvNprn61df93rdvn1+OLu/U1dTUoLS0FPn5+bj11lsNHyvaeOWVV3DHHXeE+zS8oH6JwvKJiYlBEWEVFRVIS0tDQkJCuxF11H7tdrsIvwqiH7lTR6KutLQUAPDMM89g1KhR2Lt3b9A/mx9kok0cUe5QtJ030CrkO3To0OaFvjdt2oRTTz0VmZmZKCoqCuhYU6ZMYSuQA+XQoUPs39GW3yl36tTCr4mJiZqirqKiAscffzwuvPBCw+cgd+rWrl0LAFi4cKGkMHEoCKfDHy7H2xd79+7FI488AuBo/cKEhAQ2EQ2E8vJyv0XdoUOHsHjx4oDPIdjQM2O1WoWoE0Qf99xzj+T/fE7ds88+iw8//BDA0dyJgwcPSt4XTKJZ1EVD+JUGHb5+JNAq6mpqatg9bgtaWlqwZcsW9O/fH0OHDsWyZcsCOt7zzz+PCy+8MCiigdo4EH2J/iRCzWazz/Crlqij+oWHDx82fA7ynLp169YBaJ0YfvPNN4aPp5e//voLZrOZfV5bs2fPHgBgW7RFCnfeeSeLrpCoS0xMDJqoS01N9UvUPfnkkzj//POxbdu2gM8jmDidTsTGxjK3W4g6QUDU1dVJEshDSUtLC1577TXJayTW1q5di3/961/sdXLqgrlySg5fuTuSxZES0RB+JYHSrVs3yesUcm9LiouLUVdXhxNOOAEjRozAunXrgtKmgpGYzzt14Uj0nzlzpt9bLvnKqSPR52tHCXoWqfyFEejZ7dChA2pqanDgwAEMHjwYXbp0Ya5dMNm9ezcaGhqwfft2AAhZeogv/v77bwBAly5dwvL5avCL3LKysgD4FnVlZWV44403fDrVgTh1mZmZAID58+cb+r1Q43Q62TgnnLp2RFVVFUwmE6t431Zcd911OOWUU9rks5QeahJ1/MAGHBUr1Njr6uqwc+dOLF++3Ofn6HVPhFMXWujedurUSfH1ULFv3z788ccfktdIwCUkJKBPnz5obm7G/v37A/6sYLSbcIq61atX4/bbb2fhMqP4yqlzu93MxdMS0STq/Cl5U1dXB5vNBofDgerqapSVlSEtLQ0dO3YMiRvcvXt3jB07loVew5WfSXnHtGNDpOBwONi/SaT7EnUjRozAP//5T2zYsEHz2JRT50+OXkxMq7QJZV+/bds2mEwmViBZD3JRJxZKtBMol2z69Olt+rm0uXlb5IYoDeZKr8XHx3vN3KuqqtCrVy/k5+drfsaGDRsQExOj66FqbGxkD1M0iTq32w2n04m0tLSIPm81URdqp+7444/HySefLHmN2pPFYsHxxx8PAH7nafKThmCI6sOHD6Njx44AvEVdqHPCyKXv2rWrX7+vJ6cuNjYW8fHxmteKQvXk1Dz++OOS1cpa1NXVITExEcnJyfB4PCgsLERaWho6dOgQshD/8uXLmaiiqEJbQ88XiZVIgcpT8fgSdTQJ89WfHTlyBOnp6X7tvEBtLJQTYQr3GwnJO51OtqBEOHXtkN27d7fp59GgQQ/TokWLQpasrTSYK+XQ5ObmKoo6PWzatAkAdDl6jY2NbFeBSBZHcqhzzMzMjOjzJoGSnZ3NXktOTg5L+JWvf5ibmwuz2ey3qOPdpGB0wNXV1TjuuOPYvy+88EI888wzqKurQ0xMDGbPno2ZM2eGxMWnyaS/m4jrCb/GxsYiOTlZ8xnmnTqPx4OpU6fi8ssv13UOtbW1SEhIYPt279u3D2lpacjMzPQrR08PLpeLibpgOL7+QM9RpC2YoPvM7/aktVDC4/GwMUfreSJ3vUuXLoiLizP8vamthlLU7dy5E4Ax97aqqortyEJud7BNFiHqwgB1hpQn0VaQqKurq8OKFSswevRozJo1y9Ax5s2bp6savrxTb2hoUPw9h8PBHlhq3HrDUrSxuJ7Zs8vlinhRN3v2bNx9992S1+g6ZmdnR3T4VUnUJSUlhWUxAC/qYmNjkZOTwxLNjRLsUjjV1dWs8HB1dTW+//57PPbYY8xlmjVrFm6//faQ7F9Nos7f76E3/OpwOOByuVQ/hxd1FFZTcnyUIKeOFgxQ3lVmZmbInDqXy8VqCwZjAYA/RKqoq6iowIUXXoivv/6avabl1PF9u1Z/VlRUBLfbjW7duvkl6uj9oSxfRX2Kke0Qt2/fjp49ewI4WgLmkksuCWrbFaIuDFCn1tb5Gbyoo4ZoJFehubkZ11xzDWbPnu3zvXKHpqKiwqtG18CBA3H22Wez60GDhN76VXTuembojY2NLB8lUkXdmjVr8L///U/yGi/qampqAuqkli9fHrLwEV1TSlCmsEk46ktRe6Ik7q5du/rt1PHXO1hOHYWo+QGuLURDoKJOvvpVLfxKeVZqbh0NuE1NTew9eneVkTt1AAyFX2fNmsVcN19Qf+nxeNjvhKvGJV2nSBN1lZWV6Ny5s+T+aYk6fgzQep4oitW1a1dYLBbD7nJbhF/pnPSKuv3792PRokXo27cvgKOi7n//+x/efffdoJ1X1Ii6DRs2IC8vD8OHD8fYsWMljbugoAC5ubnIz8/HiBEjwniW+vA3/BEovKgjQWlkifzBgwfhdrt1DTzyDr26uhrFxcV49NFHkZGRAaB1D1g+p45E3a5du3SdDw2Keh6qaBB1TU1NkmtbXFzMruPll18Oj8eDN954w+/j5+fn4/zzzw/4PJWgzjM9PR0A0LdvX9jt9pBOXNTCFtQ3WCwWAK15d8Fw6oIl6lJSUmC32yWCl0SDmrPZ0NAQcKoEiTp/v4fe8KsvUcfv7kLtQ6+o43PqCHLqfE16amtrccMNN+DKK6/U9Vn89Q63qItUp66yspJFQIjExEQUFRUp5ojyE3atfpj69EDDr6G8X9SOfZkKzz33HBYuXIgbb7wRANC7d28AR0UdcDTqFAyiRtR17twZixcvxvLly9GjRw8sWLBA8vNx48ahoKAAS5cuDc8JajB79mzJYB0Joo7fxUEv9EDqEXVyp+7gwYOorKxE7969cdJJJwFoXcllsVi8rHK+lpcWJOr0zNAbGxtht9thtVojNozJi7r58+cjJyeHJeEOHToUPXr0CHhHgj///NPne3bv3o158+YZOm5DQwPMZjMT7Ndccw3sdjv++9//+nWeeuAFIz+AyPcUPv744/126kIRfk1OTkZCQoJkJSy532qiLj4+HpMnTw7os8mlDUb4NTs7G7t27ZJcdz78CvgWdR6Phw2IesOv5NTxk1Fy6gD1vsDlcrHUhoKCAl2fxedT0ncRok5KRUWF14rctLQ0NDY24tFHH8U//vEPPPbYY2xxxO+//474+Hif/XBtbS2sVitsNltA4de2cOp8ndujjz6KCy64gBXdHzt2LICj1R4AsDw7NZxOJ2bMmKEr/y5qRF1WVhYrbki5Mjzz589HXl6eV200nsbGRlRXV0v+hJq9e/diwoQJePzxx9lrkSDqqMq+kXAPiTq1kNrBgwfZNZV36FQEsnPnzmx3AYfDIbHWaQDVswPA0qVL8cQTTwDQl1PX2NgIq9WK+Pj4iHXqKMeopaWFXa9ffvkFQOu1Csa56xmUzj77bFx11VWGjtvQ0AC73Y6OHTuisLAQt912m6TTCgU7duxg/+a/l1zUde3aFYcOHVLt4LU6/mCGXz0eD2pqapCcnIz4+HhJO6dC3PxzU1tbK/n/p59+GtDnB5pnxIu6s88+G/v27ZMs9pKHX8ePH6/o1vCD4KpVq9gx9VBbW6vq1AHKoq68vBzPPfcc3n//fV2fQfCiLtxlhagdhGvsUMLj8aCystJL1N14443o3r07pk2bhg8//BDPPPMMW6G+adMm9OvXDwkJCT6fO+o/4uLiDH/vtlgoQe1Y69z49u9yuTBhwgTmyskrBWjx7rvv4t5779W1S0bUiDpi//79WLJkCS666CL22oABA7B9+3YsXboUixYtUq1/M23aNDgcDvanLbYuolwZvnHxjaAttwoilV9fX4/ffvsNgLGcOlr5pfQ7LS0tyM7OxhVXXAHgqNt28803A2ityA60iro33ngD8+bNQ1JSkuSB1Svq3nvvPYwcOZL9X0+OjMvlgsViiWhRx88uadAqKipCbGws7HZ7m507uQJ6SmzMmzcP5513HhN1AJCTkwOTySQJL4SCt99+m/2bb5PynDoqiiqvjwi0hiRpI3KlwtzBDL86nU643W7m1Cm1c97h7tGjh2TAVJqlL168GCaTSVe7aGpqQkxMTFBE3RlnnAEAkmvGr34FWtMolPoKvv+jiZmR8GtCQgKsVisLr/NOnVIoLD09HU899ZTkNT2OBy8+a2trYTKZwubUkSsdSU5dbW0tWlpavERdQkICHnzwQcXf2bhxI0499VSf5Tx4UcdHc/TSFgslqB1riTreOKIJHcEXatfbr+tJZ4kqUVddXY3rrrsOH3zwgVdipsVigcViwSWXXKJa82jKlCmoqqpif/zZUNooSrlr4drdgAbpb775hpUD8cepU/qd1atXAzga3tu7dy9Gjx6Nt956CwBYLblOnTohOTkZY8aMASB9YPVu+s67nkDrNfZVyJQ6iWgQdePHj8dPP/0EoDUU6nA4YDKZfNb/0sLI5IEGSz3345ZbbsEPP/yA2tpaLxEXaqeuqKiI5fDx4kHu1NH3URoYeCfs448/9vo5PyjMmzcvoK2oqIMnUacUSufFhlyEKgmRZ599VnJsLZqampCUlBSwqDObzaw/Ky0tZf0KhV/5gUvJOVMaBPWWdSCnDjjap/py6tSO4wu5U5eZmRk2UXf48GGkpKRElKijCYg8pw4AevXq5fVabW0ttm3bhv79+/vsh51OJytxFaklTfSIOn6SUV1dLdEBvIbxZa5Qm9fTbqNG1DU3N7MwprzB8B3aypUrvfafJKxWK5KTkyV/Qg3dVDVR15arA2lgf++999CnTx/06tXL0OdrhV/JiaPl2vv27cPxxx/PipH+9ddfSEpK8lqYYbFY0NLSgubmZkURoeQWKeXq+JrBRJOo++abb/DVV18BaB3YaSZM5+5PfUMj1ftJBOkpR0LlOXbs2OEl6kLt1KnVHpQvlNASdfx16d69u+JnEN999x0uvfRSxXPRI0qon0pKSvIKv2pBz63SZ1CtLD2DXqCijs6DSpoArU78008/DeBo+JUfrJScM6VBUG8/RE4dANZ/p6SkwGazITExUbeo01MLUy7qMjIy0Nzc3ObCyul0oqqqCjk5OREl6qjPVdrlgg8t0mRpxYoVaGlp0eXU8cXi/Qm/tmVOnV5RJ3fqgKPjpa8xidIT2pWomzt3LtasWYOpU6ciPz8fc+bMYYnDc+fOxaBBg3DmmWeic+fOGDZsWFA+s6WlBZ9++mlADYNuqtlshtPpxIIFC8Im6vjvcf311yMjIyNoOXU0a6Nw8969e1k1f4fDgeLiYiYAeGjAdblciqJOyWFSegAor662thYvvPCClxikSt6RLOrUhBflKMXHx2PhwoXo3r27zy125OgdDBoaGtj10eP+0D39448/vEQczbR79uzpMxHYH2inDUDbqaO/lTpf/roo/VyPW3n48GGYzWZJrS4l5E6d3nZIaQ9Koo7SHPTcX7fbjcTExKCEX2NiYmAymQAAn3zyCYCj4VcA+OKLLwAoO2fycz3uuON094Nypy4lJYUloHfo0EF3AWI9ok4efqVFQG3t1tF3ijRRR32+L1FHO5gsW7YMJpMJJ510UrsJvyYkJGiem5ZTB7SaIT179tTsCyh3EdC3Q0/UiLprrrkGZWVlKCgoQEFBAcaNG4d33nkHADBp0iT88ssvWLNmDV566aWgfeZnn32Ga6+9NqBNgemmNjQ04F//+hcuv/xySdHhthJ1JSUlkgaek5NjeE89rfArzdpKS0tRVVUlqZxPsxOlxFAacJuamtDY2Mg6TsKXw0ThMBJ1zzzzDB555BH8+uuvkvc1NDREjVMnhxd1hNGdGvR2ikOGDGHH1iPqKF9t+/btXqKOhHXHjh1DMhg1NjYqijoSZyQwtJw6X6KOnhmtzeep/MKSJUs0z5cXdfy99AVtJq+FHieDnLqmpia/cnn58Ctw9P6So0vhV6C1oCqgHn7ln/OJEyfq7ofq6+vZtUtOTmb3H2jNnVPKr3U4HHjxxRcxcOBA9pqets33PW63O2BRV1tbi8GDB7OoRmVlJaZOnerT5eVFXSQtlNAKv/LihfIdt27dCofDAZvNZkjUUfj1r7/+0r21ZluFXxMSEjTvCaU5Aa3fSe7UUSqD1pj03//+l63c1uNER42oCwc029S7MksJugkNDQ1MFPENTa/AqK6u9tq43Ah84wJaOwit7VzkuFwuHDp0COnp6YodMC/qyD2gAZ8aMj3cPHKnLicnR/JzX2KgX79+AI5eR/pbyanT05mEEyOizmi+Gj9A7d27VzX0x+ej6hn4+AFJLupIOIQqF0hN1DU1NSE2NpY5ScFw6pQGLvkxqC2rQeKHnDqC8sHkExpCaYEHIL32vq5vS0sLWlpa2GCrN3+Vx+l0wmw2M+FG8KKOF9IOh0NV1NGED2hNrNcj6jwej2Swl4u61NRUxclOfX09EhISsHz5cmzZsgWA8fArgIBF3Z49e7Bu3Trcc889AICffvoJjz/+uM8deuj+k1MX6j2C9UJ9PvVPalC/v337dna/fOUHy3Pq3G43vvzySzz22GO6zs1I+PXgwYO6VpXKcblcSExM1BR1n3/+ueRZV0r58uXa//DDD+zfQtQFCDVarQv+66+/sqKeSlCjcjqdXqs8AXWn7tChQ3j00UdZx33FFVd4bVxuhE2bNiE5ORknnHACAONOXX19PTweDzp16qQp6pqbm1meD21cTp0hDV48clEnd/Pkg5W8Q+N/n/+5fOPr9hJ+JYwOyvx17Nq1q66V33pEHT/AyUUdtd2UlBTJno/Bghd12WKnJwAAWBlJREFU/OSkqalJIrB4N1gO3yErXVP6floDF/2erxWc8vAr0OpoPPfccwDURZ1abiOlOgC+nTpqWxS69EeY0ApnEssEPU98+BVoff6V+ka6P4cOHcKOHTskou7w4cNeC0gOHTqEw4cPs/tHoq53796sPwNa25k8t7apqQlNTU2Ij4+H3W5nocCbbrrJpziStxe6P4FOCmkRFN1XtWfZ4/FgxowZksoBQNtWTNCisrKS1f5UYtq0aZg7dy6Sk5NhsViwe/duNjmy2+2a/bA8pw5oFeJ6+z2++LCv+zxy5EhWlL2urg7XX3+9rkiIHqeuuLgYw4cPZ/9XKvYfHx+vOQ7z/epll13m87yEqNOArHytCz5o0CCcdtppqj+nzrOhoYHdfP54asd+4IEH8Nxzz7HQDs0w/Z2lHTp0CLm5uXjyyScBtIq65ORk3RX/qYNLSUlRDb9SDh0VnKUZGnVGSqJOHn7l9w7lP5eQCx/5gK3U4fEz/EgWdUacukBEnV70LJTQK+oAY4s19MCH63kB6nK5JAJLT/g1JibGb6eO2pMvp666uhpxcXGsXiLQGtblBR7PhAkTvL4bD1+f0df9pZ/TZwQi6tTgw68AcMIJJyhGF6i8UIcOHdCzZ0/Y7XY0NzfD7XajY8eOXm79Oeecg44dO7K+ikTEq6++io8++oi9T8mpo3tD15j+Likp8VkKSd5eqf/y16njt0cDjk5E1J7lI0eO4N5778WDDz4Ih8PBrn2k5NUp7SbB88gjj+Cqq66CyWRiYwFNwlJSUlQdaMA7pw44WuVAj6htamqC2Wxmfb8W/G4zf/31Fz7++GPF8kY8zc3NaGlpQWJioqZ72tjYyMZFQNmp8zUm8c/ctddeq3legBB1mlAugy8RoLUDAj2wvKjjk33VRJ38waUGTudy4YUXenV+WlAuylVXXQWPx8Nmrbt379YlFHlRV1dX5/U7FRUVGDlyJPLz87FgwQKYzWb2wJNQU3IieKeNT3yXfy4hf0DlTh0JCZfLhdraWhQWFrJ7QCvkglV0+u+//8att96quxyDL0Ip6vwRVHquE38eWuFXIPiDEe0SkpSUJAmnNTU1SUSdnvCrWhiFilYrJYMTdJ30iLqkpCSYTCYmLnhRx4dpGhoaMGvWLMnx5fChGF9OHf89geCLOo/HIwm/AkD//v2xadMm1ldUV1djzpw5WLp0qeT+UN+mdk7Uv9K+yGqpB0pOHfWv9OzwLqOv1cdGwq/Nzc0+U1nk7Z/er/a9efGSkpKi6Ti3FfX19Xj11VfR3NysuJuEGiTqaEw444wzsGXLFtWJozz8ChyNBunp+9xuN7p06QIAPnfh4a8zTQq07kltbS173uh5UupfW1pa4Ha7JaJOKULCi7rffvvNy902mmojRJ0Kzc3NbCYcyGIGNVGntQ8p7SrA/z7dWOrgv//+e0NbRtXV1XklZ/fs2RN1dXWa4WOCGm1KSgpaWlq8HiyatZEr16FDBxYCpc+VCzbAO/wqt/LlD4uaqKOOjr9uI0aMwHHHHcd+x263IysrS/c2ZL746aef8M477+guTeELtc6a3BX+4W4Lp44P76nhdDpZhy3fikvu1IVC1FmtVjgcDk1Rp8epS0hIUA2/2mw2zfArDUzyXDM5tEUYfR4ASRFdXtRZrVbExMQgLi5OdeDjRZ2vaxvM8KsSdXV1XuHXk046CaWlpayWncPhwNVXX42SkhLJ+foSdVSk9ccffwSgvmhFyamj/oi/tt9//z0A36JOLfyqdJ6TJk1SDK19/fXXyMrKwt69ew2Jup9//hkrVqxg/09NTVWdnHg8HsyZM6dNwrKPPvoo7r//fmzYsEFxNwk15E7dWWedhZaWFq8FbYR8oQRgTNQ1NTWx0ma+9gbnxxhqP0oh9i1btmDy5MlISkryEnVKkyp6jXLLAXhFogBp+PX000/HKaecIvm50dJQQtSpUFZWJtlWy1+URF1dXR3sdjtsNpvXsZubm5GTk8MWaVDjohurJ8FXCUoW5qFGv2vXLp+/zzt1gPcK2IqKCqSmprKOjV8UQYOWkqMlD7/KO2xfTp28o+NFHW2xRdfQZrOhU6dOKC0tDcoqMgrf+FM3Tgm5gKUaRiSO+UG8LUSdHrHf2NiIc889F4D3daBBhoSMP+ewdu1a2O12xWdQS9Qp5dRpOXVquTH0GVolWUh0+WpTvKjjw690nfhJFzlKNptNV/jVqFPnT/vnV57KKSsr8wq/Ul/Q0NDg1V75tqwk6vhIAE18P//8c8n75aSkpKCyslKxn+HPe9SoUTCbzT4nxfLnkfLxlAb8zz77TPL//fv3Y8KECZg6dSoOHTqEjz76SFXUKT3LQ4YMwdVXX83+n5qaqjo5+eKLL3D11Vez2pahhKoN1NfXGxJ1FLomp45cNLUQrFJOHQkurb7P7XbjvvvuQ0lJCbtfvvZ95utAqu3x++eff6Jfv34stUjreZo/fz4+//xzdp78mCbPRwVaIzH8My7f9lKIuiDB15cJJAdLKaeutraWbVklH6x27twpmW0GU9TJO2Rq9L5mMoC3qKPzfumll5CXl8ecOhq0qNI/AFxwwQUAgMGDB3sd15dT50vUxcTEwGw2s/fRYKC0ETuJOkA7ZF5VVaUrGZpEHZ+TEQjy7yrPn+CFhb/hV/6++EKPE+x0OpGSkoL33nvPa1A5/fTTAQTm1M2cORNOp9Nr9xe3242WlhZFUWckp46fcauVNLFarZJ2KU89kCe8//e//8XSpUu9jsUXH6V7ya/SVRogrVarqlPHhxr15tRRH+DPvdBy6qZPn+7l1PHPtrwPVRJ1fF+4ceNGySSYJjiAulNHUYTa2lr8+eefkr1y+WfHbDajU6dOKCoqQklJieqklhd1MTExbOEXLVzgoYkXtY2pU6di9uzZrJ6k0+k0HH7l4Z06+XHWrl0LAIbK5CixbNkyvPnmm5rvoYleZWUlm8jrgc6dnKr4+HjExsaqLkjgw698Th2g3fdt2rQJ06dPx8GDB5GQkIDs7Gxd4xvQel3Vwq/yVac0PiiJuiuvvBLXXHONoqhTQiltgEe+6M8XQtSpQKIuOztb1anTY3fTjf3999/Zfqsk6uRL+RsaGtC3b1/J7/MuE+At6mjvRF8ohV9p5ZKelT5qou6hhx7CqlWr0NzcjJSUFMnWPUSfPn3g8XiYiOQxKur4B/qWW24BIK04Lg9bA0evGS/qtMoIpKSkYNCgQao/Jyg8GQpRN2vWLPTp00fy87vuugurV6+GxWJR7Ni0cnro2GTtK80Y5fgqtQAcDZPcdNNNkjpgQGuoZteuXazj90dI0P2UCxu+w/QVftWbU6d0Tckx4J0/+feQi7pJkyZJ9iYmlMKvLS0tGDVqFJ544gncf//9Xr+j5dRVV1ez51GvU0d9gD9OnZaoe+2111BSUiJ5frVEHe9GUN82c+ZM9trpp5+O//u//wPQOiG98cYbvd4vh9pZRUUF+vfvL5kUyfu+jIwMlJWVoVOnThLByEPX7Msvv0RhYSFSUlJwww034LnnnvOa9NHAS6/L+2lahcvja/Wr/LtRO16zZo1k7KHFKIGGX0eMGIE777xT8Wfjx4/HypUr2ferqKgw5NSNGzcOV111FSZOnAigtf8hZ1UJf8Kvv/76q6QPiouLw3HHHacYZr/11lvx/PPPS15rampSderkEzk6b3qOtSaMVqsV27dvVx0nUlNTUVFRoZrbbrTfFKJOBRJ1Xbt2VXXq9DyM9B5eifOijo69efNmSQ4F4cupoy16fKEWOtF6sPbs2cOEAp9TByiHpPnwq94ZnJKo27ZtG6tSr+bUbdy4kRWf5iuOK4m6U089FUDrNaSZoi/BoqcmYLCdOt4ZyM/PZ84mLbePi4vDmWeeCZvN5tX29uzZg6SkJIk7oXTs/v37A1BeWi9Hj6jjwyRyzGYzunfvHlCCN93P8vJytLS0YMiQIZg+fbqXqOOFj1zUmc1mmEwmr8+vqKhgz1N8fLxm+JUXdfIOnwZnX44LX1GenkVaXPDkk08qPjNaTl1VVRXL89KbUxcqpw5ojTLw9ef4+64V7aD289prr0le37ZtGwDvCama80GCuaamxit0Km+jdrudHV8NOsagQYPYZPBf//oXjhw5ggULFkjeS5MktZCqy+WSXHO3223IqeMXSlxzzTWsfwSO9kN68ySvvvpqnHjiiV6v03eQTyJaWlrw2Wef4bzzzmOvTZo0CVu2bNEt6kaNGoW5c+dKHFOtscef8OvKlSsl/4+NjUV2drYkjaSlpQV1dXV45513MGXKFMn7tZw6pYWBgHY6A52nxWJBr169JAsmeFJSUuB2uyUTnR49erDQtNEJmGFR19DQoBiWoY3c2wuHDx+GzWZDx44dVZ06pca1ceNGidBSc1QoT4eO3b9/fzZ433zzzey9JOqoI1MKv+pZfamUUwe0Nii1kG63bt1YXRxfOXWAVNTp3VeXHliqg2ez2dC7d28mPtREHd9J804ddcRK191msyEtLQ1ms1n3HpFqfPfdd6xjD4VTZ7PZcNxxx8Hj8Xh1wFar1ev70TP5yCOPaB77oosuAgBdK6dLSkp8rozmwyRqUEjO6ArcPn36sFylsrIy1NbW4ueff8Z9992n6dRRyQzCZDIpbgqelpaGe+65B7GxsbBarZrhV14kyjt8eoYbGxuxfPly1e9TUVHBHGx6Fnl3RUkcazl1VVVVLFfJ5XLB4/Hg0ksvxcaNG73ey+cO0vt59KyAb2hokIirf/zjH5Kfl5aWStx4LaeORytHzuPxePVdau+nvkd+vc4991yvBPX4+HhWL04peR2QbotG9OjRg217yENOHbUF+fWVizqn02k4/Mo/Z0o7qOgVdXPmzMHWrVu9Xqc+oXv37vjuu+/Y63TveHeSxpxACvP7curk4VdCTdTJ21hcXByys7Mlk9OXXnqJCTE5RkQd1Z7TI+p89Y80meMdxb///hurVq1i52UEQ6Ju3rx56NWrFy644AKccsopWLduHfvZddddZ+iDI53Dhw+jQ4cOmtWe+cbl8XiwfPlynHbaaZKQaGNjo1dMvK6uDnFxcapFB/Pz89m/6UGiDsafzezpM406dcDR2Y9a+JWHF3W+CrES8s3j6QFQEwJKoo536uieqIm6mJgYpKWleSWjGuX1119n/w7WQgn+4dXqCJREHbULtbAqHbtbt2745z//qbhSU6kmoC8hpuXUEVpOncfjURUU/PZYZWVlksFaS9Q5nU6v9mexWFRnvHFxcaohbaXwq7zD5ycS/LMrp7y8XFJRn/9d4Gib5t0wuajjr1VVVRXLkWxqakJNTQ2++eYbtmMBj6/wa7du3diCFzXq6+sl5/b+++973Tte1Ck5dVOnTsXy5csleWl8+6FEdKC1P6H+T49TR32P3NmkPcJ5+O+h5j4qiTqgdcIq74fpeaqtrcWXX36JxYsXS/JX5eFXXtTpifj06NEDp5xyClsJzE+qqT36s0sID60ULi0txT//+U8cOXIEZWVlkr7ebrejd+/e7P+B9KN6RZ38WVb7nvLcubi4OHTq1Eni1MndPL69aYVf1YSVXNTx50bPrZ6cOsB7NTZN2Oizd+zYoXkcwpCoe+aZZ/Dbb79h8+bNeP/99zFx4kTMnj0bgP9FcSMVXtSpOXX8jW9oaJB06HQ9nE6n1y4Jbrfby6nj4Te+p06NGo1SAr8e10kt/OpwOBQfLHmHRg2LZhV03nyHnJKSwhq5XlFHv0/5afIHWS2nTi7q5NdHSfxS552RkRGwqOMXAZSUlARl6zGlMg9KWK1Wr06HDyMqwd/P2NhYxU5KKbfI1yyRz31RQ+leUpt78cUXFfPP5GJSLur4DtNms7Hr8ccff+Djjz/2cquUnDr+Z76curFjx0pe46Hj+nJKeFFHzhP/Pek6yQUML1L4z6iqqoLD4WBONb1PKdzpK/y6d+9en3vXqoVfX3nlFfZvPsTEO3X0fFx77bUYNmyYJF+Ubz+0Ih+AZE9MPU4dH37lUTpn/hqr3Te6RvK+TB7uB45Opo4cOYIxY8bA4/GwhRWAPqduw4YNqoLlggsugNVqxa5du9ClSxdJ/6U3/O8L/nvGxsaiQ4cO6NChg2SMGjx4sKSI/L/+9S+/P09N1LndblRXV7OxRq+ok0dMzGYzsrOzceTIEXbtu3fvLnnPpEmT2L+1nDq1a0vjHR2fL2hNYlKvUydfDEYaoqmpCf3791fN/ZRjSNQ1NTWxGzpgwACsWLEC77zzDp5++mldidfRBC/q1BLQlVS5/Gf81lckBIDWjkmvqHv33XdZjSaXy+WVEKtX1KmFX+fPn+/lNtF3jo2NRW1tLRMNVK+Lfi4XdUZteYfDgezsbKxfvx6Ab1GntME6P2DT4LFmzRqvz6JBJj09PWBR53K5kJ2djTlz5gBozScymUz48ssv/T6m0gCvhJZTp/Z7/ABFeynK4We65C5riTqq1u6r05Lfy/feew+pqakoKyvDH3/8oVhyQN6JykUddZ4UGqVj03dQKn2j5tRZLBZVJ4+cur59+7LIhJpTN2/ePMXjA0edNK3wK/Wh5MYA8Cp7xLvy1dXVcDgc7NxpcF+7dq1XiRat8KuvnRUINVF31llnsX/z9fz4BSokzpQmHXwfwhdnbWpq8ioeDKgPkjTAyvtiJRHIfw/+fm7dupUNrkacOnpeFi5cyF7jyzppibrGxkY0NzdjwIABOO+887wMku+//565kCaTiQkVgr6vHlGnZb7wEwFaEUw5aMTAgQOZCLnmmmskNdiMoibqqJwYXT+9oo6vWAG0XhcKrVN+mlaBcH9Enfx54scVqrDgqyg5OXU///yz1/nQ33pNEsCgqOvQoQPbrgpoHRx//PFH/PXXX5LXQ8UDDzyAvLw8TJgwQdIpud1u3HjjjcjLy8Pdd98dlM9qaGhAx44dkZaWplqEVe7U8dTX18PpdEpEHS98yalTCu3yOR4NDQ2S8EFjYyP7HVp+zj/gSgNkc3MznE6nYodKHZd81RN1FGazGUlJSSy3zmq1SuqGyXPbKAyhlregxOmnn47Vq1ez49OxAH05dUpO3ffffy8Jfzz44IPs/7TyTQm9u0NQ+6AOjkKF8gRqI/DfVWuSpCXqeLG2Z88epKamYs6cORJRp+bUEX379mWLULSSdN1uN8uD1EK+6wfVG6utrcXhw4exe/dur6LQ9P0eeeQR9O7dGxUVFbpEHf2evB/gQ/Rq56hWfJjaJH1P+fv05ApSH6IVfgWAr776Ct9++y37v1zA8H1RVVUVkpOT2feXixl+MNYqaUKLgnxtzC7PqSP4QUtp9SvvHCqJQr79ZGdnM+fP6XQqikG19hYbGwu73a7LqaPX4uLiJP34iSeeyBZ7qIk6JaeORN3+/fvZa7xTpxR+5R02+p7Lly/3EhCUa01kZmYy8UA78dBxfMGLUXlfV19fj3vuuQfXX3+95HXe1EhPT2cixEgfr4RalIjEGYk6pZy6BQsWSJ4ToFVQPfrooygoKADQ2keQCUXXy1eFALXwq9q1pXZEfRs/6dLr1KWnpyM9PR0ff/wxgNZt8eh86O+QibqPP/5YMvsAWi/4Z599ppkgHAw2btyIgwcPYuXKlTjhhBMks+Jvv/0WOTk5WLlyJerr6xVdGqMsXrwY//3vf5Geno7y8nLFGQ7fucvF2bx582C329HU1MREGl/HifYhlTt1ZrNZ0oHJ4+j8rLdz586IiYlhDbawsBBdu3bFyy+/LPkdpbwUgsJUvDsIHLX05fl6VFdrzpw5+OOPP7zqvZ177rl48cUXcfvtt3t9lhqDBw/Gzp07AQTPqQOAfv36sX/zbrJW+FVPx/j888/jrbfegt1uZ9eUknH1LhCRY2TDey1Rx1+vzZs3o7KyEs8995xkgFJz6oiHH36YTUS0hBAVIpW3HTny4rI08NXX17MO/NChQ5I8RXpvXl4eevbsiZqaGslgzYfr+ftP73nxxRcl5yAPv/LXr6ysTDX8ypfZoe8hn8D5I+qUwq9A64bdvBiQCxh+MkLhV7lTR/B9izz8yn9XcmXkaSJy1Jw6ftCVT/Lo+Js2bQKgLLDkv3/fffdh4MCBaGxsZN+BjzJoRQGUXDSt8GtGRoaimPd4PLjhhhsUP0/pM6hv4ZPy+TCl3KlraGiQhF/5e/X444+zf1utVq8JHi/q1ELzamjVX62rq1M0GvhzS0xMZBNZrYLcelAzNfSIun//+99s4gm03q+ysjJkZWUxt7esrIxN5On509pIwKhTd+edd7J0AXqeeLGvV9TFxcVJ1if8+9//BtBaBs3j8YRW1OXk5KjarbwFHwrWrl2LUaNGAWidufDCTetnPI2Njaiurpb80cJkMiE9PR3Nzc348MMPvYpUykUd33nwjg11lm63mzVQtZy6xMREyUMsF1V8R5eUlCTJr6JEy2effVbyO1qhDxKA8nIKatcmLi6OCeeTTz6ZvU5ixmw248EHHzS0X92FF17I/u1roURjYyMsFotk8Qnv1PH3xGaz4YknnkCfPn0k55Oenq4astaTG0dL4XlRRyEbf0Udfc8333zTZ6kFuahraWlhkyp+4KBOJSYmxpBTZzabvdw1Jb755huceuqpbGapBj0XdG3pWp1wwglssAcg2TKIz51MSkryel5pULPZbF6ijsKpPPLwKj9j93g8PsOvwNEwyVdffSXp5JVEnfzz1Zw6X0JePiDwoo72kDbi1NH2Y/x3pVwk+bncdtttePTRR9n/5X0c4cupu+eee/D888/DYrEoCjKl4qqUJ6nVdymRlJTkVYpHy6nLzMxEc3Mz3G433nrrLfZzXizJz0/JqaNJP//Z8fHxWLJkCUaNGuXl1FVVVbHrzffpACSTcqXrxaeP8OehR9Txzph8EkApOnxZGsBb1NFzEKios9vtiv2tXNTJyy81NjaitLRU8n3r6urgcrmQnp7OJkVU7QA4+txoiTrSB4D3OCAX/larFa+//rqX8+2PqAOkuX50fR9++GG8/vrroRV1PPPnz/f3V/2isrKSDZgOh0MShtD6Gc+0adPgcDjYH6XNdeWQ0p84cSJ69uwp6fjk4Ve+8+CdICOijhpwQUEB0tLSFHP1+NkrP6CRUJF3zvJadzxXXHEF+vXr5zVjUquNFRcX57XTAQCvwrNG4B01Ladu/fr1+OKLL7wEo1L4DWh9qJ988kmvCvDJycmqNryRBQ92u511bJTLpVX/bfv27Thw4ACef/55r+vNry7mV5cpwYu6RYsW4eWXX2ZV5fnrRS5qVVWVrpw6ora2VldtubKyMuTm5vrMp5WLOrXBZ/369ZIFRvRdk5OTUVNTI3kWaILVsWNHL1GndA/kTp38/usJv1JfMGPGDDz22GPsPUrXSC7qqD+gQYaEgi+Xj9o6tTPq2zweD1wuF1uZ68up4++/PBRNoo5vk3///TfefvttPPfcc6w/0ePUKYk6tf9rQaJOb1kIIikpyWvCplYqBpDu53rHHXewn2v1A0pOHb2fX8FotVoxYsQIZGRkeDl1/EpjPr9OjlKEiC9DZVTU8e+XfyYtpnv22WdZbU8gdE4dv5E9z5EjR2Cz2Vh4V/45SqKOnq/09HQkJCRg/vz5+Pe//w2HwwGz2axL1PEROV9OHZU0oXZJP6+urobZbEZiYqLunDpi2bJluOyyyyT918aNG9tO1I0fPx7Tp0/XfE8wV8SmpqayBllZWSnZsUDrZzxTpkxBVVUV+yNfbaIEPfQE75xpOXX8jJqO0bNnT3ZzqNHKHyxqyMOHD0fPnj29Og++805KSlIUdfJGpJSHxmO3270eLjWnjgql8mRmZmLu3LmK79cDLwroHJWcohtuuAEFBQVe58a7LPz71b6vUviSMLKCjHfqSNSpbelSXV2NPn36oHPnzpgyZYrEFQDUV9opQcWHPR4PRo8ejYcfftjrOMDRmWJFRQUTD2az2adTR24XcDRsdMcdd6CkpAQVFRWSlV56th2j58LXdnt8MWBfTt0HH3yA9PR0xMfHIy4uDs3NzfB4PKqizmKxSAZ7uQDSE37ly8Dwx5ILs1tuucWrZExJSQnMZrMkJAf4vt/UhpOSkpCSksL6Fb5avR6njs6RRD3/XWmRFC9k+FWoO3fuRFNTE5qbm32KOv57y7+bVj6THBJ1dJ56B8bk5GRde2fS2ET3Q632oBIejwc7d+7Eu+++y/5PbZu/rnTOdL3VnrnDhw+zupxq58lDCww8Hg+75w6HQ1ffxbd7uVtNos5ms0kib2pOnRGRrkR8fDwaGhq8cvtKS0uRnp7OxgX5pFFJ1NFzQWPtFVdcgdTUVJhMJqSmprLJkFYbpHaTmJioKermzJnDTC0ar+m60q4x6enphpw6ADj77LPx1VdfSZ6bxsbGthN133zzDZ588kncddddXg2PwpXyLa8CYfDgwfjhhx8AtOa78Y1O62c8NOvn//hCPmgp1coCvAcsXtTZbDasWrUKs2bNkjh1qampEhsekCaf2u12RaeOXktOTpYIGrKt5bNMapBq9Zjo4eLRcurkQvfaa69VFdJG4cOvFOol1Arm8sI2UFFnxKnzeDxeok4tXPnhhx9K/i+fUBgRdSTC5ffo6quvRlNTEzZs2IBTTjkF//nPfwC0OnWNjY0sH1LJqePbYI8ePdh5rF69GtOnT8dbb72Ft99+G2lpaZgwYQIA/aLObDYjLi5O17WVJyrTM0uijl9ERIMN7yqqibpdu3bhs88+wxdffAEAXhNSm82mKDrVSrbw7d3tdkuclxNPPNFrUCguLkZ2drZE9Lz11luswKgavKhLT09n/QrvYBlx6mJjYyVOXVFREX755Rf06dNHsx6nVl6u2uBuNpsN71tJkKjjn4sFCxZISlAokZaWpiv8Su2fRJ38u2tNQCjthCoSkOCVQ9eFrndTU5PX4gKbzaa5alqJlJQUNDU1oaGhgX3X448/PiCnju4vuWL8fePfl5SUxMZNf3Yl4aG2JD9vWtWtBpUo4fsTei6U+iP+udES6zRR69ChAxYtWqRYCxBojUrRfaRUD3r2aH9nvh0aLdDMv9/lcnntZe0Lv0XdeeedhxUrVuCrr77CFVdcwTasnzlzJnr06IH77rsP48aN8/fwXpx66qnIyspCXl4etm7dijFjxrBVoRdffDEKCwuRl5cHu92OIUOGBO1z5Y1EXuSUqK+vR11dHR588EGv91mtVpx11lksqRlofZjJxubfyzdmX6JOzamrr6+XdDLy/WPlKNngak6d2Wz2EsOBzth4+FmNfCEJ1fyT79mnlVOnBL1faRZsRNS53W7ExMTAZrMxQa0mFuVOpny1uNpKOyUo/MIX1QSAM844A01NTVi+fDl+//13AK3Cn5KIqWNQcuro/r/44ou4/PLL2T2dPHkycwK7dOkCAPjiiy+wcuVK3aIOkObPaIVrKysrUVtbizPPPBPAUaeupqYGRUVFktwTev70iDpqzzNmzADQWjiXJykpCbW1tV5tQmk/YkAqbtxuN3r27MmEYnx8PHNSieLiYq8FJbfddhtOOukk1WtB50V/8yvxeVFnJKeOwq/0vHz77beIiYnBzTffjIaGBsVngq8z58upk0P3pm/fvj5Tdq655hr2b8oVpvO2WCy49NJL2URFjc6dO3vVK9MSddSXyVfDa4m6G264Af3792d9NeWpyXPReFFHTl1GRobkGX/mmWc0v4/S/eA/d8+ePUhJSUFWVlZATp08d5F/RvnFFYmJiZJVzYFAn6WU+qOWxhIbG8tC3Pz3pXugtG0ZLXYE9Ik6ck2/+uorNvnmP0suzCk9BDi6FSB/34yWe+PbBzl1RsbYgPZ+7devH37++Wfs3r0bgwcPRteuXfHEE0/g5ptvxr59+/DUU08FcngvXn75ZaxcuRKffvopLBYLW/0SGxuLjz76CCtXrmQrR4KFXBjwAkzu1NXV1XmFVwBpTgB1cuTUAdKQLt+YlUQdPyPnk6QBaUhIaZZhJPyq5NSR0yNvpHrtZS2oIfPHkq+OqqqqwmWXXSYJNwLSchV6nTpAuVPSEnVFRUWSorbyFYXyz+fhd0cAjtZNIow4dRR+kYs6agv8NaNisIcPH2bXmJw6vuOh9nLCCSeongd/3GHDhqGiokK3qOPdYCXhSh1lZWWl5Bmz2WxITk5GfX09tm/fLinASRu+8/XQtAYEQF10JyUlwe12e/1cbccMeWgzNjYW99xzDzweDxMR/EBQXFzsc3WpEjRI6XHq9uzZI3EC1UQd32f89ddf6NmzJ0suVxIG/Ip7o6KOfjZ48GBcccUVqu9raWmR7FtMKQb0POl1KnJycrxcM6X2Ru+hvlle44yuHbnScjIzM1nfTPeEr0MKSMOvmzdvxnfffQeLxSJJvaDcLDm08b3S9aY2UVlZib179+L444+XFODWorq6GvHx8TCbzRLxqyXq+H4mmKJOnmtLaD3DOTk5rC/lvy/dC6UyK3zpFCVRR6ucqU+eOXMm4uPjcf311zOhriXqaNJJ55GcnBzQtqn8tafJQJs4dUDrIPv++++juLgYO3fuRGVlJZYuXYr/+7//07VheDQit69pwQMlo8tDk4A0L0/JqeMXdvAC0G63ez2o5NQlJCSwVYrU8VVWVrJOXWmpu5Hwq5JTp9awguHU0feWizr+IaQHRum8yHnT49TRZygN8Er7GxITJ07Eaaedxv4vL+gqP+arr77KEupdLpdkcNm/fz8r48J/rtaG6YQvUcdfMxJ1R44ckTh1gDTkSqKOvovSPZVvY+PxeAw5dTRwKA2yvGvNh4atVivrSzZt2sScun/9619slbcep+6///0v0tLSJG7h2WefzX5OHbU850ZeXJlWGfPPF4k6Ql7CBWhdFemPqCNXJiEhQVXU2e121NbW4scff5TUGFPKqaPwK/UZ27ZtQ58+fSR5j/LQvC+nTmvA4SexWsgni0rhVz3wbugJJ5yAl156SfXzgKP3Xb64gtqq2u/zW9NR/33BBRdI3sM7dUBrPUDavYRQSymhNkyONQ8v6vbs2YOuXbvqFnU1NTXo2LEj8vLy8L///Y+9Ll+Qwk9U+X4qMTGRhZ/l39co/jh13bt3ZxNr/vvW1NQgMTFRcftDPl9WKafu1VdfBdAqXqniBZ/m0dLSIhH98nFFSdT5WuymlzbNqZsyZQq6dOmCDz/8EM899xyOHDmCq666CiNHjpSUJmgP8A2MdxFKSkqQnZ2N+Ph41ikkJSV55Z3wAx81OqvVyvJyeNdGLurkNDY2srg9IM0nczqdbMatJOr0OnUbN25ULAqp1rCCsZuIkqjjw8JlZWXYt2+fYq4FOXWULE/HUBtI9Io6uesmLy/jy6m7//77WXjF5XKxe5Obm4u6ujr06tWLdTJaTogcLVEHSO89hUwPHTokcQ748weOCgClbd6eeuopJCYmeok6ALpzKdXKFxAk6iorKyXvI6cOaBWR3bt3h8fjwdSpU9l79Ii6iRMn4pZbbmHPQkxMjGTrL6V9Q2mSwLejYcOGYcSIEZLBQd7pKom6uro6v4q10gDu8XgUw68WiwVpaWn47bffUF5ejiuvvFLymfw5ms1mllMpF3X8AEvHvvzyy9nvauXUaeXNyYWNXuQLJfTmJfEi6YsvvsADDzyg+L6HHnoIDz/8MCvHoybq1EqpUJ4n4FvU8W1DLurkpaQIcqSVtuHiRd2hQ4eQnZ0Nm80m2SecirkDrX0PrcQk0TF06FBs3rxZ8h7gaN/4yCOP4NJLLwUASfWAxMREZGdnw+PxYPDgwYrnrhd/RF1ubi57P7/IgsKeSigVpyfOPPNMdn9KSkqQlpYGs9ksqRW5YcMGyU5B8vGOFnLReSQnJ2PZsmXYtGmTV4TGKOvWrcMvv/zSNqJuwYIF+Pe//40dO3bglltuQUJCAj788EPccsstOPvss/H111/7e+iIY8WKFXjyySdhsVgkoo5m3/Hx8UzJJyQkeHUEfGdOIS8+/MqLOvlCCTkNDQ344YcfWAOWF96l2j5KSbB6cuqWL1+O0047zSvniD6LKC0tZfkteovmakHlZfiZFu/UZWRkoKWlRdOpo8GIhIZWTh2gHCrlB2KlUByPkqhTC++5XC52b/kFRM888wy6d+/OVl7pFXXNzc0oKiqSDGJ0f3iXlX5+4MABL8HGh0/4kD7/HqC107Lb7SguLvY6F71lDXhRp9ReyHGqrKyU3ANaKEHI85bk30drQCCh4Ha70dzcDJvNhl9++QVr1qzxEnV79uzB22+/DZfLhRNPPFFynMTERE2nTimsZHS2TdAkpqWlRdWpS0tLY24K3x54Ubd48WJWY5QmQR6PByUlJcjNzWVtuKGhgV1/2pN33LhxrGC0nvbJ46+o43Pq4uLidE8c+cFY6zNTUlLw/PPPs/YrF3U0qdUj6viVl4cOHfJy9/jzkIs6tXPMzc1VFU68qKP0gMzMTDbJe+GFFzB06FC2eOvGG29kzhM9H3zFCEA6SQBan3lywvnJrJH6o77QEnVqixj5MmR8iR2t3+FzqOXF2ZcsWcKeywMHDrCoGt+O/v77b83vkZSUhFmzZiE3NxcVFRVwOBzIzMxEv3790KtXL83f9QU9i3p3OgICEHVbt27F9ddf72V3Tp06FTNmzMC4cePwxhtv+Hv4iKJ///544oknJJY70KrsO3XqhLKyMnz00UcAWmde8kGF75D4HSWSkpJgNpt1OXXbt2/H7bffjo0bN2L16tXsAZbPQkjUGXHq+PDr7NmzAbQOvPKOlB+40tPT2SChp6K+L7766iu2TQp/XvIcCKUHlwYpug7kjPoTfpVv5cPjdDolToiv8CvR3NyMlpYWJmb4jumFF17A7t27WVhPr6gDWsOhfFujzolvoxSOOnDgAHuvUmFn6rhposEPNomJibDZbIqiTm9RWF7U8Z9LbSgmJoY5kPx1j4uLkxQ858Mi/HsA36KOzoF/HgYOHIghQ4Z4hV+7deuG22+/HdnZ2cjPz5cchxZVEHJRR22Uvw/+ijq6183NzaxoNp9mYLVaJZGA9PR0eDwe9OzZk52jx+PBN998w1wr6jMqKyvhdruRkZEhGWDp+tD3qKysZIuTjIo6veFXOXz41Ygg5Nujnt+jPkIu6kpLS1n+oRJyp45yzTp06MDaktI+2HJRpyZWtcRTfHw8c87JST7ppJOwZ88e1NTUMDFHwnTRokXsd8lJouR+Okel0jH8d7/jjjswY8aMoO7xrlbqSMl1o4iDPFxNLqRaag4gXagCtC4Gu++++3DSSSfBbrez73n48GEm6vh8Q1/7hNPnFhUVYf/+/brq3xpFvqJbC79FndbNnTRpEr766iuWyNxekFcSP3DgALKzsyUdvJKo46FBk7Z/SU1NlYTR1ERdTk6O5P8k2uThVzVRFxsbqxrC4MOvfNhVnhcg7+Co4wmGqMvOzvYqaqy0jYxaSQU+qZqusVFR97///U+SOyH/eUNDA0444QS2jy6tdPa1UIJeo3wwficOGpApsVbPoEnuTWFhocRZU3LqMjMzYbFYUFdXJ3F3AamAJbeBXE65U8eLOj5vSe8gT4KqpaVFMuukEJDJZEJKSgoqKiokDpfJZJLkointaGPUqeNr4PHfEfBeIMTXyyJ8OXV0DflcWbfbHbBTl5aWhubmZlxyySVeTh1/vkDrdaLBva6uDh6Ph1076jNIyGRmZrLB7MCBA16iDjgqdtvKqaP2YrScg3xPaD2fA3gvXCotLdWcsPBjQXl5ueQeUHuhds73I3JRx8OvDta6ziaTCb169cL27dvZDjvUp/z555/se9N9pOiQx+NhW4HRvaX7qlTkmb/uwdxXneDdYR6lZ3j9+vVsUY+c7OxsXeFX6odzc3PxyiuvsLQBSksAjpa4mTp1Kuuv6Tl5+umnMXr0aK/j859bWlrKBGgwUUp9USOghRJajB49mm2s217gZ2fAUVHHk5qaqtkJyUODnTt3xt69e9lrtOoJkD7Yap0Bv/KzoaGBdc78oNPQ0OBz5ldaWgq32y0RJXLrWC4KyaUNRvhVCaUdN5Ry/ajuHHVM9JAZEXUejweXXnqpxC2UC0qy+61WK+rq6ljOER8yV3Lq6JredNNNWL9+vWRbLXo/lSDR43zJnbpVq1Zh27Zt7P7IS+TwZTEAZaeuvLyclcgBlJ26+vp6ZGRk4JFHHmE/M+LUKSXh0z0ymUxITExEXV2dl0PKt3utRH29Tp1SOgIv6vhBRkkY+HLqSFjxZTKC4dTRv7/77jtFp85qtbLvNGTIEKxevVpSoJYGchrkyIHIzMxEly5d4HA4sGnTJkVRZ3S7LkKpPemB2oJRUce3Dz2fmZycDJPJhMLCQlgsFixbtgxA60CulVrAO12lpaWKoo7EFN+PaIk6fnWwrzBnnz59sH37drhcLlitVlYset++fV6OPZ0H9ZF8niq1DSWnjv93oLtHKGEkpy4jIwN9+vRRLQGkJ/wqDzHz0DWi8fOiiy7CJ598AuDoIrPHHnsM33//vdfvqrmKwSQiRB0AyUrB9gCfe+bxeNjuFfym9qmpqZKQ9IABAyTHoIefHuycnBxW0X3hwoWSkih8BxUbG8tmFldccQXrfPik54aGBlZFmx+YnE6n5syPBotnnnlGIur4qvJKqO3PGiwo/Mo7SmPGjPF6H+XfUDkbpQKa8vcDUleN8qx4q12+MEJNHPOLN7Ty9KxWK04//XTJ+0kc0LZXepwQGsSLioqQmJiIs846C926dVMMv/JFtuVOXefOnbFhwwYA3oWElZw6oHWw5c9f7yBPIX55W6Hv27FjRyb8jOzqwZ9rbW0t3G63plMHHB3I+HvJh1/5PZ6VBmD5VoRywZacnCzZmkjpPXqha923b1+cd9556NSpE8455xxFp45v72eddRYOHDiAffv2SXagAY5OBMmByMjIgMlkQv/+/bFx40Z2/fnraGQhDw9fbN0IiYmJaGlpQXV1tSFBaFTUxcTEIDk5GYWFhUhKSmJ9hx6njnYwOXjwoGRyT/eBnDq+L1YSdUuWLJEk4su/hxK9e/fGtm3bmFNHbfnw4cNsy0D5BJhSD/g8VbmoU3PqglmLlKDvyE/cyVFTe4aVSoYBrX2eXqdOqy3yxa35a6oVfWsLUSevzapFSEVde4MPB9bX18Pj8SApKUmSVBkXF8dE3dKlS71WAlNokIQKL+rkDw41erKHqXO+8cYbWTkG+UIJu93uVR1frSo+MXbsWMTGxmLr1q0SUSJfmSUvhKlUGiOY0PUmcbVgwQLF1a9U0+qVV14BcFRoqIlNus68q0bXkB+s+Twbj8ejKo75gpd0TP46UudKn6vWMZlMJl2dJ1/A1FdOHeVu8u/lXaWZM2cC8A4h8ROTxMRE9r3los5o+FV+T+Li4jB37ly8//77TPgZKQANHL2uQ4cOBaC+/y49A3Q/+GeCBtuamhqJsFcaALp27YqysjI2IMqdOpPJJFmpCvgv6iwWC3799VdMnz4dZrMZI0eOlKQa8E4df92oFMbq1au9nDqaCFL7pt/v3r079u/fz0QdL2rq6uokfZte/HXq6B6Wl5cbum7+CBHafo2vXHDkyBFNUce7sfKIjZZTFx8f79WmRowYwVx/wpdT169fP5SVlaGmpoYVoDaZTLjrrruwbt06AEfbOZ1HQ0ODqlOn5GKFWtSZzWY4HA7J5EdrlTXxwgsveK1qPnjwoE+nTs+Wc2eccQb7N/VtR44cMSTqeD0QDC677DLcdNNNut8vRJ0B+MR9+eyXh2ZqSiUMunXrBuDoLC4nJ4d1omqijl+2DUhFAb9QgkSHvO6cnvDrddddh/3790vEiPy7yUUd5ejIVwcGCwq/0uCoVj7DarVKhAxdNzVRpxR+5a8hiTR+cG9qakJLS4uiiFFy6vjwN50/3V+73c72beVRKuysRExMDOs4+DbGi7rTTz8dNpsNWVlZqk4dHQvQ3vJLy6nTO+DSvVS6J1dddRVSU1O9FjLw7925c6dqQU/5OWiFX4GjDqz8mejatSvWr18vcWiVBgBysGlVnFzUAdKtiTwej985dUCr20/nShMY3qmjaACfA0vhKl7U8U5dY2MjSkpKkJ6ezs5dHv7mr09dXZ1hl44+i/9bL9SujYo6Hr2/R5NXXtRpiQTgaJiutLSULZgjKL2C/ub74oyMDMW9hOX4utZ8oWPKz5aLRblT53Q6fTp1aqLO33vgi06dOklyyvW4aQ899BAuvvhiyWslJSWqJYP0hF9PPvlkvPbaa5LX+EU0WuWI5O0kmCuE/UGIOgPw4Vd5CQgeeliVfnbTTTdhzpw5rFwAn3SuJupITNEDyBczJqeupaUFjY2NsNvtXjXBfIVfgdZSEXJR1717d5SXl2POnDmKv9O1a1fs3r3b0CzCCHS95Un8cuSFN305dVqiDmi97ikpKRJRp1V8lRc5dEw+91Iu6gh5noqRZeskqJWcOrfbjfPOO485t1pOHYnI8vJyVVGXnJwsEXX+7PObmJiI2tpa5og+++yzXqUC6H47nU4vV6hHjx5stws5ekWdllMHtG5TNX/+fIlzoDS4kKijMK2SqOOdOmqHwRgYKdWAF3VUUkmew3ziiSfit99+Y9eZBh9aZbxu3TpJigwJb7XV8moOM39ucuhZNBp+5Z06f10ivSs1+V076HxLSko09x+lPvjw4cNe4deuXbvC4/GwdjJ+/HjJ72mJOkrO9yUMOnfuLNl/VOl35Dl1epy6tgy/Aq2ijl/ZqcdNA7wXV9TX16uOcXrCr1u2bMFdd90lec1fpy6YK4QB9V2K1BCizgC8qKNcKKNOHRU9pRuvVVdJ3kj5mm0EiTq+I5YXE/YVfgVa8wBKSkqYWI2NjcXo0aORmpoq6fTkdO3aNeiNmKBBRssVBbwfUupM1Sr4K+XU8Xl7FosFGRkZiqJO6TrSQGC329kxeedQTdRR+/Bn9xUaRJScOv6c+OMrOXV076qqqlSdiczMTImo07uLBA+JOhI4p556KnOtCX4hg5HZbrCcuu7du6Ourk6SI6s0AKSlpSExMRH79+9nq3m1RJ3RXRG04J06s9nMhEFmZqbXyuCsrCysW7eO7ZFN1yUzMxOHDx/G6tWrkZeXx95P90hN1N13332q57V9+3a24ItHa4cSLfwNv/oD9W/JycnsuWlubtZ06ugZ+Ouvv+B2uxVL7RAXX3wx3nvvPQCtrqCWqKOwoq9C1SaTyUswqzl1vKgjp46ur9ypk5dfIUIl6rKzs/0SdQMHDkSXLl0wb9489pqvvb71Hlt+vNLSUs37Eerds4xuxyZEnQGUnDq6ofv27WMFQOnB0LNiiJ/9yjsvuah788038fDDD0tmkNRg+a3A5OFXPaKOirru3r0bd999N1wuF+t06CFQq34eKuh6+3oY5Z3Z6NGj8csvv3hZ9IRSTh0v8OLi4pCZmSnJqdNy6ujzTSYTOyafT+XLqaOtvIxAg4iSUwdoizr+O8hD90qYzWbWfmhjeaNQaI/frkoOv1DCSKgvWE4d3Uc+HKTW5mgLPzUXjgQSEFxRxzt1vtwv+YSR3p+ZmYl9+/ahsrKSOUOAb6dOqZwE0atXL8VyM3I3SS98+DVUgoJQCr8CyjUxCRJGTz31FFsApQVNnhISEjRF3a233oqmpiZdziY9Q2pOndJCCXLqzGYz7HY7G8dcLhcsFotkgs4vvAll+JUXdVohUp60tDTs3bsX/fr1Y68FW9TxfZARpy7YCFEXQrTCr8cddxxziKjemp5tgXhRJ7f75aKwW7dueP755yUPHjl1vOiQh1/5yttqkKhraGjwerjpIeAXBLQFCQkJbIUZoN6xKA3OAwcOVHUQlcKvcqeO3AxCa/9cvpOgY/IhPKrGrybqtAYPNc4991wA0sUcvFDi287ll1+OPn364NRTTwUAyUBOHb8v4c8vlPAnZyQxMVGyBZWSqOMXSgTi1KlNpug7kIsqHzjpM3lRp7XVXGNjo6pI5cvxhMqp8zXw8yKLH3gyMzPZ4ia+PScmJqKhoYGdt/z4/op5pWP5IhxOXVJSEtsbF/D9XGZkZKCurg6jR49W3OmEh18AQNdCrW6o3i3R6H1qTh1ffJrOgd/L2G63s+fRV3sKpVOnlFOn9/P4du1L1CmFmLXgK06E06lrl+HXDRs2IC8vD8OHD8fYsWO9lGtBQQFyc3ORn5+PESNGhOw8tJw6nhtuuEGyB6kWvKiTh7X0dKJyUWez2STnCehz6vhK3fIHKlxOHQ3OJDzUOnf5dfbVIehx6nJycpgYA7SdOjqew+HAoUOH0NzcjLKyMsTExKBr166soruaqPNHJF122WUYP368pGAzf334tnThhRfir7/+wsCBAwFIrxeFImkGrwYffvUH+j0K96g5dTTwBCLq1J47OqZa+FXJqdMqi6NX1NHgGixRp9ep49uAXNTxxyPoHpWVlSEuLs7ru/sj6qiNG/3u9HsNDQ0hF3W0AwB9f3Lr9Ig6wLvfVoKv86dnoYQe5E6dvD3QGEWirqqqSvKc8/vFklOnRqhEXXp6OhoaGvDTTz9JJtJ6P4+/R1o5dS0tLey76j22yWRix9QKr/szKTeCUsFjLaJC1HXu3BmLFy/G8uXL0aNHDyxYsMDrPePGjUNBQQGWLl0asvNQ6qgDLcrI/77cWdIjomgWouXU6Rkk7XY7241C3onSwNXWTh11rvKSIHLUBmc1qHSIVk5dly5ddIu6oUOHIjY2Fv/617/gdDqxa9culJWVIS0tDSNHjsTWrVsVzz8QURcXF4dPP/1UsvKYv2++BmDKZyOBo9ZGKLxBoTx/VkACRwdM+jylgZrCr99//72h2ofyY2mFTIGj7UnNqeNz6tQwIuq6du2qeJ7+QJ/LOy5qKO1yAaiLOmqP+/fvV2w/gTh1ansiqxEbG8vuV6jDr7QAh9qmUVGnp5++5JJLYDabMXToUCbmgiXqqB3In9/a2lp89913TFCOHTtWYjbwC8zC5dTRNbzkkktQWlrKFvUYyXuj6+hrr28as418F3qvVu25UDp11dXVhnfmigpRl5WVxR60uLg4xVn+/PnzkZeX57UsmaexsRHV1dWSP0YgB8zj8WDDhg2IjY0N+MHUQo+Iki+UIFHHO3V6wq/A0YYrb/R8Tb22xF+nTs/gSWUdCLlT16VLF1RXV7PP1loo0bFjRzQ1NbF9YX///XdWIoSf4cmvK91fs9mMBQsWsH13/cWIqPvjjz/wwgsv4Pfff8eGDRsURd3BgwexevVqAK3FbAHpnohGoMGdrqda+LWyshJ//fWXV/kcLeT3Wy18Re2koqJC0Ymi719eXu5zQJeLOqUQsHx3jGCGX2tqany6pqNHj2ahdn7g4Z0lJafu7bffVnQm/HEk6JjynWH0oLSwJxSQqCssLJR8nl5Rp6ef7t27N9xutyQkzi948wdf4deamhrFvGLeqXM6nSgrK0NZWZmm2AnVPaC2SILLHzfN1w5CdCxyLo2kAlB/FS5Rl5SUpBotUCMqRB2xf/9+LFmyBBdddJHk9QEDBmD79u1YunQpFi1axKrky5k2bRocDgf7Y3Tj3fj4eLS0tODgwYN4++23g7aTwkMPPeS1mT2gbyYn38cxNTXVr4USwNEHTP5ADR06FG+//Tbuuecen8cIJiTkq6qqYDKZVK8HPaQpKSl44YUXdD20NCgTvKgjpw6AZP9M/pyUoM289+3bx0QdPzDIryvl4ZSWluLSSy/FNddc4/O8teA7Xl/ugd1ux3333Ye4uDisWbNGsY107NiRCWsK3dIekx9//DE+/fRT3eemR9TxE4933nlH97H1TqyoXWzYsEGSVyj/eU1NjWY5C3qvllNHC0P4FdTBcuoAsGK5WsTFxeGFF17w+mz+9/hrzkcNlAqo+rPKvX///gDg1ybnNIkMtVNHn/OPf/wDwNFi6sEUdXI+/fRTLFy40PDv8WgtlLjgggtQU1OjuHhFLuoyMjLw+uuvhy38yqOWz6mFXlHnj1NH/YCWqDO6dV6o0ZeR2UYcPHiQuR0833zzDWJjY3Hdddfhgw8+UFxpRlxyySXYvHmz4mqkKVOmSJblV1dXG+ps6OZt2bIFAPDUU0/p/l0tqOP1Bwojbtu2DfHx8cjJyfEr/Aqor1QzmUysLEJbwjt1WgMifbeTTz4ZDz30kK5jW61W1fBrXFwcC0XToFxaWgqTyeQzf4acFD2ijjoKfkFGIPDPgZ5QWWxsLBISElBdXQ232+1zoUR5eTnr5PhcPiPnplfUqZWjCQQaKKqqqjBu3Divn/PfPyUlhTk3asdqbGxk7UYp/FpfXy+5t8Fy6gDfZRYIeo/aIholpw4I3tZ/+fn5KCws9Mvl79q1K3777beQO3Umk0niDJOw8DUxovHAn1xjvnadv6g5dRdffDHy8vKwevVqnHjiiV7pBPxCCd5JDkf4Vd6f+iO8qI9V67/4bQSNHvuDDz7A2LFjvcov8ZhMJvz666+szEq4iShRl5WVhVWrVnm93tzcjMsuuwyPP/641ybzQKs4oxu7cuVK3HrrrYrHt1qthldh8dBDfP755wMA7r77br+PFSzIqfvrr7/Qp08fxMTE+B1+9bf8QKigwaeqqkqzY6d7aqRz9eXU0fUicXz48GFJ9X01SNTt3LkTI0eO1OXUHTp0SPd5a8EPynpzPRMSEljJFV/CP5CFMnrDr4TR53TZsmWsgr8atN2ex+ORlEJQ+sxAnTo+P40ItqjTM4AohTD5+8z/m+8j5OFSo3k9PP6mbfibizh9+nSvwtZGoNw6pTbCQ/c81Inyasi3YKN7SeVWampq4HK5cPXVV+Pzzz9nv8c7dSR0+ONofVawcTgcMJvNzB31R3gZDb8a+S6XXXaZrtWnAwYMwOrVq1mbDZQFCxZIymIZISrCr3PnzsWaNWswdepU5Ofnsx0OyD2aO3cuBg0ahDPPPBOdO3fGsGHDQnIe8nCHr46/LaB9HHfs2MG2CfI3/EqDaqSIOn6hhB6nzojokOfUyZ06uag7dOgQc++0sFqt2LNnD3bs2IHhw4dLOnz5d6DzDnSxjRJ6Q2Xx8fG6RV0gyPMjfTl1RkWdnhWIJpOJ5acoXXO5U6eFXlHHF+QNZvg1EKeOz9Hhv3OHDh3Ys0/hduLZZ5/1/6T9hAZII/mVAHDPPffg9ddfD/jzfbndFPY3en7BQs2ps9lsSExMREtLC0pKSpCbm4slS5aw3+Pfx7dPrWfOaF6XXmifZCKUoq62ttarXFcwOfPMMzVXyRrh0ksvZekARokop06Na665RjHfiPJuJk2ahEmTJoX8PJTyE0LNrbfeqrlyjCz08vJylr+SkJAgmYHpFXX0UEeKqOOFgNY5tZVTp1fULV++HACQl5eH3bt3s58pdSbff/+9ZlFXo3z//fdYu3at7ve3laijY2uV9wjEqdPbZmkAVvquRkVdVVWV5kIJQCrq9NYf04J36vQkaPsqKcJ/5/j4eDQ2NuLvv//2Kwcu2FB5qpUrV7bp586bN89rGyolqO5juK6VWkkTfseI4uJipKSkSIQT79RRwXwg8NW4/sI/9+QQBzP8Sseqq6uLmLEtlESFqIsUeFHHb/odSmbOnKn586SkJDQ3N0v2p0tNTUVlZSU8Hg9MJpPuCv3+br4dKiwWC2JjY306ddSZGSm5YLVa8eabb+K0007DmDFjJLmccXFxbEbX0NCAlpYWbN26VbJhuho2mw1bt26F3W5HVlaWJFFeCaM1iHwxevRoQ8dsK1HHd6xA8J06vS4YiTql4/Ov6Vn96nK5fDp1/GrhYDp19fX1ukQdXVO1nCCle84vIlmwYEHQ8uuM0rt3bzz99NNsstpWjBkzRtf7LrroIhQVFUn2725L1Eqa8KIOaJ2g8G2bfz+/nSGfstOW8OdGkz4jEyClnXJ4eKcukPSraEGIOgPw4Y62rtmmBp3ToUOHWONOS0uD2+1GTU0NxowZo7tCf6SJOuBomQutxHl/wq/0cN90001eKyHj4uJY4Umn04n58+dj8+bNGDx4sO7jdunSRbLcPlJJSEhAUVERgNCKupiYGMTGxrKBI9yiTum78p/pa0WbnoUSQOueqEbPUQu1hQ1qdOrUCT/88AOGDx+u+HNf1/nSSy81doJB5rHHHgvr5/siXIIOAAYPHoylS5cqOnW8e5iQkCBpN/xCCZ6nn3461KesiFzUGQ2R6g2/VldXR9TYFiqiIqdOoA4vGujflF9UVlbGcimMiLpw2fBKOBwOuFwuzQExPj4ePXv2ZOU29MA/3Pzeg/zPqOI6/fzBBx/0eVzqoGgRBDk+ocpJCZT4+Hi2pVkoRR3Qem20nLq2CL8SSt+VH0howNPaau7nn3/GtGnTAHh/H7rvfMJ+MESd0p6+vjj33HNVr0+o8osEoeepp57Czp07WZvgnbqePXvixx9/BNDa7vjniSYD/DPgdrtxwQUXtNWpS+DbJok6I/gSdfTdKysrQ97HRQLCqYty+Nk679QB0k3ljYRfjW4gHEoyMjJQWFioOSCazWbs2LHD0HH5To7PKwGODr58aZiMjAzF2mZyqNMgUUf3ZMKECYbOr62Ij49nq2/DLer4NmpUbBgVTL6+KznDp5xyiuLPqf18+eWXALy/Dzn5fOgyGKKOjxD4u2WboH1gNpvZfuMAWM4vtcWRI0dizZo1GDRoEFugBHiLIIvFEtaJvNypMzqh85VTR/1KSUmJrgVV0Y4QdQaZM2dORDlZSqKOd+oII06d0Q2EQwl9l2Avqec7jj/++EPyM7oOJOo8Ho/u7bHouFS8NS4uDps2bULfvn2DcdpBh3d+Qi3qLBaLblFnlGCLuu7du2PHjh2SQZNH7ibIP58qwbe0tPh9jkrwK+4jPbQvaFso95AKpgPAkCFDAEj7O3rO+AUT4YSPYvizmCEzMxPx8fGqeXj0/UpKSnRNzKMdIeoMMnbs2HCfggS+YyeBp+TU6XE+lDa6DzdUtT3Yoo7vAL744gv2b6vV6uXUtbS06K4aTp0nn2jvq95VOFErbxEKeKfO1+pXoxhtH77cALvdrrkqWf6MyAeUmJgYOBwOyYKqYLRhfsDj93AVCEjUKe1dzD/bNBZEoqirra01XA9z/PjxGDRokOoYR9/P5XIFVGszWojMRB+BbpScOnqNLwPDr3JS46STTgKgvlIuHKhtXRYovIPCk5iY6OXU1dfXGxZ10eKi0PZyQPjDr4GInmDk1PH4cg3l+0YrfR/5ABLsiYmeEjuCY4fU1FS88soreOutt7x+puWMh1vU8WLMn5w6m83Gxi61nxNC1AkiHiVRZzKZvCpb9+nTx+exhg0bhuLiYgwdOjS4JxkAoQq/UgXzGTNmYNeuXez1iRMnIj8/H8DRGoBGRB2trgxXlXmj0DZWTz31VMhXdFssFlayINgLR4ymRKgNZPS6L1FH1ekJLVFH1zWSRN0NN9xwTOQXHWvcd999qikDcqiNh7vMBy/qWlpagj6Bj42NZc+nEHWCiEdto+6PPvqI/Xv69Ok47bTTdB0vFHtuBkKowq/k1PXt21eSZ/Hiiy+yoqfk1DU0NOgWdZSPGC1OHQmOQLaB0gvVdouE/RHVRN1ZZ50FwLfzp8epo2s7ffp0AMEXskbqMsr58MMPfdZQFLRveBNAjUsuuSTkz6v880OxJZk/Za+iFSHq2hF8g+XzbaJFYCgRaqeOxJrS6lQ+/Ko3iZ9yraLFqfvwww+xcOHCoOx24AtyBKgSfzhRE3Xvvvsuxo4dy1YvqyHfg1qpfaampqJDhw648cYbQ7KVVCQt2BJEHzQuaFU7+PrrryW7ooQC+WQnkAVTaghRJ4gqzj//fNx7772SUCwfmgnFQ9JWhMqpI1FH1+bjjz/2yrOjOnVGwq/R5tRlZGTg/PPPb5PPIverrXcIUEIt5NStWzfMmTPHp1P30ksvsTA9oOzUde7cWXcoTCBoa2jiGe5qB3KnLhQ5fseSqBOrX9sBCxcu9HqNz4+KZlHXVk6dUgiCr1OnV9TRrDdanLq2hO5hOKvwE4GGQm02GwYOHIiCggIAyqJu6tSpuvYQNcqiRYtQXFwc9OMKji30OHVtgbzvDcV4RccUoi5C2Lt3LwYOHIgTTzwRQGsJCj686Ha7MWnSJPz999847bTT8Nprr4XrVCMGftCKZlFHTl2oVr9qXRu73Y5169YBAAYOHKjruBR+jRanri2haxMpW+wFCt92lERdUlJSSNrBeeedF/RjCto/8fHxuPjii9n/aeIZblEnwq/BJWrCr8OHD0dBQQEKCgq86jN9++23yMnJwcqVK1FfX481a9aE6SwjCwrHRrOoC5VTR6JOy4Hjr5veji/awq9tCblWfAHdYHP33XeH7NhyqH3ExMRE7DZwAgFRV1eHzz//nP2f+qhICb+q7UkbDISoi0BWr16NvLw8/N///Z9X0vHatWsxatQoAK35ZWqirrGxEdXV1ZI/7ZmsrCwA0S3qEhISYLFYQp5TpwT/M73Jwueeey6AwArptldI1Gk5dd999x0WLVrk1/E9Hg9mzJjh1+/6Aw0UbbHIRCAINpHi1JGoS0hIACBy6gIlKkRddnY2du3ahRUrVuDw4cP46quvJD+vrKxkDdThcEh2UuCZNm0aHA4H+5Obmxvycw8n7UHUmUwmZGRkBF3U0TJ9vaJOXpdMjaeeegrl5eVio3QF9Ii6Cy+8MKThxTfeeAN5eXlBORa1DyHqBNEITTwjxamj8wllTl17Sf3QIqJE3cGDBzF06FCvP3V1dUhISIDJZMKYMWOwadMmye+lpqYy162yslK1ftOUKVNQVVXF/hQWFob6K4UVEnXhLi4ZKL169Qp6/byZM2di6dKlmgMy37nwdf+0iImJOSZmg/7QFuFXX9xxxx1YsWJFUI4lRJ0gmomUiSedRyjThWw2G5KTk4+JMkAR1RtlZWVh1apVXq/zLsmKFSu8NkcfPHgwfvjhBwwbNgyLFy/GxIkTFY9vtVqjXuAYYfTo0Zg3bx6ztaOVRYsWBX3gTExMxDnnnKP5HrLsR40aJUpTBIFIEHXBRIg6QXsg3NtCtlX49ViZbEeUU6fGqlWrcPrppyMvLw/FxcUYP348AGDy5MkAgIsvvhiFhYXIy8uD3W7HkCFDwnm6EcPEiRNRXFwcESUkAsFqtYZlhiUG7eBCoq69TKyofYSiAr5A0Bb88ssvWL16dVjPgRYZkegKlVN3rIi6qBitRo8ejdGjR3u9/s477wBoHXT1hseONSJt269oIppzESORXr16YcuWLeE+jaAhRL8g2tFbqimUULoUVbUIRb970003oaSkJOjHjUREbyQQqCBEXXBZsmRJyLccakuEqBMIAmfGjBkYPnw49u3bByA0/e6wYcOCfsxIJSrCrwJBOKDOJVISiqOdzMzMiHAGgoUQdQJB4DgcDtx4442sgkUocuqOJYSoEwhUEKJOoIUQdQJB8KCFEuGumxftCFEnEKggwq8CLcRCCYEgeJCoq6+vD/OZRDdC1AkEKghRJ9BCOHUCQfCgbctoj2iBfwhRJxCoIHI7BFoIUScQBI/zzz8f999/P2655ZZwn0pUI3ojgUAFkVMn0EKIOoEgeFgsFrz88svhPo2oRzh1AoEKNFiLMKxAidjYWPZHIBAIIgHRGwkEKqSnp+PVV1/FtddeG+5TEUQoNptNLJQQCAQRgxB1AoEKJpMJ9957b7hPQxDB2O124dQJBIKIQYRfBQKBwE+EqBMIBJGEEHUCgUDgJ0LUCQSCSEKIOoFAIPATu90ucuoEAkHEEBWi7pdffkF+fj7y8/PRu3dvrzyngoIC5ObmIj8/HyNGjAjTWQoEgmMN4dQJBIJIIip6o0GDBqGgoAAAMGnSJFx22WVe7xk3bpyocSMQCNqUPn36oGPHjuE+DYFAIAAAmDwejyfcJ6EXt9uN/v37Y8uWLYiJOWoyFhQU4B//+AdycnJw5ZVX4u6771b8/cbGRskWJNXV1cjNzUVVVRWSk5NDfv4CgUAgEAgEoSIqwq/EsmXLMHz4cImgA4ABAwZg+/btWLp0KRYtWoQNGzYo/v60adPgcDjYn9zc3LY4bYFAIBAIBIKQE1FO3cGDB3HllVd6vf7NN98gLS0NN998MyZMmID8/HzVY8ycORNWqxUTJ070+plw6gQCgUAgELRXIkrUaaEWegVaxRmJsvHjx+PWW2/FsGHDfB6zuroaDodDiDqBQCAQCARRT9SEX3/66ScMGzZMIugmT54MAJg7dy4GDRqEM888E507d9Yl6AQCgUAgEAjaE1Hj1IUC4dQJBAKBQCBoLxzTos7j8aCmpgZJSUkwmUzhPh2BQCAQCAQCvzmmRZ1AIBAIBAJBeyFqcuoEAoFAIBAIBOoIUScQCAQCgUDQDhCiTiAQCAQCgaAdEBV7v4YDWkQhEAgEAoFAEAn4WtgpRJ0KpaWl6NChQ7hPQyAQCAQCgQAAfJZgE6JOBYvFAgAoLCwUNeyiCNr6Tdy36ELct+hE3LfoRNy36CUpKUnz50LUqUD2ZnJysmj0UYi4b9GJuG/Ribhv0Ym4b+0PsVBCIBAIBAKBoB0gRJ1AIBAIBAJBO0CIOhWsViueeOIJWK3WcJ+KwADivkUn4r5FJ+K+RSfivrVfxDZhAoFAIBAIBO0A4dQJBAKBQCAQtAOEqBMIBAKBQCBoBwhRJxAIBAKBQNAOEKJOIBAIBAKBoB0gRJ0KDzzwAPLy8jBhwgS4XK5wn45AxoYNG5CXl4fhw4dj7NixaGpqwpw5czBkyBCcc845KCwsBABs3boVQ4cOxZAhQ7BkyZIwn7UAAD777DNkZmYCgLhnUUJBQQFGjBiB4cOH4+uvvxb3LQpoaWnBDTfcgLy8PAwbNgx///23uG/HAh6BF7/99ptnwoQJHo/H43nmmWc8n376aZjPSCCnpKTEU1dX5/F4PJ4pU6Z45s6d6xk0aJCnsbHRs2rVKs/NN9/s8Xg8nksvvdSzY8cOT1VVlWfIkCHhPGWBx+Npbm72XHHFFZ5TTz3V43K5xD2LAhoaGjwXXXSRp7Gx0ePxeMR9ixI2bNjgGTdunMfj8Xh++OEHzz333CPu2zGAcOoUWLt2LUaNGgUAOP/887FmzZown5FATlZWFuLj4wEAcXFx2LFjB0488URYLBacddZZ+P333wEAJSUl6NmzJ5KTk5Geno7S0tJwnvYxz+zZs3HllVciJiYGO3fuFPcsClizZg3sdjsuvvhiXH755fj111/FfYsCcnJyAAAejweVlZXIzMwU9+0YQIg6BSorK9l+eA6HA+Xl5WE+I4Ea+/fvx5IlSzB06FDJHobNzc0AWjs0QtzL8NLc3Iy5c+di3LhxAKTPGf0cEPcs0jh06BD27NmDb7/9FrfccguefPJJcd+igIyMDMTExKBv37546KGHkJ+fL+7bMYAQdQqkpqaiuroaQOvAk5aWFuYzEihRXV2N6667Dh988AE6dOjA7hkAmM1mAEBMzNEmLu5lePnkk08wduxYdk/45wwQ9yxSSUlJwdChQ2GxWHDOOedg48aN4r5FAYsXL4bdbse2bdswf/58vPbaa+K+HQMIUafA4MGD8cMPPwBofTDOOuusMJ+RQE5zczMmTJiAxx9/HL169UKPHj2wdetWuFwurF69GqeccgqA1jDtzp07UV1djfLycmRkZIT5zI9dtm7dilmzZuH888/Hzp078e6774p7FgUMGjQIW7duBQBs3LgRo0aNEvctSkhNTQXQKsxLS0vFfTsGENuEqfDAAw9g3bp1OO644/DBBx/AYrGE+5QEHJ999hnuvPNOnHzyyQCA2267DR6PB6+99hpsNhtmzZqF3NxcbN26Fbfccguam5vx9NNP49xzzw3zmQsAYMCAAVi/fj0+//xzcc+igDfffBNz5sxBTEwM3n//ffzyyy/ivkU4zc3NuO6661BcXIzGxka8+uqr2L9/v7hv7Rwh6gQCgUAgEAjaASL8KhAIBAKBQNAOEKJOIBAIBAKBoB0gRJ1AIBAIBAJBO0CIOoFAIBAIBIJ2gBB1AoFAIBAIBO0AIeoEAoFAIBAI2gFC1AkEAoFAIBC0A4SoEwgEAoFAIGgHCFEnEAgEAoFA0A4Qok4gEAgEAoGgHfD/ACPc8dVcn7U/AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -1212,7 +1211,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1257,7 +1256,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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5+kugqz+BrcaV8Tpi3QzYDzsZgkn7XDGlFDFZxWA0iUAsifg4tkwMItkoMqz8GCtnMyIJGW0D2e33NrissJlSp9FDoRDWrVs3pnBupnlUP2kSBh+9H6Z4F0wV2lEJSgHT/N1h3esokAIdZ+dwhvFHEugOxrLqO73SDsNQblx/fz+6urrGdE+teSSKIhobGwvuW7E1ZS0sgNRecVdXV0azEuGD1yB+9zmQTAIGA9xHHAnP5GrA7oHgrIQa6ANCPgyITlgat9l4Rjnwzdfoe/xPqJjihpAhsZJKZjiP/hWk2qlZjTmWVBAY8ryIyWMXGZKQEhlOswSbkR9j5QB9oTj6wvGMfSwGEVPc1i1EaSKRQEdHR8bjc9nOo0FDBezTd9gYtu1/8zUE3/gXKqaMUKTQ7oXzB6dCdOavmCGHMxKUUnQORhGMZ35hdVkMqHVu+cCPRCLo6OhAPM6eg1vPIWWbneHZYyEqaThtHgVNHjhm7FBw+24WZS8shgmFQuju7kYstqXSFD54DeKXH8I6d3vYttkR4e++Qs2+u0KoqII4bScQQlL70M1fAJFBSNseuMXfy4EA1v/p97CKARgdmaISgHnXQ2DZaf9RRRESspoSGfEkIomx54wIBEMig3tlTFQopfBFEugNZRYWU9xWWI3pixWlFH6/Hz09PWlJneOdR7HOdnT86VY4q4yQTNomXhQC7If8FMbp24ziv5zDyR0qpdgQiG00O2RBkIpWsE7yqaqKvr4+9PX1bbFVz5pDkeVL0XTWmRCc3qzmUbEwYYTFMNFoFP39/QgGg1AUBYb7boR1+hzUn3/Npi/tm9ch1M2CUNW48e/U3laonasgbX9I2jUppeh98TlEP3oJzobMZ5eF6kY4j/4ViHH0STZJRUUwLmMwlkRoBLWcCUIAh0naeMJE5IWfyh5ZUdEViCI8gji1GUU0uDMnS1JKEQ6H4fP5EAqFoKpqTuaRmkyi89GHQNq/gbU6s623NHsX2Pc7FkTgWyMc/YjLCjoHo4iPEEn2WI2odpgz9lFVFYFAAD6fD9FoFNK9N6TNofa7rkXtgXtAHMU8KgaKL4aSZywWy0Z3v0Q4hJZkErZtdtwYRSCEgAJQA30glVM3fsFqoE/TWZMQguqjj0N02x3Qed/vUTHJCpHxxgcAak8rfI/chIrjF0GqnDSqsRtEAR6rER6rEYpKEYilcjKCozxhQikQiMkIxGQQROEwS3BbjHCYJJ6TUYaE4zI6A9GsDNcq7ZkXQyD1e7fb7bDb7SmR0daKjlHNI/a2oWAwoP60s+D/4lP4nnoArqkeEI3yz/LKz+Bvb0bFjxZBsDpGHDOHMx4opRiMJrEhGBtxrRUI4LGNbKktCAJcLhdcLhcopVjFmEO2bReAKsqo5lExULwj0wFREABCEP7uq42JaZRShFvbgZAPSvMXUHtbU2GnkA+gFDSpHUa2NDah6f/uRESchGh/ULOfoMQReOpuRL76YBxjJ3BbjZjqsWFejRNTXFa4zAaMNvhAkRIZ6wYiWN4TROdgFNGkosu5a05+oZSiJxjDen8kK1FhH6pbMxoIIRASsdHNI4KMvy/XTrui4fLb4O8jSIQyJMmF+zHwyM1IrGMc+eZwcoSipnIqurMQFQDgthohaQhiLQghIJKUPoe+/RKD3y1jzyOpsPVAMjHhtkI2R4lGsOa8nwEArHO2g23bBQh/+yUiK75B3QknwFJdidSjlwz9b0CYPAdC5ZQRrz3wwbvwv/AYKqZ6M0cBqqfBdeypIGJugkfDx1gDcRmBWBLyGG3BTZIAt8UIl8WwMauZUzokZBWdg5ERk39FgWwUHY0eG8yjFBYAEFm9HOt/fyUAxjz64fGw1NZg63kkTt8ZxJ7ZOpyqKjY8+xQSX78NR512X0opDHN3h32/H/CIGyenRBIyOgejGddRglQwW6WpaMX0SseYtpfb7vw/RFd8kzaHKvY6CJUL5gCKgi3nEYE4f1+QIhQYXFgMCQsIIkBVgAgwN87AlAuvgbL8/fQ/srogzdw1q+vHezakEtI8gubWCAAoxATXj8+B5MmtFSulFJGkgsGhLZOxemXYjRJcllTiJ8/HKH5SIdso81z9MJJAMKnCAkkgaO0Pw2GWUFcxtnPwmwuLzeeRbbudUPez06C2fJX2N8RbD7F+XlbXD61cgZ5H7oZrsgMkg8hVrV64TzwHgrm4jt5xSg9KKfrDiRFPUJkkAXUVFiRkFR2DUVTZTfDaxmZSNfD2K+h54sEt5hBUBU03/AkSjULtXpP2N8LkuRAqG8Z0v3zCX0WHUZVU8oGqwDZvexCjBbC60vtF/KCJ7Eqzm6pr0HTN75F0z0bMr31MT6RxDP7jdoS/+t8YB8+GEAKbUUKd04LZVQ7MqLSjymYadbG0UEJG+2AUK3oCWO+PpGqgTFw9WrSoQyHbrkBmUWE3Smj02mAzSjBJImqcZlSOcTFMH8SmeVSxcD8QuxdglGqn/g2gNLuj1PbZczD16tsRSjqRjGgv9EKkH76HrkO8LX0B5nCyJamoaBuIjCgqhreiTZIIh9mASpsJbmsOogebzaFhiItdO0f1j80XI99wYZEBwc3+MulA9g5qRBQx6eenw3bErxDsDmj2E0SC+Ef/ge/pB0Dz4BZKCIHFIKLWacasagdmVtpRZTfBIGYfgVAp4I8m0eILY2VPEN2BGGLJ/Nqmc7IjllTQ4gsjMMIRuBqHGZNdli32gF0WI4x5soUnggBSUZPeoCRBg9kXOROtNjScfyXE7Q5GpE87f0kUKULPP4DBN58bw2g5E51gLImW/jCiGdY1kRDUuyyocZi3OLZfaTfl7Rg/MVkBK+OkVDj7F1094cIiA6kFkWHHPQaV6Nx+AeouvBnBoAhVZv9oCSEgfc3o/fM1SPr6Rn2P0WA2iKh1mDG7yoEmjw1uy+gSP5MqRW84jtV9IazpC6EvHIc8ziJrnNFDKYUvHEerL4xkhn9/o5iy6HZbjbrnIWgL9NHNI0IIvAcfAc+vLkOwL6YZNRNEAeqqj9H78C1Q49k5JHImNiql6A5E0TEYhZohGms1iGj02mDP4LWSLwSNqAX158cqfDxwYZEBYjCBOBhJY7EQaEx7a0MLqaICDZfcAKV+ARJh7QXPIMrwP3Yrgp+P/dRIthBCYDdJqHdZMbfaiXqXBfYM+SAsokkFXYEYlvcE0eoLYzCazDg5OblBVlW0+6PoGcHwqsJiQKN3bImZOcHmBgzpWy000Dum6JylvgH1l92GmKEWSlw7QiMl/Oi79xrEWleP+h6ciUNcVrDOF4Y/mrmSb6XdhAa3tWDJ7NrbIRt0HsnIcGExAsTF9pqgg+nVGLO6HiGoPu4k2H9wJqJ+bXEhGUUkP3kevY//CWqeq7QOIwgEbosRTV4b5lQ7UOswwzTK8uzBuIw2fwQrNgTRMRhFOMHzMfJBOC6jpT+McELbKE0gQF2FBZOcloI6rRJC2IuiqoCGst8O2RzBYETd6RdA2uUoxAMZRLpJQOj5BzDw6jNjug+nfKGUwh9JoLU/nNHwShIIprqtqLSZCnrqiBhM7JNU0QBoorgic1xYjABxVoG5HRIYn0q0zZyL6vNvQkyxg2pk2hFBgBRcj567r0SiR19VahAFVNlNmFlpx4xKO7w2I6RR7JUoQ/bRzf1hrOoNYUMwhsQ46p5wUmTrTWE2iGj02uE06x+yZSGw8iwwdoE+jGvP/eE59QpEQtr/FqJBBFo+Q/efb4ASLb79aI7+ZOtN4TBJaPLaYRllFDdfMPOVMP55lGu4sBgBIhnYKjESAE2OTyVKFismnXMVMGN3yBlCuiYL4H/sVgz+7+1x3W8sDCd91jktmFPtwFS3FRVmg2a5eBYJRUVPKI6VvUE094fgiySyMmzibElCVrHOF4YvksjYz2szYqrbCmMx+Y9YKwCJtR3SM+6IlrGqGnUX3Yyks1EzfwkATCSE3nuuRGTVsnHdj1PaRBIyWvpDGYuIEQC1TjPqKixFdcSeVFQzP6fjfNHNNUW08hQvml9mjlSi59Bj4Tj+bMQj2ouiwWqE/MXL6Hr4D1CTmfcC8wUhqWqpU9xWzK1xYnKFBdZR7tuHEwo6ho6udg5G+amSLAlEk2j1hTIaXkkCQYPbiiq7ueiMogghIBUMnxY5CYT947++IKD65EUw7Xk8khn2yk12I8IvPYTe557gW3QTDEop+kJxtA1EMhpemSQBjV4bXBb9E51HghhMzNMhNOQHlTO/cOgJFxZZwFwQkdvwk3nKNFSfeyOSBu2S0IIkwhTvwvpbfotYZ0fO7j0WRIHAYzVieqUds6rsqLabRvWGrFKgP5LA6r4QmvtDGIwm+ULPQFUpugaj6BzBm8K2mTdFsaIl0NUcziP7gt3hOfUqJJLa/w6S2Qhh/ZdYf/vvIAe1j4BzyoekomJ9Nt4Ulk3eFMWKwJxHFDTQq/tYtODCIguIwayhEgdA5dxFDwSjEdW/vgTC3H2gZDiS6qw0oefe6+F7+42c3Xs8mCQRNQ4zZlXZMc1rg8dqHNXR1XBCSSV89gSxIRjLeGxyIhFLKqlTNiN4U1Q7TKjfypuiGCE2DyAwyrEHNuRUVEouN6rOuQ5q1UzNUyeCJMJui2P9zZcg8N03Obs3p/gIxZNo7Q8jkiE6KhCCyRUW1DjNBU10zoZSyLMo7pWoiNBTJVbsdwScx58DOan9A3fUuRD78Fmsu+uWoklIG3b6nFxhwdyhwmgOU/Zv0LJKU7kYPUG0DUQm7IkSSil8kTjW+cJIZOFN4bEWNls9W4ggDCVDb0UiBsS0Ta/GgiAI8P7oNJj3OQFKkv1vSAiBq8EN/5N/ROc//gaq0+krjj6olGJDIIZ2fxTKCN4UTV4bHEWS6DwSxGQFzPa0z2mwH1TRzhvREy4ssiTfeRZbY5w8Fd6zroVq1a4fYvE6YFE2YPWl5yCyprjO6guEpPwTPDbMrXZgktMMc5ZHVymAwVgSzf1hrOkLoT8cnzDJnhu9KYLxjNnqFeYCe1OMET22QzbHuu0ucP3qcshE27LcMdkL0vIZVl99CRJ9xRNO5oydYW+KgWjmvINKW2G9KcYKcx5RdVRutvmktP41Cwgx2QCTLe1zGuwDVfPzpiMYjPD8/EKIs3fTfHM32EyomuHBuluuxIbnninKN3xJFFBpM2FmlQPTvSmXz2zfr2Oyis5AbGOyZzxD1n+pE07IaM3Wm6KisN4UY4U4vKniSluRzzCu5HCj8vRrgMpGzT5mjx0uL8Xqy86F/6MP8zYWTn6hlMIfzc6bYorbikp7aUT7tkZw6vuiO1q4sBgF2ioxf/bbhBA4DzgO1oN/CkrZE0AwiKhZ0ITAG89gzXVXIukfyNt4xovVmHL5nFOTMuDKtlbJcLLnqt6hZM9Y+SR7UkrRG4xh/QjZ6maDiEZP8XhTjAUiSilxsTWxEGg8ksf7inD96EwYd9hfW6RbTajdrgGdD96JtnuXQI1nTvTjFBcbvSkC2XlTWIs40XlELA7AYE77eKxutrmGC4tRkC+Tn2wwz9weFSeeB8r4MQEpAVI5fwqM0Q1Ydt4iBL7+Mu9jGg+SkDLgml2V8sYYTS5GOKGgbSCClT1B9JR4smdCUbFuIIL+kbwprEPeFKN0Qi1G9N5W3HhfQmDb/VDYj/glKCNqAqREeu3OM5BY9glWXHwuom3r8jomTm6IJmS0ZuNN4Sg+b4qxkDq+zZhHqgwa9uk/oK0o/VVKTzKpxCxLQI8H0VsL108vhuBh24wDgHNqFapmeLH2+ivR8beHQeXiSObRYtgbo9Fjw+wqByptRohZhiaTKsWGEk72DMSSaO0PZfTyEAWCBpcVVY7i86YYK8wETuQvz2JrjI1zUfHj85gJcMBQsbN5DbDZFSy/8Bz0vvpSSf2uJhKUUvSH41g3EEEyG2+KAhThyxfsAwXFsR3ChcUo0FSJigwa0mf7QbDa4fzhb2CYsYNmH0ulE3W7z0b/K89ixaUXIN49+mqshcAoCZjktGBOjQP1FRZYskxM3DrZ0xdJQC3iZE+VDnlTDI7sTdHktcE2imhOKUAkI4jNnd4Q8YMm9dl+ED01qPjJ+RCrGzT7OKdUoXaHKWh/4I9ovvV6yKHcnlzhjI+komK9P4LeEYrwuSyGovemGBM2NyClb4vSwd6CC2EuLEZJocK4W4xBMsB20Imw7HaYZh+jzYy6PeZA7e/AsvMWwfee/nbgY0UgBG6rETMq7ZjutcE1ymTPjsEolhdpsmcsqaC1P7M3BQBU20vDm2KsaFsT6zePBIsdjmPPhHH2Tpp9LF4HJu8xB+GvP8XyxYsQWvadbuPjaLPRmyIxsjdFbYGL8OULQggIK4lTjgORQf0HtBnluWrlEWJzASJLJY6/5sGoxkEIzAv2g+3QnzHHAwCiQcKkXWbC5rWi5fc3ofXu26HEisPzIlusRgkN40z2bOkPI1DgZM9svSkMooBGjw2eAldSzDfFINCBVDKpdf8fwbLwcM0+hiGRLtIoVl5+Ebqe+jv3vCgQKqXYEBzZm8JSYt4UY0Xv49vZwoXFKCFEw+SnQCrROG0bOI5bBGJLdwYFACIQVG07Fd659eh/8zUsP/9sRNau0XmU42frZE/7KLYHQgkZ6wYiWN0XwkAkobvAGI03RZOn9LwpxgIxWgCLM+1zGvKBKvrWwiGEwLzjvrAddgogGZl9RIOE2p1nwlnvRefjj2DVVb/lnhc6s9GbYoRE50qbCVNK0JtiLBC7BxDS1wu9X3S3pvz/5fOApkoskFe7VDUZzh+enXG/uKKpJpXtvqEDKy5ejA3P/7vg+3BjYTjZs8ljw6wq+6iSPeOyivbBKFb2BtEXjkPV4b8/W2+KSc4hb4oSz1YfDczkM0pBA4Ux+TE2zU+JdLu2SK/cZgq88xoQ+v4bLFt8Jvyf/E/nUU48Joo3xVggggjiqExvSESAeFj/AQ3BhcUYIA4vWyUW0PVMsDnh+MEZMMzYTrOPtaoCdQtnQxAp2h+8F2uvuxrJQb9+g8wxJkncmOw5eRTJnkmFoisQw4qh46r5cPWklKI3lIU3hSSg0WNHhaW8Q7YsNLdDQoWbR1JlHZw/PAdizRTNPhWN1ahZMB1qOIS1N/wObfffAzVRPJUlywlFpegKTBBvijGiOY8K+Twq2J1LGCKIqRDU1kQDBS1dm0rqPAnmnQ/S7GNyWlG3+xxIVhMGP/8EyxeficDSr3QcZe4RSKrS6miTPZWh46oregLoCkRz5oeRUFS0DUTQH878W/BYU5UUy8GbYkyYbOzj28H+woZxrQ44jjkdxpk7aPax1bhQu+tMCJKI3hf/gxUXnYvoeu55kUuiSRmtvhACsczeFDVl4k0xVpgRC3BhUZIQO7u8OQ0V1pyEEALLLgfBdvBJgMhW7warCZN3nw2j04Kkz4fVV1+KjkeL3/MiG9KSPbNYbFQK9IUTWNkTRLs/Mq6TJMPeFNEsvCmqy8ibYiwQQtgunMkYkEcXzmwgkgHWA0+EeddDNPtYPA7U7T4bosmAaGszlp9/Nvpee7kktxiLiY3eFL4IksrI3hTuMvKmGAtEMgBWRr5SeKBgLpxcWIwR5oKIwqrEzTHO2B6OY88EsTqY7aLJgLrdZsPsdQCUovvpJ7HysgtLxvNiJIaTPWdVpzwxTFlEBSiAgWgSq3pDWDcQzigOtkalqZDtyN4UYll6U4yVYp5HhBBYdjoAtkNOZvoFAIDRYUHd7rNhsJlAE3Gsu+cutNx2I+RQSOfRlgfyRPemGCPEzohaqAoQ8es+FoALi7FjshZlGHdzpOoGOI8/G4KL7XQoGERM2nkGbLUuAEB45fKU58X77+g2xnwz7Ikxs9KOqW5r1nkYgZiMNX2po6qheGZHz43eFNFsvCmsZetNMRaYW4oobJ7F1hinbwvHMaeDmK3MdoPVhLqFs2GsSLUPfPAulp+3CKHl3+s5zJInFE+iZURvCpS1N8VYERzseaQWSKDzFW6MZAzjJgobxt0cweGC49hFmidGiCigesdpcExJKV41EkHLbTeidckdJed5kYnh0yTTvTY0eWxZH1cNJWS0+MJYO2RqtbnAoJRiIJLg3hTjgEhG7WOnOtjkZ4tUMwWOY7VPjKQigLNg8aYihImeDVh52YXc8yILRudNYS97b4oxYXWxDxQUSKBzYTEONPMsiiCMuzmCxQbHMadDapjFbCeEoGqbqXDN2FSDpP/1V1OeF82l53mRCUII7CYJTR4bZlTaUZHlIhVNpgqfre5NeWEklZTD54Zg5mz14TooE8GbYqwwBbqqAOHCugdujeiuhvO430Bws7PwBUlE7S4zYKsdsitXVXQ+/ghWX3MZEv35q4BcyiS4N0VOIIIAYmfZ5AdAZX19YQAuLMYF0Qg/FZuwAABiMMJ++M8zZrp7ZtXBO39TZCPesR4rLlqMnuefLZrtnVxiMYiY4rZiVpUdHqsxq5Mk8SFBsbYvhFCGSorD3hQTOVs9W7TyLAoVxs2EYK9IRQA1jqMSQUD1jk1wTt20/Rj85mssO/dM+D/9WK9hFj2UUgxGE2jxcW+KXKF9oED/ecSFxTgolTDuMESUYD3wxzBtu6dmn4qp1ajesSn1ZARA5STWP/hnrL3hGsiDxfUGmStMkojJFRbMrk5VV82kAwQCjOQqPpG9KcYCKbIw7kgIZiscR/8a0pTZzHZCCCrnT4F75qYIoBIMYO31V2P9A3+a8J4XKW+KGLoCMWR6X7FPYG+KsVBMx065sBgnmmHcAheB0YIQAZY9j4J5t0M1+9gneVC78wyQzcKOg59+jGWLz0Twm691GGVhMIhD1VWrnahxmNIiDSIBREIyvjlVDGWrT1hvijFABEGj2ulgQcK42UAMRtgP+zmMs3bU7OOeWYfKbbaMbPS88BxWXHwuYuvb8j3EoiSaVIa8KbS/12Fvisk82jc6TFbAYEr7mIb0P1DAV79xopXVXoxh3GEIIbAs2B/WfY8HNB6S1kon6hbOgrDZ20LS149VV/0WHY/9tawT0kSBoNpuxpxqB+qcKS8MiSBjFjqlFLJK0RdOoKMIq6oWO5rHTgvsC5MJIoqwHnACTNvvrdnHOaUK1TtOA9nsARltacbyC85G3+uvluUWI4tN3hThjN4URpF7U4wVQgh7OyQRAxL6JuJzYTFOiM0NkPR/xmLMs9ga07xdh6qjskONpgob6nafDcmyWWEmStH9z3+kPC82dOs00sIx7MaZaZFTKYVMsTGJc9gLo90fyZmbZ7mjLSyKex4RIsC6x5EZq6PaJ7lRu8tMkM2iWGo8hnVL7kDL72+CEi5cTQc9GI03RaOXe1OMB21fGH2Th7mwGCfa2biDuldpHAvGpvmwH3UqiDHdkwMAjDYz6nafDaPDssXn4RXLsPy8RRj44F09hqk7w94U/gzeFJRSKCqF1gvYQDSJlT1BdAWikAvkgFcymGyAxAjjloBABwDzjvvCuv+PmC8ZAGDxOlC322yIW+ULDLz/DpYtXoTQimU6jFJ/QvHUce2RvCnquDdFTtAWFvpG/riwyAGaSTNFHMbdHEPdNNgzuHRKZiMmLZwFs9u+xedKOIzmW2/Auj/eWTaeF9l6U0gCgUkSMZJcoNhkF74hTwXPygFNX5hEFLTA9t7ZYpqzM2yHZYoAWtMjgAASPd1YeekF6Hr6iYJZMOcaSil6gjG0+yMZf/PD3hRO7k2RE1IHCtLXcb0PFHBhkQM03QNL5G0LACTvJDiOOwtCBVvxigYJtbvOhKUy/RRM339fwYoLzkakpTnfw8wripq9N0WT145plfasvTBUCvSE4ljZE0RvSJ+S7aVGMdt7Z4uxcR4cR/9aMwJosJlRt8ccGOxbtasqOh/9S1l4Xgx7U/hG8Kbw2ozcmyIPMPMsVBmIBHQbA/9Gc4HZXtJh3GFEpyd1Rr+yjtkuiAJqd5oOS1W6uIi1r8eKi85Bz0v/KcmEtEhC3mjfrUXKm2LLSorDXhizqxzwWI2afzuMQim6gzGs7AnCF0mU5L9VvigFe+9skCY1plw6benzBAAkkwGTdpuVLi4ABJd+heWLF2Hws0/yPcy8MBhNoNUXRiwLb4oq+8QuwpcvikGgc2GRA1JhXMaiWEJh3GEEqwOOH5wBafJ0ZjsRBdQuYIsLmkxi/X33YO2Nv4Mc0E8djwdKKXpDMbQNRCBnCNmmvClsqLCwxYNRElJeGFUOuLLwr5BVio7BKFb1huCPcoEBAMRgAsyMMG7QV3L/PqK3Fo5jF0GoYG+TSkMW4CxxIQcGsea6q7D+wT9DTZaG54WiUnQORtEViGUswpfyprBxb4o8QmwuZq6PnicVubDIEZoqMezXdyA5gBjNsB/5KximbctuFwXU7jQD1mp23YTBTz5KeV58uzSfwxw3SUVF20AE/eHMi7fHahzyphg5W90oCWhwWTGz0g5nFvVIEoqK9f4o1vSlzvaX2gM017B9YWQgVnrVQkWnB47jFkGsqme3mwyoWzgLBgd726Tn+Wex4uLFiLWvz+cwx83ovSn4YyefEEFkHyiIDoKq+hyD599wjiA2jTBugcrWjhciSrAdfBKMc3ZmtwsENTtN1xQXyf4+rLryEnQ+/khRel4EYkm09IcylkYXBYJ6lxXVjtGHbM0GEVM9Nkz32mAzjixIYrKKdQMRNPeHEc6wHVPuMBdElKZABwDBYk/V6ZnUxGwXjQbULZyTdupqmGjzWiw//zfoe6P4PC9G400x1cO9KfSE+TyiFIgGdbk/FxY5ghjN7DLqJbogAqmjtNb9jodx7i7sdkJQs/MMWGvY4gKUouupv2Pl5Rci3rMhjyPNHpVSdAei6ByMZgzZWo3iqKqgal9HwjSvHU0eW1Yl2yNJBc2+MFp84Yyip1whVhfz81IV6ABAjCbYj/wVpLppzHbRIKJuz3ma4kKNx7Du7jvQcvvNReN5ISsq2kfhTcGL8OkLsbHXZL2eR1xY5BBic6V/GAuBKqX7BkqIAOu+x8E4b1d2O4DanWfCMW2y5jXCy5dh+eJFGPjwvTyNMjvi8sjeFABQZTehwWWFlMNsdbtJwnSvDVPcVpiysPsOxWWs6QuhbSAyoVw8iWRIeVpsRSkLdGDIAvyIX2qKC0EAJu+7HUwedsInAAy89zaWnbcI4ZXL8zXMrAgPeVOEuTdF8WKtABhlFfUS6FxY5BBi1VCJRVo3JFsIEWDd51gY5+3G7kApqubWoXL3nTSvoYRDaL7leqy75w9QY7E8jZTNsDdFa39mbwqDSDDVY4PXlp9KioQQVJgNmFlpR32FBYaRqpkBGIxtcvHMNPZyginQE1HQZOa342Jno7jQSoxWZUzeexs4ZrPbASCxoRsrLr0A3f96UnfPi2FvivXcm6LoIYLI9rMI+3XZUuPCIocwF0QAKPG3LWCTuDDNX8juQFU4PQIaTjoBELXDnn2vvYTlF56DaGtLnka6JcrQ6YuRvSkkNHrsWW1XjBdCCNxWI2ZVpWqRSFkUWhqIJrGqJ4jOwSjkMhcY2tshpS3QgSFxcfgvINXPYHdIxlE9dxJqjtAuEghFQcffHsbq312OpE+fTP+ErHJvihKD+TySE7rUDeHffi6xONh1Q0p4f3hzCCGw7P0DmLbZnd2BqjAEWjHrovNhmsT2wgCA2Pp1WH7h2eh56fm8queUN0UoozcFGfKmmOTUv5KiQAi8NhNmVzlQ4zBlLNcOpFw8+yMJrOwNoruMXTwLvT+cbzaJi5nMdpqIwm4MYdr550G0s91wASD49ZdYtngRBj//NF9DBQAMRpNo9YVG9KZo4N4URUUhBToXFjmEEGFob2tLaHiw6DK6xwohBJa9joFp2z3YHagK+fv3MOO8s+HZ70DN66Q8L/6I5puuhRzMrecFpRR9ofiI3hQmSUDTkDdFIRdDYWM1VSeqbCatgrMbUSnQW84uniYb0xa7XIQFkMolsR/+c0gNWuIiBtL6OWb/7v9gn88+9g0A8qAfa669Eusfui/nnhebvCkyJzrbjRIavTbYuDdFUaEVQddjHnFhkWOYX6YqA/HiyObOBYQQWPY8GqZt92R3oCqi7z+LyccchsYLfgvBzD6nDwD+jz/EssWLEPzum5yMbdiboi+ceT/ePQpvCr0QBYJapxmzqxzwWo2M1KstGXbxXNUbxGAZeWAQQthvW9FA2dTSAIbExWE/h9Qwi9lOEzHEPnwG085bjEk//Xkqw1ODnv88g5WXnI9YR3tOxhZLKmj1hbPzpnBZIHFviqIjdVKR4QitQwSd/xpyjGYCZxm9bQHD4uIomLbbi92BUoTffAqO+irMvfteWKez38wAINnXm/K8+PvfxuV5EczGm4IQ1LssqHGYizZb3SAKqKuwYFaWLp5JhaJtIIJWXwSxMjmiyhToVAWipeHomi0pcXEKpCmz2R0ScYRe/iuq998Xs266HYbKKs1rRdauxvLzz0L/m/8ds8iklMIXjqPVF0YyQy4P96YoDdgCPZj3k4pcWOSYQoaf9IYQAsseR8K0/d7sDpQi/NZTIKFezP793ag59kfaF1NVdD35OFZdcTESPT2jGsewN0VHNt4UXhvsptLIVt/CxdM8cpg5lJCxui+EzkC09PMvtOZRGSRwbs2wuDBMncPukIgj+OLDsHidmLfkPrh214gUAlBjMbT+4fdovfNWKJHRRUllNeVN0TOCN0UF96YoGTSfR3meR1xY5BgiGQGTNe3zckng3BpCCCy7HwHTDvuwO1CKyJv/RLL5G9SfdiZm/O5GSBUuzeuFln2HZYvPxMD/3s/q/vGhSoqF8KbQC7NBxFR3ysXTnsU+dn84leBZykXOiMUJ5jn8MhTowJDT7aE/0xYXyTiCL/4FCA9g2uW/w5TfLAYxahe9873zJpaddxbCq1Zkdf9wPFWELxtvikncm6Jk0DypyIVF6cEMP8UjoHJpFBQaLYQQWBYeDtOO+2r0oIi89S8kVi9Fxc67Yt4f74djhwWa11PCITTffB3W/fluqHH229Pm3hTxDNnqKW8Ka968KfTEakwVcMrGxXP4mO3a/jDCidIzaCOiBFjsaZ/rdQ6/EGwUF41z2R2SqciF0tuBqsOPxtw774F5SqPm9RLdXVjx2/PR/cxTmrkp2XpTmA0iGrk3Relh1jipmGeBzoVFHphI2yHDEEJg2e0wmHfcT6NHalsk2bYSBrcHM6+9GZN/dXpmz4tXXkx5Xqzb0vNiOFs9e2+K8spWH3bxnFxhGdEDI5pU0Nwfxnp/JOOeeTHCFOhyHEjqa7CmJ0SUYDvkZBga57E7JBMIvfRXKAM9sExtwtw7/ojKw47UvqCioOORh1KeFwO+LZpG400x1W2FsQSjfRMdIgiAlVGJOpJfgc5/KXmgXB04R4IQAvNuh8K8YH92B1VF6LXHIXevAxEE1B7/Y8y57Q8w1k7SvGasrRXLLzwHva+8AErpRm+KYCZvCgC1BfKm0AtCCDxDJluVNu2Q+DD+aBKrekvreOpEFOjAsLj4KQxN85ntNBZG8IWHoQb9EMxmTD37fEy77BqItvQIzzApz4szMfjFZwCAAPemmDAwBbqS35OKXFjkA7MdEMr7HL4WhBCYdz0E5p0OYHeQk6k3rv5uAIBt1hzMu/teuPfRECMAaCKBtnvvwepXXsvKm6LRa4OrwN4UeiEKBJOcFsystI+Yf6FSoDsYw+rezCWui4VCJZ4VA6nqwj+FYdo2zHYaHkTwxYehRlPl5N177o15S+6HfR67PwDIfj/W3Hwd1nz+NTpH8KawcW+KsqEQAp0LizxACGG7B0YGQWlphaPHAiEE5l0O1jwtQhOx1F5xIGVHLFptaLr4cjSefwnT84K4PTBf/H9Qd9Iw5Rpi2JvCVETeFHphNoho9FizClknlFSJ9lZfuLgLnBnMgMQ4hz8BBDoAEFGE7aCTNI+iqv5ehF76K2gilYdkrK7GrJtux6Sf/IzpeSE0NMFy9W2QG9iF0IBUtK/aYUI996YoGwoRQee/nDzBDD9RFYiVj1FWJoZPixjnsAuT0UgQoRf+AjUS3Njfe+AhmPuHe2GZtqmOgrjDLrD87k6Is9lhYaA0vCn0gBACp9mAmVV21DhGdvAMxmWs7g2hq0iPp2oK9GiwrIyyMkFEEfZDToZYO5XZrvR2IPTK30Dl5Mb+dSf/ArNuuA0Gb+XGftJBR8J8xc0QarWrEA97U3ispZ/ozNkEMZgAI+ukIhcWpQcjYQYAaDSo80AKByEE1n2Ph6GJnYimBvoRevEvUOObiuKYJ9djzu13o+q4H8P401/DfM5lIBnqJZihoLGEvCn0QCApi/DZVQ5UjJDFTwH0hRNY1RvEQBEeT2W/bdGycrIdiVRV1F9A9NQy2+XOZoTfeAJU3RR9cmy7PeYtuR/OfQ+EafEVMP3kVBCD9m/BaSDcm6KMIaznUSycN4HOhUWeIIyStQBAYxNHWACp8r22g06CVMcOvyr9XQi//DfQzeocJIkI5QcnwXDA4ZrXpYqCxDOPw3/BrxH69H85H3c5YBAFTHFbMc1rg1nKPNVllaJ96HhqpJiOp5o15tEEEugAIJissB91KgSnh9mebFmGyLvPbiEM4yYr8IuzIW3HjhoCAI2EEbv/TvguOwfRNatyPm5OccB+HuVPoHNhkS8kEyAy3hCGkq0mEsMFl8QqdhhW7m5F+PV/QJVl+LPwplB7NyB265VIvvIslGAAzTddi7Y/L9H0vJjo2IwSZlTaUec0QxwhxB1NKljbH0a7P1IU5dkJw8sCmHgCHQAEmxP2o08DsbLFVmLF54h+/AoopejNwptCWbsK0esuhvLZh4h3dWLlb8/HhmefnjDbTBMKs8Y8ypNA58IiTxBCmIviRFwQgVRBHPuRv4JQUclsj3e2Yn3zWnSP4E0hf/oBotddDLV59Raf977yApZfdA6i61pzN+gyggyVaJ9VbYfXOvLx1IFoEit7g+gLxwu7PcIF+haITi/sR52aKjDFILJqKVrau9GfwZuCqioSLz2D2G1XgfZtss+nsoz2vzyANddeieTAQM7HzikcekfQubDIJ6wwrpwATU7MN2vBYofj6F+nJeQlPJPRf+BpiFZo+1kQAO6wD/Q/TwDRCLNPbN2Q58WrLxVdrkCxIAmpAmczK+2wGTPvp6sU6ArEsLovhGC8MMdTuUBPR/JOgv2IXwLSloIrVj8P/QeehoTRpv23AoG7qxnqf58HNAr+Bb78HMsWn4HBLz/L5bA5hURngc6FRR7RVokT820LAASHC46jTwMxW0FBEJq9Jwb2OQWqxpEoYJM3Rc20Rsy7+z6499lPsy9NxNH2pz+g+ZbrIYcm7sNnJMwGEU0eG6a4rDCImbdH4rKKVl8k5SFSiO0RLtDTkCY1wn5I6lgpFQ0YXHAkBnc9FtTAjmQAm7wpahcswNwl98E2V8PdE0OeF7+7Au1/eQBqsvg9TziZ0Vugc2GRR4hG4hkm8NsWAIjuapiPOBUD+5yM8Px9mWfuh3FbtvSmEG02NF18BaaedzEEk/Yi6v/f+1h27pkIff9dzsdfLhBCUGExYFaVA9V2E6Pk15YMxpJY1RvCQFTf0yNcoLMxTJ0N6aCfof+AUxFr3D5j32r7lt4UpuoazL75TtSeeDIynUve8OzTWPnb8xHr7Mjp2DkFQEeBzoVFPjGzQ5J0gu4PDxOMJdFGKpCsnKLZh8QjqIl0o8aZ7k1BCEHlQYdi7h/+DMu06ZrXSPb1YuUVF6HrycdBNcK+nNTx1BqHGbOqHCOWZ1coRbs/inUDESR0il5wgZ4OpRS+SByd9nooDq9mPzHkQz2C8DCK8BFRxOSf/RKzbvw9DB7ta0TWrMLy889C/ztv5mz8HP3RU6BzYZFHiCACpnRxMdGOyg2jUoruQBQdg9GM9SoMvevgffMh4L+PILlupWY/c30D5ty+BNXHHJfhpio6//43rLrqt0j09Y5n+GWPURIw1Z2qnmoa4XhqylwriP5wHMiYbpsDNAX6xJxHsqqi3R9FTzCe8V/evG4pPG8+DPnlh6EM9Gj2G/a8qNh1oWYfNRpF6x23oOWu26BE2DlOnOJGT4HOhUWeYarEeGhCWHtvTlxWsM4Xhj+aYb9WVWH7/h243/8HxFgoVbTsv6miZVoIBiMaTv8Npl99PSSndp5G6LtvsGzxmfB/zD0vRsJukjCz0o5JTjMy1XBTKdAZiKEzENXulAO0BfrEi/yFEzJa+8MIZ/AaIck4nJ8+h4ovXoKgJEFjEQRfeAhqyK/5N1JFBaZfdR0azjw7o5GW763XsfyC3yDMPS9KDx0FOhcWeYawzg9TCsQnhuqnlMIfHdmbQpTjcL/3GOwr/wey+XuYnETolUehBHyafwsArl0XYu6S++DYbgfNPkowiLU3/g5t998DNZG5VPREhxCCSpsptT1iyrw9EkvmXyRPdIFOKUVvKIb1IxThM0b88Lz5MCzty7b8+3AAoZf/lnE/nRCC6qOOxZw7/ghzfYNmv3hnB1Zech42PPcv7nlRQugp0LmwyDda+1oTIIyrqBSdg1F0BzJ7UzhMEqZNqoRjCjtfgsbCqUUxHst4P6O3EjOvuwV1Pz81Y0Jo74v/wYqLzkG0TTsSwkkx7N45xWWFVMAS9BNZoA8XjesPZxbDHqsRjQ11sFSxrb+V/i6EX39yRDFgbZqOOXf9CZWHZHC+lWW0P3w/1lx7FZJ+7nlRKugl0LmwyDPMBRHlLyyiCRkt/SEE4xlCtgBqHWbUVVggCgSWhYfDOGdnZl91YANCr/9ji3oIzGuKIiadcBJm33oXjNXsBRYAoq0tWH7B2eh97WXueTECw6dHZlbZ4bJkX5Mlp/+uE1SgB2JJtPaHEEtq/+5FgaDBZUW1wwxBSpVbl2obmX2T65Yj+vErI95XNFsw9dwL0fTbqyDatH0xAl9+hmXnnonAV1+MeE1O4dFLoHNhkW8MZkBkhJLL9KgcpRR94TjWjRCyHfamcFmNG7PVU0XLjoNh6hzm38jrVyH64YtZjcM+Zx7mLbkP7r330x5rIo62e+5Cy603QA6V5/eRSyRBQIPLikb3yN4XQMpcK1MuwGjQSjwrV2GhUoquQBSdg1FkKjxrM4po8tpg22y7ihiMsB3xCwjuaubfxJe+j/iyT7Mah2fvfTH37vtgmz1Xs4/sH8Dqay5D+yMPgspFVGeGk45OAp0LizxDCGGeHy5H58CkomL9QAR9ocznol0WwxbeFJszXLRM9LKjDfHvPkLs2+wSMEWbDU2XXIGpiy/K6Hkx8OF7WH7eIoSWf5/VdSc6DrMBMysdI1qDJ1UVzf1hdA7moCy7wTRhBHosqaC1P4zBTInOGPamsG70ptgcwWSB/YhfgGgk7EXefw7J9jVZjcdUU4vZt9yJ2h//NLPnxTP/xIpLL0C8qzOr63L0Ry+BzoWFDjDDT8k4qFw+CYSheBKt/WFEMoRsBUIwucKCWqclzZtic4jRBPvhv9QsQBX98AUk27SPoW5xLUJQefBhmHPXPbA0sSusAkCiZwNWXnYhup76O/e8yAJRIKirsGCa1wZDhnwWAOiPJLC6L4hgbOwOjhNBoKe8KRJY5wtn9AgxiAIaPTamN8XmiE4v7IedAggM63ZVRfi1xzMeQ90cIkmYfMqvMPP6W2HwsCusAkBk1QosO+8s+N55K6vrcnRGJ4HOhYUOaBmTlMPblkopNgRiaPdHoWTYU7cYUiFbhzm7PXrB4YL98F+wJwGlCP33H1D6u7Mep6VhKubc/kdUH32sdidVRefjj2DV1Zci0d+X9bUnMjajhHq3ZcR+SYWidSCC9f4I5DGeJNAW6KVvOS2rKjr8UfSMUITPaTag0WOD2ZC5zssw0qRGWPf/EbONJmIIvfw3qLHsS2c7t98R85Y8gIpdMnleRNByx81o/cPvoUTzexSZMzr0EuhcWOiAZgJniWe0D3tTDEQzR14qbSZMcVthEEf3c5NqGmA74MfsxmQcoVf+BjWSvTgTjEY0nHE2pl99HUSHU7Nf6NulWHbumfB/+tGoxjtRISOagW/CH01idW8Ig9HkqJM7teYREqU9j4a9KUIZ8lEEAkxyWjYmOo8G06wdYd7pAGabGuhH+NXHQZXscyOkigpMv/o6NJxxNoik/aLQ/+Z/sfz8sxBZs1qzD0d/mNtjyfiIifGjgQsLPTCy3+hoojTVfLbeFJJAMMVtRaU9c8g2E8YZ28G868HMNjU4gNCrj476jdW16+6Y98f7Yd9Wu76CEgxg7fXXoO3+P3HPixwjqxRt/lRRs+RobMFNVubHNF668ygbbwqzJKDRY0fFKE7kpF1jl4NgmL4ts03uakHk3WdHJfQIIag++ljMuWMJTJMze16suGQxNvzn3/z0VZFAjOx5hBw+j7iw0APJyN7nLEFhMRpviiavHVZjZnOlbDAvOADGWTuyx7OhDZF3nhn1omX0VmLW9bei7me/HMHz4jmsuPhcxNa3jer6HIxoCx6Iy1jVG4Qvkl1RM6Ih0EsxYpFQVLRl6U0x1WODcYR/y5EgRIDtgB9DrGaLgMTKLxD/+t1RX9c6bQbm/uFP8B58mGYfKstof+herLnuKiQH/aO+ByfHaAl0LixKC0IIM2pRalsh0YSM1lF6U+QCQgis+/0QYu1UZnti9deIfTH6AklEFDHpxJMx+5Y7Yayu0ewXbWnG8gvORt9/X+FvXaOgxm5GXRa24B2DUbT4wkhkiH4BAIxmgLHtUmqRv2FvimiW3hRjjfZtDZEMsB/+cxA72/o++vGrSDSPvhqwaLagcfFFaLrkCghWjbdhAIHPP015Xiz9ctT34OQOTYGew+cRFxY6wQw/lciCSClF/5A3RXKU3hS5gogS7IedAsHBzkiPffYGEquXjuna9rnzMffu++Decx/NPmo8hnV/vBMtv7+Je15kCSEEXpsJM6scsI9gCx5OKFjVG0RfOK4p3ggRhsTFVpSIQB+VN4VnS2+KXCFYHbAf8UvAwD4qHH7zKci97WO6tmef/TFvJM+LAR9WX30ZOv72MPe8KBQ6bM1zYaEXJsaXqSRBleLOaE8qKtb7I+gdhzdFrhAsdtiP+AVgNDHbw28/Dbl7bFsWkt2OpkuvwtRzLgDRuD4ADLz/DpafdxZCK5Zp9uFsiVEU0Oi2ot5lgZhBcFKkTLXWDUQga+ResAR6KUQssvWmqBr2phhlovNokLyTYDvoJLYnhZxMnRQJDY7p2qbaSSnPixN+ou15QSm6//UkVl52AeLdXWO6D2fsEFFKbc9vDY9YlB6aCTNFnHi20ZsikcmbAll5U+QK0VMD+8EnA4Tx01VkhF59FEpwbLULCCGoPPQIzP3Dn2BpzOR50Y2Vl16Arqef4J4XWUIIgdtixKwqOypGOHIcjMtY3Rdi+16wBHoyVrTFsCilGMjSm2KqxwbvCN4UucLYOBeW3Y9kttFIEKFXHslYsCwTRJIw+eenYeb1t2T0vAivXIFl5y2C7723x3QfzjjIs0DnwkIvWAsiAFqEiWcqpdgQzNabwp61N0WuMEyZBcteRzPbaDSE0MuPgCYyFyzLhKVhKubc8UdUHfUD7U6qis5H/4LV11zGPS9GgTRU1GyqO3NRM1lN+V6ktg02/Qb1yGjPFYqqomMwig1ZelNYsvSmyBWm7faEcd5uzDalrwvhN54aV3Eq5/YLMHfJ/XDuvKtmHzUSQcvvb0Lr3bdDiRXfd1iuENbzKBHNWQ4ZFxY6USoRi43eFJH8eFPkCvM2u8O07R7MNtW3AeE3nhzXoigYjZhy5jmYfuW1EB0aBmcAgt98PeR58fGY7zURcZoNmFXlgGcEW/D+SAJr+zYrwqW5P1xcAj2SkNHSH0YoQ6Jzypsit4nOo4EQAutex0Cqn8FsT7YuQ/ST18Z1D0OFCzOuuQH1p5+V2fPijdew/PyzEWnOzmacM05Y84iqwBijVFvDhYVeaGa0F8eCuNGbwpd/b4pcYdnjSEhTZjPbkutWIPbF+G2FXQv3wLwl98O+zXaafVKeF1dj/QN/gprknhfZIgopi/fGEaIXMVnFmr4Q+sNxTWFRLAJ92JuiLWtviszCKt8QUYTtkJMhuKqY7fGv3h3TSZEt7kEIao45HnN+fzdMk+s1+8U71mPFRYvR8/zoPDU4o0c78peb5xEXFjqhmdFeBMJCUVPZ6t2BGDLNZ3sOvSlyARFE2A8+CYKHfVQ09tmbSLatGvd9jJVVmHXDbag7+RcZPS96XngOKy5ejFj7+nHfcyKRKmpmhyPDKQgKoDMQw/oI+wdaDAI9qbM3Ra5IFSz7JYiZ/bAJv/00FP/4t/usM2Zi7l1/hvegQzX7UDmJ9Q/+GWuvu5p7XuQRkmezueL4ZU8QWOeHC+0aGE3KaPWFEIhl9qaocZgxuUAh20wQozm1KDILllGE33wSatA//vuIIib95GeYffMdMFaxy1EDQLR5LZaf/xv0vfEqf+saBZIoYKrbiklOc0aD8EASkAWGAClwjkUglkRLFt4U9Tn2psgVYoUXtkM1CpYl4gi/9jhoDqJxosWCxvMuRtPFl0OwaHteDH7+CZYvPhOBpV+N+54cBnneUuTCQk9Y4acCZbRv9KbwRZBUtB+ARjHlTeHOgzdFrhAdbtgOYZ8UobEIQv/9+6hqIWTCPm8bzF1yH1x77K3ZR43HsO7uO9By+01QwtkXeJroEEJQaTNhRqU9o2tnQkiP/BXKbE6lFN1ZeFNYh7wpRvLzKCSGuiZY9zqG2ab4uhF5L3dbFJ59D8C8JffBOmuOZp+kz4fVV1+Kjkf/wj0vck2e3aC5sNARZiYuACT1fduSR+FN0ejNrzdFrjDUNcGykG0rrPSsR/TDF3N2L8nuwLTLrsaUkTwv3nsHy85bhPDK5Tm790TAbBAxo9IOr0ZiZ1Jk/JvnMKM9W+JyypvCn4U3RUOevSlyhXHerjDOWsBsS6z6Colln+bsXqbaSZhz612o+eGJ2p0oRffTT2DlZRdyz4sckm836OL/pZcTWuEnHd+2QvEkWorMmyJXmLbfG4am+cy2+PcfI74qd2FVQgiqDj0Cc+/6E8xTGzX7JTZ0Y8WlF6D76SeL1muhGBEIQV2FBVPd1rTtt4TIyFWiKmhy7EeMR8OwN0Vr/0jeFERXb4pcQAiBdZ9jIXpqme2RD56H3DM2Z07m/SQJ9b/8NWZedwskdybPi+Upz4v338nZvSc6TGtvHrEoPQp5Br/YvSlyASEEtv1PgFBRyWyPvPtvKP3dOb2nZcpUzL3jHlQdwfbVAAAoCjoefRirr7kMSV9/Tu9f7jiHEjs330JgRiwAbOgfgJxn8aaodBTeFHbdvSlyATEYYTv0Z2yHW1VB+L+PQ43ldovPueNOmLfkPjh32kWzjxqJoOW2G9G65A7ueZELWAmcOXKD5sJCT7RMsvKcwJkoEW+KXEBMZtgP/RnAOjMvJxF67fFxmWexEEwmTDlrMaZf8X8Q7Rk8L5Z+hWXnnonBzz/J6f3LHcOQJfhwYiczYgEgGY1gdW8oo3fEeEh5U2S+PimwN0WuEF2VsO1/ArNNDfoRfvOf4/KJYWFwuVOeF6ctApG0c1H6X38VKy44G5GWtTm9/0RDuxjZ+J9HpfsEKUGIaABExoTJUwiXUorBaAItJeRNkQtEby2s+xzHbFMH+xB+e/Rl1rPBtfuemLfkPtjnb6vZRw4MYs21V2H9Q/dxz4tRMJzYOb3SDmJiCwtJTUBWKVp8YXQFtnTsHA/ZelOYJAFNHlvBvSlyhXHaNjBtzy7MJ7etROyL3FtxE0FAzbE/xJzfL4GpbrJmv1j7eqy48Fz0vPAcP301VrS25nPwPOLCQm+k9PAilXP/gEl5U8TQVWLeFLnCNHuBpl1xsvlbxL/5IC/3NVZVY9aNv8ekn/48s+fFf57BiovPQ6wjd/vVEwGLQURjtZvZJqmbQrh94ZRjZ1weXy2XbL0p3Bu9KUpv6yMTloWHQprUxGyLffYGkuvH7xPDYqPnxQEHa/ahchLrH/gT1t5wDeTBsRVNm8gQxrMIAJCD5xEXFjpDWFXlciwsokllyJtCe6+smL0pcoV1r6MhVrGd/qIfvYJkZ0te7ktEEXUnnYLZN90BQyXb0RAAos1rsPz8s9D3xmv8rWsUiIIIiOlbXZsLCyDl2Lm6LwRfJDGmf99gNt4UhKDeZUGNw1xSic7ZQgQRtoNPArGytvgowm/kxieGhWi1ovGC36LxossgWDTC9gAGP/0YyxafieA3X+dlHGWLQSOyxoVFCWJgqEQ5N/7sm7wpwiN6U0z1FLc3RS4gogTboSezXeaoivDr/4AaCebt/vb522Dekvvg2n0vzT5qLIZ1d9+O1jtugRLhnhdZwxDoopoupCkFOgajQ9sY2eUEDHtTdGTjTeG1wW4qvUTn0SDYnLAdfJIuPjEsvPsdiHl33wfrTLZ9PwAkff1YddVv0fH4I7zicLawXnKRmwg6FxZ6w/oyFXncRxFH601hLsFs9bEgOtywHXQimHVaIkGEX38CVM3fQiQ5nJh2+TWY8pvzQIzae+++d9/CsvPOQnjViryNpZwgjLetrSMWmxOIy1kldpajN0UuMNRNy+wT87+X8np/06Q6zL71LtT88MfanShF91N/T3lebMjt6a9yhBCBGfnLRSGyiTErigjmVggwrvBTKC6jxTeyN0VdCXpT5ALDlNkw73wAs03ubEb00//m9f6EEFQdfhTm3nkPzFMaNfsluruw4rfno/uZp7jnxUgw9oeNVIYpw4N+OLGzJxRL2xopZ2+KXJHRJ+a7j3LqE8NCMBhQ/8vTMfPamyG52Hk2ABBesQzLz1uEgQ/ezet4ygLG84hHLEqRHCbMUErRE4yh3R+BkiFmO+xN4SxBb4pcYd7pQEgNM5lt8a/eRaLl+7yPwTK1CXPvvAdVh4/gefHIQ1j9u8uRHPDlfUwlC2NBJKqM6V7riKXYNwTjaBvYNGey96aQStabIhds8onxMtsj7/4bim9D3sfhXLAz5i25H84FO2v2UcJhNN96A9bdcxfUmD7GaaUIK/LHcyxKEY2EGTrKPIthbwrfCN4UXpux5L0pcgERBNgO/AmIvYLZHnnraSiD46/gOBKCyYQpv1mMaZdfA9HGKpyWIvj1l0OeF7mzUC4ntCJ/gpLE5AoLpritEDNEFAJxGWv7QvBHEll5U9Q6zZjkLN9E52whJnPKPEvLJ+bVx3LuE8PC4HZjxu9uRP2pZ2T0vOh77WUsv/BsRFqa8z6mkoT1opuDnL+J/bQpAJpbIaPwNBiMJtDqCyOWhTdFlb34KikWCsFig/2QnzGL79BELJVvoVPil3uPvTFvyf2wz9tGs4886Meaa6/E+ofvg5ocvxteWaEZ+UstihVmA2ZW2WEzakcXkoqKrkB0RG+KRo8NLkt5JzqPBsk7KaNPTOT9/+gyDiIIqDnuBMy+7Q8wTarT7Bdb34YVF52Dnpf+w09fbU2ecv64sNCbERbETCgqRedgFF2BWMZs9ZQ3ha3svClygVTTAMueRzHblN4OxD5/Q7exGKurMeum2zHppFMye1489wxW/vY8xDq558VGNCN/mwS6QUwZVtU40uecSFJlzDOJhWFvilIowqc3mXxiEqu+QmLNUt3GYps5G3P/cC88+x+k2Ycmk1h/3z1Ye+P/QQ4EdBtbsZOPnD+ACwv9GeMRn5Q3RXgU3hT8q9XCNH8hjDN3YLbFvnoHclerbmMhooi6n/4cs278fUbPi8ia1Vh+3lnof+t13cZWzGQb+SOEoNpuRqMntTVCAEgEGROYy92bIldY9zxK0ycm8u6zUEP6mVaJViuaLrwUjRf8NrPnxSf/S3lefKuf8Clq8mSSxZ8+OkMEgW3rrfFFbulNoR2emijeFLmAEALrvsdDcDEe5JQi/OZTuuwTb45jm+1SnhcL99Tso8ZiaL3rNrTccQuUiH4VcYuSUUb+bEYJLrMB0ghRCgCorTCXvTdFLiCSAbZDfsosVkYTMYTfyn09kZHwHnAw5v7hXlhnsBO1ASDZ34dVV16CTu55kbOcv63hwqIQZJkwIysq2rk3RV4gBiNsB57I3IJQgwOIfPC87mOSHE5Mu+J3mHLWYhCD9oPN986bWH7+WQivXqnj6IqMUUT+4kOJzoG4drSPUgpFpUiqFOt8kRGTojkpRKcH1r2PZbbJHWsR/+ZDfQcEwFw3GbNvuxs1x7GLqAEAKEXXU3/HyssvQrwn/ydZihVNW+9x1jHiwqIQsM4Ob/VFhoe8KcLcmyJvSNX1MO/MrkWQWPklEmu/0XlEQ54XRxyNuXf+CeaGqZr94l2dWHHJeej+99MT0vMim8gfpRT+IW+KTEX4KKVQKDDcgyLl1ply3uTJfiNhnLkDDDO2Y7ZFP34Vcn+XziMa8rw49QzMuPYmSC6XZr/w8u+xfPEiDHz4vn6DKyY0cyx4xKLkyFQvZNibYj33ptAF8477QqxlP8D13ifeHEtjyvOi8rAjtTspCjr++gDW/N8VE9PzgvW2NeQaOJzo3D2CNwUFIFMw+/giCTT3Z96C5AxtLe5zLIiNcZRbVRB+40lQuTCnmioW7JLyvNhxJ80+SjiE5luuw7p7/jDxPC/yZOvNhUUhYNULUZKIJ2XuTaEzKX+LE5nfCY1HEX77ad33iYcRzGZMPft8TLvs6oyeF4GvvsCyxWdi8MvPdBxdEaDhGhhJyGjpDyGYyZsCKW+K6V5bxiOp0aSCNX0jW4FPdASTFbYD2FsPqm8Dop+8pvOINmFwezDj/27C5F+dDoja33Xfay9h+UXnIrouP8UJi5HR5vxlC38yFQBWxCJo9qLVFxnRm6KBe1PknNQ+8THMNrl9DeLf/E/nEW2Je899MHfJfbDNZdspA4Ds92PN765A+18emDCeF1vPIwrAZ/QMFRwbwZvCm/KmMEoimjw2eG3abp3DVuB94Tj3QciAoX4GTNvvzWyLf/MBku2rdR7RJoggoPb4H2PObXdn9rxoa8XyC89B78svTJzvOg8mWVxYFILNFkSVCNhQMR0bXDMzhmztRgmNXhts3JsiLxhnLYBh+rbMtugnr0LpL2xRI1N1DWbffAcm/eRnGT0vNjz7NFb+9nzEOjt0HF2B2GweyYIRHZ558NkmZ/wTljcFIQR1TgsaXBZGqbpNdAViWO+PQs1kIjPBsex2KERvLbMt/NbTUGOFPc1kmzUbc//wZ3j2O1CzD00k0HbvEjTfdC3k4ATwvGBG/sb3csKFRSEYcn6MSTas926LoEXbv2CjN4XLAol7U+SN1D7xcSA2Z3qjIhd0n3gYIoqoO/kXmHXDbTB4KzX7RdaswvLzz0L/2/qZfRWEoRBuyORGW+W2iBkZ391w1yy8KVwWI6ZX2mHMsMU4GEtibX8IcXmCH1PUgIgSbAf+hBlep+EAIu8+W/BIgGi1oemiy1KeF2azZj//xx9i2eJFCH6nfxK3nhCGEzHGWfGZP6kKABUkDFhr0e6dj6SkbebCvSn0RTBbYTuAXZZZ8XXnvQpqtji23R7zltyPit320OyjRqNovfNWtNx1W9l6XlBBRK+jEd3u2VAF7SRmq0FEo9eWlTeFxSBiRqUdDpN2ZDAmq1jTF8poVjeREb21miXWk83fIrHqS51HxGaj58X0DJ4Xfb0pz4t/PFq+nhesHAsuLEoLWVXRkTSg39kIEO1//gruTVEQUvvEezHb4kvfR7J9jc4jYiM5nZh+5f+hYdE5mT0v3no95XmxZpWOo8s/cVlBG1wYtLHD7sNU2k1oGGWisygQTHVbUW3XOOMPQKXAuoFIqiLqRNmLHwWmbfeAVM9+YEfefx5KoDhOMZkn12P27/+A6mN/qN1JVdH1xGNYdeUlSPT06Dc4veARi9ImHJfR0h9GRNH+Zx/2ppjEvSkKhmXXQyF6tPaJ/wk1XhwRAEIIqo/8AebccQ/MDVM0+8W7OrHykvOw4dnS97zY3JsiAe2ogjQkDiptpjFF+wghqHGYMdVtRaaCpj2hONYNZD4aPhEhRIDtgBNATNb0xmQ85W5bJL9FwWBEw2mLMON3N0KqcGn2C33/LZadtwgDH32g3+D0QENYjEcwc2GhA9l6U5gho5F7UxQcIhlgO+gn7Cqo4QAi7z5XVG+p1qZpmHPnPag89AjNPlSW0f6XB7Dm2quQHBjYok1NJIrqv0eLbL0pHBJFk9cOSw4SnZ1mA2ZU2mGStJfKYFzGmr4QYskyDZWPEcHmhHVfdhVUpXsdYl+9q/OIMlOx866Yt+Q+OHZYoNlHCQXRfNO1aPvzEqjxLU9OqIkSdWtlCQtgXFELLizyTEJWR/amoBTuUAcaEMiYOMbRj4z7xGu/QWL11/oOaAREswVTz7kA0y69CqLNptkv8OVnWLb4TAS++mLjZ233/RGBL4rbAyMrbwqqoHpwLWoNMsRMYYZRYpJEzPDaUZFB8CcUFWv6QxiM8ryLzTFO3xbG2Wxzqtjnr0PuKa6KvQaPFzOvvRmTf/nrjJ4Xva+8gOUXnrPR84LKMlZfcxni3fq7jI4XZvImwIVFsRKIJtHqC2X0phCVBOoGlsMbWg9C+RtPMWHabk9Ik2cw2yLvP1c0+8Sb495rX8xdcj9sc+dp9pH9A1h9zWVof+RB9L/9BvpffzVVkCkXUQtBBAgBBBGDH70z7stRStEXio/oTWFMhtHQ9y2c0d68zCNBIGhwWTDJqX2KgFKgbai2TylEgPTCutfREBye9AZVTZ22GmddilxDBAG1PzwRc277A4y1kzT7bfS8eOUFdP7jUYS+/xZdTz4+/gFsNod0gQuLPLLZlxleNr6SuqpK0TUYRWcgikxbr9bYAKb0fQNrInVWmo4zYYaTWzbtEzNO7iTiiLzz76J8gKQ8L+5E7Yknp37TGmx45p9ovfNWAEBk7Wr4c7B3bJ29Dap+9AtYZ2+D8NJP0fOvR8d8raSiYv1ABH3hzGY9FeEu1Pd/B6MyZMes5sclkxCCSpsJTR4bpAwRke5gDJ0BntQ5DDGaYTvwx8zfojrYVzSnrbbGNmsO5t19L9z77K/ZhyYSaPvzEnQ//QQAoP/tNxBb3zam+wW/+gTAlnMIAPpffXZM18saLizyx+ZfZqx55ZgXxFhSQasvjMEMR9EIgMpAKyb5V0Kkmy2CeVoQOWNHsFdo7hPLHWuQWPkFs63QEFHE5J/9MuV54fFm9Tedfx/HkTpBhHXu9qg//xp4Dj4G9edfA+uc7eB/b2xWzsF4Eq39YUQy5C2IBJg0sAJVwXUQNs+6yLNAt5skzKi0w5LhxJYvkkArT+rciDSpEeYF7Ad0/NsPIfes13lE2SFabWi6+HJMPe/ijJ4XG1FVdD7x2JjuFVu7gjmHgp/lOVlU0MhF4sJidFBKEY1G4Q+FAaORvSC++ypkOfsHPaUUvnAc63xhJDIULRr2pnDF+9Jd/njEoigxTt8OxlnshK7o/16GGgnpPKLscWy3Q8rzYteFI/aNtbXC9/47WV9bVVVEIhGEzDaAUti22XHjCQxCCGzbLgCNx6GMQqyolGJDIIYOfxRKhjd+q0HEVKcBtrg/vVEHvwGDKGCa1waPVdsKPBSX0dwf4kXMhjDvdCDEqvr0BkoReeeZovWJIISg8qBDMfeuP8Myjb01ujkD77+DSMvarK+vKApCoRCoLLPnUCKBSCQCNV+naDRyScYTQZ9QwiKZTKKnpwcrV67E2rVr0bVhA5DU/jJXrFiBdevWpb70DIucrKpo90fRE4pnzFavMG/mTZGHs8Oc/GHd6ximKyeNRxD58IUCjCh7pIoKTL/qOjSccTaIlPnEUdc/HgUdQVDHYjF0dnZixYoVaG5uRs9gADAYEP7uq43zhFKK8LdfAgYDVqxYgY6ODkSj0YzXjcsK1vnCGIhm3nOvtA15U0i5f9MaDQIhmFyROe9i2Ewryk+MgIhiakuEsfYp/d2ILy3u0uXm+gbMuf1uVB9z/Ih9Ox9/JGM7pRThcBjr16/H8uXL0dramnEONTc3Y+XKleju7kYi16dP+FbI2FBVFV1dXVi5ciV6enq2jERk+DIBIBgMorW1FWvWrEGE4WA47E0RTmgvxhu9KSo286ZgfZlFqtg5ADGZYd2LXagsuWYpkutW6Dyi0UEIQfXRx2LG/92QsV+8qxP9b7L3vJPJJNatW4c1a9bA5/Nt8QalbLMzIsuXov2ua+F7/Xm033UtIiu+gbLtLqCUYmBgAGvXrkVLS0vawkgphT+a8qaIj1CEb4rbikr7kDeFxoKod65Spc2EKW6rZp0RWaVo7udOnQAguqs1t0Sin78Bxd+n84hGh2AwouH0s1B/2pkZ+w1++jHCK5cz22KxGJqbm9HS0oLBwcGNn2eaQ0AqstHX14dVq1ahvb19VJHATGifChn71nzZC4tQKITVq1ejv7+f2T7SlzlMPB5Hc3Mzuru7oapq9t4UBpHtTcHySOARi6LGOG0bGJrYFUbD7z0LmhhfRcB8QylFz39GTgTrfPJxqFtl6g8MDGD16tUIBoPMv1H3OhTKgj0RaV6J3n89ikjzSig77QV1z0O26BcOhzfOR0rpJm+KwAjeFCYJTV47rJt5UxBBAFiP8wLMowqzAdO82kmdw06d/SMkok4EzAv2g+CuTm9QZETeLc6E6M1RYlH0vvryiP06topaUErR09ODNWvWMKN32c4hAPD7/Vi1ahUCgRwUSctDxKKsS2UODAygoyNzlUd1r0MBAJHvPkdk5XeAwaD5ZQJAX18fIpEIpIpqRDK8XQGA12rc9Ha1NayEGS4sih7r3j9AoGMtaCK2xec0NIjop69pRjWKgcHPPkbw269H7Jfs60Xfqy+j+uhjQSnFhg0b0Nc38pukutehG+dTJiil6OrqQiQaRdxUkfEY6XARvgqLgT2PRBFQtnqzKlAStNUoYbrXjtYB7chLZyCGhKKi1mGesPV/iCjBtt8PEXz2PmArOSl3NiOx4nOY5u7C/uMioPel55HYMHK14+DXXyL47VI4tt0eqqqira0NoVDmfKxs5xCQimC0tbWhtrYWlZXaRQlHRCt5cxwREUKLXR6OkWxExXgwmsxQHJUgjHofkkAwyWmBLUMhI6X5S9DgVou10QppLrtOBad4iC/7BJF3WW/+BI7jz4JUo22vXWioqiLR24NY2zpE169DbF0rom3rEFvfBjW+SSxJLje2eeBv6PH7NaN9ucBssyNpcTMfsiZJQF2FZYsS51sjL3sXSG4ZBSAV1RAbd8j1ULNGUSnWDYQRTmgvzE6zhAaXdULb9kfe/w/i332U9jkxmuE86SIIVkcBRpUdVFEQ7+pEtK01NZfa1iHa1op4R/sWOUr2edtgxk23o62tDeFwOG/jqampQVWVdpXsTFA5CeX7t9M+F2qmQ6idPqZrlqWwCIfDaGlpyft9jBYbVPuWR/lsRgmTKswjljhXWpeCDm7Y8kPJCGn+fjkeJSfXUKoi9J8HIHe1prUJnho4f3QuCKtiYBGzheBoS4kNwz77w8/y8MgxRocbqnnLh4jLYkB1hhLnw8grPgC2qt1CHF6I09huj3qhUoqOwSj8GZw4LQYRjW4rpAnqtksTMQw+eSdoOD2cb5i+LeyHnFyAUY0PqiiId3ZsFBqx9W2QjvkhAqM4YThWpkyZAqczPcF8JKiqQvn2jbTPSVUjxLpZYxpL2f2iFUVBe7s+NrGJaBhCYtOiVm03od5lGVFUAOCnQkoYQgRY9/0h8ztUfRsQ+/q9AoxqfBBBgKmmFhW77IbaH56IyWefj0Ezo4BUHkgEBwA5ldMxfNKiNtsifEWaqyQQgvoKS8YKqdGkgrX9YcTlwo+3EBCjGda9j2W2Jdd+i0TLMn0HlAOIKMLcMAXuPfdG3UmnoPo35+kiKgCgo6NjVBYJw+QjV6nshMWGDRuQTOqXfS0HfTASikaPDZ7RVFKcwCHQckB0V8G884HMttjnb0IZ6NV5RLmDUor29nZdk+hIyAezJKDJa4NjVEX4GPOoSGKwwxVS6yssmidGEoqKtX1hhDPUQClnjE3zYJi+HbMt8v5zablMpYSeL7nD9+vs7BzbHzOfR0Va3fS4446D2+3Gj370o3zeZiOyLMPn07d+g6qqsNFYypuCM6Ew77APBE9NeoOqDGW3l6YxUiQSYR6tzifJZAJuSYGhDLcF3FYjGj02zfLrCqVo8YXhH8G/o1yx7nU0iDHdC4SGA4h+Mjbn1mLA7/ePKYIwHgKBAOLxwp88yussXrx4MR59dOy1AkaL3qJi8/vmzRWNU7QMZ7ez3prlrhYklhd3xVAt8pmsWYz31QO7KXVixCCy1QUFsN4fRU9w4tUYEawOWPY4ktkW/+5jyN3rdB7R+KGUFuz3XKjn4ObkVVjsv//+cDj0yeyllBbsH1RRFM3z/ZzyRqqZAtO2uzPboh+9ApWRmFbMyLKcm7PxYyASiRTF21a+MBtETPdmrjGyIRRHx2B0wokL45ydIdVNY7RQhN95BnTrI8VFTiQSyb1DZpYUw4vuqIWFqqqYM2cOLr300i0+f+2112A0GvH000/nbHCjIZlM6h522hy9Q8ec4sGy66EQ7K60z2kihsj7/9F/QOOg0L/jQt8/3wzXGHFmOIo+EE2i1TexCpgRQmDd93iAcZpKHehB7Mt39B/UOMjn0dKRoJQWXKCPWlgIgoDLL78c9957LwYGBgAAS5cuxQknnICbbroJJ5xwQs4HmQ0j1SHIN+W+IHK0IUYTrPscy2xLtnyPRPN3+g5oHBR6HhX6/nogkJQ1udeWoYBZQsba/hASI5jwlROiqxKWnQ9itsW+fBuKbwOzrRgp9O+40Pcf01bIySefjMrKSixZsgTt7e048sgjccopp+Diiy/O9fiyJhYrbPZwLDbx9kY5mzBMnQPDjO2ZbZH3/wMaL43s9kIvSBNFoBNCUOfMXMAsLqtY2z+xCpiZtt8bondSeoOqIFxCCdGFnkeFvv+YhIUkSbj00kuxZMkSHHHEEViwYAGWLFmS67GNilwVZBkrlFIuLCY41j2PBjGlez/QSBDRT14twIhGT6HnUaHvrzeVNhOmuq2ap89llWLtBCpgRkQR1v1+yDz+qHSvQ2LZpwUY1egp9O+40Pcfc/LmySefjEgkAkopnnjiCYiMmu6HHnooTjjhBLz88suor6/HZ5/lL0ueP9Q5hUaw2rWz25d9ArlvjGfMdaTQ86jQ9y8ETrMB0712zQJmdKiAmS8yMY6jStX1MG23J7Mt+ul/ocYmRlRrPBR6Ho1ZWJxzzjkAUkW5WKICSCV09vb2IhKJoL29Hbvskr/CMkI2bpd5ZqIWFeJswjh7AaT6GekNlCL64YsFn/AjUeh5VOj7FwqLQcT0SjtMkvZ/f8dgFL2h8j01szmWXQ6B4HCnfU5jEcQ+T7efLjYK/Swo9Dwa092vvvpqvPTSS/j4448hyzIefvjhXI9r1BiN2olQemAwaFRf5EwoCCGw7nMc02pa7mxGssgTOU0mbQvqiXD/QmIUBUz32mE3ap8Y6Q7GsGECeF0Qg1HT7jv+3cdFn8hZ6N9xoe8/amHx0EMP4Y477sALL7yA7bffHueffz5uu+02XW20WVgs+S+WlAmrVZ+6CpziR6zwwrz93sy26Ecvg8rFu1/O51FhEQWCRo8Vbou2rXlPKI7uCSAuDFNnwzB1TnoDVREp8uhfoedRoe8/KmHxyiuv4Oyzz8bjjz+OhQsXAgDOPfdcBAIBPPbYY3kZYLaYzdrZ1XpQ6C+SU1yYF+wPwij7rAYHEFv6fgFGlB2F/h0X+v7FABkqxJapgFlfOIHOQPmLC8seRwKMsL7cvhrJdcsLMKLsKPTvuND3z1pYfPHFFzjhhBNw22234fjjj9/4udPpxLnnnotbbrmloJmogiCMqWRsrijkvTnFBzGaYFl4GLMt9uXbUEODOo8oO8xmc8G2FUVRnPARi2GGC5hlOo7qiyTQ7i9vl07RVQXTthqJnP97qWgdOR0OR8G2xq1WKyRJeztND7IWFjvttBNCoRDOO++8tLbrrrsOq1at0kzi1Auv11uQ+zocjoLneHCKD+OsHSFWN6Q3yMmiPX5KCCnYPPJ4PAVPOis2Km0mTK7Qfvv0x5JoG4hALWNxYdnpQBCzLe1zdbAf8W8+LMCIRkaSJLhcroLcu1Dzd3PKahZbrdaCJK3YBrpKxriFox+ECLDueTSzLbHqq6ItruRyuQrygLf2d+h+z1LAYzWiwaUtLgJxGet8EahlagFOTGZYdjuU2Rb94i2okeKs01SIB7wkSUURPS8rYUEIweTJk3W9pzUyiPh//or+x5dAKdLwNqdwSLVTYJy1I7Mt8uELRSlIRVHEpEkM98M8Yu9cjcDT92PghcegJieGX8NocFmMKSMtjfZQQkaLL1y29UWMc3aGWFmX3pCMF21pdbPZrLu4mDx58hi3YHL7uykrYQGkohZVVVW63EsEYHjjKQBAvHk5eu69HrHVWR4nVBn5KKTsvg4OAMtuhwFS+laZ0tOOxMqvCjCikXG5XLDb7brcyyjHIX2UejhEvvwAvQ/chOSG9uz+mCHMSJlupzjNBjR6bJounZGkghZfGHKBK1vmAyIIsGhF/1Z8Abk3y9+LztTU1Oi2Te52u8dUTZxSmnJh25pxPI/KcgZWVVXpkgRm++RVCMlNhjVqJIj+f/wRg689PXJSEUtYMLwPOKWPYK+AecH+zLboJ6+CJorP9IgQgvr6ehgM2scec4FAAPOb/wTZ7I1J7utCz4M3I/Tp2yMnJqqMeVbG88huktDksUHDpBPRpILm/jCSSvmJC0NdEwwztmO0UEQ+eKEok1gFQUBDQ0PeEzlNJhNqa2vH9sesZxEACGNPAC1LYSEIAqZOnZrXIzeu5qUQ21cz20Ifv4Heh2+F3J/BxIULiwmFefu92E6CkSBiX75dgBGNjCRJaGpqyluGuSgIcH78CsSQP71RkTH4ypPwPXUvlEhI+yITcB7ZjBKmee0QNdRFXFbR3B8uy8qo1oVHMEurK93rkFzzTQFGNDIWiwWNjY15ExcmkwlNTU1jPzyhISzIOOZRWQoLILVP3NTUNKbQUDbXrdpuF4jO9AfFMMmuNvTcfyMiSz9itlPWl1ngUzWc/EEkAyy7H8Fsiy19H0qgX+cRZYfRaMT06dNz7hNjNBoxbfp0eHbeG8SonXAdW7kUPfddj3jrKnYH1hH3MhcWQMoCfJrHpllfJKGoaO4PIS6XV1E3weGCeYd9mW2Rj14GLdL8HJvNhmnTpuVcpOfkuloRi3E8jwgtxvhRDqGUIhAIoLOzU9tn491XIH7zKZBMAgYDlO12BfY9PK2by+VCbW3txi9RiYTgf+ExxFZ8nXEMlu12g+uIkyCYNkVQ5JX/A2JbvokRuwfi9J1H9x/IKRkopQg9/yDkzua0NkPTfNgPO6UAo8oOSin6+vrQ09OjHXLOch5VVVWhqqpq48kT2dcD3zMPIdmZ6ZQMgWOfI+DY98iNb1KUUijfvJ7es3IKxMkMx8YyJCGraPaFkFTY34kkEDR6bLAYykds0WQCg0/cARpOT5Y373wgLLscXIBRZYeiKNiwYQN8Ph+7Q5ZziBCCSZMmwe12jzsSQqNBKKvSX4CFhvkQPGM7DFG2EYthCCGoqKjAzJkzUVlZmR4uevcViJ+/D9uMuag+8ZewzZgL8fP3gXdf2djFbrejsbER9fX1WyhD0WqH58eLUHHESczw3DDRbz5Bz/03ItHRuunDCbY3zEn9Fi17HsUsCZ1s+R7J9jUFGFV2EEJQVVWFGTNmsBezEebR8DycMWMGampqtjjOKnmqUXXqb2HfPdMDgSL43kvoe+QOyP6h6E4e9oZLDaOUqi9iEtlLuaxSNPeHEEkUp5HUWCAGI6y7pz9sASD21btQg359BzQKRFFEXV0dpk2bln4sNItnkSAI8Hq9mDVrFjweT262V1jPImBcz6Oyj1hsjaqqCAaDCAaDiEQiUO64ErYZc9Fw8bUghIBSirbbr0F07Up4rrsHLpcrq6ze5IYO+J55EHJvl3YnQYDzwONg3/0gKMveBbaqGUFctRCnspKTOOVE+N1nkVj2SdrngqcGzhMWj2tvUy8URcHg4CBCoRCi0SjUO6/SnEdVN96LioqKrMK1sTXfY+C5v0INa3sTELMV7qNPgXnm/NQ82gqhdiaEmqZx/feVGrKiosUXRkwjr0IgwFS3DXZTeYguSimCz90HheEFY5ixHewH/7QAoxo9yWQSfr8fkUgEkZsuZs6hyNqVsF95B+x2OyoqKnLuMaMG+6A2f5n2udC0IwTn2E5Yln3EYmsEQUBFRQXq6+sxvaEeSCZh227BRuVHCIF9u51AEwlUV1dnfVTIUDMZVadfAetO+2h3UlUEXn8G/X//I5RwmDG44n+gcMaPZdeDQYzpOQuqbwPiyz4twIhGjyiK8Hg8mDJlChqIknEeeb3erPeAzTPmo3rR1TBNn6fZh8Yi8D19P/wvPwEqM962JmCukiQKmOa1w6qx5aFSoNUXRiBWvAXwRgMhBNa9jgYYzh7JNd8g2dmi/6DGgMFgQFVVFaZOnao5h5BIoKGhAW63Oz/GdRopAjx5czwQgvA3X27cN6aUIvTNFxDG4OApGIxwH3UyPCecAWLWPu4aX7sMvrf+i/iG7q0uMPEWxImIYLHDvMtBzLbYp69DjUd0HlEOyOE8Eu0V8J58LpwHHc8sQDVM5OuP0P/uW0gObrXXPkHnkTiUT2Ezsv/7KYC2gQgGo+UhLqSqehjn7MRsixap+VwmiCQx5xDJtw8GP26aB1QF4WVfo+32a9D/2nOp0NOypXDtz97DywbLvJ1QfeZVMDZM175tPA7/Rx8i+N03oMOGNhN0QZyImObvDsGVHmak8QjiXxdv9VNNcjyPCBHg2PNQVJ16KUS3djhWCQbhe/ctRFrWbkoqncDzaFhcODS2PCiANn8EA5HiPD0xWiy7HQoY0sWr0teJ5NoszQqLBMvMOcw55NwtQxQ8F2gKCx6xGDeRFd+h56m/IrLiO5hnzEHNj385rutJLi8qf3kRHPscCVa4buN916yG7713IIdCIBMo6WyiQ0RRs45I7JsPoWbybihiNp9H9h13G/c8Mk5uRPWZV8Ky7a7anVQVwaVfY/DTj6EmEhNaWACAQAimuq2oMGubm7UPRtEfLj5jttEiWB2w7HQAsy366X/Zx/qLFMeC3QFsOYcAoPKI4zP92fjRSt4cx5YiFxbDqErK1lRVYJ+/Q04uSQQRzv2PQeUvLoDgcGn2k/0D8L3zJiKrl+XkvpzSwDBlFqSGWekNcgKxr4rTNGtENptHFXuw3UZHi2CywHP8aXAf+ysQxtvpMPGuTvS//QYSnW05uW8pQwhBg8sCt0VbXHQGYugJxXQcVX4wbbcnc31VB/uQWFWclvkZ2WwO6YGm+OIRi+LG1Dgb1Yuuhnn29pp9qCxj8M3n4Xvur1DjpT/ZOdlh2e0Q5ufx7z+ByovabYF1+4WoPvNKGCZN0eyjRqPo/+eDCLzzQkm9reYDQggmV1jgtWnv0W8IxtETLO31hogSzDtr5Cx9/sbI5RUmOlxYlC6i1Q7PiWeh4vCfZPa8WPoxeh64EYmMZkGcckGqqoehaX56gyIj+sWb+g+oyJG8NSnPi4XsBwkAgFIE330RfX+7E/KghhHRBIEQgkkOM6rt2pGeDaE4ekOlvS1inLUjM2dJDfoRX/5ZAUZUQmgUxCS8CFlpQAiBfdf9UXnSWRAzWI0rvh70Pnwrgv97veQymzmjx7LrIWDl4SRWfA5lsE//ARU5RDKg4tAT4D7ihIx24Im2Nei573pEl6ef0Z9IEEJQ4zCj1qFty94djJV0zgURRE3HzdgXbxWt1XdRkId6O1xYFACDtwrefQ+AZWqjdidVQeD1f6H/H/dACQV0GxtHf0RPDYyzdkhvUFXEPntD9/GUCqaGRngPOAjGqmrNPjQWge+f98P/4t8n/MOlym5CnVNbXHQGYvCV8GkRw/RtIHonpX1OI0HEv2PXbOIgL/V2uLAoBKoCIklw7rgTKnbZDUTSTrCKr/kePfddj9hanthZzph3Pojp2ZBYvRRKfzfjLzhQFYhmM1x77AX7/G2YVunDhL94Dz0P3oxkT4eOAyw+vDYTJldoV33uGIzCHy1NcUGIkDp+yiD21bugPHeNDY9YlAnKJoMa8+R6ePc/EAaPR7O7Gg6g//G7Mfj6MzwRqUwRK7wwztmF0UIR/Sy90BYHGy3xCSGwzZwNzz77QbTZtLv3dqLnwZsR/vxd7UJqEwCP1YhJGSIX6/3RkjXRkqbMhliTntxL4xHEvilBfxgdoApDSGbIA8wGLiwKAE1uuZcp2mxw77Uv7HsdhkyeF6H//Re9f/k9ZF9vnkfIKQSWnQ5gTuhky/eQe9oLMKIiR95yQTS4PfAcfCQs27AE2vDfJOF/6R/wPX0/1CjDVn+CUGkzZcy5WO+PlKT9NyFEO2qx9AOosYn7nWsipwsLIo3P7ZMLi0LA+iJFCc4DjkXlzzN7XiQ7W9Fz/w2IfFsaNSU42SPYK2Cav5DZFv30vzqPpvihcnqyoWC1w338aXD94JcZPS9iy79Cz33XI75udT6HWNRU2U2ap0WG7b9D8dKLkBomT4c0eUZ6QzKO2FfpResmMpRS5vMIXFiUIKwv0mAEIQSmpiHPi1naVU5pIoaBfz+MgecegZrg+4blhHnBfoAhfVLL61ch2dms/4CKGY03LUIIbDvsjqozroChtkHzz5XAAPr+dgcC7764yVZ/glFtN6HKpi0uWgfCCJdgyXVNf5hv/wc1zJPhN6LIKTOurckgyrOBC4sCwHrTgrTpixStdnh+8htUHHZixr2uyNKP0HP/jUh0cafBckGw2GHebi9mW+yT/07o3IDNyeZNy1BZi6rTLoVt4YGZLoTgOy+g79GJ6XmROopqgtfKfkOlQ1VRIyUmLqSaKTA0zk1vUGTEvnhL/wEVK6xnEfhWSGmSxZ4WIQT23Q5A9a8vg+St0byU4utB70O3IPTRG/yhUyaYtt+bWVZd7m6FvH5VAUZUhGi9aUlbvmkRyQDXoT+G96fnQLDaNS+XWLc65Xmx4uscD7T4IYRgktOsaf+tUqDFF0Y0WVpOpmYNf5j48s+gBCaeiGRBWeIc4FshpQalFGCdp9f4Ig21Dag640pYd9xT+6KqgsH/Po3+J+6BwsN8JY9gssC0477MtuinPGoBQPtNi7GNBADmmduietE1MDXN0bwkjUXge+pe+F/6x4TzvBi2/3aNIC5iJSQuJO8kGGYwtpRVBbHPuastACCpFbHgWyGlhZJEavdyKzJ8kYLRBPcxP4f7h78GMWlncsdXf5fyvGhenoOBcgqJeds9QSzpb9hKbweSLd8XYETFxVjetERHBbynnAfngccBGeyKw5+/i56Hbkayp3O8wywpCCGor7BoVkVVVIoWXxhxuXTEhWWXg5jfdWLVl1AGegowoiJDax5pCPRs4cJCbzS+SK03rc2xbrMLqs+8Gob6Js0+aiiA/sfuxuAb/wZlOapxSgJiMMK8gF0dNFUOemImG25E800r8zwiRIBjr8NQdeolEF2Vmv3knk70PngTwl+8N6EiRMNVUR0mdm6XrFK09IeRkEvj9ye6qmCcvSC9gXJ/GIBvhZQN4/0iJXclqn55Cex7HQ5tzwuK0Ievofevt0Ee4J4XpYpp/m4g9oq0z9WBHiRWf63/gIoJzTet7EK4xvppqD7zqoyeF1ROwv/i3+F7+oEJ5XlBCMEUtxV2I1tcJFWKZl8ISaU0xIVl54OYTpLJtd9C7p3YTqzMeUQEQOAGWaVFDva0iCii4sBj4T3lPAiMB8/GW3W0ouc+7nlRqhBRgmUn9omG2BdvTuioRS7etASzJeV5cczPM0YMY8u/RM/9NyDetma0wyxZBEIw1WOFzci2dk4qFM394ZIQF4LDBdP83ZhtsYleQZj1PBo6sj0euLDQmxzuaZmnzUX1oqthmrmtZp+Nnhf/+Rv3vChBjLN3glDhTftcHeyf2LkWOXrTIoTAtuOeqDrjysyeF4M+9D1yOwLvvjRhBJ1ACKa6bbAa2OIioaho8YUhl8C/h3nB/gCjJlOyZfmEzrVgCvRxboMAXFjoTq73tESbA96TzkbFoT/O7Hnx9f/Q+8BN3POixCCiCMvOGuWgv5rANS9y/Ka10fNitwO0O1GK4DvPo+/Ru6AEBsZ0n1JDFAgaPTaYJfajIi6raOkPQ1GL+3coWB0wbbsHo4Ui9vV7uo+naGBZH4wzcRPgwkJ/WAviOPe0CCGwLzwQVaddmtHzQu7fgN6Hb0Xo4zcn7gOpBDHM2BaCI71IndLbDrljbQFGVHjy8aZFJANch50Iz0lnQ7BoFzNLrFuFDRPI80IUCJq8Npg0xEVMTkUuil1cmLfbi/nylVj1FdTQYAFGVFhSJnOZzRrHChcWeqOxII53TwsAjJOmoOqMK2DdIYPnhSJj8LV/ov+JP0EJB8d9T07+IYII8w77MNsmbO0DxoKYizctALDM2g7VZ10DY+NszT40Gk55Xrz8BKhcesW6RoskCGjy2GAS2Y+MaFLBuoEw1CJ+YRGsDhhn75TeoCqIffOB/gMqNJomczxiUXLka09rGMFohvsHP4f7+NNG8Lz4NlWEqWVFzu7NyR/GOTsxfS3k9tUTLrNd2857/G9aw4gOFypPOR/OA47N7Hnx2TvoefBmJHvL3/PCIApo8tpg1BAX4YSCdb5IUYsL8w77AIyXuPiyT6DGIwUYUQHRsj7gEYsShPmmlbsFcRjrtrui+syrYJicyfNiEH2P/gGDbz7LPS+KHCIZNPaIgdjXEyxqkcc3rc0hggDH3oej8lcXQ2Qk0A4j93Sg94GbEP7i/bLfYjSIqciFQWBHWEMJGW0DxSsuxAovDNMYye7JBOLffaL/gAqJhnvteM2xAC4sdIVSdVR23uNFcleh6leXwL7nYcjoefHBq+h95PeQB/ryMg5ObjBts5A56ZNrv4Uy2F+AERWIJPt0Uy7etFiYGqajetFVsMxnhNGHSHlePI6Bfz0INVbeb75GKRW5kDTERTAuo8MfLVqRZdawy49/++GE2NYahmrOIy4sSotEDCw7b2K05O2WRBRRcdBxQ54XTs1+yfYW9Nx/PSLffZa3sXDGh2CywjSPcR6fTqzMdpqIshtM+ZtHgtkK9w9Ph+voUzLmckSXfYGe+25AfH15J9WaJBFNHhtEDXHhjyWxIajxRlxgpKrJkOpnpn1OoyEkVnxRgBEViLjGPMrB84gLCx3RXBDzKCyGSXleXAPTzG00+9B4DAPPPISB5x+FmijORWGiY95uL6aLYGLlF1AjEyQZV2MvPJ8CHRjyvFiwF6pOvwJSTb1mP2WwH31/vR3B914ua88LsyElLjS0BXrDcfSHi3Md0YpaxJa+B6pOjG1hmmDMI0IAg3ZuXrZwYaEnWguiyarL7Td5XpzAfDgNE/nqQ/Q+cCMS3et1GRcnewR7BYyzdkxvUGTEv/lQ/wEVAOaCCOgi0AHAUDUJ1b++DLZd2bVcAABUReDt/6DvsT+UteeFZQRx0RmIIRArvu0FafJ0iFXp4lAN+JBs/q4AI9IfyopYGC05OaHIhYWOaEcs9BEWQKoIk33hQaj69WUQPdWa/eT+Deh96BaEPnmraPdKJyqpo6eMzPbvPwadCO6qrHlkMIFkEMu5hkgGuA7/CTw/+U1mz4vWlei573pEVy7VbWx6YzVKmOrW/jdoG4ggkpB1HNHIEEK0oxZfvTMx1jzGPCI5ehZxYaEnrDctQcromJkvjJOmoPrMK2HdYXftToqMwVefgu/JP0OJhPQbHCcjorsahqa5aZ/TRAzxZeVfF4b9pqWfON8cy+ztUb3oahgbZ2n2UaNh+J78M/yvPFm2yYF2k4T6CnbEiAJoHYgUXbl1Q9N8CBXpFW6Vvi7I7asLMCL9oKrCPhWSo6gfFxY6wlwQTbkJPY2FlOfFL1OeF0btfbXYqm+GPC9W6jg6TibMO+zH/Dy29H1QpbjeDnMJparGm5Y+2yAsRKcbladcAOf+P8jsefHp2+h96BYk+7p1HJ1+uK1G1DjYJ3MUlaLVF4FcREXLiCDAvMPezLayN57L87Y8FxY6QSllRixyFXoaDxs9L+oaNfuoQT/6Hr0Lgbee454XRYBUOwVSXbpHCY0EkVj1VQFGpBPJOJgnq3TKU9KCCAIc+xwxoudFckM7eh+4EeEvPyjLcHuVzQSPlX1qJqGoaC0yjwvjrAUgVkfa53LHWsg95Ztjlu+DBFxY6IWSBFjZxgV809ocyVOFqlMvgX3PQzP0ogi+/wr6Hrkdsp97XhQazajF1++V7WkEquWOWCTzaKPnxbwMnhfJBPwvPIaBZx4qO88LQgjqnGY4TOzt3WhSwfqBSNGIKiIZYN6OXQKhrKMWGsKCRyxKjQKfCMkGIkqoOOh4eH92HgSbtudFor0ZPffdgOj3E+jMdxEiTZkF0Vub9rnq70WydVkBRqQDeV4Qc4FgtsL9o9PhOupnIIxS3cNEv/8cPfeXn+cFIQRTXFZYNMqtB+IyOgOxohEXpnkLAWP6Fk6y+Xso/t4CjCj/5Fugc2GhE4X0sBgt5unzUH3W1TDNmK/Zh8aj8P3rAQw8/xj3vCgQhBDtqEWZllQv9ojFMIQQ2HbaG1VnXAGperJmP8U/5Hnx/itlFWUSBIKpbisMIjt/zBdJoC/MrlWhN8Rkhmn+QkYLRezr93Ufjy6wnkdS7k5WcWGhFxpn74vpTWtzRJsT3p+eA+chPxrB8+ID9D54E5Ib2nUcHWeYVEl1d9rnSs96yJ3NBRhRnmEtiIIEiNqRgUJiqKpD9emXw7bLftqdqIrAW8+h//E/QAn69Rpa3hmuKyJqJKd3B2PwR4tDXJi33VPbeC4cKMCI8gvTCyaHzrVcWOgE80RIjlzO8gUhAhy7H4yq0y7N7HnR142eB29G6NO3y/ItuZghggjT9uzM9vjS8nvb0loQC3WyKhuIZIDriJPgOfEskAyeF/GWlei59zpEV32j4+jyi0kSMdVj1axU1O6PIhQv/CkmwebULKke/+4j/QeUR1IHCfLnYQFwYaEbzAXRUNwL4jDGuqmoPuNKWLZnhQuHUGQMvvIkfE/dyz0vdMY0Z2cQc/oDK7luJZSArwAjyg+UUmZ9g0IeNR0Nljk7oGbR1TBOHcHz4ok/wf/qU2XjeWEzSmhwsR9aFMC6gTBiycKfNNM0nlv+WXkd4U7GmNWBczmPuLDQC9aCmMeiSblGMJnhOfZXcB/3KxBGotMwsZVLU54XrdzzQi+IwQjTNiyjM4r4sjIqBa0kAZWxwBfBke1sEZ1uVP78Ajj2OyYVsdQg/Mlb6H341rLxvKiwGDDJyY7OqhRo9YWRLLDHheiqhKFpXtrnNBoqK5tv7SJ+PGJRUmi7nJXOgjiMdbuFQ54XUzX7qEE/+v52FwJvPz9hCvoUGtO8XQEhfTonln9eNm++pXAiJBuIIMC575Go/OXFECs8mv2S3etTnhdffVgWW4yVNhMqbWyPi6RK0eoLQ1EL+99p2nYP5ufx7z7WeSR5RIciflxY6IHmglg6EYvNkTzVqDr1t7DvcUiGXhTB915C3yN3QPb36za2iYpgc8LQlF65lsbCSJTJ21apnAjJFtOUGag+8yqY5y7Q7EOTCfiffxQD/34YakzjTbOEqHWYUWFmJ9rGZBVtBfa4kOqmQXBVpX0ud7dC7u8qwIhyD49YlAk0HmY3lGDEYhgiSqg4+IfwnrwYgi3duW6YxPq16Ln/BkSXcc+LfGPahp0DUzbJZwUql55PBIsNnhPOgOuok4FMnhfffYae+29Aor1Fx9HlHkII6l0WWI3sk2ahhIyOwWjBxAUhJMM8KpOoBet5JIg5PVnFhYUeRNnJjMSknSFeKphnzEf1omtgmp6+NzkMjUXge/oBDLzwONRkcRwvK0ekSU0Q3DVpnysb2iD3dhRgRLmFxoLpHxKhZCMWw6Q8L/ZB9elXQKqu0+yn+PvQ+9fbEPzg1VTNlBJFICmPC5PEfvwMRJPoCRXOG8c0ayemyEus+go0XvrVgynreWSy5fQgARcWOqC5IJbY3rAWot0J78nnwnnwD5n7/MNEvnx/yPOi9B9yxUjGt63vS/9ti7kgmu0lcbIqGwzVdaj+9eWw7cwu5w0AUFUE3nwW/Y/dDSU4qN/gcowkCGh02yAJ7O+uJxSHL1KYlxBiMsM4a8f0BjmB+Kov9R9QDqGKzK5ZZdGOOo8FLix0gEYZwqKMFkRgyPNij0NQdeqlEN3pe5TDyL1d6HnwJoQ+e6csEtKKDdOsHQFDeoJcYvXXUFleKiWCXgtioSEGI1xH/hSeHy8CMWu/eMRbVqDnvusQW/2tjqPLLUZJQKPHBg1tgY7BKIKxwiQes504UwK9pNetmEb03GzP6W24sMgzqQWRcdS0zBbEYYyTG1F95lWwbLebdidFxuDLT8D3z/ugRjXyTzhjghjNMM1iJAPKSSRWlnCei04LYrFgmbsjqhddDeOUGZp91EgI/f+4B/7X/lmyJ38sBhFT3NoCqs0fQbQAHhdSZR3E2vSTb+pAT0k72lKNeQQesSgxJtiCCAx5Xhx3KtzHjuB5seLrlOfFulU6jq780Xzb+q5037a0FsRyFegAIFV4UPmLi+DY7+jMnhcfv5nyvOjfoOPocofDZMDkCnaezLDHRULWP6fEPJ/lDVPaSZzM6DkAYubCoqTQSyEWI9btF6L6jCthmDRFs48SGEDf3+7knhc5RPTWQprUmPa5OtgHuaM0K2lqLYjI8YJYbKQ8L45C5S8uguhMrwkzTLJ7PXrvvxHhr/9XkuLRYzWi2s5+CZFVitYB/T0uDNO3YTvatnxfsvVDmPl+BlPGKrxjgQuLPKOXQixWJG8Nqk67FPbdD9buRIc8L/52J+TB8rGgLiRsJ87SPXqq14JYrJimzkT1oqthnrODZh+ajMP/n79h4Nm/lGQ+TbXdBLeF/X3GZRXtfn09LogowTR3l/QGqiK+7FPdxpErKKXME4r5eBZxYZFnJvqCCAx5XhzyI3hPPjez50XbGvTcdz2iy0s787oYMDTNB7Gkb7clW5dDDZXWaQI9F8RiRrDY4PnxIriO/Glmz4tvP015XnSUlucFIQSTKyywmyRmeyAu634M1Th/N7Drh3wKqpRYhDUZY1vi5yF6zoVFHuEL4paYZ2yD6kVXwzRtrmYfGovA98/74X/x76Dc82LMEFFK2XxvDVVLr36IjgtisUMIgW3nfVF9+uWQqjJ4Xgz0ofcvtyH44Wsl5XlBCMEUlxVmDY+LnlAcgzqeFBEdbhimzkn7nIYDSLYu020cuUA7ep77fD8uLPIJXxDTEO0V8P5sMZwHHZ/R8yL8xXvoefBmJHu458VYMc3blZn0V2rVGvVcEEsFQ/VkVJ1+OWw77aPdSVUReOPf6H98CZQSilKJAsFUjw2ixjnUdn9E12qoZeNoq2MCNBcWeYQviGwIEeDY81BUnfpbiO5KzX5ybyd6HrwZ4c/fLcmEtEIj2F0wNKZHh2gkiGRLCb1tTcATIdkgGIxwHXUyPD8+M7PnRfNy9Nx7PWKrS6dmjFEUMFWj1LpKgXUDEd2SOaWGmRCc3rTP5c5mKAM9uowhFzCfR4TkxaiRC4t8whfEjBgnN6U8L7ZlhOyHkZPwv/QP+J6+n3tejAGT1pG570vnbYu9IJaPc+14scxdkIXnRRD9//gjBl97umQ8L2wmCXUapdYTioo2nZI5CRFgms/25SklR1tmvp/ZDkJyLwO4sMgjfEEcGcFkgfu4U+H6wS9BDBk8L5Z/NeR5sVrH0ZU+Uv10CBXpUSG5swWKrzR8D9gLoi0vC2KpkvK8uBCOfY/K6HkR+vgN9P7lNsgl4nnhsRo1T4qE4jI2BPVJ5jTO2QkQ05NK4yu/AE0Wrq5JtlBVYRbxy1e+H5+ZeYQviNlBCIFth91RdeaVMNQ2aPZLeV7cgcA7L4CqpZOQVkhSb1ulu0es94JYyhBBhHO/o1H58wsze150taHn/hsRWVr83z8hBHUVFlgN7GqoveE4/NH8J3kLZhuMM7ZPb0jEkVj1dd7vP250jp7zJ1ye4Avi6DEMeV7YFh6o3YlSBN99EX2Pcs+LbDHOWcCu1rj66+IPi/PtxFFjapyFqjOvGtHzYuC5R+ArAc8LgRBMcVs1C5a1+6O62H5rJnGu+Dzv9x4v2gZz+cn348IiX/AFcUwQyQDXoT+G96fnQLBm8LxYt3rI8+IrHUdXmggmK/NtiyZiSK5bUYARZY/eC2K5IFrt8Px4ESqOOIkZwh8m+s0n6Ln/RiQ6WvUb3BgwiAKmuq0MRwmAAlg3EIas5DeKKVU3QKyqT/tc6VkPZaA3r/ceL3pb4nNhkSf4gjg+zDO3TXleNKWfIR8m5XlxH/wv/YN7XowA00EQQKLIy0BrnqziAn1ECCGw77Ifqk+/AlLVJM1+ykAvev9yK4L/+29Re15YjRLqNGqKJBWqSzKnac7OzM8Tq4v7BYdGGRbkkglESq+EnAu4sMgTNOxnfs4XxOwRHRXwnnIenAcel9nz4vN30fPQzUj2dOo4utJCrJnCPDKXbFsJlWHiVizQiD/9wzwuiOWIoWYyqk6/AtaRPC9efwb9f/9jUXteeKxGeK3s7z6cUNAViOX1/oYZ2wFCer5HYtWXRSvKqKoCkXRhwXLmzRVcWOQJ5oJosvIFcZQQIsCx12Go+tVvIboyeF70dKL3wZsQ/uI97nnBgBAC4+wd0xtUFYk1S/UfUBZQRWY719pc+g+mxBEMRriPOhmeE87I7Hmxdhl67rsesTXf6zi60THJaYbNyE7m7I8k4IvkL3opmK1MJ0416Ifc1Zq3+46LWBBgiJ58ziMuLPIAlRPsxE2rS//BlAnG+iHPi23YIX0AoHIS/hf/Dt/TD3DPCwbGWQxhASCxqjjDuKnwbbpI5MJi7Fjm7YTqM6+CsWG6Zh81HET/35dg8L//KkqH1mHbb4PITubsHIwiksjfuI2zFzA/T6wszm1Freg58vg84sIiD2hug/AFcVwIZgvcx58G1w9+AWLQjvzEln+Z8rxoW6Pj6Iof0emFVNuY9rnS016cDoJa88haoe84ygzJ5UXlLy+CY58jwSqwNUzoo9dTnhe+4vttSKKAqW5bhmTOCJJ5SuY0TJkNwvAiSqz9tihPWWk+j/I4j7iwyAM0wt6j5Avi+El5XuyBqjOy8Lx45HYE3n2Je15sRim9bTEXRCIAFqfuYyk3iCDCuf8xqPzFBRAcLs1+yc516Ln/BkS+KT6HSYtBRL2LncwpqxRtAxGoedgWJaIE40yGp0UyXpRW+cxtebMDJMNpofHChUUeYC6IgsRPhOQQQ2VtyvNitwO0O1GK4DvPo+/Ru6AEBvQbXBFjmL4t20Fw9VdFlXxGKWUviBYnSIZEXs7oMDXORvWiq2GezXhQDkETcQw8+1f4nvsr1Hh+kyNHi8tiRKWNHb2MJBV0DkbzknNlnMUW6PEiO2VFEzGA4QxKbPl9yeUzNMdQqgKMiAWxVYBksNrljB4iGeA67ER4TjobglVbtCXWrcKG+65HdMXX+g2uSBFMFnZhstAg5M6WAoxIg3gYYOzv8+3E3CNa7fCceBYqDv9JZs+LpR+j54Ebkehcp+PoRqbWYYbdyB73QDSZl2ROsbqebZW/fjXUiIbVQAEo1LY8Fxa5JqqRgcsTN/OGZdZ2Q54XszX70GgYvqfuhf/lJ4pyH1RPtN62isnTgoY1thO5sMgLhBDYd90f1adfDqkyg+eFrwe9D9+K4P9eL5oIFyEEDW4LjCL7cdYZiCGc42TO1CkrxjyiKhKri+eUFTPqh/w/j7iwyDGaGbh8QcwrosMF78/OT3leZKjFEv7sHfQ8eDOSvRPX88LQMAvEbEv7PLH226IxGtNeEHmeUj4x1NSj6vTLYV2wl3YnVUHg9X+h/x/3QAkxjJcKgCSknDk1XL/RNhBBIsfJnNqnrIpJoPvTP5SMgJGdm5IruLDIMTxxs3AQYcjz4tRLILrSzaCGkXs60PvATQh/8f6E9LwgoqiRfJYomuQz5oJotGSsgMvJDYLRBPfRp8D9o9NBTNoPoPia71OeF2uL4zdjNoior2B7dOQjmVN0uCHVTUv7XOnrhNLfnbP7jBWqKqkI+lYQmyvv2/JcWOQY5oJotuc1A5ezJcb6aag+82pY5rPtd4Fhz4vHMfCvB6HG0j1Hyh2t0yHFkHxG5WQqx2Ir+DaIvljn74zqRVfBWJ/+8BxGDQfQ//jdGHz9maLwvKiwGFBtZ4vPaFJBR46TObWiFvFi8IaJDILpA6PDSy4XFjkklYGbnjXNF0T9EcwWuH/4a7iO+XlGz4vosi/Qc98NiK9fq+PoCo9YORmCuzrtc7l9NdRwYcPbhdoX5qQjuSpR+auL4dj7cGT0vPjff9H7l99D9hW+GFe13QSHif0i548m0R/O3XafUeOUVWL1VwU/5l7IPCUuLHIIXxCLC0IIbDvumfK8qEmvSjiMMtiPvr/ejuB7Lxd8MdALQghMrCROSpFY/bXu49liCNxgrqggggjnAcei8ucjeV60pjwvvv1Uv8ExIISgwWWFSSOZsysYQyiem+gKMZphaJqf9jkNByB3FPZlhfk8IkQXHxguLHIIXxCLE0NlLap+fRlsu2byvFARePs/6Hts4nheGGftANZbaMEtvll5SoLIfWAKjKlpyPNi1naafWgihoF/P4yB5x6Bmiic54UoEEz1aCdzrvdHclZmnSnQUdgkTkop+3lkcYIwiqjlGi4scggzcVOHDFzOyBDJANfhJ8Lzk99AsKSfiBgm0boKPfddj+jK4jkyli8EuwvSZEbyWX8X5L7CnJqhVGXOI2LlPjDFgGi1w/OT36DisBMzel5Eln6EnvtvRKKrTcfRbYlJEtHg0k7mXJ+jfAupYQazUmii+TtQhjmVLiQigJJ+rF6vl1wuLHJEKgOXUZrWmv8MXE72WGZvj+pFV8PYqO15oUbD8D35Z/hfebLsPS+0PS0KFLWIhgBVSf+cR/2KBkII7LsdgOpfXwbJW6PZT/H1oPehWxD66I2Cnb5ymv+/vTuPb6O88wf+eWZ0WLIs2fJBLjtXKWmaQpq0NJRQoAXCJtAWtqEtITRAC7Qh2wO2LC38+usRoNl2l2a35T5S2NKWowc/lqMcvy4EKAUKJBAgIQmJYyfyKdnWPfPsH7JdO5qRbXk0I9uf9+vFC1szmueJpWf01XN8HzeOqDKezNmbyqLdgvkWQlH7e/8Ok80gvXv7uK9fDPP9QaptKZ+BhVXiUcCg8ZQ6dSqNnRqsQd3abyD4yc8Uznnx4tOI3HY9Mm2tNtbOXp55iwCXO+/x9M5Xc8GyzZy+IdLouac1ov7i78L/4ePNT9I1RB+/Dx33/ic0hyYF11d6ETCZzHmwJ2nJTqimAfrbzgTo5sPy9nweMbCwiN7Tafg4b4jlSSgKqk5YiboLroAaKpDz4lAz2m69Fn2vPDspc14Ijxceo8ln8R5kW/faXh/Z22H4OPPAlCfF40XNp89HzT9+GcJbYXpeauf2XM6L3TtsrF2OEAKNIR9cJhMu9nXHoenja9uuuhlQw9PyHs+2vAs93juua4+VlBKyx6AduSsg3OavkZUYWFjE8IaoqABviGXN2zgfDZdeDd/CpabnyEwa3Q/dja4HbpuUOS/Mvm1l9rxhaz2k1CF7DQJ0XxDCoFeFyod/0UfRcMk1cM+aa3qO3htDx90/Q/SJByE1e3vDXKpiOt8io0k0R+Pj/uJgmNNCSmT22pxALB03TntQZf4FymoMLCwgsxmTjcdquBPjBKBU+FHzua+g+sy1BT/AEm+8hMjNky/nhWvmfAhv/k03vXu7vftB9EUN51fYeUOk4rlq6lC/7p8RWF4o54VE79bH0HbnJmS77M15EfC6TJNnxZLZcW9W5p7/IcPH03YH6Ea9FQBEIGxbHfipZwHDb1ngDXEiEUKgcsly1F/8XbgK5bzo7s958czkyXkhVNV4x9O+GLTIAdvqoZsNg7AdTRhCVRH61GdRu/brUALmvbWZA3sRucn+nBcNAS8qPcbLLVtjSSQyxfekqMEw1LqZeY9nm3dB2rjdvGlgwR6LicV0XJg3xAnHXT8dDV/+F1R+9CTzk6SO2FN/QMc9N0Dr6baraiXlnpc/zwIA0nvsm9VueENUFM5TmoAq5n0gt+Pwkcbf4oEhOS/+sMW2nBcDybNUg/kWErnNysYz38KwHekaMu+9VfQ1xyI3nGiQh8dXBeEyz0BsNQYWFjCeKOMFvOb5Eqh8CZcb1Su/iPAXvgZRIOdFas/biNz4AyTeed3G2pWGe9aRuZwrh8ns3m7LpFWpmQ0nhjmcOEGplVWo/eJ6hFacUzjnxavPoe2Wa23LeeFWFTSGjHMLpTUdLbFE0df2zFtkfF27AvR4FNDzV7mIgL1fctlix0mm4kA6/40oArXMXzHB+Y46Bkdceg08s99veo6e6EPnvT9H9yO/mdA5L4TLDffsBXmP69EO6J2HSl6+NFtVxV6/CU0IgcCyT6H+oisL5rzIdhxC2+0/Ru8LT9oSyFZVuFFXafwNvjuRQVeR8y3UmgYo1fV5j2f2vQ2ZsW6PEjN6GQyDAAwsxq0cxrOodNRgDerO/yaqTv70CDkvnkLb7T9Gpt357ZKLZbTsFLDn2xaHEyc3z/Qm1F/8HfgXF8h5oWURfey36Lj359D68rf7ttq0qgr43MbzLQ7EEkhmi5tvYdiOshlkmncWdb2xMAzQhQJRWVPysodiYDFOpjdEG2fgUmkJRUHwE6tQt+5yqCHz1zVzcD/abtmIvr9tnZA5L9yzj8otkT6MHctODQN0F4cTJxPFU4Gaz5yPmrMvgvAUynmxDZGbfojUntLOSxBCoKnaeD8RKYH9XXHoRbRjt8lwSGZ3aduR6XBiwP7ViQwsxkFK3ThCrKiCcBsva6KJy9v0PjRccjUqPmCc9wHoz3nxx1+i68HboSeLH6t1gvBUwN14ZN7jWnsrtJhxAG0F0+HEqjCHEych/4eORcOlV8M9c47pOXpvFO2/vAHRJ39X0pwXHpeCWSHj/BbJrI7W2Ngnlar1MyEMVsRk9r5Z0n9LbtKmQfZnB3r9GFiMRzxmPFGG3beTluKrRHj1xag+47zCOS+2/xWRm3+EdPMeG2s3fkZbQAOl/bZlPgxSV7IyyVmumnrUX/BtBI4/HQVzXjz7KNru+ldku9pLVpeQz42w33i+RWc8jWhibHOnhBDwzM3vtZDpJLItu4uq42jIHuO/kd0TNwEGFuPC+RVTkxAClUtPQP3F34GrIX/d+gCtux1td25Cz7OP2ptoahzccxYCBr0EpUzyUw4Jfch+QlUROuWs/pwXQdPzMs17ELn5h4hv/2vJ6jI9WIEKl/HHYXM0jnR2bO3XdPl2CTclM+w9d3mAivydV0uNgcU4GCb0EYptW9OSs9z1M0bOeaHriD35O3Tc/TNoPfnjn+VG8VXCNT0/LbN28D3oJdhESkppnGCuIsDhxCliMOfF+4znJgCATCXR9cBt6PrjL6Gnrd+KXBECTTXG8y10mdtPZCzzLVzT5kBU5M8Pyux5oySJ9XLDifnbDYgqZ1YnMrAoktSyuRTEh8ml8TaeaUyTj3B7cjkvPv9ViArjsVoASO15C5GbfoDkzm021q44ppPP9pRgz4N4FNA4nDjVqZVB1J67HqEVqw0nEA+I/20r2m7ZiPTB/ZbXwetSMSNonN8ikdFwqGf08y2EohgOK8pEL7RD1ufrKLfszwwsipR7Ictjogw5z7dgMRouvQae2fmTHwfo8V50/Oo/0f3Yb8s654Wdy07L7YZIzhFCQWDZKaj/8r9ADTeYnpftOIS2265H71+esnz1VY3fg2qf8dyp9r40epKjb7e2tiPT4UQGFhMK51fQ4VyhMOrO/xaqTvq04TyFAX0vPJnLedFR+sRTxVACIagNjXmPZw/stnx3V91owpkD6+6pfHimN6Hhku/Cf8xx5idpWUQf/Q06f/0LaBZvSz4j6INXNf5o3B9NIKONbijDNWs+4MkfzsvsfsPSgCg3nGjweeTgcCIDiyKY7nfv0EQZKh9CURA8cRXq1l0BNWj+4Zg5uB9tN29E36vPlWXOC4/R5DOpI/PeDsvKMB9OrOZw4hSneCpQ89l1qDnrwoI5L5LvvN6f8+Jty8pWFYHGGr/hWhVNl9jfPbot1oXqgmd2/uZ+ek8XtI5WC2rarwyHExlYFCPVZzxRhmm8qZ+36X1ouPQaVHzgw6bnyEwK3X/Ygq7f3QE9VV45L+xYdipjbeBwIhXiP/pjaLjkarhnzDE9R+/pRvsv/x2xp34PqVuTJ8LnVjE9aBzQ9KU1RHpHN4HUvB1ZNxyix4y3n3dqGARgYFEUGY0YPi6CXHdPf5fLeXEJqletAQrlvNj2Yi7nxYHyyXmhVtdDCefv7ZDZ/w5kxppZ+abtiPkraAhXuB71F/4zAh8/rcBZEj3PPIL2O3+CbLc1OS/Cfg+CFcabp0V6U+hN5fcSHM7ddJThBmxpKwP0qMGQqqJABJwbTmRgUQTd6IYoBAMLyiOEQOVHPoGGr3wHrvoZpudpXe1ou2MTerY+VjY5L4yS/EDLIrPvnXFfW+qacUIfj5/DiZRHqC6ETv1H1J73dSiV5jkv0s27EbnpR0i88fL4yxQCs0J+uFXjXuj93XFkR5hvIdweuJvyNzHUuw5B6zLuaRgLmewFUkbLTOscHU5kYDFGMp0AEvnr+UUgDKGafyulqc3dMAMNX7kKlR850fwkXUfsiQfRcc9maL3O57woZZIf2dsJGHRbi1ADhxPJVMX8hWj46jXwvs/4vQkAMpVA5/23oOuPd0Mf546iqpLbT8RIVpfYH02MON/CdDjEgqRzpr1+IfOdZO3AwGKMzF9I8+VRREB/zotV5yJ8zqWFc17s3oHIjT9EcmfpdxUtRK2dDqUqP/tldv874x7LNmtHCtsRjSCX8+IyBE/73Ag5L55F2y3XInOoeVzl+T0uTKsynm/Rm8qic4Qt1t2zPwAYbAKWeW/8m6wZ9p7D+d5zBhZjZD6/gjdEGh3fBz6cy3nR9D7Tc/R4Dzp+9R+IPnafYzkvhBBwz82f1S5TCWiR4m/WUkrImEE7cnkBf/7mTUSHE0JB1XGnov6ibxfOedHeisit16H3xafHtfqqrtKDgNd4vkVrLIlUgS3WlQq/YTbb7KF945q0LdPJsu09Z2AxBjKbhuzryj/gr2b6YRoTVyiMui99C1UnnlEw50XvC0+g7Y5NyDqU88LdtMDw8cy+cSzv6+sCDIIlEarnMAiNiWfGHDRc/F34jllmfpKWRfSRX6PzNzcWnfNCCIHGkA8ug5zfEsD+7sJDIu6mo/IflDqyzTuLqg8A4+Ac5dF7zsBiDKTJsh5231IxhKIieNKZqPvStwrnvGjdh8jNGxF/7Xkba5fjmjHXcEVL5r3iAwvj7tvyuCHSxKN4KxD+7AWoOesCCIOEVAOSb7+Wy3mxt7j3rktVMKvaPOV3oSWohoEFxteOzIfl64u+plUYWIwB51dQKXhnvz+X82LBYtNzZCaFrt/fhU6bc14I1QX3zPwhG639APR4z5ivJ6U0bkeKC6KSu5lS8fxHL+vPeTHb9By9pxvtW/4dsaf/WNQ8oSqvG7UmW6xHelOIp42XoCo1DVCqqvMez+x/p6hVYDKbhuw16j0PQbjNE4rZhYHFKEkta5xtsyIA4TWfiEc0GoqvEuFzLkVo5bmG694HJF7/CyI3b0T6wF7b6mb6bauYZaeJHiCTv5mTCNZDGExwIxoLV7gB9Rd+G4HjTi1wlkTP/zyM9rt+imy38dYMhUwLVpim/G7uThjugiqEgKsxvx3JeA+09rFn4TRLLlcuvedsyaMke9oBg8iSvRVkFSEEAh89sT/nxXTT87SuNrTd8WPbcl64DNbhA8XNs9DLeFyYJgehuhA67XOoXfNPUCqrTM9L738XkZt/hMSbr4zp+ooQpkMiKU3HwZjxLqjmAfrY21G5954zsBgl0+VxXA1CFnMfMRP1X/kO/Es/YX7SQM6L//qPkue8UINhKDX57/Nilp0atiOhMI03Wa7ifR9Ew6XXwDt/oek5MhlH5303o+uhe8aU88LvcaEhYDyfoyOeRk8qf3Kye9Z8w+WxYw0szHvPKyG8lWO6VqkwsBgFqeuQMYMsge4KwGceERMVS3F7UHPGGoRXX1I458W7byJy0w+R3GVdimAjRtkDZToJ7dD+UV9DpuJAMn9WvqiqhSgw/ENULDUQQu2aDQie+o+GuSQGxF95Bm23XovMoQOjvnZDwAuf2ziPRnN3Apo+fKhCuL25ydCH0Q7tg26QPdOM7Okw7j0POpsUaygGFqOQyxJosHscswRSifkWLsnlvGicb3qO3teDjv/ajOjj9+d2DC2BQstOR7uVuuGeBiif7luanIRQUPXx01B/4ZVQa8xXTGTbWhG59Vr0/vX/j273UiHQWO0z3AU1q0sciOZPsjZediqR3b8TMtk3YpmA+TLTcplfATCwGJWJ8ELS5OUKhVG37nJUfWJV4ZwXz/8pl/Oi0/j9Oq46TJ8DuPJnwydfewZ9j/5yVNcwXWYadH55HE1+nplz0HDJd+H70LHmJ2lZRP/7XnT+9iboiZE/6L0u811Qo8kMuhPDh1fM5ln0PX0/En99YsTypNSN0x6UWe85A4sRmC6Pc7mBSud2j6OpRSgqgid/GnXnf8tw2dqATMt7iNz8I8Rff8GysqWU0Hu6oBjl2hhlD4nMpIB4/lwQUVkDYRCwEJWC4vUhfPZFqPnsBQWTGibfejWX8+K9kVc+hf0eBDzGQ3kt0SQy/RuV9STTSCaTgNvg/T7K7Lqyt8uwzZVb7zkHNkcSjwLZ/Ek9IlheLyRNDd45uZwX3X/8JZJvv2Z4jkyn0PW7O5F8dweqV34Rirf4de16og89v78Jevf4dmIs91nsNLX4j1kGz6y56HzgNmRa9xmeo8W60L7l31B1wkpUnbjKdLdQ0b9KZGdbL7TDhlA0KdHcnUBFTwTuJ++Gmh79XAojE2U4kT0WI9C7Dxo+Xm4vJE0dqj+A8Oe/itA/fGGEnBcvIHLLRqRb3iu6LMVXCd/HVgCGI8mjJ9mOqMy4ao9A/UVXIrDsFPOTZH/Oiy3/hmy00/Q0t6pgRshko7J0Fu3eMHrnLR1XfaXUjQML1Q1RWT2ua1uNgUUBUurGN0RFhQgwSyA5RwiBwLEno+ErV8FVVyDnRWcEbbf/GD3P/anonBeeeYvgO+HTxVYVMpM03mPHF4TwGOcDILKDUF0IrViN2nM3QPEXyHmxbxciN/0QiR3mOS+qfR6EKsw3/+o56nik5xcfXMieTpM9dhogRHl9lJdXbcqM7O0yHgYJHWHaLUZkJ/cRs1B/8XfgX3KC+Um6htif7kfHr/4TWm/+boijUbHoOFQsObmo55r1Vig15gERkZ0qjlyEhq9eA++8/N18B8hkHJ2/vRnd/++/IE1yXswIVRhuVAYAEAI9H14B91yTvBojZJ417fWrnlbweU5gYFGA7DJOtSpqyu+FpKlLcXtQc+Z5CH/uYgiveQ9AatcbuZwX77457PFsd8eotmavOPY0eBZ8JP+AwSZlQ+ldZjfE8ll3T6QGQqg9758QPOXsgh/yfS//DyK3XodMZHjOi0z7QXT0pZHVzZeqpjWg8pQvwjVtTt4xJVBt+jypa8bDIC5PWfaeM7AwkXshjVaDlOcLSeT74NJR5LyIoeOenyH6pwdyGfy0LDrvuwV9rzw74vWFEPB/4iy4Gocny1KrzZeLylQfkMjvJRGBmrLYLIloKCEUVB2/AvUXfhtqTZ3pedm2FkRuvQ59L/0ZUkpofTFE7vwpOt950/Q5QG4yp664UPkP50M5rN2oBVZ7yVg7YJDlVlRPK8tFBAwsBigqIBRAUdG7/dXc3iBGSbGqp5XdeBbRAFd1bS7nxQkrUWjCZe9zj6Ptjn9F98O/QqZlL3qe+e9RpTQWqorAaWugDpnXodYM6XkY0o6iW5+CNO2t4DAIlS/PzLlouORq+BZ91PykbAbdD/8Knb+9CV2/uxOIx1D96hPwmA2F9MtoOpQKPwKr1kH4AoOPK4H+5dxD2tAA2W3ce16uw4n8hOxXueBDaPj8OlQu+BCS776FQ7+61fA8pQzHs4iGEoqK4Cc/g7ovfXOEnBd7Ef/bVgCA3htD34tPj+76Hi8CKy+AqAwBAJQh2QyHtqPeV1/EoQfuMbiAgAhxGITKm+L1oebsi1D9mXUj5rxI9Q8v6s3vojG2D9ODFTCLL9L9eS3UYC0CK780OJTYt3cPgOFtCADaH77PuPfc4wN8wWL/eSXFwEJRUblwMRqv+D5qV3wWjVd8H/6FxyD66qtAIAxlxvuBgaEPRQX8IUerSzRa3jlHoeHSa1Dx/qNHdX7P1segp/LTEBtRKoOoWnUB4PFCrWkwb0evb8tvR+4KiBHmZRCVAyEEKhcfh/qLvwP3tMZRPaf36T+g1u/BUQ1VqPXnJ8MaCCwAwNXQiMpTvghRUYnknl2GbSj2Yv8w5eHtSPWU5TAIMEUDi2Qyiba2NhyItAFSovLoJYMvkBACgaOXQmayUOcthVI/B+q8pbkXU8qyfSGJjKj+AMJf+NqIOS8AQCb60Pv8yGmFgVw2zmRFFZInr0F7Vd3Y2lEmNe5/F5Gd3HXTUH/Rlahc9qkRz8207kPyrVfhUhTMCPlwZF1gWGbOzJDAQkqJZO0sxD/xechs1rgNpTNAIJzfjlL5G/qViykTWEgp0dXVhXfffRe7du3CoUOHEOvpAdxu9L3+yuCmM1JK9L7+MoTHM+wFVoJ1hjvKEZW7wZwXX/4XuOoKD+X1Pv8EtLj5DUvTNLS3t2Pnzp3Ys2cPOuJp9MbjbEc06QmXG9UrzkHtuZcVzHkBALGn/wip597nFW4Vc8J+zK7xw6MqSGs6MpkMDh06hLfeegv79u1DZyJt3obcLijBuvx2ZDCZs1xMiZTeiUQCzc3NSKXyvylpRx+Lvpeewb6f/B8Ejl6K3tdfRvzN11Bz3Mch+3sopJTQY+2QQhl8jGiicU9rRPUZ56H9rp+YniPTSfQ+9zhCp5yddywWi6GlpQXZbP6k5rG1I5XtiCasiiM/hKoTVyH6yK9Nz8m2tSDxxkvw9294JoRAsMKNSo+Kg23teOedvXk7qJq1odDSJdBj7RB1s4e3ozLOpSTkaPaHnaCklIhEImhrG2Gfgz8/AvX1F4FMBnC74T16MZqWfzQ3phWsgx5rB3o70S4q0eNrQFNTE7xe88k8ROVITyURuWUjtBF2PxUuN474+kaogdx8Il3XceDAAUSj+ZuIDTOGdpQMzkRjYyNUtXxvjkRGsp1tiNz8Q8h04SE9NdyAI9b/38FkiplMBvv27UMiUWAe02FtSDvmY6hbfhzqZJ9hO9Lr5mL69OllF6RP2sBCSonm5uaRb4Ym6vQowjIBAR0SCjqFD+1K7karqirmzJkDn4/piGniSO15G70vPoVMpAVaV25+kZnKYz+J6n/4PDRNw969ewvfDAso1I68Xi/mzJkDt5sTOWniiL/2POLb/4psWyu0AvuHAED1mWtRuWQ5UqkU9uzZY9jbNxqF2lEgEEBTUxOUETJ32mlSBhbjDSpGQ1EUzJs3DxUVTPJDE4/MZpBtP4hMWysykRZk21qQaWv9e8ChulC//vvY3xktOqgYDY/Hg3nz5sHlmhKjsjTJ6KkEsm0HkWlrQTbSgkx7K7KRFmix3N44aiiM8CXXYPd7+4oOKkYjEAhg9uzZZdNzMSkDi87OTrS0tJS8HK/Xi/nz55dVpEg0HjKTRqbjELKRFrR7qxDLlH6iZTAYRGNjY9ncFInGKxdwtCLd1oqD/lokM6ULKgY0NDSgoaE8dguedJ+IqVQKra3GWcpKUVYkUni8mmgiEW4PPNMaoc1daEtQAeQmhZayd5HIborXB8+seUg2HmVLUAEAkUikpL2LYzHpAouWlpa82bal1N7ejmQyaVt5RKU2MFnTTi0tLdC08l0+RzRW6XQaBw8ap7QvlebmZls//8yULLDYv38/TjrpJCxcuBBHH3007rvvvlIVNSiZTKKvr6/k5Ryuvb3d9jKJSiUWi5V0PNiIruvo6uqytUyiUursLDyxsxRSqZQjn4GHK1lg4XK5cMMNN+DNN9/EE088gW9+85sl/wd3dHSU9PpmotGo7TdiolJxqh11dHSUxbctovHSdd2RwAJwrv0OVbLAYvr06Vi8eDGA3KSScDhc0j+0lBLd3d0lu/5IZcdi+VtDE000qVTKsXHaTCZTNmPEROPR09MDXXcmw2xPT4/jw4pjDix0XceCBQtw5ZVXDnv8scceg8fjMRzyeOmll6DrOhobR7eJSzFSqZSj33bi8bhjZRNZxekPdrYjmgycbkdOlz/mwEJRFFx11VW48cYbB8dEX3vtNaxevRrXXnstVq9ePez8jo4OnH/++bjlllusqbEJp/+QvCHSZOB0O3K6fCIrOP154HQ7KmooZM2aNairq8PmzZvR3NyMVatWYe3atbjiiiuGnZdKpXDWWWfhqquuwsc//nFLKmzG6ZUZ6XSa48M04Tndjpwun8gKRvtSTaXyiwosXC4XrrzySmzevBkrV67EkiVLsHnz5mHnSCmxbt06fPKTn8TatWstqWwhTo1nDcXAgiY6p8dmy6EdE42X0+9jp8svevLmmjVrEI/HIaXEvffem7eZ0NatW/Gb3/wGv//977F48WIsXrwY27ZtG3eFiah0mP2SiMar6AT9l112GYBcDgejHQqXL19ua9RUDrsk8qZME53T6emdLp/ICoqiONr753Q7Kqr0a665Bg8//DBeeOEFZLNZ3H777VbXa8yc3gysoqKCgQVNeE7v2Ot0+URWKIfPIyeNObC47bbb8NOf/hQPPfQQjjnmGHzjG9/Apk2bkMlkSlG/UXP6huR0+URWcPp97HT5RFbw+/2Olu90OxpTYPHII49g/fr1uOeee7Bs2TIAwIYNGxCLxXD33XeXpIKj5fF4HO3+cfqNRGQFp29IbEc0GTjdjiZMj8XLL7+M1atXY9OmTTj77LMHHw8Gg9iwYQOuv/56R8eUhBCoqalxrOxgMOhI2URW8ng8qKysdKRsr9fr+A2RyApVVVVwuYqewjguoVDI8TmHQk6iNZKpVAo7d+60vdza2lpMnz7d9nKJSiEWi2Hfvn22lztz5kzHvhwQWS0SiSASidhe7vz58x3vMZlUU7C9Xi+qqqpsLzccDtteJlGpVFVVwePx2Fqmy+VCKBSytUyiUgqHw7ZP6Pf7/Y4HFcAkCywAYMaMGbbOtZg2bRq8Xq9t5RGVmhACs2bNsrXMmTNnOr5EjshKLpcLM2bMsK08IQRmzpxpW3mFTLqW7Ha7bfvj+nw+1NbW2lIWkZ38fj/q6+ttKaumpsaRnkaiUquurkYgELClrHL6kjvpAgsgN3mlrq6upGW43W40NTUxdwVNWg0NDSW/Kfp8Ps5PoklroPev1B/41dXVZTUkPykDCwA44ogjShZcuN1uzJ07F263uyTXJyoHQgg0NTWVrDfB7/djzpw5HAKhSc3lcmHu3LklCy6qq6sxc+bMsvqSO6lWhRjp7OxEa2urZRuEBQIBzJo1y7GlRER2k1Li0KFDaG9vt+yaNTU1mD59OoMKmjI0TUNLSwui0ahl1xz4Al1OQQUwBQILILel+YEDB9DX11f0NRRFwYwZMxAKhcruRSSyQyKRQHNz87i2ZHa73Zg1a5ZjuTKInBaLxXDgwIFx5X3y+Xy2DLEUa0oEFkDuW1dfXx86OzsRi8VG/TyPx4Pa2lpUV1c7nnSEyGlSSsRiMXR2do4pUB+Y6BwMBtlLQVOepmmIRqPo6OgYU6BeVVWFcDiMQCBQ1l9wp0xgMVQ2m0Vvby8SiQQSiQTS6TSklBBCQFEU+P1+VFRUDK4JLucXkMgpqVQKfX19SCaTiMfjyGazg+3I5XLB5/PB5/MNticiGk5KOdh+Bj6PNE0bbEdut3uw/QQCgQkzr29KBhZERERUGuyTJCIiIsswsCAiIiLLMLAgIiIiyzCwICIiIsswsCAiIiLLMLAgIiIiyzCwICIiIsswsCAiIiLLMLAgIiIiyzCwICIiIsswsCAiIiLLMLAgIiIiyzCwICIiIsswsCAiIiLLMLAgIiIiy7icrgAR0Vh4PnwhhKJCKCoUlwdCVaEM/u7++zG35+8/D5xrcEx1uSAUASEEFEVAcSlQhIBQBFSXAqHA/JgQEApyx1QFipI75nEpUBUx+J938Hfl78dE7jzXkPOGPm/YNYSAW8nVYejPihBQBeBWlcN+BlSRO+5WxbCfhQAUCKgKBn8WAlAFcv+2wecOHAdURUAAuesO/pw7V+gahNQBqQNDf5Y6oGWNj+n9j+s6ICWEnh18jsxmAF0DdA1S04BsBlLXAF2HzKZz/9f6jw89t//n3LHcuVLXITUdeiYLqeV+1jKZwZ+lrkNLZwd/1tNZ6P3PkZo2+Dx98BoSuqZD6hJaWuv/XULLaH8/pvUf03PH9LQGXZP9ZfXXR5OQmkRG16FJQJMSaV0O/qxJDPt96M86Bo4NPS/3801yr6NtcwB7LIiIiMgyDCyIiIjIMgwsiIiIyDIMLIiIiMgyDCyIiIjIMgwsiIiIyDIMLIiIiMgyDCyIiIjIMgwsiIiIyDIMLIiIiMgyDCyIiIjIMgwsiIiIyDIMLIiIiMgyDCyIiIjIMgwsiIiIyDIMLIiIiMgyDCyIiIjIMgwsiIiIyDIMLIiIiMgyDCyIiIjIMgwsiIiIyDIMLIiIiMgyDCyIiIjIMgwsiIiIyDIMLIiIiMgyQkopna4EEZETUqkUrrvuOlx11VXwer1OVycP61e8cq4bUP71Gw8GFkQ0ZcViMYRCIUSjUQSDQaerk4f1K1451w0o//qNB4dCiIiIyDIMLIiIiMgyDCyIiIjIMgwsiGjK8nq9+N73vle2k+dYv+KVc92A8q/feHDyJhEREVmGPRZERERkGQYWREREZBkGFkRERGQZBhZENCVdccUVOOGEE7BmzRqk0+lhxxKJBM444wyceOKJOPXUU9HZ2VlW9Rtw3XXX4SMf+Yij9clms1i3bh1OOOEEfP3rX7elLmOp3wA7/1ZGzOpXDu81qzGwIKIp529/+xsOHjyIZ555BgsXLsT9998/7PgjjzyCRYsW4c9//jPOOecc3H333WVVPwDo6enB9u3bHa/PQw89hFmzZuGZZ55BPB7Hc889Z0udRls/wN6/lZFC9XP6vVYKDCyIaMp5/vnncdpppwEATj/99LwPwyOPPBLxeBwA0N3djfr6+rKqHwD87Gc/w/r16x2vz2jq6mT9AHv/VkYK1c/p91opuJyuABGR3bq7uzFjxgwAQCgUyut+nj9/PrZv345FixZBCIG//OUvZVW/aDSKbdu24eqrr3a8Pt3d3YN7XRjV1en62f23MlKofk6/10qBPRZENGkdPHgQy5cvz/tPSolYLAYgd9MPh8PDnrdlyxacdNJJ2L59O77//e/jBz/4QVnV74YbbsBll11WkjoZqampMa1PoWPlUD+7/1ZGCtXPrveanRhYENGkNW3aNDz77LN5/61cuRKPP/44AOCxxx7D8ccfn/fcgZt/dXU1uru7y6p+u3btwsaNG3H66adj586duP7660tSvwHLli0zrU+hY3YpVAe7/1ZjrR9gz3vNVpKIaAq6/PLL5fLly+W5554rU6mUlFLKiy++WEopZTQalStXrpQnnniiPP744+Xbb79dVvUbaunSpY7UZ6AumUxGnn/++XL58uVyw4YNttRlLPUbyq6/lRGz+pXDe81qTOlNREREluFQCBEREVmGgQURERFZhoEFERERWYaBBREREVmGgQUREQ1at24dhBC49NJL84597WtfgxAC69atG3zs4MGD2LBhA+bNmwev14vGxkaceeaZePLJJwfPmTNnDm644QYbak/lgIEFEREN09jYiF//+tdIJBKDjyWTSdx7771oamoafGzv3r1YunQpnnrqKWzatAnbtm3Do48+ipNPPtnRFNrkLKb0JiKiYZYsWYLdu3fjwQcfxJo1awAADz74IBobGzFv3rzB8wZ6MF588UVUVlYOPv7BD34QF154oe31pvLAHgsiIspzwQUX4M477xz8/Y477hgWLHR2duLRRx/F+vXrhwUVA6qrq+2oJpUhBhZERJRn7dq1ePbZZ7F3716899572Lp1K84777zB47t27YKUEgsWLHCwllSOOBRCRER56urqsGrVKmzZsgVSSqxatQp1dXWDxweSNgshnKoilSn2WBARkaELL7wQd911F7Zs2ZI3Z+LII4+EEAI7duxwqHZUrhhYEBGRodNPPx3pdBrpdBorVqwYdiwcDmPFihX4+c9/jr6+vrznTopdOqkoDCyIiMiQqqrYsWMHduzYAVVV847/4he/gKZpOPbYY/HAAw9g586d2LFjBzZv3ozjjjvOgRpTOeAcCyIiMhUMBk2PzZ07F6+88go2btyIyy+/HK2traivr8fSpUtx44032lhLKifcNp2IiIgsw6EQIiIisgwDCyIiIrIMAwsiIiKyDAMLIiIisgwDCyIiIrIMAwsiIiKyDAMLIiIisgwDCyIiIrIMAwsiIiKyDAMLIiIisgwDCyIiIrLM/wJA3E/c2VZ9swAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/tutorials/causal_discovery/tigramite_tutorial_causal_discovery_overview.ipynb b/tutorials/causal_discovery/tigramite_tutorial_causal_discovery_overview.ipynb
index d52c4390..791cf745 100644
--- a/tutorials/causal_discovery/tigramite_tutorial_causal_discovery_overview.ipynb
+++ b/tutorials/causal_discovery/tigramite_tutorial_causal_discovery_overview.ipynb
@@ -20,8 +20,7 @@
     "See the following paper for theoretical background:\n",
     "Runge, Jakob. 2018. “Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.” Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310.\n",
     "\n",
-    "Last, the following Nature Communications Perspective paper provides an overview of causal inference methods in general, identifies promising applications, and discusses methodological challenges (exemplified in Earth system sciences): \n",
-    "https://www.nature.com/articles/s41467-019-10105-3"
+    "Last, the following Nature Review Earth and Environment paper provides an overview of causal inference for time series in general: https://github.com/jakobrunge/tigramite/blob/master/tutorials/Runge_Causal_Inference_for_Time_Series_NREE.pdf"
    ]
   },
   {
@@ -76,6 +75,7 @@
     "| PCMCI  | Causal stationarity, no contemporaneous causal links, no hidden variables |  Directed lagged links, undirected contemporaneous links (for tau_min=0)  |\n",
     "| PCMCIplus | Causal stationarity, no hidden variables    | Directed lagged links, directed and undirected contemp. links (Time series CPDAG) |\n",
     "| LPCMCI | Causal stationarity    | Time series PAG |\n",
+    "| RPCMCI  | No contemporaneous causal links, no hidden variables |  Regime-variable and causal graphs for each regime with directed lagged links, undirected contemporaneous links (for tau_min=0)  |\n",
     "\n",
     "\n",
     "| Conditional independence test | Assumptions                                  |\n",
@@ -102,6 +102,10 @@
     "LPCMCI:\n",
     "Gerhardus, Andreas and Runge, Jakob (2020). High-recall causal discovery for autocorrelated time series with latent confounders. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F., and Lin, H., editors, *Advances in Neural Information Processing Systems*, volume 33, pages 12615–12625. Curran Associates, Inc. [https://proceedings.neurips.cc/paper/2020/file/94e70705efae423efda1088614128d0b-Paper.pdf](https://proceedings.neurips.cc/paper/2020/file/94e70705efae423efda1088614128d0b-Paper.pdf).\n",
     "\n",
+    "RPCMCI: \n",
+    "Elena Saggioro, Jana de Wiljes, Marlene Kretschmer, Jakob Runge; Reconstructing regime-dependent causal relationships from observational time series. Chaos 1 November 2020; 30 (11): 113115. https://doi.org/10.1063/5.0020538\n",
+    "\n",
+    "\n",
     "References for the conditional independence tests are covered in the respective tutorial ``conditional_independence_tests``. The following steps will walk you through a typical causal analysis."
    ]
   },
@@ -166,7 +170,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
@@ -235,7 +239,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 16 Axes>"
       ]
@@ -265,7 +269,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 16 Axes>"
       ]
@@ -326,7 +330,7 @@
     },
     {
      "data": {
-      "image/png": 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LFi1ahDfeeANHjx5VNMo04AmBH3/8cbz55pvIzMxEXV0dBg0aNJDVXNbSpUvxL//yLxg1ahR++tOfwul0Ytq0aTGXX2zbtm2oqKiIPA6FQvjoo49kTaDInao5P1NaWlpc/39Kyc0Q82KuzyS1Q77mmmsiB6vGxkZdJruWkyNmyLjPdP78+agCIDs7G1999VXMgsOMGQL65yjW5/ryyy+jiq6lS5dGCg6bzYbbb78dAPotmzlzJl5//XXMnj076rUpKSmRImYgrxVCRL3ujTfewJw5cwb8WqnPEKv91157DXPmzIn6vADiav/f/u3fcM0116C9vT3yfzF8+HAIIaKWnTt3DrNmzYqso++E/HETA7BlyxYxefJk0dzcLCZPnixefPHFgawmLmvWrBEnT54UQgixbt06cfz48Usuv1ggEBCdnZ2Rn88//5xd0SQLM6S+WEMMcoYj4n1trGELvYfomSN96JkXs2ZI6ecaPny4ABD5cTgcksuEEJq81kptxXq/nLzEOt0oXrILuerqajF+/Hhx7NgxIYQQO3bsEDNmzBDBYFDuquLidrvF9u3bRW9vrygsLIycCxdr+eXwnAJSihkyx8Ey3tfK2VHriTmKn5y86ZkXs2ZI6efS4u9Qzmut1Jac4kxpwRaLrELu0KFDYuzYseLtt9+OLOvs7BS5ubli7969qmzQxbq7u8W8efNEbm6u2L59u9i0aZNoamqKWh4v7jxJqUTOkF6FmJ7fuM3SI3exRM6RElr0JgmhTV7MmiGln0tOEaLFa63UllbFmRxxF3InT54UN998s3C73VHPbd26VRQVFUVduWpG3HmSUomQITlXT5n1YBnva+XsqPWUCDlSKt6iTWlvkhDa5MWsGbLS3wEpN6Bz5KyMO09SKhEyFOtgp1chpuc3brNKhBzFS84XBy16k/pug1XzIiVWhqz+uUgeFnJEMlktQ1IHUaUn6CbTwVIrVsuREnK+OLA3KX7JlCGKjYUckUxmzZDS4dK+62Ahpj2z5kgppV8cmLf4JWqGSB4WckQymTVDcno9eLA0nllzJEe857jJ+eJA8UuEDJFyA76zAxEZp6WlBWVlZaivr49MRBrrHrT5+fmora1FKBSK3MM2fL9cIiXKysoi2aqtrY1k8uIcNjU1oaysLDKZbvhuLMwhkXJJU8h5PB54PB709PQYvSlkUWbKkNQBVKpgAwC32y15ECVjmClH8ZL64pCdnS1ZtPGLg/asmCHSzoBv0WVVHR0dGDp0qKxbdBH1ZYYMORwOeL3efo+lej20vF0QKWOGHMXL5XL1K87CtxCSWi71xYE51IaVMkTasRm9AUR0aS0tLXC5XHA4HHC5XGhpaUF+fj5SU1MBIKrXo7W1FTU1NTx40oBI5S3WsL3b7YbT6YTD4YgUccwhkb7YI0ckk94ZYq9HYjLrvkgqbwAke+TIWGbNEOmLPXJEJiHVEwJAsjeEvR6kFam8SfW8EZE5sJAjMonwBQxerzdyAQMAyWFUIqVifXHgsD2RtbCQIzIJOechESkV64sD80ZkLaaefqStrQ333HMPAoEA/vmf/xl33XVX5LkTJ05g3rx5+Lu/+zvk5OTA4/EYuKVEysWaPoRTN5AWYn1xYN6IrMXUPXI7duzAhg0bUFdXh61bt/Z77tSpU9i4cSMOHTrEIo4sR2pYiz0hpCcO2RMlBlMXco2NjSgoKEB6ejoyMzPR3t4eea6pqQlutxvTp0+/5LfHYDAIn8/X74dIDi0yJDWsxfOQEpuR+yJ+cUgMPJ6RFFMPrXZ2dsJutwMAMjIy4PP5kJWVBQAYP348Fi5ciJycHMycORNFRUWRb5d9VVZWoqKiIvI4PJRAFC8tMhRrWIsSl5H7Iqk7gdTU1HAI1WJ4PCMppppHrry8HNXV1ZHHDQ0NaGtrg91uR0lJCXbv3o1hw4YB+Hb+nPC8OQsWLEB5eTmuvvrqqHUGg0EEg8HI446ODuTk5HDeHYqbFhmKNVM+JS4j90VSdwJpbW3VtE1SH49nJMVUQ6tr1qzBH/7wh8jPP//zP+Pw4cPw+/3wer2RIg4AFi9ejA8++ACBQACffvoprrrqKsl1pqWlwW639/shkkNJhmJN8cBhreRj5L6I58MlBh7PSIqpCrmLPfjgg9iyZQsKCgqwdu1aAMD69evh9Xrx1FNPYenSpbjtttvw+OOPw2Yz9UehJBVrigeeD0da4flwRMnFVEOreuAtTUgpORnikBbFotW+iMP2yYPHMwJM3iNHZHUc0iK98UIaouTCQo5IQxzSIr3xywNRcmEhR6QSqXOTeC4caYUX0hARwHPkjN4csqBYGeK5SSSH0n0R80Y8nhHAHjki1fDcJNIT80ZEgMnv7KAmj8cDj8eDnp4eozeFLOpyGYp103uivtTaFzFvyYvHM+qLQ6tEMsXKUEtLC8rKytDQ0IC8vDy43W6eE0cxydkXhbNVX1+P/Pz8yHlvzFty4/GMABZyRm8OWRAzRGqQypFUwZadnc3z4UgS90UE8Bw5IiLTiHUnEJ4PR0SxsJAjIjKJWAUb54YjolhYyBERmUSsgo1zwxFRLElz1SoRkdm53e6oCxgARCaWJiK6mOl75Hp6ejBlypSo5W1tbSgsLMTUqVOxf/9+A7aMiEhdvBMIEcll6kLO6/WiuLgYH3/8cdRzO3bswIYNG1BXV4etW7fqv3FEREREBjN1IdfV1YUtW7ZgzJgxUc81NjaioKAA6enpyMzMRHt7u+Q6gsEgfD5fvx8iOZghUgNzREoxQyTF1OfIjRw5EiNHjpR8rrOzE3a7HQCQkZEBn8+HrKysqNdVVlaioqIi8jh8RRhRvJghUgNzREoxQyTFVBMCl5eXo7q6OvL4iSeegMvlwowZM3Do0KF+r507dy48Hg/sdjtKSkqwe/duDBs2LGqdwWAQwWAw8lgIgUAgAIfDgZSUFK0+CiUQZojUwByRUswQSTFVIReLVCG3ceNG5OXlobCwEC6XCwcPHjRm44iIiIgMYupz5KSsX78eXq8XDz74ILZs2YKCggKsXbvW6M0iIiIi0p0leuSIiIiIKJrleuSIiIiI6FumvmpVb0IIdHZ2Gr0ZJMOVV15pqpN8mSFrMlOOmCFrMlOGAObIqgaSIxZyfbS1tWHEiBFGbwbJ8NVXX+Gqq64yejMimCFrMlOOmCFrMlOGAObIqgaSIxZyfQwaNAjXX389Dh8+HJmjTis+nw/Tp0/XvC292jGqrUGDBmnajlyJmKFkaMtMOWKGrNmWmTIEMEdWbWsgOWIh10dKSgpSU1ORmZmp+X+azWbTpS292jGqLTMNZQCJmaFkaMtMOWKGrNmWmTIEMEdWbWsgOeLFDkREREQWxUKOiIiIyKJYyPWRlpaGhx56CGlpaQnTViJ+Jr3bkiNRfwdsSz+J+vnZlr4S9XfAtqJxQmAiIiIii2KPHBEREZFFsZAjIiIisigWckREREQWxULuO8FgEA888AAWLFiA6upqTdvy+XyYNm0aSktLcf/992va1sMPP4yzZ8/i2LFjuOeee1BaWorPPvtM07b27NmD+fPno7S0FLt27VJt/efOnUNpaSkWLFiAnTt36vKZ5GCG1GsrWTME6JcjPTMEMEd64r5IvbYskSFBQgghXn31VeHxeEQoFBKLFy8Wfr9fs7aOHTsmKioqNFu/EEIEAgGxevVqMW3aNPHZZ5+JFStWiPb2dvHhhx+KJ598UtO2nnzySfHFF1+o2oYQQjz33HPi6NGjQgghFi1aJBYtWqTZZxoIZki9tpI1Q0LolyM9MiQEc2QE7ovUa8sKGWKP3HeampowadIk2Gw23Hjjjfjoo480a+vDDz/EkSNHcN999+F3v/udJm0Eg0GUlZXh1ltvBQD4/X4MGzYMN9xwA86cOaNpW6dPn8bTTz+NJUuWqPptaeXKlZg0aRIAoLe3FwA0+0wDwQyp11ayZgjQL0d6ZAhgjozAfZF6bVkhQyzkvtPV1YUhQ4YAAAYPHoyvv/5as7auvfZabNiwAS+88AL27dsHr9ereht2ux15eXmRx+GgAIBQecaZi9sqLCzE5s2b8ZOf/ASbN29WrZ1hw4YhNTUVL7/8MsaMGaPpZxoIZki9tpI1Q4B+OdIjQwBzZATui9RrywoZYiH3nSFDhqC7uxsA0N3drel91SZMmICJEyciLS0NEyZMwNmzZzVrK6zv/dtsNm3/2++55x5kZGTgBz/4Adrb21Vd92uvvYYDBw5g/fr1un6meDBD6knWDAH65ciIDAHMkR64L1KPFTJkfOJMYuzYsWhoaIAQAs3NzRg1apRmbZWXl+Odd95Bb28vmpqaMHLkSM3aCktPT4fX68Wf//xn5OTkaNaOEAKlpaXo6enB6dOn4XA4VFv3+++/j3379mHbtm1IS0vT7TPFixlSRzJnCNAvR0ZkCGCO9MB9kTqskqErVNsqi7vzzjuxbt06vPLKK7j77rsxaNAgzdpavHgxfvzjH2P79u2YN28esrKyNGsrbPXq1Vi1ahVSUlKwadMmzdpJSUnBkiVLsHDhQqSnp+OZZ55Rbd3PP/88WltbsXz5cgDAI488ostnihczpI5kzhCgX46MyBDAHOmB+yJ1WCVDlrlFV0lJCf73//7fuO666wAAgUAA//RP/4TW1laUlZVFfhlEREREycL0Q6vBYBDz58/H0aNH+y2vrq5GcXExjhw5gr1798Lv9xu0hURERETGMH0hFwgE8Oijj2LmzJn9ljc2NqKgoAA2mw3jxo1Dc3OzQVtIREREZAzTnyN35ZVXYvr06fjlL3/Zb3lnZ2fkSpyMjAz4fD7J9weDQQSDwchjIQQCgQAcDke/q0SIYmGGSA3MESnFDJEU0xdysdjtdnR1dQH4ds6czMxMyddVVlaioqIi8jgUCuGjjz7ChQsXYr6HqC9miNTAHJFSzBBJsczFDmVlZXjqqaciFzv86le/QldXFx544AHMnDkT+/fvR3p6etT7Lv4G09HRgZycHAaf4sYMkRqYI1KKGSIppj9H7mKbN2/GqVOncO+996K2thb5+fkoKSmRLOIAIC0tDXa7vd8PkRzMEKlB7Ry1tLTA5XLB4XDA5XKhpaVFpS0ls+K+iKRYpkdOLR0dHRg6dCi/wdCAyclQS0sLysrKUF9fj/z8fLjdbmRnZ+u0pWRmSvdFLpcLtbW1CIVCSE1NhdPpRE1NjQZbSmbF4xkBFuyRI7KSsrIy1NbWwuv1ora2FmVlZUZvEiWI+vp6hEIhAN+eK9XQ0ACAPXVEyYaFHJGGYh1siZTKz89HamoqACA1NTVyo29+eSBKLizkiDQU62BLJIdUL5vb7YbT6YTD4YDT6YTb7QbALw9EyYaFHJGGYh1sieSQ6mXLzs5GTU0NWltbUVNTEzn3kl8eiJILCzkiDcU62BLJIaeXTerLA8+bI0pcLOSIDMADK8khp5dN6ssDz5sjSlyWvbODXB6PBx6PBz09PUZvClmUmhkKH1hDoVDkwMqpI5LDQHLkdrtRVlaGhoYG5OXlyR6i53lziYXHM+qL88gRyaRGhhwOB7xeb7/Hra2tam0iWYBUjrSad5BzziUmHs8I4NAqkSF4QjpJ0WoIlBfdECUuFnJEBuCBlaRoNQQqdd4cz9MkSgws5IgMwAMrSdGzp5YXQBAlBhZyRCbBAyvp2VPLCyCIEoPpC7lAIIC5c+di6tSpqKqqiizv6OhATk4OZsyYgdtvv93ALSRSBw+spOe8gzxPkygxmL6Qq66uRnFxMY4cOYK9e/fC7/cDAE6dOoVVq1bh0KFDqKurM3griZTjgZX0FKv3j0P8RNZi+kKusbERBQUFsNlsGDduHJqbmwEATU1NeOuttzB9+nS88MILBm8lkXI8sJKeYvX+cYifyFpMPyFwZ2cn7HY7ACAjIwM+nw8AcN1112HTpk3Iy8tDUVERZs2ahREjRkS9PxgMIhgMRh6H308UL70yFD6wXoyTBycGq+yLOMRvXlbJEOnL9IWc3W5HV1cXAKCrqysy6eGUKVMwePBg2Gw2TJkyBadPn5Ys5CorK1FRURF5HN5BEcXL6AzxwJoYjM5RvPLz8/tNHpyXl6fZRMUkj1UyRPoy/Z0dfvWrX6GrqwsPPPAAZs6cif379yM9PR3r169HUVERnE4n7rjjDvzmN7+Bw+GIev/F32DCF0lwJmyKl9EZkpqVP3zLJh5YrcPoHMUrXLT1vR1Y315h3hnCOFbJEOnL9IWc3+/HD3/4Q5w9exaLFy9GV1cX7rrrLgwdOhQ/+tGPEAwGsWjRIqxcuTKu9fGWJqSU3hnigTUxWWlfxFvKmZOVMkTaMf3Qanp6Ol599VXJ5w4ePKjz1hDpT+rcOQ63kp6khlsB7e4NS0TxM/1Vq0QUTWqqEl7dSlqJdUU1r3AlMh4LOSILkjqw8qBKWok1VYlUzzC/UBDpi4UckQVJHVhjDbfywEpakeoZ5hcKIn2xkCNKELHuDMEDK2lFqmeYvXRE+mIhR5QgYp3HxAMraUWqZ1hOLx1zqA3+XpOL6acfURsv1yalrJYhqXnoAHD6EoNZLUfxkpouZ8yYMZLTl0hlkzmMX6wM8feaXJKmR87j8eDOO+9ESUmJ0ZtCFmXVDMU7/AXwm7werJqjeMXbSwewt3igLpchTk+UZESSuXDhggAgLly4YPSmkEUlQoaKi4tFamqqACBSU1NFcXFxzOXnz58XxcXFYvjw4ZHHpFwi5Che4Qw5HI5+GZLKGzMYv1gZivX3TYlJ00Ju9erVIjc3Vzz88MNaNiNLMu08SRuJkKFYB9bhw4cLAJGf8PM8sKovEXKklFQO481g3/cnaw5jZSjW3zclJk0LuT/+8Y+irq6OhRwllETOkNQBU+mBNdkPtrEkco6UiDeDsV6bTHmTk6Fk+r0kG82HVv/zP/+ThRwllETOkNQ3eaUHVjkH22QqBBM5R0rEm0Eh4u9B7rvey2XLSnmTkyEOtyYu2YVcKBQSd9xxh9i0aVO/5YcPHxZjx44Vb775Zr/lLOQo0SRbhpQeWOUcbOMtBPtul1UPzMmWIyXknGOn9EuGlXqb5WQo1u/F6n9HNMAeuVdeeUVMnDhR/OUvfxFCCNHc3CwmTpwodu3aFfVapYWc3+8Xc+bMEbfeeqvYuXPnZZdfDneepBQzpPzk9VgHlXgLQTltmfX8KuZIOS2+ZCjNm57U6JEzusCV834t/maN3g+oYUCFXE9PjygsLBTbtm0T586dE9OmTRNPPfWU5GuVFnJ79uwRO3bsEKFQSDidTtHd3X3J5ZfDnScpxQzFJnVglXOwlXMA0eLArCfmSBtKv2QozZueBnKOXDwXOGlV4EoVTXLeL7Vc6WkaWvX461mgDvgcuZdeeknk5+eLWbNmiZUrV4pvvvlG8nVKC7m1a9eKEydOCCGEWLdunXjvvfcuufxyuPMkpZgh5WIdVOItBIXQ5sCsJ+ZIX/FmS2ne9KRGhvQscOPtnZfTY6/0S6FWPf5KXyvHFRig2bNn42c/+xkA4Oc//3lksse+li5diqamJnR3d2P69OmoqKjA+PHjZbXT2dkJu90OAMjIyIDP57vk8osFg0EEg8HI41ivC/P7/QAAm80G8W2hG3nc29sLAEhJSQEA1Z9LSUmJPO7bvhbPJcJnSk9Pv+T/pVrkZkgKc9X/uaFDh2Lfvn39ngsGgxg6dCheffXVqPe99tpr/dYZDAbxi1/8AsuXL0djYyNyc3Pxi1/8AikpKVi+fDneffddTJo0CTt37oQQAsuWLev3Or/fj9zcXBw4cAC9vb39JqnVipwcMS/qf6Zwti7etr7ZCj8XzmB4u/1+P3bu3Illy5bh3XffRW5uLnbu3Ine3l4sX74c7733XuQuFlrS4nhWWVkZ9TcT/lx9/2YAYMWKFZHXVVZWIhAIYNKkSairq0Nvby9sNhtuueUWpKSkRP62bDYbJk2ahEAggKNHj/abqLi+vr7f32H4tUKIqHUGAgHJ10qtM1ZbQoi42vf7/XG/X2pZMBiM+/1Sr5U7gfOAC7mnn34aANDe3i5ZxAHA7t27B7r6CLvdjq6uLgBAV1dX5DYksZZfrLKyEhUVFZHH4V8WWVdLSwuWLl2KxsZG5Ofnw+12Izs7W7P2mCFzys7Oxuuvvx51YH7jjTeiDsyvv/56VEGxa9euSIGnx0GYObK2cN4uLk5ff/113b5UapGhvp8L+GuRF/6bAf5aIPf92wo/V1VVFSn6Jk2ahKqqqqgvVLt27QIA5Obm9ivQws/1LZB37doVVUju3LkTwLd/s+F9f25uLqqqqrBs2bKodQJAXl5eVIEGQLL9i9cpta25ubkA0G+dubm5UUWnnPbD6+zb1oC+VMrqv/vOli1bxOTJk0Vzc7OYPHmyePHFFweymri43W6xfft20dvbKwoLCyPnwsVafrFAICA6OzsjP59//jmHM0xK6TkNWmGGSA3MESll9QxpMVGxGqdpxLteOetU+lo5UoT4rtSO0969e/HMM8/gV7/6FW6++WY8//zzqK6uxr//+7/je9/7nrwqMg5+vx8//OEPcfbsWSxevBhdXV2466678P3vf7/f8tWrV8e1vkS9UbWVhG+qXV9f369HLd6bu9fX10vegFsvzBCpgTkipZghAiCvR+7QoUNi7Nix4u23344s6+zsFLm5uWLv3r2yKkij8ARjfcnpUVM6B5lemCFSA3NESjFDJIQQtngLvv/3//4fHnnkEWzYsAFFRUWR5Xa7HT/60Y9QVVXFcz6SWEtLC1wuFxwOB1wuF1paWgAAZWVlqK2thdfrRW1tbaQnTurEzvz8/Mj5luHzBKSWud1uOJ1OOBwOOJ1Ozc9tIiIiMivZQ6tWx65o5aSGRsMFW98h0JqaGjgcjqhh0Ly8PMnXhtfb0NDQ7+Tzi5dpeWFDPJghUgNzREoxQwQouGqVklffoi2eXra+RVu4GJMq2LKzs1FTUxPVntQyIiIiAuIeWqXkJDVkKlW0SQ2BApAcBg0XbK2traipqTG8h42IiMiq2CNHlyTV+6ZGLxsREREplzSFnMfjgcfjQU9Pj9GbYkqxpgSR6n1ramqSPG8t0Qs2ZojUwByRUswQ9cWLHQgAJOdwq6mpibk8mTFDpAbmiJRihgjgOXJJKd7z3gDpc9yIiIjIHJJmaJX+Kt7z3gCe40ZERGRm7JFLYLEm6ZXqfWPPGxERkfWwkEtgUndVAKTvoMApQYiIiKyHhVwC43lvREREic3UhVxbWxsKCwsxdepU7N+/v99zJ06cwPe//33MmDED9913n0FbaB5Sw6ixJull7xsREVFiMHUht2PHDmzYsAF1dXXYunVrv+dOnTqFjRs34tChQ/B4PMZsoIlIDaOy542IiCixmfqq1cbGRjzyyCNIT09HZmYm2tvbkZWVBQBoamrCf/zHf6Cqqgr/43/8D7hcLsl1BINBBIPByGOfz6fLtutNahiVV5yqI1kyRNpijkgpZoikmLqQ6+zshN1uBwBkZGTA5/NFCrnx48dj4cKFyMnJwcyZM1FUVBQZRuyrsrISFRUVkcfhYsfKpO7CEGv6EFIuETNE+mOOSClmiKSY6s4O5eXlqK6ujjxuaGhAW1sb7HY7SkpKsHv3bgwbNgzAtzNah2eyXrBgAcrLy3H11VdHrfPibzAdHR3Iycmx9EzYUndbkLrXKc99U0ciZoj0xxyRUswQSTFVIXexjRs3Ii8vD4WFhXC5XDh48GDkuZKSEjz77LMYNWoUbrvtNvzhD3+AzXb5U/6sdEuTWPc/dTgc8Hq9kdc5HA60trYauKXJxUoZIvNijkgpZogAk1/s8OCDD2LLli0oKCjA2rVrAQDr16+H1+vFU089haVLl+K2227D448/HlcRZzVy5oEjIiKi5GPqHjktWOkbTKyet3BPHYdRjWGlDJF5MUekFDNEgMkvdkh2vP8pERERXUrijUdalNSEvpwHjoiIiC6FPXImET4fLhQKRc6Hq6mpYc8bERERxcQeue9I9Yjp2Vas+6ISERERxcJC7juxrhDVqy1eiUpERERyJc3QqsfjgcfjQU9Pj+TzevaISbXV1NQUdSUqmcvlMkQUD+aIlGKGqC9OP/IdqbslKD0/LdaEvlq0RfrhJf+kBuaIlGKGCODQaoQWV4jGGq7l1ahERESkhqQZWr0cqbnZYvWoxSvWcC3ngSMiIiI1sEfuEuRcACF1JSovYCAiIiItsZC7BKketVjTlEgVfRxCJSIiIi3xYodLkLooAYDkhQqx7otKiYcnGJMamCNSihkiwAI9cj09PZgyZUrU8ra2NhQWFmLq1KnYv3+/Jm1L9ajFOu+Nw6hERESkN1MXcl6vF8XFxfj444+jntuxYwc2bNiAuro6bN26VZP2wxcltLa2oqamBtnZ2TELNg6jEhERkd5MfdVqV1cXtmzZgjVr1kQ919jYiEceeQTp6enIzMxEe3s7srKyol4XDAYRDAYjj30+n6JtcrvdkhP38krUxKV2hig5MUekFDNEUkxdyI0cORIjR46UfK6zsxN2ux0AkJGRAZ/PJ1nIVVZWoqKiIvI4PCw6UCzYko/aGaLkxByRUswQSTHVxQ7l5eWorq6OPH7iiSfgcrkwY8YMHDp0qN9r586dC4/HA7vdjpKSEuzevRvDhg2LWufF32CEEAgEAnA4HEhJSdHqo1ACYYZIDcwRKcUMkRRTFXKxSBVyGzduRF5eHgoLC+FyuXDw4EFjNo6IiIjIIKa+2EHK+vXr4fV68eCDD2LLli0oKCjA2rVrjd4sIiIiIt1ZokeOiIiIiKJZrkeOiIiIiL5l6qtW9SaEQGdnp9GbQTJceeWVpjrJlxmyJjPliBmyJjNlCGCOrGogOWIh10dbWxtGjBhh9GaQDF999RWuuuoqozcjghmyJjPliBmyJjNlCGCOrGogOWIh18egQYNw/fXX4/Dhw5E56rTi8/kwffp0zdvSqx2j2ho0aJCm7ciViBlKhrbMlCNmyJptmSlDAHNk1bYGkiMWcn2kpKQgNTUVmZmZmv+n2Ww2XdrSqx2j2jLTUAaQmBlKhrbMlCNmyJptmSlDAHNk1bYGkiNe7EBERERkUSzkiIiIiCyKhVwfaWlpeOihh5CWlpYwbSXiZ9K7LTkS9XfAtvSTqJ+fbekrUX8HbCsaJwQmIiIisij2yBERERFZFAs5IiIiIotiIUdERERkUSzkvhMMBvHAAw9gwYIFqK6u1rQtn8+HadOmobS0FPfff7+mbT388MM4e/Ysjh07hnvuuQelpaX47LPPNG1rz549mD9/PkpLS7Fr1y7V1n/u3DmUlpZiwYIF2Llzpy6fSQ5mSL22kjVDgH450jNDAHOkJ+6L1GvLEhkSJIQQ4tVXXxUej0eEQiGxePFi4ff7NWvr2LFjoqKiQrP1CyFEIBAQq1evFtOmTROfffaZWLFihWhvbxcffvihePLJJzVt68knnxRffPGFqm0IIcRzzz0njh49KoQQYtGiRWLRokWafaaBYIbUaytZMySEfjnSI0NCMEdG4L5IvbaskCH2yH2nqakJkyZNgs1mw4033oiPPvpIs7Y+/PBDHDlyBPfddx9+97vfadJGMBhEWVkZbr31VgCA3+/HsGHDcMMNN+DMmTOatnX69Gk8/fTTWLJkiarfllauXIlJkyYBAHp7ewFAs880EMyQem0la4YA/XKkR4YA5sgI3Bep15YVMsRC7jtdXV0YMmQIAGDw4MH4+uuvNWvr2muvxYYNG/DCCy9g37598Hq9qrdht9uRl5cXeRwOCgAIlWecubitwsJCbN68GT/5yU+wefNm1doZNmwYUlNT8fLLL2PMmDGafqaBYIbUaytZMwTolyM9MgQwR0bgvki9tqyQIRZy3xkyZAi6u7sBAN3d3ZreV23ChAmYOHEi0tLSMGHCBJw9e1aztsL63r/NZtP2v/2ee+5BRkYGfvCDH6C9vV3Vdb/22ms4cOAA1q9fr+tnigczpJ5kzRCgX46MyBDAHOmB+yL1WCFDxifOJMaOHYuGhgYIIdDc3IxRo0Zp1lZ5eTneeecd9Pb2oqmpCSNHjtSsrbD09HR4vV78+c9/Rk5OjmbtCCFQWlqKnp4enD59Gg6HQ7V1v//++9i3bx+2bduGtLQ03T5TvJghdSRzhgD9cmREhgDmSA/cF6nDKhm6QrWtsrg777wT69atwyuvvIK7774bgwYN0qytxYsX48c//jG2b9+OefPmISsrS7O2wlavXo1Vq1YhJSUFmzZt0qydlJQULFmyBAsXLkR6ejqeeeYZ1db9/PPPo7W1FcuXLwcAPPLII7p8pngxQ+pI5gwB+uXIiAwBzJEeuC9Sh1UyxFt0EREREVmUZYZWS0pK+l3JEQgEMHfuXEydOhVVVVXGbRgRERGRQUxfyAWDQcyfPx9Hjx7tt7y6uhrFxcU4cuQI9u7dC7/fb9AWEhERERnD9OfIBQIBPProo/jlL3/Zb3ljYyOWLl0Km82GcePGobm5GRMnTox6fzAYRDAYjDwWQiAQCMDhcPS7SoQoFmaI1MAckVLMEEkxfSF35ZVXYvr06VGFXGdnZ+SS6oyMDPh8Psn3V1ZWoqKiIvI4FArho48+woULF5CZmandhlPCYIZIDcwRKcUMkRTTF3Kx2O12dHV1Afh28sNYIV65ciUWL14cedzR0WGKy8PJOpghUgNzREoxQyTFsoXcLbfcgsOHD2Ps2LE4fvw4nn32WcnXpaWlIS0tLfK47+zJRPFghkgNzBEpxQyRFNNf7HCxzZs349SpU7j33ntRW1uL/Px8lJSUID093ehNIyIiItJV0s0j19HRgaFDh/KcAhowZojUICdHLS0tKCsrQ319PfLz8+F2u5Gdna3TlpJZcV9EgAV75IiIkk1ZWRlqa2vh9XpRW1uLsrIyozeJiEyChRwRkcnV19cjFAoB+PZKxYaGBoO3iIjMgoUcEZHJ5efnIzU1FQCQmpqKvLw8g7eIiMyChRwRkcm53W44nU44HA44nU643W6jN4mITMKy048QESWL7Oxs1NTUGL0ZRGRCSVPIeTweeDwe9PT0GL0pZFHMEKlBrRzxStbkxX0R9cXpR4hkYoZIDUpz5HK5UFtbi1AohNTUVDidTvbaJRnuiwjgOXJEqmlpaYHL5YLD4YDL5UJLS4vRm0QJjFeyEhHAQo5INZzri/TEK1mJCGAhR6Qa9pCQnnglKxEBLOSIVMMeEtJT+ErW1tZW1NTURC504BA/UXJhIUekEvaQkBlwiJ8ouZi+kAsEApg7dy6mTp2KqqqqyPKOjg7k5ORgxowZuP322w3cQqJvSfWQsHeE9MYhfqLkYvpCrrq6GsXFxThy5Aj27t0Lv98PADh16hRWrVqFQ4cOoa6uzuCtJJLG3hHSG4f4iZKL6Qu5xsZGFBQUwGazYdy4cWhubgYANDU14a233sL06dPxwgsvxHx/MBiEz+fr90Mkh5IMsXeEwvTaF0kN8bNnODHweEZSTH9nh87OTtjtdgBARkZGJLjXXXcdNm3ahLy8PBQVFWHWrFkYMWJE1PsrKytRUVEReRw+qBLFS0mG8vPz+03ayt6R5KXXvkjqdl59Jw8O9wxz8mDr4fGMpJj+zg5r167F8uXLcdNNN2H9+vVYtGgRJkyYgK6uLgwePBg2mw2PP/447r77buTn50e9PxgMIhgMRh6Hz63jTNgULyUZCt9GqaGhAXl5eZHbKPH2SsnHyH2Rw+GA1+vt97i1tVXTNkl9PJ6RFNMPrd5yyy04fPgwhBA4fvw4Ro8eDQD46U9/itraWvT29qKxsRHXX3+95PvT0tJgt9v7/RDJoSRDsaaI4LlzycfIfRHPm0sMPJ6RFNMXcvfeey9qa2uRn5+PkpISbNu2DadOncK6devw3HPPYdq0afjHf/xHOBwOozeVKG5S587xPCbSCs+bI0pcph9aVRtvMkxKqZEhqRueA+BN0JOI0fsiqQwyb9ZidIbIHEzfI0eUiKR6SHiFK+kpVt7YU0dkLSzkiAwgde5crPOYeGAlLcTKG8/fJLIWFnJEJhHrFl88sJIWYuWN528SWQvPkSOSSe8MSU0d0dTUxOlLLM6s+yKev2kdZs0Q6Ys9ckQmJzUExl460oqc8zfZU0dkPNPf2UEtHo8HHo8HPT09Rm8KWZRRGXK73VGTCo8ZM0Zy+Iu9dOZn9n2R1J0hYt2hJPyFgneM0JfZM0T64tAqkUxmyJCc4S8WeOZkhhzFK9YdSjjsbywrZYi0w6FVIguSM/zFYVhSKtYdSuId9ucQLJF2NCvkzp07h9LSUtx5552YPXs2u9uJVCRn+hJehUhaifcLRawvE1I5ZDaJZBIaaWlpEadOnRJCCNHa2iqmTZsmurq6tGoubhcuXBAAxIULF4zeFLIos2bo/Pnzori4WDgcDlFcXCzOnz8vhBCiuLhYpKamCgAiNTVVFBcXSy7ru47hw4dH1iG1jJQza46UksrW8OHDBYDIj8PhiPlaqWXMoLREzRDJo1khd7FZs2aJL774Qq/mYmLwSSmrZUiqwNPqwBpvIcgDs/VyFC+pvMX64iCVQ6llanzxSMTMJWqGSB7ZhVwoFBJ33HGH2LRpU7/lhw8fFmPHjhVvvvlm1HtOnDgh7rrrroFvpYoYfFIqETKk1YE13kJQqwOzlQ7WiZCjeCntLVb6xSPWa5Xmxei8xcpQshe4yWZAPXKvvPKKmDhxovjLX/4ihBCiublZTJw4UezatSvqtV6vV7hcLtHY2DigDfT7/WLOnDni1ltvFTt37rzs8stJpp0naSMRMqTVgTXeQlCrA7PS3kM9JUKOlJLKoRY9erFeqzQvsbZLL7EyZHSBG4vSHvtk+qInx4AKuZ6eHlFYWCi2bdsmzp07J6ZNmyaeeuqpqNcFAgGxcOFC8eqrrw54A/fs2SN27NghQqGQcDqdoru7+5LLL4c7T1IqkTOk9MCqtEdO6YFZae+hnhI5R2pT4/zPeL+QKH2/nmJlyOgCN9Zrle4fjP6ip2fRKceAz5F76aWXRH5+vpg1a5ZYuXKl+Oabb/o939vbKx599FFRXl4+0CaEEEKsXbtWnDhxQgghxLp168R77713yeUXCwQCorOzM/Lz+eefX3Ln2d3dLbq7u0UgEBB+v7/f4/C//X6/Js/1fdy3fS2eS4TPpBdmKPq5M2fOCKfTKYYPHy6cTqc4c+aM8Pv94pNPPoksLyoqEmfOnBFnzpwRRUVF/V776aef9nvdJ598Irq7u4XT6RQ2m00AEDabTRQVFYmioqKoZd3d3ZLLL17mdDpFVlZWvwPV8OHDRSAQiFqu9UFYbo6kJEO25GybnGxd/NpPPvlEMkPx5iW8nvD79fgyEG+G+hYsl/rbiPV3dLnPGn6d3++PWu50OiXX6ff7Jdcb7zI575fz2lj7l3j3RVKfPxAISP6+pdYZCAQU52jAd3aYPXs2fvaznwEAfv7zn0emPQhrbGzEm2++idGjR+PAgQMAgE2bNmH06NGy2uns7ITdbgcAZGRkwOfzXXL5xSorK1FRURF5HL4snqyrpaUFS5cuRWNjoy4TjjJD0bKzs/H666/DZrOht7c3ajmAfs+Fl4nv5h+32WyRZSkpKZHnqqqqsGLFCrz77ruYNGkSqqqqkJKSEvn/zs3NRVVVFQCgqqoKy5cvj7x2165dEEJg2bJlkdfu3LkTy5cvR11dHXp7e2Gz2ZCbmwsAyM3NjSzvO12LVpgj9fXN2+WylZ2djTfeeCOSyZSUFOzatSsqW8uWLeuXl0mTJgEA8vLycODAgX7Lw3ltbGyMTJSspXgzFL4bTH19/SX/NgDpv6OlS5dG/Q7efffdyO+ut7cX7777LgBELW9sbIQQQvK1ff/mwutNSUnp93vNzc2FECKu/wM5rw0vv/i1DQ0NcX2ud999V/JzXbyssbERAKLWe6nfS9+2+s4BGreBfjN47LHHxLhx48TUqVOF3+8f6Goua82aNeLkyZNCiG973o4fP37J5RdT41swqUtpV7TeQ2LMkLXFGpaLtVwrzJE1mCUvUvTKkBanU8Rab7zL5Lxfzmu1Ok1E6WvlGFAht2XLFjF58mTR3NwsJk+eLF588cWBrCYubrdbbN++XfT29orCwsLIkFqs5ZfD81K0ocYJwvEG36znpRDJwRyRUnpmSI3iyoyUfi6tik45ZBdy1dXVYvz48eLYsWNCCCF27NghZsyYIYLBoNxVxaW7u1vMmzdP5Obmiu3bt4tNmzaJpqamqOXx4s5TOaXFmVYnr+uFGSI1MEekFDNEQsgs5A4dOiTGjh0r3n777ciyzs5OkZubK/bu3av6xmmBwZcn3qJN6RVRQsi/ysiob3vMEKmBOSKlmCESQkYhd/LkSXHzzTcLt9sd9dzWrVtFUVFR1JWrZsTgS5NzubjS4szqXfTMEKmBOSKlmCESQogUIb671CdJdHR0YOjQobhw4QIyMzON3hzTcLlcqK2tRSgUQmpqKpxOJ2pqauBwOOD1eiOvczgcyMvLi3pt+CqphoaGyNVb2dnZaGlpkVxuZcwQqYE5IqWYIQKAAU8/QtYVLq7q6+sj03fU19dHLmXve/lzfn5+v6ItXIxJFWc1NTVRbcVaTkRERMrZjN4A0k5LSwtcLhccDgdcLhdaWloAAGVlZaitrYXX60VtbS3KysqQn58fmQuw75xabrcbTqcTDocj0vMWLs5aW1tRU1Nj+R42IiIiq+LQagKTM1za1NSUcEOgWkmmDJF2mCNSihkiIImGVj0eDzweD3p6eozeFE0oHS7lEOjlJXqGSB/MESnFDFFf7JFLEFK9bwAke+QS8QIEPSVqhkhfzBEpxQwRkEQ9cokk3t43qeFSgBcgEBERJQoWchYUvlghFAr1u1iBw6VERETJhYWcBcnpfSMiIqLExelHTCzW9CFSU4VwShAiIqLkw0LOxKTmewOk53YjIiKi5GPqQq6trQ2FhYWYOnUq9u/f3++5EydO4Pvf/z5mzJiB++67z6At1Fas6UPY+0ZERESAyQu5HTt2YMOGDairq8PWrVv7PXfq1Cls3LgRhw4dgsfjMWYDVSQ1jBrrbgtEREREgMkLucbGRhQUFCA9PR2ZmZlob2+PPNfU1AS3243p06df8srMYDAIn8/X78eMpIZROYRqDlbJEJkbc0RKMUMkxdRXrXZ2dsJutwMAMjIy4PP5kJWVBQAYP348Fi5ciJycHMycORNFRUWR3qu+KisrUVFREXkcHqo0G6lhVE4fYg5WyRCZG3NESjFDJMVUd3YoLy9HdXV15HFDQwPa2tpgt9tRUlKC3bt3Y9iwYQC+ndE6PJP1ggULUF5ejquvvjpqncFgEMFgMPK4o6MDOTk5hs6ELTWhb9+54frehYGMZ8YMkfUwR6QUM0RSTNUjt2bNGqxZsybyeOPGjTh8+DAKCwvh9XojRRwALF68GM8++yxGjRqFTz/9FFdddZXkOtPS0pCWlhZ53Nvbq9n2x0tqQt9wMcd54MzHjBki62GOSClmiKSY+hy5Bx98EFu2bEFBQQHWrl0LAFi/fj28Xi+eeuopLF26FLfddhsef/xx2Gzm+yix5oG71DAqr0QlIiKieJlqaFUPet5kWOpG9jU1NTGXkzXwRtWkBuaIlGKGCDB5j5zVxZoHjlejEhERkRpYyKlEzjxwHEYlIiIiNbCQUwnngSMiIiK9meqqVSvjPHBERESkN/bIyRTrSlTeTouIiIj0ljQ9ch6PBx6PBz09PYrWIzUHXE1NDeeBSwJqZYiSG3NESjFD1BenH7kEqTswjBkzBl6vN/Iah8OB1tZWrTebTISX/JMamCNSihkigEOrEVJDplIXMHAIlYiIiMwiaYZWL0dqyFTqAoampiYOoRIREZEpsJD7jlTRlp+f3+8ODHl5ebwSlYiIiEyDQ6vfkRoy5TxwREREZGbskfuO1FWn7H0jIiIiMzN9j1xPTw+mTJkStbytrQ2FhYWYOnUq9u/fr7gd3jaLiIiIrMbUhZzX60VxcTE+/vjjqOd27NiBDRs2oK6uDlu3btV/44iIiIgMZupCrqurC1u2bMGYMWOinmtsbERBQQHS09ORmZmJ9vZ2yXUEg0H4fL5+P0RyMEOkBuaIlGKGSIqpz5EbOXIkRo4cKflcZ2cn7HY7ACAjIwM+nw9ZWVlRr6usrERFRUXkcfjKVKJ4MUOkBuaIlGKGSIqp7uxQXl6O6urqyOMnnngCLpcLM2bMwKFDh/q9du7cufB4PLDb7SgpKcHu3bsxbNiwqHUGg0EEg8HIYyEEAoEAHA4HUlJStPoolECYIVIDc0RKMUMkxVSFXCxShdzGjRuRl5eHwsJCuFwuHDx40JiNIyIiIjKIqc+Rk7J+/Xp4vV48+OCD2LJlCwoKCrB27VqjN4uIiIhId5bokSMiIiKiaJbrkSMiIiKib5n6qlW9CSHQ2dlp9GaQDFdeeaWpTvJlhqzJTDlihqzJTBkCmCOrGkiOWMj10dbWhhEjRhi9GSTDV199hauuusrozYhghqzJTDlihqzJTBkCmCOrGkiOWMj1MWjQIFx//fU4fPhwZI46rfh8PkyfPl3ztvRqx6i2Bg0apGk7ciVihpKhLTPliBmyZltmyhDAHFm1rYHkiIVcHykpKUhNTUVmZqbm/2k2m02XtvRqx6i2zDSUASRmhpKhLTPliBmyZltmyhDAHFm1rYHkiBc7EBEREVkUCzkiIiIii2Ih10daWhoeeughpKWlJUxbifiZ9G5LjkT9HbAt/STq52db+krU3wHbisYJgYmIiIgsij1yRERERBbFQo6IiIjIoljIEREREVkUC7nvBINBPPDAA1iwYAGqq6s1bcvn82HatGkoLS3F/fffr2lbDz/8MM6ePYtjx47hnnvuQWlpKT777DNN29qzZw/mz5+P0tJS7Nq1S7X1nzt3DqWlpViwYAF27typy2eSgxlSr61kzRCgX470zBDAHOmJ+yL12rJEhgQJIYR49dVXhcfjEaFQSCxevFj4/X7N2jp27JioqKjQbP1CCBEIBMTq1avFtGnTxGeffSZWrFgh2tvbxYcffiiefPJJTdt68sknxRdffKFqG0II8dxzz4mjR48KIYRYtGiRWLRokWafaSCYIfXaStYMCaFfjvTIkBDMkRG4L1KvLStkiD1y32lqasKkSZNgs9lw44034qOPPtKsrQ8//BBHjhzBfffdh9/97neatBEMBlFWVoZbb70VAOD3+zFs2DDccMMNOHPmjKZtnT59Gk8//TSWLFmi6rellStXYtKkSQCA3t5eANDsMw0EM6ReW8maIUC/HOmRIYA5MgL3Req1ZYUMsZD7TldXF4YMGQIAGDx4ML7++mvN2rr22muxYcMGvPDCC9i3bx+8Xq/qbdjtduTl5UUeh4MCAELlGWcubquwsBCbN2/GT37yE2zevFm1doYNG4bU1FS8/PLLGDNmjKafaSCYIfXaStYMAfrlSI8MAcyREbgvUq8tK2SIhdx3hgwZgu7ubgBAd3e3pvdVmzBhAiZOnIi0tDRMmDABZ8+e1aytsL73b7PZtP1vv+eee5CRkYEf/OAHaG9vV3Xdr732Gg4cOID169fr+pniwQypJ1kzBOiXIyMyBDBHeuC+SD1WyJDxiTOJsWPHoqGhAUIINDc3Y9SoUZq1VV5ejnfeeQe9vb1oamrCyJEjNWsrLD09HV6vF3/+85+Rk5OjWTtCCJSWlqKnpwenT5+Gw+FQbd3vv/8+9u3bh23btiEtLU23zxQvZkgdyZwhQL8cGZEhgDnSA/dF6rBKhq5Qbass7s4778S6devwyiuv4O6778agQYM0a2vx4sX48Y9/jO3bt2PevHnIysrSrK2w1atXY9WqVUhJScGmTZs0ayclJQVLlizBwoULkZ6ejmeeeUa1dT///PNobW3F8uXLAQCPPPKILp8pXsyQOpI5Q4B+OTIiQwBzpAfui9RhlQzxFl1EREREFmWZodWSkpJ+V3IEAgHMnTsXU6dORVVVlXEbRkRERGQQ0xdywWAQ8+fPx9GjR/str66uRnFxMY4cOYK9e/fC7/cbtIVERERExjD9OXKBQACPPvoofvnLX/Zb3tjYiKVLl8Jms2HcuHFobm7GxIkTo94fDAYRDAYjj4UQCAQCcDgc/a4SIYqFGSI1MEekFDNEUkxfyF155ZWYPn16VCHX2dkZuaQ6IyMDPp9P8v2VlZWoqKiIPA6FQvjoo49w4cIFZGZmarfhlDCYIVIDc0RKMUMkxfSFXCx2ux1dXV0Avp38MFaIV65cicWLF0ced3R0mOLycLIOZojUwByRUswQSbFsIXfLLbfg8OHDGDt2LI4fP45nn31W8nVpaWlIS0uLPO47ezJRPJghUgNzREoxQyTF9Bc7XGzz5s04deoU7r33XtTW1iI/Px8lJSVIT083etOIiIiIdJV088h1dHRg6NChPKeABowZIjUwR6QUM0SABXvkiIiIiOhbLOSIiIiILIqFHBEREZFFsZAjIiIisigWckREREQWZdl55OTyeDzweDzo6ekxelPIopghUgNzREoxQ9QXpx8hkokZIjUwR6QUM0QAh1aJiIiILIuFHBEREZFFsZAjIiIisigWckRECa6lpQUulwsOhwMulwstLS1GbxIRqYSFHBFRgisrK0NtbS28Xi9qa2tRVlZm9CYRkUpMX8gFAgHMnTsXU6dORVVVVWR5R0cHcnJyMGPGDNx+++0GbiFRbLF6QthDQlqRylZ9fT1CoRAAIBQKoaGhweCtJCK1mL6Qq66uRnFxMY4cOYK9e/fC7/cDAE6dOoVVq1bh0KFDqKurM3griaTF6glhDwlpRSpb+fn5SE1NBQCkpqYiLy/P4K0kIrWYvpBrbGxEQUEBbDYbxo0bh+bmZgBAU1MT3nrrLUyfPh0vvPBCzPcHg0H4fL5+P0RyKMlQrJ4Q9pAkH732RVLZcrvdcDqdcDgccDqdcLvd7BW2IB7PSIrp7+zQ2dkJu90OAMjIyIgE97rrrsOmTZuQl5eHoqIizJo1CyNGjIh6f2VlJSoqKiKPwzs4ongpyVB+fj5qa2sRCoX69YRILW9paUFZWRnq6+uRn58Pt9uN7Oxs1T8PGUOvfZFUtrKzs1FTU9PvdS6XK/K6cM/dxa8hc+HxjCQJk1uzZo04efKkEEKIdevWiePHjwshhPD5fCIUCgkhhHjsscfE0aNHJd8fCAREZ2dn5Ofzzz8XAMSFCxf0+QBkeUoydP78eVFcXCwcDocoLi4W58+fj7m8uLhYpKamCgAiNTVVFBcXa/3RSEd67YtiZe5iw4cPFwAiPw6HQ9XtIPXxeEZSTH+Lrl/96lfo6urCAw88gJkzZ2L//v1IT0/H+vXrUVRUBKfTiTvuuAO/+c1v4HA4Lrs+3tKElNIqQw6HA16vt9/j1tZW1dZP5mL0vqhvj1xqaiqcTidqamrYM2whRmeIzMH058jde++9qK2tRX5+PkpKSrBt2zacOnUK69atw3PPPYdp06bhH//xH+Mq4ojMLNYJ6TyXibQgdd4cwAtxEhX3I4nL9D1yauM3GFJKqwyFe0IaGhqQl5cX6QmJ1XNC1mbWfRF7hq1DToa4H0lcpu+RI0oW4RPSW1tbUVNTExnO4hWupCepnmH25lgf9yOJi4UckcnxwEp6khpy5XCr9XEuwcTFoVUimfTOkNSQa/jAymES67LSvojDreYkJ0OxTt0g62OPHJHJSQ25Sg2TsJeOtMILcawv1qkbZH0s5IhUoudBTerAyuGv5KJn3niFK5GJGTmJnZ5efPFF4XK5xMyZMzmBIg3I5TKk54S+UpO+xprgNfza4cOHX3KCWNKHWvsiM0wgLZU55k17PJ5RXzxHjkimWBky+jyiWNMLSC0Pn2fHSV+No3RfZHTeAOnMAeD5mzrh8YwADq0Sqcboq8JiDX9JnU/HITHrMzpvgHTmeP6mtfD/JgFo1dXX2dkp7r77bjFnzhwxa9Ys8fLLL2vVlCwXLlxgVzQpEitD8d7jUm9SQ3AchjWenH2R1P+LlfIWaxiYeVNGjeOZGYboSRnNCrlvvvlGfP3110IIIb7++mtRWFgovF6vVs3FjYUcKWW1DEkd8GPtvKWW82CrDTk5stLBVs75m/HmLVYGkz2bauyLYv3fkHXocrFDe3u7mDFjhmhra9OjuUuy2kGYzCcRMhSrN0dqpy6nuEv2A6sccnJk9YNtrEI03rzxi4c09siREAMo5EKhkLjjjjvEpk2b+i0/fPiwGDt2rHjzzTcjyy5cuCBmz54txo8fL1588UXlW6uCRDgIk7ESOUPxDsMqPbCyEJTOUazPb/WDbawvDvHmLVYhKyeb8ebQShlUY19k1iH6eFnp/0srA+qRe+WVV8TEiRPFX/7yFyGEEM3NzWLixIli165dkq//6quvxL333iu++uor2W35/X4xZ84cceutt4qdO3dedvnlJPJBmPSRyBmKdxhW6YFVjR4Wq+/ApXJ0uSLEqgfbWOLNm5y8yBnGtfr5fGbYF2nxe5HzN2/1LzlqGFAh19PTIwoLC8W2bdvEuXPnxLRp08RTTz11yff89Kc/7ddbF689e/aIHTt2iFAoJJxOp+ju7r7k8ssxQ/DJ2pItQ0rPsdOqh8XqvX9SObL6EKoapPIWq5CVk814cyinEDSaVvsiOX9HWgxvq1G4K+2B1XP/oLStAZ8j99JLL4n8/Hwxa9YssXLlSvHNN9/0e/6rr74SnZ2dQohvr2C98847RXNzs+x21q5dK06cOCGEEGLdunXivffeu+TyiwUCAdHZ2Rn5+fzzzy8Z/O7ubtHd3S0CgYDw+/39Hof/7ff7NXmu7+O+7WvxnJU/06effiqcTqduB2BmKPq5M2fORP4PnE6nOHPmjPD7/eKTTz6JLC8qKoq8zmazCQDCZrMJp9MpioqK+i0rKioS3d3dUa8tKioSWVlZ/XbUw4cPF93d3ZLLL16v0+mUXGcgEOi3XI8Dczw56nugCm9romZIq8905swZUVRUFMnmJ598EvX/fakcSmUoVt76/h2YcV8kJZ7/B6nfi9TvL9bvReq1fffdWVlZoqioSHzyySeSGVHyN3+pfUk8+wen0xkzL+FsZWVlRbIV6zN98skn/V776aefRvaP4deeOXMm6vc9kH3RgAs5n88nxo0bJ2bNmiW6urqinj958qSYM2eOmD17tpg1a5bweDwDamfJkiXi448/FkII8eSTT4rDhw9fcvnFysvLxY033hj5uf7665P+IGylzyT1h6M09HIxQ8oydPGB9cyZM/2K8b47P6mDcKwdtdRyqR291LJAIBC1XOver3hy1PebeXjnzwyp85n6Fl3hg2g4X31/31JfRmLlTe8vA3L3RVLi+X+I9+9Izt+h3++XfO3F+/jw43iLs1j7kng/Q7z7B6kCVao4vNT+Kd4vCXL3RQMu5B577DExbtw4MXXqVOH3+we6mstas2aNOHnypBDi256348ePX3L5xeL9BmP0MEsyUXr+g97DT2p8C6aBUzqsJmeYRkvMkbXJmVZFK3plaCDnDg50ePtSQ7Px/M0r/Qx6niai1bD9gAq5LVu2iMmTJ4vm5mYxefJkTa9IdbvdYvv27aK3t1cUFhaK7u7uSy6/nFjnFJjx/AcrUePk1Hj/cIz+v0q2c+SsROn5VXpijqwvUfdFcv6O4n2/EPLOcdPrM+j5RVFOMSyH7EKuurpajB8/Xhw7dkwIIcSOHTvEjBkzRDAYlLuquHR3d4t58+aJ3NxcsX37drFp0ybR1NQUtTxesYLPk4zjp0VxJoTyb2t64QGY1MAcWR/3RfLI6b0zI6WFoFZ5kVXIHTp0SIwdO1a8/fbbkWWdnZ0iNzdX7N27V5UN0pqcHjkOtyof7lTrKkQzTbtgtZ0nmRNzREolQobMuI+3mrgLuZMnT4qbb75ZuN3uqOe2bt0qioqKoq5cNSM598m00jcFpeRcWq5VcWaVP+hE2HmS8ZgjUooZIiGESBFCCCSRjo4ODB06FBcuXEBmZuYlX+twOOD1evs9bm1t1XoTDeFyuVBbW4tQKITU1FQ4nU7U1NRI/g7y8vKiXut2u1FWVoaGhgbk5eXB7XYjOzsbLS0tksutTE6GiGJhjkgpZogAwGb0BphZfn4+UlNTAQCpqanIy8szeIvU0dLSApfLBYfDAZfLhZaWFtTX1yMUCgEAQqEQGhoaAEj/DtxuN5xOJxwOR6SIy87ORk1NDVpbW1FTUxMp1mItJyIiIuVYyF2CVMFiJVIFGwCUlZWhtrYWXq8XtbW1KCsri1m0yinaiIiISF8cWpUpPFRYX1+P/Px8Uw8VyhkubWpqSrghUK1wOIPUwByRUswQAcAVRm+AXjweDzweD3p6ehStJ9ybFQqFIr1ZNTU1Km3lwEkVmJcaLu1b4OXl5UV62Sg2tTJEyY05IqWYIeqLPXIymeECCKmirW+BGe59AyDZI5eIFyDoid+CSQ3MESnFDBGQRD1yapHqzdKbVK+gVO+b1HApAPa+ERERJQhe7CBTrAsgYl1YoESsdUoVbVIXK/CiBCIiosTGQk6mWMWR1JWgckgVbbHWGe+UIERERJTYWMipRKqXLFaPWrxFW6yLFTglCBEREQG82EG19UpN9QFIX2wg9dr6+vq47qDAc9uMxxOMSQ3MESnFDBFg8h65trY2FBYWYurUqdi/f3+/506cOIHvf//7mDFjBu677z6DtvCvpHrJYvWoxXuOG4dLiYiI6FJMfdXqjh07sGHDBtx2222YPXs27rrrrshzp06dwsaNG1FaWmrgFv6V1JWgsa5wlVoe616l7IEjIiKiWEzdI9fY2IiCggKkp6cjMzMT7e3tkeeamprgdrsxffr0SxY7wWAQPp+v349eYvWo8Rw3azEyQ5Q4mCNSihkiKabukevs7ITdbgcAZGRkwOfzISsrCwAwfvx4LFy4EDk5OZg5cyaKiooiQ5N9VVZWoqKiIvI4PKSph1g9auxpsxYjM0SJgzkipZghkmKqix3Ky8tRXV0dedzQ0IC2tjbY7XaUlJRg9+7dGDZsGIBvT/IMn9y5YMEClJeX4+qrr45aZzAYRDAYjDzu6OhATk4OTw6luDFDpAbmiJRihkiKqXrk1qxZgzVr1kQeb9y4EYcPH0ZhYSG8Xm+kiAOAxYsX49lnn8WoUaPw6aef4qqrrpJcZ1paGtLS0iKPe3t7Ndt+SkzMEKmBOSKlmCGSYupz5B588EFs2bIFBQUFWLt2LQBg/fr18Hq9eOqpp7B06VLcdtttePzxx2GzmfqjEBEREanOVEOreuC8O6QUM0RqYI5IKWaIAJP3yBERERFRbCzkiIiIiCyKhRwRERGRRbGQIyIiIrIoFnJEREREFmWqeeS05PF44PF40NPTY/SmkEUxQ6QG5oiUYoaoL04/QiQTM0RqYI5IKWaIAA6tEhEREVkWCzkiIiIii2IhR0RERGRRLOSIiIiILIqFHBEREZFFmb6Q6+npwZQpU6KWt7W1obCwEFOnTsX+/fsN2DIiIiIiY5m6kPN6vSguLsbHH38c9dyOHTuwYcMG1NXVYevWrfpvHBEREZHBTF3IdXV1YcuWLRgzZkzUc42NjSgoKEB6ejoyMzPR3t4uuY5gMAifz9fvh0gOZojUwByRUswQSTH1nR1GjhyJkSNHSj7X2dkJu90OAMjIyIDP50NWVlbU6yorK1FRURF5HAqFtNlYSljMEKmBOSKlmCGSYqo7O5SXl6O6ujry+IknnoDL5cKMGTNw6NChfq+dO3cuPB4P7HY7SkpKsHv3bgwbNixqncFgEMFgMPJYCIFAIACHw4GUlBStPgolEGaI1MAckVLMEEkxVSEXi1Qht3HjRuTl5aGwsBAulwsHDx40ZuOIiIiIDGLqc+SkrF+/Hl6vFw8++CC2bNmCgoICrF271ujNIiIiItKdJXrkiIiIiCia5XrkiIiIiOhbpr5qVW9CCHR2dhq9GSTDlVdeaaqTfJkhazJTjpghazJThgDmyKoGkiMWcn20tbVhxIgRRm8GyfDVV1/hqquuMnozIpghazJTjpghazJThgDmyKoGkiMWcn0MGjQI119/PQ4fPhyZo04rPp8P06dP17wtvdoxqq1BgwZp2o5ciZihZGjLTDlihqzZlpkyBDBHVm1rIDliIddHSkoKUlNTkZmZqfl/ms1m06Utvdoxqi0zDWUAiZmhZGjLTDlihqzZlpkyBDBHVm1rIDnixQ5EREREFsVCjoiIiMiiWMj1kZaWhoceeghpaWkJ01Yifia925IjUX8HbEs/ifr52Za+EvV3wLaicUJgIiIiIotijxwRERGRRbGQIyIiIrIoFnJEREREFsVC7jvBYBAPPPAAFixYgOrqak3b8vl8mDZtGkpLS3H//fdr2tbDDz+Ms2fP4tixY7jnnntQWlqKzz77TNO29uzZg/nz56O0tBS7du1Sbf3nzp1DaWkpFixYgJ07d+rymeRghtRrK1kzBOiXIz0zBDBHeuK+SL22LJEhQUIIIV599VXh8XhEKBQSixcvFn6/X7O2jh07JioqKjRbvxBCBAIBsXr1ajFt2jTx2WefiRUrVoj29nbx4YcfiieffFLTtp588knxxRdfqNqGEEI899xz4ujRo0IIIRYtWiQWLVqk2WcaCGZIvbaSNUNC6JcjPTIkBHNkBO6L1GvLChlij9x3mpqaMGnSJNhsNtx444346KOPNGvrww8/xJEjR3Dffffhd7/7nSZtBINBlJWV4dZbbwUA+P1+DBs2DDfccAPOnDmjaVunT5/G008/jSVLlqj6bWnlypWYNGkSAKC3txcANPtMA8EMqddWsmYI0C9HemQIYI6MwH2Rem1ZIUMs5L7T1dWFIUOGAAAGDx6Mr7/+WrO2rr32WmzYsAEvvPAC9u3bB6/Xq3obdrsdeXl5kcfhoACAUHnGmYvbKiwsxObNm/GTn/wEmzdvVq2dYcOGITU1FS+//DLGjBmj6WcaCGZIvbaSNUOAfjnSI0MAc2QE7ovUa8sKGWIh950hQ4agu7sbANDd3a3pfdUmTJiAiRMnIi0tDRMmTMDZs2c1ayus7/3bbDZt/9vvueceZGRk4Ac/+AHa29tVXfdrr72GAwcOYP369bp+pngwQ+pJ1gwB+uXIiAwBzJEeuC9SjxUyZHziTGLs2LFoaGiAEALNzc0YNWqUZm2Vl5fjnXfeQW9vL5qamjBy5EjN2gpLT0+H1+vFn//8Z+Tk5GjWjhACpaWl6OnpwenTp+FwOFRb9/vvv499+/Zh27ZtSEtL0+0zxYsZUkcyZwjQL0dGZAhgjvTAfZE6rJKhK1TbKou78847sW7dOrzyyiu4++67MWjQIM3aWrx4MX784x9j+/btmDdvHrKysjRrK2z16tVYtWoVUlJSsGnTJs3aSUlJwZIlS7Bw4UKkp6fjmWeeUW3dzz//PFpbW7F8+XIAwCOPPKLLZ4oXM6SOZM4QoF+OjMgQwBzpgfsidVglQ7xFFxEREZFFcWiViIiIyKJYyBERERFZFAs5IiIiIotiIUdERERkUSzkiIiIiCyKhRwRERGRRbGQIyIiIrIoFnJEREREFsVCjoiIiMiiWMgRERERWRQLOSIiIiKLYiFHREREZFEs5IiIiIgsioUcERERkUVdYfQGEBFZ0dmzZ3H77bejrq4O1157raHbMnr0aAwaNAjTpk0DABw5cgSBQAB/+tOfDN0uItIeCzkiIpNZtWoVDh48KPncjh07cPvtt0ctr6qqwuTJkwEAR48exaJFizTdRiIyBxZyREQq+P3vf4+dO3fik08+wddff41x48bhmWeewXXXXYdTp07h6aefxgcffIC/+7u/Q3FxMfbu3Yvf//73kuv6X//rf+Gbb77B119/jaKiIuzcuRNjxowBAGRlZen5sYjI5HiOHBGRQufPn8fatWuxYsUK/PGPf8ShQ4cghMD27dvh8/mwbNkyTJkyBUePHsWmTZtQXV19yfVlZWVhxIgR8Hq9SElJwaRJkzBixAiMGDECV1zB799E9Fcs5IiIFBo+fDj279+PwsJC+Hw+nD9/HllZWWhpacHvf/97pKam4uGHH0ZaWhpGjx6NZcuWxbXeP/3pT8jJyYHdbtf4ExCRVfGrHRGRQt/73vfwb//2b/jNb36DlJQU3HjjjfD5fLjiiitw/vx5/O3f/i1str9+bx45cmRc6/3Tn/6E0aNHa7XZRJQAWMgRESlUU1ODF198ES+99BL+23/7bwCAp59+Gv/1X/+Fv/3bv8UXX3wBIQRSUlIAAF988UVc6/3888/x3//7f9dsu4nI+ji0SkSkUGdnJ2w2G9LT0yGEwOHDh7Fv3z709PSgsLAQQgj84he/QDAYxMcff4zdu3fHtd7e3l588cUXOH/+PIQQGn8KIrIiFnJERArNnz8ft956K+666y5MmTIFzz//PO6//36cPn0aV1xxBXbs2IG6ujrk5+dj3bp1mDp1Kr73ve9ddr2lpaV477334HK5WMgRkSQOrRIRDcC1117bb8LdTZs2Rb1mzZo1aG9vR09PD377299Glv/617/GBx98cNk2/uEf/gH/9//+X3U2mIgSEnvkiIg0FAqFcP/990cKsrNnz+Jf//Vfcdtttxm8ZUSUCFIE++uJiDR14MAB/J//839w9uxZZGZmYv78+XjooYdUmxOOt+giSl4s5IiIiIgsikOrRERERBbFQo6IiIjIoljIEREREVkUCzkiIiIii2IhR0RERGRRLOSIiIiILIqFHBEREZFFsZAjIiIisigWckREREQWxUKOiIiIyKJYyBERERFZ1P8HK5TCP9pTFcAAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 16 Axes>"
       ]
@@ -611,7 +615,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -638,7 +642,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -669,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -743,7 +747,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -770,7 +774,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here, due to small sample size and weak link strength, we miss both links from 0 to 1 (lag 0) and from 2 to 1 (lag 2) and get a false positive."
+    "Here, due to small sample size and weak link strength, we miss both links from 0 to 1 (lag 0) and from 2 to 1 (lag 2)."
    ]
   },
   {
@@ -796,7 +800,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/tutorials/causal_discovery/tigramite_tutorial_conditional_independence_tests.ipynb b/tutorials/causal_discovery/tigramite_tutorial_conditional_independence_tests.ipynb
index 05f977ff..d5e31213 100644
--- a/tutorials/causal_discovery/tigramite_tutorial_conditional_independence_tests.ipynb
+++ b/tutorials/causal_discovery/tigramite_tutorial_conditional_independence_tests.ipynb
@@ -622,8 +622,9 @@
     "\n",
     "Runge, Jakob. 2018. “Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information.” In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. \n",
     "\n",
-    "CMIknn involves no assumptions about the dependencies. The parameter ``knn`` determines the size of hypercubes, ie., the (data-adaptive) local length-scale. Now we cannot even pre-compute the null distribution because CMIknn is not residual-based like GPDC and the null distribution depends on many more factors. We, therefore, use ``significance='shuffle_test'`` to generate it in each individual test. The shuffle test for testing $I(X;Y|Z)=0$ shuffles $X$ values *locally*: Each sample point $i$’s $x$-value is mapped randomly\n",
-    "to one of its nearest neigbors (``shuffle_neighbors`` parameter) in subspace $Z$. Another free parameter is ``transform`` which specifies whether data is transformed before CMI estimation. The new default is ``transform=ranks`` which works better than the old ``transform=standardize``.\n",
+    "CMIknn involves no explicit assumptions about the dependencies. However, since the density is implicitely approximated by local sample hyperpoints, there is an assumption that the density is constant inside these hypercubes. The parameter ``knn`` determines the size of hypercubes, ie., the (data-adaptive) local length-scale. \n",
+    "\n",
+    "Now we cannot even pre-compute the null distribution because CMIknn is not residual-based like GPDC and the null distribution depends on many more factors. We, therefore, use ``significance='shuffle_test'`` to generate it in each individual test. The shuffle test for testing $I(X;Y|Z)=0$ shuffles $X$ values *locally*: Each sample point $i$’s $x$-value is mapped randomly to one of its nearest neigbors (``shuffle_neighbors`` parameter) in subspace $Z$. Another free parameter is ``transform`` which specifies whether data is transformed before CMI estimation. The new default is ``transform=ranks`` which works better than the old ``transform=standardize``.\n",
     "The following cell may take some minutes."
    ]
   },
@@ -683,13 +684,15 @@
     "\n",
     "Another note: CMIknn has a huge computational cost because of the shuffle testing. You can decrease the number of shuffle samples by ``sig_samples`` (at the cost of a larger error in the p-value estimate).\n",
     "\n",
-    "Alternatively, you may use the option ``significance='fixed_thres'`` and choose a threshold ``fixed_thres`` $I^*$ in any conditional independence test. Then no hypothesis testing on conditional independence is conducted. The criterion for conditional independence for a test statistic $I(X;Y|Z)$ is then \n",
+    "Alternatively, you may use the option ``significance='fixed_thres'``. Then ``pc_alpha`` is interpreted as a (one-sided) threshold $I^*$ in any conditional independence test. Then no hypothesis testing on conditional independence is conducted. The criterion for conditional independence for a test statistic $I(X;Y|Z)$ is then \n",
     "\n",
     "$$\n",
     "I(X;Y|Z) < I^*\n",
     "$$\n",
     "\n",
-    "$I^*$ should then be regarded as a hyperparameter. Note that picking $I^*$ for CMIknn is tricky because the value of $I(X;Y|Z)$ depends on the dimensionality of the variables due to an estimation bias for finite samples. On the plus side, then even CMIknn is pretty fast.\n",
+    "$I^*$ should then be regarded as a hyperparameter. Note that picking $I^*$ for CMIknn is tricky because the value of $I(X;Y|Z)$ depends on the dimensionality of the variables due to an estimation bias for finite samples. On the plus side, then even CMIknn is pretty fast. \n",
+    "\n",
+    "You may then also use something like ``pc_alpha = [0.001, 0.005, 0.01, 0.05]`` (or any other list of your chosen thresholds). Then the graphs for all these thresholds are computed and the (new) ``get_model_selection_criterion`` function of CMIknn is used to choose among these graphs. This function uses sklearn's ``cross_val_score`` (based on k-fold cross-validation set with ``model_selection_folds``) with a ``KNeighborsRegressor`` (with same number of neighbors as the estimator, but standard euclidean norm) model on each node using its parents in the graph resulting from the given threshold. It will return the result for the smallest score (prediction error).\n",
     "\n",
     "This option only makes sense for conditional independence tests that rely on permutation testing, these are CMIknn and CMIsymb."
    ]
@@ -701,7 +704,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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e37Qa0l8BkZRic2VE9hNCQLTsBKVlJ8hzr4Pctt4IBuuXARUltdp2zWmEU1s3xoznp9UEAyvIrWugbV0DLSUdSs+BxsZg2c0tOz4R1Y1h4Ahq91PDDgMIBaH//rO5ywtEMUAIBaJtdyhtu0OePwZy6xojGPz6HeCvgKoo+GrcuQCAi17+yvoCKkqhf78Q+vcfQnTuB7X/UIg23bhuAZFNGAaOoHTqA3i8QCgYVnuNYYDinFA9EB36QOnQB/LCmyB/X21cRtiwEgj6bT67hPxtBUK/rYBo1g5K/yHGhkker83nJUosDANHEEkpUDr2gh7m4EB942qbKyJyD+HxQXTuD6Vzf0h/BfR13wIzl0fl3HLvFmgL/gvti9ehnnw+lJP/yNk8RBbhbII6qF1OCbutLNgJWbzfxmqI3EkkpRhTA1t0iO6Jy4qhLZ5lzEL4YBr0fIf3ZiCKAwwDdVA6nGSqvfb7TzZVQhR7lD8Mh2jX0/4TaUHoq79A6IV7EHzjMegbVkJK6wYzEiUSrkBYBykl/E+PhywOYxEVAGrvgfCN+IvNVRG508SJE/HUU08BAHw+H3bs2IGcnBzIA3uhrf4C+k9fHnOaoqUat4B6ykVQep3N3ROJTGAYqEdg7lRoq74Mr3F6FpInvsCRzpSQgsEgFi5ciPz8fJxzzjno1KlTrfulrkFu+hHa6kWQG34AovHuPTkNSp/BUPtdCJHZ1P7zEcU4hoF6hFZ9geDc58Jun3T7M1CatbGxIqLYJ0sPQP/pK2irFwEH9tp/QqFA6TrAmIXQqrP95yOKUQwD9dCL9sH/9Piw23uH3ADP6dzFkCgcUuqQ29ZB/3ER9PXLAS28qbyRECd0hvqH4cZeCOzFI6qFYeAYKp+5DbIwvPXSlc79kJR3v80VEcUfWVEKfc1i6Ks+gyzYYfv5RPMORijo3I/bKRNVYRg4hsB706Ct+DS8xr5kJN//CoTKpRuIGkJKCbnlZ2jfLYTctMr284mcNlD+MBxK1wEQCkMBJTaGgWMI/fQNgrOeCbu976Z/QG3bzcaKiBKD3L8L2vcfQv/5K/tXOWzSEuofhhsrG3IrZUpQDAPHIEuLUPl/x4bd3nPOVfAOvtrGiogSi6wohf7j59BWfAyU2Ly4V3ZzqGdcBqXnWezho4TDMHAclVP/Cpm/Pay2SpuuSLr5nzZXRJR4pBaC/ut30L9fCLlzg70ny8yBevqlUHoN4h4IlDAYBo4j8MGL0JYtDK+xoiD5b69AJKfaWxRRAtN3/gb9u4XGLAQ71yxo1BjqaZdA6XMuhNdn33mIXIBh4Di0X5Yj8OaTYbf3Xfc3U3sbEFHDyIMF0FZ8DP3Hz4HKMvtOlJYJdcAwY2MkrmpIcYph4DhkeQkqn7gBCPNhUk8fCt+QG22uioiqyUAl9J+/gvb9h0DhbvtOlNII6qlDoZxyAUQSe/8ovjAMhKHy/90Duev3sNqKnFZI/vO/ba6IiI4kpQ65aTW07z6A3PKzfSdKToNyykVQTx3KS4IUNxgGwhD86BWElswPu33yPTMgMhrbWBERHYuevw36t/Og/7I07F4901IaQf3D5VBOPp8DDSnmMQyEQfttJQIzJ4Xd3nvFHfD0GWRfQUQUFrl/F7Sl86CvWWzfYMPMHKhnXw2lxx+4oiHFLIaBMEh/BSonXQ/o4f0xUXufDd+IP9tcFRGFSx7YC+3b94ztlHXNlnOI3LZQzxkF0aE39z6gmMMwECb/9Puhb/81rLYiuxmS7/qvzRURkVmyuADasvnQf1xk2+ZIom0PqOeMgtKy0/EbE7kEw0CYgp+9gdBXc8Jun3zvSxDpmTZWREQNJUsKoS1fAP2HT4FQwJZzKF1PgzpoJETjFrYcn8hKDANh0jb9hMD/Hg27ve/a+6B27W9jRUQUKVlWDO27D6Cv/BgIVFp/AkWF0udcqGdeAZGeZf3xiSzCMBAmGfSj8p/XA1oorPaegcPh/eO1NldFRFaQ5SXGxkgrPgT85dafwJsEZcDFUAcMg0hKsf74RBFiGDDB/9JD0DevDaut0uEkJN3wiL0FEZGlZGUZ9BUfQft+IVBRav0JUjOMHRJP/iM3QyJXYRgwIfjFLIQWvRVeY18ykv/Pq9wSlSgGSX8F9B8+gbZ8AVB+0PoTZDUzpiN2P53TEckVGAZM0LauQ+CFB8Jun3TbZCjN29pYERHZSQb90Fd9Bm3pe0B5seXHF83bGzMP2vey/NhEZjAMmCBDQVROGg0E/WG19146Hp5T/mhzVURkN+mvgP7dAmjL3g/7+W+G6NgHnj+O4cwDcgz7p0wQHi+Utt3Cbq9v/83GaogoWkRSCtSzroT3lilQ+l0AWHz5T276EcEZExD64g1IO2Y1EB0Hw4BJSuvOYbdlGCCKLyI9C54LboR33GQo3c+w9uBaCPq37yH4/J3Q1i4BO20pmniZwCRt7TIE3noq7PbJ978KkZJmY0VE5BR99+/Qvngdcssay48t2nSHev4NUHLbWH5soiOxZ8AkYXJAoL5zo02VEJHTlBYd4LnmAXhG3g/RrJ2lx5bbfkHoxXsR+uR/kJVllh6b6EgMAyaJ7GaALzns9rxUQBTfhBBQOvSG58bHoV56B5CZY93BpQ59xYcITvsrtNVfQNq18yIlPIYBk4SiQDQLv9tO38EwQJQIhFCg9jgT3j89A/WPY4CURtYdvPwgtA+mIfTKg9B3sbeRrMcw0ABK83Zht9X3bLGtDiJyH+HxQu1/Eby3ToHyhysAb5Jlx5a7NiL0vwcQWvg8ZJkNiyFRwmIYaAClmYlxAwcLIctL7CuGiFxJJKXCc/ZV8N7ybygn/xGwbKVBCf3HRQg+/1doKz6C1DWLjkuJjGGgAUwPImTvAFHCEunZ8Fx4E7zjnoboOsC6A1eWQfvkZYReug/6tnXWHZcSEsNAAygmxgwAgNy71aZKiChWiCYt4R1+FzzXPgSR09qy48r8bQi99ghC702BLCm07LiUWBgGGkAkp0Fk5YbdXt/DMEBEBqVtD3jG/l9jkGFSqmXH1dcuMRYs+u4DSJ2zDsgchoEGMnOpgGGAiA4nFNUYZDj+WSi9z7HuwIFKaJ+9itCrD0LP32bdcSnuMQw0kJndCGX+NkiNg3yIqDaRlgnP0PHwjP4HRIsOlh1X7tqI0Ev3IfT1LMhQ0LLjUvxiGGggxcxqY6Eg5P7dttVCRLFNOeFEeMb8E+qQcdatT6Br0L+ZbQww3Mn1TujYGAYaSLQwOaOAgwiJ6BiEUKD2Ode4dNDvAkAIS44rC3Yg9MpDCH36CndEpHoxDDSQyG5majERyemFRBQGkZIOzwU3wnPjExCtulp0VAn9+4UIzpgAffNPFh2T4gnDQAMJRTW3LDEHERKRCUqzdvDkPWLsd5Cebc1Bi/ch9OY/EVrw/yArSq05JsUFhoEImBpEyJ4BIjJJCFGz34Fy2jBAUS05rv7TlwhOvwv6+uWWHI9iH8NABMwsSywP7ueyxETUICIpBZ7B18F701MQ7XtZc9CyYoTmTkZw9r8gSw9Yc0yKWQwDETC9LDHn/RJRBETTE+AZeT88V0ywbKtk+dv3CE6/u2qLZGnJMSn2MAxEwNT0QgBy/x57CiGihCGEgNKlP7w3/wtK/4sAWDDroLLM2CL5zX9AHtgb+fEo5jAMRECkpEFkNg27PdcaICKrCF8yPH8cA8/1f4do2sqSY8otaxCcMQHa8gVc0jjBMAxESDRvF3ZbnWGAiCymtOoMz41PQDlzhDUDDEMBaJ/PROjVByELdkZ+PIoJDAMRMjWjoJCXCYjIesLjhWfglcbaBC07WXJMuWsjgi/dB23lJxxLkAAYBiIkzMwoKNzDJxUR2UbJbQPP9Y9BPe96U4ui1SsUgPbxiwjNehKytCjy45FrMQxESDFxmQCBSqCEU3iIyD5CUaCeOtSYhtiupyXHlBt/QPCFe6BvWGnJ8ch9GAYiJJo0Bzy+sNvrhRw3QET2E9nN4LnmAahDxwPJaZEfsPwgQrOeROjDGdzjIA4xDERIKCpEbuuw23NGARFFixACau9z4B33NESXUy05pr7qMwRfug/6ro2WHI/cgWHAAkp2bthtGQaIKNpEeja8V9wNz/C7gLTMyA9YuBuhVx6E9s0cSF2L/HjkOIYBC5hZa0DnwkNE5BCl6wB4x02G0vucyA8mdWhfv4PQa49woaI4wDBgAZHFhYeIKDaIlHR4ho6H55r/A2SF36tZH7njNwRfnAjtpy85WyqGMQxYQGSYCAOcXkhELqC07wXvTU9B6T8k8oMFKqEt+H8IzZ3MDdliFMOABcz0DCDo5/RCInIFY0nj0fBc8wDQqHHEx5O/fmdMQfx9tQXVUTQxDFjAzJgBANAP5NtUCRGReUr7k4xegm6nR36w0gMIvTUJoU/+BxkMRH48igqGASukZgCqJ+zm8uB+G4shIjJPpKRDvewvUC+5HUhKifh4+ooPEXr5b9D3bom8OLIdw4AFhKKY6x0oKbSvGCKiBhJCQO15Frxjn4Ro3TXi48mCHQi9fD+07z7gWCmXYxiwiMhsEnZbeZBhgIjcS2TlwnPtw1AHXRP5Toi6Bu2zVxGa/S/IilJrCiTLMQxYRGTmhN2WYYCI3E4oCtQzLoNnzD+AJi0jPp7csALBF++FvuO347ctK474fGQOw4BFTPUM8DIBEcUIpXkHeG98Akq/CyI/2MEChF57BNqy+ZBSr7OJ3L8LwZfvh/SXR34+ChvDgEXMjBlgzwARxRLhTYLnghvhueq+yJcz1jVoi15HaNZTkOUHa90lg36E5k4GDhZA/+HTyM5DpjAMWMRsGOBgGiKKNUqnvvDe9C+IzqdEfCy58QcEX7wP+vb1NbdpH78IuW+78fXyBdwdMYoYBixiajZBKABwIA0RxSCRlgHPFROgDhkHeJMiO1jJfoReexTa0nnQflwE/aevDt1XfhD6j59HdnwKG8OARcwuPMRLBUQUq4QQUPucC+/Y/wvRslNkB5M6tC/fhLbw+aPu0pa9DxniwkXRwDBgEZGcCiSlht2egwiJKNaJxi3gyXsUypkjACGsP0Hpgdq9BWQbhgELmRs3wFUIiSj2CdUDz8Ar4cl7FDCxaVu4tG/fg9RClh+XamMYsBAXHiKiRKW06mJcNjgx8sGFtRTvg75msbXHpKMwDFiI0wuJKJGJlHR4RkyAet71ka9ceBht6TxIve51CcgaDAMWMhUGjphfS0QUD4QQUE8dCs/1fwdMrMx6TAf2QF/3rTXHojoxDFhIpGWE37iizL5CiIgcprTsZFw26NzfkuPpS+bWu2ohRY5hwErJaWE3lQwDRBTnRHIaPFfcDfWPY0xt814XWbADcsMP1hRGR2EYsJBISQ+/cUWJfYUQEbmEEAJq/4vguf6xiC8baMvmW1QVHYlhwEJmwoCsZM8AESUO0aixsfpqBOSOX2stX0zWYRiwUkr4lwngr+DcWSJKCFLXEZr3b8CCrYm15e9bUNERpA7oIUAPAlrA+NCDVR8akAB7yUR2EYdqMXWZAAAqyyLfAYyIyOW0r9+B3PaLJceSv62ALNgJ0fSEBvywBKRmvPAf/hlhvNgL1fhQPId9jp/30/Hzm7hBUqqpJTk5iJCI4p2+aRX0pe9aekzTvQN6CAiWA/4iIFAMhMoArRKQIYQVBICq8BAAQuVAsATwHwACBwHNHxc9BwwDFhKKYmpGAXcuJKK4FwxAOfl8iBM6R77LYRV9zeLj7+8idSBUcSgAaBUALJ6aqAeBYCngLzQ+60Frjx9FvExgMZGcBhnmi3y47YiIYpXSdQCUrgMAGGMHULgL+t4tkHu2QO7dDLl3i/k3RloI2oqP4Dln1NH3SWm8Ww+VI+x3/VbQ/MaH4gO8qcalhBjCMGC1lHTgwN6wmvIyARElEqEoQNNWUJu2AnqcCQCQUgIH90Pu3Qx9zxbIPb9D7toIHGeVVv2HTyDPuAzi8N1itYBxCcDJxYn0AOAPAGoK4EmxZzdHGzAMWEykpIefRSvZM0BEiU0IAWQ2hchsCqVqtUIpJVCUD33XBsidGyF3bYDcs9kY2V/NXwF91edQTxtmvPi7rZteqzDGJXjTAdXndDXHxTBgMWFieqEsZxggIjqSEALIbgY1u9mhHoRQAHLvFsidGyB3bYS+cwO07z6AcvK5EHolonpJIGzSGGyou7+XgGHAamamF3LhISKisAiPzxiEeELnmtvkwXyg8gDgS3awsjBoFcbMBW8j1wYCziawmEhKCbutjHA1LiKihBWqhPCpEG4PAtX0oDEV0aWbLbFnwGqqt967Csr9mLJsA77ZVoDCigDaL1yPawqTkJeXB0VhLiMiCovmNwYKxhoZAgIlgC/DdT0EfAWymqfuMPB7YSlOef4zzFi7F50GDcHQ62+GbNoKY8aMwfDhw6Hr7kyLRESuooeMwYKxSoaqpj26C8OA1eoJA7cs+AEZLVrjt99+w+jRo9G+fXtMnz4d8+fPx/z58zFjxowoF0pEFGOkbryzjnVapdG74SJCyjhYR9FFQssWIvjBi7Vu21NaiQ7PLsQbb7wBRVEwcuRIAEDTpk2xefNmXH311dA0DR999JETJRMRuZ+sHpnvoumDkfJlGnscuAB7BqxWR8/ArpIKAMDChQvx9NNP46TcTPxnaF8UFBQgPz8f7dq1w+7du6NdKRFR7ND88RUEAONyh0vej7sjksSTOsJAn+ZZ+OtpJ+LTj+dDCOCGvu3xyBe/4Nxzz0W7du3wxRdfoG/fvg4US0QUA6Q09hmIN1IzAo4LFiViGLCY8Bz9P1URApPOOwmTzjsJS7YV4Kp3vsUJnbrg7bffxqRJk7Bu3Tq89NJLDlRLRBQDdD8s32TILULlgOJ1fHYBLxNY7RhTC+et24mLXluMPqefiW+++QYvvfQSHnzwQfz973/HaaedFsUiiYhiRLz2ClSr7h1wGMOA1bx1h4HPNu3FqDnLceXVI/HBBx/ggQcewMSJE3HHHXdgwoQJUS6SiChG6AHXLtRjGReEHYYBq9XRM1AeDOHm91fgwgsvxEsvvYRRo0bhueeeAwBMnToVWVlZmDp1arQrJSJyP835lVq/+GoJLrhkJM4fdhVmzZ1v/QlkyPHAw6mFFtO3/wb/9L/Vuu2tn7dh7PyV2LRpE1JSUrBkyZJa97/22mv49ddf8csvv0SzVCIi96sshJObEIVCIQy9/Fq8+sJzSEtPxfCRN+Cd115AVmaGtSfyNnJ0ICEHEFqtjtkEe0orkZqaildeeaXOH9m3bx+Sk2NkfW0iomiRGpzejfCnNevQqWN7NGuWAwAYeObp+GbpMlx80fnWnsjhWQUMA1ZTj35IR/Rohfd/3Y0XJj9Z+w4BiIwmyMzMxKRJk6JUIBFRjNBD9h1a1zHk8lE4d9BZuOfO22puX7xkGW7580Q89fgjuOj8wcjftw/NcnNq7m/eLBd78wtsKMjZQYQMA1ar46pLq4xUfD7m7KPb+pKR8uDrUSiKiCgG2fgCqSgK/jT2ejz2xGSMG5uHzIwMrP91A/5yzwO488/jcdH5gwHUvSaQLZMApWaczKEphhxAaDUzQzBctmsVEZGr2DykbdiQ85GdlYWZb8zCnr35GHf7BFx68YUYO3pUTZtmuTnYm7+v5vs9e/ORk9PUpoqcuyTCMGA1hgEiIovY++Lo8Xhw8w3XYeYbs3DzbXeje7cueODeO2u16dWzGzZs+h179+5DaVkZvv7mW5x5xgCbKmIYiB9mpocIPvxERPWKwmvjsKHno6KyEpASk594FKqq1rrf4/Hg3rvvwPU33Y7Lrx6DsaNHITsr055iHBwryTEDVmPPABGRNYSw/QXysccnAwAOFBVDVet+g3buoLNw7qCz7C0EcPQ1gW9NrcYwQEQUE559bjq+XLwU78ycgZCmYfa7C5wuyTEMA1bjZQIiImso9nVez5o7Hy/PfBPTpjyJrl1OxOhrr8IL/3sNwaB90xmPTTj6msBXI4uZWtCRPQNERPWzKQx89c23eHTS03jqnw+jT6+eAIC8a65EaWk53lvwkS3nPC6l/k3uonJ6R88ejxgGiIisIawPA2t+WY+/TngA99x5K84/b1DN7enpabjumhGY8dJMaJpm+XmPy+EwwL0JLKZt+QWBFx8Mq63IbIrkCc/bXBERUQzzFxsb+cQ7X6atl0WOhz0DVmPPABGRdRx+xxwdAhDq8ZvZiGHAaqYGEDIMEBEdkycBNnHzJDv+esAwYDVTPQN8+ImIjkkogJrkdBX2Up0PPHw1slooEH7bOnY4JCKiI3hSnK7APmqKK94YOl9BnJH+yvAb+5xPg0REridUQInT3gGXXAZhGLBaIPwwIBgGiIjC402BTZsHO8eT6opeAYBhwHomwgB7BoiIwiRUwJvudBXWUbyuGCtQjWHAYpI9A0RE9lB98TF+QChGsHHRjDKGAauZ6RlIYhggIjJFTYn9tQe8jVxzeaCau6qJA2Z6BuBlGCAiMkWIqnfVzi7S02DeRo6uNFgfhgGr8TIBEZG9hGIs32vD3gW28mYYlzpciGHAahxASERkPyEAXwaguPPFtTZhhBfVvZc3GAYsZmoAIccMEBE1XPUlA0+a05XUT3iApCxXXho4nLuri0WmxgzE6SIaRETRIoSxcI/iAULlgB50uqIqwlhHQE1y1ayB+jAMWMzc1MI4mCJDROQGise4bKAFgFCZuU3jrKamGFMgYyAEVGMYsBqnFhIROUf1GVMPNb/RUwATm8dFSvEB3tSYnOnAMGAxWVkefmNeJiAisl71pQM1CZAhIxhofpvO5THOo/pct3aAGQwDFpK6DpSXhN1epDaysRoiogQnBCC8Rk+BJ80YT6D5jYDQ4MsIwnjnr/qqAkDs9QLUhWHAShWlpv6BibRMG4shIqIaQhx6AQcAKQGpGR961WfjjuofqPqkVO2aqBqfY/jd/7EwDFhIlhWH31goQEocbbpBRBRLhKhatMgDxMeb+4jEZ8RxiCw7GH7j1EYQCh9+IiJyHl+NrGSiZ0CkZdhYCBERUfgYBixkpmeAYYCIiNyCYcBCZsYMcPAgERG5BcOAhUyNGWDPABERuQTDgIXYM0BERLGIYcBKHDNAREQxiGHAQuwZICKiWMQwYCGOGSAioljEMGARqYWAChP7ErBngIiIXIJhwCKyuMBY6zpMDANEROQWDAMWkYV7w2/sS+a+BERE5BoMAxaRB8IPAyKzKYQQNlZDREQUPoYBi5gKA1k5NlZCRERkDsOAReSB/LDbMgwQEZGbMAxYRGfPABERxSiGAYuY6xnItbESIiIicxgGLCAry4ByE2sMsGeAiIhchGHAAmZ6BQBAYRggIiIXYRiwgKkwoHqA9CzbaiEiIjKLYcACpgYPZjaFUPiwExGRe/BVyQJcY4CIiGIZw4AFzCxFzDBARERuwzBgAS44REREsYxhIEJS1yGLTISBbK4xQERE7sIwEKnSIiAUDLu5ktPKvlqIiIgagGEgQmZmEgCAaHqCTZUQERE1DMNAhEwNHsxoApGUYmM1RERE5jEMRMjU4EFeIiAiIhdiGIiQLNwddluRw0sERETkPgwDEdJ3bw67LQcPEhGRGzEMREAG/JD7dobdnpcJiIjIjRgGIqDv2QJIPez2Ci8TEBGRCzEMREDu/j38xinpQFqmfcUQERE1EMNABPRd4YcBJecECCFsrIaIiKhhGAYiYCYMcLwAERG5FcNAA8lgADJ/e9jtGQaIiMitGAYaSO7dBuha2O0VLkNMREQuxTDQQPquTabai2ZtbKqEiIgoMgwDDaSbmUmQmgGR2dS+YoiIiCLAMNBApmYStOzAmQRERORaDAMNIENBY8xAmJSWHWyshoiIKDIMAw0g87cDWijs9gwDRETkZgwDDWDmEgEACIYBIiJyMYaBBjA1kyAlHSIr175iiIiIIsQw0ACmti1u0Z6DB4mIyNUYBkySWghyz5aw2ystO9pXDBERkQUYBkyS+3YAoWDY7TlegIiI3I5hwCSzgwc5k4CIiNyOYcAkU2EgORWicXP7iiEiIrIAw4BJ0szKgxw8SEREMYBhwASpa9D3mJlJwEsERETkfgwDJsh9O4FgIOz2SpsuNlZDRERkDYYBE0ztVAhAadPVpkqIiIiswzBggpnBg6Jxc4hG2TZWQ0REZA2GARNMDR5s193GSoiIiKzDMBAmGQqa6hngJQIiIooVDANh0nduAoL+sNsrbbvZWA0REZF1GAbCpG9eE37jtEyIJi3sK4aIiMhCDANhMhMGlLZdudgQERHFDIaBMMhQEPq2X8Nur/ISARERxRCGgTDo238DQmYWG2IYICKi2MEwEAZT4wW8SRAt2tlWCxERkdUYBsJgarxA684QqsfGaoiIiKzFMHAcMug3LhOEiVMKiYgo1jAMHIe+7VdAC4XdXmnLxYaIiCi2MAwch6nxAh4vVx4kIqKYwzBwHObGC3SB8CbZWA0REZH1ONKtSvDruZB7tkLp1Btqp94QGU0g/RXQd2wM+xhKx142VkhERGQPhoEqIrMpQp++Du3nbxAEIHJbQzRtCeha2MdQO5xkX4FEREQ2YRioorTsUOt7mb8dMn97+AdIToVo2dHiqoiIiOzHMQNVRJMWgC+5wT+vtOsBoaoWVkRERBQdDANVhKJCad6u4QfwV0DbsAoyEP42x0RERG7AywSHES07ANvWN+hn9c1rENi8BlA9UNp2g9KpD9TOJ0Np1sbiKomIiKzFMHAYpUV7hD9csB5aCPrvP0Pfug5Ki3YAwwAREbkcLxMc5shBhJHwDr8daqc+lh2PiIjILgwDhxE5rQCPN+LjeIfcAE+vsyyoiIiIyH4MA4cRqgcikkGEADwDh8Nz+sXWFERERBQFDANHUFq0b/DPqicPhue8URZWQ0REZD+GgSM0dNyA0rU/vJeMhxDC4oqIiIjsxTBwBKVZW/M/07YbfFfdyUWHiIgoJjEMHEE0PcFc+9w28F17H3crJCKimMUwcASRkgakZ4XXNrMpkkY/AJGSbm9RRERENmIYqIOSE0bvQHoWfKMfgshoYn9BRERENmIYqIPIaXXsBqmNkDTm4fBCAxERkcsxDNRBOda4geRUJI1+iHsOEBFR3GAYqEO9PQO+ZCRd/6ClyxYTERE5jWGgDqKu7n+vD768+6G07hz9goiIiGzEMFAHkdEE8CUfusHjhW/UfVDb9XCuKCIiIpswDNRBCHFovQFFhW/kBKidejtbFBERkU0SJwxIediHXvVR9X0dlJwTAKHAd+WdULucEuViiYiIokdIWc+rYSyTOqCHAKkZH9Vf10sAigcQas3n4OL5EFlN4ek9MGplExEROSF+woCuAXoA0PzHeeEPjwwFIXypgJoEKD6AGxAREVGciu0wIHVACwBapSUB4JgUX1Uw8DIYEBFRXInNMCClEQBC5dE/t1ABb5oRCoiIiOJAbIUBKY1LAcFyALqztSg+wJtqhAMiIqIYFjthQA8BwTJAhpyupDY1BfCk8NIBERHFrNgIA5ofCJY6XUX9hAfwNQJE4szUJCKi+OHuMCAlEKoAtAqnKwmDAHwZxtREIiKiGOLeMCClcVlA9ztdiTneDEDl4EIiIood7uzXltK4LBBrQQAAggcBPeh0FURERGFzZxjQ/MasgVgVKDHWQCAiIooB7gsDehAIlTldRYRkVSBw5xUYIiKiw7krDEjdeBGNBzLkzKJIREREJrkrDARLAcTRu2mt0lgumYiIyMXcEwb0YHwOvAuV83IBERG5mnvCQCgW1hJoAKnFZ8ghIqK44Y4woIfi+wUzXoMOERHFBXeEgXgfaCfjPOwQEVFMc37tXCkdf6HcvWcvJv6fv2N/4QGoqopbx92Ai84fbO1JND+3PSYiIldyfjliLWis2ueg/H0F2L+/EN26dsb+/YW4fOQN+Oi9t5CammLdSYQCJGVbdzwiIiKLuKBnwPnu89ycpsjNaQoAaNKkMTIzM1B88KC1YUDqxgd3NiQiIpdx/pVJsy8M6LqOCy8diaee+U+t2xcvWYae/Qbiw08WHfUzP69dB6nraNG8mQ0Fhaw/JhERUYScDQNSGoPrbKIoCv409nq8OetdFB80LkWs/3UD/nLPA7jzz+OPGhdwoKgY9z7wGP7+0H32FMRBhERE5EIO9wzYP1xh2JDzkZ2VhZlvzMKevfkYd/sEXHrxhRg7elStdoFAALffeR/G3ZiHk/ucZE8x3LyIiIhcyNkBhFID/EW2n+atWfPwzNRpyM3NwQktW+A/zzwOVVUPlSEl7r7vYbRv1wZ33HKTfYUoXsCXYd/xiYiIGsDZMKBrQKDI9tOUlZfj9EFD0LZ1K7w9c8ZRAwNX/LAa1914K7p07lRz25P/fAhdTuxobSHCCyQxDBARkbs4O5tAROc0jz0+GYAxJkBVj74ycsrJvbH+xyX2FyKi9AsTERGZ4PCYAftfHJ99bjq+XLwU78ycgZCmYfa7C2w/JxERUSxxQRiwLxDMmjsfL898E9OmPImuXU7E6Guvwgv/ew3BoENT/BT1+G2IiIiizNkwIASg2HOl4qtvvsWjk57GU/98GH169QQA5F1zJUpLy/Hego9sOedxCS5HTERE7uP8okM2rNe/5pf1+OuEB3DPnbfi/PMG1dyenp6G664ZgRkvzYSmaZaf97hsCj5ERESRcH5vAj0IBJzdmyAqhAokZTldBRER0VGc7xkQCfJumTsWEhGRS7kgDAhASXK6CvupCfA7EhFRTHI+DACAx8LdAd1I8XK8ABERuZY7woCiAorP6Srs40l1ugIiIqJ6uSMMAPH7gsleASIicjn3hAFFjc+xA/EacoiIKG64JwwAgDfNmIIXLzxp7BUgIiLXc1cYEALwNkLUdjCyk+LjDAIiIooJ7goDgHG5wJvudBWREVW/A3cpJCKiGOC+MAAAqi+Gr7VX9W4wCBARUYxw7wXt6rUHQuXO1mGKAvgyuDshERHFFOf3JjgezQ8ES52u4viEagQB4c7OFiIiovq4PwwAgB4CAiUAdKcrqZuSVDUTgpcGiIgo9sRGGAAAqQPBckD3O13JYYQRAjhrgIiIYljshIFqesgIBTLobB2eVEBNZm8AERHFvNgLA9W0ABAqM3oMoklJArypHBtARERxI3bDAABICciQMchQCwCw6VcRqnEpQPXF1wqJREREiPUwcDgpAT1oBAM9iIiDgVCMAKAkcaogERHFtfgJA4eTEoAEdM3oOZCaMdag+vYawlj5WKjGh+I59DXHAhARUYKIzzBAREREYeMoOCIiogTHMEBERJTgGAaIiIgSHMMAERFRgmMYICIiSnAMA0RERAmOYYCIiCjBMQwQERElOIYBIiKiBMcwQERElOAYBoiIiBIcwwAREVGCYxggIiJKcAwDRERECY5hgIiIKMF5nC6glsr9gNQBSONzzdeyjtt0yOrbIQGpHdFOHvpZqR32tQ5AP3T7kceWeq2fkfKI+yAB/bBjQAK6dsQxZD0felW5h9V41PfH+dCPaKtph27TdKO2Ou6Th9+nVf3eh3+vyyPuq/rddVn1a0tIvfr2qu+12l8bD3HVZ804npQSqG6nVx1DouqzhK7pkLoO6BK6ptXcLg//Xter2lX9LlX3VR+v5mspIXXjd5W68bWu6VW3G+fSq76WUkLXDn2tVf/MYW2llNAP+/rQz+nQq7/WJar+NUACCFV91gFo9dxefd+R7arbake0e0RKO55t0cfnN5/ffH679vnNngEiIqIExzBARESU4BgGiIiIEhzDABERUYJjGCAiIkpwDANEREQJjmGAiIgowTEMEBERJTiGASIiogTHMEBERJTgGAaIiIgSHMMAERFRgmMYICIiSnAMA0RERAmOYYCIiCjBMQwQERElOIYBIiKiBMcwQERElOAYBoiIiBIcwwAREVGCYxggIiJKcAwDRERECY5hgIiIKMExDBARESU4hgEiIqJEJ13C7/fLKVOmSL/f73QpR3FzbVKyvki5uT4312aGG34P1sAa3FaDm7gmDJSUlMjOnTvLkpISp0s5iptrk5L1RcrN9bm5NjPc8HuwBtbgthrchJcJiIiIEhzDABERUYJjGCAiIkpwrgkDPp8Pt99+O3w+n9OlHMXNtQGsL1Jurs/NtZnhht+DNbAGt9XgJkJKKZ0ugoiIiJzjmp4BIiIicgbDABERUYJjGCAiIkpwjoSBQCCAW265BSNHjsQ777xz3NvdUl+1O+64Azt27HCgMkN99e3evRt5eXkYOXIkpk+f7qraSktLMXbsWFx11VV4//33HantWPVVmzp1KubOnetAZYZjPX5nnXUW8vLyMHr0aMfqOxYzz+tVq1bhyiuvRF5eHrZv3+5IDa+++iouv/xy5OXl4YUXXrC9hmqH//2I9uNQVw3Rfhzq+jsV7cehrhrsehxihhMrHb377rvy9ddfl5qmyRtuuEFWVlYe83a31Of3++Wtt94qzzrrLLl9+3ZHajtWfU888YRcvny5lFLK66+/3pGVteqr7c0335Rz5syRuq7L6667Lup1Ha8+KaXcv3+/PPPMM+WcOXNcV9+qVavkc88951hd4TDzvB43bpw8cOCA3LBhg3zwwQcdqeHBBx+Uu3btsuzcx6uhrr8f0X4c6qoh2o9DXX+nov041FWDXY9DrHCkZ2Dt2rXo168fFEVB586dsWnTpmPe7pb6AoEAxowZgzPOOMORuo5X35/+9Cf069cPAKDrOjwej2tqGzlyJC699FKUlZVBOjiB5Vj/xl544QVcdtlljtUG1F/fhg0bsHjxYlx77bWYM2eOozXWx8zzurKyEllZWejUqRO2bNniSA2bN2/GY489hhtvvNHSd6Nm/n5E+3Goq4ZoPw51/Z2K9uNQVw12PQ6xwpEwUFZWhtTUVABASkoKysvLj3m7W+pLT09H//79HanpcPXVl5WVBVVV8fbbb6N79+5ITk52TW0AUFBQgGHDhqFnz55Rr6taffXt3r0bZWVlaN++vWO1AfXX16pVK9xzzz14+eWXMW/ePBQWFjpZZp3MPK91Xa/5OSvDoZkaBg8ejKeeegoTJ07EU089ZXsNdf39iPbjUFcN0X4c6vo7Fe3Hoa4a7HocYoUjYSA1NRUVFRUAgIqKCqSnpx/zdrfU5xbHqu+9997DZ599hrvvvtt1tTVr1gyLFi3Cjh078Pvvv7uqvueffx4333yzIzUdrr76evfujb59+8Ln86F3796Ojlmpj5nntRCi5ucUxbo/Q2ZquPLKK5GWloauXbviwIEDttdQl2g/DnVx4nE48u+UE4/DkTXY9TjECkfCQI8ePfD9999DSol169bVvBur73a31OcW9dW3evVqzJs3D1OnTnVsVa36aps5cyaWLFkCIQSSkpJqPfndUN/PP/+Mv/3tb5g+fTqmT5+OrVu3uqq+KVOmYMmSJdB1HWvXrkXr1q0dqe9YzDyvk5OTUVhYiI0bN+KEE06Ieg3t2rVDXl4egsEgNm/ejCZNmtheQ12i/TgcSUoZ9cehrr9T0X4cjqzBzschVjiyAqHf78ddd92FPXv2YPjw4aioqMCgQYPQunXrWrdfe+210S7tmPV16tQJAHDffffh9ttvR6tWrVxV37/+9S/s3LkTWVlZAIB//etfaNasmStqy8zMxD333INAIIABAwbgL3/5S1TrOl591f9vq2cSDB8+3FX1NWrUCBMmTEAwGMRll12GkSNHOlLfsZh5Xv/444+YNGkShBB48skn0bZt26jX8P777+PVV19FcnIy/vGPf9heQ11/P6L9ONRVQ7Qfh7r+Tu3evTuqj0NdNXz33Xe2PA6xgssRExERJTguOkRERJTgGAaIiIgSHMNAgrBy7i4REcUXhoEEsGjRIowdO7bBP798+XJ06dIFAwcOrDUfuNr48ePRpUsXLF++vOa2kpISPP3007jgggvQt29fnHnmmZgwYQK2bdtW02bu3LkYPHhwg+siImvNnTsXXbp0wdVXX13n/Zdccgm6dOlSa2prfn4+Hn30UQwePBh9+/bFoEGD8PDDD6OgoKCmzdSpU5GXl2d7/dRwDAMJoKioyJKFPAKBAJYsWVLrtoKCAqxatarWbYWFhRg+fDi2bt2KadOm4YcffsD777+PzMxMXH311di5c2fEtRCRPRo1aoS1a9cetRbIzz//fNRzd+vWrRg2bBh0Xcfrr7+OVatW4a233kJRURGuueYalJaWRrN0igDDQAxZtGgRRo4cidNPPx29e/fGddddhy1bttT5DjsvLw9Tp07F8uXL8fDDD2PXrl3o27cv9u7di8rKSjz55JM4++yz0b9/f+Tl5eGnn3467vmHDRuGefPm1brt3XffxQUXXFDrtqlTpyI5ORnPPPMM2rdvDyEEsrOz8eCDD2LQoEH49ddfI34siOLB2rVrkZeXV9N79u9//xvLli3D2WefjbvvvhunnHIKpk+fDl3XMX36dJx33nno168fRowYgcWLF9cc5+OPP8bQoUPRr18/XHTRRfjvf/9bc98bb7yB8847D6eccgqGDRuGWbNmHbOmjIwMDBw48Kjn+pw5czB06NBat/3zn/9Er1698Oijj6JFixYAgObNm+PJJ59Et27dsGHDhggfIYqaaG2CQJHZvXu37Nmzp/z888+llFIWFhbKUaNGyQkTJsg5c+bIc845p1b76667Tk6ZMkVKKY+6/95775XDhg2TW7ZskX6/X/7vf/+Tffv2lTt37qzz3MuWLZOdO3eW69atk7169ZIHDx6sue/CCy+Uq1evlp07d5bLli2TUko5cODAsDbVqatuokRx4MABeeqpp8qpU6dKv98vt27dKgcOHCjffPNN2blzZ/ncc8/JQCAgS0pK5JQpU+TAgQPlmjVrZDAYlB988IHs2bOnXL16tayoqJAnnXRSzfNv7dq1sk+fPnL16tVy27ZtsmfPnnLTpk1SSim//vpredJJJ8m9e/fWWVP1c/LTTz+VAwcOlJqmSSmlrKyslAMGDJArV66UnTt3ltu3b5d+v1927dpVvvvuu8f9XadMmeLoBmV0fOwZiBGNGzfGBx98gMGDB6O0tBR79uxBdnY29u7da+o4fr8fCxYswN133422bdvC5/Nh9OjR6NChAxYsWHDMn+3atSvat2+PhQsXAgBWrlwJVVXRq1evWu0KCwuRk5Nj7hckSjBffPEFkpKScNttt8Hn86FNmzZ4+eWXkZKSAgAYMWIEvF4v0tPTMWfOHIwbNw49evSAx+PBkCFDMHjwYMyePRuAsYLf7Nmz8e2336Jjx45YuXIlevXqBVVVIaXEW2+9hZUrV+L000/Hjz/+iNzc3GPWdvbZZyMQCGDp0qUAjJ6H3r171/q54uJi6LrO53qcYBiIEV6vFwsWLMDAgQMxdOhQTJ48Gfv37zc9FqC4uBjBYPCo1RNbtWqFHTt2YP78+ejbt2/Nx/z582u1Gz58ON59910ARrfhiBEjjjpHTk4O8vPz6zx/YWEhNE0zVTNRPNq3bx9atGhRa2nuDh06oHnz5gBQ64W3oKDgqCWoW7VqhZ07dyI5ORlvvvkmdF3H3Xffjf79++Pee+9FcXExWrZsiZkzZ2Lnzp0YP348Tj31VEyaNAl+vx/Tpk2r9VxfsWJFzbG9Xi8uueSSYz7Xs7Ky4PV6sW/fvjp/v4b8fSLnMAzEiA8//BCvvfYaZs6cia+++gozZsxA9+7dARgbewQCgVrt69too2nTpkhKSjpqi85t27YhNzcXl1xyCVatWlXzcckll9RqN2zYMKxZswbr1q3D559/ftT9gLEL2ieffHLUi76UEjfddBMeffRR078/Ubxp3rw5du/eXesF87PPPsPu3bsB1N6854QTTjjqObt9+3bk5uaitLQU+fn5ePrpp7F06VK8/fbbWLNmDaZNm4b9+/dD0zT85z//wfLlyzF9+nQsXLgQs2fPxvjx42s910855ZRaxx8+fDg+++wzrF+/Hps2bcKgQYNq3e/1enHmmWfW9BQeLhAI4NJLL8X06dMjfZgoShgGYkRJSQkURUFycjKklPj6668xb948BINBdOzYEQUFBVi2bBmklHjvvfdq9u0GgKSkJFRUVCAUCkFRFFxxxRWYPHkytm7dikAggFdeeQUbN248anBQXbKzs3HOOedg4sSJGDBgABo3bnxUm1tvvRXFxcW46667ajb82bt3L+6//37s2bMHN910k3UPDFGMGjRoEEKhEKZNm4ZAIIBt27bVvGs/0pVXXonp06dj7dq10DQNH374IRYtWoTLL78cZWVluPnmm/H+++9DSonc3FwoioLs7Gzs2rULN954I7799lsoilKzV0l2dvZx6+vSpQs6duyIe+65B8OGDYPX6z2qzcSJE7Fy5Uo89thjNZcst2zZgttvvx1paWn1TlEk9/E4XQCF5/LLL8fKlSsxdOhQqKqKDh06YPTo0Xj99dfRpUsX3HLLLbjvvvtQVlaG8847r9YI//79+6NJkybo378/3nrrLUycOBFTp07FmDFjUFRUhC5duuDFF18Me3fG4cOHY/z48ZgwYUKd9zdu3BizZ8+udY709HScdtppePPNN9GmTRtLHhOiWJaRkYEXX3wRjz/+eM1YgWuvvRbt2rU7qu0NN9wAXddx5513Yt++fWjbti0mT56MU089FYCxq+Wzzz6Lhx56CMnJyRgyZAjGjBkDn8+Hhx56CI888gjy8/PRqFEjjBo1ChdddFFYNQ4fPhyPPfYYnn322Trv79ChA2bPno3//Oc/GDFiBEpLS5GVlYWzzz4bkyZNqtkIiNyPGxURERElOF4mICIiSnAMA0RERAmOYYCIiCjBMQwQERElOIYBIiKiBMcwQERElOAYBoiIiBIcwwAREVGCYxggIiJKcAwDRERECY5hgIiIKMH9f/6BZtLKyWv0AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -711,12 +714,13 @@
     }
    ],
    "source": [
-    "# Pick fixed_thres\n",
-    "fixed_thres = 0.05\n",
+    "# Pick pc_alpha here interpreted as fixed threshold with significance='fixed_thres'\n",
+    "pc_alpha = 0.05  #[0.001, 0.005, 0.01, 0.025, 0.05, 0.1]\n",
     "\n",
-    "cmi_knn = CMIknn(significance='fixed_thres', fixed_thres=fixed_thres, transform='ranks', knn=0.1)\n",
+    "cmi_knn = CMIknn(significance='fixed_thres', model_selection_folds=3)\n",
     "pcmci_cmi_knn = PCMCI(dataframe=dataframe, cond_ind_test=cmi_knn, verbosity=0)\n",
-    "results = pcmci_cmi_knn.run_pcmciplus(tau_max=2)  # if fixed_thres is used, pc_alpha (and alpha_level) is ignored.\n",
+    "results = pcmci_cmi_knn.run_pcmciplus(tau_max=2, pc_alpha=pc_alpha)  \n",
+    "# if fixed_thres is used, pc_alpha (and alpha_level) is interpreted as a threshold on the test statistic.\n",
     "\n",
     "tp.plot_graph(\n",
     "    val_matrix=results['val_matrix'],\n",
@@ -735,6 +739,13 @@
     "    ); plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, picking ``pc_alpha = [0.001, 0.005, 0.01, 0.025, 0.05, 0.1]`` does not give the correct result. Thresholding does not come with the statistical rigor of significance testing, but can help in tasks like causal feature selection or if causal discovery is used in a larger ML pipeline."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/tutorials/causal_discovery/tigramite_tutorial_heteroskedastic_ParCorrWLS.ipynb b/tutorials/causal_discovery/tigramite_tutorial_heteroskedastic_ParCorrWLS.ipynb
index 31c5ed4d..d07bb288 100644
--- a/tutorials/causal_discovery/tigramite_tutorial_heteroskedastic_ParCorrWLS.ipynb
+++ b/tutorials/causal_discovery/tigramite_tutorial_heteroskedastic_ParCorrWLS.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "W. Günther, U. Ninad, J. Wahl, J. Runge, Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery, Advances in neural information processing systems 35 (2022)\n",
     "\n",
-    "Last, the following Nature Communications Perspective paper provides an overview of causal inference methods in general, identifies promising applications, and discusses methodological challenges (exemplified in Earth system sciences): https://www.nature.com/articles/s41467-019-10105-3"
+    "Last, the following Nature Review Earth and Environment paper provides an overview of causal inference for time series in general: https://github.com/jakobrunge/tigramite/blob/master/tutorials/Runge_Causal_Inference_for_Time_Series_NREE.pdf"
    ]
   },
   {
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "id": "155d7755",
    "metadata": {},
    "outputs": [],
@@ -141,8 +141,8 @@
     "    stds_matrix[:, 1] = stds\n",
     "    noise_X = random_state.normal(0, stds, T)\n",
     "    noise_Y = random_state.normal(0, stds, T)\n",
-    "    data[:, 0] = 0.7*Z[:T] + noise_X\n",
-    "    data[:, 1] = 0.7*Z[:T] + noise_Y\n",
+    "    data[:, 0] = 0.8*Z[:T] + noise_X\n",
+    "    data[:, 1] = 0.8*Z[:T] + noise_Y\n",
     "    return data, stds_matrix, noise_X, noise_Y\n",
     "\n",
     "def generate_parent_dependent_stds(Z, T):\n",
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
    "id": "5155bc6f",
    "metadata": {
     "scrolled": true
@@ -185,7 +185,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 26,
    "id": "26546c89",
    "metadata": {
     "scrolled": false
@@ -216,7 +216,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1000x400 with 2 Axes>"
       ]
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 27,
    "id": "8c51c08e",
    "metadata": {},
    "outputs": [],
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 30,
    "id": "feb478b5",
    "metadata": {},
    "outputs": [
@@ -301,10 +301,10 @@
       "## Resulting lagged parent (super)sets:\n",
       "\n",
       "    Variable $X$ has 1 link(s):\n",
-      "        ($Y$ -1): max_pval = 0.00098, min_val = -0.148\n",
+      "        ($Z$ -1): max_pval = 0.00459, min_val =  0.127\n",
       "\n",
       "    Variable $Y$ has 1 link(s):\n",
-      "        ($Z$ -1): max_pval = 0.00216, min_val =  0.137\n",
+      "        ($Z$ -1): max_pval = 0.00099, min_val =  0.147\n",
       "\n",
       "    Variable $Z$ has 0 link(s):\n",
       "\n",
@@ -329,10 +329,11 @@
       "\n",
       "## Significant links at alpha = 0.01:\n",
       "\n",
-      "    Variable $X$ has 0 link(s):\n",
+      "    Variable $X$ has 1 link(s):\n",
+      "        ($Z$ -1): pval = 0.00459 | val =  0.127\n",
       "\n",
       "    Variable $Y$ has 1 link(s):\n",
-      "        ($Z$ -1): pval = 0.00216 | val =  0.137\n",
+      "        ($Z$ -1): pval = 0.00099 | val =  0.147\n",
       "\n",
       "    Variable $Z$ has 0 link(s):\n"
      ]
@@ -354,13 +355,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 31,
    "id": "755e99ce",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -391,13 +392,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 32,
    "id": "1f3e8755",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -438,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 33,
    "id": "3cfb4b66",
    "metadata": {},
    "outputs": [],
@@ -455,13 +456,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 34,
    "id": "378ab9c4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -485,7 +486,7 @@
    "id": "e425644c",
    "metadata": {},
    "source": [
-    "We see that the standard partial correlation test is not able to recover the link between $Z$ and $Y$. It even finds a wrong link from $X$ to $Y$."
+    "We see that the standard partial correlation test finds a wrong link between $X$ and $Y$."
    ]
   },
   {
@@ -507,13 +508,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 51,
    "id": "7f827281",
    "metadata": {},
    "outputs": [],
    "source": [
     "random_state = np.random.RandomState(42)\n",
-    "\n",
+    "T = 2000\n",
     "def generate_time_dependent_stds(Z, T):\n",
     "    stds = np.array([1 + 0.018*t for t in range(T)])\n",
     "    return stds\n",
@@ -537,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 52,
    "id": "0f209e3c",
    "metadata": {
     "scrolled": true
@@ -545,7 +546,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -568,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 53,
    "id": "0f69e3a8",
    "metadata": {
     "scrolled": false
@@ -576,7 +577,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1000x400 with 2 Axes>"
       ]
@@ -612,7 +613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 54,
    "id": "89b0bec7",
    "metadata": {},
    "outputs": [],
@@ -631,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 55,
    "id": "fe855878",
    "metadata": {},
    "outputs": [
@@ -657,10 +658,10 @@
       "## Resulting lagged parent (super)sets:\n",
       "\n",
       "    Variable $X$ has 1 link(s):\n",
-      "        ($Z$ -1): max_pval = 0.00019, min_val =  0.167\n",
+      "        ($Z$ -1): max_pval = 0.00125, min_val =  0.072\n",
       "\n",
       "    Variable $Y$ has 1 link(s):\n",
-      "        ($Z$ -1): max_pval = 0.00002, min_val =  0.188\n",
+      "        ($Z$ -1): max_pval = 0.00001, min_val =  0.099\n",
       "\n",
       "    Variable $Z$ has 0 link(s):\n",
       "\n",
@@ -686,10 +687,10 @@
       "## Significant links at alpha = 0.01:\n",
       "\n",
       "    Variable $X$ has 1 link(s):\n",
-      "        ($Z$ -1): pval = 0.00019 | val =  0.167\n",
+      "        ($Z$ -1): pval = 0.00125 | val =  0.072\n",
       "\n",
       "    Variable $Y$ has 1 link(s):\n",
-      "        ($Z$ -1): pval = 0.00002 | val =  0.188\n",
+      "        ($Z$ -1): pval = 0.00001 | val =  0.099\n",
       "\n",
       "    Variable $Z$ has 0 link(s):\n"
      ]
@@ -702,13 +703,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 56,
    "id": "d75d0ee1",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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DU23d10VVRUv36HGLAGDwmVm66ZslaUfv/cxltZYGei/zSec7xc6Dq9TSuoKoutUh8qSPhxHddNNNet+FF+qhX/1KS5Ys0c9//nP995e+VB/60Id0wXvf2/Pnd86pNjTU8+fZ5vMTBgCgePLawe8MF1YUDm9ZbNhsNPpWMxDHsQ497DCdecYZestb3qITTjxRr37Vq3T11Vf35fklqTY0v2LL+ehzGBhXsmFNW/d1UU3R0t173CIAyK+0Q09kSSLz8fS/iTR9ubM6rAIIQoVDY2mxoXNqNBp93WPg2muv1coPf1h77LGHlj/vebrlllv6WuVPGNgKV6kpWkIYADC4zCzt7FsfSTz9/+nOPufv6HvGBQqHRpW4SL6PeWd8fFx7P+952nffffX9u+7S6Oho/55cUq1W26kVF93Ql6WFZqZf/epX+vGP7tbP/vePNTk1pQP3309nn/lGVavd2ZgCAPLILC3Gs2T6Hf10p5++wx/8ZXM7ZXqpnfX5TfIF73ufJOnpNWsyWfdvyq54vi9h4E//9E912223yTmn5z//+VqwYIH+8aZbtHDhAp3x+tf1owkA0FNmNt3pN+f8S4ffmaA2onB4gVwQytfrPT/3oOUjH/mIvvnNb+r7d92lV77qVbrhhht07rnn9uW5WwZ6n4F169bptttu0+WXX661a9fq0Ucf1f33369ly5bp/zz2+HY+k8WFAPLHzOTjppL6pOKJDWpufEaNdb9Xc+0Tijc8pWTTOvmpcVmzThDoQFAdVmXRcxWNLpYLpt+V96lzvP766/XJT31KX/rSl3TYYYfpXe98p/7uqqv6tpIhD3oeBlpDLT/5yU/05je/WW9605t6/ZQAMG+td/q+MalkcqPi8WfUXP/krE5/7XSnP0WnPw+uUlO0cJmisSVy4dzB6qAPYeBb3/qW3nvBBbruH/9RR/+3/yZJ+vM//3Nt3LhRn//853v+/LMN9MjAggULdMEFF+inP/2pfvjDu/XEE0/0+ikBoCNmXr5ZTzfVGV+r5vqn1Fz7xMwGOcnkRvnGVMe752HbXFhRtGAXVRbsoiCqbPU+QY/n7e+77z6dceaZ+uhHP6rXvva1M9cvXLhQf37uubriyiv7trSx11/rjvR1NcEZb3qjfvvEat11113ac889dfYZp+mS979n6w1jaSGAHpiZ248b6bx+3KCT76cgUjSyoK0DjMxM9ampPjUsW1GloijK7rig3B5UZIO2fhZA37WW7vm4KUsasrgpi5sauPX5veKc5IK003bBnMsuCGZut2ZDvjGxg8cKFA4vmNlDoL2nd3LODfyphZIUZLSksCW3YUAl+OED6C7zfuYdv4/Tzr+0a/W3ZaYjD9N/g0DOhem/s69vdfxtSCRpO2EgGBpVOLRgp9bQB2GoJB78kZss6wWkfoeBTr5YwgCA7UiH+6eH+eO08y9tIV8POviOnn4bj+kqQ4pGFm5RGNiJKIoGPgxEUVSOMPDwww/r05/+tO6998faa++9Z67/6je/rUajofPOfot23/W5z/oswgCAzcxMNn2KXhoAGlk3qX9cIBeEcmEoBdH05fRfBWHmHcmzl4K7sKJwZKGCSm3+j+ycwjDsWyFfFsIMawVa+tKCc845R7/+9a+111576aCDDpIkHXnkkXryySd17U236Kk1T+vTf3f53E9iZAAotVbnPzsADC433dGHckEkF6b/ajoAOJftfPIOtdoXBAqHFyqoDnc1oERRNLBhIMzBqIDUp9UEy5cv12mnnaa//Mu/3OK2t7/97Vq/5ve649abntUyp8qy5b1uGoCcmBn2b9blm613/gP0psAF6bv52e/oW5d7NHzfL639GIKh0Z4Fl34fWtQvWR5ONFtfRgaOP/54XXHFFbriiiu2evvKD1yw5ZWMDAADbXPn35CP67LmgHT+M51+ZXPnH1YyO4CmH1wYKRxe0NPnqESR6gMWBvJQK9DSl5GBOI71ve99T+vXrVWy8ek5tz33Obvo2KOP2uo3JFq2PDffKADzs3l9f+udf73Yod+5LTv81jt99ESSJGo2BmO6KAgCVarV3PRxfd10yHyieM1/tn3/aNnz8j9XBmCbzCfyzXq6e1/cKOgyP7dlhx9VCj+0X1Rxs6m44KsLnHOq1mq5+v3pcwljh1+4GecVAQUy8+6/OTW9fW/BDnpxgVxUURBVZzr+fFTroyWMInnv5X0Rg2UqTyMCLfndZ0Aq9hAiUBJmNl30NyXfnJIK80fayUWVOZ0/HX/+OedUqVbVqNcLuTNhpVrNfLfBrcl+ceP2FPAHDZSB+US+kXb+RSn8S4f3p9/xR9X0/3T8hdQaZm80GrLChM80CIQZH0i0LX0NA+me1q79Tr6Q84vA4GlV/vvGlKxZz//wfxAqiCpyYXXm3T/1R4PFOadqtapms1mIJYfVWi2XIwIt/R8ZcIFk7f3gzDwlA0BGzPz08H9aAJjbcO7cTKcfRNOdPxX9peCcU6VSURIEipv5DKguCFTNYY3As/U/DARh+/uHF2j4BxgEc4f/61k3ZxucXKWadvyVWjrsn/M/tOgd55yiKFIwHQjyVFgYVSoKw2LUofQ9DDgXtD27aHl9JwIMEPNevjkpX5/M7Za/LqoqqNTkotr0kH/+/7iiv4IgULVWU5IkipvNTIsLwyjK1YZC7chgZKCDOZOynkAG9JiZT0cAGpO5HAFwUXVWAMj/ECvyIwxDBUEwEwr6KQgCVSrF3G0ym5qBdjEyAHRNugRwSkl9Utacyro5c6RFfrXpzp9iP8xPa+ogDEOZ90qSpGcHHbkgUBiGhZkO2Jb+TxMEHUwT5GjuByiimT0AGpNpEWBOlgC6sCJXqU3P+1fp/NETzqWnQQZhqMhMfjoYmPfzmkZwQaAwCHJz4mA3MDIADJjW0b9pAJjMx34dLlBQqSmoDqXz/gUcRkWxOedm3sFL06+T6Q8/OxzMfr04J6e08w+ckwsGdwvqnNcMEAaAdmwOAFPTASD7144LI7nKkILKEEV/yB3n3MzvZF43AuqnDFYTtP9NZzUBsH0+bso3JtIpgBwU3LpKTUFlKJ37D/O9wSmAzXI+MpD9Hzcgb8y8fH1Svj6R/U6ALkg7/2otXfPP3D9QSNQMAAWQTgM05eubMi8EnCn+qw6x4Q8wIDJZTdC26eIO/tigrMwn8vVJJfUJyc89w93MFMexgumlTb00M/xfHWKrX2AA9X9Mr9NhRKYKUDJmJt+cUjz+jJrrfq9kcsNMENi4cVyXX/lJvfL1p2vP/Q/V2G77aJfl++vvP3tdl1vhFFSHFI0tUWXJbqos2EXh0ChBABhQzvq8Z6OZKX7qcbU7zBku3k1Bdai3jQJywJJYvtEaBdh6CP7A/3uprr3xZp144ol60YtepH322Uf/8i//ovvv+9966L575t0GVxlSUB1OawCY/wdKI4PVBE4KQymJd3xnaYuhUWCQbN4VcKKtbYEf+7//qeOOO0433XST7rvvPh155JF67LHHdO89Ox8E0vn/4XQJIOv/gVLKZO2PC0JZm2HACnBONdApS5pK6hPy9c72BFj+vL30D9dcryVLlsjM9NBDD+3U87uoOj0CQA0AgIzCgIJIUpuHozAygAFhZumugPWJnT4d8MMfukj7/sE+euzx/9Tf/c//r6PPdWFFQW1YQXWYAABgjkzGBF0Hlc/tjiAAeWU+UTK5MS0G3LRuXscEj4wM63+c9Rad/LL/3tb9XRgpHF6gyqLnqrJomcKhMYIAgC1kNDLQQRhgNQEKyrf2BahP9PeJg1BBdVhhbVgurPT3uQEUUkY1Ax08LSMDKJDWKYHJ1Pi8RgB2RlAbUVAbYSMgAB3L/ciAzMvMs8wJudbaIjiZGu/53hjee01MTs69TlLDVTUSVXv63AAGU+5rBiRJrChATlkSK55Yn9YDTKzveRD4P4/9X/3hkX+kU047c871q1ev1ujoqN7whjf09PkBDKYMVxO0z3wsJ+Y+kQ+bzwkYnz4noD9cpaav/utdWrd+g/75n/9ZkrT77rvr9a9/vQ444AD98pe/1GWXXaYrr7xSe+21V9/aBaD4sqkZcC7dlrjd9dXUDSAHZpYGTm3q32mBQaiwNpIuBwwjDY2OKY5jrVq1SpJm/pWkxx9/XJJUqRCcAXQmuwPHw0hqs8CKFQXIknkvX9+kZGpT307SDKpDCqoj08cCby4GfOMb36g77rhD119//RafE4ahVq5cqV133bUvbQQwOPp+NkFLvO73ssbkju8oyQ2NKVr4nB63CJjLkkTJ1Mb+LQ0MonQUoMamQAD6K7ORARdG7Z/IzjQB+siSWMnUeJ9CQHo6YFAblYtYEgggG9lOE7SJaQL0gyWxksmN8m2OWM2LCxQOjaYhgMOBAGQsw5GBDoqcfCwz410TesLHTfmp8b6EABdWFAyNpgWB/D4DyIlMpwnaZpYWbjnmUdE9Pm4qmdwoa/Z+eWA6FTDGVACAXCrENIGkdOMhiqrQBT5uTIeANk/O3FnOKaiNKKyNdhZ+AaDPshsZcEHaubdZD5BuPMRWq9h5vllXMjkui3scAoJQ4dBYuiqAbbQBFECmb1dcWGm7ONDiplTrcYMwcNLdAqdHAnp8cJCLagqHRrfYGwAA8i7bMBBV2p6v7fcJcCi2zacHbkyDZA+lpwWOKojY+Q9AMWU7kdnJ8kLCANowEwImN/Z2y+CZpYEjbBAEoPAynyZoW9JkeSG2yzfrSiY29DYEBJHC4TGWBgIYKBmHgQ6fPm5IFQoHMFe6RHBDT1cHuDBSMLRAQXWIEABg4GQ8TdDZHKvFTTnCAKb1Y8dAF1YUDo/JVQgBAAZXtiMDzklBJPn2zh6gbgBSeopgMrVRfmpTz54jDQELWBkAoBQy3wnFhZGMMIA2mHn5qU1KpsbTXSl7wEXVNAREVUIAgNLIPgx0uLyQIsLyMTP5+oSSyY3pttQ94KJaWhjINBSAEspBGOhgV0Hz6Y6FbO1aCukywSnFExvbnkrqlKvUFA4vUNDJ7yEADJjse9UO/whb3GSf9xJItw7e0LMNg1xlKB0JIAQAQPZhwHW4a5vFDak23KPWIGu9XiYYVIcVDI2xWyAAzJJ9GHBBOuyfUERYZr1eJhhUh9PCQEaVAGALufjL6KKqjDBQSmZeyeS4/NR4Tx4/rQlYyEgAAGxHfsJAfaK9O7Mt8UDYXBy4oe1jrDvhworCkYWsDgCANuQmDHQkaXZceIj8sKSpeNP63ozyBJGikQXsGAgAHShkGLBmo/MAgcyZ92ldQL0HOwe6IF0iWBshBABAh3IRBhSEknNt7ypnCXUDRWJm8o1JJRMbur9pkHMKh8YUDI2mxagAgI7lIgw459K6gTaXk1mTMFAUPm4omVjfk/0CgqFRhUML5AJCAADMRy7CgKTOwgAjA7lnPpmeEmizMLQDLBMEgO7KzV/TjmoAvJf5RC4Ie9cg7JTN5whs6PphQiwTBIDeyE0Y0E7sROiq7ESYJ75ZT6cE2twzol0sEwSA3spNGOh4RUHckAgDuWA+UTKxofu7BwahopGFLBMEgB7LTxhwQVo30Obac3YizJ6ZyU9tSo8WVjenBFy6THBolBAAAH2QmzAgpXPChIFi8M264k3ru360cFAdVjiykHoQAOij3IUBTW5s784x2xJnId04aEPXVwm4MFI4soi6AADIQL7CQNRhRxA3JDqPvvGNKcWb1nV34yDn0hUC7BwIAJnJVRhQGEkuaLuz8c26QsJAz6UFguvlG1NdfdygNqJweCGbBgFAxnIVBpxzad1Am1Xp1piURhb2uFXltXkb4fVd3TPARZV0SoDzJQAgF3IVBiR1FgaaU9QN9IglseKJ9W3vCtkWF6T7BVSH+ZkBQI7kMgy0zSzdfIipgq5JdxDcpGSiu8sFg6HRdAthDhMCgNzJXxjosIjQGlMUEXaJJU3Fm9Z19VAhF1UVjS6SC9lCGADyKn9hIAiksCIl7XVI1pyUtKi3jRpw6eZB49ObB3UJuwcCQGHkLgxI03UD7YaBRp26gXnwcUPJpnVdPU8gGBpTODzGlAAAFEQuw0BQqSmZGm/z3iZr1uWqQz1t06Ax8+kRw1ObuvaY6ZTAYo4WBoCCyeVf7U4LAq05JREG2pZuJbxO8kmXHtGlqwTYOAgACimXYUBhRQoCybe3+ZA1pqTRHrdpAJj59HTBLm4l7Co1RSOL5ULOEgCAosplGEg3HxqW1dsbwk73G/DMUW+HbzYUb1rbvdEA9gwAgIGRyzAgSUF1SEmbYUDSdN3AcA9bVExmNl0b0G4Nxo5xsiAADJbchoFOO3ZrTEmEgTksiRWPr217ZcYOBaGikUUKqM8AgIGS3zAQRunBRW0uebMuH6JTZOkughNKJjaoW7sIBrWRdDSAqRgAGDi5DQNSOhzt29wIx+K6zPvSn4BnPkl3EezWmQJBpGh0kQJ2eQSAgZXrMOCqw1IHu+JZc0quNtLDFuWbb0ylSwbbPAJ6R9LNgxZQIAgAAy7fYaDS2dy0NaekEoaBbi8ZdGFF4ehiBRHnCQBAGeQ7DASBXFSTxe0NefvGlMpW3+7jhuLx7i0ZDIcXKhgaZTQAAEok12FAklx1qO0woLgh80kplrx1+3AhthIGgPLKfbVdx0sMm4O/qsCSWPHGNV0LAuHwQkULdiEIAEBJ5f6vf3pOgVO7S+SsMSXVBnNvYjOTb0wq2bRe3Vgy6MJI4egSagMAoOTyHwacS6cKGpNt3X9Q6wbSJYPruzbyEdRGp/cNoDYAAMou92FAUkdhQElz4OoGfLOeFgl2Y8mgCxSNLVbQ4UoNAMDgyn3NgJRuPtSJtoNDzrXOFYg3Pt2VIOAqQ6osei5BAAAwRyFGBhRWJBe03SFaY0oaGutxo3rLvFe8aW2XdhJ0CkcXccIgAGCrChEG0rqB9o809s1i1w10c+8AlgwCAHakMD1ER0caJ7EsbsoVrEo+PWBo0/QBQ/MXDi9QMDTGaAAAYLsKEwY63W/ANyYURot61JruS6cF1nVntUAQpUWCUXX+jwUAGHjFCQOdHmlcn5BGihEGfNxUPP5MV6YFOGoYANCpwoQBqcMjjZv13C8xTKcFJpRMrJ//g7lA0ehiBVVWCgAAOlOot4+dHk9sXTrFrxfMvJJN67oSBNIlg8sIAgCAnVKokQFXGepoiaGvTygYXtDjVnXOkqbi8bWyNqc8ticcWaSgNkKRIABgpxUrDDgnVxuWTbW3qsAakzLv5YL8DIAk9YnunC0QhIrGllAkCACYt/z0km0KOp0qyMluhGameNM6JZvWab5BwFWGVFm4jCAAAOiKQo0MSK0lhu2fYujrEwqGsj3F0JJ4elqgOe/HCocXKhgaZVoAANA1xQsDLujo4CJrTMjMMus8fWNS8aZ1ks13WiBQNLpUQYXRAABAdxVumkDqcKrArGvH/nbCzBRPbJg+bXC+0wK1dFqAIAAA6IFChoG8LzE07xWPPyM/NT7vxwqHFygaW5rr/RIAAMVWzDAQhHKVWtv39/V0qqAfLGmqueGp+Z826AJFC3ZROLyA+gAAQE8VrmagxdVG2+5wXVSV+UTaxrvrbnW2vjGpeHyd5r1aIKoqGlvCaAAAoC8KGwaC2rD89kbhK0NSdVgKa0okxY2mpG1X8wdBIBcECpyTC4J0T4M2Q4KZKZnc2JVpgWBojNEAAEBfFTYMuLAiRVUpbmy+MqrJRzVZVEt3KpTaLt7z3kvea/ZRQUEQKIyiNChso3M284rHu3DaoHOKRpewpTAAoO8KGwakdFWBd4F8WJ0bALrEey/fSMNGEIYKw3BOMLAkVnPjM5Kf37bCLqyk0wJhoX8cAICCctavyrouMjMlcaw4nv/e/p1yzqlSrSoIAiWTG5W0eYritgS10ekjh5kWAABko1BhwMzkvVez0djxnXssCAJFUaRkYv1OThE4RWOLFVSHu942AAA6UZgw0AoBeWtuEIbS5DqpkxMIg1CVBUvTugcAADJWiDCQJEkuRgO2xZmXq29oq1jRVWqKRpfk6iRFAEC55ToMmJniOFaSQW1Ax5Kmgsb2lxaybBAAkEe5DQNmpmazKZ8kO75zXsR1Bc2tbX3sFI4uVlijPgAAkD+5XMtWyCAgSVFN5hO5ZNbOiEGoaGypgoj6AABAPuUyDCRJUrwgMM0qw5Ilcj6e3lZ4KfUBAIBcy900gU8SNXJcLNgW83JJQ5XRRQoIAgCAnMtVT2VmxQ8CkuQCWTSkpKCjGwCAcslVGMjz8sGdkcQxgQAAkHu5CQPe+/SwoAETN5u52ygJAIDZchMG4ua2jxcustYWygAA5FUuwsCgjgq0DGrQAQAMhlyEgUHvLM2ssEslAQCDL/MwUJZhdAoJAQB5lXkYKEMQkMrzdQIAiifzMGB96iS/+c1vanhkZJsfZ5x5Zk+f38xYVQAAyKXMdyCs1+t9CQSTk5Nav379nOuSJNH/OPdc/exnP9Md3/iGDjnkkJ62oVKtKgzDnj4HAACdyvRsAjPr28jA8PCwhoc3nxqYJInedtZZfQsCUjpVQBgAAORNLg8q6rVWEPjud7/btyAgiWkCAEAuZVszkEHnmCSJzjr7bH33u9/VN77+dR166KH9e3LCAAAghzINA/3uGltB4M4779Q3vv51HXbYYX19fqIAACCPMp0mcM717blaQeA73/lOJkFAkvr31QIA0L7Mlxb2Q5IkOvvtb9d3vvMdff1rX9Phhx+eTUP6GH4AAGjXwBcQeu919tvfrq9+9av6/M03a7fddtPq1avn3GfZsmV9qfIPCAMAgBzKfJ+BRr3e0935fvzjH+uEE0/c7n2e+N3vtHjx4p61oYV9BgAAeZR5GIibTcVxnGUT+qY2NNTXOgkAANqRec1AEGTehL5wzhEEAAC5lHlP7EoSBsoSegAAxZN5D+WcU1CCefQwGvhaTQBAQWUeBiQpGvCOMggCRgYAALmVix4qCIKBHh2IKpWsmwAAwDblIgxIgzs6wKgAACDvctNLDeroAKMCAIC8y00YkKRKpTJQy++iSoVRAQBA7uWqp3LOqVKtZt2MrgiCgN0GAQCFkKswIKWdaNEDQSvUDNIoBwBgcOUuDEhSGIaFLigkCAAAiiSXYUBK59uLWHxXrdWoEwAAFEqu335HUSQnqdlsZt2UHXLOqVqrMSIAACiczE8tbIf3Xo16PetmbFMQhgO3EgIAUB6FCAOSZGZqNpvySZJ1U+aoVCqcOwAAKLTChIEW772azabM+0zbEUWRwihiNAAAUHiFCwMtSZIobjbV7+YzJQAAGDSFDQNSOnVg3itJEiU9nD5wzimMIoVhSAgAAAycQoeB2cxM3nslcSzfhSkE55zCMFQQhiwVBAAMtIEJA7O1viTv/czogfdelt44577OufQjCBRM/9u6DgCAMhjIMAAAANrH+DcAACVHGAAAoOQIAwAAlBxhAACAkiMMAABQcoQBAABKjjAAAEDJEQYAACg5wgAAACVHGAAAoOQIAwAAlBxhAACAkiMMAABQcoQBAABKjjAAAEDJRVk3YLbqC85SEFXlglAuCBVWNl92QbD5tjBUEFUVzNwWbnGbC0IFgZMLnMIwkHvW5SBwCkI3c5/t3uacwihQGDiFgVN1+nI08/9w823h5vtFs+4bbu2ycwqcU+ikShjMXI7CQKFT+v/AqRK4rVxOb68Ewczl0Dk5JwVOck7Tjy85SWHgFEjp1xJo5nLgpNDNvpw+hjOTzMv5WJpz2acfftu3OfNSkmy+7GPJJzLvpbghSxLJ+/S6uCnzSXq52ZRal1v3bd2v2dj8OT6Rb8ayxMu8l2/E8kn6OZZ4+WYsn2y+bNOXk2Ysm3W/pBHPupzIvMknNv3/6c/3lt6WmCwx+cQrafrpxzQlzWT6czZ/njdTYqaGNyWmZ11+9v/Ty17p5cQ0fdvmy5+2xzJ9XXYLr29e37y+8/v6ZmQAAICSIwwAAFByhAEAAEqOMAAAQMkRBgAAKDnCAAAAJUcYAACg5AgDAACUHGEAAICSIwwAAFByhAEAAEqOMAAAQMkRBgAAKDnCAAAAJUcYAACg5AgDAACUHGEAAICSIwwAAFByhAEAAEqOMAAAQMkRBgAAKDnCAAAAJUcYAACg5AgDAACUHGEAAICyswE1NTVlK1eutKmpqaybsoU8t82M9s1Hnts2SPL8fc5z28xo33zkuW3z5czMsg4kvbBhwwYtWrRI69ev18KFC7Nuzhx5bptE++Yjz20bJHn+Pue5bRLtm488t22+mCYAAKDkCAMAAJQcYQAAgJIb2DBQq9W0cuVK1Wq1rJuyhTy3TaJ985Hntg2SPH+f89w2ifbNR57bNl8DW0AIAADaM7AjAwAAoD2EAQAASo4wAABAyQ1cGHj/+9+vF7/4xTrjjDPUaDTm3DY5OalXvepVOuGEE/TSl75UzzzzTK7a1/Lxj39cL3rRizJvUxzHeutb36oXv/jFes973tO39rTbvpZ+f79m21bb8vC7Noh4fXevTby+d6xMr++BCgP333+/Vq9erR/84Ac66KCD9KUvfWnO7XfccYcOOeQQff/739dpp52mz33uc7lqnyRt3LhRDz74YC7a9NWvflV77bWXfvCDH2hiYkI/+tGP+taudton9f/71W7bsv5dG0S8vrvbJl7fO9+2rH/XemGgwsA999yjl73sZZKkl7/85Vv8cu+3336amJiQJK1bt07Lli3LVfsk6ZOf/KTe+c535qJN7bQ3y/ZJ/f9+zba9tmX9uzaIeH13t028vrevbK/vKOsGdNO6deu0xx57SJIWLVq0xdDNvvvuqwcffFCHHHKInHO69957c9W+9evX6xe/+IUuvvjiXLRp3bp1M/tvb629Wbcvi+9Xu23L+ndtEPH67m6beH3vfNuy/l3rhUKODKxevVrHHXfcFh9mpg0bNkhKf5BLly6d83k33nijTjzxRD344IP6yEc+oksvvTRX7bv66qv1rne9qydt2pYlS5Zss03buy0P7cvi+zXb9trWr9+1QcTru3t4fe+8sr2+CxkGdtttN919991bfJx88sn613/9V0nSt771LR177LFbfG7rB7p48WKtW7cuV+179NFH9dGPflQvf/nL9cgjj+jyyy/vSftmO+aYY7bZpu3d1i/ba0MW36922yb153dtEPH67h5e371pmzSAr+/sTk/ujQsvvNCOO+44O/30061er5uZ2Tve8Q4zM1u/fr2dfPLJdsIJJ9ixxx5rDz30UK7aN9uRRx6ZWZta7Wk2m/Znf/Zndtxxx9n555/ft/a0277Z+vn9mm1bbcvD79og4vU9/zbx+m5fmV7fbEcMAEDJFXKaAAAAdA9hAACAkiMMAABQcoQBAABKjjBQAjfccIMWL17clcd67LHH5JxTFEX67W9/O+e2J554QlEUyTmnxx57bM5tX/7yl3XiiSdq0aJFGhsb02GHHaZLL710ZiOPbrYRwPzts88+cs7pC1/4wha3HXzwwXLO6YYbbphz/f33369TTz1Vu+66q4aGhrT//vvrnHPO0cMPPyxp89+PBx54oA9fATpBGMBO2WOPPfRP//RPc6678cYbteeee25x37/6q7/SG97wBh111FG644479OCDD+rKK6/Uz372s4HY0xvotWazmcnz7r333rr++uvnXPfv//7vWr16tUZHR+dc/7WvfU3HHHOM6vW6br75Zq1atUqf+9zntGjRIl1yySX9bDZ2RtZrG7Fjd9xxhx177LG2aNEiW7p0qb3yla+0Rx991MzMvve975kkW7t27cz977//fpNkv/nNb2Zun/2xcuVKMzN75pln7M1vfrMtXrzYhoeH7eUvf7k9/PDD223Lb37zG5NkF198se23335zbjvggAPskksumXluM7N7773XJNnVV1+91cdrtfv666+3RYsWdfy9AYoqSRK7/PLLbd9997VqtWp77723XXbZZTOvsVtuucVOOOEEq9Vqdt1111mSJPaRj3zE9txzT6tWq3b44YfbHXfcMfN49Xrd3vnOd9puu+1mtVrNli9fbh/72Mdmbl+5cqXtvffeVq1Wbffdd9/h3gLLly+3D37wg1ar1ezxxx+fuf6cc86x888/3xYtWmTXX3+9mZlt2rTJnvOc59hrX/varT5W63Xe+truv//+nfumoWcYGSiATZs26X3ve59+8pOf6M4771QQBHrd614n7/0OP/eP/uiPdPXVV2vhwoV64okn9MQTT+j973+/JOmtb32rfvrTn+orX/mK7rnnHpmZTj755LbehZxyyilau3at7r77bknS3XffrWeeeUavfvWr59zv5ptv1tjYmM4777ytPg5TAyirD33oQ/rEJz6hSy65RP/xH/+hz3/+89p1111nbv/ABz6gd7/73Vq1apVOOukkffKTn9SVV16pK664Qj//+c910kkn6ZRTTtEjjzwiSfrUpz6lr3zlK/riF7+ohx56SDfddJP22WcfSdKXvvQlXXXVVfrMZz6jRx55RLfffrsOPfTQHbZx11131UknnaQbb7xRkjQxMaFbbrlFZ5111pz7fetb39KaNWt00UUXbfVxeJ0XQNZpBJ178sknTZL94he/2OHIgNnW33U//PDDJsl++MMfzly3Zs0aGx4eti9+8YvbfO7Zyf69732vve1tbzMzs7e97W12wQUXbPHcr3jFK+ywww7b4dfEyADKZMOGDVar1eyaa67Z4rbWa+zZo2l77LGHffSjH51z3VFHHWXnnXeemZmdf/759sd//Mfmvd/iMa+88krbf//9rdFotN3G5cuX21VXXWW333677bvvvua9txtvvNFe8IIXmJnNGRn4xCc+YZLsmWee2e5jMjKQX4wMFMCvf/1rnX766Xr+85+vhQsX6g/+4A8kSY8//vhOP+aqVasURZGOPvromet22WUXHXDAAVq1apUk6RWveIXGxsY0Njamgw8+eIvHOPvss3Xrrbdq9erVuvXWW7d4tyBJZibn3E63ExhEq1atUr1e10te8pJt3udFL3rRzOUNGzbod7/73Rb74x977LEzr9e3vvWteuCBB3TAAQfo3e9+98y++pJ06qmnanJyUs9//vN1zjnn6LbbblMcx5Kkj33sYzOv87GxsS3+rrzyla/U+Pi4/u3f/k3XXXfdNl/nKDbCQAG8+tWv1tNPP61rrrlG995778xxmY1GQ0GQ/ghnvxjbGebf1ot3dud97bXX6oEHHtADDzygb3zjG1vc95BDDtEf/uEf6k1vepMOPPBAHXLIIVvcZ//999evf/3rzAqggDwaHh7e4X2eXaAnaYtgPfv1+sIXvlC/+c1v9Nd//deanJzUaaedpte//vWS0kLAhx56SH//93+v4eFhnXfeeTr++OPVbDZ17rnnzrzOH3jggZlje1uiKNKb3/xmrVy5Uvfee6/OOOOMLdq1//77S5J+9atftfcNQO4QBnLu6aef1qpVq3TxxRfrJS95iQ488ECtXbt25vZly5ZJSpf1tTx72U61WlWSJHOuO+iggxTH8ZxzuJ9++mk9/PDDOvDAAyVJe+65p1asWKEVK1Zo+fLlW23fWWedpbvuumur7xYk6fTTT9f4+Lj+4R/+Yau3D8RpX0CH9ttvPw0PD+vOO+9s6/4LFy7UHnvsMVOj0/KjH/1o5vXaut8b3vAGXXPNNbrlllv05S9/eWb57vDwsE455RR96lOf0l133aV77rlHv/jFL7R06dKZ1/mKFSsURdEWz3/WWWfp+9//vl7zmtdoyZIlW9z+spe9TM95znP0N3/zN1ttP6/z/Nvyp45cWbJkiXbZZRd99rOf1e67767HH39cH/zgB2duX7Fihfbee299+MMf1mWXXaZHHnlEV1555ZzH2GeffTQ+Pq4777xThx9+uEZGRrTffvvpNa95jc455xx95jOf0YIFC/TBD35Qe+65p17zmte03b5zzjlHp5566jYLhI4++mhddNFFuvDCC/Xb3/5Wr3vd67THHnvo0Ucf1ac//Wkdd9xxes973rNT3xugqIaGhvSBD3xAF110karVqo499lg99dRT+uUvf7nNqYO/+Iu/0MqVK7XvvvvqiCOO0PXXX68HHnhAN998syTpqquu0u67764jjjhCQRDo1ltv1W677abFixfrhhtuUJIkOvroozUyMqLPfe5zGh4e3mbIf7YDDzxQa9as0cjIyFZvHx0d1bXXXqtTTz1Vp5xyit797ndrxYoVWrNmjb74xS/q8ccf3+p+BciRLAsW0J5vf/vbduCBB1qtVrPDDjvM7rrrLpNkt912m5mZ3X333XbooYfa0NCQvfjFL7Zbb711ThGfmdm5555ru+yyy1aXFi5atMiGh4ftpJNOantp4bYKgJ5dQNhyyy232PHHH28LFiyw0dFRO+yww+zSSy9laSFKK0kSu+yyy2z58uVWqVTsec97nn3sYx/b5mts9tLCSqWyxdLCz372s3bEEUfY6OioLVy40F7ykpfYfffdZ2Zmt912mx199NG2cOFCGx0dtWOOOca+853vbLd9rQLCbZldQNjyk5/8xP7kT/7Eli1bZrVazVasWGHveMc77JFHHjEzCgjzjCOMAQAoOWoGAAAoOcIAAAAlRxgAAKDkCAMAAJQcYQAAgJIjDAAAUHKEAQAASo4wAABAyREGAAAoOcIAAAAlRxgAAKDk/n+M3fx5NcJowgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -734,12 +735,12 @@
    "source": [
     "#### Comparison with ParCorrOLS\n",
     "\n",
-    "In the time-dependent heteroskedasticity case, we also expect degraded performance of the standard ParCorr test. This is also what we see in the plot below, the link from $Z$ to $X$ is not discovered."
+    "In the time-dependent heteroskedasticity case, we also expect degraded performance of the standard ParCorr test."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 57,
    "id": "a7cbc1ca",
    "metadata": {},
    "outputs": [],
@@ -756,13 +757,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 58,
    "id": "5ca025b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -799,6 +800,14 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "243f8968",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/tutorials/causal_discovery/tigramite_tutorial_latent-pcmci.ipynb b/tutorials/causal_discovery/tigramite_tutorial_latent-pcmci.ipynb
index 776ed7c5..36ad3667 100644
--- a/tutorials/causal_discovery/tigramite_tutorial_latent-pcmci.ipynb
+++ b/tutorials/causal_discovery/tigramite_tutorial_latent-pcmci.ipynb
@@ -15,6 +15,8 @@
    "source": [
     "TIGRAMITE is a time series analysis python module. It allows to reconstruct causal graphical models from discrete or continuously-valued time series based on the PCMCI framework and create high-quality plots of the results.\n",
     "\n",
+    "The following Nature Review Earth and Environment paper provides an overview of causal inference for time series in general: https://github.com/jakobrunge/tigramite/blob/master/tutorials/Runge_Causal_Inference_for_Time_Series_NREE.pdf\n",
+    "\n",
     "This tutorial explains the **Latent-PCMCI (LPCMCI) algorithm**, which is implemented as the function `LPCMCI.run_lpcmci`. In contrast to the [PCMCI](https://github.com/jakobrunge/tigramite/blob/master/tutorials/tigramite_tutorial_basics.ipynb) and [PCMCIplus](https://github.com/jakobrunge/tigramite/blob/master/tutorials/tigramite_tutorial_pcmciplus.ipynb) algorithms, respectively implemented as `PCMCI.run_pcmci` and `PCMCI.run_pcmciplus`, LPCMCI allows for unobserved (aka latent) time series.\n",
     "\n",
     "**Note:**\n",
@@ -589,7 +591,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -661,7 +663,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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ra0T1ff7555K+uKTdfPECR/jnvr4+SV9csj516tTwpe6SlJ2drRMnToQvZx/s/w21LCUlRX19fVqxYoUaGxvV19en1NRULVq0SDt37gyve2lt06ZNG1DHyZMnZYzpt83hjOny2vLz8yOOMZYxXf73b7/9du3evbvfGGtra69Y93DGdOrUKVVVVenAgQMqLi7Ws88+q7y8vKjqHmxZtHUPdR9GM6ZItXV0dGj9+vU6cOCAZs2apeeee055eXnDrvu3v/3toPtp+fLl/dYvLS1VbW3tsOseatmlj++0tDQtXrxYdXV1sl20fW24vUqKrR8NtSyW/R7tsvb2dq1bt04HDx5UUVFR+DhO5DEN1qsuH2OkupcvX97veaG0tFQ7d+50ZUzLli0bUMvF/jDSuk+fPq2qqiq1traqqKhIzz//vHJzc0d9P0Vb96U9lr72V/S12M/BGJO9Y7r4/BfpPHY4/Xjnzp2Djmmo3HB5bfHoazF/+vR9990nSTpz5kz4/T2XW7Fihd5//3198MEHCReIJamrqyvcCH0+X7+vlBpq2aU2bdokn88XvhUUFDhf+GWKi4uVmvrFrk5NTdWsWbPitu2tW7dq0aJFys7OVmlpqbZu3TrsOoqKiuJWR1FR0aiNMZ7fuV1VVaXGxkadPXtWu3fvVlVV1ZDrd3R0aPny5Zo2bZpWrFgx5Hsmoq3bycdJXl6edu7cqRMnTqi2tnbIDzlqbW0NN7m+vj4dOHBgyG1fvv7BgwfjVvfF+9CJ960mMi/0NS/Jy8tTbW2tTpw4oZ07d8b1+5i94uIYT548ecVjeOvWrSotLR3W84LTLq1l0aJFQ9Zy4MCBqHpJVVWVdu/erbNnz6qxsVHr1q0bdN2Ojg6tWLFiWL07WtHWfWnP5MPK/oq+BkQvmvPYaHvV5ef2Q+WGuJyvmRg88sgjZvLkyebIkSNm8uTJ5qmnnoplM5739NNPm1/84hfGGGNaW1vNd7/73fCyhx56yLz55pvGGGNeeeUV85Of/CTiNj7//HMTCATCt7a2NtfeU+zEpWleqcMrY7y0luFcHuKlS5y9ch+OxvsRuWxwZLzQ14B4i7b3JOrbh7xyub/XDLev8TyC4bDlbQ1O9sHRPi+NOhRv3brVjB071rS0tBhjvniv7dVXX216enriXpzbLn9P8UsvvRRedvl7ipubm4e1Td6jkvyc/BAvr7zHdTQ+NC3RPojIZvQ1JAMnP8TLyd7tZM+02WB9zUsfYukVtgTAaCTqC/xOvu/X670nqlD8xhtvmIyMDLNjx47w7wKBgLnqqqvM888/H/fivODyT59ev369MeaLDxJbu3atmT9/vvne97437O1x8jh8ido0nfwkWq+EOq/UES2vvKiQbOhrsJGTL8gl6vNfMhmsr3npCi+vsOVqBSevBByNGVcnPsXZ60E3GsMOxQcOHDDjx483P/vZzwYs+9GPfmSuvfbaAZ9AjYG8fvLopSfiRG2aNlzinKjhMlEfU17n9b4GuC1RXwC1WbxmihP1E9C9EgCdnIX20td5OnnliVeuavG6mL+SCbHx+smjl56IE/XA9EpwdZKXHifRsGHfuMHrfQ1INIn6/JdMButrTr7AMRovqidaAPTSthP1O+m98vkHXkcoHmVeP3n00hOxzQem1xEucSmv9zUg0fD857549bVoni+dPAdL1ADo5Cx0ol4K7+QLMzaf3xGKR5nXTx69csAbY/eBCSQSr/c1INHw/Oc+N/qak+8VTdQA6KWZ4kQ9LhO17tGWYsz//w3IGBXBYFA+n2/QL4N3W0dHhyorK9Xa2qri4mLV1NQk5XduAogfr/c1AIiWG30t2nOw8vJyNTQ0qLe3V2lpaVq8eLHq6upGvG4stTgl2jqiWd8rY4Q3EIpHGSePAJINfQ1AskmEvpaTk6POzs5+P585cybiugRAYGjpbhcAAAAAIDolJSX9Zn+Li4sHXTcvL2/ImWHAdqluFwAAAAAgOjU1NVq8eLFycnK0ePFi1dTUuF0SkLCYKQYAAAASDLO/QPwwUwwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxaNky5YtKiwsHPLj8gEgkdDXACQb+hpgpxRjjHG7CJskwpfBA0A06GsAkg19DbALM8UAAAAAAGsRigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQPITq6mr5/X5VVFSop6en37I9e/aooKBACxcuVGlpqUsVAgAAAABGglA8iMOHD6u9vV1NTU0qLCzUq6++OmCdu+66S3v27FFjY6MLFQIAAAAARopQPIiWlhYtWbJEklRWVqbm5uYB6+zYsUN+v1+bN28edDuhUEjBYLDfDQASGX0NQLKhrwF2IxQPoqurS1lZWZIkn8+nzs7OfsuLiop0/PhxNTY26ne/+50OHjwYcTubNm2Sz+cL3woKChyvHQCcRF8DkGzoa4DdUowxxu0i3NTe3q5Vq1YN+H15ebkKCgq0du1aHThwQDU1NXrqqacibuOZZ55RZmam7r333gHLQqGQQqFQ+OdgMKiCggIFAoFw6AaAREJfA5Bs6GuA3dLdLsBt+fn52rt374DfHz58WD/96U+1du1a1dfXa968ef2WB4PBcJNsamrSt771rYjbz8zMVGZmZvwLBwCX0NcAJBv6GmA3Lp8exE033aT8/Hz5/X4dO3ZMK1eulCRt2LBBkrR9+3aVlJRo7ty5mj59uhYsWOBmuQAAAACAGFh/+fRoCwaD8vl8XI4DIGnQ1wAkG/oaYBdmigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqF4lGzZskWFhYUqLi52uxQAiAv6GoBkQ18D7JRijDFuF2GTYDAon8+nQCCgrKwst8sBgBGjrwFINvQ1wC7MFAMAAAAArEUoBgAAAABYi1AMAAAAALAWoRgAAAAAYC1CMQAAAADAWoRiAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLULxILq7u3XLLbdowoQJOnr06IDlFy5cUGVlpfx+vx544AEXKgQAAAAAjBSheBBjx47V66+/rlWrVkVcvnPnTs2YMUNNTU367LPP1NzcPMoVAgAAAABGKt3tArwqPT1dubm5gy5vaWnRsmXLJEllZWVqbm7W3LlzB6wXCoUUCoXCPweDwfgXCwCjiL4GINnQ1wC7MVMco66uLmVlZUmSfD6fOjs7I663adMm+Xy+8K2goGA0ywSAuKOvAUg29DXAbtbPFLe3t0e8RLq2tlbZ2dmD/r9JkyaFX0Xs6uoadN2NGzfqwQcfDP9sjFFPT48mTpw4wsoBwB30NQDJhr4G2M36UJyfn6+9e/dG/f9mz56tXbt2acGCBaqvr9e9994bcb3MzExlZmaOtEwA8Az6GoBkQ18D7Mbl00NYunSpdu3apaqqKtXU1EiSNmzYIElavny52tra5Pf7NXbsWM2ZM8fFSgEAAAAAsUgxxhi3iwAAAAAAwA3MFAMAAAAArGX9e4q9xBij7u5ut8sAEMHEiROVkpLidhkJh74GeBd9LTb0NcC7Yu1rhGIPOXPmjKZMmeJ2GQAiOH369JDfXY7I6GuAd9HXYkNfA7wr1r5GKPaQjIwMSVJbW1v4O5CTTTAYVEFBQVKPUbJjnDaMUfrrOC8en4iODX1NsuN4YIzJg742MvS15GHDGCU7xjnSvkYo9pCLU/1ZWVlJ+4C9yIYxSnaM04YxSuISwxjZ1NckO8bJGJMHfS029LXkY8MYJTvGGWtf44O2AAAAAADWIhQDAAAAAKxFKPaQzMxM/fjHP1ZmZqbbpTjGhjFKdozThjFK9ozTKbbcfzaMkzEmD1vG6RRb7j8bxmnDGCU7xjnSMaYYY0ycawIAAAAAICEwUwwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIPqa6ult/vV0VFhXp6etwuJ+7+/Oc/Kzc3VwsXLtTChQv1ySefuF1SXHV3d+uWW27RhAkTdPToUUnSr371K82ZM0e33Xab2traXK5w5CKN8dprrw3v04aGBpcrHLmDBw/K7/fr1ltv1Te/+U2dP38+6fbjaEr2viYld2+jr9HXMBB9LbHR1+hrERl4wqFDh0xFRYUxxpjHH3/cvPjiiy5XFH8ffvihWblypdtlOOb8+fPm9OnT5p577jHvvfee6enpMSUlJSYUCpm9e/eaqqoqt0scscvHaIwxs2bNcrmq+Dp16pQ5d+6cMcaYjRs3mu3btyfdfhwtNvQ1Y5K7t9HXkgN9LX7oa4mPvpYc4t3XmCn2iJaWFi1ZskSSVFZWpubmZpcrcsa+ffvk9/v1gx/8QCbJPvg8PT1dubm54Z//+Mc/6vrrr1dGRobmzZun9957z8Xq4uPyMUrSp59+qltvvVVr1qxRZ2enS5XFT35+vsaNGydJ+tKXvqT3338/6fbjaLGlr0nJ29voa/Q19EdfS3z0NfpaJIRij+jq6lJWVpYkyefzJcWD9XJTp07VBx98oLfeekunT5/Wa6+95nZJjrp0n0pSb2+vi9U4Z9++fXrzzTdVVlamxx57zO1y4uajjz7S7t27NX/+fCv2oxNs6GuSXb2NvpbY6GsjR19LPvS1xBavvkYo9ohJkyYpGAxK+uLgzM7Odrmi+MvMzNT48eOVkpKilStX6siRI26X5KhL96kkpaWluViNc3JyciRJq1evTpp9GgwGdffdd2vbtm2aMmWKFfvRCTb0Ncmu3kZfS1z0tfigryUf+lriimdfIxR7xOzZs7Vr1y5JUn19vebNm+dyRfHX3d0d/vdbb72lmTNnuliN82bOnKljx46pp6dH+/bt04033uh2SXHX09OjUCgkKXn2aW9vryoqKvToo4/qK1/5ihX70Sk29DXJrt5mw/FAX8NQ6GvJx4bjgb52ZSkmmd4kkOCqq6v1zjvv6Oqrr9a2bduUkZHhdklxVVdXp0ceeUTjxo3TNddcoxdeeEHp6elulxVXS5cu1ZEjR/TlL39ZGzZs0JgxY7R582aNGTNGv/zlL1VQUOB2iSN26RjvuOMObd++XePHj1dmZqZeeOGFhB/jyy+/rPvuu0833HCDJOnb3/62jDFJtx9HS7L3NSn5ext9jb6G/uhriY++Rl+7HKEYAAAAAGAtLp8GAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFAAAAAABrEYoBAAAAANYiFAMAAAAArEUoBgAAAABYi1AMAAAAALAWoRgAAAAAYC1C8RC6u7t1yy23aMKECTp69Gi/ZRcuXFBlZaX8fr8eeOABlyoEAAAAAIwEoXgIY8eO1euvv65Vq1YNWLZz507NmDFDTU1N+uyzz9Tc3OxChQAAAACAkSAUDyE9PV25ubkRl7W0tGjJkiWSpLKyMkIxAAAAACSgdLcLSFRdXV3KysqSJPl8PnV2dkZcLxQKKRQKhX82xqinp0eTJ09WSkrKqNQKAPFEXwOQbOhrgN2YKY7RpEmTFAwGJX0RkLOzsyOut2nTJvl8vvDtqquu0pQpU9Td3T2a5QJA3NDXACQb+hpgN0JxjGbPnq1du3ZJkurr6zVv3ryI623cuFGBQCB8a2trG80yASDu6GsAkg19DbAbl09fwdKlS3XkyBEdP35cGzZsUEtLi37+859r+fLl+s1vfiO/36+bbrpJc+bMifj/MzMzlZmZOcpVA4Bz6GsAkg19DbBbijHGuF2ETYLBoHw+nwKBQPg9yQCQyOhrAJINfQ2wC5dPAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtQjEAAAAAwFqE4lGyZcsWFRYWqri42O1SACAu6GsAkg19DbBTijHGuF2ETYLBoHw+nwKBgLKystwuBwBGjL4GINnQ1wC7MFMMAAAAALAWoRgAAAAAYC1CMQDAEzo6OlReXq6cnByVl5ero6PD7ZIAAIAFCMUAAE+orKxUQ0ODOjs71dDQoMrKSrdLAgAAFiAUAwA8Yf/+/ert7ZUk9fb2qrW11eWKAACADQjFAABPKCkpUVpamiQpLS2Nr0QBAACjglAMAPCEmpoaLV68WDk5OVq8eLFqamrcLgkAAFgg3e0CAACQpLy8PNXV1bldBgAAsAwzxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKL6C6upq+f1+VVRUqKenJ/z7PXv2qKCgQAsXLlRpaamLFQIAAAAAYkUoHsLhw4fV3t6upqYmFRYW6tVXX+23/K677tKePXvU2NjoUoUAAAAAgJEgFA+hpaVFS5YskSSVlZWpubm53/IdO3bI7/dr8+bNbpQHAAAAABghvpJpCF1dXZo2bZokyefzqbOzM7ysqKhIx48flyTdfvvtmj9/vmbNmjVgG6FQSKFQKPxzMBh0uGoAcBZ9DUCyoa8BdmOmeAiTJk0KN8Wuri5lZ2eHl02YMEEZGRnKyMjQihUr9O6770bcxqZNm+Tz+cK3goKCUakdAJxCXwOQbOhrgN0IxUOYPXu2du3aJUmqr6/XvHnzwssufQWxqalJM2fOjLiNjRs3KhAIhG9tbW3OFg0ADqOvAUg29DXAboTiIdx0003Kz8+X3+/XsWPHtHLlSm3YsEGStH37dpWUlGju3LmaPn26FixYEHEbmZmZysrK6ncDgERGXwOQbOhrgN1SjDHG7SJsEgwG5fP5FAgEaLgAkgJ9DUCyoa8BdmGmGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFAAAAAABrEYoBAAAAANYiFI+SLVu2qLCwUMXFxW6XAgBxQV8DkGzoa4CdUowxxu0ibBIMBuXz+RQIBJSVleV2OQAwYvQ1AMmGvgbYhZliAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLUdD8Z133qlJkyZp1apVTv4ZAAAAAABi4mgovv/++/XLX/7SyT8BAEBcdXR0qLy8XDk5OSovL1dHRwe1OMiGMTqJ+w8ARs7RUPx3f/d3mjhxopN/AsPg5BMmT8ZIZjy+7VRZWamGhgZ1dnaqoaFBlZWV1OIgG8boJO4/AEPhXGZ4og7FfX19uu666/Twww/3+319fb0yMjL0yiuvxK04xEc0T5jRHjhObpuDGG7jZNNO+/fvV29vrySpt7dXra2t1OIgG8boJO4/wC5OnqvbLOpQnJqaqo0bN+qZZ57R2bNnJUnvvvuuVq9erSeeeEKrV6+Oe5EYmWieMKM9cJzctpOBG3aK9nHCyWbyiGbfl5SUKC0tTZKUlpam4uLi0SpzVGvxSt/00v2diKK9/7yy3wHExslzdauZGJw/f95cc8015rHHHjNtbW1m+vTp5jvf+U7EdX//+9+blStXxvJnPOH73/++mT9/vlmzZo0JhULh358/f97cc889Zv78+eb+++8f9vYCgYCRZAKBQL/ft7e3m7KyMpOdnW3KyspMe3t73MZQVlZm0tLSjCSTlpZmysrKBl03OzvbSArfcnJyXNt2NOtHU4fTnNyXXpGoY4z2ceKlx5WXDdbXvCSafXnx8Z2Tk+P649vJWrzy+HZyjF7pVU7WEe3955X97nWJ0Ne8YjQe324fw17i5Lm6k7y+L2MKxcYY88///M8mOzvb3HDDDWb58uXmwoULEddL5FB86NAhU1FRYYwx5vHHHzcvvvhieNmvf/1r88Mf/tAYY8y6devMvn37hrXNwZpsLCdsw31QRfOEGe2B4+S2nQzc0dyH0d7fTu5Lr/BKgzUmuvsw1seJF8KRlyXCyWO0+94pXjrmo7lPvFR3NEbjuXU463upZ3rlWPC6ROhrXhHruWOiHTvR8FI/8cq5jJOPk3hIMcaYWGaYP/30U+Xm5mrmzJl6++23NX78+AHrfOMb39ChQ4d07tw5ZWdn67XXXkuoy6KefvppTZgwQWvXrtXBgwe1bds2PfXUU5Kkhx56SMuWLdOCBQu0Y8cOffjhh6qurh6wjVAopFAoFP45GAyqoKBAgUBAWVlZ4d/n5OSos7Mz/HN2drZOnjypi7snNTVVfX19kqTbb79du3fvVl9fn1JTU7Vo0SL99re/lSSlpKRIUsT/d6VlKSkpOnXqlKqqqnTw4EEVFRXp2WefVX5+vlJSUsLrpqamynzxgkr4/w1n2SeffKJ169bpwIEDmjVrlp5//nnl5uYOWlukWvLy8iKOafny5WpsbAzfJ6Wlpdq5c+egtUW6D2trayPWPdT9fek2L/6cn58ffmvBxX154sSJqOq+0n4a6b6ItOzi/X3gwAEVFxcPeX9PnTp1wBhPnjw5osderHUvW7Zs2Ps+lvtbkjIzM4W/Gm5fi9Xnn38uKfLxNdzH1OXLIj1OamtrR+34crp/D7XMjT4Yj7pjGdPly6ZNmxaxH0f6fytWrBhyjCPp35HqOHXq1Kg+9iL1wbS0NC1evFh1dXWyXbR9zYk+NRrPmdEuG05t0Zz3GGO0YsWKfsfOokWLtHPnzoh/P9Kxc/H82MkxRVrW0dGh9evXq7W1VUVFRXruueeUl5c37HP1wfpramqqli9f3m/9S5+jBjtfG875sZcee5EeJydPnhz0/0V6nFy8DwereyTnazF/+vR9990nSTpz5kz4vSyXq6+v1yeffKLPPvtMH3/8cUIFYknq6uoKN0Kfz9cvtA617FKbNm2Sz+cL3woKCiKud+l7glJTU1VUVDRoXa2treEHQV9fnw4cOBD94AaRl5en2tpanTp1SrW1tcrLy4v7tk+cODGsbV9c/+TJk1dcf+vWrSotLVV2drZKS0u1devWIbcdzX0Y7f1dVFSk1NQvDq3U1FTNmjVr0HUPHDjQb9sHDx4cctvR6Ojo0IoVKzRt2jStWLHiiu8bq6qqUmNjo86ePavdu3erqqpq0HWjGaPTorkPo32cILLh9jUvuXTfL1q0yLV972T/vnjM5+fnD+uY37p1qxYtWhQ+Hp577jlX6nZScXFxv14Vz+fWaHqPl3rmxWMhJydHixcvVk1NjWu1eEki9jWviPbxffmxM9SxFs0x7LSqqirt3r1bZ8+eVWNj45DnSdH2k8vXH6qfOHmuHu2548X1p06dquXLlw+5vpOPk7gYch55EI888oiZPHmyOXLkiJk8ebJ56qmnYtmM5z399NPmF7/4hTHGmNbWVvPd7343vOyhhx4yb775pjHGmFdeecX85Cc/ibiNzz//3AQCgfCtra1tyPcUO3EZMgaK5j700iXlTl5eE8ullF54DyDHw+gbbl/DQE4+Xr2ybS9dau2Vt/h45fJFDI6+Fjsn39fupWPHyc+68cq5jJf64GjfJ1GH4q1bt5qxY8ealpYWY8wX77W9+uqrTU9PT9yLc9vl7yl+6aWXwssuf09xc3PzsLYZj/eoeKlBJKpo7sPRCIBONAgvfRCDDe+VsRnvvRs+Jx+vTr5X1Csv3HrpA6voPcmNvuacRD12nAyAXrlPnPxQ3GiN9n0SVSh+4403TEZGhtmxY0f4d4FAwFx11VXm+eefj3txXnD5p0+vX7/eGPPFp0+vXbvWzJ8/33zve98b9vZoshgJJ1+l9Mqn3PIhMInH9r7mlZlRr8w0OHkMe2WMSH70NW/0tWg5+UGqXgmuTkrUGe54PF6HHYoPHDhgxo8fb372s58NWPajH/3IXHvttYN+AjX+yvYmi5HhkiN4kRt9zUsnbF55zHrlmHfy/rDlRTMvPb5tZfv5mlf6WrSiqTtRx+ikRJ3hjse+jPkrmRAb25ssRsYrzSdaiRrmMTxu9DUvnczYEtSGyytXnSQyW8bpZbafryVqX4um7kQdIwaKx76M+dOnAYy+vLw81dXV6cyZM6qrq4vrJw46qaamRosXLx7Wp50m6hgxuvbv36/e3l5JUm9vr1pbW12r5dJvD0hLS0u4b1qINyeP4Wh6SSLz0uMbdkrUvhZN3Yk6RgwUj31JKAbgOIIu4s1LJzO2BDUvsKWXeOnxDTslal+Lpu5EHSMGise+TDHm//8GZIyKYDAon8836JfBA0CicaOvdXR0qLKyUq2trSouLlZNTU3SBiTYh8e3+zhfA+xCKB5lNFkAyYa+BiDZ0NcAu3D5NAAAAJDEOjo6VF5erpycHJWXl6ujo8PtkgBPIRQDAAAASayyslINDQ3q7OxUQ0ODKisr3S4J8BRCMQAAAJDE+ERzYGiEYgAAACCJ8YnmwNAIxQAAAEAS4+uHgKGlu10AAAAAAOdc/I5vAJExUwwAAAAAsBaheJRs2bJFhYWFvIcDQNKgrwFINvQ1wE4pxhjjdhE24cvgASQb+hqAZENfA+zCTDEAAAAAwFqEYgAAAACAtQjFAAAAwDB0dHSovLxcOTk5Ki8vV0dHh9slAYgDQjEAAAAwDJWVlWpoaFBnZ6caGhpUWVnpdkkA4oBQDAAAAAzD/v371dvbK0nq7e1Va2ura7Uwaw3ED6EYAAAAGIaSkhKlpaVJktLS0lz96iZmrYH4IRQDAAAAw1BTU6PFixcrJydHixcvVk1NjWu1eGnWGkh06W4XAAAAACSCvLw81dXVuV2GpC9mrRsaGtTb2+v6rDWQ6JgpBgAAABKMl2atgURHKAYAAAAc4OSHYV2ctT5z5ozq6uqUl5cXt20DtiEUAwAAAA7gw7CAxEAoBgAAABzAh2EBiYFQPITq6mr5/X5VVFSop6en37I9e/aooKBACxcuVGlpqUsVAgAAwKu89BVOAAZHKB7E4cOH1d7erqamJhUWFurVV18dsM5dd92lPXv2qLGx0YUKAQAA4GV8GBaQGPhKpkG0tLRoyZIlkqSysjJt27ZNa9as6bfOjh079M4772jVqlV64IEHIm4nFAopFAqFfw4Gg84VDQCjgL4GINk41de89BVOAAbHTPEgurq6lJWVJUny+Xzq7Ozst7yoqEjHjx9XY2Ojfve73+ngwYMRt7Np0yb5fL7wraCgwPHaAcBJ9DUAyYa+BtgtxRhj3C7CTe3t7Vq1atWA35eXl6ugoEBr167VgQMHVFNTo6eeeiriNp555hllZmbq3nvvHbAs0iuPBQUFCgQC4dANAImEvgYg2dDXALtZf/l0fn6+9u7dO+D3hw8f1k9/+lOtXbtW9fX1mjdvXr/lwWAw3CSbmpr0rW99K+L2MzMzlZmZGf/CAcAl9DUAycYLfa2jo0OVlZXav3+/SkpKVFNTw3cPA6OEy6cHcdNNNyk/P19+v1/Hjh3TypUrJUkbNmyQJG3fvl0lJSWaO3eupk+frgULFrhZLgAAABIY32kMuMf6y6dHWzAYlM/n43IcAEmDvgYg2bjR13Jycvp9hk1OTo7OnDkzKn8bsB0zxQAAAIDL+E5jwD2EYgAAAMBlfKcx4B7rP2gLAAAAcBvfaQy4h5liAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxaNky5YtKiwsVHFxsdulAEBc0NcAJBv6GmCnFGOMcbsImwSDQfl8PgUCAWVlZbldDgCMGH0NQLKhrwF2YaYYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFAAAAAABrEYoBAAAAANYiFAMAAAAArEUoBgAAAABYi1AMAAAAALAWoRgAAAAAYC1CMQAAAADAWoRiAAAAAIC1CMUAAAAAAGsRigfR3d2tW265RRMmTNDRo0cHLL9w4YIqKyvl9/v1wAMPuFAhAAAAAGCkCMWDGDt2rF5//XWtWrUq4vKdO3dqxowZampq0meffabm5uZRrhAAAAAAMFLpbhfgVenp6crNzR10eUtLi5YtWyZJKisrU3Nzs+bOnTtgvVAopFAoFP45GAzGv1gAGEX0NQDJhr4G2I2Z4hh1dXUpKytLkuTz+dTZ2RlxvU2bNsnn84VvBQUFo1kmAMQdfQ1AsqGvAXazfqa4vb094iXStbW1ys7OHvT/TZo0KfwqYldX16Drbty4UQ8++GD4Z2OMenp6NHHixBFWDgDuoK8BSDb0NcBu1ofi/Px87d27N+r/N3v2bO3atUsLFixQfX297r333ojrZWZmKjMzc6RlAoBn0NcAJBv6GmA3Lp8ewtKlS7Vr1y5VVVWppqZGkrRhwwZJ0vLly9XW1ia/36+xY8dqzpw5LlYKAAAAAIhFijHGuF0EAAAAAABuYKYYAAAAAGAt699T7CXGGHV3d7tdBoAIJk6cqJSUFLfLSDj0NcC76Guxoa8B3hVrXyMUe8iZM2c0ZcoUt8sAEMHp06eH/O5yREZfA7yLvhYb+hrgXbH2NUKxh2RkZEiS2trawt+BnGyCwaAKCgqSeoySHeO0YYzSX8d58fhEdGzoa5IdxwNjTB70tZGhryUPG8Yo2THOkfY1QrGHXJzqz8rKStoH7EU2jFGyY5w2jFESlxjGyKa+JtkxTsaYPOhrsaGvJR8bxijZMc5Y+xoftAUAAAAAsBahGAAAAABgLUKxh2RmZurHP/6xMjMz3S7FMTaMUbJjnDaMUbJnnE6x5f6zYZyMMXnYMk6n2HL/2TBOG8Yo2THOkY4xxRhj4lwTAAAAAAAJgZliAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUe0h1dbX8fr8qKirU09Pjdjlx9+c//1m5ublauHChFi5cqE8++cTtkuKqu7tbt9xyiyZMmKCjR49Kkn71q19pzpw5uu2229TW1uZyhSMXaYzXXntteJ82NDS4XOHIHTx4UH6/X7feequ++c1v6vz580m3H0dTsvc1Kbl7G32NvoaB6GuJjb5GX4vIwBMOHTpkKioqjDHGPP744+bFF190uaL4+/DDD83KlSvdLsMx58+fN6dPnzb33HOPee+990xPT48pKSkxoVDI7N2711RVVbld4ohdPkZjjJk1a5bLVcXXqVOnzLlz54wxxmzcuNFs37496fbjaLGhrxmT3L2NvpYc6GvxQ19LfPS15BDvvsZMsUe0tLRoyZIlkqSysjI1Nze7XJEz9u3bJ7/frx/84AcySfbB5+np6crNzQ3//Mc//lHXX3+9MjIyNG/ePL333nsuVhcfl49Rkj799FPdeuutWrNmjTo7O12qLH7y8/M1btw4SdKXvvQlvf/++0m3H0eLLX1NSt7eRl+jr6E/+lrio6/R1yIhFHtEV1eXsrKyJEk+ny8pHqyXmzp1qj744AO99dZbOn36tF577TW3S3LUpftUknp7e12sxjn79u3Tm2++qbKyMj322GNulxM3H330kXbv3q358+dbsR+dYENfk+zqbfS1xEZfGzn6WvKhryW2ePU1QrFHTJo0ScFgUNIXB2d2drbLFcVfZmamxo8fr5SUFK1cuVJHjhxxuyRHXbpPJSktLc3FapyTk5MjSVq9enXS7NNgMKi7775b27Zt05QpU6zYj06woa9JdvU2+lrioq/FB30t+dDXElc8+xqh2CNmz56tXbt2SZLq6+s1b948lyuKv+7u7vC/33rrLc2cOdPFapw3c+ZMHTt2TD09Pdq3b59uvPFGt0uKu56eHoVCIUnJs097e3tVUVGhRx99VF/5yles2I9OsaGvSXb1NhuOB/oahkJfSz42HA/0tStLMcn0JoEEV11drXfeeUdXX321tm3bpoyMDLdLiqu6ujo98sgjGjdunK655hq98MILSk9Pd7usuFq6dKmOHDmiL3/5y9qwYYPGjBmjzZs3a8yYMfrlL3+pgoICt0scsUvHeMcdd2j79u0aP368MjMz9cILLyT8GF9++WXdd999uuGGGyRJ3/72t2WMSbr9OFqSva9Jyd/b6Gv0NfRHX0t89DX62uUIxQAAAAAAa3H5NAAAAADAWoRiAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFAAAAAABrEYqH0N3drVtuuUUTJkzQ0aNH+y27cOGCKisr5ff79cADD7hUIQAAAABgJAjFQxg7dqxef/11rVq1asCynTt3asaMGWpqatJnn32m5uZmFyoEAAAAAIwEoXgI6enpys3NjbispaVFS5YskSSVlZURigEAAAAgAaW7XUCi6urqUlZWliTJ5/Ops7Mz4nqhUEihUCj8szFGPT09mjx5slJSUkalVgCIJ/oagGRDXwPsxkxxjCZNmqRgMCjpi4CcnZ0dcb1NmzbJ5/OFb1dddZWmTJmi7u7u0SwXAOKGvgYg2dDXALsRimM0e/Zs7dq1S5JUX1+vefPmRVxv48aNCgQC4VtbW9tolgkAcUdfA5Bs6GuA3bh8+gqWLl2qI0eO6Pjx49qwYYNaWlr085//XMuXL9dvfvMb+f1+3XTTTZozZ07E/5+ZmanMzMxRrhoAnENfA5Bs6GuA3VKMMcbtImwSDAbl8/kUCATC70kGgERGXwOQbOhrgF24fBoAAAAAYC1CMQAAAADAWoRiAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAADAAR0dHSovL1dOTo7Ky8vV0dHhdkkAIiAUAwAAAA6orKxUQ0ODOjs71dDQoMrKSrdLAhABoRgAAABwwP79+9Xb2ytJ6u3tVWtrq8sVAYiEUDxKtmzZosLCQhUXF7tdCgDEBX0NQLKJd18rKSlRWlqaJCktLY1+CXhUijHGuF2ETYLBoHw+nwKBgLKystwuBwBGjL4GINnEq691dHSosrJSra2tKi4uVk1NjfLy8uJYKYB4SHe7AAAAACAZ5eXlqa6uzu0yAFwBl08DAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFAAAAAABrEYoBAAAAANYiFAMAAAAArEUoBgAAAABYi1B8BdXV1fL7/aqoqFBPT0/493v27FFBQYEWLlyo0tJSFysEAAAAAMSKUDyEw4cPq729XU1NTSosLNSrr77ab/ldd92lPXv2qLGx0aUKAQAAAAAjQSgeQktLi5YsWSJJKisrU3Nzc7/lO3bskN/v1+bNm90oDwAAAAAwQuluF+BlXV1dmjZtmiTJ5/Ops7MzvKyoqEjHjx+XJN1+++2aP3++Zs2aNWAboVBIoVAo/HMwGHS4agBwFn0NQLKhrwF2Y6Z4CJMmTQo3xa6uLmVnZ4eXTZgwQRkZGcrIyNCKFSv07rvvRtzGpk2b5PP5wreCgoJRqR0AnEJfA5Bs6GuA3QjFQ5g9e7Z27dolSaqvr9e8efPCyy59BbGpqUkzZ86MuI2NGzcqEAiEb21tbc4WDQAOo68BSDb0NcBuhOIh3HTTTcrPz5ff79exY8e0cuVKbdiwQZK0fft2lZSUaO7cuZo+fboWLFgQcRuZmZnKysrqdwOAREZfA5Bs6GuA3VKMMcbtImwSDAbl8/kUCARouACSAn0NQLKhrwF2YaYYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFAAAAAABrEYoBAAAAANYiFAMAAAAArEUoBgAAAABYi1AMAAAAALAWoRgAAAAAYC1CMQAAAADAWoRiAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUj5ItW7aosLBQxcXFbpcCAHFBXwOQbOhrgJ1SjDHG7SJsEgwG5fP5FAgElJWV5XY5ADBi9DUAyYa+BtiFmWIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtx0JxW1ubFi5cqMLCQt1444165ZVXnPpTAAAAAADExLFQnJ6erp/97Gc6duyYdu/erX/4h3/QuXPnnPpzAAAAAADLdHR0qLy8XDk5OSovL1dHR0fU23AsFE+dOlVf//rXJUlTpkxRdna2Ojs7nfpzAAAAAADLVFZWqqGhQZ2dnWpoaFBlZWXU24g6FPf19em6667Tww8/3O/39fX1ysjIiHiZ9IEDB9TX16eCgoKoCwQAAACQHOIxq+fGtr1Sh1fG6CX79+9Xb2+vJKm3t1etra1RbyPqUJyamqqNGzfqmWee0dmzZyVJ7777rlavXq0nnnhCq1ev7rf+X/7yF61du1bPPvts1MV5QXV1tfx+vyoqKtTT0xP+/YULF1RZWSm/368HHnjAxQoBAACAxBCPWT03tu2VOqLdtg0huqSkRGlpaZKktLQ0FRcXR72NmC6frqio0OTJk/Xkk0/q448/1t///d/r7rvvVnV1db/1QqGQ7rzzTm3cuFFz586N5U+56vDhw2pvb1dTU5MKCwv16quvhpft3LlTM2bMUFNTkz777DM1Nze7WCkAAADgffGY1XNj216pI9pte+WFAifDeU1NjRYvXqycnBwtXrxYNTU1UW8jplCcnp6uhx9+WE8++aSWLl2qm2++WU8++WS/dYwxqqys1G233aa77747lj/jupaWFi1ZskSSVFZW1i/4DrUMAADEX7QnVdGs7+S2vSRR68bIeOly3njM6rmx7WjG6aUxeuWFAifDeV5enurq6nTmzBnV1dUpLy8v6m2kGGNMLH/8008/VW5urmbOnKm3335b48eP77d87969WrBggW688cbw7/7lX/5FN9xwQyx/zhVPPPGECgsLdccdd+iDDz7Qo48+qpdeekmStH79en3nO9/R17/+de3evVv/9m//pieeeGLANkKhkEKhUPjnYDCogoICBQIBZWVlDVj/888/l/TFZerGGF3cPampqerr65MkpaSkSFLcl6WkpOjUqVOqqqrSgQMHVFxcrGeffVb5+flKSUkJr3tpbRf/X7TLRnNM8ax7OGOKdB/m5eUl9JiScT9FU3dmZqbwV4nW17z82ErGMbW3t4d7YFFRkbZu3ar8/PxB/9/p06dVVVWl1tZWFRUV6fnnn9eUKVMi1nb77bdr9+7d6uvrU2pqqhYtWqTf/va3g45p2bJlamxsDK9fWlqq2traiHUvX758wLo7d+4ctO6harm87k8++UTr1q1Ta2uriouL9dxzzyk3N9eV/XRp3WlpaVq8eLHq6upku2j7WrTc7oORjoWdO3cO+piK13EZqbaL50kHDx5UUVFRTOdJV6r7wIEDmjVr1pB1R3t8RdNPLvbBgwcPatasWVfsg9HUdrGfXOyxV+on0dTt5PPVtGnTwm+9laTs7GydOHEiqn1xpf00ZswYxSrmT5++7777JElnzpwJv1pxqfnz56uvr09HjhwJ3xIpEEvSpEmTFAwGJUldXV3Kzs4e1rJLbdq0ST6fL3wb7MPGLr76NG3aNK1YseKKr2avWLFiWOteuv7UqVOvuH5VVZUaGxt19uxZ7d69W1VVVVfc9vLly12v++K6+fn5w9p2NKKtO5r7MNq6R+P+duI+RHIZbl/zkovHznD7yXCPs4vrXzzWli9fPqzjcjh1xFKLF1zaAxsbG6/4PFJVVaXdu3eH11+3bt2g67a2toZPhvr6+nTgwIEht33xgz4vrn/w4MG4rBttLevWrQuPcffu3UOOUYquH0fbuy+t281ZI69JxL4WjWgf304el3l5eaqtrdXJkydVW1s75KxetI/vvLw87dy5UydOnLjitqMVzX14cYynTp2Kex0Xtz3cMW7dulWlpaXKzs7WokWLtHXr1kHXjeW8dLjnmsXFxUpN/SJ6pqamqqioaMhtjzoTg0ceecRMnjzZHDlyxEyePNk89dRTsWzG8w4dOmQqKiqMMcY8/vjj5qWXXgov+/Wvf21++MMfGmOMWbdunWlubo64jc8//9wEAoHwra2tzUgygUCg33plZWUmLS3NSDJpaWmmrKxs0LqiWTfa9bOzs42k8C0nJydu23ay7mi33d7ebsrKykx2drYpKysz7e3tcdt2NPehl+6TaNaP5v6LZX1423D7mpd45djx0nEZjWi2He3ziA09M1GfW22SiH0tUc9louGlx6uTtXjlPGk0nqNycnI8ee4YdSjeunWrGTt2rGlpaTHGfBEWr776atPT0xP34rzg+9//vpk/f75Zs2aNCYVCZv369cYYY86fP2/Wrl1r5s+fb773ve8Ne3uBQCBik42m+XjphMMrdTt5wuGlbXvl/vbSiX006xPmnTFYX/PSvvTKseOl49KpE2ovnlQNZ30nt+2l59Zox2mrwfqak6Lta04eO05uOxrRPr6jEetzlBPj9Er4d/I5KlqjfZ9EFYrfeOMNk5GRYXbs2BH+XSAQMFdddZV5/vnn415cMhqsySbqCYdX6vbSK6BOnih55f720om9V+4Tm8Wjr0W7PtseyCsv4DkZLhOVl55bMTxuhGInz2Wi5ZXj0snHt5eOHSf3ZTS8dJ402vfJsEPxgQMHzPjx483PfvazAct+9KMfmWuvvdZcuHAhrsUloyvNqLj9ana0vFJ3or4C6qX7xCthPlFn6WwWjytgol3fydkxW45Lr1weiYGcfExheNwIxU6+sJWoEnUWOlpe2ZdeyhijfZ/E9J5ixM6NJouBOIkYGS+d2Htlls5miTBTbINEfXEQ8KJEmCnmuBwZLz2PsC8HGu37hFA8ygjFwNASdZbOZvG4Aiba9dk3I8d9CAzOzfcUc0yODu5vXCrm7ylGbILBoHw+X9y+9w4A3EZfA5Bs6GuAXWL+nmIAAAAAABIdoRgAAAAAYC1CMQAAAADAWoRiAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBaheJRs2bJFhYWFKi4udrsUAIgL+hqAZENfA+yUYowxbhdhk2AwKJ/Pp0AgoKysLLfLAYARo68BSDb0NcAuzBQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFAAAAAABrEYoBAAAAANYiFAMAAAAArEUoBgAAAABYi1AMAAAAALAWoXgI1dXV8vv9qqioUE9PT79le/bsUUFBgRYuXKjS0lKXKgQAAAAAjASheBCHDx9We3u7mpqaVFhYqFdffXXAOnfddZf27NmjxsZGFyoEAAAAAIwUoXgQLS0tWrJkiSSprKxMzc3NA9bZsWOH/H6/Nm/ePNrlAQAAAADiIN3tAryqq6tL06ZNkyT5fD51dnb2W15UVKTjx49Lkm6//XbNnz9fs2bNGrCdUCikUCgU/jkYDDpYNQA4j74GINnQ1wC7WT9T3N7ervnz5w+4GWPCDbGrq0vZ2dn9/t+ECROUkZGhjIwMrVixQu+++27E7W/atEk+ny98KygocHxMAOAk+hqAZENfA+yWYowxbhfhRYcPH9ZPf/pT/a//9b/0j//4j/qbv/kb/ef//J/Dy4PBoLKysiRJa9as0be+9S0tWLBgwHYivfJYUFCgQCAQ/v8AkEjoawCSDX0NsJv1M8WDuemmm5Sfny+/369jx45p5cqVkqQNGzZIkrZv366SkhLNnTtX06dPjxiIJSkzM1NZWVn9bgCQyOhrAJINfQ2wGzPFoywYDMrn8/HKI4CkQV8DkGzoa4BdmCkGAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFo2TLli0qLCxUcXGx26UAQFzQ1wAkG/oaYKcUY4xxuwibBINB+Xw+BQIBZWVluV0OAIwYfQ1AsqGvAXZhphgAAAAAYC1CMQAAAADAWoRiAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAAAAAMBahGIAAAAAgLUIxQAAAAAAaxGKB9Hd3a1bbrlFEyZM0NGjRwcsv3DhgiorK+X3+/XAAw+4UCEAAAAAYKQIxYMYO3asXn/9da1atSri8p07d2rGjBlqamrSZ599pubm5lGuEAAAAAAwUuluF+BV6enpys3NHXR5S0uLli1bJkkqKytTc3Oz5s6dO2C9UCikUCgU/jkYDMa/WAAYRfQ1AMmGvgbYjZniGHV1dSkrK0uS5PP51NnZGXG9TZs2yefzhW8FBQWjWSYAxB19DUCyoa8BdrN+pri9vT3iJdK1tbXKzs4e9P9NmjQp/CpiV1fXoOtu3LhRDz74YPhnY4x6eno0ceLEEVYOAO6grwFINvQ1wG7Wh+L8/Hzt3bs36v83e/Zs7dq1SwsWLFB9fb3uvffeiOtlZmYqMzNzpGUCgGfQ1wAkG/oaYDcunx7C0qVLtWvXLlVVVammpkaStGHDBknS8uXL1dbWJr/fr7Fjx2rOnDkuVgoAAAAAiEWKMca4XQQAAAAAAG5gphgAAAAAYC3r31PsJcYYdXd3u10GgAgmTpyolJQUt8tIOPQ1wLvoa7GhrwHeFWtfIxR7yJkzZzRlyhS3ywAQwenTp4f87nJERl8DvIu+Fhv6GuBdsfY1QrGHZGRkSJLa2trC34GcbILBoAoKCpJ6jJId47RhjNJfx3nx+ER0bOhrkh3HA2NMHvS1kaGvJQ8bxijZMc6R9jVCsYdcnOrPyspK2gfsRTaMUbJjnDaMURKXGMbIpr4m2TFOxpg86Guxoa8lHxvGKNkxzlj7Gh+0BQAAAACwFqEYAAAAAGAtQrGHZGZm6sc//rEyMzPdLsUxNoxRsmOcNoxRsmecTrHl/rNhnIwxedgyTqfYcv/ZME4bxijZMc6RjjHFGGPiXBMAAAAAAAmBmWIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhR7SHV1tfx+vyoqKtTT0+N2OXH35z//Wbm5uVq4cKEWLlyoTz75xO2S4qq7u1u33HKLJkyYoKNHj0qSfvWrX2nOnDm67bbb1NbW5nKFIxdpjNdee214nzY0NLhc4cgdPHhQfr9ft956q775zW/q/PnzSbcfR1Oy9zUpuXsbfY2+hoHoa4mNvkZfi8jAEw4dOmQqKiqMMcY8/vjj5sUXX3S5ovj78MMPzcqVK90uwzHnz583p0+fNvfcc4957733TE9PjykpKTGhUMjs3bvXVFVVuV3iiF0+RmOMmTVrlstVxdepU6fMuXPnjDHGbNy40Wzfvj3p9uNosaGvGZPcvY2+lhzoa/FDX0t89LXkEO++xkyxR7S0tGjJkiWSpLKyMjU3N7tckTP27dsnv9+vH/zgBzJJ9sHn6enpys3NDf/8xz/+Uddff70yMjI0b948vffeey5WFx+Xj1GSPv30U916661as2aNOjs7XaosfvLz8zVu3DhJ0pe+9CW9//77SbcfR4stfU1K3t5GX6OvoT/6WuKjr9HXIiEUe0RXV5eysrIkST6fLykerJebOnWqPvjgA7311ls6ffq0XnvtNbdLctSl+1SSent7XazGOfv27dObb76psrIyPfbYY26XEzcfffSRdu/erfnz51uxH51gQ1+T7Opt9LXERl8bOfpa8qGvJbZ49TVCsUdMmjRJwWBQ0hcHZ3Z2tssVxV9mZqbGjx+vlJQUrVy5UkeOHHG7JEdduk8lKS0tzcVqnJOTkyNJWr16ddLs02AwqLvvvlvbtm3TlClTrNiPTrChr0l29Tb6WuKir8UHfS350NcSVzz7GqHYI2bPnq1du3ZJkurr6zVv3jyXK4q/7u7u8L/feustzZw508VqnDdz5kwdO3ZMPT092rdvn2688Ua3S4q7np4ehUIhScmzT3t7e1VRUaFHH31UX/nKV6zYj06xoa9JdvU2G44H+hqGQl9LPjYcD/S1K0sxyfQmgQRXXV2td955R1dffbW2bdumjIwMt0uKq7q6Oj3yyCMaN26crrnmGr3wwgtKT093u6y4Wrp0qY4cOaIvf/nL2rBhg8aMGaPNmzdrzJgx+uUvf6mCggK3SxyxS8d4xx13aPv27Ro/frwyMzP1wgsvJPwYX375Zd1333264YYbJEnf/va3ZYxJuv04WpK9r0nJ39voa/Q19EdfS3z0Nfra5QjFAAAAAABrcfk0AAAAAMBahGIAAAAAgLUIxQAAAAAAaxGKAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFAAAAAABrEYoBAAAAANYiFAMAAAAArEUoBgAAAABYi1AMAAAAALAWoRgAAAAAYC1CMQAAAADAWoRiAAAAAIC1CMUAAAAAAGsRigEAAAAA1iIUAwAAAACsRSgGAAAAAFiLUAwAAAAAsBahGAAAAABgLUIxAACIysKFC/Vf/st/cbuMARYuXKiUlBSlpKToyJEjkqTKysrw737zm9+4Wh8AwJsIxQAAIGlUVVXp1KlT+trXviZJ2rx5s06dOuVyVQAAL0t3uwAAAIB4GTdunPLz88M/+3w++Xw+FysCAHgdM8UAACBmv/vd7zR//nxdddVVysnJ0bJly/SnP/2p3zrd3d2qqKjQ+PHjNXXqVP3P//k/r3gJ9ooVK8KXPV9+q62tdXhUAACbEIoBAEDMzp07pwcffFCtra1qbGxUamqq7rzzTvX19YXXefDBB7Vv3z7V1taqoaFBTU1NOnTo0JDb3bZtm06dOqU//vGPkqQ33nhDp06d0qlTp7R06VJHxwQAsAuXTwMAgJitXLmy38/PP/+8pkyZomPHjulrX/uauru79Ytf/EIvvfSSSktLJX0ReKdNmzbkdnNyciRJLS0tSklJ0fz58zVx4kRnBgEAsBozxQAAIGZ/+tOftGbNGv3N3/yNsrKydM0110iSPvroI0nS//k//0fnz59XSUlJ+P/4fD599atfHdb2//3f/13/4T/8BwIxAMAxzBQDAICYLV++XAUFBdq6daumTZumvr4+fe1rX1NPT48kyRgjSUpJSen3/y7+/kr+/d//XTfeeGN8iwYA4BLMFAMAgJj85S9/0R/+8Ac98sgjKi0t1d/+7d/q7Nmz/db5j//xP+pLX/qS9u/fH/5dMBgMv1f4Sv785z8Pe1YZAIBYMFMMAABiMmnSJOXk5OjZZ5/V1KlT9dFHH+m//bf/1m+diRMn6p577tF//a//VdnZ2ZoyZYp+/OMfKzU1dcDscSR9fX36v//3/+rjjz/W9OnTh/V/AACIBjPFAAAgJqmpqfrXf/1XHTx4UF/72tf0D//wD/rJT34yYL1/+qd/0pw5c7Rs2TItWrRI8+bN09/+7d9qzJgxV/wb999/v/bt26frrrtu2JdcAwAQDWaKAQBAVPbs2RP+96JFi3Ts2LF+yy8PrxMnTtSLL74Y/vncuXP67//9v2v9+vVX/Fvl5eVqa2sbWcEAAAyBmWIAAOCow4cP6+WXX9af/vQnHTp0SBUVFZKk22+/Pe5/6+mnn9aECRP03nvvSZK+9a1vacKECXH/OwCA5JFiuBYJAAA46PDhw1q3bp2OHz+ujIwMzZo1S//0T/+kG264Ia5/58SJE/p//+//SZKuvvpqZWRk6PTp0woGg5KkqVOnavz48XH9mwCAxEcoBgAAAABYi8unAQAAAADWIhQDAAAAAKxFKAYAAAAAWItQDAAAAACwFqEYAAAAAGAtQjEAAAAAwFqEYgAAAACAtQjFAAAAAABrEYoBAAAAANYiFAMAAAAArEUoBgAAAABY6/8DPCeZA3//0j0AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x600 with 9 Axes>"
       ]
@@ -806,8 +808,8 @@
       "Found nothing\n",
       "\n",
       "ER-01:\n",
-      "Marked:    (0,-1) oL> (0, 0) ==> (0,-1) -L> (0, 0) \n",
       "Marked:    (1,-1) oL> (1, 0) ==> (1,-1) -L> (1, 0) \n",
+      "Marked:    (0,-1) oL> (0, 0) ==> (0,-1) -L> (0, 0) \n",
       "Writing:   (0,-1) oL> (0, 0) ==> (0,-1) -L> (0, 0) \n",
       "Update:    Marking (0, -1) as anc of (0, 0)\n",
       "Writing:   (1,-1) oL> (1, 0) ==> (1,-1) -L> (1, 0) \n",
@@ -1006,9 +1008,9 @@
       "with rule list:  [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-00-d'], ['ER-00-c'], ['ER-03'], ['R-04'], ['ER-09'], ['ER-10'], ['ER-00-b'], ['ER-00-a']]\n",
       "\n",
       "APR:\n",
+      "Marked:    (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n",
       "Marked:    (1,-2) -!> (2, 0) ==> (1,-2) --> (2, 0) \n",
       "Marked:    (2,-1) -!> (2, 0) ==> (2,-1) --> (2, 0) \n",
-      "Marked:    (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n",
       "Writing:   (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n",
       "Writing:   (1,-2) -!> (2, 0) ==> (1,-2) --> (2, 0) \n",
       "Writing:   (2,-1) -!> (2, 0) ==> (2,-1) --> (2, 0) \n",
@@ -1106,7 +1108,13 @@
       "ER-00-b:\n",
       "Found nothing\n",
       "\n",
-      "ER-00-a:\n",
+      "ER-00-a:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Found nothing\n",
       "\n",
       "Orientation phase complete\n",
@@ -1210,13 +1218,7 @@
       "(0,-4) independent (1, 0) given () union {(0, -5), (1, -1)}\n",
       "(0,-4) independent (2, 0) given () union {(0, -5), (2, -1), (1, -2)}\n",
       "(1,-4) independent (0, 0) given () union {(1, -5), (0, -1)}\n",
-      "(1,-4) independent (2, 0) given () union {(1, -5), (2, -1), (1, -2)}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "(1,-4) independent (2, 0) given () union {(1, -5), (2, -1), (1, -2)}\n",
       "(2,-4) independent (0, 0) given () union {(0, -1), (2, -5)}\n",
       "(2,-4) independent (1, 0) given () union {(1, -1), (2, -5)}\n",
       "Writing:   (1,-4) oL> (0, 0) ==> (1,-4)     (0, 0) \n",
@@ -1278,10 +1280,10 @@
       "with rule list:  [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-00-d'], ['ER-00-c'], ['ER-03'], ['R-04'], ['ER-09'], ['ER-10'], ['ER-00-b'], ['ER-00-a']]\n",
       "\n",
       "APR:\n",
+      "Marked:    (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n",
       "Marked:    (1,-2) -!> (2, 0) ==> (1,-2) --> (2, 0) \n",
       "Marked:    (2,-1) -!> (2, 0) ==> (2,-1) --> (2, 0) \n",
       "Marked:    (0,-1) -!> (0, 0) ==> (0,-1) --> (0, 0) \n",
-      "Marked:    (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n",
       "Writing:   (0,-1) -!> (0, 0) ==> (0,-1) --> (0, 0) \n",
       "Writing:   (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n",
       "Writing:   (1,-2) -!> (2, 0) ==> (1,-2) --> (2, 0) \n",
@@ -1394,7 +1396,13 @@
       "ER-00-d:\n",
       "Found nothing\n",
       "\n",
-      "ER-00-c:\n",
+      "ER-00-c:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Found nothing\n",
       "\n",
       "ER-03:\n",
@@ -1530,7 +1538,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1562,7 +1570,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1685,7 +1693,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1740,7 +1748,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1803,7 +1811,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1844,7 +1852,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/tutorials/causal_discovery/tigramite_tutorial_pcmciplus.ipynb b/tutorials/causal_discovery/tigramite_tutorial_pcmciplus.ipynb
index a3f5e5c2..0461e9c5 100644
--- a/tutorials/causal_discovery/tigramite_tutorial_pcmciplus.ipynb
+++ b/tutorials/causal_discovery/tigramite_tutorial_pcmciplus.ipynb
@@ -14,8 +14,7 @@
     "J. Runge (2020), Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets\n",
     "http://www.auai.org/uai2020/proceedings/579_main_paper.pdf\n",
     "\n",
-    "Last, the following Nature Communications Perspective paper provides an overview of causal inference methods in general, identifies promising applications, and discusses methodological challenges (exemplified in Earth system sciences): \n",
-    "https://www.nature.com/articles/s41467-019-10105-3"
+    "Last, the following Nature Review Earth and Environment paper provides an overview of causal inference for time series in general: https://github.com/jakobrunge/tigramite/blob/master/tutorials/Runge_Causal_Inference_for_Time_Series_NREE.pdf"
    ]
   },
   {
@@ -849,13 +848,7 @@
       "    Subset 0: ($X^{1}$ -1)  gives pval = 0.97777 / val =  0.001\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{2}$ -2) -?> $X^{1}$ (13/18):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link ($X^{2}$ -2) -?> $X^{1}$ (13/18):\n",
       "    Subset 0: ($X^{1}$ -1)  gives pval = 0.96279 / val = -0.002\n",
       "    Non-significance detected.\n",
       "\n",
@@ -933,7 +926,13 @@
       "    Subset 0: () gives pval = 0.00000 / val =  0.996\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
-      "    Link ($X^{3}$ -1) -?> $X^{2}$ (10/27):\n",
+      "    Link ($X^{3}$ -1) -?> $X^{2}$ (10/27):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.00000 / val = -0.893\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -1303,13 +1302,7 @@
       "    Subset 0: ($X^{2}$ -1) ($X^{3}$ -1) ($X^{1}$ -1) ($X^{3}$ -2)  gives pval = 0.22099 / val = -0.055\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{3}$ -2) -?> $X^{2}$ (5/6):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link ($X^{3}$ -2) -?> $X^{2}$ (5/6):\n",
       "    Subset 0: ($X^{2}$ -1) ($X^{3}$ -1) ($X^{1}$ -1) ($X^{1}$ -2)  gives pval = 0.10135 / val =  0.074\n",
       "    Non-significance detected.\n",
       "\n",
@@ -1392,7 +1385,13 @@
       "    Subset 0: () gives pval = 0.00000 / val =  0.852\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
-      "    Link ($X^{4}$ -3) -?> $X^{3}$ (15/27):\n",
+      "    Link ($X^{4}$ -3) -?> $X^{3}$ (15/27):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.00000 / val =  0.838\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -1712,13 +1711,7 @@
       "    Subset 0: () gives pval = 0.20119 / val =  0.058\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{6}$ -2) -?> $X^{4}$ (20/27):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link ($X^{6}$ -2) -?> $X^{4}$ (20/27):\n",
       "    Subset 0: () gives pval = 0.18668 / val =  0.060\n",
       "    Non-significance detected.\n",
       "\n",
@@ -1931,7 +1924,13 @@
       "    Subset 0: ($X^{4}$ -3) ($X^{4}$ -2)  gives pval = 0.00000 / val =  0.436\n",
       "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
       "\n",
-      "    Link ($X^{7}$ -2) -?> $X^{4}$ (15/17):\n",
+      "    Link ($X^{7}$ -2) -?> $X^{4}$ (15/17):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: ($X^{4}$ -3) ($X^{4}$ -2)  gives pval = 0.92613 / val =  0.004\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2163,13 +2162,7 @@
       "    Subset 0: () gives pval = 0.11534 / val = -0.071\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link ($X^{7}$ -2) -?> $X^{5}$ (23/27):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link ($X^{7}$ -2) -?> $X^{5}$ (23/27):\n",
       "    Subset 0: () gives pval = 0.21199 / val = -0.056\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2399,7 +2392,13 @@
       "    Subset 0: ($X^{6}$ -1) ($X^{5}$ -2)  gives pval = 0.00000 / val = -0.383\n",
       "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
       "\n",
-      "    Link ($X^{5}$ -2) -?> $X^{6}$ (3/3):\n",
+      "    Link ($X^{5}$ -2) -?> $X^{6}$ (3/3):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: ($X^{6}$ -1) ($X^{5}$ -1)  gives pval = 0.66754 / val =  0.019\n",
       "    Non-significance detected.\n",
       "\n",
@@ -3014,7 +3013,13 @@
       "    Link ($X^{2}$  0) o?o $X^{7}$ (28/86):\n",
       "    Iterate through 1 subset(s) of conditions: \n",
       "    with conds_y = [ ($X^{7}$ -1) ]\n",
-      "    with conds_x = [ ($X^{2}$ -1) ($X^{3}$ -1) ]\n",
+      "    with conds_x = [ ($X^{2}$ -1) ($X^{3}$ -1) ]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.54563 / val = -0.027\n",
       "    Non-significance detected.\n",
       "\n",
@@ -3399,13 +3404,7 @@
       "    Link ($X^{3}$  0) o?o $X^{4}$ (8/17):\n",
       "    Iterate through 1 subset(s) of conditions: \n",
       "    with conds_y = [ ($X^{4}$ -1) ($X^{3}$ -1) ]\n",
-      "    with conds_x = [ ($X^{3}$ -1) ($X^{1}$ -2) ]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    with conds_x = [ ($X^{3}$ -1) ($X^{1}$ -2) ]\n",
       "    Subset 0: ($X^{2}$ 0)  gives pval = 0.00000 / val =  0.374\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
diff --git a/tutorials/causal_discovery/tigramite_tutorial_regime_pcmci.ipynb b/tutorials/causal_discovery/tigramite_tutorial_regime_pcmci.ipynb
index bc65d2de..ef990c83 100644
--- a/tutorials/causal_discovery/tigramite_tutorial_regime_pcmci.ipynb
+++ b/tutorials/causal_discovery/tigramite_tutorial_regime_pcmci.ipynb
@@ -7,12 +7,13 @@
     "# Regime-PCMCI: Detecting causal regimes from multivariate time series\n",
     "\n",
     "Main reference: Elena Saggioro, Jana de Wiljes, Marlene Kretschmer, Jakob Runge; Reconstructing regime-dependent causal relationships from observational time series. Chaos 1 November 2020; 30 (11): 113115. https://doi.org/10.1063/5.0020538\n",
-    "\n"
+    "\n",
+    "The following Nature Review Earth and Environment paper provides an overview of causal inference for time series in general: https://github.com/jakobrunge/tigramite/blob/master/tutorials/Runge_Causal_Inference_for_Time_Series_NREE.pdf"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -115,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -143,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -181,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -217,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -274,14 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -293,314 +287,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "################# Annealing iteration a = 2 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 490.94465076110276\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 36.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n",
-      "\n",
-      "################# Annealing iteration a = 7 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 488.8982226990487\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 36.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n",
-      "\n",
-      "################# Annealing iteration a = 0 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 503.5399195755775\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 29.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n",
-      "\n",
-      "################# Annealing iteration a = 4 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 498.27206003227985\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 40.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n",
-      "\n",
-      "################# Annealing iteration a = 8 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 517.2240554833973\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 36.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n",
-      "\n",
-      "################# Annealing iteration a = 1 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 506.8945776858816\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 38.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n",
-      "\n",
-      "################# Annealing iteration a = 6 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 497.3406581357441\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 172.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 253.0\n",
-      "\n",
-      "###### Optimization step q = 3\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 75.0\n",
-      "\n",
-      "###### Optimization step q = 4\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 12.0\n",
-      "\n",
-      "###### Optimization step q = 5\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n",
-      "\n",
-      "################# Annealing iteration a = 3 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 490.9369752339443\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 48.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n",
-      "\n",
-      "################# Annealing iteration a = 5 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 511.59244891703486\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 38.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n",
-      "\n",
-      "################# Annealing iteration a = 9 ####################\n",
-      "\n",
-      "\n",
-      "###### Optimization step q = 0\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 494.923101079663\n",
-      "\n",
-      "###### Optimization step q = 1\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 248.0\n",
-      "\n",
-      "###### Optimization step q = 2\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 350.0\n",
-      "\n",
-      "###### Optimization step q = 3\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 31.0\n",
-      "\n",
-      "###### Optimization step q = 4\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 7.0\n",
-      "\n",
-      "###### Optimization step q = 5\n",
-      "################ Regime k = 0\n",
-      "################ Regime k = 1\n",
-      "\n",
-      "Optimal objective: reached.\n",
-      "Difference in abs value between the previous and current gamma (shape num_regimesxT) : 0.0\n",
-      "Two consecutive gammas are equal: (local) minimum reached. Go to next annealing.\n",
-      "\n"
-     ]
     }
    ],
    "source": [
diff --git a/tutorials/causal_discovery/tigramite_tutorial_sliding_window_analysis.ipynb b/tutorials/causal_discovery/tigramite_tutorial_sliding_window_analysis.ipynb
index 25667844..b2c4a46b 100644
--- a/tutorials/causal_discovery/tigramite_tutorial_sliding_window_analysis.ipynb
+++ b/tutorials/causal_discovery/tigramite_tutorial_sliding_window_analysis.ipynb
@@ -8,6 +8,8 @@
     "\n",
     "TIGRAMITE is a time series analysis python module. It allows to reconstruct graphical models (conditional independence graphs) from discrete or continuously-valued time series based on the PCMCI framework and create high-quality plots of the results.\n",
     "\n",
+    "The following Nature Review Earth and Environment paper provides an overview of causal inference for time series in general: https://github.com/jakobrunge/tigramite/blob/master/tutorials/Runge_Causal_Inference_for_Time_Series_NREE.pdf\n",
+    "\n",
     "This tutorial explains the function ``PCMCI.run_sliding_window_of`` which is a convenience function that allows to run all PCMCI causal discovery methods on sliding windows across a multivariate time series."
    ]
   },
@@ -70,7 +72,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
@@ -159,7 +161,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 2000x1000 with 13 Axes>"
       ]
@@ -231,7 +233,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 400x200 with 1 Axes>"
       ]
@@ -265,7 +267,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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/c+cOypQpI5kaRdcKHB06dAAAvHv3Dnv27JEcEwJIY+XLly9F56XG5MmT0a5dO+TJk0dj/WD1lsiPHz/i48ePAIBmzZqhR48eqFChAk6fPg2lUilZ05Yxxpj5qc7nqhq0bdu2TSOv+g95Qf/+/VNdD45eVHBQl3oKhQJdu3bV2oRcvXp1PH/+XDKKVhhRevLkSclkwKqKFi0qdiQV2Nra4vjx40bX0dfX1+hzTEm9uV1O5cqVNdKGDBmCefPmwcnJCZ6eniapy5cvXwzq98cYY0w7bd1mtDVwpFk9SNcioRlQREQEHB0dER4eDgcHB8mxAwcOoGvXrvj06RNy585tphpmPkSE0NBQ8Tn39/dH0aJFxWOqFAoFSpUqhVevXknS1Lm6uuLu3bsarYARERHImTOn5JzixYuny3nhiEhn/zrV5yYpKQkKhUL8QVK9enVYWVnhwIEDKFasmNYyFAoFqlSpAm9vb5PVmzHGMpMbN25g6NChePLkCYgIxYsXx7t370x+P9myZdO7Njk3SangljrzUCgUkiC6SJEiAL4u26Xq9u3buHHjht4yDx06hCxZsqB58+aSdGGiYNXlVgoUKCCZGyi9OHfunMF5ra2t0bJlS9y5cwcKhQL37t3DnTt3ZJeze/78uWQuvPv375ukvowxZqmyZs2KcePG6cwTFRWlMVp1+PDhqFevnjgYomfPnmkS0BnK6OglLCwMe/fuxdKlS7Fs2TLs2bNHMqrRknFQl368ePFCY0FjIHklDfU+cbt27RJvz5kzB2FhYShZsiQA4OzZs1i/fj169uyJuXPnivlKlCgh3s6SJYtkuHl6oT7RsT7nz5+XvbQbHx8v2S9Xrly6Gj3MGGOppVQqceHChRSfHxcXh+XLl+PatWta83Tp0kWySlBcXBw2bNggyfPvv/+muA76CKtE6WJU9LJx40bUrl0bnp6eUCqVSEpKgqenJ+rWrYuNGzemuKLpBQd16YeLi4vs8ltyevfuLd6ePn26xhJfw4cPx549e8RlywAgb9684m0rKyvxUmbHjh313l/p0qUNqldaUygUICJxUIU2Qv/FS5cuifMcHj16NF3N9ccYY6mxbt06NGvWTHYKKWNom+Dfw8NDnL7KyckJM2bMQLVq1VJ1X8ZSXaJTKzJC2bJlKTIyUiM9IiKCypQpY0xRZhMeHk4AKDw8XOPYzp07CQBFRUWZoWYsNQCQMW/npKQkOnz4MAGgFStW0PPnzwkADR06VCxLbvvrr78k92fu7cOHD+To6Kg337lz5wwqLyoqisaOHUsxMTFp9VIxxpjJTZ8+nQDQmTNnxLSIiAgaO3YsxcXFaeRPSkoSv+vHjBkj+RwUhIaG0uDBg+ns2bNm/6xv0qQJ3b9/X+/zYFSTlEKhwJcvXzTSv3z5kiEmTOWWuszDysoKHTp0QGJiIn766SeUKVMGu3btwsyZMwEAZcuWRWxsLACgW7du4nmDBg0CAI2+euZSqFAhhIeH681naH2/++47LF++HPv27Utt1Rhj7JshmTGfa9aswfLly8WVHYKCgsT1VMePH4/s2bPjy5cvWLFiheQ8hUKBwMBArFu3Dps3bza6K4ypCF2GLly4gAsXLsDV1VXvOdonCJOxZMkSNGrUCJUrVxbnGfP398fjx4/x559/pqDKqTdhwgR4eXmhWLFi2Lx5M2xtbVNcFgd1mY/q1Cu9e/dGYGAgAKB8+fKws7NDYmIiFAqFmE+4lDl48GCjBjJYCmG6l7CwMCgUCty+fRs1atTQec7JkyeRlJRkEZNBM8YsGxHh8ePHkmmfEhMTERAQACC5a4l6ENa5c2eEhYVh6NChOHbsGAYOHCj+cNXWzadgwYJo27ZtGj0Kw0ydOhVt27Y16jKvUUFdu3bt0KZNG9y8eRMfPnwAEaFw4cKoXbt2qpe2SIl79+4hMDAQV69exbx587Bv3z706dMnxeXxMmEsb968aNasmfgLSXhfnz59Gi9evBADfmFS5Izq/PnzAJLn1Ktbt65kxHFISAg2bNiAyZMnQ6FQiB98wi/luLg4JCQkyK75yxhjqbFx40YMHz4cPj4+qFChAgDpeqnHjx/H6dOnUb16dZQtW1ZMV13RiIgMillU1279Vvz8/FC0aFE4OTnBysrK+H57qbuKbV5r1qyhrVu3EhHR7du3afTo0Rp5YmNjKTw8XNz8/Py09qm7desWTZo0iZKSktK87sy0goKCKCgo6JveZ3h4OHXq1IkmTJhAAGjw4MGUL18+AkC1atUyex8MU26q+vTpQwDo3bt3RKTZn9HV1VXjHMYYMwXh8/by5ctimrbPrd69e8umb9q0yeyfqbo+awFQvnz5UvT8GNVSp2r//v3o2rVrSk83ibCwMBQqVAgA4OjoKFlDTTB//nzMnj3boPJq1qxp0Gz/LP1xdnb+5vfp4OCAgwcPQqlUYsKECcifPz8SExNx4sQJtG7dWrJqRkbw3XffITo6WpxTUNvybg8ePPiW1WKMpWPHjh1D+/btERcXl6ruUQK59VW10TbCf8iQIamuR3qV4s5jffr0wbJly3TmMeRJTw0nJydEREQASA7w5FaBmDp1KsLDw8XNz88vTevEMh8rKyvkz58fQHKg06FDB53r2VoihUKBR48ewdfXF7dv3waQ/FiF/z/GWMYTGRmZ6v9xYW1rfSsh6JOYmIjo6GixK4ilr4H922+/6Tye0m5gKQ7qjhw5glmzZuHnn3/WCN6SkpKwZcsW8Xp3Wqlbt644b8zp06fh7u6ukcfOzg4ODg6SjbG0ZmVlBQ8PDwCQrFmr3mrcrFkzg8pLj5MFW1lZSeYEnDBhguR4UlLSt64SY8yEnJ2dNeb9FISGhhoUqJlqAGLv3r2RPXt2cXLg1atXW9xnjDC7wt27dzFt2jSdeYUJ9I2V4me5VatWuHLlCg4ePIguXbogJiYG8fHxWLt2LVxcXPDLL79oLMBuatWqVUOBAgXQoEED+Pj4mP1yMGOqqlSpAgAYOXIkRo4cCSB5RJWqUaNG6SxDyF+pUiWxvPRi7dq1kv0///xT8uvy0KFDsueFh4db3IcxY5lRXFycbLqvry9y586NihUraj3X19cXV65cEYM6Q6/ceXp6IioqCq9evcLTp0/FdPVpls6dO4dOnTph4sSJBpVrTvXr1wcRYfbs2SAiVKtWTWdL3MWLF3Hs2LGU3Vkq+yySv78/ubq6kqurKxUqVIicnZ1p3rx5FBERkdqi04SuyYcZSyvr168nAPTPP//QgQMHCADNnTuXiL528q1QoQIBoKNHj4ppJ06cIAD022+/icctZWvSpIns54BqHh8fH0naxIkTiYgoICCAANCVK1e+3YvEGJMQ/i/Vde3aVXLs/fv39OrVK0meLFmyEABq1qwZAaBPnz5Jjp8/f54A0KtXr+jZs2e0ZcsWSkpK0hjgsH79eoqNjTX751lqtgYNGmg8hwkJCZI87du3p4oVK6b6NUtVx5/w8HBs2rQJ79+/R3R0NBQKBTw9PfHdd9+lpljGMiyFQoH27dtj3rx5+OWXXyTHtm3bhsmTJ6Nt27b4/vvv0aJFC9jb2wMAbG1txV+8luLixYv43//+h5YtW6J///6yedauXSuZU2rx4sVYuHCh2EJ58OBBrcv2MMbMQ/2zSJi3NiYmRlzKSmiNF/6qnnPt2jWx64nqsovC1CSqAxxGjBiR7lvjSpYsidevX4v7uXLlkvT5k2uVE9IqVaqELVu2mG6QZkqjwSlTppCjoyOVKlWK1q1bR1++fKGBAwdSvnz56ObNm6mONtMKt9Qxc1i3bh0BoA0bNmgcg5Zfw0TJS9ksW7aMoqOjqWzZsmb/xZnSzdPT0+C8qr9gx40bR02bNqUdO3ZQQkICRUdHa32O//rrLxo8eLBBVwkSEhIMf/EYy0RevnxJ586do7dv30o+m2JjYykxMZGIiDp27Cg5Jtx2dHQkIqKmTZuKacI0T6NGjSI/Pz9JfvVt5syZZv+sSsnm5+dHR44coYIFC1KxYsU0HuOgQYM0nmehVbJ58+Ymff1SHNSVL1+etm7dKr7IgunTp1P27Nnp0KFDqa5cWuCgjpnD3r17CQDt379f45iuoE5VmTJldH6wODg4iHM4WfIWGhoqCeqE25UqVdL6PF27dk3MV7t2bZ3P44cPHwgAHT582LAXj7FMRO5/Ukjv1q0bERF16NBBPKZ6KVY1r9z2/fffk4eHh9k/Y/Rtp0+f1nlcuKQsbCEhIURElJiYKMZEwrHLly9rXU9+zZo19OHDB9O+fik9UalUaj32zz//kJ2dHa1atSqlxacZDuqYOSiVStq3b5/s/43qh6EuLi4uBHxt9WrQoIHkA/L9+/dElDwRs/A+t8RN6E8HSIM69edJtQ9P8+bNZfPI8fLyIgD0008/GfryMZZpyP1PqqYTEbVr107r/29gYKDWY7rO+9abs7Oz1mNnz56VBK7C1qVLFxo7dqxGfzi5ie8N/Vw3tTS7xxMnTlDOnDnTqvgU46COpTdubm5Us2ZNvfm6d+8ufkgcPHiQPn/+TEREW7ZsEX8pqgIMX9li4MCBZv+QlduE1jn1LxghMDt69CgREbVs2VI2qHv37h1duHBB8rwMGzaMAFDTpk1T9oIxlsHEx8fT2LFjqX///gYFdbr+Z3v06GH2zw31LUuWLOTr6yvud+zYUefjOH/+PHXu3Fnc/+GHH+jNmzeS5+zOnTvk5uZGALR+/maooI4o+UGnNxzUMUsVFRVFjx49Mjh/cHAwRUdHG/Shd+/ePbN/8Bq6EZE4gnjhwoW0atUq2TwfPnwgOzs7jQ/WRo0aEQCqWrUqERH5+PhQjx49eHlAlmlER0dTw4YN6fXr10RENGPGDL3/c8Jt1ZZ0S9kEwv68efMk+wUKFKCOHTtS6dKlxSDN39+fChUqRB06dNB6ZfLLly9au3FkyKAuPeKgjmU2mzZtokOHDun80AsMDKRixYqZ/cPXkK1169bipZPZs2fL5qlWrZrGh7rQalmjRg0CklsxiYjatGkjfpCnhaSkJA4YWZrx9/fX6NsuR6lU0pYtWyg+Pl7stjFu3DgiIho8eLDZ/6+/ZVAn/D8uWbKEbt26lSavy8GDB8nDwyNNytaFgzrGMgmhdUt1s7Gxofj4eCIijQ7PGWkj+vqBnjt3bgJAbm5uRPS1f9DHjx/p2rVrFBsbS58+faJq1app7cT8888/08yZM8na2poCAwN1Pu8ODg7k6upqwleSZWbPnz8nAPTu3Tuxb9e0adP0nnfx4kUCkluphNGro0aNop9++sns/59puT179kx8Duzt7SVBXkaUsR+dDA7qWGb28OFDyQees7OzeOzLly90//59yfHmzZtT/vz5CUj+Vf/PP/+Y/UM6tUGdcElWSG/RogUBoAsXLhAA+vnnn2n37t0EgJYtWyb7PKqWffDgQZ3Puep9MZYSz549o6dPnxIRUZ06dQhInshcmJRXbnJbVfv27SMbGxuz/x+mxVaiRAmtx4TJzQUBAQHi85hRZbpPGg7qWGaWmJhIrVq1IisrKwJAL1++1MijOj2IHEM+aB0cHMz+Ya+6CXNCqW+qo4SFy8/t27ennTt3il+WcXFxtHbtWkm/GtUy/vvvP5owYQJFR0eTUqmkPXv2UGJiIt26dYt27dqVJkGd0BeKZQ6q7yHhdrdu3SSB2pUrVyTzOCYlJdGmTZsoMTHR7P9/ptz+97//SfaHDBlCCxculKQ1a9aM3r59a66Xy6w4qGMsE4qLi9M5kW/x4sUNDupu3bqlkebo6Gj2D3/VzcfHRzZdaJ1T3Tp06CC5JLV06VICQOfOnZN9Dnr16kUA6O+//6Zz584RAHGyadXNGPfu3aOzZ8/KHhOeb2HkL7M8Y8aMoZUrV2qkv379mm7fvk09e/akZs2aiemq7yFdywWOHDlSPOfIkSMEgAYMGGD2/z9TbsLzsW7dOlq3bp34Xa6ap0WLFmn58qVrqVomjDFmmWxtbXUef/z4MeLj42WPLVmyBG3atEFwcDASEhJkl7exsrIyST1NRdvC402bNtVI8/f3x5EjR8T9jx8/AgCaN2+Obdu2YfTo0ZL8wtJGsbGxaN68OQDAx8dHo9zPnz8jd+7cknKzZMmCvHnzauStVq0aAMgugv7u3TsAwJMnT9CuXTvZx8XM76+//kL27NkxcOBAjWMrVqwAAPz000+S9JIlS0r2FQoFxo0bJ0lr1qwZnjx5Inufr1+/xvv371GkSBFxuc5t27al+DGkV3L/F6q0/b9nBgrS9+xkMBEREXB0dER4eDgcHBzMXR3GMgQ/Pz+MHj0aR48eBQDkzp0bnz9/BgCEhYUhV65cZqxd2hoyZAg2bdokScuSJYu45qUqIkJwcDCyZcuGHDlyiGnqhHUh5Y4dOHAAXbt2xYgRI7Bu3TpTPASWBtRfw1evXuHQoUNo2rQpqlevDgBYtmwZjh49Cjc3NygUCsydO1dnmffv30fNmjWRkJCgNc/AgQOxdetWEz0K80lMTIS1tWa7k7aQRXi+PTw8UKNGDb0/XDMss7URmglffmUsbfzwww/i5Y+8efNqXC7hTX62fXUrVqzQeoyIaP/+/eLxffv2pfj1ev78OQUEBGg9/unTp0w3FYu/vz+1aNFC67JORETr16+n7t276y1LeI2EaS1cXV3N/v5Lz9uzZ8+oU6dO4r5SqRRvq07JpOv5FtaezczS1zUSxpjFIpVf0MKvZvUWLFX37t3D/fv3cf/+fcycORPR0dFpXkdzK1CggNZjK1euxI0bNzBmzBiNY8+ePcOrV6+gVCrF5xYAunXrJltWaGgoEhMTddalbNmyKFy4sOwxIkKePHkwe/ZsnWVkNGvWrMHZs2dx8+ZNAMDBgwfx6NEjSZ4RI0bgv//+M7jMevXq4eLFi3jw4IFJ65rRlC1bFhUqVBD3Vd/nHTt2BABky5ZN6/nnz5/H/fv3066CFoKDOsaYSSiVSvF21qxZAcj3WRNUrVoVrq6ucHV1xezZs2Fvb48+ffqkeT3Tm9mzZ0OhUGDMmDGoV6+e5JhCoYC3tzfKly8PFxcXtGvXTvJlJyAizJ49W7zknTt3bvzvf/+Tvb/IyEjkz58fQPJrFhQUpJFHuLx37tw5rfW+ffu22DdMXVRUlNbz1AUHB0OhUMh+IRMR3rx5o5EeHR2NAwcOID4+HjNnzkRsbKzB9wcAGzduxIgRIzTSw8LCAAAnT56EQqFAly5dxL5p6q5cuaK1/BcvXkj2df0fZHTbt2/XSNu7d694u3z58oiLiwMATJ06VSPv9OnTxdtFixbVej9NmzZF8eLFU1PVjMG8DYXfHl9+ZSxtDBkyhADQ1KlTxYmM3717R0TS0XsTJkygIkWKyJaRkJCgMUny2rVryc/Pz+yXh8y1CaNvha1p06aSfSKi69evE5C8RqXwfBcqVEj2Ofb09NS4j//++48CAgKobNmyFBgYSJGRkQSA6tWrp/X1zpIli+zlsCtXrhAAWr58udZzvby86MqVK0RE4ojhqVOn0s2bN6l58+bi9DHr168nAHT69GmaMWMGKZVK+vLlC/34448EQFwibtWqVWLZr1+/Ji8vL4qLi5Pcp+qlZOFxq0574e/vL6Znz55d8vxMmTKF+vbtKzkXAL1//1728Zn7PWOKrW/fvgblmzp1qtZjNjY2ss9HSEgIAcmrw6ibMmWK7PvqxIkTWp9v9hUHdYwxk1iwYIH4RdelSxcCQH5+fkRk/AS8ql8AJ06cEGfON3YT1nLMyFtoaKh4O1u2bJK+SImJidSyZUvJdCxeXl4aZUyePJnGjBkjfhGXLVuWAN1Bneq6uoMGDaL8+fMTEdHy5cvFcoOCgsT8p06dIgA0duxYyftBWOlg0qRJVL9+fQJAcXFxso918+bNkv2ZM2cSAJozZw4BEANKYXv8+DERkfic/Pnnn5L3V69evejZs2ey0/LIbb1795bsV65cmT58+EDXrl2jQYMGEQC6evWq2d8Tqd3mzp1Ls2bNkqT16dOHIiIiNPImJCTQjh07xH0hII6MjBQDa+HYvXv3aPv27ZSQkEANGzaku3fvaryvlEqlRkDODMdBHWPMJBITE8nb25uIiE6fPk0AxLnwVL/EDSHkV539Xb2Fys3NTbK/YcMGjS+cX375RSNNLp8lb5UqVdJ6bOTIkeLtO3fu0M2bNzUCEwA0YcIE2fPd3d1lXx9hcmYAkpY/Hx8fGjdunLj/5s0bIiJJoKm6TZ06lfr06UMAKFeuXGK6tqBO/T2gb+vZsycRJS9gD4Dq1KkjeX+ZYhNaLDPSNm/ePDFQBkC2trbia1+1alUCIE58rP4/++HDB405+IRjLO1lumeZgzrGvj1jP9Tl8p86dYqyZctGUVFR9ODBAwoPD6e2bdtSUFAQxcTEiJfqVDdvb2/JfoECBUipVKa7FS/SaitSpIhB+X7++WfZ9Jo1a9K4ceNow4YNRES0ceNGWr9+vcGTS/v6+hIRaZ38WXXLmjWreFtY/soUGxGJLUx16tSh77//3uyvS3rbgoODJfsXL16kefPmEZC8rJ6q8ePHE5A8+bWnp6eY/vvvv5OLi4tJ/v9ZymW6Z5mDOsa+PQCUJ08eg/OfO3eOtm3bZtR9yK3ioN5CFB8fT0Rf+6Bl9O333383KJ8hQe78+fONvn9hObPHjx8bdd7nz59N9hwIl5J5074J/6PA1/8RYemtrVu3Sv7P9u7dSwDo1atXBv9vqt4PS1s8+pUxluZCQ0NlRzFq06xZM/Tv39+o+yAtk/jeuXNH3BdWf5DLmxHNnDnToHwRERF688iNTNSnRYsWAIxfYWTo0KFG35c2z58/N1lZlkx1FKkq9YmMhf8RQc6cOSX7PXr0QHBwMEqVKmXaCjKT4KCOMZbmcuXKJa6gkFaEJbgOHTokSRdm7zclXfPvsa9evnyJjRs3wt3d3ajzDh48mEY1ypyaNGmCOXPmyB4TVm04cuQIFi9erHFc7geQ3NJ2uly7do2D62/EIoK6yMhI1KlTBzly5JBMBLl37164ubmhadOm8PPzM2MNGWPm1q1bNxw5cgQdOnQw+tz8+fPjwIEDqFKlit68Q4YMweDBg9GqVauUVDPTGTZsmDh/Hvs21Oc7NKRlun379pgwYUKa1Mfd3R1lypRJk7KZlEUEdfb29jh27Jhk9vSEhAQsXboUly9fxpw5c7T+CmGMZQ4KhQLt27eXnZxXneqEslu2bEFgYCA6d+4MLy8v8fJTkSJFxDyqi6ILK1+otwgylh5MmzYNq1evBpDcQpdSffv2RdWqVVNVBvv2LCKos7a2hrOzsyTtxYsXqFSpEmxtbeHu7o6HDx/KnhsXF4eIiAjJxhjL2Pr27SvZL1u2rGTfwcEBf/zxBwDA0dFRTLezs8PDhw+xZMkS+Pn5ITo6Gm/fvpW04O3ZswfA11UzGDNGr1690qTclStXIiwsDPPmzYOLiwuA5FblEiVKYMaMGQCARo0aGVxe4cKFce/ePTg5OaVJfVnasDZ3BVIqLCwMDg4O4n5SUpJsvvnz52e69QsZy+y2bt2KDRs2iPu3b99GfHy8JI/QeV/90lS5cuVQrlw5AMlXCYoVK6ZzzVZtVq1ahZ9++sno81jGJgRcxlIoFHj+/DkiIiJQo0YNMV3u0mrOnDmRlJQEKysr9OvXT0xfsGAB3NzcUnT/zDKkq5a6wMBA1K9fX2OT64/h5OQkaXXLkiWLbJlTp05FeHi4uHHfO8YyvixZskha0nLmzIk8efJI8rRv3x4AULt2bb3l2dra4uXLlwAAV1dXMX3jxo2SfKotgj/++KN4e/DgwWKLx6FDh+Dp6anz/gICAvTWKa35+vqauwoZkrZRqOoKFSoEIPnHR3x8PJRKJVxcXAwe+CM34rhu3bribUO6KTDLk66CugIFCuDatWsamzCqTZWLiwt8fHwQHx+P69evSz5oVdnZ2cHBwUGyMcZYxYoVQUQoXLiwQfnz588PAGjbtq2YNmTIEEke9R+XQrcQ4ccpEaFjx46oU6cOvL29td5XgQIF8OjRIzx9+lRcOL5t27Y4duyYmEepVBpU75QqWbJkmpafWdnZ2Ym3P378KN5WncLnypUrYuB/6dIljWlGhPed8EMjJd6/f48XL16k+HyWPinIQiZsatu2Lby9vVG8eHGMHDkSgwYNwp49e7BixQpkzZoV27ZtQ9GiRfWWExERAUdHR4SHh3OAxxgzysOHD1GhQgVxGghA2uJRp04deHl5Afh6Wez+/ftwdXWVbRnx9vZGtWrVNNJ1fSwL5RBRmrW2nDt3Ds2aNePWHC22b9+ucx7FgQMHIm/evPjxxx81gmPV10319ufPn9G0aVN4e3un6TyKqvfNMqBvPt2xmfGKEowxU9qyZQt16NCBNmzYQP7+/hprYuoDLTP8a5MtWzZq2LAhERHZ2dmlaiUBKysrWrp0qUZ6RESE1roJm9y6uqbYSpcurTdPzpw50+S+BwwYoDePg4ODuJas3CY8d4LKlStrvLYRERH08uVLIiJq27YtAcmraHwLhrzHmOVKV5dfGWPM0gwcOBCHDx/G0KFDUbhwYfj4+ODcuXMpKmvSpEniSEVtoqKicPnyZQDJqyUcPXoUwcHBKbq/pKQktGvXTiNdtSVSm8TERK3HVKeAMcatW7f0zmdWsWJFRERESC5jmsrWrVvF26rPy61bt/D+/XsEBwcjLCwM9vb24jGhJa5ChQrw9vbWWIFBdb9r165iWunSpQEAv/76K1xcXDTOSyv79+/H3bt3v8l9MTMwd1T5rXFLHWMsPcH/t5w8efLEJOUIC66rby4uLuLtuLg4ev/+PRERPXv2TExftmwZdevWjZRKpaRMuW3Xrl1aj+k6t0CBAlqP3b59m5o3b67zfitVqkRERK9fv6Zjx44Z1RI3cuRIAkAtWrQQWyg7depEW7dupU+fPon17t69OxER7dixQ2urllBmiRIlKCYmRlwzVZ2/vz8tWbKEqlevTm/fvk3Va8yYPhzUMcaYGb1584Z8fHxSXY4QZCQlJdGUKVMIAOXPn5+2bdumEYSpevLkiXjpUVuZ6ltkZCQREZ04cYK+fPmicTlS17lTp04Vb2/dulVy7M6dO9SoUSMCQGXKlJEcq1ixIgGgypUrG1RHuW3lypUEgC5fvkweHh4EJF9GVhUVFUUJCQkGP98VK1Y06nViLC3x5VfGGDOj4sWLo0KFCiYrz8rKSlybc+XKlejfvz/i4+PRu3dv2fz0/x3m5QZFPH36VPYcYR3fNm3aIHv27LC3t9dYr1UYLSzIly8fiAidOnUCkDzly4ABAyR5rK2tsWnTJvz666948OABAKBq1apYv3497ty5I1sX9ZU9KlWqpJHH1dUVgwcPxogRI5CUlISGDRuKo4fVH3e2bNkMuvwsDJQ4fvy43ryMfSsc1DHGWAYnTInh4uKiEcQUL14cDg4O+N///qdxXrly5VCqVCkAwMiRIwFIV+BQ1alTJ3FuNUAz2BKCOYFcEFm4cGGUKlUKc+fOFaeHsbOzw/Dhw8U+dOrntW/fHkuXLsWlS5eQI0cOXLt2DSEhIeIE0gCQO3dubNq0CXZ2dhqTTqd0hO+2bdtARChRokSKzmcsLXBQxxhjGcDMmTPFAKx169YApJPNAsDjx48RExMjScuWLRvCw8O1TsJ848YN3L59G3///TcA4JdfftFah3///Vdcok31vq9du4bly5fLnlO5cmUAyUGW6gTRQvDVuHFjAF+DL/Ul4KysrDBu3Dg0atQIkZGRyJUrF/LkyYOrV6+KA0rkCBNFqwebjFkyi5mnzlR4njrGGEuZuLg42NraGty6tWHDBhQrVgwtW7YU07y8vFC3bl389NNPWLlyJRITE6FUKmFra6tx/vv371GgQAGtKwbpExsbC3t7e7Ru3RonT55MURmMWRIO6hhjjH0zERERKFasGC5fvowqVaqk+f2tWbMGXbt2TdH6vYxZGg7qGGOMMcYyAO5TxxhjjDGWAXBQxxhjjDGWAXBQxxhjjDGWAWS6PnVEhMjISOTMmTPF8xMxxhhjjKU3mS6oY4wxxhjLiPjyK2OMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBsBBHWOMMcZYBpDpgjoiQkREBIjI3FVhjDHGGDOZTBfURUZGwtHREZGRkeauCmOMMcaYyWS6oI4xxhhjLCPioI4xxhhjLAPgoM6EYmNjcezYMXNXgzHGGGOZkMUEdZGRkahTpw5y5MiBR48eAQD27t0LNzc3NG3aFH5+fmauITBz5ky0b98eCoUCSqXS3NVhjDHGWCZiMUGdvb09jh07hm7dugEAEhISsHTpUly+fBlz5szBnDlzTHp/s2fPRo4cOYw6JzAwULydlJRk0vowxhhjjOliMUGdtbU1nJ2dxf0XL16gUqVKsLW1hbu7Ox4+fCh7XlxcHCIiIiSbIWbNmoWoqCij6qhQKMTb6i11vXr1Qt26dY0qjzHGGGPMUBYT1KkLCwuDg4ODuK+tZWz+/PlwdHQUt6JFi5q0Hnfu3MGbN28AAFZWX59OpVIJIkJISAiA5EvFXl5eOst68uQJLl++bNL6pdaxY8fQvn17c1eDMcYYY3pYbFDn5OQkaXXLkiWLbL6pU6ciPDxc3Azpe6d6GRUAfv31VxQoUEAjX1RUFGrWrImSJUsCkLbUJSUlYdmyZXB2dkZYWJjGua9fv8bcuXMlaRUrVkTjxo311u9b6t+/Pw/+YIwxxiyAxQZ1Li4u8PHxQXx8PK5fvw5XV1fZfHZ2dnBwcJBs+hQsWFCy/8cff+Djx4+StKSkJEmfu4cPH2pcfr169SoAYNq0aWK6EDD27dsXM2bMSPcrW/CAD8YYY8wyWJu7AsZo27YtvL298ezZM4wcORJjx45Fo0aNkDVrVmzbtu2b1EGhUGDy5Mn4/vvvJenLly/XuPwq7Pv6+orpBQsWBBGJwZxSqdTayigICAhAvnz5ZPOtWrUKcXFxmDBhAgDgzZs32LRpE2bPni0JMlOKgzrGGGPMQlAmEx4eTgAoPDxcax4A4qa6/+jRI8kx1a1WrVrUqlUrcT8kJES83bBhQ40y69WrRwAoLi6OSpUqRWvXrhWPx8bG0o8//kiLFy+mmJgYAkA9evSgGTNmEBHR2bNnSalU0sGDByVlEhE1aNCAAFBkZCRVqVKF7t27R0RE06dPp5S83NmyZUvReYwxxhj7tiyqpe5b+Pz5s9ZjlStX1nrs1q1bkn3VFq4rV65o5Bda8ZKSkuDr64vx48eLx1atWoXVq1cDAIoUKQIA+PfffwEATZo0QYsWLbBw4ULs2rVLa30+fPiA+/fv448//sC///6LJUuWaM2ri/A44uPjsWPHDgwePNgkLYCMMcYYMy2L7VOXVoYMGSLZf/bsWYrKCQoK0nps/vz5uHbtGgDgwIEDAIDo6Gjx+MSJE8XbX758kZzbtGlTAMDkyZNx//59Mf3ff//F48ePxWBROC8xMRHA19HBvr6+qFWrljhdS2Jios5+fUJQt3LlSgwdOlTsJ6gqJiYGnTp1QkBAgNZyGGOMMZa2OKhT8+nTJ8l++fLlU1SOrlY91YET/fr101nO8OHDDbq/nj17onLlymIr2i+//AIAYqCVkJAAAFizZg1u376Ne/fuISkpCTY2Nli5cqXWcoWgTgg0VYNPwc2bN3H48GEsW7bMoLoyxhhjzPQ4qFNj6ZcWL126BAAIDQ0F8LWlTiC02FlbW4u3hdZCOeoDJeRa9WxtbQF8DRwN8fnzZygUCty9e9fgcxhjjDGmHQd1aiw9qBMIAdvt27cl6UKQZ21tLQZoRIShQ4dCoVBIgrb4+HiNoE51Pz4+HrGxseIlX+GYUqnEihUrEB8fr7V+jx8/BgCd/QIZY4wxZjgO6tTITRRsiYSgCQAGDhwo3haCvbdv32LLli0AgKtXr2LTpk0AkgdwzJw5E8+fP8esWbM0ylUN+qpXrw57e3tJUJeQkIDWrVtj7NixWL9+vdb6CeesWrUKffr0AQCsX78e/v7+KXi0jDHGGFOQrl7yGVBERAQcHR0RHh4uOxFxRmmp06Z69eqpuuR5+PBhODo6IioqSpyr78CBA+jSpQsAoHXr1jh16hQAYNGiRZJBH6o8PDzg7u4u7hMRFAoFatWqhZs3b6a4fowxxlhmxS11mUxq+7B17NgRjRs3lky+LAR0AMSADgDCw8Ml5/br1w8FCxbE+fPnMXXqVNnyb926haFDh0rKAYCFCxdi4cKFqao7Y4wxlpFxS50KIpKsCsFSz8/PT5xrT1crqNBSp54mEI7JvV2zZMmCuXPnYurUqWjfvj1cXFx4JC5jjLFMhyMYFRmlP116UrRoUWzevBl+fn4680VERGg99uHDB/H25s2bAQAvXrwQp4ZRKpWYM2cOAODYsWNYvnx5KmvNGGOMWR4O6lToGq3JUm7IkCHo2LGjzjyOjo4aaZ8+fUJMTAxOnDghpgktcP369cP8+fPFEbcxMTGSc4XJlRljjLHMgoM6FbGxsbLpjRs3/rYVyYDu3btn9Dl58+ZF06ZNJUucCa+RMJhCfR4+gSF9B7W93sb49OmTScphjDHGUsuig7o3b97A2dkZjRs3RuPGjREcHJyq8rR9OR89ejTFZXIfvdTx9PSULNX24sULvH79Wtx/+PChePvp06eyZSxYsAAKhUISAEZERMDe3h558+ZNVf3y5s2L5s2bp6oMxhhjzBQsPuJo1KgRLl26hEuXLsHZ2TlVZQlzuKnLkSNHiss0dJWF0aNHp/g+MhvVOfgmTZok3hbW0wWAhg0bivmEkbaqo3GF2+rLwgmio6OhUChw/PhxvfW5fv26EbVnjDHG0obFB3XXr19HgwYNMG3aNNmRkXFxcYiIiJBs2qivnqBqypQp4u0OHToYXD/VlrqWLVuKt9UD0FWrVmlMAcLktW/fXrx94cIF8faoUaMk+Ro1aoQnT56I+5cuXcKjR48AJI+Y1UVo9d2+fbvB9SIi7NmzR+uPA8YYYwwA5s6dizdv3pi8XIsO6goWLIiXL1/iypUrCAoKwsGDBzXyzJ8/H46OjuJWtGhRreWpfhnv2bMHhw8fFgPF8ePHi8d0BQStW7fWeqx3797iKFDVYM/b2xsKhQIODg44ePAg9u/fr7UMpp16/7pPnz6hYsWK4n63bt3w3XffQaFQSC7V379/H0qlUnz9//nnH5QoUcLo+7906RJ69+6tcyUNxhhjmZtSqcSMGTPE1ZRMyaKDOjs7O2TPnh0KhQJdu3aFt7e3Rp6pU6ciPDxc3HRNraHaUtezZ09Ji5ydnR0AYOLEiWJgeOjQIY0ymjVrJt4WVlMQWggHDRqEIkWKYMaMGTh58qSYr0qVKuLtTp06SSbzFabt0EW1NYoZpmrVqpLb5cqVg7W1NZ48eYIRI0aIx/bu3YuZM2caVOaXL18AAKGhoSatK2OMsYxDiDXS4qqORQd1kZGR4u0rV67AxcVFI4+dnR0cHBwkmza6Lr/mzJkT3t7emDdvHiZNmoSOHTuidevWqFatmkY+waJFi8Q01fTff/8d1apV01kXwbx582QvKwvq1q2LggUL6i3Hy8tL5/G9e/fqLSMje/nyJQBIWvYECxYsEG/HxMRgxYoVsq+JMEHyli1b4OTklEY1ZYwxZgmISNIHXCDEGqrfI0lJSbJLZEZHRyMuLs7g+7TooO7atWuoUaMGGjRogPfv36e6KVOImuvWrSt7vEqVKrCxsUHhwoVx6NAh2NnZ4eLFiym+v6dPn+LBgwcpPh+A3suEfn5+ICLUrl1bZz65YMYQq1atStF5liQhIQFv375FWFgYJk2ahLFjx+L777/X2j/zxYsX4kTWdevWxdixYw26nw8fPmDXrl0mqjVjjDFzWrNmDSpXrgxfX19JumoL3X///Yd+/fphwYIFqFOnDvz8/HDw4EFkyZIFRITs2bOLqzIZwqKDujZt2uDOnTu4evUqtm3bBmtr61SVJ0TP//zzj8HnODo64sKFC/jjjz8AJPeVe/r0KW7cuKH33IIFC+K7776TPWZjYyPZl4vgu3btKq6koI2hbwZbW1vZ9EqVKgHQPjVLZlll7uHDhyhatChWr14NADh58iTmzp0rHg8ODhYvv6ry8vLCihUrEB4ejr179yIuLk58zh48eIDLly+LeTt16oS+ffumSf1LlCiBAwcOpEnZjDHGNAnTbH3+/FmSrtpS16NHD+zcuVO8WhQZGYklS5ZAqVSKjUYhISEG36dFB3WmJkTPxs4t16RJE+TLlw9A8iW4cuXKaW3tM1RISIikb1atWrXw5s0byZxt+/btg4uLi9jfT50xc7DJBXU5c+bEw4cPcfToUfj4+Miel1mCuunTp2sEbaoDM/Lly4fevXtLjqsOxvjhhx/Qq1cvZM2aVQzEq1SpIpnYOi2XqXv79i1mzJiRZuUzxlhm5+XlBScnJ3GFI+H7UX21KiGou337tpgmt765ah99Q3FQp0J4ovVNd6HrXFNNNuzg4IBcuXJJ0ooXL46yZctq5M2aNatkOpThw4fD2dlZ7NOnzfDhw8W+X+pz8eXKlQuPHj2CQqFAu3btUK5cOQCawZ/qII+M7P79+xppwpJl2giBPiD9paZtdLPw3lHtw2dKmSUAZ4yxb+3UqVNo164dwsLCxCt3hw8fBgBs3bpVkleu/75qUCfcVj8eHR2ttx4c1KlITWAmBIKpvQScUg4ODmjYsCEA4M8//0RQUBAGDx4syRMWFoYvX76IA0oGDRokBmmqrXqPHz9GaGgoihUrpnE/CQkJ6Ny5s7jfqFEjyfHUrL6Rkan2nXzw4IFsy6fwjyxMlizn4MGD6N69uyRN3/yLjDHG0labNm3Ey6Rz586Fn5+fePXly5cvuHfvHt69ewdA/6hXuaAOAH799Ve99eCgTkVKL78CQN++fTFz5kz06tXL1NUymraWRkdHR2TPnh3btm0DAJQtWxZ///03mjRpIsmna9AEEYl9DmfNmgUAksEibdq0keQfOnSoZP/jx48aZaZmxQ5LERgYKNkX+iqq0vaPrKpHjx7Yt2+fJC137txwdHTUeo7QQsctdYwxZnpyA9ySkpIQFRUFAMiWLRuqV6+OkiVLApBvqdu0aROA5D51qqsjqcqePbveunBQpyI1l1/t7Owwe/ZsrQMOvgUhKNAXlLq5uYGIkDdvXnTq1EmyKoM+1apVQ548efDo0SNMnz4dANC4cWMULVoUxYoVk9x327ZtMWvWLDGA2b59u0ZL5qxZszL9Cgx169aFQqGQzDe4d+9e8QNBlXrg16VLF73PHwdzjDFmGqGhoZIBbgD0DnDbsGEDgOQYIzQ0FPfu3dOat169elqPyQ3G00CZTHh4OAGg8PBwjWOnTp0iAPT27Vsz1MxwAwcOpLlz52qkt2jRggBQTExMisp99eoV+fr6aj3+7NkzCg0NlT2WlJRESUlJREQEgFTfWpUqVRL3hecfAH358oWIiKpUqUIAqGbNmpLzeQMVK1aM/Pz8xOdSSE9MTNR4rvz9/Sk4OFjM++nTJ6pZsybdvXtX4zVJjc+fP0vuhzHGMrLLly9TfHw8ERE1b95c47P0W34n6GOeDmDpVGpa6r6lLVu2yKZv3rwZO3fuRNasWVNUbqlSpXQelxukIVBtoQsPD8eHDx9k8wktde7u7mJTstC6qT7n36BBg8TH6uHhofUXTL9+/dCoUSMMHz5cZ/0t0bt37zBy5Ei4ublJRlD5+/sjW7ZskrxFihSBjY0N4uPjERoaiuPHj+P27duoXr26mKdgwYIICAhIVZ3y58+PhIQEbgFkjGV4/v7+aNSoEaZPnw4bGxtxxOratWsxYsSI9BcvmCqSvX37tqmKSlO6WuqOHTtGAOjDhw9mqFnGNWvWLPEXRnx8PAGgunXrisfr1KlDACgqKoqIiAYNGkQAaOzYsZJfJ9Dzy0Xb8Yy4nT9/nsqWLSt77N9//yUAVKJECa3Pl5eXl/jL01jqzztjjFmqffv2UUJCgtbjixYtIgBUqlQpjc/Sfv360Y4dO9JVS53JPpmLFi1qqqLSlK6g7vDhwwSAAgMDzVCzjEupVFJcXBwRJV+mBUC1atUSj9erV48AUHR0NBERRUVFUe3atenly5cpDuq+//57yf65c+cM/qfZvHmz2YO21Gzt27fXeTwkJIQA0NSpU3W+brt376bnz59rpHNQxxizRD4+PuTu7i5+H/3zzz8EgBYvXizJd+fOHZoxYwZt2LDB7J/nxgZ1Rl1+7dGjh2w6EWnMmGyJLOXyq6VRKBTiJVYrKyu0a9dOMjR74MCB8PDwEC/NZsuWTedatTly5JDtMGptbS1OCKw+IEP9UqWqv/76C//73/8Mf0DpnL5pZYS5jl69eqUzX+/eveHk5ITPnz9j9+7d6NSpE+zt7U1WT8YY+5bmz5+P69ev482bN0hMTBS77Kxfvx4TJ05EcHAwWrdujTt37pi5pilnVFB37tw5bN++XWMKCiLClStXTFoxc0jNlCbMcOpBx4gRIzB8+HCDpvQAvr5Od+/elazq8ObNG7x48QJNmjSBg4MDhg0bJhl1pK5q1aqoVKkSfvjhB+TKlSvVawdbCrn5GBMTE7F582YMGjRIskRdZGQkfH190adPH4wfPx5LliwRj/Xo0QM7duww64hvxpjlOnPmDCpWrGjU2qb6JCQkwNraWvb7ZPv27QCS52Lt0qWLmP7ixQsAwJgxYyw6oANg3DWUzp0706VLl2SPtWrVypiizEbX5VehL1JYWJgZasbkQKXJ+YcffiAAZGtrq7MZesOGDeJrWLJkSQJAt2/fJgC0YMECAkD58uXTel+WfvlV37Zt2zbJpdo2bdpIjm/dupXu378vPtdPnz4lADRo0CDJ8wSAbty4karXNyAggJRKZarKYIxZJgBUpkyZFJ3bvHlzypYtm2yZEydO1Hp/lr7pfU5T9GymI+PHj6f69etTnz59xOvkuugK6vbs2UMAKCIiIi2qylJA9Y2sVCopNjaWrK2tDXpzq1IqlbR7925KSEggAOTm5qb1vs6ePWv2f9z0tC1evJgA0IABAyTPEwDy8PBI8Wsr9O1bsWJFistgjFkuAJQrV64UnwuA6tevTz169JCkFyhQQCP/1atXzf5ZaopNH4u+znjv3j0EBgbi6tWrqFixosZM+8biy6/pm0KhgJ2dXYomK1YoFOjVqxesra1x6dIlnf3O3N3d8enTJ3G/bt26Bt9PhQoVjK5bejdx4kQAwPv37zWOJSQkyJ7z5MkTBAcH6yxXWELH09NTb15VZ86ckb2czhizPLq+bx89eoRy5crhv//+05rn2rVr+Pfff/HmzRvxkmtgYKC43uqKFSuwevVqNGjQwLQV/4YcHBwAACdOnNCbN8XRi7ZFyb+lGzduoGXLlgCA1q1bw8PDI1Xl8UCJ9Of169d49OiRJG3v3r3o2LFjists1KgR8uTJo5F+7tw53Lx5E/b29sidO7eY3qpVK/G2arAnp3DhwimuV3p3/vx5jWVqtm/fDk9PT0nfRiB5qTlXV1eDyj169Cjy5csnux6uusuXL6NVq1YaC2QzxtKfoKCgFK9zCiSvYPT8+XP06NEDr1+/1lmO+jyrgwYNwpgxYzB27Fj89NNPhlc6HRo9ejSISGMZTjkpDur69OmDZcuW6cxDaTw5aVhYmBjBOjo6yo7AjYuLExc817fwObfUpT8lSpTQWCe1e/fuOHTokMnvq1mzZqhVq5a4X6xYMQDJAyoePHgADw8P5M6dG1OmTNFaxpYtW/QuGWPJhJGzAicnJ7i5ucHGxgZv376VHFNf7zYgIEA8PyYmBpGRkQC+Ln2jfr4c4X88KCgoZQ+AMfZNJCUlIX/+/Pjtt9905lP9oXz//n1UqFABcXFxACD5sViqVCmdLXZy8cbKlSuNrXa65O7ubnDeFEcvR44cwaxZs/Dzzz9rPJlJSUnYsmVLml+KcnJyEoO0sLAwSeuKYP78+XB0dBS3okWLai3PwcEB5cuX56COAUgO5oDktXK/++47uLm5Afg6LF6uVa5w4cJaV/zIiFT/50qUKCFp1QSSf1StXr0asbGxKFSokHi8evXqqFatmtH3J/yq1xVYM8bS1vPnz1G+fHmda5EKjSQ7duwwqMyFCxeiatWqePr0Kd69eyeb5+rVq3B3d9dYe9WS/Pzzz0blj4mJwffff2/4CSnqofj/vL29qUiRItSpUyeKjo6muLg4+uuvv6hEiRLk5OREM2fOTE3xet29e5f69u1LRERz586lXbt2aeSJjY2l8PBwcfPz8yNAfqAEY6oePXpEbdu2FddZlZM7d27ZTqxys49nxK1q1aqyHXmF28JqIePHj5c9rrodP35c8tyuWbOGAFBQUJCYdvDgQYM7DDPG0obw/3z16lWteWbPnq3zf/XOnTvi8TFjxkg+C06dOkW+vr5m/3xLi23y5MlG5TdWqj8Z/f39ydXVlVxdXalQoULk7OxM8+bN+2YjSE05+pUxY+3Zs4dq1apFS5YsoRo1aojprq6uBID+/vtvUigURv/jL1261OwfPindVD/Mha148eLibWEEsvqmHtQJ6aVKlRLTOKhjzPy6detGgObI9VevXtGePXuISPrDLS4uTuP/u1OnTmb/rEqrTf3xq243btwQbzs7OxMA6tq1K4WHh1PHjh1lyzJGqj4Zw8LC6Pfff6c8efKQvb09ZcuWjR48eJCaItMcB3XsW3j79i3t2LFDvH3+/HkKCAigli1b6vwwsLGxIQCUmJgoTiWS0TZhjWX1TVtQp/rBdujQIUnasmXL6NChQ9/uhWUsE1MqlfTo0SOaO3cuAaCDBw+KP+I+fvyoN6hZsGABvX37lhITEzWWcrT0TfUxExE1a9ZMI09oaCgplUpx/86dOxQcHCw2SG3cuJEA0IgRI2jIkCFUqVIlo1+jFAd1U6ZMIUdHRypVqhStW7eOvnz5QgMHDqR8+fLRzZs3U1psmuOgjpmT6nq2AGjUqFGSfS8vL8mEvub+oPqW23///UfLli0TF9dW/8Ak0gzq1I8zxtLOpk2bCEheyB6ApOVcmD8UAOXMmVPr/7mLi4vZP2tMvU2YMIGIpJ9HUVFR5O/vL8kXGhoqPpchISEaz69SqUx1bJLiT8Py5cvT1q1bNfobTZ8+nbJnz55ufz1zUMfSAycnJwKkqzsA0OhCoP7hcezYMWrfvr24/+eff5r9A83UW/PmzSk+Pl6SFhsbS0REhw8flnxwCreDg4Pp77//1nie4+LiKCkpKe1fUMbSuR07doj9zjds2EBTpkyRzRcdHU0AaOfOnRrHRo8eTUDyijwAqEWLFmb/vEgPm0B9n4jIxcWFihcvTnny5BF/sKalFAd1upb2+eeff8jOzo5WrVqV0uLTDAd1LD0ICwujt2/fklKppCtXrtAvv/yi8WFAJA3qVIMTIU14P2f0be3atXT+/HlJS93du3c18kVERNCpU6fI1dVV/JU8dOjQb/nSMpYuqQYccsGH4NOnTwSA2rRpI0kXltHMLFurVq00flxq2wRXr16lP//8M+1eRAOk2XWLEydOUM6cOdOq+BTjoI5ZEuFDY9iwYbLpqrdPnz5t8AeWcBnFUja5/ikFChTQSJMLcm1tbbU+v0OGDKHx48dL0qKjo2n16tWyP1wnTpxIjRs3NsEry5hhoqOjKTY2looUKUIjRozQmffKlSsUHBwse0xXMKIqLCyMgOQWcyKi06dP04kTJ6h58+Zm/xww9Xby5Emtx+bNm0dERG/evNE7Ejc9SdPa3LlzJy2LTxEO6pglET40Ll++LEm/desWXb9+XcwzadIkSX5924YNG8z+gWrMJgwgUd0cHR010oRWBkM+dB0cHCTHg4KC6OXLlzRjxgwCkvs3hoSEUGRkpMbrwdi3AoCKFCli0HsPANWsWVPrMV3/F0FBQRQUFEShoaGyeTPipdbnz59rPbZgwQK9z196XCc+TWfZrV69eloWz1imUbt2bcl+zZo1Ua9ePQBAZGQk5s+fb3BZp0+fTvPVXkxNbo3Z8PBwjTT15coEDx48wPXr1/H69WskJiZi/fr1GqvLlClTBi4uLuKEqomJicibNy++++47o+vr7++PP//8UyP9hx9+SFF5LHPw8/MDoPl+9/f3l83/6tUrjXWsX716pZHv119/1XqflNy4g3z58iFfvnxaPxvOnj2rs+6WSNcSZcOHD5fst2jRQiNPzpw5TV6n1OKlExizAFmzZtV6LEeOHOIqKA8fPoSvry+2b98uyXP+/HmcOHECERERaNmypbjOMZD8JSAsy2Pp9u3bJ5tepUoV1K9fH6VKlcKUKVMwcuRIyfFXr16JQaLqcwMAb9680SjvypUrsveTmJiIyMhI9O3bFxMmTNAoa926dRprGTMGJP/wKFasGObPnw9bW1tcvXpV7znVqlVDhw4dJGnqQZm/vz/++OMP2fOJCHPmzJGsomRpP/hSQ271qNKlS+PFixcaK1SdOnXKIpYn5KCOsXTM29sbGzduNDh/5cqVUbJkSeTJk0eS/t1336FNmzbiL0vhg3vBggUoVaoUbG1tTVdpMwoODtab59q1axppbdu2FW+vWLECgO4vt0aNGsmmDxo0CA4ODmJLi77FzE0hMTHxm9wPS51NmzZBoVDItjoDX1vpdu/eDQC4deuW3jKF9ZOzZs2KJ0+eiOnR0dGIiIhApUqVcOfOHa3nt2zZUmNtVksP6urVq6f1/1OdekvdggUL4OnpCRcXF428VlZWsLOzM0kd0xIHdYylY1WqVMGQIUOMPq9169bYtWuXuK/+4SVcppRbv9aShYSE6M3j5eWlkfb8+XONNEPWgL527Ro+fvwo7u/cuRPA1+c7KSkJR48e1fiivHDhguR2w4YNJcdDQ0OhUChw+/ZtvXWwsbFB06ZN9eYLDw9HVFSU3nwsbezZswdAcsAl2Lx5s/hDRP3Sq42NjWw5b968watXr7B//34xLS4uDhUrVhT3s2fPjiZNmsDHxwdr167VWqdz585ppFn6Z8L69etx6dIljfQiRYpopFlZWeHZs2fi/uTJk5E3b16tZTs4OMDLywsREREIDAw0SX1Nzkx9+cyGB0qwzAQqAwhUPXv2jADQu3fvNPLKbcL8VMK2evVq8XaHDh00JtnMaBsRSWaCB5LXpAVA5cqV03gO3d3dCQA1bNiQANCePXtkyyQiqly5slheSEiIJM/YsWOJKHm+PdUBG3KvsSHvhYIFCxr+5mEm1bp1awIgjk6Ni4uTvHbq74+1a9fKpteuXdvs/w/m3oRlyrT9r8o9byNHjiRvb28CIK7ZLfxP+fj40NmzZ7/xOyJtcEsdYxlYWFgYtm/frtE/pGzZsiAiFC1aVExzcHAAkNyi8OOPP0ryr169WrJfunRp8fbhw4ct/te9PkSk8Zzky5cPAPDs2TMkJiZKBmlcv34dwNe+d7169dIo08PDAwqFQuxjly9fPvTo0UOSR+iT5+7ubnSn7OPHj4t9gA4ePAgACAgIMKoMS/DgwQN4e3trpCuVShw6dEj2cuLevXvF50QQEBCAc+fO4e3bt1AoFLh06RK8vLxQqVIlrQNwjHHq1CkAXy/JC5f5tZk7d65sR371fpqZkbW1tdHn9O/fX3wv9OzZE0SEHDlyAAAqVKiA5s2bm7SO5mL8M8MYsxiOjo7o16+fQXk/ffoEpVIJW1tbPH78GACwdu1axMbGauS1srIySd+bevXqwcPDI9XlpDV9l2JtbGxQqVIlo8pUvTwuUL0sC3y9nKx+GTY6OhrZsmXTWX67du0AJAekXbp0Mapuxpg3bx6aNWuGunXrptl96FKlShUA0Hg/bt26FUOGDMG5c+fQrFkzyTEhyFY9p3HjxpLL8KtXr0Z8fDx8fHwQGhoKZ2dng+oTGxuLx48fo0aNGjh48CBiY2MlgxmUSiWuXLkiGU36v//9T6Oc9+/fy5ava8SmpStWrBjevXunN5/q69a9e3esWLECBw4cQMGCBTXyxsfH4+LFi3B3d0doaCiA5KAuwzJXE2FqvX79mvLmzUuNGjWiRo0aUVBQkEHn8eVXxvSLiYmhDRs2SCbghcqljPPnz2ucAwMum6hu2bJlIyKiHDlymP1yjjm2//3vfwblu3jxouTS0pEjRwhIXmJO9XnX9nqovzbaKJXKFC2pBiSv+2mI+vXrU7NmzbQeP3z4MC1dutSgspRKpWQSbVV2dnZi+oEDB2TrDIDmzp1LREQBAQEaz3uPHj3EJfn0fb88ffqULl68KC6xBYASExNlX8+M3lUhNVtERIR4u0GDBlrz9ejRQ7zds2dP2dcEAPXp08eg91JGYtFBXdeuXY0+j4M6xlLGzc1NEmiok/vwTUxMpHbt2on7J06coIULFxIAmj17NhERTZ482exfJubYDA3q5s2bJ95Wf55V92NjY+no0aP05MkTse+QtnPk9OnTR+vxyMhIyWLkcq+7IfTlNaasmzdvyj6upKQkSbpqUBcVFUVjxoyRHNe21J5qUPfx40eDHtedO3fE20KfS94M31SfSz8/P5o5cyaVKVNGkufLly/07t076tixIwGgMWPGGPR+ySwsOqgrUKAA1a9fn6ZOnapzLVpVHNQxljLR0dE0ZMgQAuSDutjYWDp27JjGh7TqKg+enp7k5+dHZcqUkSxn1LhxY4M/+A8cOGD2Lx9TbKNGjTIon9CpG4CkJQhIDuT0na++QgARUeHChcne3l62JbZGjRoar62zszMBEFsJ58+fr3GeIYS8QpD09OlTmjp1qsZxAKRUKqlBgwbk4eGhUU5SUhJt3rxZkj8mJob8/f3pyZMnGu8XouSWPdUWHmF7+vSp3ufw/fv3Bj0u1ZVaatSoYfb3mCVtx44d0/p+2rlzJ+XLl08j/ciRIxQTE2PQey+zsNigLjY2lr58+UJKpZKGDh1K+/fv15ovPDxc3Pz8/AjgoI6xlBBa1eSCOqLkL87du3dTQkKCGDCoBhXaGDOiL6Ncvho5cuQ3uR/VoBCAZBRv06ZNaeXKlUQkDahu3rxJpUqVori4OHr06JGY3r9/fwJAOXLkEF874dibN28kr+mXL180Xmf1+8iTJ4/4vjhz5ozkuLCYev369Wn16tWULVs2WrhwISUkJFDdunU1Hqerq6vW52DMmDEaPziErWvXrnqfw5MnT4qP4dOnT9SkSRM6c+YMTZs2ja5fv27291J63jw8PHQe/+GHHySX/VXfE6q8vLxo8ODBWj9DWLJ0H9QFBASQu7u7xqY6RcOJEydoxowZsuf/9ttvsm8kDuoYM96kSZMI0B7UyRH6yTRt2lRrnqpVqxKQfLn23LlzlD17dkl/KdUtMTGRvv/+e7N/WaV2s7e3N8v9JiQkaKSpTq8BgLJnz04AyMfHR5JeoUIFAr72hySSBmozZ86kffv2iYF3vXr1JK+zat5du3aJt9UvmQLJ0+0AyUGd6jq9HTp0MMvz9uzZM3r37h0tX75ckr527Vqzv5fMsVWsWNGgfERE69atIwC0aNEisra2lhxfvny55D2SK1cu8TxmPIt95lQX0p0yZQpt3bpVNh+31DFmOkJQd+nSJYPPiYyMJADUvHlzrXmEudrUKZVKevbsGY0cOZKGDh1Ky5YtE4+pfjHcunVL9gulcOHCZv/ys4StevXqKTpv1qxZBn2pExHdvn1bkq4a1AktgKqbMK9bsWLFzP78qG6Z/bKqi4sLAaBWrVrpzNevXz+6cOGCxv/0q1ev6Pz58/TTTz8RADp9+rTk+KFDh6h169YGf74wKYsN6k6cOEHVq1en+vXrU//+/SkhIcGg87hPHWMp5+/vT61bt6bo6GiDzxFa6lq0aKE1jzC5sTFUv0DUW5oA0Jo1azT6oPH27Tcikh0UkllHPVv6lpSURE+ePKHevXvrzPfq1Su9/8N+fn5G/c8z/RREFr7Qm5EiIiLg6OiI8PBwcbJVxljaISKMHz8eo0ePlkxarCoxMREhISEoUKCAweWqztdFRBr7QPLySVmzZpU9f+HChXB2dk7RMmzMcG/fvkXx4sXNXQ1mIsL/Vs+ePfHvv/9qHM+ZMyciIiK+dbXY/+MVJRhjaUqhUGDp0qVaAzogeYZ4YwI6Q2mbef7gwYOYNGkSBg8ebPL7nDBhgsnLtGQc0FmmSZMm6TyuvrJFZGRkWlaHGYiDOsaYRdK24PnVq1fF21myZJFN79Spk97ytbXw6TNw4MAUncdYemFjY4Np06bpzFO7dm3x9uTJk8UltzLyiheWgIM6xphFWrZsmWx6/fr1tabb2dkZXH5K19hMybqUjKXGhQsXMHbs2FSVcfHiRfF227Zt4ejoiNjYWLi6uorpqoHc+PHj8ezZMzRq1EiyzBkHdebFQR1jzCKNHj0alDzYCwCQL1++VJeZPXt28ba2lkB99K0Ty5gpODk5ibebNGmCZcuWYe7cuQA01zY9duwYTp06JVtO5cqVASSvfSsQuhCo/whS7UNnZWWFsmXL4tKlSyhWrJgknZkPP/uMsQzB19cX4eHhGum5cuUSb+saFxYUFAR/f39x387ODv/884+kBQMArl27hlmzZknSpkyZIt5Oi76BjKkbOXIkAMDe3l5M+/XXX0FEKFy4sCRvmzZt0KpVK3H//v37YovylStX4OPjI8mv2to9btw48bYh7+3WrVsb8SiYyZlr2K258JQmjGUugYGB9OTJEyIiatu2Lal/7AGQpG3dupUAUMeOHcU0d3d3ST7VKVRKlChBL168IKVSSYmJiZIyU7IJ84Dxlnm3GTNm6Dzu5eVF8fHx9OLFCwoMDNR4z48bN06SX+69Lkx+ret/wVgfP36kuLi4FJ/PUo87fzDGMrT8+fMjf/78AIB9+/YhNDRUZ/4BAwagbt26KFKkiJh2+PBh5M2bV9xX7Tf0+vVr8bbqwIyUUCgUSEpKSlUZ2uzatQt9+vRJk7KZaXh7e6Nq1aoYPHgw5syZo3Hcw8MD/v7+Yt82FxcX2XLo/1ukt23bJnl/qp5jY2ODmJgYU1bfJF0gWOrw5VfGWKZhb2+PQoUK6c1XtmxZZMuWTdwXgrUWLVoA0N9vSBg5a2dnh/79+4vpnTt31nmeUqmU9E8CTDdFiurlt/Qsd+7c5q5CiugaLeru7o5y5crpPL9mzZqoUqUKiAglS5YU02/dugVPT09MmzYNbm5u6N69u966lCpVSixz5syZYvqHDx9w584dAICtra3ecpjl4aCOMcb0EFrm1P927dpVNv+FCxcAAKNGjcK2bdvECY7VR9T++eefGufu379fst+0aVPJfvXq1TXO0RYwvHv3Trxt7Jf4rl27JK2V30q3bt2++X0CyYMADh06hNu3b6NXr15Gnz9v3jzxdtasWSWPY8OGDXj69CnCwsIk/T6FwQl//fUXLl26JFtuzZo1UadOHUn5+owePRpeXl6oUKGCJL1gwYLipPs7d+5Ely5dDC6TWQYO6hhjTA/hcpbQYmdlZYXjx49j48aNsvnLli0LAOjRowcAiNNCVK1aVczTu3dv/PLLLxrn5smTB58/f8YPP/wAGxsbjVZBIsLChQvF/axZs+LYsWPivmqLUdGiRcWWL2EeMUP17t3bLJfTfv31V60jNXXR1xKmT6VKldCxY0fUqFEDo0aN0ppPCNY8PT3FAPzu3buSPC9evBDznTt3DuXLlwcAODo6SlYyEgY0uLi4SEZep5aVlZVk+hE5LVu21PgBwTIA83bp+/Z4oARjTNWcOXOoePHiOvMolUqaPXs2+fv7p+g+lEqlOJhi27ZtBIAuXbpEREQvX77U2UH99OnTkk7vnTt3pt27dxMA6t+/v5ivYcOG1KRJEyJKXm9XrgM91Drc79+/X2tnfCLNDvdpvX38+FG2riNGjKCTJ0/SgwcPyNHRUfbc1q1bp+q+VV27dk1yzNraWrzdo0cPAkBv377V+vwKr/nNmzdlX1Mh37179+iff/4hpVKpkefChQs0ZcoU2fPTiq73IbMMme7V46COMWZuMTExkn1dX6aPHj0Sjzds2JDCw8PFoG7ZsmVG3e/58+fFsr777jvJfatuwsjfhIQECggIIACUI0cOIiKKjY3VGhidO3dOvN2tWzdq1aoVhYSE0NKlSyX5ihQponHuly9fZJ8TLy8vyfMVExNDnp6eGucPHz7coODNycmJ/vjjDwIgBq3Dhw/XeH1atmwpnlOsWDECQAcOHKCAgACaNGmSbCBmaFC0cePGdBk8cVBn+Szi1YuIiKDatWtT9uzZ6eHDh2L6nj17qG7dutSkSRN69+6dQWVxUMcYS2/0fZn6+vpK9lMa1Mm5desWZc2aVdLCpB6w2NnZ0aJFi4iIKDExURIk1alThwDQwYMHiYjo33//JQC0ePFi8Xz1c9zc3CT73bp106iXrudEqVRqBGt37941KKhLSkqi69evEwD65ZdfKCEhgZKSkmTvRzjn1atX9Oeff+p9Li09KOrTp49F158RWUSfOnt7exw7dkzS8TQhIQFLly7F5cuXMWfOHNnh34wxlhGojoYEvg7UIB2TKRuqZs2aWLVqlaRs9aWeYmNjMXHiRABfR/4uWLAARISWLVsCSF7VAPg6IEN1uTTVqV7Cw8M1JrGtW7euUXVWKBRo06YNgOTRoU+fPkW1atWwZMkSMc+CBQtkpwaxsrJCrVq1MGzYMEyePBnW1tZaRzO3bdsWBQsWRKlSpWT7P2Y0O3fuNMl7ipmPRcxTZ21tDWdnZ0naixcvUKlSJdja2sLd3d1kw/4ZY+xb+/fff1G0aFGz3f+wYcMwbNgwg/IqFAokJCSIgdqMGTPQo0cPODo6AgDatWuHefPm4YcffpA938HBARUrVsTBgwexa9cuZM2aFe3btze6zkIgVqxYMXFAx/jx43Hr1i3s3bsXvXr1QvHixXH+/HmNc21sbPDPP//ovY/jx48bVae9e/emeq5CxlLDIoI6OWFhYZJRRNom7IyLi0NcXJy4HxERkeZ1Y4wxYxgy95iqVq1aoX79+mabTFi1Fc7GxkZcPxRIbpXTNWcbAMyaNQtdunSRnZ5Fla7HZ+jC8d9yBK8w2pkxc0lXQV1gYKDsHEVHjhzRmJDSyclJEqBp+3U0f/58zJ4927QVZYwxM8qVKxeuXr1q7mqkmLW1td6ATt9lQG2XoIW1foXvhO+++w6PHz/G+/fv8ccff6SwxoxZhnQV1BUoUADXrl0zKK+Liwt8fHwQHx+PW7duifNAqZs6daqkL0RERIRZL3Mwxlhm9OOPPxo9V54uwuVX9aBu8eLFqFOnjmTi5IoVK6JixYriiiCMZVQKspBekW3btoW3tzeKFy+OkSNHYtCgQdizZw9WrFiBrFmzYtu2bQYFaxEREXB0dER4eLjk8i1jjDHLcffuXQwbNgxeXl6wsbExd3UYSxcsJqgzFQ7qGGOMMZYRWcSUJowxxhhjTDcO6hhjjDHGMgAO6hhjjDHGMoBM16eOiBAZGYmcOXMaPM8RY4wxxlh6l+mCOsYYY4yxjIgvvzLGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQAc1DHGGGOMZQCZLqgjIkRERICIzF0VxhhjjDGTyXRBXWRkJBwdHREZGWnuqjDGGGOMmUymC+oYY4wxxjIiDuoYY4wxxjIADuoMdOXKFTx//tzc1WCMMcYYk2Vt7gpYgujoaDRq1AgAeIAFY4wxxtIlbqnT4/bt28iePbu5q8EYY4wxppPFBHWRkZGoU6cOcuTIgUePHgEA9u7dCzc3NzRt2hR+fn5pcr8PHz6U7G/cuBFXr14FEaFXr164f/++eOz9+/dpUgfGGGOMMX0sJqizt7fHsWPH0K1bNwBAQkICli5disuXL2POnDmYM2fON6nHsGHD0LBhQ8THx2Pv3r346aefAACXL19GkSJFcO3aNZPf57Nnz3D37t1UlaFUKhEfH2+iGjHGGGMsvbGYoM7a2hrOzs7i/osXL1CpUiXY2trC3d1do0VNEBcXh4iICMlmDIVCIZuelJQEALCyssLevXvRuHFjAMkBGJDc905uYMXTp0+RmJgIAAgICMCxY8f01qF8+fKoUaOGUfVWKpViHQFg9OjRsLOzM6oMxhhjjFkOiwnq1IWFhcHBwUHcVw1gVM2fPx+Ojo7iVrRoUaPuR1tQl5CQACC5ha5Xr14a6Rs2bEC5cuWgUCjw6tUrAMmXkCtUqIDZs2cDAL7//nu0b98eABAfHw8vLy/xMrJCoUD37t2NqiuQHFQ+ePAAnTp1grX113EwO3fuNLosxhhjjFkOiw3qnJycJK1uWbJkkc03depUhIeHi5sxfe/8/PwwaNAg2WN58uSRTReCS29vbzFt69atAIDY2FgAEPvhBQcHi3kKFSqEunXrwsXFRUzbt2+fJKjMnTu31n57b9++xaVLl1C+fHlUqVIFR48elRzX9vwwxhhjLGOw2ClNXFxc4OPjg/j4eNy6dQuurq6y+ezs7Iy+7NimTRsUKFAAW7Zs0ZpHW8tgYmIiqlevjnv37olpWbNmRe/evREeHg7g67Qotra24jmfPn0CAJ393kJDQ3H69GkMGTJE41jFihURHR2t9VwO6hhjjLGMzaKCurZt28Lb2xvPnj3DyJEjMXbsWDRq1AhZs2bFtm3bTHY/p06dSvG5iYmJkoAOADw8PHD8+HFx/9ixY3B3dxeDOvXWQ19fX63la+sTqCugA5L7/gFAq1at8OHDB619EAUxMTFYuHAhpk+fLrmMa4wGDRqgffv2mDRpUorOZ4wxxpjhFJTJZtONiIiAo6MjwsPDJX3yBB8/fkSBAgW+aZ3c3d1x/fp1cb906dJiPzw5BQoUgIeHB0qWLCmmaev7R0TYuXMn+vXrJ0k/c+YMWrRoofU+li9fjnHjxuHAgQPo3Lmz3sfg4eGBevXqSdKEOul6i/3999+YMWOG5FI0Y4wxxoxnsX3q0sr27du/+X2GhIRI9nUFdAAQGBiItm3bIjY2FklJSdi7d6/O/OoBHQC0bNlSNm+lSpUwYcIEcYSu8FebmJgYseVRtTXSUNOmTdN4/IwxxhgzHgd1aiZOnPjN79Pf39/oc54+fYr58+cja9asktG3xjh8+LBk/86dO/Dx8cGff/6p0cr2/v17zJ07V6PVLVu2bOII3sDAQDFd3+VdQSZrKGaMMcbSDAd16UBUVFSKzrty5YreljRhxK2cXr164e+//8a///4LAKhZs6Z4TD2oGz58OGbMmIH79+/DxcVFNhhTvQQsN3DlzZs3+Pz5syRNV1C3fv16nD17Fu7u7jofB2OMMcYsbKAEk7p06ZLePN9//73WY7GxsRg1ahSA5OlTVAkBWlJSEq5evQqlUgkAqF27NhISEvDw4UP06dNHcs7kyZMxZMgQ7N69W5KelJSELFmyoGTJkihQoAACAgLEY8Jo3xcvXqBMmTKS80aOHCnefvr0KapWrarxGL58+QIbGxueWJkxxlimxy11GdyFCxcMyvfff/9J9oWgbvHixWjYsCFOnz4N4OvkynZ2dnj8+LHknJCQEDx48EAj2BOWUgOSL9Fu2bIFM2bMAJDcJw8A5s6dq7N+M2fORFRUFIgIixcvFgdW5MyZE7Vr1zboMTLGGGMZGQd1TNaCBQsASCdRVqVtPr0qVapopK1du1ayP3jwYJ1B3MaNGxEWFiZJO3r0KP78808EBgZi0qRJkkDxwYMHWssKCgoSWw6VSiV69eolLuXGGGOMZSR8+ZXJ+vjxo87jjRo1Mqq8/fv36zy+bds21KxZE6dPn8bx48dx/vx5jTyJiYnipM/6+hJ++PABq1atgqenJy5duoTevXsjJCQEe/fuRUxMjMYgEcYYY8zScVDHUiQ0NNSo/N26ddNIW79+vWT/559/Fm+r98sDkgdVCH37hMmU1T169AgbN27Eu3fvcODAAXE5N0POZYwxxiwZTz6sRtskvnIqVqwIHx8fU1aP6fHkyRNUqFABQPLKG6VKlQLwdRSt8Pq5urpKLssmJibi48ePKFy4MLp06aK35ZAxxhizNNxkkQpJSUkICgoydzUyFdXLpkJAByQHc6rLran3s0tKShIDP/WWuj179uDkyZNpUV3GGGMsxQYPHowTJ04YnJ9b6lQ8f/4c5cqVM7is+vXr4+rVq2LrUNWqVTUGFjx+/BiVKlVKdb1Z6owePRo2NjZYvnw5ACAuLk5ce1d4/Xx9fdGwYUNs3boVTZs2NVdVGWOMMQDJ3082NjZaBydq5Oeg7quzZ8/KLp81ZMgQbNq0SZJWrVo1nDp1Cvny5RODgsjISOTMmVOSj4iMuqQrEOqoqnDhwkhISODWQRO4dOmSONhD/fWpWrUq7t27Z45qMcYYYyJD1lBXxZdfVaxbt87gvHfv3kW+fPnE/UqVKiFHjhy4evUqXr9+LclbsWJFo+tSt25d8XZMTAxy5syJJUuWSNJZyjVu3BiRkZF4/vy5uavCGGOM6SSs/KQPB3UqrK2/DgZ2d3fXOC5MmKvu+fPnuHbtGoDkS7IlSpRIVT3atm0rthi2bt0aWbNmRUREBHr16oWdO3emqmz21dKlS/VOenz37l0sW7ZMdoqV9+/fQ6FQ4OLFi2lVRcYYY5nI6dOnJaspCQICAgxaUpSDOhXClBcAcPXqVY3jPXv2lL2UWqZMGeTKlStF91m/fn3J/q5du3D8+HExwOzfv7/keI4cOQwqV25JrW/JmFZPc5k1axa2b9+uke7t7Q1XV1fcuHEDNWrUwC+//ILmzZtj6dKlkvnxhFa+s2fPapQRGBioc1JkxhhjmcuRI0e0TugvaNeunTjd15cvX8T0sWPHonr16nrvg4M6FcJqCCdOnJAEbz179gSQ3KfN0P5xs2bNwsSJEyVpSUlJmDhxIqZPny6m/f777wCS++gBQIECBQAYfv1cm/nz52P06NGpKiM1HB0dzXbfpvDw4UPUq1dPkjZ+/Hjs2bMH7969g4eHh/ga7du3DwqFQrIKRuXKlWVX1xB4enpiy5YtaVF1xhhj6cTbt2+hUCjw6tUrdOzYUfyuN4QQewgM6S7EQZ0KoXVLvZWrZcuWICKjWuN+++03LFq0SJJmZWWFRYsWYc6cOWJakyZN8PDhQ7Rp0wbA12Cue/fuqFKlClq3bq1RtjBPW9WqVTF+/HgAyUGcKnt7e9l5+AAgf/78AJLXcT137hyGDBli8OMylPA4li1bZvKyzal///4oXrw43N3dERkZCQB48eIFAEimVPn06ZPOctzc3DB48OC0qyhjjDGDXbx4EW/fvjV5uZcuXQIAnDlzxqjzHjx4gDt37hh9fxzUqVCfwFZOSkay7t27Vwy+5FSuXFljhEuhQoXg7e2N3Llza+S/e/cuIiIicPfuXSxZsgQBAQGYMmWKJE+ePHkkl5MFderUEZfaypIlC5o1a5YmKywULVoUAFC6dGmTl51eqAesCQkJqF27NlavXi1Jv3//vmStWlWnT58Wg0MAiI6ORkxMjOkryxhjTKumTZumuNtSQECA1mPC9+uNGze05jl69Ci2bdsG4GsMUKVKFdnvcL0okwkPDycAFB4ernHs8OHDBIA+fvxIREQAyM7OTpLn119/JVM8be3bt6fFixeL+15eXgSAPnz4kOIyARAA8vLyIiKiCRMmiGkAKDIykoiInJycJI/Bw8ODAFDBggUl+QHQs2fP6OLFi/TPP/9oHNO1ERH5+PhI6pUZt8jISMlzcvDgQQoODpbk6d27t/haWFtbk5OTU4rfA4wxxoyn+jltjKdPnxIA+u+//2SPb9++Xfb7kYjo6NGjNHfuXEl6lixZxH3hu1ruXK2Pw+hHYOF0BXWHDh0iABQUFERERM+fP6eAgIBvXcUU+/fff+n8+fPi/tWrV2XfDA4ODrJvDhcXF51voA0bNmgcj4+P1xrUCcwdWKWXTXguGjduLEmvX7++xnPFGGPs20npZ++5c+cIAE2bNk32+PDhwzW+Cz59+iS5T2Fbu3atQd8juvDlVxXqC76XKVNGHLhgCbp37y5ZCaF+/fqyAy6Ey6/a2NjYyKYPHToUlPxDAEDypVVteVXVqlULR48e1Zsvo3N1dQUAjX4buprYL1++jMePH+Ovv/7SOPbixQvJ67t3717Mnj3bRLVljDGmj74uWf/8849GmouLi+wl21GjRqW6PhzUqVAP6jKqhIQE2fQ9e/bgf//7n+yqGurWrFmDU6dOSdKEwRpdunSRpN+8eRPt2rUTB2gY69atWxppderUSVFZ5vTw4UMAmkFcfHw8du7ciSNHjohpnz59wrhx49C4cWNUrlwZo0ePFlcYCQ4OxtmzZ1G2bFnJCNpevXph1qxZ4j4RaX2tGWOMpZ4Q1MXExGDZsmWSz3dtgyNCQ0NRqFChtKmQ0W2NFk7X5df//vuPAFBoaOi3r1gaglqz7YABA3Q246r3AzO0/M+fP1PevHnp5s2bsvmSkpIoKiqKbG1tdTYvd+vWjQDQ3LlzqW3btpL7EDbhMWTU7ccff9RIe//+PYWEhFD27NnFtEmTJmm8DvHx8URENH36dAJAvr6+Wl87Ly8v8vf3N+h1ZoyxjMqY7zxVly5dknxOnzlzhmbNmkV3796lihUrmvy7Qe/jSMmDt2S6grq9e/dqPWbJChYsSP379xf3ExMTKSYmRuc5fn5+dPbsWYPKN/afQRiYIbf16tWLHj16RE2aNKGEhATxHCFAAUDPnz+n0NBQ+uuvv8wefKXVZm9vb1C+yZMnExHRrVu3JOm3bt0ihUIheW1CQ0NJqVQSEdHly5dp9+7dBIDy5Mmj9bV6//59hvuRwxjLHD59+kRTpkyhxMREOnXqFNnb21NiYqJGvk6dOqU4qLt8+bLeH+Qc1KUhXUHdnj17CABFRESYoWaWa/HixdS5c2eD879//54A0NGjRyk0NJQSExPp3bt3BIBWrlyp9Tz1N7VSqTR78GXubcqUKZLnRtgWLlwo2e/Xrx8BEEdcG/pBAYDy589v8GvLGGPpxdixYwlInhGiefPmBHydBUKVMUFTQkIC1atXj+7du0dERFeuXJGc3717d7MGdUZ3HouJicH79+810h8/fmxsUelOZulTZ2oTJkzAgQMHDM5fqFAhJCYmol27dsiVKxeyZMmCokWL4tmzZ/jxxx8NLkfoy1CqVClxZQ7g67q92bJlM7gsS6VQKCRLl6mmq9qxYwcA4MKFC0bfx8ePH1NWOcYYMyPhuzw+Pl5jLlhtTpw4ASA51pEbVBgUFAQPDw/xO0f9s/a///5Ldb21KVWqlN48RkUv+/btQ9myZdG2bVu4urrCy8tLPKa+RqklEl5sDurSXpYsWTTSypYtq3ckkbCahiqFQiFZes3V1RVRUVEIDQ1F4cKFUaZMGUn+yZMnp7DW6Y+VlRXGjRunkT5p0iTZ/Nqe34cPHyI+Pt6kdWOMsfRAdWUGpVKJmJgYbNq0CSNHjoSHh4ck7/fffw+FQoFs2bJh4MCBGmUJccLTp0/x22+/oUGDBmlbeRV79uzRn8mYpswqVaqIc7jdunWLKlasSDt37iQioqpVqxpTlNnouvwqTBKor78ZMw8ANHDgQI200qVLExFRnz59CACNGjVKkickJERsut69ezcRkaS/meo2Y8YMs19SNWZzdXU1Kr+2gScAqEKFCkREFBERQX369KGAgACDm/wZY8xcIiIi6PPnz5K0mzdvyn7ODRgwgLp06WLwZ2ZCQgLFxsZSbGwsvXnzht6+fWu2z/v79+/rfS6MapJKSEiAs7MzAKBmzZq4cuUK1q1bh99//z1Fy2elN8QtdenakydP8Pfff2ukN2vWDAAwd+5cAEC5cuUkx4X3ZuvWrdGrVy8AX+cD2rp1K5YuXSrmdXNzM33F09CDBw+Myq/r//TJkycAkpeY27VrFwoWLCge+/z5c8oqyBhjaax06dLInTs3Nm7cKKapdzVp27YtAGDbtm1GdRcqV64csmbNimzZsqFEiRIoXry4aSqdRoyKXvLlyyf5EsmTJw/Onj2LJ0+eGP3lYioTJkxAgwYN0Ldv31RfPhL61GWEADUjKl++PLJmzSpJ+/jxo7jWasmSJeHt7a2xzqpckC6kVahQQXL50pA5+izZ8ePHsWbNGq3Hp0yZIju3XZ48ebSe4+npKXsJmDHGvoXg4GAAwLBhw+Dp6QlAcz5QYZ5QY/n6+sqWZw6GxCZGBXXbt29Hvnz5JGm2trbYvXs3Ll++bFztTODevXsIDAzE1atXUbFiRezbty9V5ZUsWRK9evXiljoLki9fPsmqFlWqVNF4/RwdHTFz5kysX79eTKtXrx4AaEyILNfXzxApXQjaHHQNRlm4cKHWY6dPn5YdEPW///0Py5cvN0XVGGPMYG/fvtWY4NfNzQ1xcXGYNm2aJN3Pz+9bVs1sjIpeihQponXZLGHE4bd048YNsWWldevWGh0eASAuLg4RERGSTZuGDRti9+7dKf5iZ+mTQqHA7NmzUbRoUTGtd+/eCAkJQbFixTTy37p1SxLcrFy5UnKcZEZP2dramrDG6VPr1q1RuXJljXS+NMsY+5aKFCmCn3/+GY0aNUKrVq00jqe2gSe9WL16Ne7duyfum7ylTtX+/ftTeqrJhIWFwcHBAUBya4zcl8v8+fPh6Ogobqpf7Cxz03ZJsWbNmpg0aRKSkpJARJLLuUOGDAEAvHnzBgBQsWJFANqXXsuoiAi7d+/GixcvJGnR0dGylyni4uIwY8YMHmHLWAb16NEj8VJlWnv//j1WrVqltfWtX79+36QeaW306NGoWrUqhg0bZvA5KQ7q+vTpg2XLlunMI9eiYUpOTk5iy1tYWBhy586tkWfq1KkIDw8Xt8zSBMtST+4y/Lp16wB8DQiFARfq06Y4OjrqLb9Dhw6praJZdO/eHVZWVujTpw/q1KmDt2/fAkieNiB79uyYPXs2gOS58SIiInD79m3s3r0bc+fOFefLM9bnz5+xdetWkz0GxlgypVKJDRs2aMzJ5ufnh3PnzhlcznfffYfSpUsbdd/FixfHhAkTMH36dERGRurM++7dOxw6dAiLFi2S1D0j6tSpE3bu3CnuC4MzcuTIof/klA4hPnXqFDk4ONBPP/0kLj0kSExMpM2bN1O5cuVSWrxB7t69S3379iUiorlz59KuXbv0nqNrShOWeUHP1B26jt+7d49u374tGXqeK1cuyXlyW0xMjMFD2Tt37mz26VP0bRUqVCAAVK1aNXHVEGHqmIkTJxIAsrGxoYoVK9K7d+8Mfm0OHjxIdnZ2BIDCwsKMe2EZYzoJy2MCoC9fvojpBQsW1PjMe/nyJRElrxm9ZcsWyTFdn5Fv376l/fv3ExFRWFgYBQQESM4BQEWLFiWi5PhBWCIyMTFRXNarQIECZv+MS+3m6Ogo3v7+++/F23fv3hVvC8+TqoSEBLp8+bJBr2eqJqDy9vamIkWKUKdOnSg6Opri4uLor7/+ohIlSpCTkxPNnDkzNcUbZPz48VS/fn3q06cPxcXF6c3PQR2TM3DgQK0fSEREuXPn1nn83r17kn/eDh06EBGJgYyQ/vjxY/F2fHw81a5dW9zXNnceABo6dKjZP5CM2RYsWKDz+MKFC8UPdiKipKQkat++PXl7e2s8t6rnXb16NRWvMmNM3aZNm8T/r0ePHonpQppA+Iz777//NI7J5ReofuZ9+fKFChUqRABkl3kMCgqiBg0aiOUUL16cAFBoaKjZP9NSs128eJF8fX1p6tSpYtr9+/fp77//psDAQCIiWrRoEdna2qb69Uz1rKL+/v7k6upKrq6uVKhQIXJ2dqZ58+al2/VTOahjKSE3uaUq4QOvXr16BIA+ffpERF+DukmTJomtd25ubgSAkpKSKDIyUvwnL1GiBPXq1Uv2Q2H8+PFm/2BKi23AgAEEgKpUqSKmxcbGis/r/fv3Nc7p1atXmrzGwcHBdPToUZOXGxAQwJ83zKyUSiW9f/9e9phqUPf48WMxXUgTWsuOHj1KAGjOnDniMVVyaap51c/19PTU+dnw8eNHs38+mWIrU6aM+HwcP36cAJCvr68JX12pVM3dER4ejk2bNuH9+/d48eIFwsLCcP78eUybNg05c+ZMTdGMpSs5c+aEk5OT1uNC/7vKlSuDiCT9OxUKBRYuXIjQ0FAAwObNm/Hzzz9DoVBI+kgoFAqto5usrKyQK1cuEzyS9GXbtm0AgPv374tpjRo1wrt371C6dGnZ+e/0LZUTExMDhUKB3bt3G1WXbt26oX379rh06ZJR5+lTsGBBVKpUyaRlMmaM9evXo3Dhwvjw4YPOfHKfPwUKFEDhwoVl8wv98MqXLy+mzZs3T/wsmzFjhiS/6n7dunV11kV9uqn0rHnz5lqPeXt7i7fbtm2LmJgYlCxZMs3qkuKgburUqShevDi2bNmCP/74A8HBwejevTuaN2+OW7dumbKOjKV7wgeQ+jqA3bt3x969eyVp5cqVw4oVKzQ+QBUKBWrWrClbvpWVFTZt2mRQXcaOHWtgrdMnLy8v7NmzB76+vhqzwstRKBRYvHixuB8TEwPga8CoT0hICKZPn473798DSJvnz9/f3+RlMmao27dvAwA+ffqkM5/wmSSsLgMk/398/PhR/L9SXfM9KSkJsbGxePbsmZimug53ZqE+h+eaNWswdOhQLF68GNmyZZPkVZ9A3+RS2sRXvnx52rp1q9iJUTB9+nTKnj07HTp0KLWtiGmCL7+ytJLS9xT+v5m+bt26lJSURE+fPtVowp8xYwYRER04cEDjWI8ePST7s2fPFm9XrlzZ7Jcf0mJTf/7Kli0r7quu9ava8VsboT+l0KfR1dU1Ra+jvteXsW/F29tbcil1xIgRBEC2z+rmzZvF9+iTJ0+ISPcAL9Vt/fr11LJlS7N/Hhi7dejQQedxoV+ftq1ixYqyn0fp4X89xS11Pj4+GDBggMZEvXPmzMHy5cvRs2dPcfkmxjIDYc7ElDp48CCsrKxQrlw5PH/+HGPGjAGQvFrFpEmTAABt2rTROG/v3r1o2rSpuG9lZYVFixbB09MTd+/eTVWd0qvevXtLLq8+f/4cr169AiCd5qBNmzYICwuTLePChQuYNm0a4uLiAHy9hE7/PxWTn5+f3pYNZrksYW7Ju3fv4suXLzrzREVFaUwfVrVqVcklf0OXvtyxY4c4B6chRowYobGigyXQ1x1C23Rtzs7OOHr0qNgqZ2VlleZTtxktraLFEydOUM6cOdOq+BTjljqW3kDLrzthBKl6q7cwaGLv3r105coVIiLatWuXWM68efNkyxemHMlIW1JSksYvZrnBFTExMeLz8c8//1CtWrXEY23bttXIL4zMM8VnmLbXl5nP4cOHCQD5+fmZuypaeXl5EQBq06aNJN3Hx4e6du1KRMlTXQDJo8lVqb/nhJa6Bw8eiGm3bt2ipKQkSUtdZtisrKxoypQpOvN4e3vLpguzGgjPsUKh0Pm8m0OaLXLapk0bk3c4ZiwjKl26tLhShTEKFSok9uHr2bMn5s2bBwCyy+ZUrVoV3t7eWLNmTeoqm86oT5gKyE/q/PjxYzx8+BAKhQLDhw+X9Ps9ceKERn5hMEZkZCTatGkj6QidkJAAIkKuXLkM7ufIvj0fHx+txy5evAggbdcDDQ4Oll3lyNPTU5ywW5c6deoASO5o7+vri1OnTgFIXsVm//79UCgUYkvyyZMndZYltF4L7/WnT5+iVq1akol8MzJSaU3LkiWLbOta165dERQUhJkzZ+K7777DxYsXsXv3bhw+fFjM06VLF8k5AwYMkOwvWLAA48ePN3HtjWTWkNIMuKWOWYrPnz/TgAEDKDo6WpIutNSlZM62169fEwDKly+f1l+pqpNiAqC1a9ea/de1tu38+fOS/apVq8rmK1GiBI0bNy5V91W+fHmKiooiAGRtbU3A1wlTVfn4+Ej2hfPZt3P9+nUCQAcPHpQ9PnbsWAJA169fN9l9vn//nhYtWiTuQ6Yl5/Pnz3rfD58+fZK8r/PmzUvZs2cXz1F9T/72228EgBo3biwpQ/U+IiIiJOcMHjxYsi9MMpyRN9XnpFy5cuJk6MIUIwDo5MmTWl8TT09PWrZsmSQtNjaWkpKS9L8xvrFM90nDQR2zdMIM8Cmd6+jt27f09u1bjQ++n3/+mQDQDz/8IEl/8+aN2T+U08sWGBgo+4UhuHXrFgGgffv2iWly+Yxx5coVmjx5Mt26dSvFZWQ2wgS5qkGWKiHAN2VQ17p1awJAkZGRRKT5ukdGRtLff/+t9/3QpEkTg4ITuU14j6jmlxt4lZm2LFmyEBFRrVq1yMHBgT58+EATJkwg4OsPY7kBJJYqzS6/MsbSRo8ePRASEpLiuY6KFSumMcAJgDgPX758+STpxYsXF6cHcXd31zjv999/T1E9LNH69es10oRLwElJSeKcVPfu3UOnTp1kO6grFAps2bJFY6obVWPGjEHjxo0BAA0bNsTChQtRq1Yt8fj79+9x9OhR2ct7DOL7W9/aoPT/l+EiIiKgUChw/vz5FN+nMPCCtHScHzBgAH744Qe95ei6NKtv0MT06dM1LinLrWGd0Qnzf96/fx+JiYkAgJs3byI8PBwFCxYUXyPhualSpYp5KpoGMt+rzVgGkCdPnlSdr/pBX6JECQDJ/e4OHz6MX3/9FdHR0ZL8/fv3BxFh3759GmXZ2dml/dxL6cTMmTM10p4/f47g4GBMmDABw4cPB5A8AatqXxwAePHiBTw9PQEAgwcPRq9evXD//n18/PhRki8kJAQrV67E5cuXtdajSJEi6NChA/LkyaNz9F1AQAAUCgU8PDwMfozp0aNHj6BQKBAYGIioqCitIzQ9PT2RI0cOcUSztqBOCLZJZaQzAKMnrNZW5l9//aVxXK6P38ePH9GyZUvJYva6Rqrqm9TfyspK8sNLoVBkqr7twvMzYcIEHDhwAK6urrL5hgwZggIFCqBy5crfsnrfBAd1jGVinTp1wrVr1+Dm5oamTZuiQ4cOsLW1hb29vWx+uS+c0qVL621ByMgqVqyIfPnyYfny5TrzlS1bFm5ubpK03377DQUKFMCDBw8AAJcvX4azs7PWMuQGhkybNk0jbf/+/Xj58qUYSOjrSG9qly5dQpYsWRAbGyt7/MKFC9ixY4dG+osXLxAcHKyRfvz4cQCAh4cH2rdvr7WVet26dYiKisK7d+/01g9InkYI0JzORp+DBw9KVkFRLUOpVGL06NEa56j/7+zatQtbtmzB2bNnJS2EhtZBzsmTJzVa6kaMGJHi8tKrXbt2aQRs8fHx4lQjHTt2ROfOnbWeX7FiRQQEBKR6Gqr0iIM6xjIhoQUjS5YsKFy4MDw8PDQ+4JydnfHzzz/rLUuhUMhezmX6Ca15L1++BAC9o6CF4EbV5s2bMWnSJCQkJCAsLAwfPnxAt27dUKNGDTEIVL8EFxMTg6NHj5riIchas2YNlEql1nn+mjVrhv79+4v7u3btwqlTp1C2bFlUrFhRI79qwCSMXBV07NhRDJhsbGwAJH/Byzl16hQUCgWsra0BQAwChPP1Xa4VdOnSBVWrVkVISIiYpt76p5ru4+OjEdT17dtXfFzCJcKJEyfC19fXoDpkZkQkvsYTJkxA586dYWNjg6JFi4KIULVqVfNW0Iw4qGMsExK+eHRd6gkKCsKKFSu0Hp8yZYreMtSVKlVKvC1ciswoVCeANlbXrl3RqFEjvV/o+/fv11hy7OPHj1i8eDHOnj0LJycncZ3OiIgIMUhRD+omTZqEDh06ICgoCEDyOt579uzBxYsXERgYKLZgpdT+/ftl0+Pj48UARvDgwQP07dtXnFhbCJTCwsLElj5tAVN8fDyOHDki7qv3pVPPv337dgBf+4+ePn1aUr664OBgja4IqpydncXJrYUyatSooZHv5MmTsvchXHYV1itesmSJ1vtKD7Jnz27uKgBIfl2FS+yLFi3CgQMHzFyj9IODOsYyIW1f9vrkzZsXrVq1wsOHD9G7d28A0LperT4FChTA27dvMWrUKLx9+9bigzxD1qnV5cqVKxppZcuWlexv27ZNY31hgXqwBHy9XKvakurv7y+u9lO5cmUsWLAAo0aNQu/evdG0aVMULFgQXbp0gUKh0Np389mzZ+Ki7XKEYEp91QY7OztJ0KNQKPD333/LluHk5AR7e3uULVsWUVFRAKCxStF///0n3vby8hIfp/C41RewF973QjCnWg/V44J8+fKJwfrPP/8Ma2trcf441XoCX/+XXr9+rfFYEhISZJ+rOXPmAAC2bNmicSw9krvUbwqurq5YunQpAKBatWpi+nfffYdXr15pDDAhInG9bWN+VGYK33q4rbnxlCaMkTjf2vHjx01WJrRMKVC4cGHxtuo6kW/evDG4DN70b3LrAh85coSAr6uM7Ny50+hy9b3WRMlzoV25ckWct0s4Jqx8YOzr++XLF8l+165dNfIIqymobiNHjiQA9Ouvv8rWv2fPnrKPb8OGDQSA+vXrR0RE58+fp+7du4t5Bg0apPc5ateundbjM2bMMPv7wxTb8+fPTVpegwYNxNcmPj6edu7cSR8+fBCPu7m5ERGJ8/EJ27Zt2+jjx4904sQJ4z6kMgEO6hhjJpGUlCQuR6S63b17V7wdFhZG+/fvpxkzZpBSqdQoQ9uHf+fOnWW/kHn7ugnzF6puBw8eJAA0f/58evjwIXXp0sXocgMCAqhDhw7k7e1N165do9WrV0uOnzx5UpywOm/evJJ5DYsUKWLQ66u+NWvWTG+eBw8eaD1WuXJl8TZR8uSxRCT5gSFsnTp1kuy/ePEiRRPytmrVyuzvgbTeDH0NfX196dKlS7JL8Klu9+/f1/gMEH5wAiAvLy8iIjp69CgByT9OANDp06dN+tmVkXBQxxgzmT/++EPyoZ2YmEhEhk/Aq3ruwIEDCfi6rmXv3r3N/qWWnreOHTtqpO3fvz/V5T5+/JgAUNOmTY0+t0CBAuJrq7qawrfc/v33XwKSV10w92tkKZu9vT1t375dI139f1R9U5/s+fTp01rz1q5d26jPlk+fPpFSqTTphNEZEfepY4yZjPqUG8b22RP6L7Vo0ULslC2su6qtrIIFCxpbzQxJfV48IHlkYGoJ/dkoBVNtREdHY/fu3ahWrRpy586d6rqkRI8ePQAgU83XllpDhgxBv379cP36dYPyC/PyjRkzRpLesmVLre8bY99PuXPnhkKhQL169Yw6L7PhoI4xZjJCR/PWrVsDML4T8+nTp/Hu3TscOXJEY8RjSjpEN2rUSCPt+++/N7ocSyXXad9Ywhe1MA2IMSIiItCnTx9xpQ1mGYT/OV0B1JMnT3Dq1Cm8efMGo0aNAhHB1tZWNu/o0aOxb98+lClTRkxbtmyZaSvNAHBQxxgzISGoGzNmjOSXeEhICN6/f6/3fGtraxQtWhRZs2aFnZ0dAO3BXOfOnXHo0CEMGDAAQPLcYYB02bJjx45pLAH0ww8/oHjx4kY8qsztxo0bAICzZ8+auSYsrXl5eenNU6RIEQBA+fLl0apVK4P+l1avXo2uXbvi9u3bAJJb3eSWHGSpx0EdY8xkhGk11Ft18uTJg0KFChlV1m+//YZ58+aJUxw0bNgQAMQpMOrVq4eOHTtiwYIFSEpKwqBBgwBIg8AcOXLg5s2b4v7nz5/Rrl07cfqE+fPni+tEMmaJfv31V63HunbtiqioKAwZMgRXr14VJ2fWxpBJxO/evYtHjx4ZXU8AcHBwwLJly3D37t0Unc/046COMWZyKblUp87BwQHTpk0Tg7Rhw4YhJCQEefPm1chrZWUlzltVokQJtG/fXlxuS/WSkPpyUFmyZMGLFy9k73/u3Lk669evXz8jHxFjqSP33hd+zMhxd3dHtmzZsHHjRtSvX19vH1fh8ujAgQO15nF2dkalSpUMq7CMsWPHckt5Gkr9Jy9jjP2/SZMmAYDWCXJTQ30yXPXLsrVr18atW7dQo0YNrQGX3FqPBQoUQN++fbFz504x7c2bNyhevDguXrwoWZcTSF4Vw9fXF+PGjZNdv5Sxb8nFxcXgvHJBXYMGDXD16lUAyf8f6gMYoqKi8OHDB61Lr7H0hVvqGGMm4+DggLlz56bpWrBCi1zRokU1jtWsWVPngAptx9S/7ISy5UZsCqNxU/sY1VeLYBmPoaNHDeHk5ISpU6fqzDN48GCdx9VHpwPJK5lUr15d6znZsmWDi4uL7Jq8LP3hoI4xZlHq168PLy8vdO/e3WRlqgd1wr7wJejj44MWLVoA+LpYvLb+SYaM0o2MjDRJXz71xe1Z+iLXitaqVSvZJeFUlShRQrasX375BVevXtXap03bkmsCba1tly5dMslIaWZ+HNQxxixO7dq1TbLmo1CG6nqTqoSgLleuXDhz5gyICCNGjECuXLlQsmRJjfxFixYVA0JhNC6QvManMGfcoUOHkCNHDtmWvpkzZxpc98ePH6Nx48YG5zfWoUOH0qzszEJ9LVkgecBPgwYNJJf7AWD27Nnw8fEBkDyyVCCMEhW6NNSvXx+VKlXCgQMH4OHhISnD1tYW/v7+ePjwIQCgXbt2svXatm2bZD9nzpyygSSzPBzUMcYyHfW5737++Wc8f/4c2bJlk+QTLsNmzZpVTHNzc0NoaCjs7e01FrwvVaqUWPb+/fvF9F9++UUM9hwdHbXWS+iAXq9ePYSGhiI2NlY2X1pfDouNjUXHjh3TrPz0qEKFCiYvU+5ypzCIqE+fPvDz8xPTJ0+eDHt7ewDJQVZsbCyICG3btgWgOedi586d4ebmplF+4cKFUblyZRCRZF44Vf3798ft27dx4sSJlD0wlm5xUMcYy3TUgzqFQoEyZcpotKwsWrQI586dg5OTk2w5/v7+kn0rKyvZKSZsbW3FS3FCWeqrPdy8eVMMHr///nvkypULdnZ2uHHjBu7fvy+ujKBa79QaP368uIqHKn1TX2REaXEpW/XHgEC1hVaY8w1Ifu8Ix4hInKdR+GEh9CWVY2Njg1GjRumtT6lSpcTbNWrUQJs2bfSewywLB3WMsUxH2yoV6h3G7e3t0axZM63lqH9pW1lZ4ffff9cYQZglSxYMGzYMN2/eFCdDVg3SAKBWrVribdV61a1bF66urlpn6wcgeynYEEuWLJG0Tnbt2hVKpdLo5d2+tT///NOk5ZUtWxb58+fHx48fTVbm/fv3kSdPHri6ukrStQ2wsbKyEp931R8X/fr1g6enJ+rUqaP1vuLj48WlunQx1Y8Bln6l7//c/xcZGYk6deogR44ckg6ie/fuhZubG5o2bSppxmaMMUOof8mdOHHC6IlVVb9s1ctbtWoV5s6dC4VCAYVCIQncVAnzgulaEu3HH39Ezpw5AQD58uUT0x8/fiyZYFlf60vp0qUlo3pVLzv+9NNPkvvet2+fzrJSQrV1Sp0hq47MnTsXv/zyi9bjwiVMOfPnzxdvC/3XgOT+bID0eSUiJCYmSlYoMdQvv/wiBnPqI6i1zeEovEcAaVCnUCh0BnTGkFs2j2UwZAESEhIoKCiIBg4cSA8fPiQiovj4eKpduzbFxcXRtWvXaPjw4QaVFR4eTgAoPDw8LavMGEtHHj9+TOfOnRP39+zZQwBo2bJlqS47NjaW/P39CQC1aNHCqHMBkL29vbj/6dMnKlWqFL1580brOdu2baOPHz/KlgWA4uPj6fDhw+K++hYZGUmvXr2igwcPEhHRx48fxWPa6mjM1rx5cwoMDNR6vH79+pL9z58/S+5fX/nz5s2TzTdw4EACQJs3b6b4+HiN4yEhIUREVLlyZfG+Tp48SUFBQbKPV6BUKunNmzf066+/EgBas2YNLV68mLJlyybmvXjxIp0+fZqGDh2q8fwFBATQjh07xLzr1q2THC9VqhQBIKVSKb4WPXv21Pr6p0ZcXBz5+fmlSdksfbCIoE6gGtQ9fvyYBg8eLB6rW7eu7DmxsbEUHh4ubn5+fhzUMZbJhYeHU4sWLSg4ONgk5YWGhhIAatmypVHnqQd1qaEajLx48UJrUBQTEyM5Tz2o0lau+jZlyhTZ9AEDBhBRcjAjF8y5ublJ0tXrXrt2bQJAx48fJwA0YsQISf65c+cSEdGFCxfIw8NDTH/58iUBoNevXxMR0ezZs8nJyUnjcQcFBdGNGze0Po/C/asLCgqiPn36iOUEBwfTo0ePaN68eaRUKg1+fc6cOSNJd3V1ldzfsmXLTPa+ZJmPRVx+lRMWFiaZHV5ulBGQ3Nzu6OgobnITljLGMhcHBwecOXNGdtmllCAdl011WbVqFW7cuGGSOqhycXHBqVOnJGlTpkzB06dPNfoBGlpnYV1fAPjrr7/Qu3dvAECTJk0k+YR+YQUKFMD9+/exefNmEBFWrlwJAKhSpQqmT58uOUd1JO/169cRGxsrjhIWBpaMHDkSwNdl35o0aSKO/hw8eDBKly4NIhKn5pg5cyY+f/4sXo4VHrezszPq1q2r9XEK96/O2dkZO3fuFMvJmzcvKlWqJFnKzhDCfIfajB071mTvS5b5pKtlwgIDA9GtWzeN9CNHjmj0S3ByckJERIS4r63z6dSpUyX9LyIiIjiwY4yZVI4cOWBjY4Nx48YZdd6PP/5osjrUqlULt27dEvfr168vOW5tbY1y5cppnKdvnd7ffvsNly5dEj9jXV1dMWrUKMTHx+OHH35A165dJSNHVQdZuLq6in3LypYtC2dnZ4wcORJVq1aVrK177do1BAUFifWxtraGm5sbfv/9d4wbNw4LFiyAUqlE6dKl8dNPP0nqR2qDUtT5+fkhLi5OZx5Vwv2b2pMnT2RHsLq6uuLBgwcmvz+WOaWroK5AgQK4du2aQXldXFzg4+OD+Ph43Lp1S2OEkcDOzk4cGs4YY2nBxsbG7GtjXrx4EeHh4eK+ocuY6VvZYtasWeLtU6dOoWrVqgCSW8zWrl0LIsL69etx5swZ7Nu3D87OzrLlZM+eXQzcAODs2bPiwu5OTk4a08ZYWVlhxowZkv2JEyca9JhUqc8laC6qEwqrWr9+fYoeF2Ny0lVQp0vbtm3h7e2NZ8+eYeTIkRg0aBDGjh2LRo0aIWvWrBozZDPGWGaSPXt2ZM+eXdw3dlqSvn376s3TqlUrjTSFQoHhw4fj/9q716CozvsP4F+XOwIrICIIXriKiIo3XFhAEAEBcdpxEo2TNtNpmrzpTNrYTJ3pNM1MG/umnXYm7YvkhW0nTdpM+sJFUC4KykVA7uGiICiiIIi47MplF5bn/8Jw/hDvZuFwdr+fmWd2j+yyv/WBw3ef55znHDhwAF9//fVjS7U8zew1dO2dm5vbUwcliF6WYkLdk1a+PnLkCI4cOSJDNURES9vsSF1QUNBjiyR/15MuZ/WygoKCnjsVSkQLSzGhjoiIXpyDgwOqqqpw7969517yi4vSEtkGxZ79SkREz6bRaJ65GC8R2RaGOiIiGxYfH4+0tDS8++67cpdCRAtsmbCzgyAMBgPUajVGR0fnrXNHREREpGQcqSMiIiKyAQx1RERERDaAoY6IiIjIBtjdMXVCCBiNRnh6evI0fiIiIrIZdhfqiIiIiGwRp1+JiIiIbABDHREREZENYKgjIiIisgEMdUREREQ2gKGOiIiIyAYw1BERERHZAIY6IiIiIhvAUEdERERkAxjqiIiIiGwAQx0RERGRDWCoIyIiIrIBDHVERERENsDuQp0QAgaDAUIIuUshIiIishq7C3VGoxFqtRpGo1HuUoiIiIisxu5CHREREZEtYqgjIiIisgEMdXPU1tbiF7/4BWZmZuQuhYiIiOilMNTN0dHRgb/85S+wWCxyl0JERET0Uhjq5lCpHv13MNQRERGR0jDUzeHg4AAAnH4lIiIixWGom2N2pI6hjoiIiJSGoW4OTr8SERGRUjHUzcHpVyIiIlIqhro5OP1KRERESsVQNwenX4mIiEipGOrm4PQrERERKRVD3RycfiUiIiKlYqibY3akjtOvREREpDQMdXNwpI6IiIiUiqFuDoY6IiIiUiqGujk4/UpERERKpehQZzQaERcXBw8PD7S2tn7v78eROiIiIlIqRYc6Nzc3nDlzBocPH7bK9+M6dURERKRUig51jo6O8PPzs9r34zp1REREpFSOchew0EwmE0wmk7RtMBie+tjZkbqf/OQnUKvVcHBwgIODAxwdHR+7dXNzg7u7O9zd3aX7c//N3d0dK1asmNfc3d2xbNmyBX/PREREZH9sPtSdPHkSH3300Qs9dvPmzXjnnXcwOjoKi8WC6elp6dZkMknbU1NTmJycxPj4uNQmJiYwNjb2zFE+JyeneSHP29sbq1atmtf8/f3nbbu6ulrrv4KIiIhs2DIhhJC7iO/rrbfewvHjx7F58+bHvvakkbrg4GCMjo7Cy8vLqnUIITA1NYXx8XGMjY1hdHQUer3+qW1kZAT37t3D4OAghoaG8ODBg8e+p5eXF4KCghAUFITg4OAn3lr7fRAREZHyKD7UZWVloampCevWrcM777yDt95665mPNxgMUKvVCxLqvi+z2Yx79+5haGgIQ0NDGBwcxN27d3Hnzh309fXh9u3b6Ovrw+DgIOZ2m5eXFzZs2ICQkBCEhoYiNDRUur927Vo4OTnJ+K6IiIhoMSg+1L2spRzqXpTZbEZ/f78U8vr6+nDjxg10d3eju7sbvb290hm8Dg4OWLt2LUJDQxEWFoaoqChs3LgRUVFRCAoK4jF+RERENoKhzgZNT0/j1q1b6OnpkYJeT08POjs70dnZKU1HL1++XAp4s7dRUVEICwvj6B4REZHCMNTZGYvFgps3b6KjowMdHR24evWqdF+v1wMAnJ2dER0djS1btmDLli3YunUrtmzZYtXlY4iIiMi6GOoIwKOTPIaGhtDe3o7W1lY0NzejpaUFra2tmJiYAAAEBATMC3nbt29HZGSktBQMERERyYehjp7JYrHg+vXraGlpkYJeS0sLent7AQAeHh7YsWMHdu7cKbXQ0FAeq0dERLTIGOrolTx48ACNjY2oq6vDlStXUFdXh5s3bwIAVqxYMS/o7d69G8HBwQx6REREC4ihjqxmeHgY9fX1qKurk9rt27cBAGvWrIFGo0F8fDw0Gg1iY2Ph4uIic8VERES2g6GOFtTdu3dRU1ODqqoqXL58GVeuXMHk5CRcXFywY8cOKeRpNBoEBATIXS4REZFiMdTRojKbzWhqasLly5eloNfX1wcAWL9+PbRaLZKTk5GcnIywsDBO2RIREb0ghjqSXV9fnxTyysvL0dTUhJmZGQQEBCA5ORlJSUlITk5GVFQUQx4REdFTMNTRkjM6OorKykpcvHgRFy9eRF1dHSwWC/z8/JCUlCSFvJiYGC6nQkRE9C2GOlryHj58iMuXL+PSpUu4ePEiampqYDab4e3tjdTUVOzbtw9paWmcriUiIrvGUEeKMzExgdraWpSWluL8+fOorq7G9PQ0goODkZaWhn379mHfvn1YvXq13KUSEREtGoY6Ujyj0Yjy8nKUlJSgpKQE33zzDQAgOjoaaWlpSEtLQ1JSEvubiIhsGkMd2ZzBwUFcuHAB58+fR0lJCXp7e+Hg4IDdu3cjIyMDmZmZ2LlzJxwcHOQulYiIyGoY6simCSHQ09MjjeKVlJRAr9fDx8cH+/fvR2ZmJjIyMrhGHhERKR5DHdmV6elp1NbW4ty5cygsLMSVK1cghMDWrVuRmZmJzMxMxMfHw9nZWe5SiYiIXgpDHdm14eFhFBcXSyFvcHAQHh4eSE1NlULehg0b5C6TiIjouawe6nbu3IkdO3ZIbcuWLXBycrLmS3wvDHX0NDMzM2hubpYCXmVlJaanpxEREYHs7GxkZ2cjMTGRo3hERLQkWT3U/fznP0d9fT2am5sxMTEBZ2dnREdHIz4+HkeOHEFCQoI1X+6lMdTRizIYDLhw4QLOnj2L/Px83LlzB56enkhPT0dOTg4OHDgAf39/ucskIiICsIDTrxaLBW1tbairq0NdXR1KSkrQ3d2NN998E6dOnZJtkViGOnoVQgg0NzfjzJkzyM/PR01NDQBg165dyMnJQXZ2NmJjY7n4MRERWZXFYpEW3d+7d+8zH7uox9QVFxfj2LFj+Pjjj/HTn/50sV52HoY6soahoSFpBK+wsBAGgwGBgYHIyspCTk4O0tLSsHz5crnLJCIiBRobG0NJSQl0Oh3OnDmDoaEhHDhwAAUFBc983qKfKPHpp5/is88+w5UrVxbzZSUMdWRtZrMZFRUVyM/Px5kzZ9DZ2QkXFxfs3btXGsXjyRZERPQsAwMDOHPmDHQ6HUpKSjA5OYmNGzfi0KFDyM3NRVxc3HPXV130UNfS0oLk5GQ8ePBgMV9WwlBHC62rq0sKeJcuXcLU1BSio6ORm5uLQ4cOYdeuXVCpVHKXSUREMhJCoK2tDTqdDjqdDjU1NVCpVNBqtcjNzcXBgwcRERHxUt/T6qHub3/7G3bt2oWtW7fCxcXlsa+fP38eR48exdDQkDVf9oUx1NFiMhgMKCoqQl5eHs6cOYORkRGsXr0aBw8eRG5uLvbt2wc3Nze5yyQiokUwNTWF8vJyKcjduHEDHh4eyMzMRG5uLrKysuDr6/vK39/qoc7b2xsGgwGOjo7YtGkTdu7ciZ07dyIqKgrj4+M4ceIEQkND8fXXX1vzZV8YQx3JZXp6GlVVVdDpdDh9+jSuX78Od3d3pKenIzc3Fzk5OfDz85O7TCIisqLR0VGcO3cOOp0OBQUF0Ov1WLNmDXJzc5Gbm4uUlJQnDoK9igWZfu3u7kZ9fb3UGhoaoNfrAQAbN25EQUEB1q9fb+2XfSEMdbQUCCFw9epVKeBVV1cDAOLj46XjJyIjI2WukoiIXkVvb680GldWVobp6WnExsZKQW6hVktYtGPqbt++jampKasfMH78+HHU1NRg7dq1OHXq1HMXhmWoo6VocHBQOkC2uLgYExMTiIiIkAKeRqN57gGyREQkj5mZGTQ0NEhBrrm5GU5OTkhJSZGOj1u7du2C16Hoy4Q1NjbiT3/6Ez7//HP84Q9/wIYNG/DGG2888zkMdbTUjY+PS6ey5+XlYWhoCCtXrkROTg4OHTqE/fv3c7kUIiKZTU5O4sKFC9K+ur+/H97e3sjOzkZubi4yMjIWPWcoOtT9/e9/h4eHB370ox+hvr4ep06dwieffPLM5zDUkZJYLBbU1tZK07QdHR1wdXVFWlqadBxeQECA3GUSEdmF4eFh5Ofn4/Tp0ygqKsLY2BhCQkKkWZWEhARZL43qKNsrW4Fer0dgYCAAQK1WY2Rk5LHHmEwmmEwmadtgMCxafUTfl4ODAzQaDTQaDU6ePImuri5peP/dd9/FzMwM4uLipOM0oqOjeVULIiIrunbtmrTfraqqghACe/bswW9+8xvk5uYiKipqyex3Fb1Y1uyZtsCjgOfj4/PYY06ePAm1Wi214ODgxS6TyGrCw8Px/vvv4+LFixgaGsI///lPrFmzBh9//DFiYmIQGhqK9957DxcuXMDU1JTc5RIRKY7FYkFFRQU++OADREZGYuPGjfjwww/h4+ODTz/9FAMDA6iqqsKvf/1rbNq0ackEOkDh06/fPaYuJCQER48enfeYJ43UBQcHc/qVbMrk5CTKysqkT5N37tyBWq1GVlYWcnNzkZmZiRUrVshdJhHRkvTw4UMUFRVBp9MhPz8fw8PD8Pf3n7emqLu7u9xlPpeiQx3As1+JvksIgcbGRingNTY2wtHREcnJydJZWLxsGRHZu/7+fuTl5UGn0+H8+fMwmUyKv/qP4kPdy2KoI3vT19cn7bhmp2VjYmKk4/B27typuB0XEdHLEkKgqalJ2h/W19fDwcEBSUlJ0gfe0NBQucv8XhjqiOzI7GXLZqcYeNkyIrJlcw9NycvLw+3bt+Hl5YUDBw7g4MGDyMrKgre3t9xlWg1DHZGdmnvZMp1Oh66uLri5uUmXLcvOzoa/v7/cZRIRvZR79+4hPz8fOp1OWnZkw4YN0mhcYmLicw/VUiqGOiICMP+0/crKSgDAnj17pB3hUjvLi4gIeDSt2t7eLk2rzl52cc+ePdIshL3svxjqiOgx9+7dQ0FBAXQ6HQoLC6UFNmePw9NqtbIusElE9m1qagqXLl1CXl4e8vLy0NPTA3d3d2RkZODgwYPIzs7GqlWr5C5z0THUEdEzTU5OorS0VBrF6+/vx4oVK5CVlYWcnBxkZGQ8cY1IIiJrGhkZwblz56DT6XDu3DmMjo5izZo10mxCSkoKXF1d5S5TVgx1RPTChBDSRatPnz6N5uZmqFQqaDQaZGVlISsrC1u3brWLaQ4iWlhCCLS0tKCgoAAFBQWoqqrCzMwMduzYIU2rbtu2jfubORjqiOiV3blzB2fPnkV+fj6Ki4sxNjaGwMBAKeClpaXB09NT7jKJSCGMRiNKSkqkINff3w8PDw/s378fWVlZOHDgANasWSN3mUsWQx0RWYXJZEJFRQUKCgqQn5+Pa9euwcnJCUlJSVLIi4yM5KdqIpIIIXD16lUpxJWXl2NqagpRUVHSfkOr1drs2arWxlBHRAuiu7sbZ8+eRUFBAUpLSzE5OYkNGzYgKysL2dnZ2Lt3L9fEI7JD4+PjKC0tlYLczZs34erqitTUVGk0LiQkRO4yFYmhjogW3NydeH5+Pnp7e+Hq6oqUlBSkp6cjPT0dUVFRHMUjskFCCLS1taGoqAhFRUUoKyuDyWTChg0bkJ2djaysLH7IsxKGOiJaVEIIdHR0oKCgAIWFhSgvL4fJZMKaNWukgJeWloaVK1fKXSoRvaKhoSGUlJRIQW5gYACurq5ISkpCRkYGsrOzERERwQ9yVsZQR0SyGh8fR3l5ubTzb21txbJly7B9+3Yp5MXHx/OYGqIlzGQyobKyUvo9bmxsBABs2bJF+j1OTEy0+yVHFhpDHREtKf39/SguLkZRURGKi4tx7949LF++HHv37kV6ejr279+PjRs38hM+kYxmR9yLi4tRWFiIixcvYnx8HKtWrZo34h4QECB3qXaFoY6IlqyZmRk0NzdLn/4rKipgNpsREBCAlJQUqYWEhDDkES0gIQSuX7+O0tJSXLhwAWVlZRgcHISLiwsSExOlIBcTEwOVSiV3uXaLoY6IFGNsbAzl5eW4cOECSktL0dDQgJmZGaxdu3ZeyFu7dq3cpRIpXm9vrxTiSktLcfv2bahUKuzatUv6XdNqtXB3d5e7VPoWQx0RKZZer8elS5dQWlqK0tJSNDc3AwBCQkLmhbzAwECZKyVa+vr7+1FWViaFuJ6eHixbtgzbtm1DSkoKUlNTkZiYyL+dSxhDHRHZjPv37+PixYtSyGtrawMAhIeHQ6vVSi08PJzTtWTXhBC4du0aysvLUVFRgYqKCvT09AAAoqOjkZqaipSUFCQnJ/PazgrCUEdENmtwcBBlZWXSH66WlhYIIeDn5zcv5MXGxsLJyUnucokWzNTUFBobG+eFuOHhYahUKmzbtg2JiYnQarVITEyEv7+/3OXSK2KoIyK7MTo6isuXL0t/1GpqajA5OQl3d3fExcVBq9UiISEBu3fvhre3t9zlEr2y4eFh1NbWorq6GpWVlaiursb4+Djc3NwQFxcnhTiNRsPrM9sQhjoisltmsxkNDQ1SyKuoqMD9+/cBABEREYiLi0NcXBx2796NrVu3cq08WpJMJhOam5tRU1OD6upq1NTUoLu7GwDg6+uLhIQEKcRt376dP8c2jKGOiOhbQgh0dXWhpqYGtbW1qKmpQVNTE6ampuDi4oLY2Fgp5MXFxXEpFVp0MzMz6O7uRl1dnRTgGhsbYTab4ezsLP2Mzjb+jNoXhjoiomeYnJxEU1OTFPLmjoJ4e3tj27ZtiI2NlVpkZCQcHR1lrppswfT0NK5evYqGhgY0NDSgsbERjY2NMBqNAIDQ0NB5AW7btm1wcXGRuWqSE0MdEdFLun//Pmpra1FfXy/9ob1x4wYAwM3NDTExMfOCXkxMDC9WTs80NjaG9vZ2NDU1SSGupaUFk5OTAICwsDDExsZi+/bt2L59O2JjY+Hn5ydz1bTUMNQREVmBXq9HU1OTFPIaGxvR0dEBi8UClUqFkJAQREdHY/PmzYiOjkZ0dDQiIyM5smJnTCYTrl27htbW1nlt9kOBSqVCVFTUvPC2bds2qNVqmSsnJWCoIyJaIBMTE2htbUVzczPa2tqk1t/fDwBwcHBAWFiYFPaioqIQHh6O8PBw7p8UTq/Xo6urC52dnejs7ERHRwdaW1vR2dkJi8UCAAgKCsLmzZuloL9582Zs2rSJV2igV8ZQR0S0yB48eDAv5LW2tqKtrQ1DQ0PSY/z9/aWAFx4ejoiICISHhyMsLIx/9JeIhw8f4ubNm/PC22yb25cBAQGIjIyUAtxsiFuxYoV8xZNNYqgjIloiHjx4gK6uric2vV4vPc7f3x/r1q17auNUnXUYjUb09vbi5s2bT2yzy98AgJeXFyIjIxERETGvhYeHcx04WjSKDXVGoxFpaWloa2tDdXU1Nm/e/ELPY6gjIqURQmB4eFgKeL29vfParVu3YDabpcer1WoEBwcjICBAaqtXr563HRAQAA8PDxnflTyEEBgbG8PQ0BD6+/vntYGBgXnbBoNBep6TkxPWrVuH9evXP9bCw8Ph5+fHpUNIdooNddPT03jw4AF+9atf4fjx4wx1RGS3ZmZmcPfu3XlB7/bt27h79y4GBgakNjExMe957u7u8PX1ha+vL3x8fJ546+3tDU9PT3h4eEhtdluuS6tZLBYYjUYYjUYYDAbp/uy2Xq/H/fv3MTw8LN3OvT83AAOPzlgODAxEYGAgAgICpPuBgYFScAsICIBKpZLl/RK9KMUupuTo6PhCp3ObTCaYTCZpe+4nLyIiW6BSqaQQotFonvgYIQQMBsO8kDc4OIiRkRHcv39fuu3u7pa2Z9dDexpnZ2d4eHhg+fLlcHFxgZOTE5ydnZ/YZtfuE0Jgdizhu7cWi0XaZ882s9k8b3t8fPyxcPpdbm5uWLlyJVauXAlfX1/4+/sjOjoavr6+0r/7+flJIU6tVnOUjWyCYkPdizp58iQ++ugjucsgIpLVsmXLoFaroVarsXHjxhd6jtlshl6vx8OHD/Hw4UMYjcYn3h8bG4PZbH6sTU1NSfcnJiak4LRs2bJ592dvnZyc4OHhARcXl6c2Nzc3eHp6wtPTE15eXtL9udtyjSASyW3Jh7q7d+/i8OHDj/27TqeDj4/Pc59/4sQJ/PKXv5S2DQYDgoODrVojEZEtcnZ2xqpVq7Bq1Sq5SyGiF7DkQ93q1atRUVHxys+f/XRHREREZMsUfdRnVlYWioqK8Pbbb+Mf//iH3OUQERERyUaxZ7++Kp79SkRERLbI7kKdEAJGoxGenp4824mIiIhsht2FOiIiIiJbpOhj6oiIiIjoEYY6IiIiIhvAUEdERERkA5b8OnWLafYkCiIiIqKl5nkneTLUzTE8PMyV04mIiGhJet5ybAx1czg7OwMA+vr6uIadwsxe/o19pyzsN2VivykX+07ZPD09n/l1hro5Zoc0vby8+MOuUOw7ZWK/KRP7TbnYd7aJJ0oQERER2QCGOiIiIiIbwFA3h4uLCz788EO4uLjIXQq9JPadMrHflIn9plzsO9vGy4QRERER2QCO1BERERHZAIY6IiIiIhvAUEdERERkAxjq5jh+/DgSExNx7NgxmM1mucuhb9XX1yMxMRHJycl47bXXMDU1hf/+97/QaDRITU1FX18fAKC9vR1arRYajQYlJSUAgIcPH+LQoUPQarX44x//KOfbsFtffvkl/Pz8AID9phBlZWXYt28fkpOTcfr0afabQszMzODHP/4xEhMTkZSUhO7ubvadvREkhBCioaFBHDt2TAghxO9//3vx73//W+aKaNbAwIAYGxsTQghx4sQJ8dVXX4ndu3cLk8kkKioqxNtvvy2EEOLQoUOis7NTjI6OCo1GI4QQ4s9//rP47LPPhBBCZGRkiL6+PnnehJ2yWCzihz/8oYiNjRVms5n9pgATExMiJydHmEwmIYRgvylIfX29eP3114UQQhQVFYn33nuPfWdnOFL3rcuXLyM9PR0AkJmZiaqqKpkrolmrV6+Gu7s7AMDJyQmdnZ2Ijo6Gs7MzEhIS8M033wAABgYGEB4eDi8vL/j6+mJ4eHhev+7fvx/V1dWyvQ979MUXX+Dw4cNQqVTo6upivylAVVUV3NzccPDgQfzgBz/AlStX2G8KERQUBAAQQkCv18PPz499Z2cY6r6l1+ulS6ao1WqMjIzIXBF9161bt1BSUgKtVjvv8jYWiwXAox3ZrNk+ZL/Kx2Kx4KuvvsLrr78OYP7v2OzXAfbbUjM4OIgbN24gLy8PP/vZz/C73/2O/aYQK1euhEqlQlRUFD744APs3buXfWdnGOq+5e3tDYPBAODRHx8fHx+ZK6K5DAYD3nzzTZw6dQqrVq2S+goAHBwcAAAq1f//OM/2IftVPp9//jlee+01qV/m9gXAfluqVqxYAa1WC2dnZ6SmpqKxsZH9phCFhYVwc3PD1atX8b///Q9//etf2Xd2hqHuW3v27EFRURGAR78YCQkJMldEsywWC44dO4bf/va3iIiIQFhYGNrb22E2m1FZWYktW7YAeDRN29XVBYPBgJGREaxcuXJev5aUlECj0cj5VuxKe3s7/vWvfyEzMxNdXV349NNP2W8KsHv3brS3twMAGhsbkZ6ezn5TEG9vbwCPwvnw8DD7zt7IekTfEvP+++8LrVYr3njjDekgYZLfF198IXx8fERycrJITk4W//nPf8SXX34p9uzZI/bu3Stu3bolhBCira1NJCQkiD179oiioiIhhBAGg0Hk5uaK+Ph4cfLkSTnfhl3bsWOHEEKw3xTik08+EYmJiSI5OVl0d3ez3xRienpaHD16VCQlJYm4uDhRWVnJvrMzvEwYERERkQ3g9CsRERGRDWCoIyIiIrIBDHVERERENoChjoiIiMgGMNQRERER2QCGOiIiIiIbwFBHREREZAMY6oiIiIhsAEMdERERkQ1gqCMiIiKyAQx1RERERDbg/wC95c3/92pUhwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
@@ -346,7 +348,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -373,7 +375,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x1000 with 13 Axes>"
       ]
@@ -465,7 +467,7 @@
     },
     {
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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/tutorials/causal_effect_estimation/tigramite_tutorial_general_causal_effect_analysis.ipynb b/tutorials/causal_effect_estimation/tigramite_tutorial_general_causal_effect_analysis.ipynb
index a1886ee0..7d62361c 100644
--- a/tutorials/causal_effect_estimation/tigramite_tutorial_general_causal_effect_analysis.ipynb
+++ b/tutorials/causal_effect_estimation/tigramite_tutorial_general_causal_effect_analysis.ipynb
@@ -486,7 +486,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -540,7 +540,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -589,7 +589,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -713,7 +713,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -801,7 +801,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -863,7 +863,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -914,7 +914,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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wgB9//FH12G+//TZTp04lICAg3deiYxPYf/UORiNWdzDEtOfPmbrF8+KhoNmzI0tOTubMmTPplpLDwsJ4+PChpdOzGJ1O99RG17ly5dI+gcREqF4dwsJsb9/f0yxdCm3bWjoLYWOkABTiHzExMYwePZr58+erPnNTuXJlpk+fTsOGDZ/5uTsJyRy8epfYJOv6g6lUTg8qF8yJSzZ6/QkTg8HA5cuX0y0lh4aGEhMTY+n0LKpIkSLpWtYEBARQqFAh9fYZjhwJY8daX6+/7PDwgMhIKFbM0pkIGyIFoHB4iYmJzJkzh3HjxnH37l1VYxcuXJhx48bRpk0b9PrMzZwZjEaibj/kdEysxWcDvVz0vFA4FwU85W5ac7h582a6peTQ0FAuX75s6dQsKnfu3BnOGJYuXVpZo+sHD6BoUVPTZ3ui15tOBE+YYOlMhA2RAlA4LKPRyLp16xgwYABnz55VNbaHhwf9+vVjwIAB5MiRI0sxHialcPj6PW7GmXefUto8S/l8OSifN4cmbV6EMrGxsYSFhaWbMYyKinLoRtceHh5PNLpOe/n6+uKa0d258+aZDn7Y4x97uXJBdDR42scNQ0J7UgAKh3TgwAH69OnDX3/9pXrsVq1aMX78eEqUKJHtWEajkegHCUTdeUhMfDI6tJ0RdNJBCW8PfPN6yUEPG5CYmJiu0XVoaCjh4eEkJCRYOj2L0ev1lCtX7oml5Ffq1qVUw4amhs/2+MeeTgcLFkCnTpbORNgIKQCFQ7l48SJDhgzhm2++UT32K6+8wvTp06levbrqsQEeJKVw/l4c5+/GkWQwqlIMpsXI7eZCmdyeFM/pjovcHWvzUlNTuXDhwhPLyGmve/fuWTo9i1jUowcd58yxdBra0enAxwfCw+VEsMgUKQCFQ4iNjWXSpEnMmDFD9ZkRHx8fpk6dyrvvvmuWhrgGo5FrDxK59jCROwlJxCamkLYImPb0x39TP55R2vsuTjpyu7uQx92FEjk9yCWzfQ7BaDRy7dq1dDOGp0+f5tq1a5ZOT1O7qlen7pEjttv0ObOkObTIJCkAhV1LSUlh6dKlDB8+nBs3bqgaO0+ePIwYMYJu3bplvN/ITAxGIw+SUribmMK9hGQSUw0YjEYuX7pI4aLFcdE74eSkw8tFT243F3K5u+Cud7LN2xuEZu7evZvhDSjnzp3L9rWH1uCWiwv5kpMtnYa2nJ2hXz+YONHSmQgbIAWgsFu//vorffv25eTJk6rGdXZ2pkePHgwfPpy8efOqGltNH330ES1btuTDDz+0dCrChsXHxxMREZFuKTkiIoJkGymoCuTJw407dzSLbwAqAO8Ckx97fyvwP+AboJlmT/+PV16BP/4w19OEDZMCUNidU6dO0a9fP3755RfVY7/33ntMnjwZPz8/1WOr6fjx41SpUoVKlSpx9OhRZa0yhMiElJQUzp49m65lTVhYGA8ePLB0ek94xdeXP6KiND38sQL4HLgA5AGOAfWAEUA/zZ6aAU9PuH/f1BpGiGeQAlDYjRs3bjBy5EgWLVqkemuMF154gRkzZvDqq6+qGlcrH330ET/88AMAa9eulVlAYTZGo5HLly9nuJx88+ZNi+TUuXx5Fpw5o+nNHymAH9AaaA+8hGlGcK5mT3yGU6egQgVLPFnYECkAhc1LSEhg1qxZTJgwgViVG7wWK1aMiRMn8umnn9rMLFra7F8amQUU1uLWrVsZFoYXL17U9LmzChSglxmKz4XAEKAYUBpYD1hkHm7ZMmjTxhJPFjZECkBhs4xGI6tWrWLw4MFcuHBB1dheXl4MHDiQvn374mljjVUfn/1LI7OAwpo9ePCA8PDwdG1roqKiSFXh1O6vOh2NzPBH3QOgAOAD7AO8HvvaJaAVcANwBoaj0b5AFxfo0wcmTdIiurAjUgAKm7Rnzx769OnD33//rWpcnU5Hu3btGDt2LEWKFFE1tjn8d/YvjcwCCluUlJREVFRUuhnDsLAw4uPjMx3nElBcuzQfaQOsBnID5wD3x752FbgOVMVUBL4AhPNkkagKFxfo0gVmz1Y7srAzzpZOQAglzp49y6BBg1izZo3qsV9//XWmT5+eYQFlK8aMGZPh+ydOnGD9+vUyCyhsiqurKxUqVKDCf/azGQyGR42u//u685/Tvq6urhRL0v46xeHAZkwzfw2BYKD7Y18v8s8LoCCQF7iNBgWg0QgKimPhuGQ6QNiEu3fv0r9/fwICAlQv/vz9/fnpp5/Ytm2bTRd/x48fT7f0+7jRo0c79L2xwn44OTlRsGBBChcunO7l7PzkvIY5ul0uAaYDm4AqQG9gCvC0JjkHMbWOyf5lkU8hC3siE2QGUFi15ORkFi1axMiRI4mJiVE1dv78+Rk9ejQdO3bExcX2b8J42uxfGpkFFLbo9u3b6W4tCQ0NzfS+38SkJK4DhTXK72dMM33fYTr5C6Z2MNOAlUC7/3w+BgjCVDRqQqcDDw+togs7IgWgsEpGo5HNmzfTr18/wsPDVY3t6upKr169GDJkCLlz51Y1tqU8b/YvzejRo3n//fdlL6CwKkajkejo6HTNpkNDQ1W5wSdUp6OwBrNihzAd5JgCfPDY+zkxFYGTMLWFSTsJnAi8DwwG6qiezWOkABSZIAWgsDrHjh2jb9++/Pbbb6rHbt68OZMmTaJMmTKqx7ak583+pTlx4gTr1q3jo48+0jgjIdJLTU19onn04y+1Wzg97nSePNS/fVv1uC9iOvmbkTH/vNIYMR0SaYDpNLBmUlLAx0fLJwg7IaeAhdWIjo5m+PDhLFu2TPW7R2vVqsWMGTOoU0fTn7st4mknf59GTgQLrSUkJBAREZFu6TYiIoIkMxzI+K/ufn7MOXtW00bQz/MX8ApQ+bH3VgKVtHjY0aNgw/uZhXnIDKCwuIcPHzJ9+nQmT55MXFycqrFLlizJ5MmT+fjjj9HpzLEd3PwyO/uXRvYCCrXcu3ePsLCwdEu3586ds6oDR6EAKvQTzI66mA5+aM7NDQIDzfEkYeNkBlBYjMFg4Ouvv2bIkCFcuXJF1dje3t4MGTKEXr164WHH+2GUzv6lkVlAkVlGo5Hr169nuGwbHR1t6fQypUj+/ETfumXpNMyjdm3Ys8fSWQgbIDOAwiJ+//13+vbty+HDh1WN6+TkRKdOnRg9ejQFCxZUNbY1Ujr7l0ZmAcV/KemtZ2uu3rrFPb2eXBaeBdSci4upABQiE2QGUJhVZGQkAwYMYMOGDarHbtKkCVOnTiXQQZY/sjr7l0ZmAR3Tf2/XSFu+DQ8PV3S7hq3ZW7kyL506ZfGlYM39+CP873+WzkLYAJkBFGZx+/ZtxowZw9y5c0lReSN2xYoVmTZtGm+88Yaqca1dVmf/0siJYPv24MEDwsLC0s3mqXW/rq0JrVOHl44ft3Qa2ipeHN56y9JZCBshM4BCU0lJScydO5cxY8Zw9+5dVWMXLFiQcePG0bZt23Td/+1ddmf/0lSsWJFjx47JLKANu3XrVobLthcvXrR0ahaVM2dOAgICqFChAgEBAbz9xhtUePNNuHrV0qlpQ6eD6dPhiy8snYmwEVIACk0YjUY2bNjAgAEDiIqKUjW2u7s7ffr0YdCgQXh7e6sa21Z89NFHmWr8nBlr1qyRWUArZzQauXz5coY3YtxylMMNT1G4cGECAgLSvYoUKZL+5P/UqTBoEFjRCWXVeHiYittcuSydibARUgAK1R08eJA+ffqwa9cu1WN/+umnTJgwgZIlS6oe21aoNfuXRmYBrUdKSsqjRsmPt1YJCwvjwYOntRy2fzqdjtKlS2dY6OXJkyfzge7cgaJFISFBu2QtwdkZunWDL7+0dCbChkgBKFRz6dIlhg4dysqVK1WPXbduXWbMmEGNGjVUj21r1Jz9SyOzgOYVHx9PeHh4umXbyMhIizRKthYuLi74+vo+Ku7Slm/9/Pzw9PRU5yFffAGzZ9vXLKCzM4SFQblyls5E2BApAEW2xcbGMmXKFKZNm0aCyj9Zly1blilTpvDBBx/YbSNnJW7evEmrVq3S3ZRy8OBBbmfyqqsKFSpQvHjxJ96rWLEi06dPVy1PYXL37t0M9+edO3dO9dtubImXl1eGs3lly5bFxcVF24c/eACVKsGlS/ZzInjaNOjb19JZCBsjBaDIstTUVJYtW8awYcO4fv26qrFz5crFiBEj6N69O25ubqrGtkeNGzdm27ZtmfpscHAw7dq10zgjx2E0Grl27Vq6vXmhoaFcu3bN0ulZVP78+TMs9IoXL27ZLQd//w116tj+LKCzM7z8MuzYAbKFQyjkWEcnhWq2bdtG3759OXHihKpxnZ2d6datGyNGjCBfvnyqxhYiO1JTUzl//nyGM3r37t2zdHoWVaJEiScKvLSl2/z581s6tYzVqgWjRsHIkWCrcyA6HXh6wtdfS/EnskQKQKHI6dOn6d+/P1u2bFE99rvvvsuUKVPw8/NTPbYQmZWYmEhkZGS6Ii88PFz1LQ62RK/XU65cuUfFXdrL39+fHDlyWDo95QYPhs2b4dAhULk3qVkYjbBkian3nxBZIAWgyJQbN24watQoFi1apHoT2apVqzJjxgzq16+valwhniU2NjZdo+TTp09z9uxZh2yUnMbd3R1/f/90y7a+vr64urpaOj31ODvD99+brk67ccP2isA+faBZM0tnIWyYFIDimRISEvjyyy+ZMGEC9+/fVzV20aJFGT9+PK1atUKv16saW4g0N2/eTLc3LzQ0lMuXL1s6NYvKnTt3hsu2pUqVcpyWQCVLwq5dpn10t27ZThHYs6fp4IcQ2SAFoMiQ0Wjk+++/Z+DAgVy4cEHV2J6engwYMIB+/frh5eWlamzhmAwGA5cuXcpwf15MTIyl07OoIkWKpFu2DQgIoFChQnKyHqBsWfjrL2jQAKKjrb8I7NvX1NBa/tuJbJICUKSzd+9e+vTpw759+1SNq9PpaN26NePGjaNYsWKqxhaOITk5mTNnzqQr8sLCwnj48KGl07MYnU5H2bJl0xV5/v7+5M6d29LpWb9y5eDAAWjSBI4ds772ME5Opj1/M2ZA796WzkbYCSkAxSPnz59n0KBBrF69WvXY9evXZ/r06VSrVk312ML+xMXFPWqU/PjSbVRUFMnJyZZOz2JcXV3x8/NLV+j5+fnh4eFh6fRsW8GC8OefpkbRixeDXm8dhaCTExQoYDrw0bSppbMRdkQKQMG9e/eYMGECX375JYmJiarG9vPzY9q0aTRt2lSWm0Q6t2/fznDZ9sKFCw7dKDlHjhzp9uYFBARQpkwZnJ3l27ZmvLxg0SIICsLQrh1OUVGWaxOj12NITcWpWzcYPx5y5rRMHsJuyXcSB5aSksKiRYsYOXKk6hfK582bl1GjRtGlSxftO/sLq2Y0GomOjs6w0FO7gbitKViwYIaNkosVKyY/MFmI0Whky717LPD1ZVNQEIwZYyoCzbU3MO2/e/nybG7blp8iIxkdF0dhKQCFyqQAdEBGo5Gff/6Zfv36ERoaqmpsFxcXevbsydChQ5Vd0i5sXmpqKufOnUvXViUsLEz1E+S2plSpUhkWetLs3LocOXKEfv36sWPHDho2bAjDhkHz5jBxIqxaBYmJpgJNixtEnJ1NRaaPj+mUb+fOlAoNZVH//nzzzTcMHDiQPn36yME5oRopAB3M8ePH6devX6avDVPio48+YtKkSZSTC8ntWmJiIhEREenaqkRERKi+hcCWODs74+Pjk27ptnz58vKHtpW7fPkyQ4cOZeXKlY+2Hjz6PubnB8uWwZdfwnffwYIFcPTovwVbdqQd7nBzgxYtoGNHU1/Cf2YBy5YtC8DDhw8ZMWIECxYsYNy4cQQFBUnrLJFtUgA6iGvXrjF8+HCWLl2KQeWfXmvUqMGMGTOoW7euqnGFZd2/fz/DZduzZ8+q/v+QLfHw8MDf3z9daxUfHx/Z7mBjYmNjmTx5MtOnT093y4uPj8+TH86ZEzp3Nr2OHjVdwbZvn+nv006gOzubCrr/Hh7R6f49VJK2p7BUKahRAxo2hJYtM9zjlyNHDgoVKvRoq0R0dDTt2rXjyy+/ZNq0aaZZSiGySApAOxcXF8eMGTOYNGmS6m0ySpQowaRJk2jRooXjNI61M0ajkRs3bqQr8k6fPk10dLSl07OovHnzZrhsW7JkSfn/3calpKQQHBzMiBEjuHHjRoafeeZKRtWqpheYCrrz5+HIEVMxGBEBCQkQFwdJSab7ej08IG9eqFLFNK5y5Uwf6ihXrly6vbLHjh2jUaNGNGnShKlTpxIYGJipWEI8TgpAG2IwGDL9B4/BYOCbb75hyJAhqt94kCNHDoYMGULv3r2l9YQN2rp1K3v27HlU7N25c8fSKVlUsWLF0hV5FSpUoECBAnIQw84YjUa2bNlC//79n7v/OdNbWXQ6KFPG9PrgAxWyTJ/Hnj17Mvzazz//zNatW+nQoQOjR4+mcOHCqj9f2C8pAG2EwWCgZcuWzJ8/n7x58z7zs3/++Sd9+vTh0KFDqubg5OREhw4dGDNmDIUKFVI1tjCf77//3tIpmJ2Tk9OjRsmPL936+/uTU05XOoTHD3hkRtr+O0t7XiFqMBhYtGiRHBQRikkBaCMWLFjA999/T7FixZgxY0aGn4mKimLAgAGsX79e9ec3btyY6dOnU7FiRdVjC+UePnxIWFjYo1m8Y8eOWTolq+Dm5kb58uXTzej5+vri7u5u6fSEBWR0wON5ChUqRI4cOTTOLHPS7UV8CjkoIpTSGR2526qNiIqKokqVKsTFxeHi4sLp06ef+KZw+/Ztxo4dy9y5c1W/JSEwMJBp06bx5ptvqhpXZE5MTEy6vXmhoaFcvHjR0qlZVM6cOTPcn1emTBn5Q08Azz7g8Twvv/wyf/31l0aZKbNv3z5q166teFyVKlXkoIh4JpkBtHKpqam0bduWuLg4wHQX6sCBA/nhhx9ISkpi/vz5jB49WvV9XAUKFGDs2LG0b99ebh7QmNFo5MqVK+naqoSGhnLz5k1Lp2dRhQoVSrc3LyAggCJFisj+PJGhlJQUlixZwsiRI596wON5rKmVVVZzefygyJQpU2T1RqQjf7JbuVmzZqX7SXTdunWMHz+eFStWEBkZqerz3Nzc6NOnD4MGDZK9USpLSUnh7Nmz6Yq8sLAwYmNjLZ2exeh0OkqVKpWurUpAQIA0ExeZZjQa2bx5MwMGDMh2g3trKgDz58+Pt7d3lr9HpB0Uad++PWPGjJGDIuIRWQK2YqGhoVSrVs1szXVbtmzJxIkTKVWqlFmeZ6/i4+OJiIhIV+hFRESQlJRk6fQsxsXFBV9f33RFXvny5fH09LR0esKGKT3g8Txff/01n376qSqx1FCtWjWOHj2a7TheXl4MGDCAvn37ykER4ZgzgKmpEB5uatl09CjcvAnx8abWTXo9uLub2jaVLftvu6ciRf69otEcUlJSCAoKMkvxV6dOHWbMmEGtWrU0f5Y9uXfvXrq9eaGhoZw7dy7Tm83tkZeXF/7+/umWbsuWLSuNkoUmnJycVL3P3JpmAMGUjxoFYHx8PHFxcbKtRwAOUgCmpMCWLbB5Mxw8CKdOma50BHBxMfXxNBhML53OdDuPTvdkQ/e8eeGFF0yN21u2hEqVtM150qRJHDx4UNNnlClThsmTJ/PRRx/JfqqnMBqNXLt2LcMbMa5evWrp9CwqX7586dqqBAQEULx4cWmULMyqSpUq7N+/n9GjRzN58uRs31RjjQWgGjFCQkKoU6eOChkJe2DXS8BRUbB0KSxZYprly+7VjWm3+aSkmIrBLl1M1zd6e6uXM8DRo0epWbOm6id60+TKlYthw4bx+eef4+bmpskzbI3BYOD8+fMZFnp37961dHoWVaJEiQxP3BYoUMDSqQmRzt69ewkKCiIqKipL43PmzMndu3et6ofixYsX06lTpyyP79atG1OmTJFlX/EEuysAjUbYsAFmzYI///z3+kW1PX6Hd8uW0L8/BARkP25SUhI1atTg+PHj2Q/2H3q9nq5duzJy5Ejy58+venxbkJSURGRkZLql2/DwcMWtIuyJXq+nXLly6Yo8f39/vNX+CUcIjU2cOJEhQ4ZkaWy1atU4fPiwyhllz44dO3j99dezNDZ37tyEhYVJ836Rjl0tAZ87Z5qV+/VXU+EH2hR/YFouBtO+wZUrTa/Bg2HIENMewqwaM2aMJsVf06ZNmTp1Kv7+/qrHtkYPHjwgLCwsXWuVM2fOkKrV/xQ2onjx4tStW/eJ5VsfHx+ZDRZ24cSJE4waNSrL461t+Reyl9Pdu3fp1KkTGzZssKpZTWF5djEDmJICM2fC8OGmgi87y7zZkXYlZHAwvPaa8vH79++ndu3a2d6/8rgqVaowffr0LP/0aO1u3ryZ4bLtpUuXLJ2a1QoODqZdu3aWTkMI1SUlJVGrVq1sHZgYOHAgkyZNUi8pFaSmpuLh4ZGtbUHLli2jTZs26iUlbJ7NzwAeOQJBQaaDHZYuZY1GuHAB6teHNm1My9C5cmVubHx8PK1bt1at+CtcuDDjx4+ndevWNn8zgtFo5NKlS+mKvNOnTxMTE2Pp9CyqSJEiBAQEEBkZKUWvcHhjxozJ9mlZa5wB1Ov1lClThoiIiCzH6NmzJ/Xr15c2X+IRmy4AN22CZs1MM36WLv7SpK0urlwJf/9tWo4uXvz544YNG0ZYWFi2n+/h4UH//v3p37+/1dxlmVkpKSmcOXMmXVuVsLAwHj58aOn0LEan01GmTJl0bVX8/f3JnTs3YLqrWQpA4cj+/vtvJk6cmO04mb1719zKlSuXrQIwNjaWtm3bsn37djmlLwAbLgAXL4bOnU1/by3F3+NSUyEy0tQ2Zvt2CAx8+md37drFzJkzs/1Md3d3Tp8+TenSpbMdS0txcXGEh4enm9GLjIzU7OSzLXBxccHPzy9daxU/Pz88PDwsnZ4QVisuLo6goCBVVlCscQYQ1ClMd+7cyZw5c+jZs6cKGQlbZ5MF4Lx50L27pbN4vpQUU/uZevVg166Mi8AHDx7Qpk0bVRoHJyQk8PPPP/PZZ59ZxcnNO3fuZLg/7/z58w7dKDlHjhwZtlUpW7asNGgVIgsGDx6seHasXLlylC5dmt9+++3Re66urhQrVkzt9FTx38LU3d2d5s2bExISoijOwIEDeeONNyhfvrya6QkbZHN/2ixaZBvFX5rUVLh/H155BXbvhv8ewh0wYABnz55V7XkDBgzgm2++SXd/sFaMRiNXr15NtzcvNDSU69evmyUHa1WgQIEMC73ixYvLaTwhVLJjxw5mz56taIxOp2PFihXUrl2befPmMWDAAOLj4ylTpozV7pl+vACsWbMmISEh+Pn5ERcXx9q1azMdJyEhgaCgIHbv3i0/cDo4m/qvf+AAdOtm6SyUS02Fe/fg/fdNh1bS2sRs27aN+fPnq/qsBw8ecODAAYxGo6pFRmpqKufPn0/XViU0NJT79++r9hxbVLJkySf25qX9fb58+SydmhB27d69e1k62dq/f39efvllAHr06EHjxo1p3bo1efPmVTlD9ZQrVw5nZ2dGjRrFwIEDHxVv8+fPZ9euXYp+4N6/fz+TJk1i2LBhWqUrbIDNtIF5+NB0/drFi9r19tOakxP07GlqWXPv3j0qVqzI5cuXNXnW3r17eemllxSPS0xMJCIiIl2RFx4ebpZ7ia2VXq/H19c33Wxe+fLlreKwTePGjdm2bVumPittYIS9aNu2LcuXL1c0pmLFihw8eDBd38uUlBSOHj1K9erVVcxQPYmJiZw+fZpq1aql+9qmTZt45513FMVzdnZm//79GcYTjsFmZgC/+MLUYkXFFnlmZzCYWsM0aQLffdc7U8Wfu7s73t7e5MiR49Er7Z89PDw4efIkx44dSzcuLCzsmQXg/fv3CQsLS7d0e/bsWVX7ENoaDw8P/P390xV6Pj4+uLq6Wjo9IcQ/fvzxR8XFn7OzMyEhIRk2PXd2drba4g/Azc3tqcXa//73P9q2bcuyZcsyHS8lJYWgoKAMi2HhGGyiANy40XTq1x44OcEnn9yjd+/KLFq06ImC7r8FnpeX11P3aISFhfHJJ59kWPyBqcAzGo2PGiX/d+n2ypUrWv4yrV6ePHnStVUJCAigZMmS0iJBCCt38+ZNOnbsqHjcqFGj7HbGa9asWezYsYMLFy5keszJkycZMWIEkydP1jAzYa2sfgn4/n0oXRru3rXOdi9ZoddD8+bw7bfKxxqNRoKDg+nVqxdxcXFP/VzBggVJSUnh9u3b2cjU9hUtWjTd3ryAgAAKFixoVwcxZAlYOAqj0chHH33EunXrFI2rVasWf/31l10ffNi5cycNGjRQNEan0/Hnn39St25djbIS1srqfycsW2ZfxR+Y9jCuWgUTJ4KSpuy3b9+mY8eOmfrGd+PGjWxkaFucnJwoW7ZsumVbf39/cmX2KhYhhE345ptvFBd/Hh4erFixwq6LP4D69evTq1cvvvzyy0yPMRqNtG7dmmPHjlnFfmZhPlb9uyE1FaZPt6/iL42TE8yZA1OnZu7zv//+O5999plDL926ubk9apT8+MvPzw/3tKPVQgi7dfnyZXr06KF43KRJkxym793EiRP55ZdfCA8Pz/SYs2fP0r9/f9W7UgjrZtUF4KZNYK+3W6WmwoIFMHIkPOuHruTkZEaNGsXEiRMdpnmyt7d3hm1VrLlHlxBCW0ajkfbt23Pv3j1F4xo0aJClotFWeXh4EBISQp06dUhV0DJjwYIFvPfee7zxxhsaZiesiVUXgNOnm/bL2Wrbl+d5+BBWrHh6Y+szZ87wySefsH//fvMmZiaFChXKsFFy0aJF7Wp/nhAi+xYsWMCvv/6qaEzOnDlZtmyZwx3sqlmzJkOGDGHs2LGKxrVr146TJ0+SJ08ejTIT1sRqC8ATJ8BMl1lY1MyZ6QtAo9HI119/Tbdu3Xjw4IFlElNR6dKlMyz0rLnpqhDCekRFRdGvXz/F47788ktKliypQUbWb9iwYfz0008cOXIk02Oio6Pp0aMH33zzjYaZCWthtQXgjh2mfXL23JLOaIQzZyA6GooWNb137949unXrxrdZOSJsQc7OzukaJVeoUIHy5cvj6elp6fSEEDYqNTWV1q1bP7PrQUbeeecdWrdurVFW1s/V1ZWQkBBefPFFkpKSMj3u22+/5f333+ejjz7SMDthDay2ANy/HxxlFfDAAXj3XdizZw+ffvop58+ft3RKT+Xp6Ym/v3+6tirlypXDxcXF0ukJIezMtGnT2LNnj6Ix+fPnZ9GiRQ6/laRixYqMGzeOAQMGKBrXpUsX6tatS+HChTXKTFgDqy0Ad+82x96/FKA88Dbw38vEuwDbgL1AQc0ycHaGvXtTOHZsAmPGjFG0aVdL+fLly3DZtkSJEg63n0YIYRknTpxgxIgRisctWLCAQoUKaZCR7enTpw8//vgjfynYUxUTE0OnTp3YuHGjwxfR9swqC8Dbt03XvmnPGRgE9AZGAvn+eX8isBbYg5bFH5iK3N9/v0Tt2ncoXry4oi7uaihevHi6Iq9ChQoUKFDArHkIIcTjkpKSaNWqlaLlS4DPPvuMDz/8UKOsbI9er2f58uVUqVKFhw8fZnrcpk2bWL58OW3bttUwO2FJVnkTyC+/mO7LNY8kwAfoAIwAvgE6AtuBOmbJwMvLdOOJTmfk2LFjrF+/ng0bNnD8+HFV4js5OVGuXLl0bVX8/f3x9vZW5RnCsuQmEGFvhg4dyoQJExSNKVasGCdPniR37tzaJGXDFi5cSJcuXRSN8fb25vjx45QuXVqbpIRFWeUMYGSkaf+feUpTV2AAMAaojqkQ/BpzFX9gagdz4wYULqyjatWqVK1aldGjR3PmzBk2btzIhg0b+Ouvv7LUB3Dy5Mn06tVLLvsWQtiMffv2MWnSJMXjli5dKsXfU3Tq1IkNGzbwyy+/ZHpMbGwsbdu25bfffpOtP3bIKv+LxsWZ+v+ZTwdAD/wP0/Kv+ZcP4uPTv1euXDn69OnDn3/+ybVr11iyZAlvv/22omLu/v37UvwJIWxGXFwcrVu3xqCwBUTXrl1p3LixRlnZPp1Ox5IlSxQXyL///jtfffWVNkkJi7LKAjAhwdwngN2B+phm/Xqb88GPZFQAPq5gwYK0b9+en376iZs3b/L999/zySefPPeu29OnT6uYpRBCaGvQoEFEREQoGlOuXDmmZvZeTQdWrFgx5s6dq3jcoEGDCAsL0yAjYUlWWQBaZlficaCWJR4MKPs1e3t706xZM7755htu3LjB1q1b6dq1K0WKFEn32dDQUBWzFEII7fz222+KZ5ucnJwICQnBy8tLo6zsS8uWLWnWrJmiMQkJCQQFBZGSkqJRVsISrLIA9PAwdxEYD4QBL5rzoU/Iaq9kV1dXGjduzLx587h8+TL79u1j0KBBjy4+j4qKIjk5WcVMhRBCfffu3cvSidP+/ftTp4759mzbOp1Ox7x58xS3yTlw4AATJ07UKCthCVZZAHp6mvv+36NAKvCCOR/6BA+P7MdwcnKiVq1aTJw4kbCwMEJDQxk7diy3b9/OfnAhhNBQr169uHTpkqIxlSpVYvTo0RplZL/y58/PkiVLFI8bM2YMhw8f1iAjYQlWWQD6+pp7BvAwkAPwNedDH/HygoIatBv09/dn0KBB0hBVCGHVNm7cyIoVKxSNcXFxISQkRA65ZVHTpk0Vt4NKSUkhKCiIhIQEjbIS5mSVBWCNGuZ+YncgFkv869DpTL9eOWEvhHBEN2/epFOnTorHjRo1iqpVq6qfkAOZOXMmpUqVUjTm1KlTWbqdRVgfqyw78uWDkiUtnYV56PVQu7alsxBCCPMzGo107tyZGzduKBr30ksvKb7fVqSXM2dOli9frnjctGnT2LVrl/oJCbOyygIQoG5dc/cCtIyUFKhZ09JZCCGE+X399desX79e0RgPDw9WrFiBs7NV3mNgc1577TV69+6taIzRaKRNmzY8ePBAm6SEWVhtAVizpqXawZifFIBCCEdz6dIlPv/8c8XjJk+ejJ+fnwYZOa4JEybg7++vaMzZs2fp16+fRhkJc7DaArB+fVDYCN7m6HRQtiwULWrpTIQQwnyMRiPt27fn3r17isY1aNCA7t27a5SV4/Lw8CAkJAS9wmW3hQsXKrpaTlgXqy0AK1eGl1+2/2XgL76wdAZCCGFe8+fPZ9u2bYrG5MyZk2XLlsmdtBqpUaMGQ4YMUTyuffv20mrMRln176S+fc3dD9C8vLygTRtLZyGEEOYTGRlJ//79FY+bPXs2JR3ldKCFDBs2jBdeUNYPNzo6mh49emiUkdCSVReA77wDJUpYOgtt6PXQpQvkyGHpTIQQwjxSU1Np3bo1cXFxisa9++67BAUFaZSVSOPq6pql3orfffcda9as0SgroRWrLgD1eujTx7RXzt4YDCA/NAkhHMnUqVPZu3evojH58+dn0aJF6OzxDwIrFBgYyLhx4xSP69q1K9euXdMgI6EVqy4AAdq1g9y57asI1OuhRQtQ2H9TCCFs1vHjx7PUQHjhwoUU1OKqJPFUX3zxBfXq1VM0JiYmho4dO2J0lPYddsDqGynlzAnLlsF771k6E3U4OUHu3PeoUGEZS5bkIEcO08vb2/vR3z/+UnoqSwghrE1SUhJBQUEkJycrGteqVSs++OADjbIST6PX61m+fDmVK1fm4cOHmR73008/sWzZMsVXzAnLsPoCEODdd6FDB1i61PZbwxgM8O23ufjmmyMMHx7y3M97eHikKwr/Wyxm9M8vvvgiPj4+ZvgVCSHEs40ePZpjx44pGlO8eHFmz56tUUbiecqWLcuMGTPo3LmzonG9evWiQYMGlC5dWpvEhGpsogAEmDULfvsNLl603ZPBTk7w+efQuDHUrPklv/32G1euXHnmmPj4eOLj47l582amn+Pm5kZYWFh20xVCiGzbu3cvkyZNUjxu6dKl5M6dW/2ERKZ17NiRDRs28PPPP2d6zIMHD2jTpg07duyQlj1Wzmb+63h5werVtns7iF4Pvr6Q9n0wd+7cBAcHa/Ks3r17y09fQgiLe/jwIa1bt8agcOmmW7duNGrUSKOsRGbpdDqWLFlCnjx5FI37448/ZPbWBthMAQhQowbMn2/pLJTT6yFXLtiwAdzd/33/jTfeUDy9/jwFChRg8ODBqsYUQoisGDRoEJGRkYrG+Pj4MGXKFI0yEkoVLVqUuXPnKh43ePBgWYmycjZVAAJ06gRz5lg6i8zT600HWf78EzK6anHq1KmUKVNGteeNGTOGXLlyqRZPCCGy4rfffmOOwm/WTk5OhISE4OXlpVFWIitatGhBs2bNFI1JSEigVatWig/+CPOxuQIQoHt3WLDA1BrGmtvDODtD/vym4i8wMOPPeHt7s2zZMlWe5+HhQZMmTVSJJYQQWXX37l3aZOGaowEDBlC7dm31ExLZotPpmDdvHoULF1Y07uDBg0ycOFGjrER22WQBCNC5s2lJ1dXVOu8L1uvBxwcOHoSKFZ/92VdffZXevXtn+5nx8fEEBAQwcuRIHjx4kO14QgiRFb169eLy5cuKxlSuXJlRo0Zpk5DItvz587NkyRLF48aOHcuhQ4c0yEhkl80WgGC6Km7PHtPSqrXMBKYVo61awb59ULx45sZNmDABPz+/bD8/Pj6eMWPG4Ofnx7Jly0i11SPTQgibtGHDBkJCnt/i6nEuLi5ZuoJMmNfbb79N+/btFY1JSUkhKCiIhIQEjbISWWXTBSDACy/AkSMweTK4uZmWXS1Fp4OSJWHHDlPzaiVb8Tw8PFixYoVqx+avXr1Ku3btqF69Ojt27FAlphBCPMuNGzfo1KmT4nGjR4+mSpUqGmQk1DZjxgxKKbzG6vTp0wwfPlyjjERW2XwBCODiAv37Q2go1K9ves+c7YecnU2vYcPg9Ol/c1DqpZdeYuDAgarmdvToUV5//XXeeecdwsPDVY0thBBpjEYjXbp0UdSzFEzf9/r3769RVkJtOXPmZPny5YrHTZ8+nT///FP9hESW2UUBmKZMGdi6FX74AV5+2fSeVvsDnZxMM37u7qbl3mPHYMyYJ9u8ZMXIkSOpVKmSOkk+ZtOmTVSsWJHPP/+cW7duqR5fCOHYvv76a9avX69ojIeHByEhIThbculGKPbaa6/xxRdfKBpjNBpp06YNsbGxGmUllLKrAhBMRdkHH5hO3kZEmGYG8+UzfU2N7zFpMapUMZ1Evn7ddEVdhQrZjw2mWzxWrFihyTfElJQU5syZg4+PD9OmTSMxMVH1ZwghHM+lS5fo0aOH4nFTpkzB19dXg4yE1saPH09AQICiMefOnaNfv34aZSSUsrsC8HG+vjBxIly9ajox3LYtVKtmOjmcxsXFNEuYtmSs05n+2dn5ydnD3Lnh9dehb184ehQOHzb1JMyZU/28q1WrxogRI9QP/I979+7Rv39/AgICWLt2LUZbvV5FCGFxBoOBdu3acf/+fUXjXn/9dbp166ZRVkJrabO3eoXLbIsWLVJ0tZzQjs7ogH/6p6RAeLipkDt6FG7ehIQEuHMnDi8vDzw8dLi7Q9myULWq6VW0qHlPGicnJ1O7dm2zHJ9/+eWXmTFjBjVr1tT8WUIbjRs3Ztu2bZn6bHBwMO3atdM4I+Eo5s6dq3j2L1euXJw4cYISJUpolJUwl5EjRzJmzBhFY4oUKcLJkyfJmzevRlmJzHDIAvBpmjdvTrNmzRR3PNfKqVOnePHFF822VPvJJ58wceJESpYsaZbnCfVIASgsITIykipVqhAfH69o3IoVKwgKCtIoK2FOycnJvPTSSxw+fFjRuBYtWvDdd99plJXIDLteAlbi5MmTrFmzhtGjRyu+uFwrgYGBjB07NsOvjR07Fh8fH1Wf9+233+Ln58eQIUMUL+cIIRxLWn83pcXfe++9R6tWrTTKSphbVns4rlq1iu+//16jrERmSAH4j7Qp7FOnTvHDDz9YOJt/9enThzp16jzx3ocffsiwYcM4deoUM2fOJE+ePKo9LzExkYkTJ+Lr68vChQtJSUlRLbYQwn5MnTqVffv2KRpToEABFi5ciM5aOvcLVQQGBjJ+/HjF47p27crVq1c1yEhkhhSA/Dv7l8aaZgH1ej0rVqzA09MTMP20NXnyZABcXV3p3bs3UVFR9O7dW9WTwzdu3KBLly5UrVqVrVu3qhZXCGH7jh07xsiRIxWPW7hwIQULFtQgI2FpvXv3pl69eorG3L59m44dO8pBRAuRAhDSbWC1tllAHx+fR0Xf559/Trly5Z74et68eZk5cyanT5/m/fffV/XZp06d4s033+TNN9/k5MmTqsYWQtiexMREgoKCSE5OVjQuKChI9e9Pwnro9XqWL1+Ol5eXonGbN29m6dKlGmUlnsXhC8D/zv6lsaZZQIBu3brRvHlzhg0b9tTP+Pr6sm7dOn7//XdeeOEFVZ+/detWqlSpQpcuXbh+/bqqsYUQtmP06NEcP35c0ZjixYvz5ZdfapSRsBZly5ZlxowZisf17t2bc+fOaZCReBaHLwCfdnzd2mYBnZyc+O677zK13+/VV1/lwIEDhISEUKxYMdVyMBgMLFy4EF9fXyZOnKh487cQwrbt2bPn0WqEEsuWLSN37tzqJySsTseOHWnSpImiMQ8ePKBt27ZWNeniCBy6AHza7F8aa5sFdFJwwbGTkxOtWrUiIiKCMWPGKJ6Wf5bY2FiGDBmCv78/3377rVX9OxJCaOPhw4e0bt1a8e/37t2707BhQ42yEtZGp9OxZMkSxYcT//jjD5klNjOHLgCf17zS2mYBs8LT05Phw4cTGRlJ+/btVT19d/HiRT799FNq167N7t27VYsrhLA+AwcOJCoqStGYx/cvC8dRtGhR5s2bp3jc4MGDCQ0N1SAjkRGHLQBbtWr1zNm/NK1bt2bDhg3aJ6SxIkWKsGTJEo4cOcLrr7+uauz9+/dTt25dmjVrxtmzZ1WNLYSwvO3btzN37lxFY5ycnAgJCVF19UHYjhYtWtC8eXNFY7J6wEhkjcMWgJm9NSE+Pp5ffvlF42zMp0qVKmzbto2ffvoJf39/VWOvXbuWgIAA+vfvz927d1WNLYSwjLt379K2bVvF4wYOHEjt2rU1yEjYinnz5lG4cGFFYw4ePMiECRM0ykg8ziELwJMnTyo6ybpp0ya72uem0+l4++23OX78OHPnziV//vyqxU5KSmLatGn4+PgwZ84c+UlOCBvXs2dPLl++rGhM5cqVs9QnUNiXfPnysWTJEsXjxo0bx6FDhzTISDzOIQtApRdXR0dH2/xewIy4uLjQrVs3oqKiGDBgAK6urqrFjomJ4fPPP6dSpUps2rRJGn0KYYPWr1/PypUrFY1xcXFh5cqViq8GE/bp7bffpkOHDorGpF0zmJCQoFFWAhywAHzeyd+nsbYTwWrKlSsXkydPJiwsTPGejecJDw/nnXfeoWHDhhw9elTV2EII7dy4cYPOnTsrHjdmzBgqV66sQUbCVs2YMYPSpUsrGnP69Oln9r0V2edwBaDS2b809nAi+HnKlCnD6tWr2b17N7Vq1VI19o4dO3jhhRdo164d0dHRqsYWQqjLaDTSuXNnbt68qWhc7dq16d+/v0ZZCVvl7e3N8uXLFXehmDFjBn/88YdGWQmHKgCzOvuXxp5nAR9Xp04d9u7dy3fffUepUqVUi2s0Glm2bBm+vr6MGTOGhw8fqhZbCKGelStXKu5+4OnpyYoVK9Dr9dokJWzaq6++Su/evRWNMRqNtGnThtjYWG2ScnAOVQBmdfYvjSPMAqbR6XS0aNGCsLAwJk2ahLe3t2qx4+LiGDlyJH5+fqxYscIhimohbMXFixf5/PPPFY+bMmUKvr6+GmQk7MX48eMJCAhQNOb8+fP07dtXo4wcm8MUgNmd/UvjKLOAadzd3R81gO3ataui20ieJzo6mjZt2lC9enV27typWlwhRNYYDAbatWvH/fv3FY1r2LAhXbt21SgrYS88PDwICQlRPEu8ePFitmzZolFWjsthCsDszv6lcaRZwMcVLFiQefPmcfz4ccX3PD7PkSNHaNCgAe+99x4RERGqxhZCZN68efP47bffFI3JlSsXS5cuVfWHQ2G/qlevnqXDHR06dCAmJkaDjByXQ/yOVWv2L42jzQI+LjAwkC1btrB161YqVqyoauyNGzcSGBhIr1695De6EGYWERHBgAEDFI/76quvKFGihAYZCXs1dOhQXnzxRUVjrl69Svfu3TXKyDE5RAGo1uxfGkedBXxc48aNOXr0KIsWLaJQoUKqxU1JSWH27Nn4+Pgwc+ZMkpKSVIsthMhYSkoKrVu3Jj4+XtG4999/n88++0yjrIS9cnFxISQkRHGvyNWrV7N69WqNsnI8dl8A3rp1i3v37tGoUaMnXi4uLpmOUaRIkXTj9+zZo2HWtkGv19OxY0ciIyMZOnQo7u7uqsW+e/cuffr0oUKFCqxbt04aSQuhoSlTprBv3z5FYwoUKMDChQsVt/YQAqBChQqMHz9e8bhu3bpx9epVDTJyQEYHlS9fPiOQqVdQUJCl07UJFy9eNH722WeZ/veq5FWvXj3j/v37Lf1LtFqNGjXK9L/L4OBgS6crrMjRo0eNLi4uin9Prl+/3tKpCxuXkpJifOWVVxT/v/fWW28ZDQaDpdO3ec7ZriCF+EeJEiVYuXIlPXv2pG/fvuzatUu12Lt27aJmzZp89tlnTJgwQfYcCaGCxMREWrVqpfjO7tatW/Pee+9pk5RwGHq9nuXLl1O5cmUePHiQ6XFbtmwhODg44yvmjEaIjoajR02vs2chIcH0Sk0Fd3fTq0ABqFrV9CpfHpwdrxxyvF+x0FyNGjX4448/WL9+PQMGDODMmTOqxf76669Zu3Ytffv2ZeDAgar2JxTC0YwaNYoTJ04oGlOiRAm+/PJLjTISjqZMmTLMmDGDTp06KRr3xRdf8Prrr1OmTBk4dgxWrYIDB+DwYbhzx/QhvR50OlNRaDCY/urkZHrpdJD2g4+rKwQGQvXq8Pbb8NZboGCbmK2y+z2AwjJ0Oh0ffPABp06dYvr06eTOnVu12AkJCYwfPx5fX18WL15MamqqarGFcBR79uxhypQpisctW7aMXLlyaZCRcFQdOnRQ3F7s/aZNybNhA1SrZprFmzYNfvvt3+IPTDN+KSmmv6btIzcYTO89PuudlARHjsCyZfDee1C0KAweDFFR2f2lWTUpAIWm3Nzc6NOnD1FRUfTs2RNnFafZr1+/TqdOnahWrRrbtm1TLa4Q9u7hw4cEBQUpbmfVo0cPXn/9dY2yEo5Kp9OxZMkS8uTJ89zP5s+Thx/q1ydkwwZy9+0Lx4+bvpCSkv1E0mLcugVTp4KvL7zyCqxb928BaUekABRmkS9fPr788ktOnTrFu+++q2rsEydO0LhxY9566y1Onz6tamwh7FFWtmb4+voyefJkjTISjq5o0aLMnz//mZ9518+Pk/fv88GuXaY9fWlLu1pIW1naswc+/BDeeAPOndPmWRYiBaAwKz8/PzZs2MDOnTupVq2aqrF//vlnKleuTNeuXblx44aqsYWwF9u2bWPevHmKxjg5ORESEoKnp6dGWQkBH3/8MR9//HG693N6ebE8f37WR0RQKG1Z11zSCsGdOyEgwDQzaM7na0gKQGERr732GgcPHmT58uUULVpUtbipqaksWLAAHx8fJk+eTEJCgmqxhbB1d+/epW3btorHDRo0iJdeekmDjIR40ty5cylcuPCjf369SBFOPHxI6zt3sGjHyZQUSEyEgQNN+w6PHLFkNqqQAlBYjJOTE61btyYiIoLRo0erOrsQGxvLoEGD8Pf3Z9WqVdJIWgigZ8+eXLlyRdGYKlWqMHLkSI0yEuJJ+fLlIzg4GA93d2YXLMiv169TEv6dibM0oxFCQ6F2bdi0ydLZZIsUgMLivLy8GDFiBJGRkbRr107VmwUuXLhAy5YtqVOnDnv37lUtrhC2Zt26daxcuVLRmLQru1xdXTXKSoj03ipVivPe3nx++zZOWu3xy47UVNPJ4XffhUWLLJ1NlkkBKKxG0aJFCQ4O5vDhwzRo0EDV2Pv27aNOnTq0aNGCc3a2kVeI57l+/TqdO3dWPG7MmDFUrlxZg4yEeIpTp6BePQrevm3de+2MRtOrc2d4zuEVayUFoLA6VatWZfv27fz444+UL19e1dirV6/G39+fgQMHcu/ePVVjC2GNjEYjnTt35tatW4rG1alTh/79+2uUlRAZCAsztV25f996lnwzo1s3WLzY0lkoJgWgsEo6nY7//e9/nDhxgq+++op8+fKpFjspKYkpU6bg4+PDvHnzSLHmnzKFyKaQkBA2btyoaIynpycrVqxAr9drlJUQ/xEfb2rCfO+ebRV/abp2hYMHLZ2FIlIACqvm4uJCjx49iIqKol+/fqruRbp16xbdu3encuXKbN68WQ6KCLtz8eJFevbsqXjc1KlT8fHx0SAjIZ5i0CCIjLTN4i/Nxx/Dw4eWziLTpAAUNiF37txMnTqV0NBQmjVrpmrs0NBQmjZtSuPGjTme1lVeCBtnMBho27Yt9+/fVzSuUaNGdO3aVaOshMjA1q0we7Z2TZ3NITUVzp+HPn0snUmmSQEobErZsmX5/vvv+euvv6hZs6aqsbdv307VqlXp0KEDV69eVTW2EOY2d+5cduzYoWhMrly5WLp0qaon8YV4plu34LPPwMkOyhGDwXQq+McfLZ1JptjBv3HhiF5++WX27t3Lt99+S8mSJVWLazQaCQ4OxtfXl7FjxxIXF6dabCHMJSIigoEDByoeN2fOHIoXL65BRkI8Rc+ecOeObc/+PU6ngzZtTAdZrJwUgMJmOTk50bJlS8LCwpgwYQI5cuRQLfbDhw8ZMWIEfn5+rFy5EoO9fHMSdi8lJYWgoCDi4+MVjfvggw/49NNPNcpKiAycPw+rVtn2vr//Mhrh7l1YtszSmTyXFIDC5nl4eDB48GCioqLo3LkzTiouJVy5coWgoCBq1qzJH3/8oVpcIbQyefJk/v77b0VjChYsyIIFC2TpV5jXnDn2sfSbkRkzrL6wtdN/88IRFSpUiAULFnDs2DHefPNNVWMfOnSI1157jQ8++IDIyEhVYwuhlqNHjzJ69GjF4xYtWkSBAgU0yEiIp4iNhYULrb5IyhKjES5ehJ9+snQmzyQFoLA7FStW5Oeff+bnn38mMDBQ1djr168nMDCQL774gtu3b6saW4jsSExMpFWrViQnJysa16ZNG959912NshLiKZYvt6mWKYrp9TBtmqWzeCYpAIXdevPNNzl69CgLFy6kYMGCqsVNTk5m1qxZ+Pj4MGvWLJKSklSLLURWjRw5kpMnTyoaU7JkSWbNmqVNQkI8i73/f5eaCn/9BSdOWDqTp5ICUNg1Z2dnOnXqRGRkJIMHD8bNzU212Hfu3OGLL74gMDCQDRs2SCNpYTG7d+9m6tSpisctW7aMXLlyaZCREM9w5QqcPWtaKrVnTk6wc6els3gqKQCFQ8iZMycTJkwgPDycTz75RNXYUVFRvP/++7z22mscOnRI1dhCPM+DBw9o3bq14pPqn3/+OQ0aNNAoKyGeYf9+zUIbAH/gv02QtgKuwBrNnpwBnU7TX2t2SQEoHEqpUqX45ptv+Pvvv3n55ZdVjf3nn39SvXp1WrduzeXLl1WNLcTTDBgwgDNnziga4+fnx6RJkzTKSIjn2L8fnJ01Ce0EDAbmA3f+ee8Y0AyY8M9fzSY1FXbvNucTFZECUDikmjVrsmvXLtasWUOZMmVUjR0SEoKfnx8jRozgwYMHqsYW4nG//vor8+fPVzTGycmJkJAQPD09NcpKiOfYuxdSUjQL/ymQH5gNXAbeBloB/TR74jOcPw9WemBQCkDhsHQ6HR999BGhoaFMmzZN1b1Q8fHxjB07Fl9fX4KDg0m1x1YHwqLu3LlDu3btFI8bPHgwtWrV0iAjITLBYIADBzR9hDOmJeDZwFvAC//8vcVo/OvNKikAhcNzc3Ojb9++REVF0aNHD/R6vWqxr127RocOHXjhhRfYvn27anGF6NmzJ1euXFE0pmrVqowYMUKjjITIhBs3wAxXbH4KxAFG4Dvgv9/V3wfyAB9pnYhOBxERWj8lS6QAFOIf+fPn56uvvuLkyZP873//UzX28ePHadSoEU2bNiU0NFTV2MLx/PDDD3z99deKxri6uhISEoKrq6tGWQmRCWa6X73HP3+9RfriD6AnEGKORPR6UHgto7lIASjEf/j7+/Pjjz/y22+/UbVqVVVjb968mUqVKtGjRw9u3rypamzhGK5fv07nzp0VjxszZgyVKlXSICMhFDBDMTQc2AzsA1KA4Aw+Ux/w1jwTTDOAUgAKYVsaNGjAwYMHWbp0KUWKFFEtbmpqKnPnzsXHx4epU6eSmJioWmxh34xGI506dSImJkbRuDp16tCvn0W2wAthVkuA6cAmoArQG5gCKLsfxzFIASjEM+j1etq2bUtERAQjR47Ew8NDtdj3799nwIABBAQEsGbNGmkkLZ5rxYoV/Pjjj4rGeHp6smLFClX3tgqRZSp+D/2vn4HuwNfAS/+89zlwH1ip2VOfw2jU9NecHVIACpEJOXLkYNSoUURGRtKmTRt0Op1qsc+dO0fz5s2pW7cu+/btUy2usC8XLlygV69eisdNmzYNHx8fDTISIgs0aj90CFOPvynAB4+9nxNTETgJsEgvhtRUKQCFsAfFihVj2bJlHDx4kNdee03V2Hv27KF27dq0bNmS8+fPqxpb2DaDwUDbtm25f/++onGNGzemS5cuGmUlRBYULKhJEfgi8ADI6EekMUAEGR8G0ZzRCH5+lnjyc0kBKEQWvPDCC+zYsYONGzfi6+urauxVq1bh7+/P4MGDFf+BL+zTnDlz2KnwTtFcuXIRHBys6my1ENnm5AQ1alg6C97ANGO4BSgOaNqpzwp+vRmRAlCILNLpdLzzzjucPHmSL7/8krx586oWOzExkUmTJuHj48OCBQtI0bBrvrBu4eHhDBz435tNn2/u3LkUL15cg4yEyKbatTW7Ci6ztgI3MfUKvAxoVqKVLg0q/tmgJikAhcgmV1dXevbsSVRUFH369MHFxUW12Ddv3qRr165UqVKFX375RbW4wjakpKQQFBREQkKConEffvghn3zyiUZZCZFNNWtqehWc1dDrQeU759UkBaAQKsmTJw/Tp0/n9OnTfPjhh6rGPn36NE2aNOGNN97gxIkTqsYW1mvy5Mns379f0ZiCBQsyf/58WfoV1qtmTUtnYB5Go1X/WqUAFEJlPj4+rF27lj///JPq1aurGvvXX3+latWqdOrUiWvXrqkaW1iXI0eOMGrUKMXjFi9eTIECBdRPSAi1FCsGZcuamiTbM4MB6te3dBZPJQWgEBqpV68ef//9N19//bWqe7EMBgOLFy/G19eXCRMmEG+lXeZF1iUmJhIUFKR472fbtm155513NMpKCBX17m3pDLSl10PdumDFt+9IASiEhpycnPj0008JDw9n3Lhx5MiRQ7XYDx48YOjQoZQvX56rV6+qFldY3ogRIzh58qSiMSVLlmTWrFnaJCSE2tq2BS8vS2ehndRUsPLbd6QAFMIMPD09GTp0KJGRkXTs2BEnJ/V+6126dElxsSCs1+7du5k6dariccuXLydnzpwaZCSEBnLkgM6dTTNl9kang5IloWlTS2fyTFIACmFGhQsXZtGiRRw9epRGjRpZOh1hZR48eEDr1q0VXwvYs2dP6lvxXiMhMtSjh2mfnD3q08fqi1spAIWwgEqVKrF161a2bNlChQoVLJ2OsBL9+/fnzJkzisb4+fkxceJEjTISQkOlS0OLFlZfKCmi00Hu3KYlbisnBaAQFqLT6WjSpAnHjh1j/vz5cnLTwW3dupUFCxYoGqPX61m5ciWeGt2vKoTmZs82NUpWcVuMRRmNsGIF2MB2DDv5Ny6E7XJ2dqZLly5ERkYycOBA3NzcLJ2SMLM7d+7Qrl07xeMGDx5MTSvuMybEc+XPDytX2sdSsJMTdOoE//ufpTPJFCkAhbASuXLlYtKkSYSFhdGiRQvNnnPhwgXNYous+fzzz4mOjlY0plq1agwfPlyjjIQwozfegJ49bXsWUK83LWnPmGHpTDLNhv9tC2GfSpcuzXfffcfevXupXbu26vHHjh1L27ZtuXLliuqxhXJr167lm2++UTTG1dWVkJAQXF1dNcpKCDObNAl8fW17P+Dq1TbV2kYKQCGs1EsvvcTu3btZvXo1pUuXVi2u0Whk+fLl+Pn5MWrUKB4+fKhabKHMtWvX6NKli+JxY8eOpWLFihpkJISFeHjAhg2QK5dtFoHz54PKNz9pTQpAIayYTqejefPmhIaGMmXKFFX7vMXFxTF69Gh8fX1ZtmwZqampqsUWz2c0GunUqRMxMTGKxr388sv07dtXo6yEsCB/f/jzT9MBClsqAufNg44dLZ2FYlIACmED3N3d6d+/P1FRUXTv3h29it8cr169Srt27ahevTo7duxQLa54tuXLl7Np0yZFY7y8vFixYoWq//2FsCqBgbBrl+lwiLOzpbN5Op3O9Fq4ELp2tXQ2WSIFoBA2pECBAsyZM4cTJ07w9ttvqxr76NGjvP7667z77ruEh4erGls86cKFC/Tq1UvxuGnTplGuXDkNMhLCigQGwsGD4ONjnTOBej24ucGPP5pO/dooKQCFsEEBAQH89NNPbNu2jcqVK6sa+8cff6RixYr07NmTW7duqRpbgMFgoE2bNsTGxioa98Ybb9C5c2eNshLCyhQvDvv2QatWpn+2lkJQp4OAANizx+qvenseKQCFsGENGzbk8OHDqt8mkpKSwldffYWPjw/Tp08nMTFR1fiO7KuvvuL3339XNCZ37twEBwej0+m0SUoIa5QrFyxbBjt2mO7WteT//87O4O4OU6bAkSNQrZrlclGJFIBC2Di9Xk+xYsU0iX3v3j369etHhQoVWLt2reI7asWTwsLCGDRokOJxc+fO1ey/sRBWr359OH0ahg0zFWLm3BuYNvPYoIEph379rHtvogJSAAohnuvs2bM0a9aMevXqsX//fkunY5NSUlJo3bo1CQkJisZ99NFHtGzZUqOshLAR7u4wZgwcO2ZaFnZ3N80IatU8Oq3we/ll+OEH+OUXKFNGm2dZiBSAQohM2717N7Vq1eLTTz/l4sWLlk7HpkyaNElx8VyoUCHmz58vS79CpKlQAZYuhevXYcECqFLF9L6zc/aXiNNm9vLnhwEDIDIS/vgDPvjAssvPGpECUAih2Lfffkv58uUZOnSo4sMMjujw4cOMHj1a8bjFixeTP39+DTISwsblzGk6gXv4sGlWsF8/eP11yJPn38/o9aaiTq//t4BzcjK95+Ly7+dcXeGFF6BdO9i4Ea5ehQkTTKeQ7Zh9LGQLIcwuISGBCRMmsGTJEsaOHUu7du1wtpO9MWpKSEggKCiIlJQURePatm3L/2zkUnkhLKpyZdMLwGg0FXBHj5peZ89CQoLplZpqWjp2d4cCBaBqVdPLz89u9vUp4Xi/YiEcXKNGjdi5c6figuRpbty4QefOnZk9ezbTp0/njTfeUCWuvRgxYgSnTp1SNKZUqVLMmjVLm4SEsGc6HRQtanq99Zals7FqOqOdH+u7desWn376abrTi7///jvJycmZilG4cGEqVar0xHuBgYHMnDlTtTyFyI7GjRuzbdu2TH02ODiYevXqMXDgQNavX696Lm+++SbTpk0jMDBQ9di25q+//uKVV15RfHp6x44d1K9fX6OshBDCAWYA8+fPT65cuVizZk2WY1y7do1r16498V5HG7z3T4g0vr6+rFu3jj/++IO+ffty6NAh1WL/8ssv/Prrr3Ts2JHRo0dTqFAh1WLbkgcPHtC6dWvFxV+vXr2k+BNCaM4hDoGMGDFC1XiBgYF8+OGHqsYUwhJeffVV9u/fT0hIiKp95gwGAwsXLsTX15dJkyYpbn1iD/r168fZs2cVjSlfvjwTJ07UKCMhhPiXQxSAFStWpFmzZqrFGzlyJE5a9R4SwsycnJxo1aoVERERjB07Fi8vL9Vix8bGMnjwYMqXL893333nMI2kf/nlFxYuXKhojF6vJyQkBA8PD42yEkKIfzlMFaPWLKDM/gl75enpybBhw4iMjKR9+/aq9p67ePEin3zyCS+99BK7d+9WLa41unPnDu3bt1c8bsiQIdSsWVODjIQQIj2HKQDVmgWU2T9h74oUKcKSJUs4cuQIDRs2VDX2/v37qVu3Ls2bN1e8PGorevToQXR0tKIx1apVY9iwYRplJIQQ6TlUJZPdWUCZ/ROOpEqVKvz6669s3rwZf39/VWOvWbOGgIAA+vfvz927d1WNbUlr1qzh22+/VTTG1dWVkJAQXF1dNcpKCCHSc6gCMLuzgDL7JxyNTqfjrbfe4vjx48ydO1fVWymSkpKYNm0aPj4+zJkzJ9NtmazVtWvX6Nq1q+Jx48aNo2LFihpkJIQQT+dw1UxWZwFl9k84MhcXF7p160ZUVBQDBgxQdbYqJiaGzz//nEqVKvHTTz/Z5EERo9FIx44diYmJUTSubt269OnTR6OshBDi6RyuAMzqLKDM/gkBuXLlYvLkyYSFhdG8eXNVY4eHh/O///2Phg0bcvToUVVja23ZsmX89NNPisZ4eXmxYsUK9Hq9RlkJIcTTOWRFo3QWsGjRojL7J8RjypQpw+rVq9m9ezcvvfSSqrF37NjBCy+8QPv27RUfprCE8+fP07t3b8Xjpk+fTtmyZdVPSAghMsEhC8CKFSsqup3gf//7n8z+CZGBOnXqsGfPHlatWkWpUqVUi2s0Glm6dCm+vr6MGTOGhw8fqhZbTQaDgbZt2xIbG6to3JtvvkmnTp00ykoIIZ7PYauaRo0aZepzHh4evPnmmxpnI4Tt0ul0fPzxx4SFhTFp0iS8vb1Vix0XF8fIkSMpX748K1aswGAwqBZbDbNnz+b3339XNCZ37twsWbJE1T6LQgihlMMWgCtXrszUXsAVK1bw3nvvaZ+QEDbO3d2dgQMHEhUVRdeuXVWdNb9y5Qpt2rShRo0aigsurYSFhTF48GDF4+bNm6fqtXtCCJEVDlsAwvP3AsrJXyGUK1iwIPPmzeP48eM0adJE1diHDx+mfv36vPfee0RERKgaW4mUlBSCgoIU33HcrFkzWrRooVFWQgiReQ5dAD7vRLCc/BUi6wIDA9myZQtbt26lUqVKqsbeuHEjgYGB9O7dm9u3b6saOzMmTpzIgQMHFI0pVKgQ8+bNk6VfIYRVcPjq5mmzgDL7J4Q6GjduzJEjR1i8eLGiw1fPk5KSwpdffkm5cuWYOXMmSUlJqsV+lsOHDzNmzBjF45YsWaJqI20hhMgOhy8AnzYLKLN/QqhHr9fToUMHIiMjGTp0KO7u7qrFvnv3Ln369KFChQqsW7dO00bSCQkJtGrVipSUFEXj2rVrR9OmTTXKSgghlJMKh/SzgDL7J4Q2vL29GTduHBEREXz22Weqxj5z5gwffvghr776KgcPHlQ1dprhw4dz+vRpRWNKlSrFzJkzNclHCCGySgpA0s8CyuyfENoqUaIEK1euZP/+/dSrV0/V2Lt27aJGjRq0atWKS5cuqRp3+vTpisctX76cnDlzqpaHEEKoQaqcf6TNAsrsnxDmU6NGDf744w9++OEHypUrp2rsr7/+Gj8/P4YPH664UfN/PXjwgDZt2iheXu7duzevvfZatp4thBBakALwH2mzgDL7J4R56XQ6PvjgA06fPs2MGTPInTu3arETEhIYN24cvr6+LF68mNTU1CzF6devH2fPnlU0xt/fnwkTJmTpeUIIoTWdUcsd01bKYDBw69Ytrl27xrVr14iLiyMlJYX79+/j6emJi4sLzs7O5MmTh8KFC1O4cGG8vb2lfYOwWo0bN2bbtm2Z+mxwcDDt2rXTOKOsi4mJYezYscydO1fxYYvnqVSpEtOnT8/0TUAAP//8M2+99Zai5+j1evbu3UuNGjWUpiiEEGbhbOkEzCE1NZXIyEgiIiKIjo7m5s2bj66UcnJyerSsk/ZXnU6HTqfDaDQ+es/d3Z3ChQtTrFgxKlasSOHChS3zixHCzuXLl49Zs2bRrVs3BgwYwMaNG1WLfeLECRo3bsxbb73F1KlTqVChwjM/f/v2bdq3b6/4OUOHDpXiTwhh1ex6BjAmJoYjR45w+PBh4uPjcXJyyvZdomkxChcuzIsvvkilSpVwc3NTKWMhssaeZgD/6/fff6dPnz4cOXJE1bh6vZ5OnToxatQoChYsmOFnPvnkE7777jtFcV944QX27duHi4uLGmkKIYQm7K4ANBqNhIaG8vfff3Px4sVHM3la0ev1VKpUiTp16lCgQAHNniPEs9hzAQimbRsrV65kyJAhREdHqxo7Z86cDBkyhF69ej3Rn3DNmjU0b95cUSw3NzcOHTpEYGCgqjkKIYTa7Oq0w507d/j6669Zs2bNo/YPWte3qampHD9+nAULFrBjxw7V9ywJIUwz761btyYiIoLRo0fj6empWuz79+8zaNAg/P39WbVqFUajkatXr9K1a1fFscaNGyfFnxDCJtjFDGBqair79u1j586dGI3GbC/zZpVOpyNXrly88847lClTxiI5CMdk7zOA/xUdHc3w4cNZtmyZ6j/kvfTSSzg5ObFnzx5F4+rVq8fOnTvR6/Wq5iOEEFqw+RnAq1evsnDhQrZv305qaqrFij8wzTbeu3ePkJAQNmzYQEJCgsVyEcKeFS1alODgYA4fPkyDBg1Ujb1v3z7FxZ+XlxfLly+X4k8IYTNsugAMDw8nODiYW7duWTqVR9JmI44fP05wcDD37t2zcEZC2K+qVauyfft2Nm3aRPny5S2Wx4wZMyhbtqzFni+EEErZbAF48OBBVq1aRWpqqub7/LLCaDRy+/ZtFi9ezI0bNyydjhB2S6fT0bRpU06cOMGcOXPIly+fWZ/fpEkTOnbsaNZnCiFEdtlkAbh//342b95s6TSey2AwEBcXx7Jly6QIFEJjLi4udO/enaioKPr164erq6vmz8yTJw9LliyRJvFCCJtjcwXgwYMH+fnnny2dRqYZjUYSExNZvny5VS1VC2GvcufOzdSpUwkNDaVZs2aaPmvevHkULVpU02cIIYQWbKoAvHLlClu2bLF0GooZjUYSEhJYtWqVtIkRwkzKli3L999/z19//UXNmjU1ecb27du5evWqJrGFEEJLNlMAJiUlsXbtWkunkWVpewK3b99u6VSEcCgvv/wye/fu5dtvv6VkyZKqxg4ODsbX15dx48YRFxenamwhhNCSzRSAW7du5d69e1Z54COzjEYjf//9N2fOnLF0KkI4FCcnJ1q2bElYWBgTJ07E29tbtdgPHz5k+PDhlC9fnpUrV1q0FZUQQmSWTRSAYWFhHD582KaLvzQ6nY5169bJbIEQFuDh4cGgQYOIjIykQ4cOqsa+fPkyQUFB1KxZkz/++EPV2EIIoTarLwATExPZuHGjpdNQjdFoJD4+3qYOsghhbwoVKkSuXLk0iX3o0CFee+01PvjgAyIjIzV5hhBCZJfVF4BHjhyxuxs1jEYjJ0+e5O7du5ZORQiH9OeffzJjxgxNn7F+/XoCAwP54osvuH37tqbPEkIIpay6ADQYDOzdu9fSaWhCp9Oxf/9+S6chhMOJjY2lTZs2ZtlSkpyczKxZs/Dx8WHWrFkkJSVp/kwhhMgMqy4Aw8PDuX//vqXT0ITRaOTQoUPyB4IQZtavXz/OnTtn1mfeuXOHL774gsDAQDZs2GAX+5mFELbNqgvAvXv32nWH/aSkJI4ePWrpNIRwGFu2bGHRokUWe35UVBTvv/8+r732GocOHbJYHkIIYbUF4PXr17l06ZLd/6S8b98+S6cghEO4fft2lk7+dujQgZdfflnVXP7880+qV69O69atuXz5sqqxhRAiM6y2ADx37pxdz/6luXPnjt0ucwthTbp376741o4XX3yRefPmsWvXLtauXUvZsmVVzSkkJAQ/Pz9GjBjBgwcPVI0thBDP4mzpBJ4mOjpas9gRERF8++23T/16hQoVaN68uWbP/6/o6Ghy5sxptucJ4Wi+//57Vq1apWiMm5sbISEhuLi4APDhhx/StGlT5syZw9ixY7l3754qucXHxzN27FgWL17MuHHjaNOmDXq9XpXYQgjxNFZbAF68eFGz5d8yZcrQt2/fJ94zGo1s2LCBa9eu8eqrr2ry3Iw4OTlx5coV/P39zfZMIRzJ1atX6dq1q+Jx48ePp0KFCk+85+bmRt++fWndujVjxoxh3rx5pKamqpLntWvX6NChA7Nnz2b69Ok0bNhQlbhCCJERq1wCjouLU+2n64y4uLjg7e396OXl5cWvv/7KtWvXaN26NYUKFdLs2f9lMBhkD5AQGjEajXTo0EFxH7569erRu3fvp349f/78zJ49m1OnTvHOO+9kM8snHT9+nEaNGtG0aVNCQ0NVjS2EEGmssgDUcvn3vwwGA+vWrePMmTMEBQWZtfhLc+XKFbs/7CKEJQQHB7NlyxZFY3LkyMHy5csztQxbvnx5Nm7cyI4dO6hatWoWs8zY5s2bqVSpEj169ODmzZuqxhZCCKssAGNiYszynMeLv9atW1O4cOFHX1u1ahWTJk1i9erVmueRnJzMw4cPNX+OEI7k3LlzfPHFF4rHzZgxQ/Fhj/r163Pw4EGWLVtGkSJFFD/zaVJTU5k7dy4+Pj5MnTrV7m5FEkJYjlUWgMnJyTg5aZvas4o/gFq1avH+++9rmsPjkpOTzfYsIeydwWCgbdu2ik/WNmnSJEutYgD0ej1t2rQhMjKSkSNH4unpmaU4Gbl//z4DBgwgICCA77//XlYMhBDZZpUFYEpKiqbx/7vs+9/iD0wHRVxdXTXN43FSAAqhni+//JI//vhD0Zg8efKwZMmSbLef8vLyYtSoUURERNCmTRtV21mdP3+ejz/+mJdffll6iAohssUqC0AtGQwG1q9f/6j4U3O5JjscoeehEOZw+vRpBg8erHjcvHnzKFq0qGp5FCtWjGXLlnHo0CHq16+vWlww3ZJUu3ZtWrZsyfnz51WNLYRwDFZZADo7a9OdJq34CwsL44MPPiBHjhzExsY+8TIYDJo8+3m0+jUL4UiSk5MJCgoiMTFR0biPP/6YFi1aaJJTtWrV+O2339i4cSN+fn6qxl61ahX+/v4MHjxYGsoLIRSxyqrDxcVFk0IsOjqaEydOAPDNN99k+JmBAwfi4eGh+rOfJ63ZrBAi6yZMmKD4jt3ChQszd+5cjTIy0el0vPPOOzRp0oQFCxYwatQoxa1pniYxMZFJkyYRHBzMmDFj6NChg/xAKYR4Lqv8LpEvXz5N4hYvXpxRo0ZpEjs7XFxc8PLysnQaQti0Q4cOMW7cOMXjgoODNfue818uLi58/vnnfPbZZ4wbN46vvvpKtf2/N2/epGvXrnz11VdMmzaNN998U7aWCCGeyiqXgIsVK2bpFFi5ciVr1qwhMjKS6dOnc+XKFc2eVaxYMflGLUQ2xMfH06pVK8UHyDp06MBbb72lUVZPlydPHqZPn05oaCgffvihqrFPnz7NW2+9xZtvvvloxUMIIf7LKmcAPTw8yJUrl6a3gTxPq1atzPIcJycnihcvbpZnCWGvhg0bpvjWjNKlSzNjxgyNMsqccuXKsXbtWnbt2kXfvn05cOCAarF//fVXqlatSvv27RkzZkyG3Q6EEI7LKmcAAUqWLOkQs2IGg8EqZjyFsFV//PEHM2fOVDRGp9OxYsUKvL29NcpKmXr16rFv3z6+/vprSpQooVpcg8HA4sWL8fX1Zfz48cTHx6sWWwhh26y2AFSzHYO1kwJQiKyJjY2lTZs2ihsjf/HFF7zyyisaZZU1Tk5OfPrpp4SHhzN+/Hhy5MihWuwHDx4wbNgwypcvz9dff22xbgdCCOthtQVgmTJlHKLbfZ48eaxmFkIIW9O3b1/FffACAgIYP368NgmpwMPDgyFDhhAZGUmnTp1UvRXp0qVLtGrVilq1arFr1y7V4gohbI/VFoCFChWiRIkSdr8M/NJLL1k6BSFs0pYtW1i8eLGiMXq9npCQENzd3TXKSj2FCxdm4cKFHD16lMaNG6sa++DBg7zyyit89NFHnDlzRtXYQgjbYLUFIECdOnXsehbQ1dWVqlWrWjoNIWxOTEwM7du3Vzxu+PDhVK9eXYOMtFOpUiW2bt3Kzz//TIUKFVSN/cMPPxAQEEDfvn25c+eOqrGFENbNqgtAPz8/cuXKZek0NKHT6XjxxRfNet+wEPaie/fuXLt2TdGYF198kSFDhmiUkfbefPNNjh07xvz58ylQoIBqcZOTk5kxYwY+Pj7Mnj1b7iUXwkFYdQHo5ORk10uktWrVsnQKQtic1atXs3r1akVj3NzcCAkJsfkbd5ydnenSpQtRUVEMGjQINzc31WLfvn2bXr16UbFiRTZu3GjXqy9CCCsvAMF0j6Yt7NdRQqfTERgYaLezm0Jo5erVq3Tr1k3xuAkTJqi+fGpJOXPmZOLEiYSFhdGyZUtVY0dERPDee+/RoEEDDh8+rGpsIYT10Blt4Me88PBwVq1aZek0VJF2qMXV1ZW8efOSI0cOvL29yZEjx6PX4/8sd3qKzGjcuDHbtm3L1GeDg4Np166dxhmpz2g00rRpU7Zs2aJo3CuvvMLOnTtVPU1rbfbt20efPn3Yu3evqnF1Oh1BQUGMHz9e2lUJYWdsorooX748L7zwAkeOHLH5ZQmj0fjoHtDly5c/9/Nubm7PLBCf9s8vvvgivr6+2v+ChDCTJUuWKC7+cuTIwfLly+26+ANTN4Hdu3ezdu1aBg4cyLlz51SJazQaWbFiBWvWrKFfv370799f1f6EQgjLsYkZQICkpCTmz5/PvXv3bLYI1Ol01KxZkzfffJN79+5RqVIlLl26pPpz3NzcCAsLo3Tp0qrHFtbJ3mcAz507R+XKlXnw4IGicYsXL6ZDhw4aZWWdEhMT+eqrrxg7diz3799XNXaRIkUYP348QUFB6PV6VWMLIczLZn4sdnV15aOPPrJ0Glmm0+nIly8fDRs2BCBXrlwsXbpUk2f17t1bij9hNwwGA23atFFc/L399ttZahVj69zc3OjXrx9RUVF0795d1ULt6tWrtGvXjurVq7Njxw7V4gohzM9mCkAwXZn29ttvWzoNxXQ6He7u7nz88cdP7Olr2LAhXbt2VfVZBQoUYPDgwarGFMKSZs2axZ9//qloTN68eVm8eLHdN5J/lgIFCjBnzhxOnDhB06ZNVY199OhRXn/9dd555x3Cw8NVjS2EMA+bKgDB1MvrrbfesnQamabT6XBzc6Nt27bkz58/3denTJlC2bJlVXve6NGj5XSxsBunTp3KUu++efPmUaRIEQ0ysj0BAQFs2rSJ7du3U6VKFVVjb9q0iYoVK/L5559z69YtVWMLIbRlcwUgQI0aNVT/iVYLOp0OLy8v2rVr99TGrWmb1NWYqXB3d6dJkybZjiOENUhOTiYoKIjExERF41q0aMHHH3+sUVa26/XXX+fQoUMEBwdTuHBh1eKmpKQwZ84cfHx8mDZtmuL/XkIIy7DJAhBMM4EtWrRAr9db5TJP2p6/jh07Prdrf7169fjiiy+y/cyEhAQqVKjAiBEjFO+XEsLajB8/XnEfuiJFijB37lyNMrJ9er2edu3aERkZyYgRI/Dw8FAt9r179+jfvz8BAQGsXbvWZg/rCeEobLYABFN7mPbt26t6LVJ2pRWjVapUoX379uTMmTNT48aNG4e/v3+2nx8fH8/YsWPx9fUlODiY1NTUbMcUwtwOHjzIuHHjFI9bsmQJefPm1SAj+5IjRw5Gjx5NREQEQUFBqsY+d+4czZo1o169euzfv1/V2EII9dh0AQimn/g7d+5Mo0aN0Ov1Fu/3lTt3blq3bs27776r6AYTDw8PVqxYoVr+165do0OHDrzwwgts375dlZhCmEN8fDxBQUGKf3jp2LGjTe0PtgbFixdnxYoVHDx4kFdffVXV2Lt376ZWrVp8+umnXLx4UdXYQojss/kCEEx3BtepU4cePXpQpkwZALMuCzs5OeHk5MQrr7xCt27dstyCpWbNmgwaNEjV3I4fP06jRo1o2rQpoaGhqsYWQgtDhw5V/P9qmTJlmD59ukYZ2b8XX3yRnTt3smHDBtUbyH/77bf4+fkxZMgQ1fsSCiGyzi4KwDS5c+fm008/pXnz5pQsWRLQrhBMi+vs7EyVKlXo0qUL9evXz/bVbSNGjKBy5cpqpPiEzZs3U6lSJXr06MHNmzdVjy+EGv744w9mzZqlaIxOp2P58uV4e3trk5SD0Ol0vPvuu5w8eZJZs2aRJ08e1WInJiYyceJEfH19WbhwISkpKarFFkJkjc3cBJIVt2/f5siRIxw+fJi4uDicnJwwGAzZipkWo0iRIlSvXp3AwEDc3NxUytjk2LFj1KhRg+TkZFXjpsmZMyfDhg2jZ8+equcuLMMebgKJjY2lcuXKnD9/XtG4vn37Mm3aNG2ScmC3b99m3LhxzJkzR/XvRYGBgUyfPp033nhD1bhCiMyz6wIwjcFgIDIyksjISKKjo7lx48aj/UVpe+6MRuOjU2s6nQ6dTvfEex4eHhQpUoSiRYtSsWJFChUqpGnO48aNY/jw4Zo+o0yZMkyaNIlmzZpZ5UlqkXn2UAB27NiRJUuWKBpToUIFDh06pGi/rVAmKiqKgQMHsm7dOtVjv/HGG0ybNo2KFSuqHlsI8WwOUQD+l8FgICYmhmvXrnHt2jXi4uJISUkhJSUFnU6Hs7Mzzs7O5MmTh8KFC1O4cGGzLy+lpKRQp04dDhw4oPmzateuzYwZM3jppZc0f5bQhq0XgJs3b1bc29PZ2Zl9+/bx4osvapSVeNyff/5Jnz59OHTokKpxnZyc6NixI6NHj9b8B2shxL8csgC0FaGhoVSrVs1sjVVbtGjBxIkT5R5hG2TLBWBMTAwVK1bk2rVrisaNHj2aESNGaJSVyIjBYODbb79l8ODBXL58WdXY3t7eDB48mN69e6van1AIkTG7OgRibwICAhg/fnyGXxs3bpzqp/VWrVqFv78/gwcPltN6wmy6deumuPirXr263HltAU5OTnz22WeEh4czduxYvLy8VIsdGxvLkCFD8Pf359tvv832fm0hxLNJAWjlevfuTd26dZ9474MPPmDo0KGcPHmSL7/8UvXTepMmTcLHx4cFCxbIaT2hqVWrVvH9998rGuPm5kZISAguLi4aZSWex9PTk2HDhhEZGUmHDh1U3UN88eJFPv30U2rXrs3u3btViyuEeJIUgFZOr9ezfPlyPD09AXBxcWHy5MkAuLq60rNnT6Kiovjiiy9U/QPx5s2bdO3alSpVqvDLL7+oFleINNHR0XTr1k3xuIkTJxIQEKBBRkKpIkWKsHjxYo4ePUrDhg1Vjb1//37q1q1Ls2bNOHv2rKqxhRBSANqEcuXKMXXqVAB69OiBj4/PE1/PmzcvM2bM4PTp03zwwQeqPvv06dM0adKEN954gxMnTqgaWzguo9FIhw4duHPnjqJxr776Kr169dIoK5FVlStX5tdff2Xz5s2qF+dr164lICCAfv36cffuXVVjC+HIpAC0EV26dOHjjz9+ZmsYHx8ffvjhB/7880+qV6+u6vN//fVXqlatSqdOnRTv1xLivxYvXszPP/+saEyOHDlYvny5xa97FBnT6XS89dZbHD9+nHnz5pE/f37VYiclJTF9+nR8fHw06UsohCOS76Q2wsnJiW+//TZT+/3q1avH33//zcqVKylevLhqORgMBhYvXoyvry8TJkwgPj5etdjCcZw9e5Y+ffooHjdr1iw5oW4DnJ2d6dq1K1FRUQwYMABXV1fVYsfExPD5559TqVIlNm3ahDSxECLrpA2MnYuLi2PmzJlMnDiRhw8fqhq7RIkSTJw4kZYtW8qsjIXZShuY1NRU6tevz65duxSNe/vtt9m0aZM0LLdB586dY/DgwaxevVr12A0aNGD69OlUrVpV9diZZjTCw3Nw+wjcOQqxkZAaD6lxYEgGvYfp5ZYXcleBPFUhTxVwyWm5nIVACkCHce3aNUaMGEFwcLDq7RVq1KjBjBkz0p1WFuZjKwXg9OnT6devn6IxefPm5eTJkxQpUkSjrIQ57N27lz59+rBv3z5V4+p0Otq0acO4ceMoWrSoqrGf6s5ROPc13NoLd45B6j8/XOucAQMY//s9Vgc6PRhTgX/+yPUsCfmqQ+HGULqlFITC7GTaxkEULlyYRYsWcfToURo1aqRq7AMHDlCvXj0++ugjzpw5o2psYT9OnTrF0KFDFY+bP3++FH92oHbt2uzZs4dVq1apupRvNBpZtmwZvr6+jB49WvWVjkeS70PkAthSFX6uBuFfwq09/xZ/AMaUDIo/AKPpazw23xJ3ES5tgANd4YdCsLct3NxjmlEUwgykAHQwlSpVYuvWrWzZskX103o//PADAQEB9O3bV/HpTmHfkpOTCQoKUnyrTcuWLWnevLlGWQlz0+l0fPzxx4SGhjJ58mRy5lRv1isuLo5Ro0bh5+fH8uXL1VvpuB9hKs5+KAgHusHd46b3jWr0SDUARjAkwPmvYdvL8FN5CP/KtHwshIakAHRAOp2OJk2aPDqtV6BAAdViJycnM2PGDHx8fJg9e7ac1hOA6eaaw4cPKxpTpEgR5syZo1FGwpLc3d0ZMGAAkZGRdO3aFb1er1rs6Oho2rZtS/Xq1dm5c2fWA6UmwokxsDnQVJwZEjHN4Gk0Q5dWUMZGwaFe8HNVuPW3Ns8SAikAHVraab3IyEgGDhyo6mm927dv06tXLypWrMiPP/4op/Uc2IEDB556peGzBAcHkzdvXg0yEtaiYMGCzJs3j+PHj/PWW2+pGvvIkSM0aNCA9957j4iICGWDb+yCLZXgxKh/lnXNeSPSP0Xm/XD4tTYc6GFafhZCZVIACnLlysWkSZMIDw+nRYsWqsaOiIjg3Xff5fXXX+fIkSOqxhbWLz4+nqCgIFJTUxWN69SpE02aNNEoK2FtKlSowObNm/n111+pVKmSqrE3btxIYGAgvXr1IiYm5tkfTn4Af3eE7a/Ag7NoNtuXGWkHRqLmw4++cOUny+Ui7JIUgOKR0qVL891337F3715q166tauydO3fy4osv0rZtW65cuaJqbGG9hg4dSlhYmKIxZcqUYdq0aRplJKxZo0aNOHLkCIsXL6ZQoUKqxU1JSWH27Nn4+PgwY8aMjPeixl83FX5nl5n+2ajshxbNGA2QeAv+eAfCZlk6G2FHpAAU6bz00kvs3r2b1atXq35ab/ny5fj5+TFq1CjtTusJq/D7778zc+ZMRWN0Oh0rVqzA29tbo6yEtdPr9XTo0IHIyEiGDRuGu7u7arHv3r1L3759CQwM5Icffvh3a0rsGdhaE+6esJ7C7wn/HBY5/AUc7isnhYUqpAAUGdLpdDRv3lyz03qjR4/Gz8+PZcuWKV4eFNbv/v37tGnTRvG4vn37Uq9ePfUTEjbH29ubsWPHEhERQatWrVSNfebMGT766CMaNGhAyp1w2FYX4qPNvNcvi8JmmA6JSBEoskkKQPFMaaf1oqKi6Natm+qn9dq1a0f16tXZsWOHanGF5fXp04cLFy4oGhMYGMjYsWM1ykjYqhIlShASEsKBAwd45ZVXVI1dvmwxnHfWNy2x2kLxlybiKziirKG6EP8lBaDIlAIFCjB37lxOnDjB22+/rWrso0eP8vrrr/Puu+8SHh6uamxhfps3byY4OFjRGGdnZ0JCQlRd7hP2pXr16vz++++sW7eOcuXKZTuet7c3oxuFQuJN2yr+0oTNgItrLJ2FsGFSAApFAgIC+Omnn9i2bRuVK1dWNfaPP/5IxYoV6dmz5/NP6wmrdOvWLdq3b6943PDhw3nhhRc0yEjYE51Ox/vvv8/p06eZOXMmuXPnznKsIe1qUchwxDaLPwB0sK8DxF22dCLCRkkBKLKkYcOGHD58mCVLllC4cGHV4qakpPDVV1/h4+PD9OnTFd8cISzHaDTSrVs3rl+/rmhcjRo1GDx4sEZZCXvk6upK7969iYqKolevXjg7OysaX7J4YXpX/Q2LtnnJNiOkxsGez55y/ZwQzyYFoMgyvV5P+/btiYyMZPjw4Xh4eKgW++7du/Tr148KFSqwdu1aaSRtA1atWsWaNcqWpNzd3QkJCcHFxUWjrIQ9y5cvH7NmzeLUqVO89957mR43qXkK7m528MefMQVu/AFhyk7bCwFSAAoV5MiRgzFjxhAeHq76ab2zZ8/SrFkz6tWrx/79+1WNLdQTHR1N9+7dFY+bOHEi/v7+GmQkHImfnx/r16/n999/f+5WgloVC9PixdtW2u4li44OMrWyEUIBKQCFarQ8rbd7925q1arFp59+ysWLF1WNLbLHaDTSvn177ty5o2jca6+9Rs+ePTXKSjiiV199lQMHDrBixQqKFSuW4WdmNLuNDjtcMg2fbekMhI2RAlCoLu203vr16/Hx8VE19rfffkv58uUZOnQosbGxqsYWWbNo0SJ++eUXRWO8vb1ZtmwZTk7yLUioy8nJiaCgICIiIhgzZgyenp6PvtbsjcrU8bHVQx/PYEyBM4sh6Z6lMxE2RL77Ck3odDree+89Tp06xcyZM8mTJ49qsRMSEpgwYQI+Pj4sWrSIlBQ7/IZuI86cOUPfvn0Vj5s1a5aqt8wI8V+enp4MHz6cyMhI2rVrh4eHB5PeuQr2OPsHkJoAZ5daOgthQ6QAFJp6/LRe7969FZ/We5YbN27QuXNnqlWrxtatW1WLKzInNTWVNm3aKL7Sr2nTprRt21ajrIR4UtGiRQkODub8ri8pm/umpdPRkNHUG9BgR3sbhaakABRmkTdvXmbOnKn4tF5mnDx5kjfffJMmTZpw6tQpVWOLp5s5cyZ//fWXojH58uVj8eLF6HQ6jbISImMFY1aATr2bjKxS3GWI3mLpLISNkAJQmJWS03pK/fLLL1SuXJkuXboo7kUnlDl58iRDhw5VPG7+/Pmq9o0UIlNSEyDmb/s6+ZsRnQvc+N3SWQgbIQWgsIi003ohISFPPa2XFQaDgYULF+Lr68ukSZNISEhQLbYwSUpKIigoiKSkJEXjPvnkE5o1a6ZRVkI8w51jmt74seUo6D59+qu5uQ7oGpPh1l4zPUzYOikAhcU4OTnRqlWrR6f1vLy8VIsdGxvL4MGD8ff357vvvpNG0ioaN24cR44cUTSmaNGizJkzR6OMhHiOmP2AdtsO6leAq3OffF3+ChpVhPzeMPw9zR6d3u3DYJCDceL5pAAUFvff03pq7g+7cOECn3zyCbVr12bPnj2qxXVUBw4cYMKECYrHBQcHq3oSXAhFYvZruv/PwxUK5/73VSAn9PsWjlyAHUOgUknNHp2eIRHuyV5o8XxSAAqrUaRIEYKDgzly5Aivv/66qrH//vtvXn75ZZo3b87Zs2dVje0o4uPjCQoKIjVV2T6qzp078+abb2qUlRCZcPMvTZeAH5dqgM/mwbYT8Ju5iz8AdKb9jkI8hxSAwupUqVKFbdu28dNPP6l+TdiaNWsICAigf//+3L17V9XY9m7IkCGEhYUpGlO2bFmmTZumUUZCZILRCA/Nc3tQqgFaPVb8Vf6n+IuNhxrDoepgqDQQFu/QMAmdM8RGafgAYS+kABRWSafT8fbbb3P8+HHmzp1L/vz5VYudlJTEtGnT8PHxYe7cuSQnJ6sW217t3LmTWbNmKRqj0+lYsWIFOXLk0CYpITLDkIQ5mj+nFX+//lP8VSn179c83eCPYXB0Ivw9Bib+CDFaXmSUGq9hcGEvpAAUVs3FxYVu3boRGRlJ//79cXV1VS12TEwMPXr0oFKlSvz0009yUOQp7t+/n6XGzf369aNu3boaZCSEAmYohtKKv60nYPvgJ4s/AL2TqQgESEg2fV677zZGKQBFpkgBKGxC7ty5mTJlCmFhYTRv3lzV2OHh4fzvf/+jUaNGHDt2TNXY9uCLL77gwoULisYEBgYyZswYjTISQgltm46nGiBo/r/FX9XSGX/u7kOoMhiKfw4DmppOB2tHGq2L55MCUNiUMmXKsHr1anbv3k2tWrVUjf3bb79RrVo12rdvT3R0tKqxbdWmTZtYulTZ/aLOzs6sXLkSd3d3jbISQgG9h2ahDf8UfxsOwdddoUhuuHb3yVfqP6vPub3g2EQ4Nwu+3QPX72mVlU7TX7OwH1IACptUp04d9u7dy3fffUepUqWePyCTjEYjS5cuxdfXlzFjxii+59ae3Lp1i44dOyoeN2LECKpVq6ZBRkJkgZMLWv1Rd+CsqZiLS4S3pkKR7ulfsf9ZjS2Uy3Q45E9l56mUcfbUMLiwF1IACpul0+lo0aIFYWFhTJo0CW9v9dZU4uLiGDlyJOXLlyckJASDQftN5NbEaDTStWtXxVfq1axZk8GDB2uUlRBZoNOBt48moWv5gPGbZ79ye5lm++7HmcbcjzMVf+WLaJKS6TaQnOp2TxD2SQpAYfPc3d0ZOHAgUVFRdOnSBScn9f63vnLlCq1bt6ZGjRr8/vvvqsW1dt999x1r165VNMbd3Z0VK1bg7OysUVZCZFGBl03tUSzk8m14ZaxpD2DdMdCj8b8tYjSRr6aGwYW9kAJQ2I2CBQsyf/58jh8/TpMmTVSNffjwYerXr897771HRESEqrGtzZUrV+jevbvicZMmTVK9b6MQqshXE4zKGpir6cUyphYwxybC8UnQtaGGD9N7gnd5DR8g7IUUgMLuBAYGsmXLFrZu3UrFihVVjb1x40YCAwPp3bs3t2/fVjW2NTAajXTo0EFxk+z69evz+eefa5OUENmVryZaNl6xKvmqg5N2194J+6EzSvMzYcdSU1NZunQpw4cPV7yf7Xly587NiBEj6N69u6r9CRUzpLBy0VTOhh2AhGuQEme69sqQYtr/pHMGJ2dwzQPuRXjnwyCq1W5s+tp/LFy4kC5duih6vLe3NydOnFD1MI4QqjIkw/c5/mkKbcd0zhDQD6pOtHQmwgZIASgcQmxsLJMnT2b69OkkJCSoGtvHx4cpU6bw3nvvocugqFKdIRmubIboLRBzAO6fMr0HoHMBjGA08Oj2A50Tpsl+47/LYK55IM8LppmRUi0gT2XOnDlDlSpVFJ98Xrp0aZYaRQthVjsawfWdFl0KNov6v0KRRpbOQtgAKQCFQ7l48SJDhgzhm2++UT12vXr1mDFjBtWrV1c9NgD3I+FsMEQtgaQY00/72b3gPi1Gnhe4U7AVn0/fyzfffZ/p4f/73//YuHGjeQpfIbLjymb4o6mls9DQP6edm4ZnOLsvxH9JASgc0oEDB+jTpw9//fWX6rE/++wzJkyYQIkSJbIfzGiESz9A+Jdw8y/Q6TWawfhnhlDvxtozteky/Qgxt+8+c0S+fPk4efIkhQsX1iAfIVRmNMAmX3hwDrvdD1hzIfh0snQWwkbIIRDhkGrUqMGff/7JDz/8QLly5VSN/fXXX+Pn58fw4cOJjc3Gje8PzsKOxvBXM7i5x/SeZstXBkx3iCbwUZldnBofyzuv+T1zxIIFC6T4E7ZD5wT+fS2dhXZcckHpzyydhbAhMgMoHF5iYiJz585l7Nixik+/Pk+hQoUYN24cbdu2Ra/P5Mk8QzKEzYDjI00FX3aXebPIaIQV+wvQc+lDYh/EPfG1Tz75RJNldCE0lfwA1heFlGz8YGaNdHoIGABVJ1g6E2FDpAAU4h8xMTGMGTOGefPmkZKibtFVqVIlpk+fTqNGz9mcffsQ7A2Ce6FYxTKVTs+FG6m0DSnMzsPXAChatCgnT54kT548Fk5OiCw4PhJOjsUqfn+pRe8B/4sEz2KWzkTYECkAhfiPiIgIBgwYwMaNG1WP/dZbbzF16lQqVKiQ/ouXN8Jfzf+Z9bOuk4oGoxNz/ijAoK/vsX79Bt544w1LpyRE1qQmwi814H6oxWbXVVdrKZSTk/hCGSkAhXiK33//nT59+nDkyBFV4+r1ejp16sSoUaMoWLCg6c3I+XAg7fYNK/0tqXPmRkIeCr6/E3IHWjobIbLuXij8XNX2+wLq9FDsf1BvnZz8FYpJASjEMxgMBlauXMmQIUOIjo5WNXbOnDlZsmQJzSpfg0M9VY2tGZ0eXHJCo78gVwazmELYioi5cLCHpbPIBidwyw9NT4NbPksnI2yQnAIW4hmcnJxo3bo1ERERjBo1Ck9PT9Vix8bGUt79qO0Uf2Bamk6+D9tegfvhls5GiKzz7QZF3jD9UGOTDFDnayn+RJZJAShEJnh5eTFy5EgiIyNp27atKo2P233SlMqxk1TIzsyMqZB8F/58H1LVvVVFCLPR6aD2SsjhY2qIbmuqTpYbP0S2SAEohAJFixZl6dKlHD58mAYNGmQ5jpeXF2NfP2K7+3aMqaYZwKNDLJ2JEFnnXgAa/gE5ythWEVhlAlQYYOkshI2TAlCILKhatSrbt2/nxx9/pHz58orHD/q0PEXco63utK8yBgifCVe3WToRIbLOoxA03AW5Aqx8OfifHxarTYPAwZZNRdgFOQQiRDYlJyezcOFCRo0aRUxMzHM/X6xwXiIm3MbTzQzJaU42ogs7kRwLuz6Ea9uxupP4Oj3wz5J16RaWzkbYCZkBFCKbXFxc6NGjB1FRUfTr1w8XF5dnfn7iRwl4utno0m86BkiKgUO9LJ2IENnj4g2vbYaKI0zLwVazJKwDb1/TyXsp/oSKZAZQCJWdOXOGQYMGsXbt2nRfezGwOPsHXcbJ7n70coJ3z4FXSUsnIkT23QuDvzvArd2Wy0HnbLq/uNIo0x3GelfL5SLskt39MSSEpZUrV441a9bw119/UbNmzSe+NqNFgh0Wf5gOs0TMtXQWQqgjlz80+hNqLQHnnGbeG/jPswrWg7dPmfb7SfEnNCAzgEJoyGAwsHr1agYNGsSLAYVZF7Tf0ilpx9kb3o8GlxyWzkQI9cRfh4jZELUYEm+aikEtDm+lxS34Gvj1gBIf2G6XAGETpAAUwgzi4+OJ3fQKBVOOALZ88vdZdFB9Dvh1s3QiQqjPkALRWyBqkemv6EwFWnaKQZ0LGJPBrSD4dIRy7SBHWdVSFuJZpAAUwhzuHIWfq1k6C43pIEc5eCfS0okIoa24K3BhFcQcgNsH4ME5TCeHdeDk/M8hYuO/76Ez/b0xxTRe7wl5qkLeF6FwQyj6lmmcEGYk/8cJYQ7Xd2LacmuwdCYaMsKDKIi/Ch5FLJ2MENrxLAYBff/955SHcOc43D0K9yNMN+QYEsCQDHp3cHIHt7yQu4qp8MtRxnTAQwgLkgJQCHO4tf+f5SL1Q285Cm9PffrXm9WC78153XDMASj+jhkfKISFOXtBgdqmlxA2QgpAIczh1m7Nbv2oXwGu/ucAbqoB2i6EIxdg+HuaPDZjOmeI+VsKQCGEsHJSAAqhtYRbEHdJs/AerqZXmlQDfDbPVPztGAKVzNmaz5gKt/aZ8YFCCCGyQjYhCKG12wfM9qi04m/bCfjN3MUfAEaI2Q9Ge97rKIQQtk9mAIXQWmwk5jgAkmqAVo8Vf5X/Kf4uxUCr+XDjHjjrYfj7pn2Bmkl5AAk3waOQhg8RQgiRHVIACqG1lDjTiT8NZ8XSir9f/yn+qpT692vOTjDrM6ha2lQEvjAU3qoCXu6apQOp8RoGF0IIkV1SAAqhtdR4TH3ANAr/T/G39QT8NvjJ4g+gSB7TC6BgLsibA24/lAJQCCEcmewBFMKGpRogaL6p+Ns+2DTL9ywHz4LBCCXymSU9IYQQVkpmAIXQmt4DLRoAGv4p/jYcgrU9oUhuuHb3yc8UyAn6f37Mi4k1fX5JR9VTSU/vYYaHCCGEyCopAIXQmrOnJvv/DpyFb/eY/v6tpzSCvrMIcntBYjK8PxMGvwN1/FRPJT0pAIUQwqpJASiE1rx90eIEcC0fMH7z/M8ZjdBmITQIhFb1VE8jPecc4F7ADA8SQgiRVVIACqG1vDUs+vjdEbB6H1QuARsOmt5b2VWrHoE6yFdT7jkVQggrJwWgEFpzzw+eJTS9DeRZ6pYHw9dmephOD/nlPlQhhLB28mO6EOaQ/2VTcWTvjCmmGUAhhBBWTQpAIcwhf03TZjxHkM+yS95CCCGeT5aAhTCHQg3Q+io4y9NBjnLgUcTSiQghhHgOmQEUwhzyVPlnb5ydLwP7f2HpDIQQQmSCFIBCmEtAPyDV0lloxzkHlG1t6SyEEEJkghSAQphLsXf5f3v3DiJXGYdx+D179kY2ZteNhICJrtHElSBe4mIgEBBiWu1CsBAUvKCV2IhY2icgYuGlEuyChQhiZURBLEQsghhsIgpRRGJhzDpjcSb9bjY738x8zwOHgZni/KcY5sd3btmxr/QU26Npk4MvJtMLpScBYAMEIAzLVJusvpKkKT3JzdfvJ4deLj0FABskAGGYDjyTzCxmoiKwaZOV08nC/tKTALBBAhCGaXYxOfpBkkm5JcxUMrs7OXK29CAAbIIAhGHb/2Ry97OZjJ9fLzn2YTK3u/QgAGzCJPwDwfh5+EyycMeYPx1kqjunce+J0oMAsEkCEEqY2Zkc+2h8nw7StMmue5MH3iw9CQA3QABCKbc9mqy9XXqKzWvaZHYpOX4uaedLTwPADRCAUNLB55NH3io9xcY1bXcV84nz3QogAGNJAEJph15K1t5Jd2uYEb49TDOdzO1JHj+fLN5XehoAtqDp98f1JCSYMJc+Tr48lfTXk/6IPTLu+jl/j32W7Li99DQAbJEVQBgV+55ITn6V7FrNyKwEXr9K+a6nk5Nfiz+ACWEFEEZN71py4Uzy/RvdSmB/vdAgTbLzQHL0/WTP8UIzALAdBCCMqr9/Tr55Lvnt83SL9b3h7LeZ7l4Pv54cfi1p54azXwCGRgDCKOv3k0vnkgtnk8tfdIdkt+X8wEFgtvPJylPJ6qvJ4uo27AeAUSAAYVxc+Sm5+F5y8d3k6u/dSt2WDg83g6BcT5aPJPe8kNx5Kpm55aaNDMBoEoAwbnrXkl8+SX79NPnj2+SvH5Lev91nzUySftLvpTtk3CSZSppm8P5g9XD21i76lteSldPJ0v1FvgoAZQhAGHe99eTKj8mf33Xb1cvJf/90W9N2h3Xb+e6CjqUHk+WHkvm9gygEoEYCEACgMu4DCABQGQEIAFAZAQgAUBkBCABQGQEIAFAZAQgAUBkBCABQGQEIAFAZAQgAUBkBCABQGQEIAFAZAQgAUBkBCABQGQEIAFAZAQgAUBkBCABQGQEIAFAZAQgAUBkBCABQGQEIAFAZAQgAUBkBCABQGQEIAFAZAQgAUBkBCABQGQEIAFAZAQgAUBkBCABQGQEIAFCZ/wEm4CKTEKbU9QAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1032,7 +1032,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 7 Axes>"
       ]
@@ -1061,7 +1061,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 7 Axes>"
       ]
@@ -1088,7 +1088,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 7 Axes>"
       ]
@@ -1158,7 +1158,7 @@
     {
      "data": {
       "text/plain": [
-       "<tigramite.causal_effects.CausalEffects at 0x7faac02a05e0>"
+       "<tigramite.causal_effects.CausalEffects at 0x7fd3567447f0>"
       ]
      },
      "execution_count": 21,
@@ -1257,7 +1257,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1349,7 +1349,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 7 Axes>"
       ]
@@ -1410,7 +1410,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1460,7 +1460,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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mT57MFJC1tTWboDSppO9Gf1+6dEn2LKV+4N1vKSkpomvOnTuHFi1aGHQJUTsWLFggU4Q89u/fD3t7e7Z46Z3pPXkTdUpKilGLR2hoKMaMGSMzbfOWuuzsbMycORO9evVi3xPp43eG3bp1w8qVK2X9Q9axq1evYvfu3ahVq5ZsvlWoUAFt27ZliQKUuaxG6miOkCKnhU+uJqmwpvEm0tC4cWNMnz5d8d485s+fLyod8f7776NLly4ixUd9JRWc9Mxbt25h6dKlAJSVn9R1y78PD7X1R8IuKioKH330ETw9PWUuNzWLgo2NjWobpDhy5Ahq1KiBU6dOsfHftWsX4uPjZRZt2mzMnz9fVJKDxuXo0aOizReQuymj8aFx/+WXX/DNN9+Izrr++eef8fDhQ1lfKpE6qodl7L0GDRoEQE5oGjZsKKoXmpmZaZJlFJC7i5KTkxUVYuXKlfHll18CyLXqqhFeInX8PIuLi0Pz5s0VXYzUH0S+TFXs0nnGjy1PaoyROrK8RUVFsbXQsGFDODk54dChQ6JrO3bsyOQjERTa7KjFHd+6dQsHDx7E8uXLceDAASQlJcHR0ZHJd2oTLwukSRPSucGTOl72nDp1Cjk5OTIjgCkbieTkZPTv3180b9566y00btxY5i4lnWdojo0ePRoODg6id6F5y69z+uzJkycYOnSo6B5Xr15VPGqUd79KZUjXrl0BqCfG5AfS/lSa++Qt0khdHiEIApYsWQKdTqcaXybdlURFRSElJQW7d++Gr6+v6LuEhAQ2GErZdSQ4tm/fzhbt3bt3AbywstAEj4+Px7Zt2zB+/Hj8+eefrCbVypUr8e+//yIlJQWfffYZ263ygofIzpIlS1g7rKys2LvQpJIKMkOBoko7aV4pZmdniwSCn58fAGXFKq3ifujQIbYz4rF27Vq0bNlSRjI/+eQT7Ny5k70nryh3796NBg0aGCyZMn36dKxcuVJW/oW31Hl6egJ4sfPmv6d34vtPTdCpEYfMzEz2/jQX6P9K4yAIAlNwtNClpE4qAKSWOn9/fyxcuJB9v2PHDuh0OpFSVxqv27dv499//8X8+fNl946JiRH1kXS9zJ8/X1FomeL6HDhwIJo0aYLU1FQZWaB3p/5NSUmRZQKrkToS4qaEGZDl4+rVq8zyUrFiRbi6usLFxUV0rdSiQeCJALmcgNz536VLF2zcuJG9y549e5ji2bJlC+rXr48nT55g/Pjx6NWrl4xIKJG39PR0kXubn6dz5szBxIkT0adPH/z111+q782P46xZs/DWW28ZtdCMHDkSDg4OWLx4MY4cOYKtW7di5MiRaNSokcHYoKioKFVSR3GXPDkjGakUCkHrUC3ZCMjtD2mSjCAIiI6ORrNmzbBq1SqRdbtSpUqMcBgjddRHycnJiI2Nha2tLfz9/dGxY0f4+vqqulU/+ugjpKSkMFl348YNJCUlyVy8/POfPXuGpKQkODg4wNraGunp6TJSl52dLbOAGSJ1hEuXLqFt27ZYtWoVgoODUaZMGdF79+3bV0ZSeSxduhR///03k8O8TJPKSn7dqK2hgIAAAMDy5csZcQZy9WZGRgbi4+OxZcsW0btRPO3NmzexYcMGvP3222wjwYO31Cm51IEXXpr8YMGCBaLi0NKNxnfffSfjC/QeGqnLByZPnsz+LQgCjh49KiIEvPmUkhjs7e3x/vvvy+7Fkzol0ABZWFjICIWFhQUL8gVeKCtPT0+kp6czoTdmzBh06dJFtjD5xc4nShB49ysfqNmjRw+sXr0ap06dEsVkGXPzxMfHyyY53yZahLRgUlJSkJGRgeXLlzPBZywIdOTIkfD19VU8HWT27NnM/bhy5Ur2OQUKGyqGTIqCSj6Qi5z6/PLly6w/y5Yty9pKY0BkgRfqz549Q0REBCIjI0VKVBoATe/MB4fTIidSR+SY75+EhASZpY7en0hdcnIyMjMz2TOlljopKE4kOTkZe/fuxejRo9m8VHKHzpo1S/ZZTEwMMjMz8cEHHyg+Y+bMmTh48KDsczVLUocOHTB79myMGjUK27dvh7+/P+zs7ETrFMh1s/j5+YneLTMzE5cuXcKlS5fQrVs3VWsHjZsppM7e3p49j0hd6dKlFQmFmkLiNzu8cpQWbI6Li0Pv3r3Z37t378b169fx+++/A8gdJ1MsdW3atBGROl4mzZ49G8uWLVOVU/x7ZWRkQKfTsQ2loVMLBEHAhg0bkJaWhtu3b6NTp04YMmQI6zNekUkt0evWrWNxo1LQnOfHmV/bx44dw6pVq9jfJGOlypkn4Js2bZKdXJSTk4ObN2/iwoUL+Pzzz2WkkDapUVFRsLCwgE6nUyR1JDeJ1NExiq1bt8a1a9cMks19+/YhOTkZJUuWxNGjR+Ho6IiKFSuKPA+RkZHQ6XQoWbIknj9/LiN19P6pqak4ceKEIikw5H4FcseS3O8nTpxAZmamqD5nTEwMdu7cySy9UmzatInFhJOsJX2gBOqTu3fvwtPTU3Ed0ZxJT08XyRoXFxdkZmZi1qxZ+PDDD3Hz5k32Hc2TMWPGsBpxSqA5n5iYaHCO03dSY0hWVpbM+sgndM2YMUP1nkBuGFKXLl2QlZUFnU6H3bt3M/loilVUI3UGkJyczGLKiNHzO2s1oU2Ij483GNNFpUZsbW0VSZ1SjEj58uWRkZEh28kuWLBA9TlZWVkygWNpaSlzv0ZGRmL//v0YPXo02rZtK5r4vMJ59OiRTAFKrZSAWFGToKB229vbQ6/Xi3ZKUoHDK5ozZ86wfyuRuoCAAFFtJgI9b9myZYwgS0Hut/bt26NSpUpo06YNkpOTGanjg2xJCfMWN14IECIjI+Hp6Ql3d3fRHOAVDt8+/n5EEomETJw4EVOmTBHFyMXExLC/qd+I1JGwSUpKwuTJk+Hm5oaMjAwRqZMq8Zs3b+LixYvsPTZs2IDVq1ezWB69Xs82B9WqVZMpQb5dqampBktnSAnZpUuX8NVXXylee+LECcyZM4cVHyfwrkhCixYtRPPSzc0NTZo0QZMmTRSJJIEndYaCvmfOnMl20M+ePWPrU/obQRBYVrAaKIuSHwepqyc2NlY05vQcWkvZ2dmyNaMk9C9evMhcwYCyRUkt4YcPg5CSzsePH+OHH35QvF9KSorihoWsy/xnpiZiAbnWysuXL4tI4dixYwHkytF3330Xn3/+OfuO+kNK6hwcHLB06VKULl1a0c2Xk5MjauPff/8t+p7WQnR0NEqVKgUHBwfWD0ePHmXEhCd1z58/Z2SSwhek9+VBZIUqIRD4d4mKioKLiwvc3NwQExOD2NhY6PV6maXu3Llz6NChA3bv3i17zo0bN3Ds2DH2t5TUnT17lrnMb9++DQsLC3h7e4t+T32mhI8//pgRH9IJhtaGlOgqZQLzpJ6v8efi4oKMjAwmC2kTQejevbuMxErbTfP2wYMHRpOgADkpXrlyJZo1a8a8btL2mgrSCatWrWLz+LW21E2ePBne3t4YPHiwSClmZWVh2LBh8Pb2ZoVuCxKurq7s37ywWb58uYyRSyeMFLt378bJkycVv0tJSWExRjY2Nnj48CGbiEDuRFba+aSlpSE9PR2pqaki0kT3UsKWLVuwbNky0WfZ2dlsMtJ9DCUo3L9/HzExMWjZsiUqV64sy4q6desWE9j8OxLIRXLv3j2myKTuG+niJSVy9uxZ0XFwaladOXPmyD6je964cQN+fn6IiYnBnDlzRIuVSB2PYcOGKWZfSd18ANjZm1JLHYFXtNL4Pt69TqC28cJbenpKdHQ0Ez7SmDoidcnJyfj5558B5Cbo0HjHxsaKlKKdnR2GDRvG/ubfgwKR9Xo9nJ2dUb58eVy6dEm1zheV7Slbtiybz2rX0rvy1ihTwe/AefDCk1/LhkDv+/jxY1hZWWHz5s0QBAELFixg8Y1paWmYP38+1q5dCyBXKdH48II2MjISvXr1QsmSJQ3GBL311lt49913RUROidTxa4rWy6+//gogl+RJ3flqbkuaB/z7GoMgCKKjx6Tvc+jQIUybNo0lBtHYL126FM2bNweQuwni1wJZ2nilnleF16hRIwwYMACAeG7x1jeSMbQ2pCUo0tLS8PXXXyM8PJxZX3kQqbO1tYW1tbWIFNPvgVwZ5erqCkdHRyQkJCA7OxsdO3ZEkyZNAKhb6jw8PFC9enVZ4hwANGjQQPS3lHBHR0fj7NmzqFKlCg4cOIBSpUrBxcUFMTExiIyMRKlSpWBjYyOKqSMouVZ//fVXvPvuu6rXtG7dmoVaBAYGomHDhizEgnd9muKOJNmtVrYGkJM6pfvyupnvL7LUlS5dGoBcRx84cEAmg1NTU7FgwQIsXrwYs2bNYnrHWCWG8PBwnD59WjY3aMyvX7+OtLQ0tGnTxuB91ED3tbCweP3dr1evXkVERATOnDmDmjVriirM79+/H2XLlsWZM2eQkpKiaCF6GfALXLqDlJIO6RFZBJrwixYtwpQpUxSv4S1zFhYWCA4OhpeXl+gapfR73qVryhFgBGlJlLS0tDxlLXXo0AGurq6sv6UEMDIyUhb4T9au2NhYpnxWr16Njz76SPEZ0v4ll5E08453JZGSUIN0hz5r1izMnj1b1LdKbt9//vlHRuJLly7NrCX83FiyZAmWLVsmEqB82RBDtdTi4+ORlJSEwYMHy74z5LqPjo5mmx2ppY7ayuPPP/9kz3j+/Lmor1NTU0WnjsTGxjLBRALWwsICmZmZGDduHPR6veo7kdXXwcEBbm5uAGAwASktLU2WpZtfWFhYiAhCdna2olVXCho3yo6dPn06fHx8MGPGDPTr1w+CIMhIZGRkJCMKPGnv3LkzqylpKEHHzs4Ozs7OBkldXFycbKPEIysrS7Zm1Oqb8e1fs2YNixHV6/Wq95cqEKklnOLQtmzZgqysLLi5uaFLly5Yvnw5e161atUU1+uzZ88QEhKCq1evvlR9QH7uuLi4sHkpJdy0aRo4cCDatWuHtLQ09u5qySURERFwd3dH6dKlcffuXVEIwvPnz/HgwQOsXLkSbm5ucHR0RGJiIiMRz549E7m9yVJHpA4AqlevrnjaD++Sv3Hjhmx9REdHw9/fHw8fPoSfnx/c3NxQsmRJPHr0CJmZmShVqpTMUkcwlMX86NEjjB071qDLEQDeffdd1s884SQ5aiiMhsiJoQLD0udLSa20JiZfSNzFxQWCILCNOq+jSXcp1XacMWMGvvrqK8ybNw/NmjUD8EIeKJ0eZW1tjePHj6NNmzbo06eP6DvaXNy6dQvvvvuuInGXQiqvgVydC+TG41MWcqGQurCwMMycORPt2rWDl5cXatasiXbt2mHmzJkFlvbu5+fH3BOdOnUSETdD3/GguDP+PzXwyjsmJoZV35bGYdFioywrNUudKWfS8oQsPDwc2dnZqFmzptHfJSQkMGX+MofJ55XUGUNkZCRT4oTOnTujRIkScHFxEWULqRVWVSJ1giDg1KlTos95N5CxGknSHTqNdXJyMm7cuIEdO3bIBB8f6M/Dw8OD9b2U8Pv5+Ynab+rh6vHx8di6datqQK4aYmJiZNYIPrmHJ1JmZmYs+B6QkzopmjdvzmJeyKKSnJyMjIwMppSVyLSlpSUjgba2tqwfKQ6RB1mhv/vuO1nM57Rp01TbZgjOzs6IjY2Fi4sLGjVqhMTERJOEII0/KZOnT5+KxqNly5bM8kKIjIxk1/AKhne5GIKdnR30er2IyEmtF7GxsQYLMmdnZ8tcomobTR4//vgjIiIiMGrUKNVaYEeOHJHFTEpJHR8XtWvXLgCAj4+PyGJWtWpVkQInmRMREYHPP/8cb7/9NvttfsB7cSwsLJiMJvkqJWyLFi1Cnz59kJ6ezkidIfcrkbrU1FQ4Ojqy70+ePIlPPvkEQK4MIlJHiQ0TJkzA6dOn2fOVSF21atUUY315C3P16tVlGZrBwcEi65CbmxtcXFzY3DNE6qSGAH5z1rNnT6xYscLoySrNmjVjOpDXIdnZ2bh79y4sLCxw9uxZRYvw3Llz8dtvvxm01EnBuzfv3r2LkJAQCIKAIUOGYOLEiaLNLM09JaLevHlzmRVUen9BEGT9rUTqSpQowcqwSMeQ7rdz506TynMBEB23aQimWEPzROrOnj0LLy8v7N69G/Xq1cOHH36IIUOGoF69etizZw9q1ar10lX8gVwhSTtsvV4v2skZ+o7HggULoNfr2X/lypVTfR7P3JOSklCtWjUAkMX5EIH48ccfAUBxl7Vu3ToMHTpU0aRPaNq0KWbOnAkzMzNRJpJacDClUQNiS520HAIgtjTOnj1btQ1paWmKGYe84DIVgiAgKipKRuqMxRxKQURp7ty5sLKyQkBAAM6ePSuLpyJCEhAQgFGjRhm8p9Q9RTu4mJgYNG7cGAMGDJAJn7p16yreS6/XIyMjA0OGDJFVlk9PT2dHCKmRQiXcvn3b6DsowZiljkhdp06dZLW5nj9/LiMDUpCgIjft48ePkZaWxixHRMT4GNNatWqxkgy2trZMGCodUUR99OOPP8pIHa2/vILkgYeHBypWrIiwsDDk5OSgZ8+esmv5qvVKG76YmBjY2tqiXLlyLCieR0REBJ49e4aSJUuKiJmh0iEVK1ZkRWDt7e3h7OwsWoNSRSR1vwJiGZGSkiJruynyl8i/dF3x9+7UqRNTbrNnz4a1tbXM8kibSp1OJyqxxF9XrVo1EfHiT42gOciffGJITiuBZHKvXr2QlpbGCBG1TdqnfBIB6RElj4ggCCJSB4hl4+HDh5kF5tatW4zUhYSEoHTp0li6dCnq1avHrk9OTkZISIjo/dTmOU+UrK2t4e7ujs8++4x9tmrVKtEmjUgd9aebmxvbDFy7dk20vnhS5+vrK2oPWVeNFWz38vJStNTn5OSw2Gdvb29VK/mYMWPydBQYv0a8vLwYAfr4449lRYCJNKslRUl1FABGzglSz4IaqQPEoTs6nQ6CILD2mlr2ZMOGDSaFoHzyyScGYzAJeSJ1EyZMwPDhwxEQEICffvoJ06dPx4wZM/DTTz/h9u3b+PTTTxVThPOKEiVKiALI+Z2foe94TJ8+HfHx8ew/Q1ZEqcWKBK/arpcmlRKpo92f1HXGuzkuXryIW7duoUuXLszUC7yYTPxuDgBWrFjB/p2Zmcl21yS4+MOBeaLXpUsXmUt31KhRqFq1KlJTUxUtdWrE0hCysrIUSV1+8NZbb2HmzJl47733sHv3braj69SpE1vAe/bsQZUqVeDl5YWVK1eKgnaNgRbhzZs3mbIJDAxE1apV8f333wNQF7ZWVlbIzMxUPBnC2tqaud14d4AxUHyU0v3UUK5cOcTExLD2kzLh5yvNJWdnZ9kaiYmJkYUF9OvXTzX5gQdZ6IhA8rXBeEszb6nz8vKS1Sbj30/qXlJSGDExMTh8+LBimygBhtyvLi4usLe3Z1baSZMm4ezZs6JNDr8eExMTZQooKCgILi4uqvEw1Pdubm4mVb4HcpUebbrI/crDFFLHywZSHnSNjY2NoqWQvwcpHh7ffvstI8NKGDNmDNzc3FTlYatWrViFgFKlSom+69u3r+i+ZMmNiIhgGwfe+sDPQWNu+U8++YSNQ+XKlZGamspI3sOHD5GSkiLKhAdyyTTJAPq/EqnLyclBcHAwypcvz0idg4ODYsb3li1b4OTkhMTERJEcbNq0KbsmJiYGT58+RZUqVdhn0r4iSN3w1G41uLm5oWHDhqL78mSDJ6M8qStZsmSeinwTeNLDPyc7O1u2kVciiGlpafDz8xPF4xkCzXNaZ7SJlepJ4IXsWLduHUqVKiVLkKLfDBo0iMXk80lXNjY2snGh8eSJNfUb75UTBEEUq04wFEJhbm6OTz75xKRxMDVGOE+k7tatWwatCiNHjjTJ/G8MzZo1Y0LiyJEjIkuDoe94WFtbw8nJSfSfGqTkhuJNCLzrJSwsDBUrVpSx+8aNGwN4oeyk1gfpoDVo0AC7d+8WKRd6rnSySpUyKcjQ0FCYm5uLglz54sd6vV62k1+1ahXq1Kmj6n6tWrWqrEbfwYMH0aVLF9m1bdu2BZDbfy9L6qh/6P8dOnTApUuX2ELev3+/6KB3UgSGagoquQhJ6PNW2Bs3bqBp06bMNK+2wCwtLVWzmdu2bYsHDx7AzMyM/d5QRnJISAgsLCxUU/v5uBop3NzcRJa6iIgI1KpVS3QNzRlyfys9n8fatWtNch2SgBoxYgSsra1FVk1+3WRlZTFl7uDgICMM/fr1Q8mSJeHs7MzWiqOjIxYsWIBu3brJ+sXFxUUx7mTkyJE4ffo0vv32WyQmJrJgdHt7e2ZldHNzQ8uWLfHdd9+x3/FKMjExUZatGxAQgBIlShiNyTP17NlZs2bh4MGDrP/I/cpDeuJFXFycjNwrxcCRklUqvguI43X4NUobShcXF5QoUUKV1JUsWdJg7B0lRVSsWFG2qS9XrpwoezMtLQ0ODg548uQJwsPD0a5dO9H1/Bwy1vcbNmxgbnyyepJMe/jwoWKmp6WlJetTkgVKmb/Z2dm4d+8eqlWrxvrMwcEB33//vYiY/fbbb+jSpQvKlCmDR48eicJQKOzA1taW6UU+sUPNK6IU00ZrRGmz5+bmJpKNLi4uouuk9UMJ9vb2In1hKvjMXykBlZ5AoWa5fvDggWg9Eijjl5dZdA+pW5jXk/PmzcPYsWNF7lVLS0t06dIFvr6+rEgxrfvOnTsrcpkyZcrI9Al5kPgsW9KrtWvXRnJyMrOcpqenIyUlRUTApJs3HkR6DRE/Aq0zY8gTqfP09DSYmODn5ycjRPlBgwYN4OHhAW9vbwQEBKBPnz7MhdO9e3eEhobC29sbtra2Jr+oIUjJjaurK0aPHs3+5i0E9H7SGBu6nhYyHfdFkBKFpk2bwsLCQuSqowWm0+nYIDdo0EB1RxMWFiYjrPxzypUrp0jcbGxsEBcXpziW7u7uMlLZsGFDUZkA/logNwsuPDzcKKnjaxtJdx2kmGjnV6VKFaSnpyMoKAh2dnawsLAQCUHeEqoUZ3Ds2DHWn7wQVjve5d69e6wfBUGQuX3J9UjWOCnIjcG3UVqKQHo9bQSkGwTAMKlzdXUVWeqePHkiix0jK8Gnn36qas3mx8DOzk6VHPMgoty4cWOkpaWJiABPeKtWrYqyZcvCzs4OVlZWKF26tMgl4ubmhrlz5yIxMZEpCUtLS0ybNg3m5uYikkXKWYls169fH+bm5nB0dMTTp0+xf/9+uLi4sDWj0+kU+5K3MCQmJsrcfrdv34azs7Oi4uXXh6HdM3/O45QpU1C7dm3Wx3Z2djJCyM9NGxsbRUudkiwgazzfZ7xc4d+fxqtPnz6MkFO/KpE6nU4Hc3Nz7N+/HxUrVkT9+vVx6tQp7Ny5k11DirREiRIYMWIEunXrxr6ztLQU9VG1atXw0Ucf4fr16yxTlMc777zDan5K+166cQFys/6Tk5NZtifJuz179mDIkCEAILO20tjzcbHSDQOFWlStWpWNNxECvvg59dnbb7+NO3fu4OjRo0wO0j1zcnLY+uRlkRJpbdq0KQvvUYKSEaNUqVKwsrJCYmIigoKCYG5uLiJ15MnhrXlA7hw01VL3/vvvi+reAblkXzqHpUYdQ0dQKulu8pLxc5ksX1KLKi/XZsyYgV9++UX03iSrmjdvzuYCfa9GaGnd8ESMYkn5vqe5U7lyZdjZ2bG5mp6ejri4OBEPMpbMx7fLEAqF1E2ePBmjRo3C2LFjsXfvXpw/fx4XLlzA3r17MXbsWIwePVo12zOvWLx4Mc6cOYOtW7fCysqKKVgLCwv8/vvvOHPmjChN/2UgdZW6urqKymOUKFECy5cvx/Lly5klTioE3n//fSQkJDBlOnnyZNGOS+pKIEHB765J2JiZmSExMRGZmZm4cuUKUwRlypQRDX5YWBgcHR1Fwo+fQPb29jh69Kgss9LGxgZ+fn4ICQmRTbikpCSZqd/a2lpR4NNnAwcOREZGhlG347Rp01hJCOlzpaSOFvW1a9eY8OMVGk9WlUhdhw4d2KL97LPPWP/TYepSmJubs2sEQUDLli1F5nZra2tZm3kSRIKcHwt3d3f2vkqYMWMGvLy8MHHiRGzatEkkzJWSCwglS5ZEdHQ0m7dKO/tq1apBEAQ0bNhQkdRVrVoVM2fOBJA73wwJHn7spdfRXGnUqBHrg7t376Jy5cr4+OOP2Rm8gDheysnJCS4uLsjOzmZuR95iQvPcwsKCHc2mJIRpDPh+J/crkOvO47/bsWMHrl+/Di8vL+a2T0xMFLlczM3NERsbq0rqeKJFlhfpJs/R0VE09jSvaY3wMYdKqFmzJmJjY0VxkoByEgmFTPBntu7ZswdBQUH4+eefsW3bNvY5n/VKfUz9Su3hZRvNnUqVKuHBgwe4cuUKWrduLZJb9evXB5ArO1xdXUXnBAPidWtvbw93d3cWW8dv9IDcNUNxmTx5vnz5Mm7evCmz4JqZmTFyQpY6qWGBziUmKLldmzRpIioyT5v0KlWqsD6guca3iyd1QO5conVA850sgqVKlRL1LT+3Zs2ahYkTJ+L8+fPo27cvbt68qZiIJ7VsAi82Fg4ODix0hMa2Xr167GQV6fmq9vb2iqROSRZ89tlnsLKyUnQDS+coD0OeO7oXL0epH/k2EKmTJuoorU3+d0qbVPrewcFBUZ4Q6eYJ//z581G/fn2R0YK+pw0T9fesWbOwbt062NrasusNbdAJSqSOH5vDhw+bHKudJ1L3+eefY/PmzfD390ffvn3RokULNG/eHH379oW/vz82b96cr6DvogZPEGjXL7VWjRs3ThQUTovT29sby5Ytg16vl00yfgGTNYJKi9D9+Wt4UmdlZSUigocOHYKvr6/IghgdHW3Utfz222/LjtziJwsJnHnz5mH06NEYOXKkbILZ2NgoEjbpJPPy8sLBgwdl1bSB3AVIyTWNGzeWHXpvCqnj+4MfM2ncBmWz0QL28PAQlcXhMXDgQBw/fhw7duxgC5pPTOF3bLwy//TTT0WWTp7UtWrVCkCuEOetNVKQm7FWrVr46KOPRC5hQ6TO1dVV5H6VZrK2adNGJICUSJ1erxe5vA1Z6QYMGMDiT6SWI3d3dyxevBj//vsvpk6digMHDjCS4ejoKIor4kMSHBwc2C5f6Zgnag+vMHghTO9H85efG+R+BeTWiX79+qFu3bowNzcXkS5+TZCilrpfifjxmXs9e/ZEWFiYKNgfkM9Jah8lbWRmZhr0atSqVQuZmZlMeQQGBiIjIwONGjWSXUvEkp8zZcuWRbVq1fDFF1+IPqd3c3JyYuuD3r18+fKwsLAQWSR4K5uZmRkbF35+EZHgLdPnz59ntTH5cbO0tBSR2cqVKyM4OJhtoMh1OGvWLJEr7+2334ZOp1ONLbO1tUVGRgaSk5MxfPhwUcFwGjfaNPHybcyYMRg/fjxGjBghiqWdNm0aVq1aherVqzNZrRR6QTKwXr16zMJIbv8OHTrg22+/Zeu6ffv2onXG64vvv/9eFPRfu3ZtUYwqzafmzZvLQgWUiBm9o7W1NXr37o2MjAzUrl0bqampbEwtLS0xadIk0e8cHR0VNxuG4t8MkTpDCQA0L3gdSOuVyB3wInbcz89PRGqVZBYvn5Q2+/S9lZWVIqmjMiI8PvzwQxmh/Pbbb0WhI3TfdevWAcgdrwcPHuD58+eKYQAEGlclUrd+/Xq2zpTapYY8lzQZMGAAzp8/j5SUFDx58gRPnjxBSkoKzp8/LwrYf53AEwQHBwfodDqDExV4IRz5g6gNge5HAoYWNE1MV1dXtjiVnt25c2eUL19etriUSF2NGjVExWSl8TC868nBwQE7d+7E1KlTsXLlSjRu3FjmirK2tlZ0M0kXf5UqVdClSxeZ1cLMzAwJCQks1f7ixYui9gEvLBjUNnt7e5QoUQKRkZGi96OzSg25X0k4U1/p9XrVmIWKFSuiffv2KFu2LNzd3SEIgmgBhYaGsvgJfhc4ceJEkWKjf9vb2+P777/Ho0ePVE3qSse2EUj5S3d3vMWrVKlSiIyMREZGhmxcBgwYAB8fH1FblXaKlStXZvONJ0R//vkncwsT9Ho9e46UrOh0OkyaNAmurq6wsrISEWIlkBvf3NyckU3K2pPGNrm7u4vOpiUhbG9vj19++QXAC1LHZ8GXKFGC7e7Vji4CxOPJrwnqL6mlTqkcgq2tLcqUKSOzbqhlEA4ePBg7duxA9+7dFWMECaTQw8PDMXXqVFSvXl3RgmJubs7Ghl8HUjnh4+ODGzdusI2Lo6Mjux/1a79+/eDn5ydyEaplo/IbBXNzczx58kREbJs2bcrkIk/ELC0tZZbA8uXLs3Gka7///nvFhCU1ckHjFxcXB1tbW3z88cf47LPPsGnTJtjY2CApKYmRK36snZyc8NNPP6FLly6yeORRo0ZBp9Oxd1UidbTuzczMmEWQ3Nrm5ub45ptv0LdvX3Tt2lW2kc1PpQFra2tZgp5SgpM0GYTG2sbGBseOHcOUKVOg0+nQtGlTtqmqW7cu9Hq94mbDUKIGyTOy/JsKahsvn8qVK4fw8HDRZnjJkiXYunUrLl++bFS+8GvEEKnLzMwUzYPPP/8c/v7+snNxle5fqVIlzJo1S2TN4+8L5FqtyThUunRpxWNEeSjpCktLS7b+8lLLM9/Fhy0tLeHp6QlPT0+TfMbFGTypk05etdpxpUuXxuLFi7F48WKTnkGDQ4PPE7eQkBAEBASwSWbIaiJtn5OTk8xidufOHVHKu1R48JM5KSkJ77//vohoSM/tVEtGkPaNKXEBam0isstPXp6UEchaygf8qtXuod85Ozurts1YnTsHBwfWVhq74cOHo2bNmqJ5zwdHm5ubi1xhUiFpqEYTPUtqqeOzpD08PBAZGYmnT5/KCLRSJiZZDglbt27F2rVrGanjx/aDDz5gFgMaE2dnZzYWptR9M4Rff/2VlZCRxuNI+ykiIgITJkxgf1N/63Q69m8av08//ZS119LSEiNHjsTmzZsVE3yk9wMgiu2idpQqVYqNh4uLC37//XecPXtW5BajeUV9SIpNjdTpdDr069dPFiMqBa2ttLQ0xRNPCIIgMGtyXFwcJkyYgB9//FEWnN2mTRvUqVOHxeVERETIMpktLS3RqFEjtlnz9PQUyREe0o1J6dKlDW6cCJaWlqhduzb7m/qNTogwVtLEkKUOyA2qp/5as2YNK3TO/44P3uflglrSC1nqlGSFNNwlKipKZv1q1KgRDhw4IFuHeSF15O52dXVlm9wKFSpAEATFDTe9l5IVr169eqI6mk5OTvjtt99w5MiRfJG6+fPnQ6/XGyyFsmPHDtGc7NChAyPL0pAtDw8PWbuHDBmC9PR0xfJEPPg5qNQeSiipU6cOm/fW1tZYsWIFGjZsaDSuOCEhQTGhTKpfpJnsxoxEaqTu4MGDqln/aiiWJ0q8aqiRumfPnim6EgGxhcIUbN261WCQvZubm8j9qgYlUqeWGk+QCmDewqT0fsOHD2dnXBoCn/lo6tFDBOmOm+7Fm7lp58xb6pQUnDFSp2SpoxpTamdeKoEEONWf4gmoUlIGwdfXF/v27cPRo0cRGBhoMGOSBL20tAy9t5WVFSPxgYGBKFOmjMjCwLs7CeSO9PLywtmzZzFo0CA4OzsrkjogN6QgPDyc3cvT07PASJ1Op2P38vT0ZM9u2LCh0crrdK1Op2NjS/dydnZm7r7U1FS4ublh6NChBoU0r5DfffddZjEgK1/Dhg3Z3Pvwww/h7u6Oli1bijYzJIzffvtt/P7774olL4y9D49SpUrBwsICzZo1Y+9miNTl5OSw9W9mZoalS5eqnqMLvIj9K1u2LJu/0jVAynfmzJkGwwBMRffu3dkZspaWloqhHB07doQgCEYzXtWII78WDfUXkJvkQzKWLz9hbm6OAQMGyJ6hZKlbuXKlrFYlkEu6DFni1dpsDJ9++ilu3LiBGjVqwMzMDBMmTFANKQFezEtjfUH4+OOP4eHhgTZt2ogSS+jdDblfJ06ciLi4ONlGgrcgenl54dy5c9i0aRNiYmJw4MAB2NnZQRAEUWINQYmMVqtWjSVSqMGYpa5+/foQBEFkCOEzh3n89ddfssLYNjY2inNQ+pmppE7N/dquXTt07NgRHh4esmQiY9BIHcSkjncL0kHNBYGSJUuie/fuLDhbqf6VIfcrQcn9CuTGG0jTydXQo0cPALm136SWHiBX2XTq1En2ORXX5dvbtGlT1KhRI8/9JH1HcvnxQkiJ1CkpQr6kCw9S/HZ2dqJF89tvvzFilZcyLCSsqV28AGnSpAnGjx+v6IKoWLEiunfvjnfffddoLTiyktauXVuULarT6RASEoKwsDCR2zs6Ohr3799HSEgIIiIiVElFYmIiAgICRPFStEFQskB4eHgwl2bp0qWZhYePdXlZkLUfyM3GNKW+39tvv42VK1eiffv22LJliyiZhdYUX3rA2PN5BAYGIj4+nimDJk2asHmttnHgLXUffvhhnqzVQO7JBHz8V9euXdlRTxQ/a+yederUwYYNG0w6icPJyQn379/H9OnT2dxXy7B9WQJP0Ol0THFbWloyy39+5pIaSec3NsaIjL29PdtASJXvX3/9JSvDwcsRwujRo3HkyBHTG/6S0Ol0olMHli5dqhhfSSDCmNf5uGLFCpF1nNaSKfJ93LhxLKYMyPUYUT97enqiZs2a+Oijj+Di4iIaIyVPnxKpk9ZcVYKxmDopoqOjReuPx8CBA00+l9oYqeMt0ErhWtLfb9u2LV81BAGgYA5dfM1BpO7cuXMFUiLFEN566y1VM3V+3a9ArnuOd9EpgbLUKleujCVLlhhVfubm5iI35zvvvIPk5GRER0ezdyios3fNzMxw/fp1kRVLidQReAIwd+5cTJw4UWaxnDFjBgICAkSlXZydnfHxxx8DyM0QNJUAAC8sGkqkztraWhYsnx/Mnz8f06dPByB2WwEvBAO/CQkODjapjJDSnCIhqaa8eVJXtWpV5OTkmFT2JC8oV64cnj59arIA48t+UJkCQpkyZYxWw+ehlsm7bt06/PvvvyhZsiQj8sbWrBRTp041qQ1t27YVrTHeakbjb6yGlU6nUyyLowayJn/22WdwcHCQZaAS2VKrAUrYuHGjyeNG/URkgy9lUxDo0KEDPvnkE/z2228mERlyqUrnPpVw4WFubo5169bl2WJSlKB+zu/pLIR169Zh0KBBJrmKra2tMXz4cNStWxdhYWEwMzODt7e30TWpZMRQmlfUBh8fH8XCw4B4TZsiC0ytM2kM0jUqLSU2d+5c3L17FwcPHoSDgwM2bdqEkSNHstJn0jlrSt06NWikDi9cApRhVVSggTS0EKWDbWpcRmRkpGinOXHiRKO/uXHjhuiMQSCX0PCEylisgCmgQrjS47nUSF1AQIDI7W1ubg43NzfcuHFDpGTr1q3L2i893gqA0fgMKUjhKiUYFBTMzMwMFnoFxJXo1Y7DMQWGSmoAL0gdkcbCWBt8Md5XDbVY4IoVKzIrGa0vtXICSgSCVyYTJkzAyZMnDbaDdwHypI5cn9Jzhq9du4bjx4/LYrfyCgsLC8XA8EqVKpmkEKXJToZA/UR9bihGi0eZMmUMWqQIOp0ODRs2xG+//WY0ThZ4MZ58fJ8hDB8+3KTr8gO+IHFBoVy5cjh27Bhat279UvextbU1GJeqhCZNmih6gPL6XMLevXvRs2dPNnfUTnkB8m6pKyjQ/CadI90o2djYoHfv3jh48CBsbGzw0UcfsXhP/veNGzfGnDlzDBYsNgaN1CFXuedVwZsKKoppCnQ6HXx8fAy6JaSKyFShlJ/THmrWrKmaKFKQUAuOJkUvJTlqZnhDhyLzdc/yC7KoKFnqCgs9e/YUnVMK5L7L9u3bMXny5DzFcCmhQoUKqnMjrwo4P2jfvj3OnDmjGFdT2CAFYGhOeHl5Yd++fYrhCIBx99bSpUuNtoNfY3wMGxEPaWJNvXr1ULNmzZcmda8S1Nd5XTNKR2apgU8YMYaSJUvi0aNHeTrSrzDw9OnTQltfeSmDUdzAb747dOiAmjVrmmSIMBZTV1jgqxYcOXJE0btEbVOSN3wM5MtahIsdqbt8+TK+/PJLmJmZwd3dHVu3bhUNlI+PD4YOHYoqVarA3NxcdG5bcURoaKjJpA4wvAsB5EJRmlH1JoF2a8aCp02BkqUuryALAJG6wrDUSfHPP/8oWh769+9fICWE6GgzJRw7dkz1BI6CwjfffIPp06fnOfanIEBzwdj86t69u+p3BdHuypUrQxAEnDhxQuTypPhLJeX8ulUcIAVbEO328/NTdJvRZs+UYq+APLyhKFAQJzAVBk6dOqVY/LgoYG9vb3JbjGW/FhbI8DB69GhVD4gppK4g2lzsSB0xXTs7O8yYMQN79uyRZacMGDDA5FIiRQ1Ts2NNBU2Itm3bokaNGkZdaG8CCoLU0aJ5GVInjakryJggNVhYWBQqeTT0Dm+99ZYsC7egodPpioTQ0bMBddeqKSjIsZG6bEqUKFEocYxFgbJly6J8+fIm1fQ0BrXY4QYNGuDu3btGk5E0GEfr1q1f2nVbFCgqS52jo6NRQkbr2BCpK4gYv2JH6qRHEil1wM6dO3HhwgX07duXVbr/r4DOP5w/f36hJ3W8DH744QccPnwYQUFBqoL8wYMHBhcCWagK0lL3MkpYGlOn4fXHzz//XCSuX1NhiNDNmjXrtbHU29jYIDg4uNCfU9ibEA2vFnnNkC6qmDpTQOE7ShtpS0tL/PXXX4plcvKKYkfqCCEhITh27Birb0Ro1KgRO7OvZ8+eaNWqlewoICD3xAH+1AE6FeB1B516UNwxZcoUo+cAGwsQJhJlLHHAFBRETJ0aqXsVblgNhYMvvviiqJuQb3z//fdF3QQNGgoEffr0kcWtPnz4MM+WK3NzcwQEBKBmzZrFTk+S/lDTF3RG78uiyLRRREQE+vbtK/t83759sLCwwNChQ7Fx40ZZDAZfL6dHjx64fv26IqlbsGABvvvuu4JvuIZXBloExc1SJ3UXaqROgwYNGvIPpULK0jNuTQVVjyhuljrSH4UdtlNk2sjDwwNnz56VfZ6dnY1evXph9uzZirERCQkJTMmfOXMGo0aNUrz/9OnTRdkyCQkJRo+g0VC8UJCkjtxY0vMX84LGjRtjz549smw1OsJHgwYNGjQULWiTXVxJXWEbAYrdiRI7duyAr68v5syZg7Zt22L79u0AgJEjR7LvmzRpghYtWqBMmTKqwZzW1tbssHulQ+81FH9QDEJBjZ0gCC9F6qZNm4bg4GBRbcDTp0/j6NGjBdE8DRo0aNBQQChu7lfSZ4VN6nRCcXvzQkJCQgL0ej3i4+M1gveaYPHixfjqq6+QkZHx2pVw0PBm4+rVq7h3716BlJXRoEFDwUKn08HKykoUV1/UOH/+PJo3b47r16/LCu0XJDRSp6HYQhAEJCUlmXxqhgYNGjRo0KDT6dCuXTucOHGiqJsiQkZGxksdAWYKtAhvDcUWOp1OI3QaNGjQoCFPePDggews8OKAwiZ0gEbqNGjQoEGDBg1vEArjPN3XBcUuUUKDBg0aNGjQoEFD3vGfiakTBAGJiYlwdHR8I47d0aBBgwYNGjRo4PGfIXUaNGjQoEGDBg1vMjT3qwYNGjRo0KBBwxsAjdRp0KBBgwYNGjS8AdBInQYNGjRo0KBBwxsAjdRp0KBBgwYNGjS8AdBInQYNGjRo0KBBwxsAjdRp0KBBgwYNGjS8AdBInQYNGjRo0KBBwxsAjdRp0KBBgwYNGjS8AdBInQYNGjRo0KBBwxsAjdRp0KBBgwYNGjS8AdBInQYNGjRo0KBBwxsAjdRp0KBBgwYNGjS8AdBInQYNGjRo0KBBwxsAjdRp0KBBgwYNGjS8AfjPkDpBEJCQkABBEIq6KRo0aNCgQYMGDQWO/wypS0xMhF6vR2JiYlE3RYMGDRo0aNCgocDxnyF1GjRo0FBcEBsbi6tXrxZ1MzRo0PCGQSN1GjRo0PCK0blzZ7z99ttF3QwNGjS8YdBInYY3BoIgwMfHR4ub1CBCTk4OJk+ejKdPnxZ1UxiuXbtW1E34T0IQBNy7d6+om6FBQ6FBI3Ua3hgcPXoU7dq1w+7du4u6KRqKER4/fowlS5Zg8uTJRd0UBjOzXNGrbUBeLbZs2YLq1asjMDCwqJuiQUOhQCN1xRBt27bFunXriroZrx2io6MBoFhZZDQUPYg46XS6Im7JC5ibmwMAUlNTi7gl/y0QmdNkhDqCg4Px4MGDom5GscKmTZug0+lei02YRuoKCF27dkXHjh3z/fvbt2/D398fAHDq1Cl89tlnBdW0YgNBEDBjxgw8evSoUO5P1o+cnJxCub+G1xM0H4oTqaO5mpKSUsQt+W/B0tISAJCRkVHELcmVh9nZ2UXdDBkqVqyIqlWrFnUzihV+/fVXAEBWVlYRt8Q4iiWpu3z5Mry9vdGmTRv0798fmZmZ7DsfHx+UK1cObdu2RYcOHYqwlWIcOnQIR48ezffva9eujcaNGxdgi4ofMjIysGDBAnz44YeFcn+N1GlQQnFQ4FIQwUxOTi7ilhQegoOD8dNPPxV1M0SwsrICkPc5ERISgpUrVxZoW9auXQsLCwukpaUV6H01FDyIzL0OY1UsSV2ZMmVw5MgRnDp1ClWrVsWePXtE3w8YMAA+Pj44fvx40TTQAEJCQoq6CcUWRM4La7djiNTl5OS8FrssDQWP9PR0AC9nqVu2bFmBWvporr7JpG7v3r2YMGFCsdpkkaUur8p54MCBGDNmTIG+y86dOwG8Ohe8lpyTf5BFVSN1+YSHhwfs7OwA5C5CCwsL0fc7d+6Et7c3li9frnqP9PR0JCQkiP4rTNAO8PHjx6rXXLhwAU+ePDF4n+Joji8o0O64sN7REKn78MMPmUDX8N9CQZC6zZs3AwDi4+MLpE2vI6nLycnBmDFjTA6fIJlL/V8cQDIgKSkpT7+jDWlBFq8nOfUqLMmHDx9GgwYN8L///c/k3xRHC/erwNq1a6HT6UR6hHRWcZrLaiiWpI4QEhKCY8eOoVu3buyzRo0aITAwEMePH8fhw4dx+fJlxd8uWLAAer2e/VeuXDmTnjl79mxUq1Ytz20lhWFo19WsWTOjtalCQ0Pz/Gw1ZGZmQq/X48SJEwV2T0OIi4vDL7/8ohpMSkLCVIuZIAh5WkS0CJVI3datW02+jwYx6tati1mzZhV1M/INU0idIAjYv3+/6tz18PAAUHDrkxIl8krqEhMTodPpXtma5hEdHY2VK1fi9OnTJl1PpM4US9R7772X7+SwM2fOoGrVqibJFVLOeSVn1tbWAAqO1AMv5NSrIArkQcqLJykyMrKwmlNgyMnJwZkzZxAbG1tg91y/fj0AManVLHUFgISEBAwdOhQbN24UWVgcHBxgZWUFKysr9OjRA9evX1f8/fTp0xEfH8/+M1UYz5kzB/fv30dcXFye2mtq4LN0oZw+fVqkbO7fv5+n5/JISUmBIAiIjo5GcnIyQkNDkZCQYNCiWZCYNm0axo0bh+DgYMXvaZHwMZKGsGzZMtjY2Jhs2StsS+DLYOfOnTA3NxeRBp1OhyVLlrC/L1y4gF9++aUommcQN2/exNy5c4u6GfkGCWJDpG7btm3o0aMH/v33X8XvC5rUUVvymigRFhYGAPjjjz8KpB15wfPnzwGY3ua8kLr//e9/BpPD4uLisH//fkVSNWfOHDx48IBlvxsCzYW8kjobGxsABUvqXoX1JywsDOvWrctXvHFERERhNavAsHHjRrRu3RpTpkwpsHvSeJA+iY2NzTepu3jxIiOJrwrFktRlZ2dj8ODBmD17NqpXry76jnej0g5NCdbW1nBychL9ZwpatmwJIDcD9fr160yIGkN+hfRvv/0m+vvs2bN5+j0hIyMD9vb2WLZsGdzc3FCtWjVUqVIFAGTu68ICvbtaH/CkzhQLBSkuUxcSLUZDpM5UoRYfH69KPvMTV/PDDz8gJyeHuX3IqsCTuGbNmmHcuHF5vrcGwzBFaVJBWrWxLVmyJICCi5nNr/uV2ke/T09Px7fffvtKXGVE6kxtM8nql7VuPH78GCVKlECPHj0wevRo2fe03qOioozei+aCIVLn5+cnC9dRs9QJgpBvi9arIHX//PMPPvvsM7aZNEV2kb4oKkvdV199ZXKoDLVx+/btJhsLeDx69Ei26aD5mp6ejvj4eLi4uLASL/xY+fj4GDX+NG3aFCNGjMhzu14GxZLU7dixA76+vpgzZw7atm2L7du3Y+TIkey7Jk2aoEWLFihTpgxat25doM92d3cHkFvPqH79+qhUqZLidXPmzEH//v3Z37RoUlJS8PTpU6xevdqk50ndPWvWrFG8LisrC7du3VK9D01McjOGh4ez714VqaOFqDbRadEFBgbCwcHB6IIgwZueno7s7GzcunXLoIvGFEugqaTb2dkZVatWhU6nEynyu3fvwtHREc+ePTPpPgQS4OQmIOVQUHWPCivY2lj7PvroIzRp0kT1+8zMTPj4+BRwq/IG3v2qRn5iYmIA5Fpk7t+/L1O0pAwLqr5ZfrNfpaRu8+bN+O6771jQfV7g6+sLnU5nsnegMC11hsDXTKNx4kFryxQSYoqlrkWLFujbty+A3HetXr06goKCAMhJ3dq1a+Hu7m6SlZDaSBYwU9yvkZGRRtcg3z/Pnj3DF198wTaNtImk9pnixbC1tQUAVZfm3bt3MWTIkELziCxevBhZWVkmyUaepF+/fj3Pni5vb2+ZrqZ7pqeny/qA36B069YNv//+e56e9ypQLEndBx98gJiYGPj4+MDHxwcDBgxgZGf48OG4ePEifH19sWjRogJ/Ng0aJTxI4zSOHDmC27dvY/bs2fj7778xceJECILAfpeSkoKuXbti9OjRoklvyg6pdevWbMFLXUVr165FnTp1VCuh00S8cuWK7DuK38kLZs6ciUGDBqFixYrYsGGDSb8hUkfCXwqpQr1x4wb799y5c2XxkaQUbt++DQsLC9SpU8egi4bub0iJKAlzQRCQlZWFDRs2YPr06WwhE5njk1uCg4ORkpJiMCFGCTSPYmNjcf78eRZ4HxMT89KWjLCwMNjZ2cmyxAsCxgLKN2/ejEuXLql+P2/ePLRr107RwpWQkICmTZvi/v37yMnJYW572iEXFKh/N23aBGtra0UlSmQhMTER1apVw9SpU9l3qampjMwVVGIDyYa83k/qSqY1YmyNBwYGijZ6AHDy5EkAMLhZ5EF99DKk7uHDhzI5YIwc8HOQCIfS76WWuhMnTkCn04m8LcYsdSSn/fz8AOTKqHv37jGyIJ2XFF9oqlWrfPny8PT0FLWb2pSQkICAgAB2bXJyMtzd3UUhGhkZGWjYsCGuXr0KADh37hyqVq3Kyml98803+PXXX3H79m0AL/qO1p8pCSK0YVDbdE+fPh1bt26Fn5+fbE4pISsrCwsXLjSZ3JMRIiQkBAsWLDBI7vi13Lhx4zzFwwuCgGfPnslCKnhSJ53rtP4EQUBycrKqrjMVd+7cMZo8mVcUS1JXlKCJxyttXiF16tQJtWvXZn8vW7YMGRkZIksdpY7zE0JNcfPkrXz58qLPBUHA+vXrkZSUxITQ33//rXgfQ7u9/Fjq7ty5g6CgIAQHB2P48OEm/YZIndoOTyrMSTBFRUVh1qxZMsJGSoEXdMALS1xOTg779507d5jiNUSSlIRa8+bNYWlpieHDh2PhwoX4/vvvRd+3aNGCEU6aH6buzAkkwOPi4tC8eXNMnDgRQK7g7tWrl+jaESNG5ImgkVAiBSMIAvz8/FQVV2pqqmrcoxRKWePffvstbt68Kfrs3r17WLhwIfv7xo0byMnJYQpViaT5+/vj4sWLuHv3Ln788UdUrFgRCQkJGDBgAJydnU1qnymQrg3p5uHatWvMwn337l32PoQOHTpg+/btAAqO1NE8ymtWPs1tUrx0H3IPKuHhw4eoUaMGPvnkE8XvTQ0nMOR+vXPnjmzd0fzjP69SpQrzuhB4Za+0aeVjuyi2jQe1R0rqDhw4AEAsv6ktauSG2pKUlIS7d+/Kwm+k85jktynyID09nc3FDRs24OLFi6I29evXD7Vq1cL9+/chCALrvwULFuDrr7+GIAgICwvDlStX8OOPPwJ40V9EOqUEWUrqTJlv1J4ff/wRrq6uAHJl9/jx43H58mXo9XoAuVau0qVLIzIyEmfOnAGQK3t+//13kbfk9u3bmD59usnJPVT5Yvjw4ZgxYwZbk0rIr+v6/Pnz+OOPP5CVlSUbOz6mTipD6QhK0mVEfH/++Wd89dVXqs9T2tTm5OSgZs2aeOeddwDkbkKGDBnCvhcEIV+hPhqpk0BqqQOAChUqmPQbQEzkeOGntkvhdyGVK1cWfff48WOMGDECQ4cOZfdVs9QZIjKmWOp8fX1FpCshISHPGUX0+02bNkEQBCxatEg0maUWHbIUkHuOYgAJau8UExODoKAgVK9enSmzmjVr4ocffjD4O0BZmF+4cEH0t5KA3rZtG4AX4xgTE4PQ0FBkZ2fjwoULimUewsPDmZuWt9RJceTIEdHf69evR+/evfHs2bM8xXDRXPLz80OLFi3Qp08f1KtXT5atOHDgQFSsWFHx91KhL1VigYGB+O6779CzZ0/R57Vr18b06dMhCAJCQ0NRr149/PTTTwaLvVKSU1paGmtjTEwMU8YFdT6nVPCTAiLwBJX+XaZMGfYZWW0AdSvVxIkTZUI9OztbNRSA5pEplg6l30lJnZpyS0lJYYlS1JZ9+/bh3XffZQpDyaWpBDX368KFC1GzZk2sXbtW9LnUUkfPk7rj+fvVqFEDQC7RVuojJUsdrTGptYzuy8dnUT9JZURERASuXLkiktPt2rUTkXsA+Pzzz0XtJVIXGRmJu3fv4uDBgwBy17TUa8In9fEbZWoTeS6qVauGOXPmMP3x/PlzzJs3DxkZGaxPSe5Re8eMGYNz586xPiY5R6TEVFLHVxwICQlhc+P27dv4+eef0aJFC5k+8fLyQuvWrREdHQ0fHx8MGzYMq1atYt/TM6V9qQZ7e3sAuZsRvn/4+9E4KMl6tTj44OBgzJkzB4IgoHnz5qwIvnQzwFvqpKRuxYoVuHTpEnsuycfx48dj8eLFqu8k5RAnTpxArVq1ALwghgMGDMDWrVshCAJOnTqFRo0awdHR0aAnRAkaqft/pKWlITs7W9FSR1BzE/BEgTelmkLq+HtKLXUkRPfs2cMmqnSSlS1bFoMGDTJIZKTPzszMxKNHj/Dnn38CyN1lt2zZEitWrGDXxMfHi4S9mtLIycnB9OnT8eeff7K2nThxAgcOHMCUKVMwbNgwALmKdMyYMaLfHjp0CCtWrGCxic+ePYOnp6csXk3qBoiKisJbb72FBw8eQBAEWRzFunXrVN130v5TMu0rmdRJWFJfPnnyBOXLl8f333+PZs2ayQg5AJQuXZplTRoidW5ubrLPnJ2dUbZsWaMbCkDuqud37zdu3MDs2bNF30tJJOGHH36AXq9nbY2KihL1Y1BQEFO6qamporlLpO3u3bus/65du2awLhgpufT0dEY4nj9/jrfeegtArmuJhyAI6NWrFzp16pSn01ukc1dKFvn3IFfkiRMnZBZi4MWaPnv2rOh3y5Ytkwn1tm3bKo4fT/aUFFCjRo2g0+kUw0to/klLKJ06dYrN0cjISLZmJk2ahJ9//ln0nsOGDcOxY8fY+jYlwQB4Qf54uZaQkIAZM2Yo3kdK6nhlSbh9+7YsLik9PR0NGjRA7dq1ERwcLLLUSUkdZfsD8mxNIl/btm1jJ1uQnOTlZWhoKMqUKYOGDRuKCHxqaqrixoLkU0hICEvmioyMhJeXFyu/1alTJzRs2FD0OxqTUaNGiT4PCgqCTqcTtf/GjRsyy2pKSgrrY7JY8oT3zz//ZPKM+p7WHVnmDZE6X19f1Zhu8gYIgiDrZ1rvu3btYvOa3xjTM4OCgnDhwgWjxJIsdfSc+Ph47N27l8mYjRs3YtWqVdi/fz/S09Ph4OAg+j2VLxMEQbRhb9GiBWbPni0j/9TWGzduiEjtnDlzFPXIrVu3ZKRO2heAnC/wVrcOHTowCyQle/LenLZt2+LKlStISUlRLdumBo3U/T9sbW3Rq1cvpKamonLlyookTM1yVbZsWfZvXhGpkbqTJ09i586dCAsLE9VPc3R0ZP/W6XSi51GWrHRBPHnyBH/99ZdBM/T27dvx7bffonv37ujbty+GDRuGypUrY/Dgwbh58yazktDun57DT9jExER07dqVKVxCWFgYFi5ciMGDB4sIE1lDaCLv3btX9Dt7e3tkZmZi7Nix7LPTp08jIiKCWWoIUpIlVR5KcRTvv/8+vv76a1EbADm5UOo3Q6SOFAUJSaVs5WrVqsmymqkvle5NyTk8SpUqxcjV06dPmbA8e/YsG69Dhw5Bp9OxOUECnXa4RBj4cQVeWGyk7079npqaivj4eJQqVUpUymTHjh2ieyhZeGrWrMkssImJicz1HxoaKrOA8qSOBHZMTAzro7i4OMTHx+PLL79Eeno6fH19sXfvXhw5csSkc5bj4+Px/fffy6yoUutnamoqzM3NYWNjw5T4gwcP2E6ax9WrV6HT6eDt7Y2ffvoJOTk5qta7s2fPIjw8XKZEaP66uLiwMXr06BELRyAhzpdpePLkCT755BPWhzSmNK9WrlzJagkOHDgQ06ZNY7/j+wN4IZfoO1NJHW+pEwQBd+/eFQXySzdMUguNEqlTKkdB7Xr48CGaNGmiGnPk6+uLVatWsbnz8OFDPH78GJcuXUJCQgJzmS9duhQTJkwQPZuXx/7+/mx98xvEsmXLMhcp8EI+Uz9++umn7Dt+jPkCv4IgYOzYsXBwcGCbZnK3Ec6fPy97t507d8os7FFRUYw0JiUlYdu2baJ4MHd3d/YeUlJHY0P/X7ZsGXtuZmYmEhMT0bJlS8Xs4qysLDZPbWxsVK3LoaGhzDLPjzFP6po1a4b3339f9tunT5/i/fffx5YtW9hvaV1duHABvXr1wrx58wC8IORZWVlIT09HiRIlFNtz4cIFNGvWDEFBQUhPT2fhOdLQkaioKJw/fx716tXDP//8w8jVrl27cPjwYdl9ly9fzt4pPj5epFP4e0vXFd9u6ecpKSlM30dHRzMXN5D3EI1iS+omT54Mb29vDB48WOS6ycrKwrBhw+Dt7Y3x48cXyLNoEh04cABpaWnw8vJSvM6Ym8Lc3JxlSQG5wlMQBMyZM0dk+Wvfvj369u2Lzz//XPR7fseRlZWlWBtMbYCNBduvWbMGBw4cwM6dO5krEcgtLEsWOz5eRboDSUxMxKFDhxAUFIRJkyYByK3Bw5uGDx06xP597NgxALkWBJ1OJ7O61K9fX0Y2CKTcCFIiZEr8yokTJzBv3jykpaWJyLVU8SjFnSmNMykuUgZE6vjdGO3g79+/L5qbgiCw/lTa+VPcCg8qoQHkugJnz56NR48ewdvbG/369QPwokimVOkRiSHydvLkSVGtQqngl36enJzMrAr8mPLHDGVlZYmy7njQGZl79uzB0qVLAQBDhw6Fm5sb5s6diwcPHiAzM5NZwnhSFxERwfoxPj4ea9aswfLly3H48GGRdY5280Du/FizZg2++eYbZGVlYdq0aQgLC8PixYvxzTffiCzQAGTxhCkpKbCzs4ONjY3MTSy15BJhBnJllF6vlxH4CRMm4Pjx46yMktR9QtaDXr16MWVZuXJlg4XJ//e//2Hjxo2MrJGljp+rRGKCgoLYfXk3O40pveM///wDQJnUPXnyRJTIlJ6eziwnKSkpmDRpEry8vFgcpb29vUhm8GP61Vdf4cmTJ4qkTilmiCcqkZGRonl27NgxREZGIj4+Hi1btmTW//r16+PBgweoXr06mjRpglmzZim6/JUsdXy7aS1ZWlri6dOnuHPnDvuO9IKUMAFiK+HQoUPZvxMTE7FixQokJydj3759AIB69eqJ2mRqTdSDBw+yd9q6dSs++OADlnBFbSbCQHJNuomld504cSKaN28OIHceSkt+8booLS2NjUlSUhLCwsLw5ZdfYsCAAaLfBAQEMLcyP8bUFtrEKW2E9+7di927d2PFihWy/qBNGM1pmvPh4eFIT09Xjb+l60JDQ0U6RXq8aHR0tGIsLaCc7X79+nVs2rQJQG5/8r95+vQp03mnTp0S/Y5IqpRUxsXFibx0S5cuRU5ODn788UdUqFAhz0ljxZLUXb16FREREThz5gxq1qzJhA8A7N+/H2XLlsWZM2eQkpICX1/fl34eH+sQHBxslNRRkLsULi4uAF4cGZacnIzIyEjMnj0b7733nux6qdChWAICTQpy4QHqpM5YwCgfSC2NiaBJxu9epc/hrYakqJs2bcpS/4FcAT1w4ECMHTtWNnGlrqySJUsqxnUBuaSAF6ZSUmdqNi6QS7T430tJHClJHkrWNCJvhkhdo0aN2DjwwpR3L1KgLQBm8YmKikLv3r1Fz5PGYt2+fZsJHiIa9CwSglJLHY9Fixbhu+++Ex2wPm/ePBFR4q2RSu5S3i0eHx+PESNGiMgnwVDszKxZszB69GjcvXuXzf9r164xq8GFCxfYe8TFxYnWEr8x4utTfvHFFxg1ahS+//57/Pvvv/jhhx/wxRdfqAYZh4aGir5LTU2Fra2tYryWMTdvUlISvvjiC/a3IAhYuXIlFi5cyO4ntWz4+fmhdOnSaNWqFSIiIkRrTUoidTodjh8/LnNrZWdnM2sZ4cGDB8jKykJ4eDhiYmLw999/45dffoGVlRUmT56sqhzOnTsnkx+1atUSkY9KlSoxeZScnIxly5YBeLEWK1euLHoPqfy4dOkS/P39AYhlFR+7SJCSbt7Seu/ePbi7u8sUeZMmTRAcHMz6Scl6mp2drWipS0hIYGNFmabDhg1DbGwsrKysmGWNLOrUjzz54EMa+E2ndIMK5PYV71o1NabRWDmdxMRE1u9KxJPaLJ1j/MaNQBm6QO548e7XqKgotGrVSlRj08rKCrt27WIEXBqfDbzol/T0dNlGlObGjRs3ZG2me5KsJTkQFhaGtLQ00VwYMmQILCwscP/+fXaf8PBwXLhwAdbW1rCyspLF1CYnJ7Nxmjlzpug7paxmb29v0bGBvCEnLi6OhRMMHDhQ9Ls7d+4gMjJSZpm9f/++aA6sXr0aiYmJKFOmDPR6Pf755x9YWVmZnBRS4KQuNDRUNcvKVPj5+TES1KlTJxFxM/RdfiGNn+NJFOHvv/9mO1qeVfMgBUeCKiUlxWD6Py+snJ2dZaSO/w7I3YnlxVLXpEkTVryZt8JJSR0RNhJ0mZmZMvezWnyIFBcuXEClSpVkJmZpux0cHGSFpYHc+DJBEERlFqQki1e048aNE2UjS/Hs2TMRGYmKimICOCIiQmZlUXoekEt4Hj9+zBYfKR5+fMPCwhTHhywoY8aMQXR0NNzc3DBz5kw8ePAA9erVw+3bt2XZriTkCGXLlmWnHdCumhY5b2m5fv06/Pz8ZHM4MjIS3377LXNDAbluhI4dOyInJwfDhw9nBCE5OVmRAEhjF2/fvm2SG1SKmJgYtpGysbERhSCsXbsW9vb2aNCgAeLj45myJVJHa4ziZrKyskRWEtrN79mzB7t27ZI9u1y5ckhPTxcRrdTUVGapk4LP6DX13TIyMnDixAk270aMGCFyXd++fRv169dH+/btIQgCpk+fzr5TItPLly+XhX6kp6fj1q1bMsvvvXv3kJOTg/Pnz7NY1bZt26JKlSqIj4+XKfTLly/j8ePHTNHFxMRgxIgRbPw///xz3Lp1S9RfSklBlSpVMkjqevfuzVyHvGzgNwW04ZAePJ+WloY//vjD4Drv0KGD6G+lWMXnz58zORkYGIhevXqxE4eklioiNRMnTkTdunUB5IZEAC9cbvfu3cOaNWtw8uRJkQ7hN3pt27YV3dfCwgJmZmaiepumnlKiFog/bdo0ODs7IzExkcllNVLH9wEgJnSOjo5sDfA6Lj09HZGRkaJNT6NGjUR9JiXnSu5XHrw1E8gtxVWuXDnFsCfaJP7+++84deoUk73Hjh1DamqqyP3avHlzZGVloVq1amzOhoeHIywsDOXLl0eVKlVw7tw5WRyylOgRlBLV2rZty+ZXXFwcgoKC4OrqCldXV8TFxYk8UBSDDOSGGlSpUkX1FCwpypYtCycnJwQFBSEzMxOdOnUyiVsVOKl7/vz5Sxfki4uLYxNGr9eLlKyh73ikp6cjISFB9J8a+vfvL8rms7W1ZRYCstr1798fy5cvh4ODg2JgO/0OyA2QB3IVkdIkJYteUFAQmjRpgvv37+Phw4eqpI7et1KlSiJLEy+g+YVKcUzW1tZsd8RPYjVX7YwZM9C9e3eT3AFqJTFmzZqlWrCZR2xsrKLbkWL2eCLGj7E0fsLe3h4HDhxglsiWLVuKsvCaNm3KBK6VlRWWLFmCTp064eTJk6qLSyl5IicnB5UqVWJB50TmSPl16NABlpaWivORkkXef/999OjRAwMGDMDcuXNRqVIlWZCvGrZt28Z2xtKYOBpjQRBQv359ALmnU/BQy8K0t7dHTEwMNmzYIIq54tcL9S2Rp3bt2mHQoEEAoGrVtrW1ZddIERAQgKtXr6JixYpwcXFBcnIyatSogRo1aiArKwsffPABPDw8EB8fz+bqmDFjcPPmTXz22Wfo06cPsrOz8b///Q+WlpaiUgm85UqpFEKbNm0AvCAOe/fuxfz582Fra6tI6vJaOJmspFIr4Z49exATE4Nx48bh1q1bqF69OipUqICWLVsyd7Wzs7OinOKVNSE9PV1kzSbUrFlT9pmjoyP0ej2ys7NFiQAA2MaK5tA///wjOtZo1apVqFOnDvvbwsJCsfB2qVKlRG2ndzKEdevWieLGyHIvzUYHcuezofO73333XVbmA8h18UlPG4qKihKRjb1797I+52OYgFx5/+233+K7775jGyRnZ2dYWFggISEBvr6+yM7ORqtWrUSKmwcfI00hAERoecXPb0q8vLxkyQr8Rgx4QS4JlFTCx0HTWPD6wsLCAjExMaK51LVrV/Zve3t75pLlE7/S0tIQGxsrkuvly5cXeX9I5xEyMjJw9+5djB8/Hv7+/oqGEiB300C1BDt16qR4De9+nzdvHsLDw+Hp6Ynbt2/j+PHjon7miSeFFUVERCAiIgLu7u4scUk6N4KCglC+fHl07doVffr0YZ8rWer4zUV8fDwCAwNRvXp1ODs7y0iddLOelJSE+/fvK8brSsMvqlevLpqXPj4+qv3II8+kbt++fQb/oyDpl0GJEiXYpIyLi2MkyNh3PBYsWAC9Xs/+MyQQAHEMAS/gmzZtyj4/efIk5s+fL0qMILRp04ZdS7u85ORkRUsdr/ibNGmCKlWqoESJEqoKnghZ5cqVkZKSwiwbvIDi/00LTK/XMwuBqbvBAwcOKJrjpeBdTkBufF16ejo+/vhjxUxQKR49eqQYC1G5cmWYmZmJBB3fX9Lxtre3R4UKFViGp7W1tYx0X7hwATqdDtWrV2eWtr/++kuxULMa1IgwCci3334bmZmZaNy4MfuOyBzB0dERe/bsEbktiMg7OjrKFC4P3lJDMYJSSx1vLeDnrSFUrVpVZgVKSUkRWeoqV67MhMtnn32GEydOsLFTK/bZtm1bUc0lHmlpaTh8+DDq1q3L1pmLiwvGjx+PkSNHYvHixdDr9YiPj2drPTMzE7GxsahQoQKsra2RlpaGjRs3yu5tqKYVkBvPVLJkSfj7+yMjI4PVTDMzM5OROt4FZSqIaElJwqFDh+Dq6opffvkFjx8/ZmSKd9s5ODgonmGZmJioaKkzpZAskEswabzoGESCvb296mZECWobWicnJzZnHj58yEI0DEFal5IUrjQG8b333kOVKlUMup/0ej0mT57MZFdSUpLMehcdHa0aViC11JUtWxbffPMNrKysmO7IyMhg8/LUqVNwc3ODl5cXSpUqpVgLlF/n0nJNQO64SOdJ06ZNZfKTt+R6eHgwiyehdOnScHR0REJCgqx0CU/qvLy8kJCQoJoYY2try2IU+c1ap06dEBUVxUidTqeTWbqk75GamorBgwfj559/xr///ou4uDhmlfX29mbWTCp9EhMTgwYNGrDfSxPyCFWrVkVERARmzpzJjAI8ueRjbWkeUWkpntRJ+UBycjJ69OiBAwcO4K+//lJ8NoEnZFlZWbh79y4qVarESB1viZb2C5BLUvl3JVy+fBk7duzA5MmTsXPnTnh4eMh+37lzZ4NtA/JB6nr16oXevXujV69eiv+pxZvlBc2aNWMutiNHjogEkaHveEyfPp2Z1uPj442SGqlAJ8YvjRnq2LEjWrduLTt4ffz48UwJuLi4wNbWVpXUAS/YPi+olCx1ZmZmLD6BFvuQIUMQEREhSgDgLWe0637nnXfQoUMHbNiwIU9FU2mHYwi8uVqn06Fhw4bMummKpa5GjRqKpK558+ZwcnISWQN4Vwq/KwPEpAjItcZJFc+tW7fg5uYm2uFeu3YtT4eiq50gQYqMXDS8EB01ahQGDx4sartUGBKRL1OmDLOyKYGvtUVzSkrqSIh9+OGH7F61atVic1lt0yBV6FL3q16vZ+SN7kVCWS0UgWosSUHzPiAgAJUqVWIC2cXFBaNGjcLq1avZRoxObuFRpkwZ2NjYIC0tTeQaoaN+SDaMGzcOixcvxvHjx5GSksIsdLVr10bdunUREBCA/v37s3mWlpYmi6lTivcCXsTMKoFIpVQuSd3ZZCmwt7fHnDlzWCas0tqLioqSBV1TDS2+ze3bt1dsU3p6umic+Fg5nU4HFxcXNgfIZbVo0SLF8hakSKVr18nJiREJUwtbS0EKV2pVJi+KoWQwMzMz6HQ6dO7cmW38pNajqKgohIWFyaw0165dkylPvl+JAERFRYlIXevWraHT6WBmZqY4V/h7SNsC5Pa9dF7Y2trK5BevF27fvi1bV6VLl4aTkxPu3bvHNpkPHjxAcHAw0tLSmA4joqYmy2xsbNCnTx9ERUWx9QLkuj+Dg4OZXFfK1peG9OzcuVO0aU5LS2Mbmfr168usvYIgiPpIiQQDubIvMzMTHh4eTJbwpE4pLpaSr9zd3dk6qFOnDj766CPRdTQHpATd1tYWnp6eWLp0KY4dO6ZYT9bFxYWROj4+jieZ/PVqsr5fv35YtGgRyxDmNxt6vZ5ZUg0hz6TO09MTO3fuRE5OjuJ/ebF+qKFBgwbw8PCAt7c3AgIC0KdPH7aj7t69O0JDQ+Ht7Q1bW1vVl7S2toaTk5PoP0PgSV1iYiL7m3cROjk5MYHQo0cP0e/t7e0ZqXNwcIC9vb2q+xXIjYNwd3cXmXqVSF1sbCwjdbwgHTRokIgwzpgxAzqdDp9//jn+/vtvTJ48mfWZsXeXwlhdHOkuw87OTmRydnJyUrWgArmm7i1btogEaenSpdG7d28MGzYMTk5OslpIBKnwoL9J0Ol0OplQvHHjBtzd3UWu20uXLinWIQNyXQLSgFm1TE/alUnjA8uWLYtGjRqJiKPSOBDR8vDwUDwVwN/fH3Xq1BEpOjoRQnquY0REBFxcXLBp0yb2rKpVqzIBTUqTspeBXPfC/PnzRc+kUiAEvV7PBC4JqSlTpqBTp06KwmnMmDGYPHmyKqkjYluqVCkRqeOh1+sVXR/u7u6wsbFBcHAwLl26BCcnJ9jY2MhiTZYvX45Jkyahffv2sLW1ZePXokULlCxZErGxsaIyO2lpaTKyRopaGk9q6PSGM2fOoGzZssxiu3fvXpm7DBAr+a+//lp0LBkAlogA5Mb1SF2tROr4PlZzeaalpaFWrVqsPpqU1EhJXYMGDTB58mRR6Q2KnSS5x/eBr68v9Ho9wsPDERUVJTuLVEmu8Qk7BGdnZ8W+bdSoEXtnKd555x2Z5YnkJJ+IZW5ujsDAQHaWK4+bN2/CyclJFP/Gb6J4UkcbzosXL4qIj5L3hlfoxk5IadeuHQBlUsfrJhcXFzbmDRs2RFBQECpXrgwnJyd2NFj79u3h4+PD3p/uR6Ruy5Ytim0g156rq6siSTVE6oyd01q3bl1Ur14djo6OKFOmDO7duyfa8AJit7JUdjRo0AClSpVi/MLT05OFGvD9o0TqwsPDERERAQ8PDzYmderUwYYNG0S6nmSmdOO9bds2PH36FBMmTECHDh1kz4iJiUGJEiWYZ4yynNXaAwBdunQR/a1WloXXkR07djTpdKg8k7qGDRsaJG50vNXLYvHixThz5gy2bt0KKysrtmu0sLDA77//jjNnzrD4poKAlNTRYPCkrmHDhoy88IseyF3AROoyMjIYqZNa6kiJly1bFhEREaJAc1IqfK0gJycnRuoo5mD48OHw9/dXtL6tWLECXbp0waJFi9g78QvEmHWqRo0aqtlYJ0+ehI+Pj0wgKwltQ9a6atWqwcHBQSToateujV27dsHGxkZmqeMhLYNCc41PHqAxq1mzJiwsLBAREQE3NzdGHPixI+LLuytXrFiB77//XmRx4C29SrFX0liH+/fvywioEskhIVO6dGnodDpMnDgRFy9exIULF5CUlISGDRuKFN3kyZMB5CosCljns+2qVq0KnU7H2liyZElZrCf//uHh4di/f7+oTVu3bhVlpzk5OTGhQ+2tXLky/v33X0Wh9euvv8LJyUnRMjhmzBimJA2ROn5u8GSLSF14eDgyMjKwaNEipKSkiN5p3Lhxsudu3rwZkyZNYu8idWempqbKstFJsUlJgCH5du7cOfTu3RvTpk3DypUr0a1bN0U3rlRxSwmlEkngkZ6ejpiYGDg4OKBVq1Zwc3MTPad58+aMCJKFi2J2pFnnLi4uWLZsGTp37ownT56w+/Cb1MOHDyM6OlrkruafRUSxUaNGIre4m5ubomWTjxMbPHgwdu/eDZ1OJyPA77//PiPVSqSuUaNGsthcGkdeBpUsWZLpLbI6jx07ls1fJycnVoYJECt2WjdkjfH390dmZqYoBkrJYm1nZ4e//voLkydPNkrqSDbY2tqKvEPe3t4ymcdfS+9Cn9nb28sstjTX6Fq+mgTB1tZWRPaU3OwVKlSAl5eXomtdmhgnxYULF9ChQwd4e3vDw8MDOTk5Mqu0m5sbunbtimrVqjH5RaSmevXqqFOnDquq4OHhwfpczf1KCAkJQXR0NNzd3dGqVSsAucTX3NwcDg4ObP4obbr/+OMPGQFTQokSJWQJROvWrYOlpaWMiA0fPlzk3g4ICFBN1KB5U7t2bVFZKkPIM6n76quv0KJFC9Xvq1atWiBxda8avKIeNmwY+5snLLRjBHIVktTKRkIwJSUFer0esbGxMkvd3r17Ua9ePcWYASLE0iwnstI0bdoUgiCgU6dOSExMZO4nSkxRUzY8mSArG7mrpJDWUOJRvnx5tGnTRqakpAQXMM0Fyy8i3l2l1+tlljoSdGpHnjVp0gTt2rXDwIED2UKoWrUqE07Ozs6MOPBnrVLxUmmauZmZmaprUckNJCVsShYHtUQYACz+bMmSJWjcuDGaNGnCrqd71apVC97e3uw3NN48CSe3BfVXx44dmeKiNlpaWmLhwoWiws88pAU39Xo961MloakG3nq6cOFCZGRkoFWrVowAuLm5iWLqePBxKzzZ4okgkCtMpTtrJeHXrl07tq6USF1aWhrLpCchTMpcaplQSzohfPrpp7CxscHo0aNhZmbGxocX3NKwDinxUXLXtWjRgr37yZMn8dNPP8HBwQFnzpzBs2fPRHPQ19eXkVGyrhFRlCoZ+vvw4cM4cuQII7M0b8iKVLJkSdYuKens1q0bBg0ahJCQEFFFAp1Oh23btrEjmZTw8ccfszVJ8WnUhqVLl7I1r0TqlOYjrU/eTebq6spIHfWLtbU1u0av16vKFgsLC5w4cQJr165FqVKlWGY+b/FUii21s7PDwIEDsWjRIoMJUU2bNhURNfIyKR3xB7xYV7zO6tChA2rVqoX169fLTjEhUqKW1ATkbhZ565yZmZnMTevq6oqAgABFN/+vv/4q8gBs3boV3t7e2L59O/z9/WFjY4OhQ4fi4MGDqnK1VKlSOHDgAIKCgpjepDZ7eHiI5nfZsmWZHuL1ntImMyUlBTk5OWjcuDEaNWoEQRBE+onuqxT/NnjwYJOsY1Ir88KFCxVr9lHbCW+99Ra8vLwUEyeAF4lP5cqVMylJAsgHqfP29lbNUgFyFRdvln5dQAvk559/RpkyZdjk4IUtb1UzNzcX7Xjs7e2ZYrKxsUHp0qWxatUqZlkBcklM+/btce3aNYMDJBVUJPCoLaS4addiqGgpICYcFStWRHx8vCxImSAldba2trhz5w5GjRrFFqN0Z6xUD8yUOcBbJnl3p5Kljiq7SxU4LeiyZcvixIkTGDFiBMzMzHDq1Cls3ryZKWS9Xs+Efc+ePdGxY0d07NgROp1OkZQqwVCyjSlZrErFlvv164eTJ08aDIAlYUFufUL58uVlgprmRoUKFRAdHY3+/fuz53bu3BljxozBl19+ialTp8rGmlcSvIBXstSZApp3FhYWmDp1KutnaqMhS500e5dvI2+B5rPWW7ZsadK8U7PUdevWDTqdjilraj8RWpp76enpii6of/75B19//bWsX8mNyW9cpASC1kLDhg3x5MkTVKlSBVZWViw7sWnTpjhx4gTS0tJEa5csJBS8XrFiRcyZMwdA7nxLSUlhyRektKTznchm//798eWXX7KTKYDcUAk+cNzc3By7d+8W1VskqG3k3nvvPdHpCzySk5NFCQ2lSpVCtWrVmCLl55vSZkppPtJaJKVvZmYGNzc3PHr0CHq9npEeS0tLJtOIlKkl97Rr1w7Ozs5s3O3t7UVWRaXj4HiCIZVbhJSUFJw5c4bNMd5DpLYJ5AkgoUuXLrh16xYGDhwos7JNnToVjx49Es1Laey7Uj9KPRKGQmqqVKkiMkYMGjQIp0+fRv/+/WXhC/zGVOm9gBexySTf3N3d2Vynum00vrGxsbC3t8e7777L+oTGg7+nWhybIUudqShRooSID/A6Uir3SZaeO3dOFisrBel2tfAfJRTL4sNFAVIuNCloAlOqfEpKiiybiv+dnZ0dKlasiNWrV2P+/PlsRxseHg4bGxv06tVL0extCE2aNAGQ64qWZiMCL86pNDYZpSnfTk5OqkJGWhLB3d0dNWrUwKpVq5iglboSlEjd2LFjVUtaEEi5eHh4iJQc73Im0ELgs0sBdUXSunVr6PV6JnidnZ0xYsQITJs2DR988AH+/fdfxSNg1DBlyhQcOXIEtWrVkhUstrCwEClKacFRqhemBEtLS1ktKyl4UscL3zZt2sgshLzFgKxBlNRQokQJ/Prrr2xu86UqALA6eEBusDtZ8vR6PVOUSjthNQsHKQWpZYtInZubG9uRS91upUqVQuvWrQFAFi9H9x0/frxo03X27FmTSpCUKFFClhwiCAL27NmD1NRUJvylxILfyfO11JYsWYI9e/agT58+jFDxWLNmDSIiIlTXHPCC1FlaWqJ06dJwd3dHamoq9u/fj2PHjuHQoUNsHvDkRlpM+9GjR+x4PCB3vOi5TZs2xXfffYfJkyejRYsWrHDwmjVr0KVLF2zfvh2LFi0SbV6qVasmIxe9evVSjLniM+K/++47kYVCTUZJycTgwYMxduxYtlnjny09O1rp90BupvGaNWtgZmaGuLg4PH/+nCnZbt26sbVqaWkpc03//vvvBrNsaZ5WqVJFNJ5KpM6UDZCtrS0sLS1lcYNubm6yjSLNRyVLHY/33ntPVF+RyD6/TqV9qUQgpd4GQ6QuL7CwsMDhw4dlmx++P2lek66zt7dnMcTS8IDnz58jKSkJR48eZX3u4uICDw8PJmu6dOmiuv5obihZ6kyFs7MzSpcuzTxhSuW6CDTWLVq0UNwc8qB1xhf5NwaN1ElASmvDhg1YsmQJnJyc8NVXX6kGPNLg2dvbQ6fTYeTIkdDr9aLBsrOzw+7du/Huu++a3I7k5GS2gzY3NxcJRScnJ+j1elag1pBbDxBbkQwpFkCeSq406fh7eHh45LvYNO0opQpd6ZSCEiVK4O7du/juu+/YZydOnJAlrEhB99br9ahVqxYWLFjAMuXygh9++AFeXl64desWFixYgNOnTzOLNQnLtWvXYty4cbId4R9//GHyUUBKIIUvtdT16tVL5gbjQwQIRLql86Rp06asUO2uXbtE5NLc3JzF6lWoUIG1QcnayGcS8655tT5u3LgxnJyc4OnpyZ6hRBJOnToFQRCwYcMGXLhwgRVxlgY25xV8UPI333wDIDeW1NzcHNbW1tiwYQNu3LjBnkPX80Lfw8ODubUmTpwoshhKYWVlZVR4K/UvzdMOHTqIFCof3mJqWRMgd0xnz54NvV6Pc+fOsXU7fPhwHDx40OT7qMHd3Z3FZdnb24sINz9WSsdEEQYPHozPP/+ckTqeuEydOlUWYqIkl728vJg1kzKpSdb07t2bbTIsLS0ZsaDsdTMzM4PZzTSO0jjLNm3ayFzMSqRLrVQHhTSRq75OnTqiDdq5c+dYEV5jpE6n0zHLqNpRmlKZq0RATSF1QUFBBsdTDR07dsTBgwdV+5rmNa09KysrFkOuROoINB/Mzc3xySefYPz48Zg9e7ZqcgjwQg+p9acpoHaSrFWKSXR1dcXTp08Nhq9JodPpkJmZqXhkqCqEYgZ/f3+hVatWQuvWrYV+/foJGRkZou9PnjwplC1bVmjTpo3Qvn17k+8bHx8vABDi4+MVv//hhx8EAMLOnTvz1N63335bACCkp6eLPp80aZIAQAAglClTJk/3NAW1a9cWAAguLi5Cenq6sHjxYuGjjz5SvDYtLY21hQd9xv/HXwtA6NGjh+I9t27dKly/ft1gG3v16iW6V9u2bWVtOHz4sJCVlSX6zMfHR9YuQmZmJvtM+jsl0DgsW7bM6LVNmjQRKlWqJPpMqd8IO3fuFAAINjY2Ru/9Mujfv78AQBg8eLBw69YtAQAb6ylTpoj6SalP+vbtKwAQzpw5Y/RZGzduZNcdOnRI6Nu3r5CZmSmsWbNGACCsX79e9bcXLlwQnj59KvoMgFC6dGnZtWlpaYIgCEKpUqUEAMLjx4+Nto2wevVqAYCwevVqk3/D4+jRowIAoWrVqoIgCMKNGzeEqKgo2XUZGRnCkiVLhJSUFAGAUKdOHdF8SExMNLoGpGjYsKHQtGlT2ec0l1q1amX0Hn/99ZfQrFkzAYBgaWmZp+cXJJTWxt27dwUAwuXLl0WfP3/+XHQ9AMHDw0P13p07d1Zdd/Hx8cKiRYsEAMKmTZtMauu3334r2NjYCElJScKuXbsEAMLChQuFtLQ04caNGybdQxBejNOUKVNk38XExIjWYnh4uOj76OhoITExUfG+OTk5wsaNG4WYmBijbbh48aIAQBgxYoTB6wICAoTs7GzRZ9S2nJwcITExUfD09BQACH/99Zfs99nZ2ex6U2WcIXmphKSkJMX7nz17VujZs6fg5+cnABDOnz8veHl5CQCE1NRUQRBy+8zT01M4dOgQ+11iYqIAQGjWrJnJbThx4oQAQCS7jh8/LgQFBan+5o8//hAGDBjA3jckJEQQBIHJiMDAQNH1p06dEsLCwkxu08vAeATgK0aZMmVw5MgR2NnZYcaMGdizZw87wJwwYMAA1SNT8gvh/3eAaofMq+Hvv//Gxo0bZXEq/I4sL3FIpoKsLvPnz4eVlZUoSFUKtRIMc+bMwZkzZ0THbvHXent7q+4QjLlWgRdFcn/55Rd07twZ5cuXl2VJKR0zRW43INeVwgfW8kGram4/HrSDMpZ9BihXsj99+rRqYHxhjKsSaEyoHACQG1wOiIO1V65cqdgnapY6JfAFkzt37sxi/agNhlxTFC7A4+TJk4o1p+h+ZJnIS6FfmkP57X+yXJOlSuqGJlhaWmLixIlMNnz44Yf46quvRPchC4+pkB7/RjBkCZVi4MCBGDhwINvFFye89dZbiglb0pIN0dHRBmNZt23bJjpTk4eTk5OozIgpGDNmDDp27Ah7e3u888476NKlCz7++GNYW1urjr8S6D2UrC1kJZo6dSoaNWoki5tW8kAQdDqdrFi5GoxZ6ghKiREeHh4sFMDBwUEUPiQFPxcLyvUqBT1XKp9btmzJYlBjY2Ph7OyMJUuW4LvvvmPvrdPpZOfh0hgonbWuhnbt2snmrFrNR8LgwYMxePBgljFOHhMKdZG6X3mdVtgodqSOXwhK6cBAbmHDCxcuoG/fvqrm5byCBjWvbrnKlSsrxtEMHz4cx48fx/bt2wtF+VOgt1rxZVPw9ddfY82aNTh69ChKliwpK2WilHmVF5AJnSrCA8qZslLodDpYWVkhIyPDpArahkBCK7+mdbWgXuDVkzoqA8MLIHLRfPHFF6JSODzyQurUQLEihgokK8FYvCAJQUMuLymIWBqqF2cITZs2xZ9//inbLKpBp9MhKysLZmZmIlJXkMgLqSNUqVLlpc/Zfhl8+OGH+Z5ThggOkEvclEIJCBRbayxJjEBncwK5pCi/7uZ27drB399fFvwP5M5HCwsLVKpUKU8xUHkFubLVQoIMwd/fX1TMnTJcjZ0CZCqpe/LkSZ50qE6nw+TJk/HBBx+oXkOEj99kqsHc3ByRkZFG51dBQVrAuGrVqggICDDJiFBYKHakjhASEoJjx46Jgn6B3JghOsS6Z8+eaNWqleICS09PN3qoMA9SfHkldWrQ6XRo0qQJtm/fnq/FZwzu7u4ICgpSjdEwFWTZ4VOwhwwZYnL6tCGQpS4/CywwMLBAClkTmZMmXhQEaFwLas6ogciLkqDw9vbGgQMHDGakE3F6GVJXt25dpKSkFPhcnj9/vux8RGN4WVJnZmZmUIkowRSr8MsgP6Tu/v37hdUck5DXM76HDRuGTZs2FcizK1eujMzMTJPKTRQ0lPQNkDt2x48fN5lo5hemWuqUUKZMGcX4VaUzg3mYSuqUSvEYw6JFi/L8G0NQO8ruVeD333/HtWvX8uzxK0gUGamLiIhQ3M3s27cPFhYWGDp0qKJbkw/679GjB65fv664yBYsWCAKqjeG/LpfDYEWn6FMmPxi+/btuHr1qsnlOADlWkr0vqT4AfWK43kFkbr87FoqVqwoK5KaH5BlSVpFvyDwqix1RCjUjufhD+VWAs3tvFjDlFAYm5Pp06eLzrY0BUTQX/Z98oOqVasquplfFvQuhU0eixK//fYbfvvttwK7X1EQOmN4FW422py9TGA/oUePHrh9+7aq3ps6dSp++OGHQnO/vmlwdnY26p0obBTZqvDw8FDMmsnOzkavXr0we/ZsWYYRkGtxI/PzmTNn2NE3UkyfPl1UiychIcFgnbFBgwZh3bp1ecpMMQYidYWxIDw9PfMUhxQaGqp4ogEpEZ7UFRSmTJmCsWPHFoqikh5qrYZWrVohIiLCaPZhfkCkrrAtdWRlzu87rFixAl5eXoWyuSgKUExtYZArY6AMxIJGfix1rxsKe538V2BmZoZy5crlSf6rYe/evQZPSFm4cCFOnTpVKPJTQ+Gg2G11duzYAV9fXyQmJmLOnDkYPXo0BgwYgJEjR2LNmjXYsWMH1q5dCwsLC7Rs2VJ1Z2RtbZ0n90ylSpVUDzrOL4hEqZ3r9iqhduwQES5jx7zkB5999plqkeOXQUZGRp6UX2EJpFflfqUahUpniJqC8uXL48cffyzIJhUpatSoIavP9rrjv0DqNBQcbt68aVLBc1NgTH7xZ0lrKP4odqTugw8+UIx3obNfhw8fzmK/ijtoB1SUQZPGQMrkddpF58XlXJh4Ve5XqnGn7ZbfXGikTkNe8DKFcvOKl43b1vBqoUmQQgRZ6tTOdSsO6N27N2bOnPnaEOXihFdlqSNSl19LnYbiD43UadCgoSCgSZBCRJs2bXD+/PlCTW9/WVhaWmLu3LmFEgT/puNVWQynTJkCR0fHV2YZ1PDqoZE6DRo0FAQ0CVLIaNq06Wvl2tSQdxT2+A4cONBoSR4NrzeIzGmkToMGDS8DTYJo0PAS6NOnD7Zu3VrUzdDwmoPIXEEFv2vQoOG/CZ1gKJ/5DUJCQgL0ej3i4+O1TB4NGjQUO/zyyy8YPHiwVhNMgwYN+YZG6jRo0KBBgwYNGt4AaO5XDRo0aNCgQYOGNwD/GUudIAhITEyEo6OjlrigQYMGDRo0aHjj8J8hdRo0aNCgQYMGDW8yNPerBg0aNGjQoEHDGwCN1GnQoEGDBg0aNLwB0EidBg0aNGjQoEHDGwCN1GnQoEGDBg0aNLwB0EidBg0aNGjQoEHDGwCN1GnQoEGDBg0aNLwB0EidBg0aNGjQoEHDGwCN1GnQoEGDBg0aNLwB0EidBg0aNGjQoEHDGwCN1GnQoEGDBg0aNLwB0EidBg0aNGjQoEHDGwCN1GnQoEGDBg0aNLwB0EidBg0aNGjQoEHDGwCN1GnQoEGDBg0aNLwB0EidBg0aNGjQoEHDG4D/DKkTBAEJCQkQBKGom6JBgwYNGjRo0FDg+M+QusTEROj1eiQmJhZ1U4oNcnJyNJKrQYMGDRo0vCH4z5A6DXKYm5ujb9++Rd0MDflEcnIysrKyiroZGjRo0KChmEAjdf9x7Nq1q6iboCGfcHBwwNChQ4u6GRo0aNCgoZhAI3UaNLzG2LFjR1E3QYMGDa8YT548QUZGRlE3Q0MxhEbqNPxnEBgYiHnz5hV1MwoUVlZWRd0EDRoYnj59Cmtrazx8+LCom/JGo2zZsvjss8+KuhkaiiGKJam7fPkyvL290aZNG/Tv3x+ZmZnsOx8fH5QrVw5t27ZFhw4dirCV/03ExcUVdRPyje7du+Prr78u6mYUCHJycgBopE5D8cLp06eRkZGBffv2iT5//vw5dDodDhw4UEQte/Nw+vTpQn+Gr68vIiMjC/05GgoOxZLUlSlTBkeOHMGpU6dQtWpV7NmzR/T9gAED4OPjg+PHjxdNA4sAERERSEtLK9I2PHv2DCVKlMCWLVvy9LvQ0FDExMQUUqtMR3p6OgC8ERm/KSkpAABra+sCve+BAwdw69atAr1nYSA6Orqom1DgIEL0OsPCwgIAkJ2dLfr87t27AID//e9/r7xNbxooOcrc3LzQn9W/f3+sWrWq0J+joeBQLEmdh4cH7OzsAACWlpZMUBB27twJb29vLF++vCiaVyTw9PREnz59Cux++cmafPr0KYDc3VteUL58edSoUSPPzyto0Dvzlt/XFUTqCtpS1717d9SpU6dA71nQ2LdvH9zc3BAYGFjUTSkwxMTEoE2bNpg5c2ZRN+WlQERDSuqioqIAACVLlnzlbXrTQJt7qV7MD/744w/88ssvqt+npKTkaUPeq1cv9O/f/6XbJcXly5excePGAr/vm4hiSeoIISEhOHbsGLp168Y+a9SoEQIDA3H8+HEcPnwYly9fVvxteno6EhISRP+97jh8+HCB3Ss/Vj8iQ/khEsXBskLtf92tIUBuOROg4C11rwOuXLkCAHj06FERt0SMu3fv5tsKTHPywYMHBdmkVw4zs1yVIiV1ISEhAIDY2FisX7/+lbersJGUlITq1asjICCg0J9FsrsgLHVDhw7FuHHjVL9PT09HbGysyffbu3cv/v7775dulxSNGjXCJ598YtK12dnZeWrzm4ZiS+oSEhIwdOhQbNy4EZaWluxzBwcHWFlZwcrKCj169MD169cVf79gwQLo9Xr2X7ly5Qq9zRkZGahSpQp8fHxMuv7JkyfYvHmzyfcvSHN7fkgduS/58SgoxMfHY/v27UavW79+fb4VH1nqipLUZWVlYeHChawv8ws1S11ycnKBlKlZunTpS9+jsEDzrzjV6Ltz5w68vLywefNm5OTkwM/PD40bNzZ5M0lkkGIlX1cYI3U//fQTRowYUaQhEE+fPoVOpytQS29YWBju3buHoKCgArunGgrSUmcMGRkZr10c9aeffgoXF5eibkaRoViSuuzsbAwePBizZ89G9erVRd/xQvLMmTOoWrWq4j2mT5+O+Ph49l9oaGihthnItUY9fPgQo0aNMun6vn374qOPPjJ6HZEQEpgFgfyQOiIShSFMxowZg4EDBxpUgmFhYRgxYgS6dOmSr2eQpe758+cYPXo0s3YVFM6dO4dt27YZvObgwYOYPn061q5d+1LP4i11MTExLJh50qRJ6NOnz0vHME6aNOmlfl+YoPn3Kt3oP//8s0EZ8uzZMwC55G7evHlo0aIF/P39cfbsWZPuT2tcSoYKEklJSahTp06hEg+193jy5Ino75fd1OQHSUlJOHLkCAsfoaSNDRs24MKFCy91b5JbJCOVsGbNmgKJKUxNTQUg31xv27YNZmZmOHTo0Es/A8jdYGRlZRUrq1dERITRa37//XcALzZKt2/fxpEjR1SvFwQBly5dQmZmJuvb1xnFktTt2LEDvr6+mDNnDtq2bYvt27dj5MiR7LsmTZqgRYsWKFOmDFq3bq14D2trazg5OYn+K2zQEWSm7gApzsSYciIFXhikLi/3pPfLi/vVVOFNSlGN1MXGxjJr69OnT/OlFEjhbNiwAatXr8bOnTtVr3306BFGjBiRJ8tJq1at8MEHHxi8RqfTAVB/T1PBW+pcXV3h7u4O4IVLsqAJqxSLFy/GyZMnC+x+eTmyjkjdq7LUZWVlYfz48RgyZIhJ1/OWelPfiWSAsfm2a9curFu3TvE7nU6HNWvWqP72+vXruHXrFlavXm1Sm/IDWpdSUifdZBgiP4WFIUOGoFOnTrJnDx8+HM2aNcO9e/dUw3mMIT4+HgBEpICfnxkZGRg1ahTee++9fN2fh5r7dd68eRAEAV27dpWRaEJMTIyii1hJB9FnxclS5+npicePH6t+z683kve1a9dGp06dVH+zadMmNGnSBA4ODiyWvzAxb948NGvWrNDuXyxJ3QcffICYmBj4+PjAx8cHAwYMYMJq+PDhuHjxInx9fbFo0aIibqkYSUlJor+nTJmCjh07qroVSfAZE3D0/cu4X1NTU0ULlwRDTk4OdDodrl69avQe9H6muF/T0tIQHR2dZ/cTCUcpiPRRO9q3b2/SfYFckn3lyhUmZE3ZjU2cOBHr1683mM4fHR1tMGkkKysL8+bNEz2PCPHLuoCJtPEEu169ejh69CgAICAggAm/p0+fYs6cOQXq8vrqq69EY3Dy5MmXEv7m5uYYP368Sde+SlJ37949nD9/HoDhDQpPxqRj+/333zMyL0VwcDD69OnDNkzGLHV9+vRRrE9GfbFkyRIIgoBz584hMTFRZF2k8c/LRi4zMxM3b940+Xo1S92rInWCIGDRokUyi86+ffuwd+9eAC8SvgCxx6J69epo1KhRvp4rJXV+fn6wtLTEnTt3AORa8QHAy8srX/fnoeZ+5eV7eHi44m+9vb1Rq1YtAMDu3bvZ50qWfZrvr8JSd/nyZdU2S6G2qQHE42nqxpZCA15VWM7XX3+dJ8twenp6nowLxZLUva4gwUxYtGgRjh49ioEDBypeTwNlTMDR5FQidVlZWdi+fbtMYR85cgTdunXDp59+CgCws7NDz5492fdS9ytvno6KilJcyETqTHER1a1bF25ubjKS9vDhQ2YeV4IaMSCrJiEvGbg1atRAw4YNWX/TuxuykNI7GlpMY8aMQcuWLVWJ67///ouvv/5aZD0hwVEYMXU3btxg/+7cuTMqVarE2jl79mzExMRg7969quNniuB4/PgxGyNK0sjJyUH79u1ZKIGp5DEpKQkJCQnsekMu6czMTKZ4SJkNGjRINXZm7NixuHjxokntMITq1avD29sbQO679+jRAxkZGYiOjmZlOgDxRoFXDoIgYOXKlewdJkyYgMmTJ7Pvv/76a+zatYuVkclvTB3N6eTkZPj7+6NVq1ZwcnJC+fLlZW1UI5hKmDp1KurWrWtyuAbNa6mCfP78uejvlJQU1XWTmpoqW+9SXLlyhZFtHgkJCZgyZYosBIY2O0BuGIfSvwk7duwwmWAQpKTuzJkzAHJdfwAYMX7rrbfydF8lqFnq+E1OZGQkwsLC2NqiNU8kUxAEUaa1UiIbjWF+Sd2JEyeMjiOhT58+Jlez4Atb//7776hYsSL7m9c3a9asMSlxRSnZLDY2FjNnzlTdOO7fvx9jx44VfbZ+/XqRYefs2bOieZdf2NjYYOLEiYiJiTEp5EQjdSYgKysLJUuWNFoXT0rqjMFUS52a+/XixYuwtLTEwIEDmdsgOTkZ2dnZ6NSpEw4ePIjffvuNXf/vv/+yf0uFdFpaGg4dOoSWLVuiVKlSKFu2rKwd9H7GCEliYiLu3bsHQG55e//99zFs2DCZ8pJa6gICAkQ7Mt5S97Ig65uSIDt58iSWLl0qIoCpqakypbRnzx52RJc0hoUEOgkEvr9orNX6cPjw4bK6jEpQstSpgYTzqVOn0KtXL8yYMUORPBjbqcbExKBSpUrMoubm5gbgxZiFhoaiZcuWaNeuneo99u/fjxYtWgDILXWj1+tNqrk3ffp0uLq6ymJjlBROdnY2VqxYgcGDB7PPoqOjTbJGG8KzZ8+wf/9+3Lx5E127doWXlxfs7e0BvBgPQRBkpE6v17Pf//TTT1iyZAn7npJ+THW/KkEQBEaIk5OTVV1vNE5mZmZYsGABI0XZ2dmqY0/E2NRYI5rXUpkmtQSdPHkSer1ekXi/++67KFWqlMHndOjQAc2bN5eRL3ou/f/w4cM4evQo7O3tUaJECQBiUhAcHCz6vbu7OwYMGID333/f4POloL6l59I40v8pLMIUpRwYGAgXFxcm89LT03Ht2jX2vZqljicg/v7+KFeuHH7//Xfs27cPFhYWIhn6/PlzUcKZ0gkgNCdSU1NN2oTycbiCIKBDhw4mleFKT09HSEiIyV4dfszGjh2L4OBgNr/4e8yYMQNz5841ej8lubNgwQLMnz9f5I738fFh/dSjRw+sWLGCfRcREYERI0bg888/Z595e3ujY8eOqs81tvldvXo1evfuDSA3XvLTTz9Fjx49jL6PRupMQFxcHJ4/f44ff/xR9l1gYCBbyDypkyobQRBkC/plSd2GDRvYv83MzCAIAhwcHDBlyhRZ+wHxzk5K6lJSUtC1a1dmAaP6RDdu3EB8fDx++uknJrikvxUEAe3bt8epU6cAQOTykZI6EjxqO2Fqa4sWLURuJiVS5+zsnK+yFmQVuX//vkyY9enTB5MmTWLtS0tLQ8eOHWX1tf7880/275MnT4pM/RTnSRYRfvHSWCspyezsbGzYsAG9e/c2qtzpeaZYXegaUvY//vgjXFxcsGzZMtF1xgQ3EVsis0TqiBzb2NjA19cXp06dwv379xXddp9//jn8/PyQk5PD1oinpyeAFwT15MmTMmsiua8uX75slHySdcDZ2RlAbp+3atUKb7/9tsHfmYqTJ08y13ZKSgpycnLYuEpJXUZGBiN1SmTr/v37AF6sk/wkSty+fZsp1JSUFFXrCL8WZ8yYwch19+7dVQk1zYnbt2+zd87IyFCcn5mZmfjnn39YOwh+fn4yhU2EkqzLz549w7Rp05CTk8PG2hBITly6dEn0Oa0Lal/nzp3RsWNHpKWloXTp0rCzs2MbTiITPMjSklfrlNRSR/1GZIPkDMnOpKQk3LlzB5cvX4adnZ1o03jkyBHExsayRI5p06ahQYMG7N70f0OkjgwQDx8+ZMSZ39RcunRJNE+Vjk/kvzcltILPmKeNsynx5cHBwRAEwWSXPE/qaLzoOdJ5JpWzdnZ2GDBggOgzpXAiug8v19u1a6daw5N0O53yYYrrl3S2GkaPHs02+Hq9Hvfu3VNNDBXd1+gV/zGcPn1aVuSQJs7Ro0eh0+lEgrdGjRoYNmwYADGpk2aYzZ8/X2ZVedmYOn6A09LSmNDlYyWAFzEDpITpeh7SHSuQG/hfr149bNmyBRMmTMBff/0FILcsAV9lPD09HSdPnmSEkN+VS0kdkaOPP/5YVHqDBAhdL/2dEqmLj483yaolBQn19evXo0qVKqLvPDw8ALyohZaamsosb/wC5IN1165dy1x0BEEQFEkdLXal2EFeIPFC/vDhw2jWrJnoPtS+vMSV8fMzPj4eEydOFLkLpKROem9qH80dGxsbAC9Ina2tLbu2WrVqqFu3ruwepGz55/IJOKdOnUL79u1FpDk7O5sp/0ePHslIXXx8PB4/fsz6lOKpiNTVqVOHCf1bt26hVatWL5Xl9tVXX4kUQVJSEhvXjIwMUT+mpaWxdvCxXATqO+rb8PBwVWuOGuHj50pmZibmz58v+j4xMRE5OTmsf3irIiC24EtB7+Lt7c3c+dbW1iIXMmHhwoVsrfD9S+SR+gF4MYcmTpwInU4HDw8P/PDDDyZVKeDnMU9gnz9/zuSYlHSmp6fDxsYGpUuXZkQ6OTlZRoCpb3jCFBYWhjNnzuDkyZPQ6XQYPny4TGbzpI6yKYHcTUyjRo3Y5pP6s2/fvqhZsybmzp2L1NRUEbkk6y+5Sqm95Co2xVJHMVseHh7M6xIcHMx+Q+ME5FrMlbJK+Xmc13hZkqNKsLKyYmFBV69eZRbDM2fOmOQuDQ8PR0ZGhmgMWrZsiSFDhsjkKr+REgQBqampbFNKUAotoM+k/ZKSkiLadNCaJJlE808pjj4xMVFWE9DU8lwODg64f/++rBqIEjRS9/8ICQlBw4YN0aZNG1mRQyn7pwEnwfX333/jzp07ImFz4sQJ0W82bdoEQLz7USJ1I0aMwKFDhxAREcEWklpMHb+IU1NTmSApU6aM6DqaaIZInZJrimKGvvjiCwAQCZ7PP/+cucLovWkB8KSOj3sRBIHFQP3vf//D0KFD2Xe0GH19fRUXtinuVz8/P1a30NfX12RzPt+PUuGVlpbGrBi8u1ZqIZT2Hx8zpGSpI0vAjh07cOPGDaSmpooE0OnTp6HT6fDo0SNcvXoVFy5cgI+PD7tm//79srYbg1J/8EJQSuoSExOxZ88eODs7QxAEmfWCxp0ndVKSbGlpKSJwpGyVSK21tTUrAcK/V1BQEFJSUlCyZEkEBATIykI4OzujUqVKaNq0KZKSklgcE5EI3ho7adIknDt3ThR/aAhq5XsiIiLYXH748CGLr0lOThat8dTUVEZ+lSx1NDdobAIDAzF69GgsWbIE8+bNg06nY0qIJ2+XLl1Cv379IAiCrC+l2YFOTk745JNP2HWG1kXVqlVFlgw1661SsDptloBcpSZdI7ycI/kjDVn56aef2L/ViuLyzwkLC2PyrV69enjnnXcAKJM6a2trkVs1OTlZJKsqVKjA/m1hYYEnT54gMjISTZs2RevWrbF161YAuR6S0qVLizZyPKlbvnw5Dh48yL67fPkyk4303qQfiHzx7aV1RvKXLL0kf3lSN2vWLBaLxm8GaNzS09PZ5vLhw4fMsMCHErm5uSkaFvh5zK/9du3aYceOHWzuzps3j2XfS0HXZGVloVu3brh79y4yMzPx22+/ISoqCm+//TbTtw8fPkStWrXg6upqsBSQIAgICgqSydytW7fK5jZvyeXlEK/jpe9++PBhFvdN48b3batWrdi/Q0JCMGfOHJk+5Y/SpO82btwoO72Dbx9vmAFekHsgdxOfkZHxepO6yZMnw9vbG4MHDxZNrqysLAwbNgze3t4mZ8sZgyAI8PPzY9YPKaSCh8gcv8urWbOmaELxwgl4MbAnT57ErVu3cOzYMSakaVJlZGRg/fr16Nq1Kzw9PVG/fn0AYlLXr18/Jph4E29qairb2UkVPbkSHR0dZe0h8ILSVFCaOC0WJVLHu6xDQkJYGwGI0rqJTP3xxx8sOwvIXUyCIKjGCfGEqUWLFqhfvz6ysrLQrl07rFy5UjVTqk2bNuzfDx48wPHjxxEeHi5zC6elpbH4HlpwSUlJRk/I2LJlCz788EMAwMyZM5krhcaa3mfAgAGoV68e7OzsRKZ9soT6+/uzvmnfvj0qVqyIzMxMNveMkTreYqikzPfs2cP6UKrA//zzT/Tu3Rvx8fGIi4tjSo1AFiBah9bW1ooWMLLwAi82Mkr9Z21tzcgWL2hJePfu3Ru+vr6qsa2BgYGoVasWKz2iVMaI5ujkyZMVy8/wwjsrK0u1f7Ozs1nQe4MGDdjn69evF1mbgoODWfulGwZ+l84Ts0OHDmHy5Mn4+uuvAeRmz2ZnZ4v67JtvvsE///yD8uXLy1yISvj999/ZM/j78OsnKysLDx48wI4dOxAWFobr16/L5gRdTxZsftMmzf6njSyVibh58yazrKtlufNy85dfflHsf7JYOTo64ttvv2WB8nzSgxqp44+wSk5OFvUFn1RiYWGBsmXLwt3dnVlYeU9LfHy8iHjwMXVKZ2NL45FpnpG8efz4MSpXrozz588zvUBrnObxO++8g7S0NCa7dTod5s6diy+//FJ0Tx7JyclsTYaEhLDf8tmXLi4uMnfh4sWL0bZtW/Y3kbqMjAxWkYLiu77++mvVKgG03kNCQnDw4EFmIABezEOpNSwmJkYWGkJwdXUFAFy7dg3+/v6wsbERHUFpaMPCrzfeGyeVWfzJTZMmTUJ8fLwsppowduxYzJ49W7RJTExMRFxcHJuXNI5KsvHOnTtITU1FSkoKRo4ciUqVKrE1Ru/Ko27duqrvRyiWpO7q1auIiIjAmTNnULNmTRanAeRaKMqWLYszZ84gJSUlz+eQKmHr1q2KGaqCIOD8+fOy4MS0tDRkZmaKdnYARBamyMhIUZAkLepOnTqhTp06ePfdd9l3pMCkEzI4OBgtWrRgE0YQBPzzzz/MvZqcnMyscnzGmNTNQwuSF9BSF21+ERkZKbPUSRcACfVatWohMDAQTZs2Rbdu3ZCdnY2dO3ciLi5O0TUF5ArSwYMHK2ap8eCF0pMnT5CRkYF169Ypln8AgCZNmrB/DxkyBO+88w5GjBgBS0tL0XepqamM1JHlwZTirVK38KZNm0TJH7SY1UBj7uTkJFKAWVlZbL44OTkZjf3hybsSkRo7diwTcFKiz2d3bd68mSlpQmJiIoKCgli/7Nq1C0+fPpVZt8hyBrwQ8kpuB0EQ2OfPnz9HamoqcnJycPXqVZQvX94kgcYTHKV4FZqjZ8+exbZt29g1mZmZ2LRpE6ysrJhSVnI58Yqd3JGGMHfuXDZ3pQSJD5/g17603bdv38aMGTNEm0v6bVhYmEhB00ZCCTSPeKt3y5YtZd8DQLly5VC/fn3RhtrW1lZUsqR169Zo3rw5+15K6gRBgCAISE9Px8qVK1G9enV07dpV1gZDiIqKQkxMDOrXr882nhERETAzMxOVB8nIyBB5IqSkLi0tDTY2NqIxM2apk0LJTUfvTH23detWXLlyBcOGDRPJ/7S0NJQsWVI1i3jOnDl49OgRzp8/z9Y0rVd+3gQHBzO5ISW8SgQ4OTmZPXPbtm3IycnBmDFjRNdYWloiNTUVw4cPx9y5c6HT6fDVV1+J5DitBT5M58CBA0azop8/f44DBw6wucvLeUNGmV27dok8OQQXFxdUqlQJ165dw7Vr11CnTh3RuMfHx8ti5Ki+qZLhQqfTYeHChaLPUlNTRbHK58+fVy3mTvHZPA958uQJkpOT2VyLiorC6NGj8ccff8h+P2fOHNjZ2aFUqVIsJv3x48fYtGmTLI57xYoVLP7YEPJM6lJTU3H27FlFF1laWlqejr1Sg5+fHyvS2KlTJ1GHGfouv1Aqi9CsWTNs2bIFzZs3l5GU1NRURYEv3TF36NCBBY+q7UyBF6RO6Ro/Pz/m5uStVWlpabhz5w5j82lpaYzUSa1NJCRSU1Nx+/Zt+Pj4YN++fSwb7GXg7u7OCOKzZ8+Qk5MjIw9EBoh41alTB+XKlcOpU6fQt29fDBo0CNnZ2aom/L/++kuV1H377bd49uyZyGJA8ViGiCAvCPz9/QHkWhL69OkjKmjNu1+nTJmCoUOHomHDhqr3JUiFgJmZGZo2bcrGJjs7WxbkzYN2vvfu3RO5coAX80Wv14sIkxI2btzIBJRaAD1Z0gwlSiits8TERJHllcD3LSB23ZCyVRKwiYmJTGk8f/4cdnZ2mDx5Mm7evIl69eqZJNB4pKamymJFpRaFx48f4++//4aVlRU+/vhjAC+CxpUIMy9o+VIKpsAQcePXvlLs3Jo1a0Skif83f1Ti/Pnz8ddff2Hq1Kmye9D4833g5+fH/r148WLZb/g5YWVlZVCJ822qV68eQkNDkZCQgOzsbCZjLSwsYGVlZbD+I4+IiAjs2rUL169fZ/M0IiICpUqVEsVtRUdHi5I9pP1L65iXecYsdVLwJWwIJJOlsrtNmzayTZCrqyvS09MVS7HQRtHc3JzpG2m8JZDbx2RJkhoBlEhdUlKSbMymT58u+ps2Khs3bsSsWbNk9wBerAXpZkxKiHicP38eDRs2RPfu3Vk5LZ7UqZ2uQWT4jz/+kMkkc3NzeHl5ISgoCDdv3kSdOnVEHqiEhASZXqtWrRoAcRwhoJ7MEBYWhvr167Pi9HFxcaqeGdL5v/76q+j3SUlJIkvd6tWrGQFUQnJyMnuPL7/8Eh9//DGuXLmCsmXLMo+CKZtIII+kLigoCF5eXmjdujXq1KmDtm3bighEfHw8E4wvg7i4OGZy1uv1sh2D2nc80tPTkZCQIPpPDUqk7sKFC6pFN6nMhRShoaGie9nb25vkAx82bBiysrJEgsHe3p65u5TesUWLFjhy5AhKlCgBnU7HLHX29vYyMzwtyJSUFNSuXRvt2rVDVlYW2+3TpM8vKE4gIiICdnZ2+PHHHxmRcHNzQ+nSpUXXm5ubszgRIDdQW6/XG6yyrbZTSkxMxBdffCEqEzFjxgwAubt3a2trzJ49W/Y7JdN2SEgI3NzcRDt2fqyDg4Nluy1vb2/FoFglUscrPTc3N8VsagCic4q/+OILGTklYaR2SgofezlmzBhGEtQUKc07Q6ROyeWZmZmJ69evw9nZWWQhkvZtaGgoUyzUFiVrZ0REBBOeZKlctmwZjhw5gho1ahgMvlbCtm3bjBIvf39/WeA0JVUolXlwdHRkczuvpI6fE9K4V14+Scn3qFGjEB8fjw4dOoiuISHPb7BtbW0xcOBAdgIPDyqLoZZ5rqSg+TmRnZ2tSOqio6MRGBgo2qRUr14doaGh7J15MmxnZ2c0fIEQERHBLBgkxyIiIuDh4SGaZ1FRUSIlLW3n8+fPYW1tLbLA7Nu3TxTDxG8alEid0gaKXH9SUufm5gYPDw/RqTWurq5IS0tTPBqSL8VC8j4hIQE9evTAhQsXWCjOyZMnGamTxiwqkboHDx6IqiT07dsXZcqUwfr163Hu3DlMmDCBxVAayrgnI4Z0TRg63cHR0RGNGzcG8MLCZyzhYtmyZaKTW5YuXYoRI0awv83NzVG5cmXcv38fAQEBqF27tkje8fyAQPpHGs/m4OCg2IZz587BxcUFvXv3hoWFBWJiYvJ07GJgYCASExPZfDK1Vh+Rvn379rHPevbsycZV6hlUQ55I3dSpU1GnTh1ERkYiMDAQTk5OaNmypUkxHXlBiRIlmJCLi4sTESVD3/FYsGAB9Ho9+49XlFJIzZwENcJIPnApaAdJMHbsCL9Tefr0qUiwN27cGIMGDYK7u7tiPBnF6Zibm8PW1paROj6Ik0DCULozoayoxo0bY/LkyYpERw28tYomfHx8PFMCdG9ra2vUq1dP5LZKTk4WZcIBubvk/FoOnz59qmqxdXZ2Frm6CWrv6urqKiLiagSe4OjoiP79+2PDhg0iYqNE6vg++Pjjj2UWOIIxixRlN/LEmAdvcQBeKAxeuPDzJDIyEidPnlSNWwTUSfWlS5dQo0YNUR9JLXUnT55EkyZNsHnzZuaGUbLUkVLR6/WyRKPq1auL+sXMzEwU+0LgXeem4NKlS4r1JQVBwLZt21C1alVRKSFra2tmBVKrpaaWpcoTGSmxMrTpHD58uGK73d3dYW9vL1LmlIFcqVIlWf88efIEVapUyVPZFF5mJCUlKco9Nzc3dO/eXdSOcuXKITQ0lMlQKakzFc+ePWObESKvaqSO78OEhATRfH727Bmz5JELGBB7P/g2KhV6T0hIULRk8ZnFBGobb0UqWbIk0tPTZclEPFJSUhAdHc1I//79+xEaGsr+/vnnn6HT6VC9enW22TMzM0NWVpZiuMG///4r2hRS33/66ado0aIFli5dqqpDCY6OjkyHSOetodIddnZ2ihtPMggR/vjjD0bcSpQoIVpXM2bMwPr169nf3bp1Q6VKlVj4StOmTWVEvWHDhjhy5Agr88PfzxAPIMTHx6NkyZLQ6XRwdXVFdHQ0YmJioNPpjFqY7ezsMHbsWCQlJbHnKm0OTYWtrS2L2TWl7UAeSZ2vry/mz58PV1dXVK1aFfv27UPnzp3h7e39Ug2XolmzZqwS85EjR0RxH4a+4zF9+nTEx8ez/wylyqtNaqniIbdMWlqaahkSXqHZ29sbFGD8TiEpKYkJBhcXFzbpXV1dRYu1adOmonskJSWJSF2TJk1ku0zaHUl3DCQoXFxcsGjRIhkZMARj536SK9XGxgYODg6iTLHExEQZIfH09FQlKVevXmWJBkq4f/8+4uPjFQsmOzo6irKICDTm0jIzrq6uoqrvagSeQETkk08+Ee0opUp6586dovgkQwRW6Ts+/opi4NQsddLFT+PPkwreOhUWFob27duzIHL+u8jISDafxo0bx45Rorlz6tQplC9fXkSMlAjzzZs3RRaKO3fuwMLCQrHuE5XA4FG2bFmRpS4nJ0dxXPkYLzXUrFkT+/btQ69evXDx4kVFQhUTE4Nr167hnXfeQWBgIGrXrg1AXKiU2jNy5EicPXsWO3fuxP3791WP4TJ0qLih8IzKlSvLPnv27Bns7e1lGwDKtAWgWPhUWnoHMBymIIUaub93756IrLi5uSEmJobJHF7GSuenoWSfiIgIUUHyn3/+GQcPHkTp0qVFsvbdd98VkdXo6GiRPIiMjGR9c+DAAZEsXLJkCVxdXVV1CY/SpUsjLCwM9+/fZ88/cOCAoqUOEJM6stRlZGSgW7duivdPSEhAYGCgbHNO6y00NBQDBgwQtdXe3l4Wz6gGJX2ktI54lC9fnul3qYU1PDxcVQ7Z2tqKTjEiVKxYUaQ/Bg8ezDb5jo6OqpulRYsWYe7cuawv3nvvPbRo0UK03u7du4fBgwfjvffeY15DflNv6oaC+rNEiRL45ptv4OvrCzc3N0XZ9sMPP7B/8x4jFxcXmJmZiY4zVTIw8OB1CJDbh1OmTMHjx49Fc8kQ8kTqUlNTZYRhxYoV6NGjB9q0aWNSALkpaNCgATw8PODt7Y2AgAD06dOHuRO6d++O0NBQeHt7w9bWVlWIW1tbw8nJSfSfGqRWI4LUB0+Bm7yilwrVvFjqpLEAJBhu3rzJ4h6kVkTpwcSJiYmwtbVlOzxPT08ZOVOLXaDFQWSKyIxSQKehtiuB+oEEqTQeQErgPDw8VE9HqF+/Prp27SpyP/GgoGtSvDycnJwUzez0LOmu2dXVVaR05s6dq2iJpjgmnnAbOhGBCF3dunXx008/iZQvub0sLCxQqlQpRVJXvnx5UQVzejclSJWmUvv5mnJSyw2fGVyyZEk2dl9++SX69esHAGjUqBGcnJxYsg5P6qSWOikcHBzw/PlzlChRgsVjDh06FBUqVMBnn32Gd955B7a2tiIS16JFC5ny4d+BQG4qQ/D29kb37t3RtWtXnDp1ShRXRggLC8OzZ8/g4eGBqlWrMiXMjzFlIq9cuRItW7bE+++/b9AKYwhKpK5Tp06oUKGC6gZAidSpFaNet24d/vjjD9mB8lS7beLEiejSpYvRdhpyJfHj5ezsjLi4OFy+fBlOTk4igsVnQFapUkXRKtaqVSt4enrif//7H65cuQJ3d3eEhYVh/PjxyMjIwFdffWXQsyB186WkpIjGztXVlbVj3LhxiIqKUjxSTQpPT0+UKVMGVapUYaf19OzZExkZGSJLvRqpS09Px/P/Y++6w6so3u6596aQHmqA0IvUhBp6702KIIiIIqLY209EpAsIKFhAQZpIVRBUUHov0qRL7xBKIAHS+81+f+R7h9nd2b17k5sC7nmePHC3zE5958yZd2YePFBNvxO++eYbJCUloW/fvjK3leLFi7OyrVKliqwfttlsqoHJiBEj2GkEPET9kSOi0717d2zevBnJyckqUn/58mXN9u7t7Y0mTZqojv8jt4XevXuznQ7IBvn5+anC27hxI9auXYt33nkHNpsNzZo1Q/Xq1Zkfm7LOU59Zo0YNSJIkGxAbVbtoupjEoIULF6JEiRLC9sUPVvv3788Gvr6+vihQoICMcE+dOhUffPCB8Jt16tRR7THp5eUFm81meOoVcJLUVa1alTmV85g5cyZ69Ohh6AgLo5g2bRr27NmDZcuWwcPDg52f6ebmhkWLFmHPnj2YMWOGS77FGxWts9oCAgJYJ9KpUyc24uU7Z0BbqeM7SoKvry/zSSC/Pw8PD5QsWZKRDjJctWrVwr59+zB27FhZGHFxcShQoAAuXboEu92OUqVK6apAfDwobCJYc+fOxe7du2UHtWcVROooz+hbBQoUwPfff68idUFBQbIRV58+fVRhrlmzRtMYApBthUIgpVCJ2rVrY+jQoaqNposUKQKr1cq2VtCS22nlHT81pqwLIsycORPvvfee7FnKmxkzZuDu3btCxVKkbtJvJdGn8GifL5EbgV5c+c7ParXi0KFDOHv2LMqXLy8rR/KBDA4OltV7R9P4tF1BZGQkK5vSpUvj0qVLmD17Nj788EMkJiayAduBAwcYgeXbp4jUVa5cGYmJiaoRLw8ifoMHD1b5exLCw8MRGRmpqsd83hQrVgxubm6ayhwPvTzx8PAQTmNt2LCB2QflOZNApi00unikRYsWGDBggGpWwt3dHRaLBdOnT8dff/2F2NhYTJ06FU8//bQwHD1Sx8clMDAQdrsdGzZsQNOmTWU2lny4QkND2ca6yjraoEEDlCxZEtu3b0d6erps8P7HH3+gatWqQjJRp04d1KpVS1i/lYOur7/+GnFxcUL/ORHRByCrL0q78swzz7B98uiektQBmTbFkatJu3btcOvWLfa9gIAAFuZTTz0lW+GZkpKiciGgGTUlskLqOnTogPj4eFy6dElF6iIiIjRJHbUZpfJGg8RVq1YxPzJSa319fVXhValSRXbqSXBwME6fPs18wZVES5ke3k58/PHHukd3EWibGJ6QKRfyTZo0CRs2bJCVpYeHB6t7vr6+skH///73P4SGhjKfbwKVU6VKlVQijjOuCgSnSF2vXr1ke07x+O6779C/f3/Dh3nnVyg7igULFmDhwoWIjo4WGgq6Rp2OUqkjgyHy2/P19WXvxcXFISYmRtVxU4H7+vqicePGsFgssukpmn4lHzv+PEoeRBKrV6/OrpFhoG/SNKmWAymlg0bd5cqVQ+fOnYXPBQYGYuTIkWwlKjXUMWPGIDg4WKXc3blzR9Y5ijpsHx8fzSliHx8fWd5T5xUdHY3ixYtj1qxZsoUv7u7u+OGHH5gjL4HC0FpuP3z4cPz9999wd3eHJEky8qmn1BFIFaZn3d3dWR2htiMiCfPnz1d1PlR3KMzmzZvjjz/+YB2JaDqaj+vVq1exc+dO4T1lnMkhnOpjUlISmwIqWbIkFixYwAyQI6WOn1qiuBYuXFhFkGjBD9+R8ufK8gbvzz//xPLly9GkSRN4eXkJF3107twZK1asYKq/1WrF6tWr2e72fLiffPIJ0tPTmSGn+sjbACNHtAGZdZDSTJ0FD8rTokWLau6VKfLlSk5OdkjqKD/JJijJBE8GLBYL/Pz88PHHH2uuanSk1E2ePBmzZ89m39m3b5+qE3Vzc8PVq1dl09HKOme322W2kHc7oeloEWn57rvvcPz4cdk9GoApv2Gz2VS27vz587qqCJ/fyncDAgKwcuVKtnE4oPapAzKJUGBgINtuSTlTNHbsWNa2qb4FBASwsJRKXVJSkuzcVXpH1A9kZfqV8jIuLg5RUVEYOHCgbD9Bvr3z9YviqJzVISWLByl1tDp6woQJ8PX1Rfv27R0uSFIuClKmkW+zhQsX1tzmivDyyy+zwTqvKittyocffohOnTrJCDZP6nx8fFi63nvvPUybNo35wPOgKVkfHx+VTRH1g47gFKkbMWKE6vByHrNmzcrSgdT5AZUrV8Yrr7yiqhCDBw9mx4DpkToiG3yl9vHxQXBwMJ555hmhkfTz82MVPjY2FhcvXlQZXWrw/Lc3b97MFKRXXnkFcXFxbLl9uXLlhI2UjvUhA1emTBlWGZVGRa+RT548mUnSV69exfr163H58mVs3bpVFcbEiROZ9E2GgcglH4+6devi3XfflXXoWiMUrWlfDw8PWQOglaWRkZGwWCx44403hNOzVqtV1sj5va9EGDJkiNDnCzBG6qijovKk6QQATCFVtqEPP/wQoaGhqtE4lROVX9myZdGjRw+WRzTVULJkSbz++uuydy0WC8qVKyfbuPP777/HmDFj4OnpicuXL8sOsyaQAU9MTGT5UKpUKRQqVIgp9bwa5O/vr5peI6LUoEEDeHl5oWXLlipfUSCTyNSuXVtzVSJf3l27dkX//v2ZURSRusKFC6Nv374yw9moUSOZIzaQuWqWnPKJ5FO5ubu7y47McoTPP/8cAQEBrG5UqFBBFTdqG+7u7prh8m2SVCQjpI5XDQB9X04ePKEKCgpirg/kW7V27VrcuHFDpugVL14cn3zyCV5//XWZTRHtnVeuXDnZdK2y7dSsWVMWB/5ZyiPR4IHaOP99IgVGlPSnnnqKuRgQ6tWrx+onr9aISF3BggVlfou8vaK8t9vtKFiwIObMmYPY2FiWTrJ5/ECTbIG/vz8LS6nUAY/8NefNm8fUL+WAlf+Go2s8iGDGxsbi/v37KF++vEwJ58tBOfNB8ejZsycrYxGpo/KiuIwaNQrR0dGaM2c8OnToIBsg6LlpBAQEsPv8Yg0aOLZv3162PcmMGTPY4hDlgIbqE29PeNLm6+vLFMjevXsL4wM8qtvUN/IzZTlO6p5kXLhwAfPnz5dl4q+//ip7RiTTU2GR4eAbMSl1q1evFm4b4uHhATc3N3h7e2Ps2LFYtGiRatqIDAdvkHx8fFC0aFFkZGTgyy+/lC0rd3NzUxmbUqVK4aWXXkKJEiWYwfrmm29USh1BpBTRyEWkTlSoUAFt27bVbVj0Db5De/jwIU6cOIEjR44gLCxM5jSvVZkXLVrEpuKBR0bcarWyPJo8eTLzczCydQKFV7RoUaF/Dw89X0L6fpMmTWSDHz5dSpJusVhQsWJFSJLECBYZcipH+rdnz56y6XNS9ihvyShQHIsWLYrp06fj4MGDmr5etMoLyHRYHj9+PCwWCypUqIC6deuqnicCkpiYiNatW2Px4sWM3FF94jsckS9gQEAAYmJisHPnTlgsFuzcuVPopN6gQQMcO3ZM86guvo4p66WjTVGV4LeXefrpp1GrVi0Aj0gddVwJCQnYvXu3oTMbixQpwnxjqWy8vb1lvqP16tVjCo6Hh4emfy/fHvi9KYnUzZ8/X0jClaqNUVLHxyMiIgJbtmxB0aJF2fYiTZo0QenSpbF27VpW/nxnzb9v5Jt8uztx4gReeeUVWVsLCAjAggULUKdOHdYelErdDz/8wAgIvzqfbLORaXJA7Y9XsWJF/Pnnn1i1apWsbivtrGj7Kjc3N5Z+vgzpmp+fHysjSi+fX6T0EKkrUaIE/Pz8NNsE79f5/PPPq3ztRMTWGVIXFRWFwoULw8vLi5E0ntSJZlL8/Pzw+++/Y+/evfjqq6+Etn3kyJFYsmSJzIXGkS3mwZeF3vQrP8Bq0KABc1Gh/rl3796q94OCglCpUiWMGzfOUFz4Nke2nK/LVqsVO3fuZLNcNFCg+rlt2zZGyE1S5wJQJjZt2lTo16XEp59+itjYWKaK8JVLSWw+++wzodLp5+eH8PBwdOnSBR9++KHqHiBWgZQdGal3yu/abDY89dRTuH37NooXLw5JktCrVy9NUqfEoEGDdPeQI3To0IEpkloSON/ZBgYGyox/3759sWbNGgDaqxiLFy+O1157DcePH8eZM2eYszI/QnJzc2NGb8CAAaowRGdKRkVFqTrq2bNny6Y1AH1SxyufnTt3xsmTJ3H69Gls2LCBOajT+5Qfoo6GlC7ltghFihSRTZfSKJBIgpLUFShQAB9++CFKlSqlqb66ubkxUqE37U6gOCUkJMBms2HgwIHM+JLKyatNw4YNQ9euXWXG3mq1wt/fP0sGi4evry/69+8v3MbEWVI3bNgw2Qp0climKWwid9RJOpp6vXr1qmyzWjLuynZx+PBh1ilS+T3zzDOYNWuW7Dm+nlB58aSuSpUqQhJObZvaA9/ewsLChNPBQKYNWbNmDduzz2KxoFGjRmyvNr7sKG945UOLnBpBSEgILBaLzJWnQIECGDx4sGx62t/fH9999x2rd3wdnzVrFrtO7cnoWdBKvzHy8+LVFrrOQ6uNXbp0CTt37pQRKt7mFihQABaLRUi8qY0TqaPFbGRrlIvLlPZJGUeRvfH09ESVKlVUpHTp0qX47LPPWBhr1qxBSkoKGwhSvvKkTk8NrV+/vuYiAS8vL9n+dM6Cr2/KNsa3VX9/f5anbm5ujMCTzRLlj8ViwcWLF1UKrhaIIPJloyyXli1bsnZMcee/7ewemDzEdP8/DKoQRkcJ5IdCDvw2mw2hoaE4efKkqpFr7dZN6tWAAQNUlYoalNbKUCBzUcHdu3dZ46J3Jk+ejBEjRmiO6oyQujNnzqBChQqGVsQC2v4c5KfCT/cpYbFY0L17d5nzrVY5kJJCjtY2m43lEeVhUlKSappCy+dT5PP4+uuvQ5IkhIaG4p133kFsbKwuEaF8J7WVV+g2btyIlJQUFjc9Ujd27FjUrFkTv//+O65du6ZJtnjnYuCRjC8aCAwYMAA3btwQugEUK1YMycnJhuq8kiTw+Pjjj9GmTRvUq1cPKSkpsjp7/fp1VKlSxWUr5AnLly8XlimRuuLFi6vOltQCX2e7dOkiC5fqo97WIzyURpkUF7Ivbm5urPyoE6C6ym9YKwKVQXBwMOrUqYNWrVppug1s2LABf/75J/vt5uaGZcuWYceOHZrnIhOUC98aNWrEwuLr1sKFCzFx4kTZNeqojAwUlKBOmHdD4LcD4vHWW29h9erVOHv2rKwz9/b2xoQJE9CnTx+m/hgtO6W6r5UGvr6ItoohFCpUCC1btsShQ4fYNb6TL1CgAAoVKsR8SEWkLiAgAO+//z6rI9T+ChYsyFb/u7u7q+ydcvAhGoxYLBacO3cOMTExWLVqFZuV6dChg4yw0Wb4vJ83ILedRlxQcgI8qdPrKz08PJidDA0NZYMU6p+c2cNRC2PHjkXz5s1lK21FYsD06dNRpUoV1v75vmDcuHH49ddfHboDiZDvSN2RI0fw/vvvw2q1IigoCMuWLZNV1J07d2LgwIFsKbzW4d5ZBe/r5Az4qbONGzdi/fr1hsOg0YJoyw5qOHq+isppYpLL+RGJVpzDwsJ0l3lTpTK6AIaMhpL8VK1aFVeuXDE0AuH9VhwZCX6Uo1xoYMSHxhEsFgtefPFFbNu2DYsXL9ZVaDw8PDTzyWKxyOJD6RKROhq10oHhyjwoXLgw7t+/zzqBBg0aYOnSpUxZFpE6f39/TJ48WUjqgoKCHO70TrBarfjhhx9UW2MAmW2GfI9EhnX//v2GTxLQQ8eOHWWdqKhMQkNDsWvXLpQqVYqRuuws4qLOzajao4RSqdu3bx+zXWTU9fZr42G1WrF161bUrVsXBQsW1N0zskqVKrJ9F4HMabnnn3/e6TTwaj1v25SKO/Co7inVLWdAedakSRPdFYtU/kqFpnfv3rh37x4jc0bL7r333pNtZ6VF6vh+ydG+nYCcBPJhFihQgO3rB2hPv/Ikm77tChtHCAgIwCuvvMJIndb+rby7ACAnLJ6enujQoYNLju90Bnz9E9mDhQsXMjeQmjVr4tKlS6hYsSKio6OxZs0apsgbaYNz587VPXDBw8NDtSOBiNQVK1YMo0aNYnnF9wXVq1fPsr3Kd9OvwcHB2LRpE3bt2oVKlSqpDkYHMpfE79y50+WEDshsbE2aNMHkyZOdeu+ZZ57BihUr0Lt3b5QoUUK1ok4Pn3/+Ob755hvh2afU+I0afCBTMRk2bBjzv9IilxUrVsShQ4cMjaZpakc0xSOCqGGVL1/e8IpBgtZGlASe1FEnYNR3xhnMmzfP8CHkRqCn1BGoDikNwsmTJ/HPP/+wOuHl5YUBAwawjrRx48aYMGGCcJT3xRdfqPavKlasmMMpeB5Dhw41vFCAR6FChQwdm+cIGzduxMiRI3WfmTZtGs6cOZOtaUAeFI7o9AkjUCp1YWFh+OSTTwA8InXODCTbtm3rkrObnYHI8V4P169fV+1R5gwoz5YsWaJLXsimiKY/+WMKjW651bt3b9jtdna8oBH7aKTstFxziNTRjA0/GOOnX3nQQP3FF19k26hoxSEyMpKtVnXG/mqFR0odv70Rn5aNGzdmefCTVThy5Rg0aJDsGDvyO3zttdeQmprK2qURpe7VV1/FhAkTnIqfEfLtrJCkhXyn1PGrnPjtHnisXr0aBw8eRJ8+fTS3nsgqrFYr/v77b837NWvWRLt27WRLuoHMxkI78jvCxIkTZQcbKw9Y5pEVUhcQEIAvvviC7SmopdQ5g7p16yI1NVUl7ytBowtnyZsIS5YscbjLO6Xtvffey1FS5+Hh4ZBgOgMjpC4sLAxXr15VOR+XLFkSJUuWZKs2lUqel5cXRo0aJQxz2LBhqmvt27fX3f/vcYSHhweqVasmI8TZUeqoPtO0v7NQkjoeDRs2xDfffGPYfuQVjO5oT3DmhBoROnbsiJUrV2oe40jQUuoI3t7eTpe91Wpl2z/pbVwPwLCvlRapa968OTIyMvDJJ59g0aJFsneULhYEmo6uVKkSunfvjq1bt2qejVykSBHme+mKgQCVx+zZs9G3b1/Z4gbl+bq5hax+02KxyHiGM/2ss99xBFf000A+JHWEGzduYOvWrarOqX79+sx5t0ePHmjWrJlspRMhJSVFttLSVSMH2uvstddey/JUkiOVgQc1ZppqcwZEwFw1AnBE6FwNo46zZLDpIOTcVjCyAtFyeBH0pqvJAGW3XJxRlR83uIrUAcC5c+c0O05H0CN1NpvN8OA0r8n3lStX2J6YOY2XX34Z/fr1c7iPGg2MsrJRqx569uyJ4cOHY/DgwZrPxMfHG/Yj49PB/5/vD5SnelAbVw7+6Mza8uXLMxVZr248++yzsNlswlMmlHB3d9ftb6hPqlatGusPR48ejQkTJrh0Ojg3QX2kK3zqnEXDhg0xfPhwptxnF3lG6iIiIoSrS9euXQs3NzcMHDgQCxcuVHVY/Iile/fuOHHihJDUTZ48GePHj3d9xP8fWXFgzAqyotQRyOchK6Ru48aNDg8vFoGm8Zwd1bsCTz/9NH7++ed8r3gA+j51RkGGN7fJ9uME3l4MHz48W2EpfdOcgR6pM4rY2FiXDdCyivLly2dp6j0r4FeEOnoOcLyJrrPw9PTU3ISZ4Mw3efJntB58//33sn3TCHReL78vKU3DimCxWAz7N965c0eX3IgGouPHj8eHH37oMrUpt6GcUnYV1q1bx3yjtWCz2RzWM2eQZyVQvHhxYWLtdjt69uyJMWPGCP1vYmNjmRy+Z88e1aaqhBEjRsi2B+G3HXmckB1Slx2lzshRKiIMHDgQVqsVXbt2zdL72YHFYsFzzz2X69/NCrK6IIeHo4UwJh4NLtauXZvlqVNXwBWkLi8GSo8DtBZn5TfwZMjoYG7IkCGyowgJXbt2xV9//YWSJUvCZrPh+PHjws3VswLRdPeNGzdQq1YtPHz4UPiOxWJxmf9qXqBly5ayradchS5duhg6U9mVyHcLJVauXIl9+/ZhwoQJaNWqFVasWAHg0VEgK1euRIMGDdCkSRMEBwejRYsWwnA8PT3h7+8v+3sc4QpSl5udvs1mw4svvpgnfhWPE4xOv+ph0qRJGDhwoFOHPf/XQEQor48vpJWEj+v0VG7g2LFjbIsiZ0D7FLp6+jU/4/XXX0dGRgYbFNaqVStHVdzSpUsjPDw8S7M3uYUaNWroHo3oCJ06dcryzMmoUaPYhvd5DYuU19Yul0DHscTExDxWBC89PR3u7u7o0aOHcCWwHm7fvs2IL+0EbyJ/gMq1VKlS7Ng1E67HjBkz8N5772Hp0qXCjahzC9HR0di4ceNjoyQ/TrDb7bh27ZrmqSn5CTSI+490u7mKjIwMWCyW/7ygYM7b5HO4ublh5cqVsvPgjCIvlDoTxkBlkhMrdU08Ail18fHxeRqPwMBAk9DlEGw222NB6EzkLExbmgmzt38MYHTJvBJEHExSl38h8pcx4Tr06NEDTZo0MbTqz4QJEyYed5i9/RMMUupovyUT+QvmFEzOo1ChQrr7TpowYcLEkwST1D3B8PX1xaFDh/J01Z8JEyZMmMjE8OHDNc+xNWHCFTAXSpgwYcKECRMmTDwBMD0LTZgwYcKECRMmngD8Z5Q6SZIQFxcHPz+///ySZxMmTJgwYcLEk4f/DKkzYcKECRMmTJh4kmFOv5owYcKECRMmTDwBMEmdCRMmTJgwYcLEEwCT1JkwYcKECRMmTDwBMEmdCRMmTJgwYcLEEwCT1JkwYcKECRMmTDwBMEmdCRMmTJgwYcLEEwCT1JkwYcKECRMmTDwBMEmdCRMmTJgwYcLEEwCT1JkwYcKECRMmTDwBMEmdCRMmTJgwYcLEEwCT1JkwYcKECRMmTDwBMEmdCRMmTJgwYcLEEwCT1JkwYcKECRMmTDwBMEmdCRMmTJgwYcLEE4DHktQdOXIEzZs3R8uWLdG3b1+kpaXldZRMmDBhwoQJEybyFI8lqQsODsamTZuwa9cuVKpUCX/88YfDdyRJQmxsLCRJyvkImjBhwoQJEyZM5DIeS1JXvHhxeHt7AwDc3d3h5ubm8J24uDgEBAQgLi4up6NnwoQJEyZMmDCR63gsSR3hxo0b2Lp1K7p166a6l5KSgtjYWNmfFtavX4+QkBAkJydj5cqVCA0NxeDBgzFx4kSsXr2aPffSSy/h6aefZmrfH3/8geeeew4AMG3aNISGhqJWrVqoWLEiOnTowN7bvHkz2rRpg9q1a2Pt2rUAgBUrVqB8+fIoX748GjVqhO+++w6hoaH44IMP8OKLL2LVqlVITExEw4YNUbZsWdSqVQuhoaEICQnBuHHjWNgjR45E8+bNERcXh27duqFevXqoU6cOfvnlF9jtdjRv3hxHjx5lz69cuRKNGjVC+fLl0bZtW0iShF9++QVTp04V5s1zzz2HPn36AAC+/fZbjBgxAp07d8auXbswbdo0/Pjjj2jTpg3ef/99vPHGG6hduzYaNGiABg0aoFWrVjhx4gRu376NqlWrolKlSiwdoaGhqFmzJiZMmMC+dfPmTTRq1AiNGjXCrVu3cPDgQbRq1QoPHz5EvXr18OOPP6J27drYvXs3xo4dixo1aqBevXo4e/YshgwZghkzZqB+/fqYOXMmypcvLysDAOjcuTPmzJmDxo0bIyUlBX/88Qdq166N+vXrIyQkBJUrV0alSpVQtmxZ1K5dG6GhoVi6dCn++OMPjBs3Dv369cMPP/yAhg0bYvr06ejWrRtefvll2Td27tyJDz74AAAwbtw4TJw4Ec2bN0dISAhCQkJQpUoV1KtXD6GhoXjzzTfxyiuvYNmyZQCAdevWoVatWqhVqxZat26NxYsXq8rjp59+wjfffKO6LkkS2rZti5CQEHTq1Al2u11Ynjzeeecd/Pjjj6rrFO+mTZvixIkTeP3111GuXDlMmTIF/fr1w+LFi9GhQwdMnToVoaGhCAsLQ506dfDss8/KwunTpw/69esHAPjqq69Qrlw5vPrqq+z+rFmzEBYWhmvXrqF///6oWbMmmjZtiiVLlsjqOGH79u2oVasWtm/fDgD49ddfMWnSJFk9qV27NkJCQjB06FBcvnwZDRs2RExMDJo0aYKQkBA888wzqnAvXryIsLAwNG3aFLNmzcIrr7yCt99+G6GhoWjUqBHq16+PefPmoXz58qhatSq6du2KuLg49OrVC6GhoejSpQu+/fZbNGjQADdu3MArr7yCLl264IcffsDs2bNZXqxbt459c8uWLayOtW7dGl9//TU++OAD7N+/H+3atcPo0aMxZcoUdOjQAW+88QZ7LyUlBY0aNcKZM2cAAJGRkazuzp49G127dsW0adNQv359jBw5EgDw77//olmzZpg8eTLKli2L8uXLo0ePHqxdVqlSBVevXgUAhIeHIywsjNnLf/75By1btsT48eNRo0YN1K1bF0uWLEGdOnUwf/58Zu8qVqyILVu2yPJ16NChOHjwIABg6dKlmDZtGl599VX8888/ADLtSc2aNVkbbN++PdLT0wEA7du3R7ly5VChQgV8+eWXCAsLQ9myZdG0aVNW/gsXLsSbb76Jo0ePomXLloiOjkZYWBhu3LiBEydOoE6dOpg7dy7CwsJw8+ZNAMDVq1cRFhaGadOm4X//+5/s2owZM9C0aVNUqFCBfbtmzZrMrleuXJnVkwcPHmDHjh2oUKECGjdujPj4ePTu3Rvh4eEYMGAAvvvuO1SvXh1PPfUUKlSogIoVK+KHH35A48aNmf2rX78+6tatizlz5qBevXq4ceMG3n33XXTo0AFLlixBs2bNMHLkSBw4cABly5ZF/fr1MXv2bNSrV4+VP49Dhw6hbt26aNeuHVJSUgBk2qNOnTph4sSJaNKkCR48eAAAePHFF/Hrr7+qyqtDhw6YMWMGwsLCcP36ddU3duzYgdq1a6Nbt27IyMiA3W5HixYtcPjwYQCZ7bxhw4a4ffs23n77bfz8888ICwvDd999pwqLsGfPHrRv3x5Tpkxhef3yyy/j5ZdfRvny5fHll1+iYcOGOH/+PAYOHIgKFSpg5syZ7N06deqgU6dOrKyrVauGOnXqoEuXLswO7tixA7Vq1UKNGjXw9ddfs3ry7rvvYs+ePQCAOXPm4P3338fBgwfRtm1bjBkzBp9//jmaNWuG0NBQ1rc1adIE9+7dQ9euXbFt2zYAQFpaGtq3b486dergwIEDiIqKQpMmTdCgQQP8/PPPeO211/Dll19i+fLlWLJkCcqVK4dKlSqhbt26zM7v2rULtWrVwubNm5GYmIhWrVqhXr16OH78OG7cuIF69erp8hgG6TFFTEyM1KJFC+n8+fPC+2PHjpUAqP5iYmJUz/7yyy8SACkuLk564403JACSv7+/FBoaKr366qvsOQojMTFRkiRJslqtEmVh69atVd9KT0+XJEmShg0bxq599NFHsrDor1q1ahIAqXz58uza/v37hWngi41+Hzt2THb/5Zdflu7evSsBkLp168aef+mll2TPpaamSi+99JJUv359YT7ScxkZGbL3atSoITVq1Ehq0aKFZhwBSLNmzZKmTZsmu9a+fXupePHiqrRs2bKFXdu+fTsLe8WKFRIAlt+TJ0+Whbd8+XKH+aSM/9mzZ6X33ntPN+4ApEGDBklvvPGGVL16ddn14OBg1TckSZLGjx8vFSpUSFjG/F/58uVVeTB8+HDJ399fCggIkABIffv2VZVH7969pVatWqmup6eny8K/f/++sDxFZat1HYD0zDPPaKahadOmsvqqld/KMAlhYWESAGn9+vWy+y1atJCqVq2qiteECRMkANLEiRMlSZKkIUOGSKGhoayeUDumPypf5XW73S4L97ffflOlrXTp0rrld/jwYVl7pec3bdokS0fHjh1Z+gMDA9k3R48eLfn6+qryt2HDhrr1+Ny5c6xeSpIkzZkzR/VsgwYNZO89++yzwjC/+OIL9v/hw4dLkvTIbq5bt06SJElq166d6j3Kb74N0G8e7u7u0pdffilJkiQNGDBAatSokeTm5iZ9/fXXqjpBf5GRkaq6rPwbNWqU7P2OHTtKwCM7MHbsWOnHH3+UAEhFixaVAEiTJk2SJEmSPv74Y1W+rlmzRgIgBQUFObQHfPlXrFiR/d6+fbsEgIWl9zdw4ECpadOm7HexYsUkANK4ceMkf39/WR4DkJo1a8b+TzZj6dKlqvYxY8YM9tyNGzckSZKY3fL29pYASAcPHpTlHQ+6VqFCBQmAtGHDBtU3Jk2axJ6LjY2VoqOjJSDTpkuSxN7dtm2b5Ofnx9JRr149VViE2rVrq9Is+nv11VdVZTd16lRVveT/Hj58qIo3/Y0dO1YKDAyUJkyYIEs/XzZaf/v27ZMASJUqVZIkSZIiIiLYvRkzZrD7FC93d3epUaNG0oABA1RhkZ2fMmWKBEAaPXq0dPHiRXZ//vz50pgxYyQA0saNGzXzkfBYKnV2ux0DBgzAmDFj8NRTTwmfGTFiBGJiYthfeHi4Zng2mw0AkJ6ezph9SkoK7HY7UlNTVc/TKMjoNf6eloKSmJioGY4RKN+z2+3sW5Q+0XOUTkffVeaDxWJBSkoKi7fee/z3gUzFU1RufN7w/7dYLACAjIwMzTRoQfp/VVW5mMZImilsyiMj39SqM0qEhYUJy6x48eIoWbKk5jdEcaF3jcTPlYiLi0NYWJjsmlZ+i0CqjDLuiYmJwvjTc/y//HNUT5RQ1j9Rvmt9SwsJCQkAwNJP7YB/LzExUbMupKSkICgoiKlFroQyfUbyhZ5R/iuCozYPZLbVtLQ0FhcqK0ftLqu2iL7JhwM8qmMEZV0AHuWXXppF8RT9NtLuxo0bJ1OM+bDofSN2VQn+21rxMGKb+DJTQhRXQJ3PVNaidqEFRzZDFAYfB2qTovta3zfaDziKi7Lv4vM5ISEBaWlpsvagF6bSxjsbv8eS1K1cuRL79u3DhAkT0KpVK6xYsUL1jKenJ/z9/WV/WiCfPLvdzipnSkoK0tPTWYbyBiM7pE5Z+QlUIV1F6lJTU9m3eJ9DESHi08lD4haVaKVP1JCUzyh9Ht3c3ODp6al6ls8brXwSxcVoQ1Fed4bUiYyWCFp5qYS3t7fqufT0dLi5ubGORyvPRXljNH6uRGxsLPNtJYiIt6SxOEmr401ISBDGn57j/zWSTqtVbuZE+a71LS1QZ0Xpp3bAv6eVDoqDh4cHPDw8HMTeeRgteyO+yCI4avPAI/JAcUlPT0dycjIkSdKNn5Ey1ctTPhzRs6I0i2y8IyjrBxESI3nv5uYmiwe9y+eNVh7rkUc9Uqc1IBaBLzMl+Gs8+RANKtPT01k6jJA6R4TTEakTEWG9tND1rNhKZXjKvksUL63BqjIMpY13Nn5Za9V5jP79+6N///4uC0+k1AFAUlISy1C+wuWWUqfVGYogIjoU56wqdUaIrKPOLyUlBX5+frJrNptNSOq0lDpRmHq/lWG6ubkJO3KjBs5qtTql1KWlpTnsIESkzm63w2azOSR1+UWpi42NhY+PjyoeNptN9n2tvHC1UqcEtR9HhDc7Sh2lPzk5WfVeYmKiivTycfD09BS2g+zCiJIGiFUrV4WvtH12u93QwNXIYMsIqdMiP3pKnREVSxk+wZmZFr6N89/llSqtPBb1Kcp7ovtaJFfvG0aUOhqUOFLqHPUTRuImCiOvlDrRzI9WmBQvLbtGduo/rdS5GiKlDpAXgqtInVblpsqflJTErhkZ3Wh9nxQmIOtKHR9XLbnfiFKnNKS5qdRpGbPsKnVaHQA956iD8PHxUZXv46bUxcXFqUiLKL+1yjKnlTqtsleWjSuUOtF7jpQ6Z0idMyqSK0mdaGBpRKlT2r709HRDxMfVSp2yjYmUOqoP8fHxut9VxpOHM6ROqdSJ+gmtPHaW1IkIgyM4o9Rp5TMRE2eUOkdxE03POlLqqGxdrdQp89eIUqdlD6htu0qpM0kdtJU6vhD0RkF0zd3dXXVN+bxe5fbw8MgyQ9cjdVlV6hxVLKOkTmlIc1upEz3jyL+BD1tLHRPB6IhYpOCQykX5ld+VurS0NFU6RPmtjJuyozFK6hwpdUoCQveUddSIUueIlCuVOlFYlA4RMXKW1FF8lGGJ/MCMkC5H0KvvWSF1uaHUUedpsVg046+n1Gl1/Eq7TvHk4Yz7jFKpozJ1BanTsh2ib2iB6poRpU5r+pXECZFbghYcxY0XPETvZEWpy8jIUH3XiG+lng3h67CbmxuLl1G7Zip1LoCWUsdPvxohdUq/PWocfAehV7mV77uK1PGkStlZ0ahA1Ik5qlgpKSnChqZ8JrtKnXKEpoyrXgesRbL4/NGDllLHQ+Sg7ShsngyQEVEqdVrqqBFlyREpcWZqXw/KcjSi1FF56nUIaWlpqjiKlDq9tkVxUI7glWUjyk9H9dqIUkf2Q6SypaamOkXqHHVOPBzF3Znv6YVvRE3nlRJ6L6eUOn67B6246fnUaUHkj+1KpU4UDyN2VYnU1FQWV70FOkaRFaWOvquceXKFUqdH2vhvie6L0qKMszPQsyF8Hfbz82PxSkpKEn5Ladd4f/isxM8kdVArdbzzshFSRw6uWqTMqFLnLKnT83nLLaXOEVyh1DlS5rKi1BkhdR4eHuw5vZVZIjLqjFJH5ZObSp2RkbMRKMvRiFKnNLRacVEaM5FSx/svatULR6SO/C6dWf1IYSqVOq1BkxJaSp1WHLRInTMuGs7AaNvmwcddpNQZCTs7Sh1P6pT5QnHTU+q0ICJ1drtdlt7s+NQ5ioeW0iu6RnHVCssZUqdlZ6jO8rZIafeUBMyoUqc3yNFaCCFabMTHUS8t/DPOwIhSZ7PZVIM+Z5U6T09PU6nLCqgTTU9PR3p6usxQGyF1VIhGSJ0rlTo9Pz8tpU70HKVbqSjoGWJHq9j495QdlbNKXXZIXXaUOh8fH/acEeLI/98ZpY6vf7nlU5cVQyZCVpQ6uudoxaDWaFg0shU974xSp1UntaCl1GkNmpTQInVa0FIcslOOemptVkid6J4o3nmh1FFac1qpowUzejCi1PFwZlutrJI6kZqsZWeozouUOq02Z5TUaS0sArSVOuXACoAsjnpp4Z9xBkaUOlH71hNPRD51ogV1jmCSOsiVErvdLqtYriZ1rlTq9OLkrFInuqdniI3sQ6YMn/C4KHXUoBw9+zgqdTlF6pxR6kTtQy+OIqVOLxwt1UAUrlad1IKWT53WoEmJrJI6Z8tZj7jxZEHp8O1qUpebSp3SNxp4lFZXKnU8+BWOjuCsUqdlB0TXjJA6EYEzYlPoGtV53hY5UuqM1icRQSNoKXUiIsjHUSstjuyPo7jyECl1RkmdnlJHwoIzMEkdsq/UUUPJaaVOqXgZJXVGlDrRPWeIlRZEJERPqeP9y4x+2wjhygqp45U6vdWHplKnjodenc8qqdNS6rTC4feHchSuqE4a2YA3u0qdVgcvep7iqvc9JfQGX/y7SrXTSP3QaxPKcHJTqRO1EXrH6FQmD2eUOiOkzlmlzhlSR9tH6YVldLW3s0odP3vjqM2JkJGRkatKXXY2/NeyTRaLJUeVOiOr4E1Sh+wrdVTZclqp06sgeqQlJ5Q6Z0idskHZbDbNFWVubm6qfeEcfVtvysNVSp0eTKVOHQ+9Op9VUuesUhcXFwcg60qdnopmVKkTra6j5yh8I2qdluKQnXIW2Y/sqBeisEXx1gvbFUqdqI1o5Z+j+ADOKXVGVgZbrdYcU+q8vb1V+0Qqn3FGlRNd01Lq+AFEVpQ6QN2eeLhaqcvOhv9atsnT0zNHlTojPrQmqUP2lTqqbDmt1OUUqcsLpU6kgpBi4ubm5hSh1Bsdu0qp00NuKXWSJKlWRhFySqlzNDLMqlJHAyi9uGgNMowqddTJZ1WpM+K07eXlpRs2oK0wZIXUZaWctWxObpK6/KLUaU2F60G5ebooHKNKHbX1nFLqiEjohZVTSh3/TWU+ZGRkGPKrc5VSR+1Sr9xzQqnz9PTMUaXOSB6apA5PvlKnFyZPurQqKpA1OZ2eEyl1IpBiYrPZnCKUeqPjx1mp44kPYHz/KCPfN1p+joxIVpU6vbqrdd1ZpY46+ZxU6pSdsygtWgpDbil1RvI3p0mdsj1r1ausKHUUVk4pdVp7SvIwqtRRW88ppc4IqcuuUufp6Qmr1SoLS0nGHW0/ogU9pU4ZH5ruFZWP1WqFu7u7brlrKXWOtnuic89FcTOVunyCJ12po2/SIdvK54wodTSVpfU9LYiMiNaZk/lRqUtLS3O4oi27Sh2/pQ6v1CnD0Rt15pRS58iIZFWpywqpy49KnRFS54xSp5XfWmX/uJA6ZXvWCj8rSh2VRU4pdY5W6vNx+C8oddRW+LAcKXVacVZCT6lTgjaQ1yKCfD44Uup4IudoICua3jaq1IkIo6nU5QD0lLr09HSZX4yoQPOjUpeenq7a+FFrabwRpY7fLkCZDj1Hb5ERyY5SJzrHNSeVOsDx5o85pdQpw9EbdSrLw5mOUW+K9XFW6qjc+PohypvsKHXKuixKC3UevEHXInWO8uJJUeq0ws+KUqdUXEQDSb22k5Ki3kuTh7IekM8vX565qdRptW9HpI7ey65SR22FD4sn4zabzSmljs9HPaVOFF5KivY2KHw+OFLqeBvvaBAv6oOMKnUimEpdDkBPqQPkRsjPz09zpJiflDrgkbqmRzR40qVVUQE1qdOLtzJ8Vyp1/v7+mvmvFaYyDCCz4Ro5n9UIsqLUFShQgP1fy6dOGY7eqJO/JsojJYyQKuDxUOoc7XfH1w8vLy9huPlFqdM6SUCr7PMTqRMNxETx1otTdpQ6/r5RpY4G7Ho2TFkPyD7x9iMnlTplnLXatyNSR+/ltFLn7++fK0od1SNXKHV83ByVYXaUOhFMpS4HoFTqPD09Vbujp6Q82iFaa6QoImXk3E5Q+knxcDWpIyKmRzR40qVVUfmwjMRbGb4rlTqRQcuKUmfk8G6jBiYrSh1flq5W6lxJ6h4Hpc5ROHz9EHV4onynZ7WQFaVO+ZyI1Dk68zM/K3V64eWGUsffN6rU0fecIXVkn/h45KRSp0R2SR2fB8pzmHlkVanz9/fPsk+dcuGRHlyp1InKUgumUvcYQKnUKUftVOhaDUZvzyqlGqQ3ElUaFmXlyiqpe5KVOqvVmiWlTi89hJxU6vijbZRKndX6qFlmRakzsgv5k6TUiZ4XHeFE8dVS6pTIKaWOHLxFpE6rLj8OSp1eeK5U6lJTU2VTdtlR6uh7WVHqROpOdpQ6I6dRAK5V6kjlzi9Knd40uCi8/4pSp9wcXA/Gc9AFOHv2LA4cOIDGjRujatWqOHfuHL799lukpKTghRdeQJs2bXIzOgw0ajpw4ADOnDmDLl26wNPTkzWylJQULFiwABaLBZ6enti2bRvmz5/PfJFWrlwJQN34d+7cidatW8uu2e12/P3338J4KJfOL1myRPabD3/r1q2yivX777+rwjt48CAAYPHixQgNDcWdO3dUz2zZsgXXrl0DAMybNw83b97EkCFDAMgrkDIuevHmYUSpGzFiBKxWK65cuSJTrfgwCEqD5unpqTuymj9/Pjw9PbFx40bZdcobPRhV6mJjY/HZZ5+hbdu2hpU6nrhJkoTPP/8cBw4cQEhIiKzDOnnyJJYuXYqiRYviyJEj7PkRI0bAYrHgxRdfxO7du7Ft2zb2jqenJ/bt24eDBw/iypUrSEtLQ6tWrVC6dGksWLAACQkJsud37NiB5557Dg8ePMCmTZtk8XRWqdu1axfOnj0ry+/Ro0fLnklJScGff/4p+y3CvXv3MG/ePERERKB169asTmzZsgWLFy/GxYsXVeHy8aL2y9cPNzc3zJ07F2PGjGHEj9QHR2njoaXUidohdRKpqamYN28eG+g5Q+pWrlyJhw8fYv369QAy82b+/PnstxZmz56NiIgI4T0+rlQmly5dAgAcPXoU69at0w1biYSEBIwfPx42m022eGH8+PG4efMme+7hw4eYM2eOMIx9+/Zh3759Dr/FD5ajo6Nl9/bs2SN0oVm6dClmzZoluz537lwcPnwYzZo10/yW6Ozq+fPnIyYmhl1zhVJnZI87INMG3rp1CwCwf/9+eHp64t9//8WDBw8Ykfjnn3+wd+9e1XvKQfb+/ftRsmRJbNiwQfWdZcuW4bXXXsPevXsxYMAATJ8+HZs3b0a/fv3g6emJHTt2YNeuXQAy8/ebb75h31GWCQB8//33qF69Ou7evYsBAwbg77//Rps2bXDixAlV/hjBlClTkJSUpGmnPTw8MG/ePHzxxRfCwSnFMTU1FV999ZXquhbc3Nxkbcdut2P27NkA5Eqdv7+/YVI3bdo0WRnMmDEDQKabzr1797B48WL2LYeQcgkbNmyQPDw8pEKFCkkFChSQNmzYIBUtWlRq166d1LZtW8nNzU3atm1bjn0/JiZGAiDFxMSo7j148EACwP6GDx8uFStWjP0+duyYBECqUKGC9O6770re3t6y5+nv4cOH0lNPPSV5eHgI7wOQQkNDpeeee051PSQkRIqKipI6dOig+W7Hjh1lv5csWaL5bEhIiFSxYkXN+wCkUqVKSV5eXqrrGRkZkiRJ0s6dOyUAUmBgILvn6emper5du3aSzWaTAEjVqlWTpkyZwu75+PhIo0aNkj1/584d6cqVK+y3h4eHZLVaJQBS5cqVpQoVKkiffPKJ1KJFCwmA1LZtW3bv/fffl4U1ePBg3TQCkOrXry8VKFBAdq1ixYpSjRo1dN/7+uuvhdctFovs99SpUyUAUoMGDaSmTZtKAKTJkyfLnlF+S5Ik6emnn1aF/cYbb0hDhgxhv9u0aaMZP5vNJgUFBcmulS9fXmrYsKHq2XLlykm7d+8WhtO1a1dJkiTpmWeeEead1vfDwsKkpKQk4b0CBQpIrVu3Ft6bPXs2C7dEiRK6aaS/unXrSs2bN9d9hsJ5+eWXpd69e0uVKlWS3W/ZsiXLm1u3brH2//rrr0t169aVZs6cKVWpUoU9T+0tLCxMqlixouTm5qb65q1bt6QPP/xQqlWrlqyd8H98e+D/fv75Z0mSJKlz58666Wrfvr1UokQJzfve3t4ye6Ws58p8EP0p66e/v79Uq1Yt1vb02oDIJmiVvVb+hISEsN9eXl5SqVKldN8he05/1apVkypWrCgrvwoVKkjVq1dn9UcvvFmzZkmhoaFS8eLFVfb75s2bUlhYmOTh4SEtWLDAcLqATNvG50+5cuUkSZKk48ePO3yXbKLyr3PnzlKJEiUkSZJU92bMmCFrT9WqVZO916JFC+nMmTPs2gsvvKD5Hf7v4MGD7P8DBgyQPvzwQ6lMmTLCZ6dNmyb7XaJECal27dqya+XKlRO+O2fOHM36qnyHynvTpk2qZ5s1ayZ169ZNAiCdPXtWatmypVS+fHnJ19dXqlKlisM28dRTT8l+d+rUSerTp480e/ZsVRxWrFjBfnfs2FHq1q2b1LRpU+nFF1+UPv74Y6fqi1bfQn8zZ850yHVybfr1s88+w7Bhw3D//n0sXLgQzz//PF599VVs2bIFW7duxccff4wpU6bkVnRkUI4OlNOvNFX35Zdf4ttvv0XHjh1VYYwYMQKBgYE4f/48UlJSULt2beG37HY7UlNT0alTJ1y5coVdP3nyJAoXLoxNmzbhr7/+YtcvXbqEkiVLAtCffuXh5+eHkydP4tKlSyqlkNCrVy+Eh4ejffv2qns0CiaVZurUqezesWPHMG/ePNnzvMy8cOFCDB8+XBbH9PR0VKhQgV2z2WwoX7485s6dCwCoW7cugoOD2T2RT11AQAAuXLiAsLAwdv2nn35CpUqVZHF5+eWXVUpoXFwcWrVqxeLdqFEjXLp0CadOnUK5cuVU6af81hoBKq/z5z5SnvEO7y1btsTWrVtV4axduxYffvih7JqyLvJqAI8qVaqgYsWKiIqKkl2nkbsSERERqukdi8WCPn36sCkYZVjAozrQvXt32fU5c+bg0KFDsgUfPFq1aoUFCxbIrn311VdsOiEtLQ1vvvkmihYtamhaJjEx0eEoNSUlBYGBgfjxxx+xatUqXLx4URbvjRs3YtKkSQDkU12k1L399ts4d+4cU54pH59//nlcunRJOGVvs9kwffp0HD9+HL6+vppxF0Frn7pZs2Yx1QMA/vzzT12lPCIiAnfv3sXly5dl17/++mtcunQJFy9eZHZAWY4+Pj4YNmyYrAw++OADDBkyRHc6kNpAYGAgkpOTsXTpUtl9I+4NQObszfDhw3H48GF2rW3btggPD0ffvn0BAL/99pvqPWWdOXPmDC5duoSzZ8+yaxUrVsTp06fx3nvvyZ4vV66cLMwCBQrgjTfewIkTJ3Dnzh2kpKTg9ddfB5CpVgUHB+PQoUNISUnB4MGDZd8lGybCW2+9hZSUFCQnJ+OPP/4AoK/UKaFlf/R8ZvWm/JRKnY+PD1JTU4Wr3/mZBEC9gnz69OkyO0vK6LBhw/D+++/L3i1SpIhqZuTBgwfCOPr6+uLixYsICAgAAJmie+XKFTz//PMAgF9//RWXLl3CuXPnNFXWsWPHAshs63a7Hc2bN0dcXBzOnTuHCxcuqJ6nb/n5+eH8+fNMLWvdujU2bNiAX3/9ldULHkqfXWd96ngoFWYtn2U95BqpO336NAYNGgQA6Nu3L+Li4tC7d292v3///jh58mRuRUcGkbwuInV6m4UaXTnHz7drNWz+Xa3/A5mGTXTcFg9HxkMUT6VzKV/RRBWVv6b8Xnp65vEx/HXKb7rm7u7O3tfyqaNnlQsMRHEROa/rxVELWr4ayut8o6Y8UzZ0rW+KBhQ8tDpHGngoG7kzB9M72tMKkDsAK991FLYybbyDNd8GjPpvOZoK5uuJKJ583ih9/vh8V7ZzvY6Yf0/L4GpNq2nZE2XeOSpTrXrN/3Zku5RT147qBbUBSr8yXKOkThR30cIEJfi48ffJRYa/zk/FA5nlJCpvERzZCj0XDZHt1vOpU8Ld3V34XHZJHbUjvcPilelWujDQtwhUH7TavbKf0vquMmwK12q1yvxkle1DBL6tp6enq+qJEvQtZXt3hph5eHg47VPHQ1mftHyW9ZAnCyWsVisKFCiAwMBAds3Pz09TlchpGFXqXEHqeBav1bCdIXWOKo3kYHdsPVLHO2vyz+uROlGaEhMTZdcpv3lyxxtikVInMiRa21Ao45CUlOQwjiIYVep4JYbyTOmcr/VN0YCCh1bnqNXRi/JEC0Sm9Tpv3gGYhxFSp0wb72DNtwGjpM6IUqe3glWL1Cl96pTtXK8j5t/TMrjOKnXKvHNE6qjDFOW3o2+lp6erSI8RUkdtQKvjc5bU8cqQMixH239oEXneZvCr3ZUdvF7eOrIVzpI6Z5Q6rbacFVJnsVgYiaN2pLegSm9wKSpzygctm6QkUY7IJIVD4erVbUf9KKXZaFkq27szxMzNze2/o9SVK1eOOeECmdNEZcqUYb/Dw8NRokSJ3IqODI+LUica7TiqNM6uXqRw+Xezo9QBmaM8PaWON145odTFxcU9sUqdKDyjhoRUjZwidflJqbNYLLBardlS6pRTUkDOK3WkUOjlN3WYWVHqaAsnnvTkhVJnsVg0CaIjpU6LyPPh8fFRdvB6sx1GZjqcsePOKHVabdnf359tii/6plb/RGWaFaWOzz9HSp2jsPSgpdTp1W1Ru+TDECl1IigHKllR6viV0Xml1OXa6tc33nhDZvRq1qwpu79hw4Y8W/2qrBT5VanTmlbUg7OrFylc/t3cVuoA9erX7Ch1iYmJLlXqPD0z9zEkFZSUGD7OrlLqtDajzYpSJyIdjjpv5UpN/j09ZFeps1qtsk4rIyMjW0qdsv4YUepo2xm9+pLTSh0RNiOdQ3aUOr6eUf3OjlKXlJSkKkMRlO2ZSKZeugDnlTo+fUam4vS+rfwWzSwo0+sKpU4UN9p+RVQ+Rkidq5Q6flsmR0odD716kRWljgYESvvgrFKnRbydIWYUj7xU6nKN1IkcDHmQA3NeQFkp8pNSJ9rPjPCkKnUWi0Wl1Iny3qhSZySOImgpdRRf5ZYZPKlwlVKnhawodVqdgF7nTfdyW6lTumOkpqZqjsj5uCo3LxWpNny6AG2lTllPRdBT6jw8PGCxWLLtUyd6RoTs+NQpfxOp03LfcKTUAcZcakSD1pxQ6ngY6eD1vq38lpubG1JTU1XpdYVSp+VTB2Sd1PFKnfJMb0BMckVKHf8ctTsjSp3Wlid82M4odfRbj9QZUeqUhDsvlDot+0XItz51+RFK0pFflDot51Ag81SE3Fbq+EUNfBjOKHXUOTvyqaMOJbtKnZE4iqCl1Cm/y29i6WqfOi1kRanLbVKXHaVOdLpKbit1RlQVPaWO6lx2fepEz4igV5ectV2enp5sM3YR9JQ6ms7U29BXFGctgugKpY6HkQ5e79vKb1FYyvS6QqkTlVdOK3WenuoFWI6m0/XIvTIP9eqFsj4RydFT6kS/+TCMKnVKwm1UqePzKrtKndIVIF8rdfkdytU0+UWp4yGSxHklTwRHlUD0vp5SJ/LvMaLU8d9R+gApDTspdfzpDCLJ32azqUaVHh4eLlPqtEidsn4YUeq0VCZXK3W835gSriR1juqdnlKXnJyMtLQ0XaVOROq0tk/hn1F+k+LpjFJnZNoVeOSnRxApBXmp1PFtIytKHaA9daxU6vj64OnpibS0NEOkTjRofZyUOg8PDxaWHqlT1in+Xy3ibLPZhO2Mts5xhVInCoNWcPLgSZ2eUGBUqdOCkki5u7vD3d3dkFKnFZfsKnWObJ2yLmZHqXM0mDOVOiegJBb5RanjIXJeza5Sp2cwRUqdKH7OKnUER0oddZKpqal5otTxBlsZb2eVOi2/HaNxUUJvmtWVpC4nfOpoyoeeUR6lB6hPKTGi1PH1RBnP7Ch1WlDez29KnZFvaX2frmnFXU+po/8bIXU8/otKnd4iDZF98/DwYIMbI6SO35LI09MTqampmofF82HokTq9duhqpY7yICtKHfUlOa3UKetiamoq8w11ltQ5Gszlq9Wv+R2uVuq0jAZP6lyh1DkjDRsJE9BX6kTxM6LUia5r+dSRwzR9n9/njv92TvvUaREnPaVOa/Wr3jd4GBmJAfrTrK4kdTmx+pVvTzabTXiMnOgcZUd1WdRZa6k2SlKn51OnBUc+flTn8kqpM/Itre/TNa246/nUZZXUPY5KHU84jJA6JWHQW9msZd/0CLeSSPBKO12nRSNaSp2np/70a24qdZQHWVHqKByyHTnlU6esi1QuplKXx+ALXNnQskLqtMBLs46WYuvFkeKV00qdxWJRyc+5odQp/XnyQqnTIk50XWlcU1NT2ekMSqVO7xs8DJ3tB9crdc7uZ2iE1GmtKufbkzL9Hh4eQmOYnp4uVPSUcKTUubm5wWq1qqZfs6LU6a3s5NPhCqXOSJ3VI5lZJXX5Vanj99VzVqmTJCnfKHWi+PHf1isbkZ+bsu2ICAZdEyl1VG/1SN3jotRRODSDkhNKnSRJKqUuO6TOVOpcCL5TUy6rdyWp45U6LWhVvqyQuuwqdSK/NT1Dk12lTjT9yj/LfzuvlTqRmkGj4LxU6rTqjxapA8DIqBayQuqU0FLqlO9pGUMtgqH8higeys1tXTH96kixoD9XKHVGwO/1ZvRbWt8X1W0epNSJtlzJKqlzRqkTnXCg/L6e0uJKpS6nSJ0jpc4IqaN8EpWpSKnTanu5pdQpyyy/K3XUn/NhiPLcKByRuny1T11+B59ZtHcTITY2FhaLRZe5Gy086jz1njfqf5UbSp3eqiL+t54K5oxSxy+UeNyUOv7/WVXqjJI6PaVOC3qkztEGwM6SOpFzsYjUiTpkrXw3Quq0Onjl/mHObGmSFVD9sFgsmvHWsidaddgIyHVBFB/Rt7S+72ihhJ6fbVZJHZWRMj5aNoXgrFInekcLRkgdCQJ6pI4Gq0oVSCt+9ExWSB3f9pxV6nKC1Dmj1CnLjPJA2SfmF6WOZt74MLKj1Iniz8NU6pwAn1m8Uufh4cHIk95GoM4WnqMVNSLklVKnBJ839NuRuuEqpU65+jU3lDplWWldJziarlaGxYNPs7NO1I7giNQZMdZ8metBa2CSHaXO0RQxoN3B8/nqKqVOD3rpED2r/J3Vb7taqdOC0s9WufoVEHfeenZP65xhZZo8PDxyTakz4jdJ7UaP1NFvipPesWjAo1X8WSF1ojhrKXVKFwIjpM6Z6VeRevykK3Vkp/i8NNrfixZb6d0XwSR1/w8tpc7HxwcJCQmyzNVqNM4gKwxeaYiU8RIhJ5U6/mgYR+qG1uok+pd/383NTVOp48lCbil1yg6M9/MQ5Z+jhSXKsHjwadbbwiMnlDo9tU5U5s6C2hU/PSHqkLMzwtXq4Pl8zQ2ljvZzzCqpy45Sp/cNV5E6pVInIimiLYGUm6vy0FqUo0yTj49Pril1jsAro0ZInZGZDyAzjVlV6kTQUur0nuPBk2hnlDpRmrOi1CnDUJYf9Rui+OS2Usd/21m7qbWBstZ9EUxS9//QUur0jj/hoXffFSQwq+E4q9TxkrzW6MZqtcLNzU2WN1lR6miU6IxSpwzTGaWO4m3UoNOeb0rjx48eKSx+Cw5HW8Aow+KhVJS0kBNKnRFS5+3tLXNFcAZKX1VnlTojyC9KnXKKXg/KZ7T2WjSC3FLqjBxkLyJneuqzllKntHsFChTItlLnKlLHn37jjFKn9xyQmb9ZUer0SL1IqVOCFkqIQDbOGaVOlGbaZ08Eo0qdciZAa5CbF0od/21necR/Wqn76KOP0Lx5cwwYMMDQqjhH0FPqgOyROqMN2RFEU1quVur4Dk9vdMMrWHzlVRpgMuKicChuzih1PJxV6vTua0Gk1FF8+dEhb9CzotTRv3yaHR007kwdUq7SUsbNGaXO01N73z09KH1VRWXhjMIlgiuVurwidVklzfRdvW+4gtQ5qnta3+IHQSJoKXWihVrZVeqyo8Iq4Qypy2mlTgtGlTrRiUEESl92lToj9tCRUid6TzSz8bgpdcq8VfYBT6xSd+zYMURERGDPnj2oXr06Vq1a5dLwXa3UZYWMGYUoHP57WpWAnhE1OEdKHT2nVOpsNu2VsqJwKG6uVur0FE2t0bIWREodKXh8J8Ub9KwodSJFSQ/OKnVK46OMmzNKXVbrrhGljso0r5U6/rSIrBBYZ9LhSiVJ1Ab5b7iC1DlqQ1qdoSNSp6XUKeEKnzpHvnLOwJnpV1codeSflVVSl5CQoHnyjB5hp/RlV6lzxh7ytlYPWVHqtH7zC/gcxddVSh2VqTJvlW35iVXq9u/fjw4dOgAAOnXqhH379qmeSUlJQWxsrOzPKHhFwRWkTuTgnZOkjkdWlLpLly5h7969uHr1qu7IX0nq9EahRpU6WiH28OFDtj2I1vuiqVSt/MiOUqfle6Kl1DlD6pQdYE6tfrXb7bh586bqOoVx6NAhxMfHa77vClJnRKlzRuGi5/V+Uzj8li2enp4IDw/H3r17sXfvXsTFxalG2NlVcrKj1NH7gLHFIaLvan3DCKnTU2uMKCdaC8q0/J4IWkqdKL58Xc6KUqe3x6CzoLrlSqWuQIECmkqd1WqFu7s7rl69ajiOfF28efOmZj3RGyzStGluKXU0QDGq1GnV7Vu3bkGSJId+edTWlOROL76pqam4e/euKv70nlEeQfcd2f8nVqmLjo5mDSggIAAPHjxQPTN58mQEBASwv9KlS+uGWbt2bfb/Bg0aoFSpUggMDGTvFStWjN1/6qmnVO8rV7e0bNmS/b9+/foAgMaNG7NrRYsWZf9/5plnhHGqU6cOAKBPnz6ycHgUK1YMxYsXl13r168f+/9zzz0nu0f51q5dOwBAzZo1AQClSpVi4S1ZsgTNmzfHTz/9hEKFCgHINNRVq1Zl4QQHB6NOnTpwd3dHUFAQSpUqJYsHkW7Kt0KFCqF169ayuJQtWxYA0LRpU/ZcwYIFUbhwYZw9exYxMTHsWYoHAFSsWFGWFoKXlxcKFiyoyiPgUX4r49m7d2/Zc56eniy/6fly5crJnilSpAgLp3DhwgCAatWqsc6sWrVqsudLliwp+12mTBn2f3q/UaNGADLrTatWrQAA9erVAyCvN0WKFAEAtG7dWlYneYSFhQmv79y5U/b7ueeeQ5EiRWCxWDB06FBhOwIy8zkoKAju7u6oW7cugoODZfcpD/k2FhISwv5fokQJAEDVqlVZnD09PeHn58fST6hcuTKCg4NlaatUqRJTVpR1qHPnzrLffD0BHpU75SWQWSc3bNiA5s2bo3nz5ggPD5e9R2VLNkFZnlRvK1euLLvepk0b9v/ixYur0qGFoKAg9n+qvxQfvs2L1IpevXrJfhcvXhzdunVTxbtkyZKsrfJo2LChavGCl5cXvL29hT5X3bt3R3BwMCu3ChUqyO536tSJ1XdKO5V/x44dZW2vevXqsnfr1q0L4FGZkY3y9/dn/6dw9+zZw35Tevn7wKM8pHQEBgaycHm/Lr69E8h+K/MLUNt/qluUTiDT/0x51J3S9gCZbZBsLwC0aNECAFC+fHkUL15cVX+o7RUrVgy7du1SxQ2Qtz3+vcDAQLi7u2PPnj0oVKiQKmybzcbqLdlvHmS3OnbsyK41adIEANClSxcAj0jLiy++qErzgAEDWNy1QOUSHBzM3uPDaNu2LQCo7Hzx4sVldpLiw9cVpW0YOHAg+3/r1q1ZO+jUqROATFvr7u4uK1cAqFWrFvt/eno6Dh48yH5Tu3B3d0dAQADc3NwQFBSE+vXrw9fXV/Yu8Ih3UDvn7RnVVx5GBv0WydmhYD7A7Nmz4ePjgxdffBGHDx/GTz/9hO+++072jHI6KTY2FqVLl0ZMTIxw9U18fDxiY2Ph7+8PX19fSJKExMRE2Gw2XL9+HcHBwTJDQKpGWloa7HY762yVYcbExLDC8fLyQnh4ONzc3FjjTEpKgru7u2oUkZKSwkZkGRkZSE5Ohre3N/tucnIyHj58iIoVKzL/M6vVioyMDJlcbbfbkZqaCovFgoyMDLi5ZZ616ePjw0hIfHw8vLy82IkId+7cYfEoXrw4AgICZPGheHt4eCApKQm+vr6yOAKZlS8tLQ0pKSm4e/cuKlSowJyKed+H+Ph4lt/Xrl1jxODy5cuwWq0ICgrCnTt3UK5cOWYw4uLikJCQwBr6gwcPIEkSPD09WRklJCTAYrHAbrcjNjZWlt+8IzrFm/LOZrPB3d2dpSU1NRWSJCEuLg42mw3R0dEsjunp6UhOTsbdu3dRsWJFREZGIjk5GeXKlcPVq1fh7e0NX19fFqfk5GRWBvwg4Pr16yhRogRSU1PZs/Hx8fDw8MCdO3dQunRpJCUlwc3t0WkIlGfXr19nRtJqtbK8jY+PZ+Xq7u6Ou3fvIjU1FcHBwXBzc5PVk9u3byMuLo7ld2JiIjIyMuDn54eIiAgULFgQRYoUkdUTnghQHUtLS0NSUhJ8fHxYOpT1RJIkXL58Gf7+/ihWrBjsdjsuX76MokWLwt3dXXbw+bVr11CiRAm4ubkhKioKSUlJKF++PBITE9mo2tvbG9euXUORIkVU9YRw+/ZtBAQEsM49MTER4eHh7L7FYkHFihVVdYLaG9/uk5OTYbPZcP/+ffj4+Mg6bjr1wmKxyPziLl++jGLFiiE6OpqRBL6sqLwSExPh4eHB7IUy3jSNZLFYcOfOHZQoUUJlO5T5zYPSQm0DeHRgelRUFKv79F5kZCSio6NRvHhxxMfHw8/PT1b+N27cQFBQEMtvsmWSJOHWrVsoV64cu5acnMzeBTJVEfo2ITExEQ8fPmRtVZIkJCQkwNfXV3aiSEZGBm7dugUvLy8ULlyYLd4hZGRk4Nq1ayhXrhwbDERGRsLd3Z0pYG5uboiPj2flIJqOVZY9IS0tDWlpabBYLPDy8kJCQgKio6MRHByM+/fvs1N4lO9S3aE0JyYmwtMz8/SG9PR03L17F+XKlUNCQoKwHZDa5OHhgQcPHiAyMhIBAQGw2+3w8/ODu7s7a5fx8fFISEhAfHw8SpYsyWze3bt3ER0djaJFi6JQoUK4efMm7HY7fHx8YLVa2b8Wi4X1BUWLFsXt27dRtmxZ2O12WX5T3+PpmennSHWV73cov+mal5cXrl+/jiJFiiAyMhLFihVDWloakpOTWfvg22BaWhqzV3yd4EH9U0pKCiwWC4tjSkoKrl27Bnd3d5QvX17lmqSMY3x8vKpvVH6LL//bt28jPT0dpUqVYmVz5coV+Pr6srQkJCTAy8sLiYmJcHd3R0REBBtMe3p64vbt2yhSpAgyMjLYQkW+nyAfyHv37sHd3V01qFbisSR1x44dw/Tp07F06VJMmjQJFSpUQP/+/XXfiY2NRUBAgCapM2HChAkTJkyYeJzxWE6/1qlTB8WLF0fz5s1x5swZ1RSaCH5+foiJiVFJ4iZMmDBhwoQJE08CHkulzoQJEyZMmDBhwoQcj6VSZ8KECRMmTJgwYUIOk9SZMGHChAkTJkw8ATBJnQkTJkyYMGHCxBMAk9SZMGHChAkTJkw8ATBJnQkTJkyYMGHCxBMAk9SZMGHChAkTJkw8ATBJnQkTJkyYMGHCxBMAk9SZMGHChAkTJkw8ATBJnQkTJkyYMGHCxBMAk9SZMGHChAkTJkw8ATBJnQkTJkyYMGHCxBMAk9SZMGHChAkTJkw8ATBJnQkTJkyYMGHCxBMAk9SZMGHChAkTJkw8ATBJnQkTJkyYV8BYZgAAywlJREFUMGHCxBOA/wypkyQJsbGxkCQpr6NiwoQJEyZMmDDhcvxnSF1cXBwCAgIQFxeX11ExYcKECRMmTJhwOf4zpM6ECRMmTJh4krFhwwZcuHAhr6NhIg9hkf4j85GxsbEICAhATEwM/P398zo6JkyYMGHChEthsVjg5uaGtLS0vI6KiTyCqdSZMGHChAkTTwjS09PzOgom8hAmqTNhwoQJEyYeI6SlpeHSpUt5HQ0T+RAmqTNhwoQJEyYeI3zwwQeoXLmyuZuDCRXyJak7cuQImjdvjpYtW6Jv374y/4CdO3eidOnSaNWqFdq2bZuHsTRhIns4cOAAYmNj8zoa/zmMHDkS33zzTV5HwwSHNm3aYM6cOXkdjccG+/fvBwDY7XZ2zZEf3f3793M0TibyB/IlqQsODsamTZuwa9cuVKpUCX/88Yfsfr9+/bBz505s27YtbyKYS3jctl85efIkwsLCkJqamtdRyRLsdjtGjx6N6OjoXPle48aN0b9//1z5lolH+Pzzz/HBBx9o3r948SJSUlJyPB6SJCE+Pj7Hv/M4YMeOHXj99dfzOhqPDTIyMgDIiVxiYqLm87t27UKRIkVw+PDhHI/bk4S0tDTUqFEDBw4cyOuoGEa+JHXFixeHt7c3AMDd3R1ubm6y+6tXr0bz5s3x7bff5kX0cgV79uyBv78/jh8/nqfxSE1Ndei7sXXrVkybNg0TJ07E4cOHcffu3VyKnWtx7NgxTJw4EePHj8/xbxHxNbcfyF+QJAlPPfUU3n333Rz/1hdffAE/Pz+Z2vJfxJM6hRgTE4PChQvj33//dXnYVGf4RRF6pO7vv/8GANy8edNh2A8fPnziVL3Zs2fD19fX6feio6Nx5swZjBw5MgdilTPIl6SOcOPGDWzduhXdunVj1+rXr4/z589j27Zt2LhxI44cOSJ8NyUlBbGxsbI/o3jw4AEbCeU0JEkSKnLHjh0DAJw5c0bz3WXLlsFischUhR07dqBChQou6yjeeustVK5cWfeZ9u3bY9iwYSzPrFbH1So1NTXfdWY0eNAzjq4ClbnNZsvxb+UWLl26hG7duj22Si0AJCQkAECuKBp//fUXACApKSnHv5Wf8aSm/9SpU3jw4AEWLlyY7bDS09Oxbt062W/AuFJHJC0gIMDhtwoVKoQiRYpkNar5El988QUSEhKQnJzs8NmkpCSmzFE+37lzJ0fj50rkW1IXGxuLgQMHYuHChXB3d2fXfX194eHhAQ8PD3Tv3h0nTpwQvj958mQEBASwv9KlSxv6bkpKCsqVKydrQACwePFi1K9fP+sJUuDEiRNYunQpli5dCn9/f5w6dUo2iiJipEcuyVhQRwQAo0aNwtWrV11GTMh3w8gyeSJpIrL2448/4ssvv2S/PT090b59e5fEMTuwWCz4+eefATzK89wgJU8KqTt69Ci2b98OABg3bhzWrVuH69ev53GsxPjiiy9w7tw53WdiYmIAQDU7kBOgtp0bg4j8DMrzJxWuEAjmzJmDbt264eDBg7IwtUidUv0kUpdftzu5f/8+Ro4ciR9//DFHBvvFixcHkCkUOcJbb72Fxo0bIz09nfUFt2/fdnmccgr5ktTZ7XYMGDAAY8aMwVNPPSW7xytue/bsQaVKlYRhjBgxAjExMewvPDzc0LcjIiIQFxenYuZvvfUWjhw54nCq4OrVq4amE2rXro2BAwdi3759AICQkBAZ8TRC6miEyyt1Dx8+BABDIxJnYMTHiBqjyGH3lVdewccffyy7tmPHDtdELoug+BLZpDTmhj9VXpK6LVu24MUXX8x2OL/99hvq1avHFixRmiwWS7bDdjUkScLw4cPRvXt33efIn5IfSOYUTFKXiSeV1JG/pCNSJ/KrVPZXZOuvXbsG4JHtKl68OCIjIwHI6xE/0AcyZ5+A3Bmw6sFiseCzzz5TXZ83bx4+//xzvPLKKznSLwQFBQF4lH96OHXqFIDMvKL8iomJybXZu+wiX5K6lStXYt++fZgwYQJatWqFFStWYOjQoexegwYN0KRJEwQHB6NFixbCMDw9PeHv7y/7M4KIiAgAalJUoEABAPqLF2JiYlChQgVMnTrV0LcAMN9BJZwhdXxcqVNyFakjgmokPJqy0luFdevWLZfEKzuQJAkJCQmoXr06+w3ISd3WrVthsVhcTo4JNDgxogjNnDlTRZQ2bNiQ5QUdzz33HJYsWZJtX6YpU6bI4kOjYKqXly9fzjerTKlsHXVqjpQ6SZJw9OhRl8SJ2nb58uXz7fSO3W7H5cuXDT8vSRL+/PNPpzpAvh7npH+dJEnZbs8ZGRlYvnw5S19GRoamMk1tXC8v1q5dCz8/PxnZWLFiBcqUKSPztw0MDAQAREVFAZArbqQ+86RO6W5EpM7ZAaskSYanx6OiolTPHjx4EBaLRVbGM2fOVL3r5+fH/i/Kr+joaKfqIY/Dhw+z2QQjpI76r5SUFJm9sNlssvppt9tx7969LMUpJ5EvSV3//v1x//597Ny5Ezt37kS/fv3YcvchQ4bg0KFD2Ldvn2w6z1XQInWenp4AgJYtW2oaHhodbd682fD3fHx8ZL/XrFmDHTt2sEagZ+Qojo5I3dWrV/HJJ59ohvXFF18w0uzoW0rwJJc6JhGpK1OmDADg9OnTwngkJyezqV5XICkpSdOYFi1aFKVKlVItUqAGnJqaiu+++w5Azm0D4IxSRwuCKF/tdju6dOmCl19+OUvfpoGEUiF56aWXsH37dpw4cQKnT592GA4/JdGlSxe2qCcxMRFTp05FpUqVdFeZ5iZIDXHUqVGeuLu7Izk5WWbUly9fDqvVinr16umuvI+Pj8fq1asdxomvn1evXnX4vCuQkZEBi8WCpUuX6j734Ycfol69evj0009RqVIlzXxLTk6GxWLBxo0bAQDr1q1D9+7d8euvvxqOE18Ps7vi/969e5qLj6ZNmwYvLy+np/cuX77MfJvXrl2LAQMG4JdffgEALFiwAOXKlZMpa7dv38aIESOYLdYb5K5cuRKAPA8OHTrE0kJQTgPyaSAipUfqqG9KSUlBZGQk0tLSDLXxOXPmwNvbW6X8iVC6dGmVm9NXX30FQD7tKVKmaYYJENeB9u3ba87KOUJYWBgLUzR4io+Px3vvvcfKlMqrfPnyKlWRj/v//vc/BAUFafarZ8+e1fT5z0nkS1KXlyBSpxxxEKk7fvw4rFYrG/nwoAKn0ZQRKEldz5490aZNG4wYMQKAvg+ESKmja3z8hwwZgqlTp2punzB8+HDMnTtXN57JycmQJElFlET5IFJDSPno2LEjtmzZwq5fvHiRxaFJkybZMuqkEkiSBG9vb4wePVr4zP3794XqAK/mkNHUmhpLTU2FxWLBTz/9ZCheyjxxhtRRntM79G9WiYCXlxcAqEaZixcvRr9+/VC7dm3UrFlTdu/kyZMYN24cq2t2u521FSXOnTuHTz75hP1WdqLVqlXDjBkzshT3rCAtLQ3t2rUDIJ7q4sGTOi8vL9StWxdRUVH49ttvMWDAAPbcmTNnkJGRIVQxBg0ahD59+jj8Fp8vrpzuLV26NPr06SO8R52zqL1HRUWxwczXX3+No0ePMvKq1S6pDpH6Qkq8ERJA4AmN3lT0/v37HU5VV69eHVWqVBHeIyIrKrPU1FQh+YqLi0OlSpVQp04dAI/KjEgcKT9EkHr37o0aNWpgypQpuHLliip9SpD6ZLfbsXr1asTGxgrtJ+WncvoVyCTSkiTpkjqyIZcvX0axYsXQs2dP1K5dWxU3JUEh3/KEhATN9k5ITk7G/fv3Zb7hlL6kpCRdm3r//n1G2vi4R0VFISYmhg0YszsFGhMTg9TUVPzvf/9jRHLv3r2YMWMGvvjiCwCPSF1MTAwj3QS+TS9atEj2vBLVq1cX+uEfOnSI9Rvz5s1T+e9rYf369diwYYPD50xSp4AjpY5w8uRJ1btZIXWOQAYoNTUVGzZskFVqEakj8NeoMTmarjt8+DB69OiBO3fusO/w06+dOnVCsWLFZO+IjLeykq9bt44ZOACy/ajIZ5KmEBwZDkJsbCyGDh2K27dvMwP7559/ylQCkVoimv4VTb9SnmmtmqZObsWKFQ7j+tFHH7H6c/PmTVgsFvzzzz8AjPmfUfzIAFOc0tPTUbZsWfTu3dthGDxIqeO3nqHyVpLMqKgoLF++HP369cP48eNZvCMjIzUVDyXZ5FUeSZJw7tw5DBs2TPZMo0aNYLFYnNo6YP78+fj444+xfv16bNy4UXNq/9y5c2xBlVFSR+Vy+vRpFC1aFO+//77sufv37+Ojjz5ieXnmzBn8+++/yMjIYKtatQjI/v37UbFiRbbCHcgsy927d2P9+vW69iMjIwPjxo3TfebmzZtYvXq1TEEj6A0m3nzzTbzzzjtC1USrHdCzlFaySTRwIKxbt05zFShPLEqUKIGzZ89izZo1sgFjWloamjRpgnfeeYdd++yzz9C8eXP2m0gFj9u3bzN1htIiInVBQUGoVauWZtyIaJFbDNnSEiVKAMhc+Z2eno7ffvuN3aMy4vNTCSI99+/fR58+ffD222+zb7355ptsIEF2ltLH9wMzZszA77//rkvqqK3OmjULQGZ9TU9PVz3Hh7Fx40aWllOnTqFEiRL47bffsHr1at1pTPLxAx6R0NjYWF3F8v79+wgKCoKXl5dsAFG0aFFUqlSJLXTgw3aEP/74g6mehNjYWBw/fhxfffUVxowZg82bNzNCTsKDXjz5uFHeOLt6u2HDhmyW5bXXXpPt7qGH77//3tAG3SapU4BIxfnz5/HJJ59g165dOHjwoKGRNBWuMxXPkZMwNbJFixahS5cuMn89o6SOOig94wIAu3fvxtq1a1GyZEmEhoYCkJO6zZs3q4ymEVKnrLQihYk6GaO+RV988QXmzp2L4OBgNrVLnTqpByLCRE6wPJKTk7F+/XqhUqfVmRE5IENAe46JGujatWsBZJIoWr1GIzXlqPzWrVuIiYnBkSNHmIpJBpziQnXGbrfjxo0b+O2334Rx1IJIqaN6ptyOZsiQIRgwYADrYCndeqvBlOXLGz1Kg7+/P+bPn4/vv/8eAFi+fP7554bT8eqrr+LLL79E165d0blzZ1kHD2S25ffee8/QijcC5a0jRSgqKgrz588H8GiD0tDQUMyePZvVI2UYqampWLRoEZo0aSIb5ACZRKxly5bo2rUr+vXrx66fOHFCRp4vXLiA8ePHa5JfpT1ZsWIFTp06hYcPHyIpKQn16tUD8Ki9SZKkGjTwtoPKS0upo3pBdkBUj27duoVp06Zplq0y7OHDh6Nnz54yf0wK/8yZM2y7ibFjx2Lv3r3sGX5RXUZGBqKiohAcHIySJUvKvpOYmIgdO3Zg2bJl7Pno6GicPXtWFTd+QJKSksJWyiunVs+cOaPq3Kl98Xb3008/xXPPPcd+U19Bcbt79y6zCf/++y+WL18uSz99VzmgiomJkdlmpd2idJB9VZbX5s2b0aVLF9nAv3Pnzix/afr59OnT6NOnj+5pTtHR0bhz5w527NjB4nT37l0V0edx//59FC5cGP7+/qq4R0VFsYUOBw8eNLywqFevXmjYsKHsWkxMDEv7d999h44dO+Kjjz4C8KivM0rqCFn19dNC2bJlMWjQINX1xMRETR98HiapU4BI3e+//46pU6eiVatWaNSokaoiiQqXnnFm2bgj9YwMBS1COHnyJH7//XdMmDCB3bty5QosFgt27tzJ3ktOTsa5c+ewbNkydt3Rt3hjfvr0aZmR4+/xpFWkfFCjoClbI3BE6pSElEgAITw8nMVLb788kVPzhQsX0LVrV9bRnj9/nqXhnXfeES58oXRTvIcPH474+HisW7cOaWlpsv0Fq1WrBiBz70Ey2KIVa5IkoVSpUggMDET9+vVZJ0XpiYmJgSRJhhyw9UBE9O7du0hPT8eUKVNYfPiyXblyJSNElP+Ubj3yrSR1fN2h9/z9/fHqq6/i7bffVnVQSvVzw4YN+PPPPx2m6+7du0hMTGRtc9CgQZgxY4auj+uePXvQtWtX9pvaiCMieP/+fdb++M6U9+dKTEyE3W5H+/btUbRoUbRv315orAH5asezZ89i0qRJsFgsqF27NsaNG4datWph8uTJrHPWsjHnz5+X/b537x5CQkLw8ssv499//2X2jertu+++i7JlyyIhIYHVJ36gxpO6//3vf2jWrJksfCJ1VI/p3w0bNjC1rVSpUti5cycuX74sIz779+/Hpk2bVLaVyvr27dtITEzEuHHjWP07cOAAGjduLFPxqE7yeRgdHY2iRYuy36tWrZIpdW3atMELL7wgzEMePKl7//332SwAqdwU97t372qSOl4Rnzx5MlasWIGoqCiZbaS4ZWRkCP0XlaROWf7e3t44duwYmyZWEiPlwJ9URIr/8ePHsWHDBk0fYtqlgeqN3gbzMTExaN68Odq0acOuKeulEnqkDni0erVHjx7o2rUrRowYgTfffFM3TBFiY2M100h5rNd/i3xz69Wrh+vXr+O9995zuPXJnj172P+Vdi8iIgKSJOHGjRtsapdHYmKiyl1LBJPUKaDVWUVGRsqkf2pc/fr1Yz5izviRKMPRwo4dO5CRkcFIXXR0NJ555hmMGTOGEQ/qSHjFZsmSJahWrZrMcDn6lpKgXbp0SabUFSxYEMAj/5Fr164JVbfU1FRkZGTAy8vL8GIWMjqi/D99+jQKFSqETZs2sWvKhlmmTBmMHTsWwCMDabVasWjRIqaUAfo7qlOYDx48YMvqT58+LfMPI9A3RKskP/30U9SoUYMZTJqimTdvnsoY8h2a1pQalcHff/8Nq9WK3bt3A1AbBbvdLvRxvHTpkqxsqZ7u378f69evx4gRI5iPG2/Q+vXrx6YIqa5Rup1R6kSkjh8Ne3h4yJ7nlQwgcxFG9+7dWT5okdmqVatixIgR8Pf3x/nz59ku+soBAI8333wT69evZ+mmdCmVNCUiIyPZO3y58flHfkhbt25FVFQUKzcReEJy584djBo1iv3euHEjTp48iU8//ZS1Ya0NvpVkdP369QAy2yqfz1Rvly1bhvDwcOzdu5flK19XqH7GxcXhq6++wt9//40LFy7gvffeQ0ZGhkzBPXr0KHOjWLRoEXr27ClT0iRJkg0UmzRpgk6dOmkqL6tXr4aPjw/Gjx+v8m3iO36RDVK2pWeffZZ1xI6myx4+fMjygu+8f/jhB/b/gwcPIiMjg7Wlhw8fqogTEejbt2+ztkqb/x46dEgmDJBLiiRJwtkbfvr1xRdfVA1yU1JScPjwYTRu3BgFChRAXFwc4uLiNHcvoOuJiYn45ptvMHz4cADiQS8AplBSPJSzIHybFK1UdbTg7NatWyhWrJgmqeM3Td65cyemTJmC2bNnA8gk2/R/RyBFUzSLQ2nTU+qGDRuGr7/+WqXq/vPPP5gxYwZWrVqlGwa/Wwcp/UCm3ShRogRatWole3716tWy2YNcU+qelGNezpw5o9kBxMTEyFb2PHz4ENHR0Vi5ciWef/55DB48GE8//bTqvbS0NHTs2FFzpOJo+nXPnj2YN28eI24iYkZGkW/ootVtgwYNQlpaGjMcyjN1laSOJ1jt2rVjx6xcunQJd+7cQfny5fHKK6+ovpOWlsbStWDBAt30EUgh4r8ZHR2NX3/9lXV4NFoE9I0EP/06aNAg9OjRg93TI3V6fkpbt26V/VZOv/KgLS+og6WO4bfffpN1QBaLRdahacWNDCbVTTKwys5w7NixKFy4MB4+fIhdu3axdlm5cmX4+fkxlYg6k99++013xbIIetOvS5YsEaaD71Coo+OfUZK0Ro0aCb997tw5zJ07FzabTdgxV6hQgbWTHTt2MJcJPVJHxp3SZeQYJUDe+fHT2DExMYzEJyYmGt7CR08ZpAGdm5sba+M2mw27d+9m712+fJlt1SPCiRMnZPaJFBfqJOLj41k5kNrDgycg8+bNw4wZMzBhwgTWDhMTE1GvXj2VH6uSTIhWXCYlJQnbEW/rlDMj/D0RqdNzgVG2G74unTlzBoUKFWK+S1qrfm/fvo3Dhw/LbK+yToaHh6NIkSJIT09n6SNiHRUVJRvgUT5JkiQbmPn5+SE1NZXVgaioKNbOeMTHx+Py5cuoWrUq/P39cejQIaaGjx49WnP3gsTERJnfmaP9XHlSl5ycjJSUFKxatUqW36I+Sk/Zu3btGm7evIlGjRrBz88PsbGxqrqspZ5dvHgR3377Ld58801DWzyRUle4cGHNtIlsYePGjQFk2pJNmzapfOqpnVNe8n1JRkYGLl68yGZsCLxvObUjfuCXlJSEPn364I033gCQy6TO09NT6I+QHXz00Udo3rw5BgwYIPM7Sk9Px6BBg9C8eXO89957Lv1mjRo1dO/zhufOnTtMuYqKilI5AZOBvHbtGjZv3oyJEycKw4yOjkZISAjrcDt06MAKkXD58mXEx8ejZMmSwoormjoT4eHDhyhTpgzatGmD/v37o1evXrIl20rDqey4qcFfuXJFd4PItLQ0Zpi0OjWlTwY1ej4Nffv2Rd++fVm+f/3116hTpw5bwaoF6mRFaoZep3337l34+voyqZ8HnX5x+vRp+Pn5sY5E1BlRfSUnYWWn4e3tjcaNG+Pll19mncK6deswadIkYbzIIFOe0lmSytWrpGa8+eabaNWqFbZt2yYzjIsWLcKMGTNw6dIl1KxZE4mJiSw/jE7lxsfH499//8W4ceNU93hVmF+BSPG/evUqq0NaA0Glagc8GqWfOnWKrdrkCT4AVKxYEcCjVaTh4eG6ewDSPaojFy5cQGxsrLBTGzhwoOoafx4yP+UaHh4uI3XUhipUqKAZF8DYTveBgYGM1FksFrRs2RJly5ZFREQEKlWqhJkzZ+r6G4lOrKG6yZM6EXjbQB3LuHHjWOemRRqUROfUqVOqTjM+Pp7ZUsKLL74o27Jn165dsvv8VjkREREqH0M9W9izZ0/2f6UtoXAiIiJw8+ZN1pmLcOPGDV1SZ7fbmQ8jAJmyFxUVxVRUHpIkyWy8t7c3JkyYoHtcJJBpG9LS0tierDSoX7BgASZOnKhJ9rdv3y6z0Y4GNURWLBYLypYti8DAQDz77LN466232DPKPqpChQqqfqB69epMkSM1t2XLlvD398fFixcxb9482VmtWoNO3o+S/4YWCSSlrlChQhg8eLDsXkJCAk6dOiWsy/yg/t69eyqRhvyfidTxC/6SkpKwbNky3dNsRP0ZtfXbt2+jdOnSuHLliutJ3Ycffij8s9vtmDJlCvudXRw7dgwRERHYs2cPqlevziRNINPfolSpUtizZw8SExNVxj2rEO1yzcNqteK1115jvx1tJ8GPvOjfCxcuqGTz6Oho+Pj4MMUrICAALVu2lD1Dlb9GjRqyBuPh4QGr1WqY1AGZlW3nzp0yZ2OC0sjcuXNH2PlevXpV91tpaWksnVrGhN9zKDk5mTWCqKgoZGRk4PXXX2fT2pSXcXFxOH78OE6dOoWEhARNx9vskDpPT0/Ng5/v3buH+fPnIz4+nqk/bm5ubJqPQGVEpC45OZmtGv7333/RuXNn7Nu3j0097du3D926dROu1k1OTmaqp7LO8dOvR44cYVOG5EM2evRotvcSgQZCtNknkQmjm2jGx8dj/PjxDp+bPHmyLA2RkZGoUKECm+bRQpEiRWTqyNChQ1n6r127xv6vVIhLlSqF1NRU1snevHlTc5pt2LBhSE9PR0ZGBqsjDRs2xDvvvCMkdcWKFVMptTx49eny5csqpc7d3V1I6jp37sz+b+TEG29vb9VqU+ARyd+xY4ewvSk7LyCzU16xYoVsoYMeqeN9fKgDA+R1XARlZ/XFF1/A29tbZlfCw8NRqFAh2XMVK1aUDXL4aVwAMv/hw4cPs0UYEyZMgLu7u65CyqsogwYNks0O0OyFxWIRKmI84uLidKdfAchIXWRkJCu3e/fuqVZUA5nEjyfQqamphuoG2U9fX1/hRvtag6gpU6bI1CFHpI4f6N+7d4+lmVfilPahUKFCKoHg7NmzTAiKjIyEr68vChYsiCJFiuDw4cMqP2YjMwl8OWr1O7xSN2fOHDzzzDPsXnJyMkJCQoRbyhQoUIARqsjISDx8+FB2Pi61iYsXL2LVqlWoW7cuu/fWW28J3WJ4iPpTCnPXrl2sXFxO6r755hvs2LEDx44dk/2Rr8SxY8fYfjLZwf79+9GhQwcAQKdOnWTETe8ej5SUFMTGxsr+tJCWliYjNyEhIapnevbsKXNSdDSyVq5MlSQJ3bt3x+jRo9kedEAmAfD09GRhe3h4qJQi6uxr1qwpqxyFCxeGj48P65CVFUNpKHmIVsIqO/bbt28LjcGFCxd0SQCv1GmB95EgslK+fHm2hxy/dFs5rU2KVOXKlYVh65G6yMhIBAcHC99zROq+/vprtiKPOiu73a5yHidjRelKSkpi37x79y4bXXbv3h2FChVC06ZN2btFixaVKUO0Hxqgb3Dr16/PSB7l/YEDBzBkyBDZc5QnRI5IZdKbHuERFxenyvdZs2ap6glf9/7991/VVjhaKFy4sIzU8fupnTx5kk17Xr9+XVYvChcujNTUVGbMw8PDZcSHNtc+ePAga9+pqamyOrJt2zYkJSWpdrz39vZWbWnUr18//PrrrwgKCpKtqL516xYjdQ8ePMDbb7+NggULCg9S51XJu3fvokqVKujUqZPquW+//RZ9+/bFgwcPWLvllQDaruX+/fvCqcW5c+eqVmUnJyfLfBcdKXW8gzevUhJ5SktLg6+vL9vri8DbJHJfUW6lceXKFZVSV7RoUdlCBz3MmzeP/d/f3x+lS5cWbrkhaveLFy/GtGnTVNdnzpypGhDx8PLyQnx8PMvv2NhY4aKxatWqsWk33s+MyoyfggPEpI6289BCtWrVHJI6ozBK6pTtXW+/TX9/fyExpfxISEhg/R/ZV6VPqxFSt2nTJhw/fhxpaWmaq7Xj4+MRFRWFQoUKwc3Nje2c4EhJt1qtrF8gUkftHJC3CaV/36JFi1ifxSuaPET1VcQxXE7qJk2ahJiYGIwePRo7duxgfzabDT/99BN27NjBjuPIDqKjo1nFDAgIkBEEvXs8Jk+ejICAAPan3OmaB/lAlSxZErGxsWwZNN+YqNKdPHkSderUcXjciHLpuCRJuHXrFhYvXiw7XolIHRl4Dw8PWWUBHo0sq1evLhtFeHp6IikpiXUqyrzgySHv5GsU9+/fVzWm1q1b4+jRo7rHP6WmpjokdTxxogbcoEED3L59W0awRSBlxBGpUy4kyMjIwMOHD1GqVCnhe0TqaGqua9eusj3g+HIjciFSg+i7165dw/bt27FlyxZZh1K1alUAmeWnXNVZsmRJzJ8/H926dYPFYsHx48dhsVhU5EwLdPQZHSukhKenJ4KCgjBhwgQAj4inUVIXHx/P6gTVqbCwMNVzvIqqVFkIFStWVI3qlUodj6VLl8rqI0+APD09ZUrd5cuXkZGRgWbNmmHx4sWYPXs2JElCgwYNGEFTkjoiKGFhYbKwfXx8VNPCv/zyC/r06YOCBQuqTjAgMkLKfuvWrTW3ROKnFYsVK4YffvhBpnICmStUn376acTHx7NOlScINJC+f/++SqEoUKAAbDYb23yZoCR/CQkJho/QunjxIqtfN2/eZHkYHx+vUs95Ujd8+HAsXrwYgHwAefHiRRWp8/b2NnSEnhI+Pj4oW7as8FQJkR8VkOnHZLVaZUr5gwcPhHuREnx9ffHuu+/ijz/+YIRG1PcFBQWxNFOZFSxYkLWJ//3vf7LnJUmSkcPU1FRGUPjFM8q4EKnz8fHJFqlzpApSv6ckTVRWvHoFQNd2Xb58Gb///juioqJY/+rn56eqC4A+qStatCh8fHwwbdo01KlTBx4eHkJfb8L58+dZHlG8eUVVq6+k2Y2UlBSEh4ezrXKAzD7Mw8MDXl5ebGqWt/l3795FuXLlNPss0bYoIoLtclI3YsQIrFixAm+88QY++ugjw87VzqJgwYJsJBcdHS0b9evdU8Y1JiaG/elV1sjISFStWhXXrl2Dn58fO+e1du3a7IgmqnQhISEICQlhpG3RokWYPn26KsyQkBA8++yzTBFJSkpCfHy8qjEkJibC09OTGf3SpUvLSB2RH19fX5XaYbPZVKvteBAp7dq1K/r376+Zfi2cOHFCRV5fe+01tGrVStYpKJdZG1Hqnn32Wfb/ixcvwtPTE6Ghobh27Rrb1X7MmDGq9wICAlij0To2hjoMpWocGxuLjIwMWWPkYbfb4enpyfL0jTfekG13wYNWheoplteuXWO+g/wAgXeYbdiwoawjLFiwIDw8PDBgwABIkoSRI0eibNmyeP755zW/w4M2UK1cubLQRy8pKQk//PADyzsic86QupiYGISFhWHo0KFIT08X7prOp4lUCSCz3KkuFi1aFGPGjJHFs0iRIsLpDzrhgjeK3bt3BwC0atUKHh4eSElJQUJCAtzc3Ngo9/3338fAgQNlq92IoNGpIEqULl1aRvZESh0hICBANbWkVOXmz58v9PGRJAktWrRgaQoODkbZsmWFq61DQ0NhtVrx448/ApB3ArTfGil1ojQp1X/lStyYmBjN47UIK1asgLe3N6Kjo5ktUrYnsp0EntS5ubmxunL37l0Z0eU78ueff162HYYelKq6r68vypYtK/TxVsaNEBERgXLlyqFjx46G1MHXXntNVj/IXivJOJBZxyltZLeqVq2K+Ph4eHh4oHz58rLnb9y4gYyMDIwaNQrDhg1jriwtW7bEZ599hrVr16oG1H5+frmm1GkNuOi6UlW0Wq0yH0Yef//9N5555hl8++23sj5EVAZ6XMPd3V3V95ELCt9nEsm6dOkSyyPKN96GaS3U4s+nvXjxoiqt7u7uCAwMxK1bt1C2bFnVVkp+fn6MlI0YMULmZiVaSJkrpA7IHMUeOXIEkZGRqF+/Pv79919Du+I7g0aNGrEM2bRpk2x6Su8eD09PT+Y0Sn9a6NatG86ePcuMDDV+b29v1jnxlY4fjXTp0gXvvfeecOHAqlWrmHOkXufv6emJp556Cn/88QdGjhwpqzzUwR06dIjFgRzSHR0xJVIaCVpKDsHPz0/odO3j44O+ffvKrimJNRki0ZQTkElkq1atylbGfvDBB2jSpImqMZOixaNmzZo4c+YMypQpI1ylpwcimnpp50ldYGAg6zTq1KmDV199VWXU+M6Vl/ArV64sI8R8/vMLcmw2m+w3fY/yLiIiAoULF1Y5bGtNyVCe6O1pVLx4cdU9PfcEgre3N44ePYro6GgWP606yJM6fnryhx9+YAaUlJNPP/2UGX5++pVXWok4BAQEYPv27Th16hRKlSqF33//HWvXroWHhwebfuXLQWQEidT9/fffQveCoKAgWaddoEABTVJHdYlXf5X13tfXV7dTorzSUpCBTFLHb0pM4NUAmg4UtQue1PHuJS1btmSDV14h4uskEaxKlSqxvOXDc0Tq3N3dERQUhC5durD3wsPDkZaWxggRX07Lli1jMyuO9idUKoM+Pj4oU6aMzO+PwNdVpRpTuXJl5sqidKcA5NPdc+bMkQ08tPogIJOsEqk7evQoLBYLK58SJUqo2g8NRsLCwthAJjIyEgULFoTFYsHTTz+Nd955R1ZWvFKXXVJnRK0VkR5yR6G6QHlos9k0fZ/5c1F5eyRStUUDPYKbmxu+//57lCxZUvXuvn37kJaWhueeew5//PEH4yrUx1Kb433gtPotfgBx7949Vb+XkJDA7EFQUJCqHPz9/WXp5Mv+wIEDsgGrzWbLPVIHZCZu0aJFGDFiBNq3b+/0AcmOUKdOHRQvXhzNmzfHmTNn0Lt3b+YT8/TTTyM8PBzNmzeHl5eX7uqkrIIncvR/fiqAJx9+fn6w2Wyq/WUI1Nh43z9lQ6ZRRo8ePeDh4SEjyVSZS5UqhTZt2uCff/5hPldae1UR+I5f+U2lTK6EXsXmz8AEHo2yQ0JCWAcWFRWF4sWL4+TJkyqHYMpTvgN47rnnVOkRERciAoMHD1ZNU+vh2LFjTFHR8pkDMjt8InUFCxZkjbBZs2aYO3euahqOV2lomgXI7IR5Ik9p9ff3VzVMvmMWTTkFBASgQIECqFatGjNCWlNJtGKwSpUqmqSOHzEaAZ0uUqRIEVy5cgWrVq3S7Dg2b96MAwcOaBpy/tt8HSTflsDAQEbqaCXZSy+9xPxoAwIC0Lp1a0Y6evbsCT8/P0bqEhMTZaROFA8qw549e7Jjz3jYbDZZXbTZbLpKHaBP6gCx0kDT1hRHvh6IVsMRGeMHJfx2LbTxsmj6ishUoUKFULZsWXZ97NixwrKkxRdApp/TggULUKdOHbbKmFdA9EhdVFQUypcvj4iICJQuXRqBgYFwc3NjgyEiLloqmuiEFn7Fo7JcfHx8NG0bbwOVR+vxHaqorKmTpuPt+MFd4cKFhbM1AJj7D5BJYkqVKsXKWasNA2B1GsgkEHyZW61WWX75+vqyPsQIqTNK+pR9Bqn7ypNbgEeKLNlkqm96wgO/YIW3VRQWX856g053d3e8+eabeP3111XtzNfXF25ubvj5559Rv359lvdE6saNG4ejR4/K6jAvqijD4iESB+hakSJFUKpUKSxevJgtiOJJXUZGhixvbty4wThEwYIFERgYmLukjvDcc8/h8OHD+O2332SGwhWYNm0a9uzZg2XLlsHDw4M5zru5uWHRokXYs2dPjh0KzpM6ykTeyJPRsFqtMgPw119/qVb3iTbTrVatmkwhEO2PVrBgQZlB8/Pzg8ViQf369VmDttlswtE7Qc/BVk8VAB41fH4EA2RWKn9/fxw9epT5Q1FYFosF8fHx+PDDD7F+/XpUqlQJISEhmsaL0lGsWDEMGTJENY0nmjKihlWqVClh50VbjyhRt25dZoxE7xHc3d1lSh01Qq2zMnnwZJzfZJLeP3funHBTW37ER99p0qQJu0adwpkzZ/D7778DEHcIXl5eKFKkCCIjI7Fo0SJNUicilnogH1O+vWmR/vbt26Nhw4aa4bu7uzOXBH7EP2XKFGzYsAElS5ZESkoK/v77b5YH/fv3Z2WmFa6npyebfnWk1GkRNOBRO+fbu9VqFW61Ajwy4nxb4/OGpp6ps/n000/x1Vdf4cqVK/j0008BPPLL5NtkoUKFEBUVJXMbIbtD7cLd3V3WGUmShPDwcHh7eyM0NFQ2tUfveHt7yzqLIkWKsHpCbb1MmTKyuly4cGEMHjwYFouFkTq+bfJkVKTU8desViuCgoLY9ChtfaN3Ju/FixdlZwXzG5orF3dYLBbNuskPmIoUKYJDhw6xQZCWrxOPmJgYthCESJ3VakXTpk01B5g06A8ICMC9e/dQoUIFVo56dsjX15fVubt376oIBG+P+EG2EVJXu3Zt3fvUZsj1iEAqnsjthfowagdGSB0PnjARqeO3AdMjdVSuonQrr9HgkYibp6cn6tSpI6vPWqROaQP4Mlm3bh2WL1/O6h7Z54EDB8qIJE/qlAP4Z555BgsWLMDevXtRqFAhocuYET/TbO9TV6pUKfTo0cPQ8RWPC0gFuH//PiN4vJEnpU5pULp27araO095QL2Pjw8b5ZKzvYjUPXjwAH/++Sf69OmjukdxstlswhVaFL4WqRs3bhzzzeFRo0YNpohShfXy8oIkSUhNTcWSJUsY8apTpw7rNKgz4hvwhQsX2PSpVsMmY1+jRg1YrVbUqlVLRnaV8nbz5s2ZYSlZsqRQhXn33XeF3+Lx9ttvY/To0bJrtMAgPT2ddcAFCxZkcefTQA1W2YHx8W3RooVsmis5ORlVqlQRkjERqQsICGAkiu+kyODw02PU0VI4RYoUga+vLzPOBQsWZP5ngPOkjvz0KlWqxL7lqOPQO+eRFCp+OwEvLy906tSJkTN+82x/f3/dDhDINLjx8fGw2+0sjoD+9CuPzp074969e6xN8u3dYrE4VOr4Tj0gIAAjR47Ejz/+yFTOGTNm4OWXX8aECRPwwQcfoHz58uwbDRo0APCoDhIKFy4sI3r9+vXDoEGD2BYlNNDjcfXqVfj4+ODo0aOyFXkFCxaEm5sbvLy8mOoJZNYZst3kD6RcIMDbdiL4fCerVOrWrVuHBQsWwM3NDdHR0ap20qpVK3amKZE6vY1jK1WqhC+++IL5IPGkQumrmJGRoeleQW2L0hMWFsY6c57UGdmzkWanHj58iOeff16T1NE3yY+2UqVKjNTpuS0plTq9+t+lSxf2f29vb01SQnBE6ipVqoS7d+/irbfekp16QIMPftB06NAhZlssFgurC9QHUfr5mawjR45g3bp1sm/ydYz2IHz22WexYcMGAI6VOkBsk5R2iPpuZR7xNlhrAKfsx6hM2rVrhy5duqB///6s7vF2nsL29/dndkSp1FE4gwcPRvXq1VGwYEHhNidG1jGYx4QJQIbr5MmTjBnzDVBv6lJZiZSkLiwsjPmLkCHQ2/Ptl19+Uflx8aQOyJwC3LhxI7tPapXWIpKxY8eiXLlyqus8iaHOir7l7u6OF154QVYRqSEWKlQIY8eOVZ3ZyRsyEcjYaw0IRMSA/PxKliyJChUqqPZF9Pf3lznmv/TSSwAyiQkpQ0WKFFHtS9ipUyesXbsWP/zwg2x6mIw3n+4rV64gOjqaPRcUFIQrV67IfAD9/f1x4MABtr+Z3tFEfCfEf4eu851ilSpV8Ntvv8kOR6dl8krDT/lasWJF2dSwr6+vzPdEbxr79ddfl6k8NOrUUkMIempYmzZtIEmScJsTT09PSJKE/fv3s2v+/v4sL7Q6XA8PD7bdB7/SXUTqlNc+/PBD/PHHHyhatKhwgKJH6khh4Be/BAQEYOLEibLNc6tWrYoff/xR6DIxb948JCcnq0idEv7+/li4cCErA5Hv0d27d+Ht7a2aQrZarShWrBi8vb3x+eef48CBA+jevTuCgoJYGgoWLIiSJUuqypbPLyKEvGO6Uqnr0qULBg8ezFxjlHnHD7zIX0+0xZISNPjlSQVvW7p164aWLVvK4s/7/9psNpw6dUqmlpONdJbUEYgc8G1I5Jf22WefoWrVqhg+fDjrP/ROYuJJnSRJulO1PKxWq6aa88MPP+CXX35h9uTAgQM4dOiQaurYy8uLtU1+FSmli2+3YWFhzM60bt2apU05w7Ft2zb2TsWKFWWrTQG5/+z06dNZ3pCNVSq5vAsUpdeRTeLjpSR1RtYFKPO1ePHiiI6Oxl9//cWuUVvi+1eqY76+viw/eFK3YsUKbNmyRRa+0paTTdPqS3mYpE6AoKAgNGzYEJMmTRIeDq9cscSDKiHJ7spjr5YvX84aERllvT28bDabauSgJHUlSpSQdSqffvop1q9fL9xqgiCqxHxHRg1Ey9eFR0BAAMaNG6eawqAp+T59+uDo0aO4du0a2/QYeGTstXzc9EhdcHAwrFYry0t+S5jQ0FA21UoqSOnSpbFp0ybs2LFDmHZ/f388/fTTqFOnDv7880989913sFgsaNiwIfr06SPzC/T390dAQADr7AIDA1V1gu4TIdIjdXwei0idEr169ZIZMHpfaajIgFmtVtk95QiRVL833ngDX331leze7Nmz0bRpUzaNQJ2LIwNqsVgMn/vLg8qRJ+a8UqdH6ni/IoKI1NWsWRN//fUXoqOjMXDgQPTv31/VxpTKvBap47cbIhjpXJTQI8FK8OWqd1+JoKAgeHl5wWazoWHDhlizZg3c3NxY3dCqbzx5DAwMxLp16zBr1ix2je/A+LpMpFbZ3ho0aICxY8di7ty5TKlzRGiBzAUUY8eOlX1jzZo1rOw+//xz2Gw2WTr4vfhoURJvb0k5492HiGAY2UaI0sa3f1FZtm/fHmfPnkXlypUZIdEjdfz0KwDdLbmUUNYLUtq7deuGfv36sfwrWbIkwsLCVAtDtFR2InXK+k3xfOaZZ1QDDZE7g7u7u6rP09pXjv/WyJEj2cpfXsEmgmjEV5Dvnx1B2Z8p01a7dm0EBATIypsUZ74/7tWrF6pUqYKGDRvKSB3Vg5YtW6q2HCJyHBwcDEmScOPGDUiSxKaP9eD8RkD/ERD5IAWMr5QlS5bEb7/9Jty2gyq4l5cX3NzcZCockQEeu3bt0iWJIoimhJWdfOfOnZlkLVpFymPnzp1o1aqVrLOnBqLX2ZBvlFZnQHHiV3zxxtMRqRMpEWPGjMHQoUNlI1dJkvDZZ59h7NixLCyaeiZjSIdFay1oIYdtILMxU4MuUKAAfv31V+E71PB4A2G1WpGRkcHyjzpYPVJH+eDj4yPbFoHyVWT8+Y6SX63Ng+/89RbVNG7cGFu3boWHhwfeffddlClTBtOnT2fTKiVKlGBKH6VD5MCuxEcffYQOHTogODjY4cIcAuVFYGAg6tati+3bt8PX15eROT1SR+BJjYjUWSwWtlUNr2DyoPyaPXu2cENgApURfyxaVkidM9AidcWKFcO9e/c0p9aDgoKEW1KQ/VF2ioUKFRLaOH66D5C3aZ5wURsUddj8MXPnzp1DuXLlHB7KXqtWLUZQCEWKFEGrVq2wefNmli98/rds2RJbt25Fu3btZGVE6NmzJzp16iSzNVTH3n77bdn0I4+lS5di06ZN7LeHhwfefPNN2WBEC9QWtOqyv78/fH19ZbbXEambOnUq29pGWS9mzpyJPn36qNxGKJ+UNl5rBSyRXXqP+hVaLBYSEsIGOXa7HSEhIcLteZSLAQFtUlerVi00bNgQBw8ehKenJxu4vf322zh8+DB69uzJ/JedIXV6i+WATNeogIAAHDhwgKVPORgWbb1CIg5P6p566im2CwblT48ePVC/fn00a9ZMeCwlkd6suLWZpM4BqHCUDvi9evUSPk+VtVevXti4cSObVujZs6dQIVI61BsBv1CCQP41kiTJfAwOHTrEDOGpU6eE/m30vNVqZQSCvqFHBnr06IEOHTqotjghOGpk5B+grLhnzpzB3bt3hfnVtm1bma8QYfjw4ahatSojZ/PmzcPrr7/OfG4c7T+lnA4wAppy4Zfb22w2ZGRksDwtWbIkvL29Vef58iCj+u2338pGoEQW9Ub0YWFhrKyUapNokY8InTt3Zoez22w29O7dW7U6kPDdd9/h5MmTspVpeiCfsuvXr+tuS0CgvChdujSWL1+OxYsXIzAwkNUFLeWY75j8/PwQEhKCf//9V9e3Tw9NmjTBzz//jN69e+tOzXz66ado06aNrNwcdRjZBbUXitedO3eQkpKCV199FVu2bFGdq0x46623hB221mKFvXv3am4czUOkGgOPOiZH2+XwZEvkFiLCmDFjGEGlfOB9AwleXl5o27Yt9u7dK9xP0WKxqOoIkRe9DnXAgAGqXQC+//57Q3Gn6ThS/zdt2oSOHTuy+zRY5tuzo4VtH3/8MT7++GNZ+ITmzZvL9qH09PSExWJh9ZTKLDAwENHR0aozSrds2YJixYqxfsTPzw/37t1j+UZ+qNWrV2e2uUyZMpqbN1Mf1LZtWxw5cgTR0dGadYRmSw4ePAh3d3fmV9mzZ0/V8Xf8QDohIUF4egPZRBGhPnv2LBNhSBHjy4WmR0eMGKE5u/bSSy/h77//1jy1qEiRIjJ7Tu5BSlD4WdqeRspnOHz4sNSsWTOpRYsW0rPPPiulpqbK7u/YsUMqVaqU1LJlS6lNmzaGw42JiZEASDExMa6OsgqRkZFSWlqaFBISIgGQWrdu7dLwExMTJQBSs2bNZNf9/f0lAFJERIShcDp16iRNmzZNOnTokARA6tChg/TGG29IAKSPP/5YAiD17dvXqbgBYH+3bt3SffbMmTMSAGnx4sWGwssKfv75ZwmANH36dN2wMzIynA6b8u2pp55i19555x2n47px40YJgLR27VrZ9VOnTkkApLfeekv43rlz56To6Gjpt99+kwBIPXr0kN2/ffu2BEBq2rSpJEmSKh/pd1xcnARA+umnn5yKd05gy5YtEgCpcePGsusZGRnSl19+Kd25c0f43uzZs1l6Ll26JF2/fl2aOnVqluORkZEh3bx5U3bNUV3MTj11BsePH5cASGXKlJFdP3funDR58mSn6/KlS5ckAFL37t2deo/SS/YIgBQeHs7uX79+XQIgFS1a1FB4MTExUkJCglNxkCRJevPNN2W2PT09PVtlERYWJgFg7Scr4Tj7Hj2/ceNGdu3kyZO69knrGxkZGdKKFSs07586dUoaPXo0+3316lUJgNSwYcMs1e/3339fdv3QoUOG4puRkSFlZGRIAKQRI0YIvylJkjRq1ChNG84jMTFR6ty5s3Ts2DEpPT1dGIddu3ZJAKTr16/rhiXCq6++KgGQjh075vS7zmL+/PkSAKlXr15Ov5vvSN2dO3dYwx4xYoS0cuVK2f0dO3ZI//vf/5wONzdJHaF+/foSAGnixIkuDTcjI0P65JNPpEuXLsmuly5dWgIg3b9/36nwqJPo3Lkza0ATJ06UAEj9+vVzKiy+44uLi3P4/Llz53Q7oeySupiYGKl3797CPMluJxweHq7qXDMyMqT09HSnw/r7779V+WC326WxY8c6LM/169dLAKQ+ffrIrlOdb968uSRJ6vRu375dOnPmjCRJkpSSkpIlYutq7N69WwIgtWvXzqn3FixYwNJ37969HIlbfiF1Fy9elABI5cqVc0l4aWlpUoUKFaSdO3c69R5POOj/kZGR7D6RvQIFCrgknlpITEyUtm3bpopbUFBQlsKrV68e6ysqV66cpTL94IMPpBkzZhh+XlR3zp07l6361rRpU0NxJ/LaqFEjqXXr1jLCZ+R7drtdSkxMdPgdrffj4uIku92u+d6XX34pAXAqP3MCJHgcPnw4x7+1du1aCYA0aNAgp9/Nd9Ov/DYc7u7uwpU8q1evxsGDB9GnTx/VFiL5CX379sXt27c1pyezCovFIjySJiAgAOHh4ZpLsrXAT7+OHDkSVatWNbzaSonvv/+eyd5G/AFEfi6uhL+/P1atWpUjYZNEzvvLWSwWw3sz8eD3pSNYrVaZ75EWaPpE6YNI+U/1b8aMGTLpv3Xr1uz/ztaZnIIjP0st8PHPzo762UGzZs0MTVdmF44WSjgLfjNgZ/DRRx/B3d1d6N8JPPJfMnqmbFbh5eWlOlbszz//dPrEGQJNzXl7e+Pff//N0ub6ygVHWUF22+SmTZvYVK4eqMwkSdI9u71ixYrCemK1WrPs5gA4buvKc1rzCp988glu3bol87/OKdDCsKy4cuQ7Uke4ceMGtm7dqjrEuH79+uyctB49eqBZs2ZCf6iUlBSZU7CRY5BcjWHDhsk2zcxpkG+L1uHhWqDnbTYbChQogAEDBmDnzp0AjC315vHmm28yUueK4+NKliypOlvTVdi0aRM7tSArIKMr2kswN6FF6uhsYOr833nnnVyPm7OgPM0OqXNmJakz+Pvvv3WPhNq6davm2ZiuhNKnLq8gWt0s8nnM6YGbCEYW8mjhyy+/xIgRI+Dm5pZrRGLQoEGqs0KpTmsNsNeuXSvbk1EJHx8fQwNrai+Sju8uAPzzzz+aCxpyEuRbbMQnNydRpkwZrFmzJle+RT55Wv6xesgzUhcRESHsDNeuXQs3NzcMHDgQCxcuVHVUvLHv3r07Tpw4ISR1kydPZice/FeQVVJHxoNXmKih53XHcevWrRyLQ4cOHWQbsWYF8fHxhrZ9yUlQWYnKPSuqYV6CVJLskLqcgkhN5eHp6ZljhJKHq5U6V+DYsWNYtWqVigSdPn1ad8um/Ii2bdtma7CXFSxcuFB1jeq0Vv49/fTTLvk21VlHBJY/xzYrqF+/Pg4fPuz0e6QC6u0g8KShfPnyePDgQZbyO89IXfHixYVTFXa7HT179sSYMWOEK+xiY2OZHLtnzx68/vrrwvBHjBgh25g2NjbWqb1+HkcEBASoNh01Aur4+ffyC6nL78gPJ6lQGTlL5vMjqA46e+wg1dfcIFWiVZS5CWqvekcE5jZq164tPKnAyP5zJsQgkpXTpNhms2Hy5MmyPf1yAlu2bBEem+kItGKV9ij9ryCrBDrfTb+uXLkS+/btQ1xcHCZMmIA33ngD/fr1w9ChQzFnzhysXLkSc+fOhZubG5o2baq5JUhujZrzEwICArKkWIg2WP6v5d3jDJqWyC9+cdlB7dq1sXr1atmxZkZA21jk9PRncnJyvlA/k5KSzDb6hCMgIAB9+/bFmDFjcvxbov3kXI3AwEDNPU318F9U6rKDfEfq+vfvj/79+6uuz5kzB0DmLt9Gdvr+LyIgICBLag2NCHr06MGuEUHIilIXHBzsUj+4Cxcu5IuONL+CTtDIz4uGnAF/JqxR8Ofs5iTyC5HK6yl/EzkPq9WqOnrxvwhS6vLap+5xQb4jdSayjooVK8oO1zYKPz8/2O124bRtVkjdxYsXnTo/0RGUx7WYkMPLywu7d+/O62jkKXJ6w18TJkzkDWrXro2PP/44VxcdPs4wSd0ThFdeeQUDBw7M0rtKQkdKgKOdzEXIzvJ2EyayiuXLl5sKlgkTTxhsNhumTp2a19F4bGCRHK1jfkIQGxuLgIAAxMTE5Nk+Vo8bNmzYgDZt2uSbKScTJkyYMGHChDZMUmfChAkTJkyYMPEEIP9sdGTChAkTJkyYMGEiy/jPKHWSJCEuLg5+fn7m3msmTJgwYcKEiScO/xlSZ8KECRMmTJgw8STDnH41YcKECRMmTJh4AmCSOhMmTJgwYcKEiScAJqkzYcKECRMmTJh4AmCSOhMmTJgwYcKEiScAJqkzYcKECRMmTJh4AmCSOhMmTJgwYcKEiScAJqkzYcKECRMmTJh4AmCSOhMmTJgwYcKEiScAJqkzYcKECRMmTJh4AmCSOhMmTJgwYcKEiScAJqkzYcKECRMmTJh4AmCSOhMmTJgwYcKEiScAJqkzYcKECRMmTJh4AmCSOhMmTJgwYcKEiScA/xlSJ0kSYmNjIUlSXkfFhAkTJkyYMGHC5fjPkLq4uDgEBAQgLi4ur6NiwoQJEyZMmDDhcvxnSN1/CUlJSUhLS8vraJgwYcLEY4mIiAgkJCTkdTRMmHAaJql7AuHt7Y127drldTRMmDBh4rFEiRIl0KZNm7yOhgkTTsMi/UeczGJjYxEQEICYmBj4+/vndXRyFBaLBQBM/0ETJkyYyAJMG2ricYWp1JkwYcKECRMmTDjAZ599hm+//Tavo6GLfKnUHTlyBO+//z6sViuCgoKwbNkyuLu7AwB27tyJgQMHomLFirDZbNi2bZuhME2lzoQJEybyJzIyMnDlyhVUqlQpr6MCwLShJsR4HOpFvlTqgoODsWnTJuzatQuVKlXCH3/8Ibvfr18/7Ny50zChM2HCxCPcuXMHmzZtyutoyJCYmAi73Z7X0TCRR/j6669RuXJl3Lt3L6+jYsLEY418SeqKFy8Ob29vAIC7uzvc3Nxk91evXo3mzZvryqApKSmIjY2V/f0XkJ9HECbyB9q2bYtOnTq5NMxff/0Vd+/ezfL7Pj4+ePnll10Yo/yNHTt24M0338zraOQbHDt2DAD+M3bahImcQr4kdYQbN25g69at6NatG7tWv359nD9/Htu2bcPGjRtx5MgR4buTJ09GQEAA+ytdunRuRdsliI2NxauvvoqkpCSn3ktOTs6hGD35kCQJy5YtQ0pKSl5HJUdx7do1l4f5wgsvYOnSpdkKY8mSJS6KTf5H7969MXv2bGRkZOR1VPIFaForPT09j2NiwoQa+/fvR8eOHfM6GoaQb0ldbGwsBg4ciIULFzJ/OgDw9fWFh4cHPDw80L17d5w4cUL4/ogRIxATE8P+wsPDcyvqTiE1NRVbt25VXV+4cCHmz5+PNWvWOBVeYmKiq6LmEiQmJqqmz/MrTpw4gRdeeAFTpkzJ0vs3btxgikN+Bk1zukrVtdvtSE1NRVRUVJbezy/q8tGjR3PtW8WKFQMAREZG5to3Hwfkh73h8kt9JKSmpuLMmTPCezExMYiPj8/lGOU+0tPThQLH/v378frrr+f49999911s3rw5x7/DQ5IkjBs3Djdu3HDqvXxJ6ux2OwYMGIAxY8bgqaeekt3j5fk9e/ZoOtZ6enrC399f9pcfMX78eLRv316TdDprYPIbqfvrr7/Qq1cvPHz4MK+j4hCk0N2/fz9L75ctWxZ169Z1+JzdbsfQoUNx/fr1LH1HDw0bNsScOXMcfh9wnSpC+ZZVUpcf6uz69etRr149bNmyJdthJSYmOvRZJFKXXwebeYXcqAupqam69/Pbxu0ff/wxatSoIWyvgYGBKF++fB7EKnfx7LPPMpcs5XVH9u5xRVxcHMaPH4+hQ4c69V6+JHUrV67Evn37MGHCBLRq1QorVqxgCVu5ciUaNGiAJk2aIDg4GC1atMjj2GYPV65cAaA2NFldZZMfOkgeFJ+sEKWNGzciIiLC1VHShNWa2Rxyekrs2rVrmDt3Lvbt2+fysA8dOqQ5cpUkCbNnz2akzlVT9dklw7mpNERHR2PixImqMqZ2KKpvx44dw9q1aw1/44MPPkCnTp10/cNMUvcIqampLB9yWqk7duwYAgMDdQcg+Y3UHT9+HIB23mR1MJUb2Lt3L2tb2YGj2R5XL7I6ePAgLBZLnirpZJ+JCxhFviR1/fv3x/3797Fz507s3LkT/fr1Y2x8yJAhOHToEPbt24cvv/wyj2Mqx19//YW5c+c69Q6NvlxVKZ31waNvu0K12bt3L3x9fWVkgf7/4MED2O12WCwWwyOrzp07u9yPQZIkhx1pTq/CJJ+2rJRVdvD333/LnPNdReoOHjwIIOudS26SuilTpmD06NEqX1zqyHlXDwC4d+8e6tatix49esiuS5IEi8Ui9AMkBVZPEfL19QUAp6dWlHF+EnYAePvtt7Fr1y4AOT8ovXbtGpKSknRtgCMlLyexdu1aWCwWmQ2ihYKubCf379/HpEmThKJBSkqK0DZkxS42b95cNdvmShDhcbUv+W+//QYAOH36NIC8mZKntqC0SY6QL0nd44qnn37aaamUOpPIyEjExMSw6yJ2XqpUKTz//PO64VFFUK4Y1kOzZs0MVZyPPvoIDRs2FN5LTk7GO++8g4SEBNm2BKTiPHjwgJGYefPmqd632+24cOGC6vrly5dlv1etWoVbt245jKsWfvjhB5QpUwZ37twBALz22ms4fPgwdu7cyYy5nlJ38+ZNNGzYUFZWziKvSJ3SKCsNoY+PD77//nunwpw/fz46d+4MIOukLi4ujv0/txYOKMtPi9RpKQRUr2fOnCm7npSUxJQJvY6G3s+OEjBp0iS0a9cuRxa+uBKNGjXStVv8lHdOK3VEjJSq8vLly1lZu0qpi4iIgMViwY4dOzSfSU9Px88//8xIA+3owKu8IlKXnJyMkydPGo6LJEmymYEPPvgAo0aNEg4qKlasyJRkwo0bN+Dm5pYlvzKjZDA1NRWJiYl4+umncenSJeEzWvbBFYOBf//9l/m3kx3IS9WW2oIzfTlgkro8BylkzZo1Q2BgILsumn69desWfv75Z93wskLqDhw4YOi56dOn49ChQ8IpqiFDhrBpAr4h8KSO4iYacX744YeoUqWKqiPkR82SJOHZZ59F79692bVz587hl19+MRR/AMwQ3rlzB3a7HfPmzUNYWBhat27NDJzeqGzRokU4dOgQdu7cafibSriK1D377LNYsWIFtm/fjvXr1ztUW5WERZnXiYmJmDBhglNxWL16Nfu/K6ZfeYInws2bN/HZZ5/Jyqht27ZsZO0IPj4+AKDafoXqrHIwReVss9lk36SyU+b5wIEDcfHiRQDGSJ2jPPvpp5808+Tq1auysPIrDh48qGu3+E4/J5W6HTt24MUXXwSgHoAMGDAAvXr1AuA6pY4GqRs2bNB8ZubMmXj++eexf/9+AI9cQPgyF5G6d955B7Vq1TIcl7/++gtNmzZlpIx8nG02m+rZW7duse8fP34cLVu2ZCTLkTLct29fvPDCC4bjxaNMmTIoWrQo/vrrL0yePFl2j9qlsu+g664YIIeGhqJ9+/YAHtlKat95qdSZpC6XMWnSJPz7779Zfl9rJJBdnzpHyhttC+MIP/74I4tLaGgogMxpPCX4USA/2uanXyluok5q9uzZAB4ZG0o3nz+krvANu1GjRujfv7/DdADAK6+8grNnzwIATp06pRrp0uhYTy2iBqYcfTpTTlqkLjk52SnlZtWqVXjuuefw3Xff4YsvvnA4BaGsa/zz1JG5u7sjPDzcsBr64MED9v/79+9nSWnj64OjNLz55psYO3asjAxt374dvXv3xpgxY4TvREZGMsJORlqZPsobJUGKjo4GkFnePOGgeFJ4SUlJSEtLk7UNZVoOHDjAiBjd0yN1N27cwMsvv4wRI0YI7+ekqkmqSXawbds2NGrUiP2+du0atm7dilatWsme49vSzJkzDaUrK2kfP348+39UVBTmzp3LypeHq9QZsgkWi0VF/m/cuIGMjAzcvn0bwKM8IFsbGxuL1NRUBAQEYM+ePQDkdk+k0t26dUsz7lT/aRaFbI+jAcGIESOwe/du9m1Hqtuvv/6KZcuW6T4jgiRJuHv3rmb/5eHhAUB70OeorkZFRWHv3r2G40N2ntppbpG61NRUHDp0CICp1GULN2/exMqVK50uuJSUFIwaNQoDBgzI0ncjIyM15Wxq3MePH4ckSYb9KfTYfXp6OsaPH4+tW7eiatWqaNeunUN155tvvmHvksrBE4/09HS88cYbbDoTkJM6MhoPHz5khkSUFjJGZGRFxonUFT8/P3aNiF6TJk101YC0tDT8+OOPzHfnpZdeUq1UJYNhhNQp4/fDDz9ovqOEFqnr1auXatrDCJKTkxEbG+uQECnv87+pzDw8PFCmTBmUKlUKFosFq1atwq5duzSdnflyt9vtWZqW5uuDoxE35btoo2MtlbFChQooW7YsgEf1S0nqiNSmpKTg9OnTLG/i4+NRqFAhAPIpW7qflpaGy5cvw9vbG88//zw8PT1VaYmJiYEkSWjcuDEqVKjAvgPoT1nTN0TEA3jUweaEUteyZUvW3rOKDz74gPlbAkD58uXx1ltvYdeuXTJywP//9OnTwi2eeOzduxc2m03orqEHnigcPXoUQ4cOxaeffqp6zlVKHdXVL774Qvbt5ORkVK1aFcuXL2dlTPWGJ3UPHjyQbZzPtxMt95zXXntNNy6kzFE/4aju0H3qG53xvXamP1UOyJT9lxapM6rUPffcc2jevLmhOCUlJbHyou8ZeU9Zb8gfTw/nz59nxB4ARo8ejYYNGyI6Otr0qcsO/v77b/Tr189pR1TqnAsXLiwbKfzzzz+G3n/llVc075Ghmz59OmbNmmV4FSgpJ6KKcO7cOYwbNw5dunRh1/jGsGbNGlVnSY0mMTGRpZHviK5fv44ffvhBSBAAsVKn9JvhG8zQoUMxYsQIIamjUSZP6mi6Yv/+/bpTokZ2qieV0AipS09Px4MHD1jc+QUISsP3zz//YPr06ew3OdIrDdHGjRtV39u8eTPGjh2rus7nWXJyMmJiYmThiXyT9EgdGS8lefvxxx/RqlUrVKxYURVeRkaGql5mxa9O6SukB6rXERERSEpKMmRs+fCJICnrOeVdQkICwsLC2EbK8fHxKFWqFHv3woULsFgszNczLS2N2YHNmzezzofSkpKSgsDAQHz11Vey7xmZfqV4a61+ywlSl5SUhK+++krmkpGUlISDBw/K9vEjJYnHhAkTMHHiROzbtw9r1qwRqjqUz7y9VD7n7u4uGzgmJibKypDUUGdnSHibSFPkIpLiKqVOSUAo7Xfu3EFSUhKOHDnC6rtIqVPGg/Jgw4YNmiRGObD97LPPcO7cOZUSbYTUnT9/nvkDkj1xhtTxbXn37t26bXXixImy30r7pSRZSjhS6shOGZkJiYiIYOVx+fJlWCwW5lpEUNbZ+Ph4FC5cmM1Y7d+/HzVr1sTrr7+u6/NatWpVlClThv0+f/48gMzyVyp106dPl/UjWjBJHYAiRYoAyOyQ7t+/r3LO1wL5GWRkZMhGjQ0aNDD0vp5TMG8wT5w4weRzR/vtEetPTEzEpEmTEB4ejunTp+Pq1aus4vPGgjcOPXv2xLPPPisLj4xMQkICiy/fcYtGtTdu3IDFYsHevXuZ0fj555+xcuVKAI8aBIXH58OePXswZcoUWbhJSUl4+eWXce7cOQCZpK5Tp07o1auXzCdEb2rAiIJEHf6hQ4c0jSZ9LyYmBoULF8bUqVMBQLZfIqW5ZcuWePrpp9GgQQN89NFHADLzi1Sib775RtYxiVZybdiwQbiwhDfGIqVONEDRI3VaAxplHeXLfuPGjaqOR4ukSJIkNOqbNm2S7WHoaMRNxn3Tpk3w9vZmU1NK/Pbbb7IRMIHqgfI7lP4bN24gKSkJERERWL58OY4dO4bg4GAAQI0aNdjWJtu3bwcg3xQ1NTVVRuqmT5+OIUOGAMicKudhZPqVBiIWiwWJiYlYsmSJLA+d2Zpmw4YNOHz4sMPnZsyYgf/973/styRJKFeuHBo1aoSwsDB2PTAwUGXnxowZg9GjR6Np06bo2bOnsD3SqlO+c1bWoU8//RTFihXD77//DgCoVq0aG8hFREQwYtGnTx+cOnXKYZpSUlIQHx8vKxvazFdkT3lb4WjQEB4ejiFDhgjTqhxIErGgenn69GlZ3QEeDVLXr18vrKPXr19Hly5dVNOvIoIvSRLGjh2LPn36sDTFxMTAz8+PbdqfmpqKCxcusH0VedLWs2dP9n96Pz09HdevX5d95/Lly8JZEp5otWzZEsuXL1c9QwgKCsKwYcPYb+UZwFR2S5cuxaVLl/Dnn3/KvunIbhQvXhzAIz9UEcj+3r59m4W3YMEC4bNKMhwVFYX4+HhGyshuzpkzB23bttWNG193+P5FOeu2bt06Q4KRSeogJ3UjR45UERstEPnbvXs36tSp4/R3tabaatasiUmTJrHfZ86cYRWDX0whAhGG+Ph4jBo1Ct27d8dHH32Er776SjiaUV7bs2eP0PdAi9SJwiSD8euvv7LKf/fuXXzxxRfsmRdeeAG+vr64fv26Q7+WzZs346effsJnn30GAChQoAA2bdqEP/74gxlBQL9hG1HqZs2aBQA4e/YsvL298c477+D777+XKXeUHspn6nji4uJYHaBOdvfu3fjrr7/Yu6mpqbh586YsPH7LFjJcfEeflJQkUwQJSjVUqdR98MEHqvwQ+fClpqYiNTVVcwTMl01sbCyKFi2KkSNHAsjsVJXg60ZaWhrLp7CwMJQsWVL27P79+9GpUyfZGc5169ZlKooIlAbaFod/lp+y6d27N7p27Sp7V5Iklh5lXlD6yehHR0cztwpS6oBHKi7VA57UJScny4jDmjVrmOKnzN+UlBRYLBbcvn0bCxcuFJIHntQtXrwYL774ItavX8/u6yl1N2/eRJcuXdjAs0uXLjJSJoLdblcRvxUrVrAOtmDBguy6JEk4ceKErkKiN8giEn3r1i3VgItUQrKvNKBdsmQJSpQogVGjRrFneT85Ef79918ULFgQfn5+srKhGQ0/Pz9Z3kuShGbNmrHffHtXuspcunQJH330ERYsWKAiCw8ePMCgQYNk1+7cuYPU1FT2LD/NryzDmTNnqsqCSJ0IojrAL+YhmxIZGSkbwKWkpKBKlSrsLGi+vdMgGnhUF+12O8qVK4eWLVuyezVr1hSublbWDb3N1pOSkhAUFMSmYZWkjgZzM2bMQO3atdG9e3fZNx2RuhIlSgBQk7pp06YxgkwDh6NHj7Jy0fItTklJgc1mw6JFiwA8ssdUr/g8vn37Ni5cuIDu3bvLfJBFoP4sJiaGhUn18+7duwgKCtJ9HzBJHQA5qTtx4oSq8qWnp6Njx47sCKhZs2Zh4cKFWT5W6Pz58/j666+FqzbT0tJw+vRpmXpB0w1Wq5X5XqSkpAg7AqU6QZVyx44dhkgdABmp5adfqaI6InV8ZdRSEciZ9siRI0JSxyt1ZIwpLXyYvFKn17CVHYdod3IlvvvuO7z99tuy1WuUXopLTEwMnn32Wdy9e5edL6w1pXHnzh38+OOPAB4ZkDt37uDmzZsIDg6W+VklJydj5MiRuH//PlJSUlRpU5K6tLQ0WT7+/PPPqik/kVIXHByMcuXKaSp1vCJA///888+Rnp4Ou92Ot956S/b8uHHj2IrYd955hxGiI0eOqKZqaUpDacB5PywlqO5RJ8OniXzAaEBw7do17N69m91fsmQJWyhjhNQRSKkDHtVL3qfOiFLGDyru37+P5ORkVK5cGZIkYfDgwbBarfjnn3+Qnp7O1Cyqs1arldXXefPmMbLDE8zt27czlSUtLQ19+/bFhg0bsHz5clnnLMKOHTuwZs0afP755ypF8YMPPkDjxo3x2muvoWTJkvjll19kadFbUKMsVx5U39asWQM3Nzd4eXmhfv36smeUdoFWrvLgj+W7c+cOypQpI3MhCA0NZWUtcklJT0+X2RqlPaM2GRoaio4dO+Knn34CkJn3lStXVs0+EGgAyuP27duoWrUqBg4cyH5Tm6A48N9X5l98fLymb6tylwBAbiOI1CnLi7dVb731libpoDKnOnbw4EHExsYiIyOD1X/lrI0yL/V8FZOSkuDl5YXx48fj008/1VTqlOni+yc9UL/JT4VKkoRhw4ahRYsWmDhxIkvj119/7ZAk3rp1CxkZGRg3bhxGjhzJ7AflH4W1dOlSJCcn488//8Sff/6J4cOHa4Y5a9Ys1n83b96cDch4YSTHSd3Dhw/xzTff4K233sLEiRNdujv6Rx99hObNm2PAgAGyypCeno5BgwahefPmeO+991zyrcKFCwPIHMWcOXMGDx48kFV2GqWNGDGCdWSDBw9WKVrkUK0Fi8WCoUOHok6dOvjwww+Fz2g1qkqVKmHkyJFISkqC3W5HgQIF8PXXX6ueu337tqwBkA/B6dOnheRJ1BhCQkJkcQa0lTrRFDL5K82cOVPliwBkngrSo0cPeHp64syZMw5JHRljMlZ8g+OnC4ySuhYtWhjyTSAMGTIEDx48QJkyZZiCSo0vJiaGdYREYLQ6+YEDB7L3+Wmfzp07y8h4VFQUfvvtN1knq6wXopWYSj8x5VYKIlIXFRWFO3fuGPInvXnzJvv/li1bkJqaimeeeUb2zJEjR5iCR4tSeJJGZShJEpvKTE5OZm0QeESwfH192fQ2Qemzx/vIeHt74/bt2+z96OhomaLw0ksvsc5CmReU/9Rp8nWSpm6ATEdmPh5paWmyeqe1qIG/XqRIEVy6dEl1Gs6aNWswceJElClTBpMnT2YLP/iVqGvWrGFT/UQkTp8+jbZt2+K1117DV199hYMHD2L//v2oUKECtmzZgmrVqqni8+DBAzx8+BCDBg1CmzZt0LNnTyGZfvDgAQYMGMDi3L9/f9l51NT+RX6oeuo41bfjx48jJCQEiYmJbNUfn2eOtny5fPkyK7Nly5YhPDycLbRQtgcRqVD66ynfSUxMxKhRo1j7Gzx4MG7duqUqZ749TpgwQaY+E+7cucMGDTQYpQF7SkoKJkyYICPgSgIWHx8v3L/Nzc1N1l9ZrVaZT5YkSYw88m2YvkuYNWuWcAES8Mh+8vZ+0aJFMgVaScSUfQsNtgYPHszIMJBpw9PS0uDl5QUgcwbr3r17uHr1KkaNGsU2rdcDtcHvv/8e7777ruZ9Pn2UlpiYGNauq1atyvx19UDT99euXcPnn3/OfA+pvsbFxcHDw4MtjKL08rtE8KLMvXv38NZbb8nu0wbpqampSEtLw/37911P6kqWLMkiffXqVVSvXh1Tp07FxYsXMWfOHISEhDgcFRrBsWPHEBERgT179qB69eqy0eOff/6JUqVKYc+e/2vvvKOrKr63/9wkN71TEgi9994hdBAEARESG2KhKCCK0uUHqFRFQZEqiICAgJQvKE1AkN57LwIBEkJCei/n/SPvHmZOufemQALMZy2X5JZz55xpz+y9Z88+JCYm5slRS87OznB3d8eZM2fYQBQWFobbt2+jUaNGzJ2oKIpgElfH3pGlxhILFy4UGoza325kot61axdcXV2RlJTEvr9q1SohsWVmZiZu376tG9QOaK14gCiEyPRrNpvRokULzJw5k3UmSldRtmxZtuOxfv36GheD+nf0Yl5ee+01bNy4EY0aNcLKlSvZQMVvgODdr+pVMD9Y8AN1TEwMpk+fjqFDh6JYsWIoXLgwRo8eDQDCCQKurq4so78thIWFYfbs2QgJCdG4X3mLKi/q9KyofPwXL7zVzygiIkJjVYiKisJ///3HBmcakEwmExMo6kS4NBHVr18fixYt0nW/EtbywwHiJEMpaHjBQKEBlPqG2hOf1qJUqVJ46623cPz4cWFjSJEiRTRlSUhIEFxtW7Zs0bhPeJEXGhqKgIAAwxQgRM2aNYVnce/ePbb4oGfGT9qenp7MzULQBKY+aNwoZkfv+arP7IyOjmYxlmPHjmWu5bi4OF2BRP2CJqolS5bg888/Z9/r0aOHxrIzffp0tGnTBk2bNkXTpk2F+9LbMZ+amgp3d3d4eXkJgf5EQkKC7oYZa9DzuHXrFnsO/MRtZ2eH6Oho5kUBsnKgUdviKV++PKZNm8ZE4ZQpUxAaGqoRiXoL0MTEROF19Rh54MABthCbN28eFEXB6dOnNYsLXhgapdbhrUTqeMSwsDCMHz9eEB1qUXfjxg2NKAOy2qB6kfLff/8JooUEuyVLHWC8yY/aH7+4PH36tDAW83OXoii6oq5ly5ZYsmQJgoOD2evUrnhRl5ycjHLlymHy5Mm4du2aofeDfpN+a8iQIZpxEHg81s2aNYtZy/Q2dZUsWRJJSUlWY7BJ1BFUt7ylzsPDg1n5qS1evHiRufH5e9ITa7wFlBaveS7q+F0hY8eORZUqVXDjxg3s2LED169fR2BgIFO8ueHQoUPo2LEjAKBTp06CcLP0Xm4oUqQIsywAWZ17xYoVOHbsGJvAAGhEK++a4XexqC0rvEjhYzb4yR0wnhQCAgLg4uIiiLrjx4+jbdu2zKV348YNxMXFaWJnyHXDD8YE3/EqVKiA1q1bY8uWLThw4IAQJEqTWLVq1diK8+TJk7orO2tuaZrsx48fj3v37uG9994DALaqAaDZKMFjtIo6dOgQRo8ejdmzZyMsLAyRkZGYPn06Tp06JbhDsivqAO2ER4M/3zFJ1KSkpAjPjo9FIiy5C9atW6eZjB89eoRy5cqhTJkyAB6n71AUhdWretMADQQnT55E//792SBx4sQJODs7CxMB3/aNoAnB1dUV27dvh729vRAXev78eQwYMAApKSlYtWqVYazXypUr2eREJ5Twk3dkZCQTxRkZGVi6dCmWLFmCnTt3onz58sJCQm9gtnZUX4sWLYQ2RBMexd0Aoqhzd3cX4uqAx/1BbanLDk5OTkKcVlRUlGAVJPTcNoqisLFYvaPv+PHjKFSoEIoXLy6IFA8PD4wePRp79uzBrVu3NKcJGLnH3Nzc4OXlxf7mhUVCQgI6d+4sjIOAOBbqQSLo9u3bLN0MT7ly5YQFE5BlSerfv7/w2pAhQwBk5VOj+NXbt29j+PDhGquWnqhLSEgQBJna28RP3nXr1oWnpyfOnj1rUdQZQa5/AKhYsaKwMYAXxeQpUQuw3bt3a54JoV40UOA+kCX6U1NT0aBBA40oVItBo/lULeocHBwQFxcnPFNetKakpGjGuPj4eDZGubi4sGdI/YfmKXWs+eXLl62GOCQlJQmhImvXrtW8T3zzzTf47bffNPkSgcfeNnUYghq+LoHH8zY9n7i4OHh6egpjCjFlyhT2GUvQuJ6amsrm2Sfqfj1y5Aj+7//+j1WEk5MTxo0bZ/PpBJaIjo5m7ikvLy9BIFl6jyclJYXl+OFz/Rjh5+eHEydOMMERGhrKvkMWBbKW8PC7HnlLXaFChaAoCv755x+sWbNGMwhQ41Hv/KIKV2Nvbw8XFxckJiZqJpEPPvgA586dYwe5q1eBNODyoo4ECO8edXBwEPJspaens9UzL+oURRFOEgCy4q2yO7m1b9+e7Q50dHQUAun5TqxeNdGqx9akjIsWLRL+dnFxybaoU7uB1O6X4OBgJta7du0qTD567i96VnppbdauXcssjAQFhKekpCAtLU1wgRkREREhDBznz59HhQoVUK9ePTg7Owv1pX5GBL9Io0mmSZMmSE1NRZEiRYSYxoCAAJQtWxZXrlzBm2++aXEXOS2OatSoAUAUdd9++62QhPrdd9/F+++/j7Nnz6JChQrCZ0nQ8JY+I6pWrYo9e/bAz89PuPeIiAjY2dkJ/Zev3/T0dE0MJu/GTUhIQEBAAOt/tpKcnCy4YCMjI21O+BsTE8NEs/r82YMHD6JUqVKCEAPE8JDU1FRNf1VbHwh3d3chXIAXGzdv3tTNtak+K1dNfHw88yzQQoWnfPnywqaQ6dOno1ChQppJctasWQgNDUXDhg2F+3FyctK0v8jISPTu3VuY8Ldu3Yrq1auzv9UuaH6BWrhwYdSsWRPnz5/XFXWXLl1iIQULFy5EmzZt2PvOzs5COISbmxu++eYbNrfwY/OYMWNQvHhxQYA1aNAAkZGRwpFqPOpdmmFhYRoRW6NGDY2QUHuGrIk6avclSpSwKOr4FFgEv4hISkpCtWrV2L8B0VLHM2PGDKupSP7++2/hhI2goCAhJEDd1vv06SPcO1mAeW+RHvQ5dZoSso7TPEGWOrPZzNJdubq6CnGL1hYCtMBISUlhZVUvLvXItqijST4lJUWjGv38/HJ1liHh4+PDGlF0dLQwGFl6j2fq1Knw8vJi/1lzjdLkUqtWLfj6+uLcuXPM7UqcO3cOf/31l/AaP9irfyM1NRVt27ZFcHCwJu8SCRi1qNPLFE7fpUavZxqOiYnB7t27BcFCEy6JOn7VTmXlxYNa1N2+fZutSMilRR1RnVixZs2acHZ21pTLGmTF8vb2FpKd8pOqkSm8YsWKNv2GenUaFhaWbVFntMuS+P7771lbULtwfH19cfnyZWFgoQGP2kHbtm0t/j7vZs9Ogl/eyrx+/XrWhvz8/HQtt2q+/PJLzSkMZF3TsyrxgssS58+fh6+vLxukeAFCqRjU7Nq1C2XKlBFc09Q3+IlZj9KlS2PLli1o1aoVs3gTERER8PX1Zc8GEN3qaWlpGlGndvu6uLgw8c5bZj///HM0bNiQLRZ37tyJ5s2bA8iyoPD97dSpU8J1La3Kw8LCDAXg6dOn4ebmJuyUp+B8S9y+fVvXvam21PGiTi9mFrBeH/Hx8bhx4wZSUlIECz3Bx1gCYP1VPena29vD398f33//vdAeCxUqpLHUhYWFoUSJEsI4xT9vZ2dnTZw0H7bh6+uLSpUq4dq1a7qirlq1akzMurm5Yffu3WjatCkAaI7Oovt46aWX4OzsLPTFl19+Gb6+vsK41a5dOxQtWtRw4UxJ4vl75QWXp6en5pkC0MT+hYaGCnVNwpTGHCpnQEAAtm3bxhblgHVRR8KEdq1S31WLOn6BVqhQId0TjEqWLCnktuPribh79y5++OEHZGRkWLX09e/fH7/99puQb1TN4cOHmWhXJ75Wu1/j4uJYHdPOYkVR4OPjw8YWI4GuJjU1lY2XemOummyLunbt2qFevXqIjY3V3NidO3dsHtQt0aRJE7b62759OxsErb3HM2bMGMTExLD/rG3ioAG5SZMmaN68OSZNmoRt27axFWrXrl3RtGlTHD9+HFWqVAEANG/eXFgNqB8436jVoo6Eli3Zy+mZUqPXs07SCmrdunVsBUCxdTRx8gOHOr0EoBV1ycnJwj107tyZpeBQx6sQeodXFy9e3LCeqOF7e3sLEyc/qRoFn+tNBmocHBwQHR2N4OBgNqhcunTJoqjTWw3xYlvvd4sWLSo8Ox4fHx9UrlwZzs7OCAkJQWhoKNvpRwPvK6+8AgCYNm0ac0cbkZ2QA7Xbjqw7ZcuW1Vid9cpvMplgb28PNzc33Lt3Dy4uLswSTNfiY6FssZgBWQukIkWKsHbI10f//v0NT+jgF0GOjo6sX1lbYS9btoxZhFxcXIRBPjIyEoUKFWKTfenSpYWzMbt37y4IPjWLFy9mcbmAuHt8xowZOHr0KFuweHt7szQnaktKeHi4MNDPnj3bMGXHhQsXLFr1xo0bJ4i6smXL6n6+WLFiQkwn5SvjoZg6ghd16oUvYS3F09GjR7FmzRq4ubkJFi1CbUGi52eUp7NFixaa003Ui7nIyEi4ubmhadOmqFy5sjAm2dvbo2fPnoI4KFKkiHANLy8vVKxYEdevX7fqfqW2MHPmTCxYsABz5swRYgdHjhzJPuvo6MjE5Pr16+Hl5YVChQoJc4ObmxsCAwN1710Ptajz9fXVWG6BrDbHp4UCxMWy2WyGyWTSHKFI8xcfY85b4hITEzVzLo036sUKlZP6GK8h1LvriW7durFdp+3bt9cNw/j666/x6aefonfv3lY9iMWKFWMbgowoVaoUE8ZGc1JkZCTi4+Px6NEj1lZpXsvMzISvry+ioqIQHR2NgQMHsu99/PHHQggMv3BNTU3FuXPnUKNGDasbRoBsiroJEybgtddeQ/fu3TF8+HDN6nXz5s3ZanhG1K1bF/7+/ggMDMTFixfx2muvsQfwyiuvICQkBIGBgXBxcWErITVOTk7w9PQU/rMEiZWhQ4eiadOmSE9Ph9lsZvFqlSpVwpYtWzB06FBMnz4dCxYswNatWwVRp54UjURdaGioYKmrVasWevbsaThAEpZEHfncPT092QBDruFixYrBZDIJg55e47W3t2cTm14c2JYtW1CsWDF0794dFy9e1LXM6cUp0AqYT2ZJ0GTs7u5uaCkx6kAkWl966SUhoJ4nPT0dp0+fRqFChVicT9u2bS2KOn6TQ/HixfH2228LA5beZhYHBwf2PCpWrIikpCTmWuP7SYkSJeDv74+tW7fi0qVL6NatG4As9+2QIUPw9ttv48cffzQsG2DdtUXoHYFEC7GyZctq4vYsCRdPT0/cu3cPnp6ezM1M1zp79ixLUWOrqDt//jyKFCnC3Gm8KGvUqJFh5vrevXuzgc3Hx4fFIun17ytXrmDQoEGoXr26EGdK8YRUzyTqyLLdokUL9t6sWbPg5ORkNQVOdHQ0uwe9EwlIlLi4uKB06dKYOnWqJtTC09NTiEP09fUV+qmdnR3OnDmDgIAATJo0yaL75qWXXhImcV9fX92YMhIQQFYb1rME8FY/Ozs7QdQdPnwYJUqU0IhqvZADviwrV67E7t270axZM93jyNTnOdNnrIn3iRMnAshyf0VHR2sWryaTCYUKFcLly5fZ+DhmzBhERUVhxowZwmfVcYJ2dnaoUKECoqKiWJJZQu3WpPI2btwYAwYMgKOjI9q3bw8A+PXXXwWrGR8e9OqrrwIQw3qArDFE7Xru3LmzJn708uXLCAwMxI0bNwSPga+vr2GOU/UiVm01d3Z21oQvqZ+Nl5eX4M1ITEzU7L5PSEiA2WzG+PHj4eTkhKJFi+KDDz5gC1wafxwcHLBs2TLs378fo0aNQuXKlTVlJsOKu7u7piwEud8pl6h6pz55nYDHBhm9eY/w8/ODm5ubJg5efY8eHh74888/WVultkCWuoSEBM3vVKtWDS1btsS4ceNw4MABoa5TUlJw4sQJwb1siWyLOv4/PnEqkBULY+n8zewwY8YM7Nu3DytWrICjoyNLNurg4IClS5di3759Vie/7FC9enUoioKqVauyFb2Pjw9bcXbt2hXOzs744Ycf0K1bNwwYMAAeHh5M1DVs2FAz2fIrFX4FGBMTwxpiWloazpw5g3Xr1qFmzZro1q0bdu3apVtGmlh4wUOrHrLUeXp6olmzZgDANpSQqI2Li2PCU88Uz1vqLLl+aOAxEsrkEqegWCrv+vXrNakP6BoODg7ZttRRPVWsWNEwkXOxYsUQGxvLBrTo6Gj88ssvNp2x+u+//+LChQtChn3AOA8dibo6derA2dmZpdzRs4B5e3ujSpUqaNWqFRRFQbFixTB79mwEBARk2zXMwwsFPoG1Om9WmTJlNMG+lkSdh4cH0tLS4OHhgcKFC6Nw4cLMJc8nH61Tp47VMrq5uUFRFBQuXFjXUqcexD///HNkZmZCURR06tQJb775JpydnfHpp58K5VNTqVIlzJkzB+fPnxfujf5N1rqIiAiWsgOAkLSYBnC+bW7cuFHzW7Gxsewe0tLS0KhRI3z88cfsfXrP1dUVJpMJo0eP1vQx6q+Et7e3IOoyMzNRq1YtDBkyBLdu3dINtP71119ZaAQ/ibu6uupa6nhRR6EYY8eOFRbn7u7uqFq1KubPn4/OnTtDURTButO4cWPN8/fw8DBMR0InpNy8edNwETB48GAhMwDVmbXF+YQJE9ChQwcm6tQTPl8nJEK6desGDw8PjWjixQ6lxiEr1qFDh4QNHuoFkp5QpfHBlv7NH+UIZC3C1OUbOXIkKlWqJLzm5OQEf39/bNiwQRi39Cx19OzVz4gPI8rMzNSIOicnJ41RoFixYoLl//79+9i/f79GSJUvXx7e3t747rvvEB4ezowmgNjH+vTpg+bNm8PV1VVIWk/wopfK7+vrK3gx1F6jKlWqCGNiixYtWF8nUcfXG8U7V65cGZUqVYKdnR1bFACPN/wZuURpHqRrZmZmGopGahtff/01mjVrxvoTnXF848YNXYu2HjL5sA7UqDMyMlCuXDmkpqYaPlBa0S9duhSurq6C/5+3Ii5atAg9e/ZkDYEqnDcvm0wm/O9//zOMr6JBmheL1MBI1Hl5eSEwMBCZmZkslQS/0qbOmF1Rxx+yTYODkTXl9u3biI2NZRYdErsmk0ljPqbGa2dnJ7hseMvYkiVL2L/5gZYEQXx8vG72eicnJzY5UZm9vLxgNps1lhc9i2+FChXg7e0tCJXu3btj8eLFGDRoEHudOqq7uztWrlzJNh3wm4iyS/v27XXriEcvF5Z6dyAxYsQIdOrUCa+//jqALFeCuv4siToadGhSffjwIduBy+Pq6or9+/ejc+fOwuv8xg5yT9auXZtNVPxER6twon79+kK7qV69OpKSkoR4UGuTPQ/d5/Dhw/Hw4UNmqZs0aRKmTZuGoKAg9ntUd3x70bOUUmA0kCXqjhw5Iiw6eUudEZ9//jn69OnDJhN3d3dd0ePr64uYmBjdzV99+/ZlFghq8z4+PoaWRuoPERERTPhPnjyZLaKp7HZ2dhg4cCBrk/zEP2/ePF1RrY533rZtG86cOcMWVPfu3dN8hq9nPnaV+jf/O3qWaCCrLaxduxYJCQmCEKpcubLwPGnM1FuIxMTEMOHw5ZdfsnOlyTtw6dIloZ1SrBWhJ+qoLdki6jp06CD8XbFiRY0oa9CggSbGztHRUXfHpbe3t/D9f//9l/Vptahr1KgRS4yfmZkJJycnIVzB09NTc38kbEhk/vbbb0hPT2fjDUGbifQ8RUZ9Q28jDd/+qPxFihSx6ClISUkRxjwvLy/2rGi+M5lM7P6HDRsGIOv8a94yS32AwnCMRB1dm/oeWeqIypUrs4Wcun+SrggICEBcXBxMJpMUdbmBF3WAfiZygixPVClGptno6Gj06tWLrfRcXV2hKIrhJEzwQfHUiPhdatQ5ePcrkNU4qaG6u7szUUcN0kjUUUfnRd3evXuFVTMNDkbHALm7u7PBNy4uTvfsUoJfkbz99ts4d+4cypYty+KOeB48eIArV64wV1rZsmUxZMgQfPXVV7oTnNlsZsJPL56EZ9CgQTh06BD++ecf1qH0BuaVK1fi/fffx5w5c3Dq1Cn88MMPQizEG2+8weqA3HmWzPVG/P3335qzEtWT2IcffqiJmXN3d8fp06dZ4C7dP+2+I0u6niXWkuAgK496kNajefPmLEaQ6Nq1K/v34sWLcf/+fUyYMAFFixZFkSJFhDhF9YDPJ8M2gp/s165da5j6AXh8n3PnzkXfvn1x+fJl+Pv7o3fv3hg1ahTs7e3Z5EB1p67Ds2fPaix2NFnrxclaEnXz58/HkiVL0KRJEyxbtgwLFizAnj17ULlyZd3Jz8fHR0hpAmTFtpELnKA2X6JECfb79vb2+Pjjj+Hg4IBChQqxzxQqVEgQVHxoBd8P6DnzOfYKFy7MwgiMoFN5atWqxcbKtLQ0zTgUFhbG4th4UUcubb6eeauL+rcI3lqpFi9//PEH5syZoxtG4unpycI1eJHk4eHBJnH+9YcPHwp1a0nUqWPYiEOHDgm/w1OuXDn23N544w2MHTsW7u7u6NChA6ZOnSr8hp7IOH/+PKtrDw8PBAYGsjatfi7e3t5skUuWOh5fX18hRMXFxYU956ZNm8LJyQnr1q1D9erVhb67Y8cOlhhYb/4xGn9q1aqFDRs2sL4wcOBAwXVKoo/SiABgLt169eqx8Tk2Nlb4noODA2bOnIlt27YJQvvIkSMIDg5G9erVkZyczDxfBHlqyKPg4OCA999/n222JNTWv8zMTGEe8vT0xIYNG/DFF19oQpNIRJJeqFKlitUjQgkp6nSgidDIEsVjq6gDsk4y4IO1bYG3QHh5ecHX11fYeUoNnVad/GBQrFgx+Pj4oGzZsqxB+Pv7w8XFBWXKlMHvv/8uZN92cHBg5adJv3v37prM93SttLQ0XLlyxWIsoLu7u8XUI7wAMplMqFGjBoKDg3VdS0WLFoWrqys2bdqE9957D3Xq1MHs2bNRsmRJQ1FHnUivQ/A73by8vNCkSRMhJpBfPdGEp663oUOHGooOmtxzYqkDtAO7enHh6Oio+YyTkxNq167NXEO7d+8WdsASeu5nfvCeOXOmcATT//73P5w9e1bIrWUJ9XOys7PD1atX8ffff8POzo7FeTo4OODu3bvo2rUr3n77bUydOlUQF40bN9aNqVFDz8HR0RG9evWyOADy97l161YkJiayfGfqz1B/UFuYa9asie7duwtHsVmKqSPxrTfZDxw4UMi9Z29vz9x9vOWBgr31XDi1atXSnL/p5OSECRMmYP369awNDhgwAD/++CNSU1NRrVo1w3gk/hnx/ZcmP17UmUwmTJ06FaGhoXjppZc0ZwK3a9dOSH3Dtw21pa5o0aKsTNTeS5QowRaV/HWM4D0ZfDvgLf5AlqXY0m5H3sLCQxa8woULY/r06UzQDh8+nH1Gr55prDOaV9SpqE6ePMl2I7u5ubFn3rNnTyZo7e3thYWdo6Mj69vDhg1jx+S99tprbCyk61A59EQd1bOiKJr0HY6OjkIIipubG7u3MmXKICAgAKmpqejataswf3Xo0IH1I72NepbmxB49erA2PGLECKE/koC7f/++kO7s1q1b2LFjBxNXMTEx6NWrF0tObDKZ4O7urgkj49Ebu8ltTx6HpKQkLF68GLt37xY+pxZ1iqJoLImurq6YNGmSRjdMmTIF/fr1Y5t5rJ3bzGNboq8XDLPZjJIlS1rNTA9oRZ3RBF63bl0EBASwFYq1wOtz587pujTKly8vZP329vaGyWTCvn37YDabhUHPyckJDx48gIODA2vIxYsXx82bN+Hn5weTyYQuXbpg//79OHnypPBdKqfe/fDuV3VMR3bh3a8En5xZD39/fyEWA8iKwfjll1+wY8cOhIWF4ZVXXoGjoyPrUHqr4+bNm6NLly7466+/hMGfBnF+Mnv55Zfx119/2bT7iKDB0pY4Mz3USVl79+6t2Q2pbiPq8lWuXFlXFFkTdZUqVRLKza9wbUFvgK5YsaJuGhoa0NT51oCsjP62TOQ5cb8SPj4+mkmGBIU1K+uwYcPYkX98TJ2abt266Z4yYg3qhxUqVGCpZNSCVe8wdYI2DtDKnxYtJpMJGzZsMByvjCZYskaoT8Og1CL8KSFAlodCbenhxz5LRyv27NkTGzduxIULF7JVv7xLkgTwlClTrCZEVhMUFIQdO3YIu5mBrE0g+/fvR+HChTFy5EgkJSVhwYIF+PDDD1lIgp6LdciQIUhKSmIbJtSox6i6devi9OnTbI5p3bo1Dh48KJzQAoh93tHRkY1bZcqUQWBgIDIyMtiiCnhcd2R91xN1NG7qeanMZrMgTPlwluLFi6NEiRK4efMmunTpYughqVatGvbv38/Sfhn9lt59qtsTP5Y5OztjxowZeP3119l9qT0mNGcZWUytsXLlSty8eZO54mkDknqxRYYRvr0XL14ce/bs0d1QyEO7o8kKS3HstiBFnQHqbOtGNG/eHPv379es7Hl27drFKpEGMWuWOrUpl6hSpYog6tzd3dlkoTeZUEehiUCd68bd3R1r165F+fLl4eDgwAYQGgDVq27AekxdduDdr4TRjmZL1K5dW3OYstlsZpZMvbMpgccTAD9Rjhs3TnN+4Jo1a3SP6LFEnTp1cOvWLd2M+bZAx/L17NkTe/fuRbVq1XDo0CHh+QwYMADR0dG6ed0soTeZ8m03Jy5jHlst0dawRdABj9uRLaJbbUXRWzzpuV27d++umeCBrDQgfn5+bCK3NjllB7PZjDp16jBxBoiTx5EjRzQWHj06d+6MrVu3ClYJSzGbNBGp7/ftt99GuXLlUKNGDXTs2NHqvepN6nzbsFSGPn364PXXX8/28/zjjz9Ynrzg4GA8evTIokWO58yZMyzhsI+PjybJOpBlPf75559Z23RxcRE27dBralxdXbPdTymlEGFtbHR0dGRzB1mwSLyULl0abdu2ZUKBxkT1gsbb2xtOTk745ptv8Prrr2P16tWChd7BwQGDBw9mB9ArisJi7gICAhAQEAAfHx80bdrUYv9t3rx5tsZUdZwrz5YtW5i4U29s8/b2xvLly9nmE7rvnIo62plNCdRJHDs4OGDSpEn4999/sWPHDtb21c9AHTNsiSFDhqBTp05WUwQJKC8IMTExCgAlJiYmT6+blpamhISEsL/v3bunAGD/lS1bVnh//PjxCgDlzz//zNHvzZs3T7j+2LFjhb+NGDp0qAJAmTRpkua9kJAQBYDSo0cPZcSIEQoAZcuWLRbv2drv2UpaWppiNpuVv/76S3idv6fs/lZ6eroCQClTpoySmpqqzJgxQ0lNTdX9bNOmTRUAyp07d3J1H0+SBw8eKBs2bFAURVEuXbqkAFDq1asnfKZGjRrZrg/18/3vv//Yv/fu3ZurMu/YsSNHdUeMGzfOpu/R9Xfu3KkAUOzt7a1+Jzw8XChb3bp1NZ+pXbu2AkD5559/slXuWbNmKffv38/Wd7JLZGQkK/vFixef2O+EhIQomZmZeX7d2NhYVv7Dhw9n+/sAFHd3d4ufoX798OHDnBbTkIsXLyoAlF9++UXzXtu2bXPcD3OD+hq2zHN79uxRBg8erCQkJAj9QW8s5N9v1qyZoiiP+3iZMmWUXr16KQCUkydPKocPH1b++OMPm+6PftvOzs5qeUuVKqUAUOLi4qx+1hKhoaGsrLmBxhF1W4yKilJmzJgh9B3+GdD8OXDgwFz9vhHSUpdLHBwchB2ZaguHeqs7WY6ycyoAj9o1SSZgICsOygg+INoIs9nMgq8tWVpsPZ7LFhwcHGxKwJwdaGUUFBQEs9msWbnx0CrL1iDU/KBo0aIskLZ06dKoXbu2Jk7u4MGDVo/C08PJyYnFx5QpUwbDhw/HjBkzcryKJXJyugjP119/rbu71ghytRht3uFRbz6w1VJnC5TG5knCW7+s5W3LDbYcSZQTbLXUGXHt2jWr992/f38cOnTI6gapnFC1alVcuHBB1+KyefNmIV9bfmGLu7pVq1YsdvP48eOIiYnBm2++qRua8corryAxMRG7du1i4z/VQfny5VnOxOLFi8PPz4+FCliD2kJQUJDVz9KYlNuxxd/fP0ehEGrIYq7O/+jt7a0755C1zcHBAWfPns116JIRUtTlMdYmgffffx/Xr1/XTcRrC3xsU3BwMN555x2WAsHSSQRkutZzu5GgCQoKYilZbJnMnqYQoo0gthIXF2c1bhF4LOpykxvuaeLi4qJ7NJOHh0e2J/jLly/Dx8fHpkOisws/cT+NdqIXeG2EUVodnpyKuqcB7855kqLuSeHg4ACz2Yy0tDSLMXVGqBPz6vHee+9ZPZklNxjFmLq6utqcJJZo2LChbujM06R+/foAwJJ5q9m0aRMURcHQoUNZrj/qR+XLl8egQYNgNpttTj5OmEwmXL9+3aYFBP1eXhoVcgOVwxaBePHiRWG3tC07+nNKgdv9euLECQQGBqJVq1YICgrSNPY9e/agZMmSaN26tZBmo6Bgbaejm5sbfvjhB5sEhx68BeX3338XGrilAZ4sGHorV4rL69Wrl83xBps2bRJi+/IadXB/dgcLd3d3m6xNK1euxKBBg7K1AeJ5oXLlyppVOT2H3K5keVHHH4r+pMhOIL0t3y3Iog7IXiLbggglYX4SlrRnjaNHjwo7zQsqJpMJs2fPZhamWrVqoXv37hg3bhxq166NTZs25cjCX758eZsyBFDeuIKGOmm4HlWrVn1qRpCCIXk5AgICsH37dri6umLs2LHYuHGjJlg3ODhYc6RLQSEvg6SN2LVrl+aYGsCyECOxZm2VY6uoU+chy2v279+Pa9euaXIE5TUNGjRgAcUvKmfPnjVcoecUEnVNmjTR7JTMS0wmU44EqJ2dHWvrlix1OU1H86Q5evQoli9fbvNGkoKGi4sL7O3tn9nyFzQGDx6MOXPmPNXfdHFx0T1dRU2/fv1ytegiPv74Y+FEkIIAnSdckChwljp/f39mxTKbzboiZN26dQgMDNTNqJ/f8GJInTw2r2jbti0++uijbH2HttFb8+PTCQxG+aueFoULF87RLlhJ9qlZsybLYE8xmjlxi/GQqDPadZxX/P3335qdyrZw//59tovtWXO/All1pnd80rOCq6trrtuY5DE//fRTnsSJPQl+/vlnfPfdd/ldjCeCr69vgVv4FThRR9y5cwc7d+4UMtEDWZaVK1euYNeuXdi2bZtwpipPSkoKOyiZPzD5adG3b1/NodT5Sdu2baEoinC8ih5BQUGIi4vLd1EnyR8GDBiAY8eOsZQQOYXcg7ZsXMgN7dq1Y4u769evC+dPWsLPz4/FtVKQN4+17P+S3OHi4iJFnUTyBMg392tYWJhuDrRNmzbBwcEBffr0wZIlSzTuTD6GpFu3bjhz5gwL8uSZOnWqJlHr0+LOnTtPJPj8afGsxulIco/JZMoTd/TTstTx8DvBbYF2/kVERGje09sBKMk7XFxccrTzVSKRWCbfRJ2/v79wTBORkZGBHj16YPz48bquwtjYWOaf37dvHzsgWM2YMWNYpnf6njUrVV7xtH6HCA4OFg5clkjyG7J0PY0Y05zi5+eHRYsW6QY6T58+HQ0aNMhx4miJZYoVK/bEUqZIJC8yJqWAOeJXrVqFIUOGsC2/H330EYKDgzFw4EAsWLAAixYtwsKFC+Hg4IDmzZvj22+/tem6sbGx8PLyQkxMTJ4EbUqeDnPnzkWVKlXYUTKSZ4dZs2ahR48e7LxjiYSIi4uDvb19jrMASCQSfQqcqHtSSFEnkUgkEonkeUZGAUskEolEIpE8B7wwljpFURAXFwcPD48XMtGsRCKRSCSS55sXRtRJJBKJRCKRPM9I96tEIpFIJBLJc4AUdRKJRCKRSCTPAVLUSSQSiUQikTwHSFEnkUgkEolE8hwgRZ1EIpFIJBLJc4AUdRKJRCKRSCTPAVLUSSQSiUQikTwHSFEnkUgkEolE8hwgRZ1EIpFIJBLJc4AUdRKJRCKRSCTPAVLUSSQSiUQikTwHSFEnkUgkEolE8hwgRZ1EIpFIJBLJc4AUdRKJRCKRSCTPAVLUSSQSiUQikTwHvDCiTlEUxMbGQlGU/C6KRCKRSCQSSZ7zwoi6uLg4eHl5IS4uLr+LIpFIJBKJRJLnvDCiTlIwSU9Ph8lkwoYNG/K7KBLJC09qaioWLlwoPRoSyTNKgRR1J06cQGBgIFq1aoWgoCCkpaWx9/bs2YOSJUuidevWaNeuXT6WUpIXJCUlAQDmzp2bzyWRSCTz5s3DwIEDsWvXrvwuikQiyQEFUtQFBARg+/bt2Lt3LypUqICNGzcK7wcHB2PPnj1y4HkOSE9PBwBkZmbmc0kkkmeTQYMG4eWXX86Ta9EiS4apSCTPJgVS1Pn7+8PV1RUAYDab4eDgILy/bt06BAYG4ocffjC8RkpKCmJjY4X/JAWPlJQUAFLUPQtkZmZixIgRuH//fn4XRcIxb948bN26NU+u5ejoCCDLDSvJe/744w/8/vvv+V0MyXNMgRR1xJ07d7Bz50507dqVvdagQQNcuXIFu3btwrZt23DixAnd706dOhVeXl7sv5IlSz6tYkuyAU0eBS2Gx2QyYezYsfldjALFjRs3MGPGDIwePTq/i2KRYcOGwWQy5XcxnknMZjMACCEvkryjd+/eeOONN/K7GJICyrJly3DlypVcXaPAirrY2Fj06dMHS5YsYQMNALi7u8PR0RGOjo7o1q0bzpw5o/v9MWPGICYmhv0XEhJi82/v2bOHuSEkTxYSdflpqRs7dix27NiheX3GjBn5UJqCC9WVvb19PpfEMrNmzcrvIjyz0Fj7olnqzp07xybTpKQk6TmQ5At9+/ZFs2bNcnWNAinqMjIy8NZbb2H8+PGoVKmS8B7vRt23bx8qVKigew0nJyd4enoK/9lCcnIy2rRpg48//tjqZy9fvozly5fbdF2JPgVB1E2dOhUvvfQS+5ushmq3P713/Pjxp1a2ggQtdMhFJ3k2yczMRHJysuF7AJCQkPA0i5Tv1KpVC1WqVAEAuLq6YsyYMflcorwlMTERn332WYE2VkRFReV3EQoE8fHxufp+gRR1a9aswcGDB/H111+jdevWWL16NQYOHMjea9SoEZo1a4aAgAC0bNkyT3+bRMb169etfrZZs2Z455138vT3Cxrh4eFsM0N22bRpk1VxXBBEnZrExEQAECzExP/+9z80bNgQ//zzD5YtW5bvA1FycjImTZqUa3fZTz/9hPLly1v8DAXPP0+iLiEhQWh7gwYN0q33ZwFbrWtffPEFXFxcdN8r6BslGjZsiPnz51v9XFRUFNasWZPj38nNd58UERERKFu2LG7fvp3t7y5atAgzZ87Epk2b8qQsderUweeff54n1wKAAwcOoHjx4vk+nj4XKC8IMTExCgAlJiZG897vv/+umM1mJSkpSXn48KECQGnevLnVa5rNZsXaIzx06JDy119/5bjcuSE5OVkBoKxYsUL3/czMTOXBgwcWrwFAGTx4cLZ/OyMjQwFg9fkcOXJEAaA0btw427+hZtWqVcrmzZuz/T0qZ1RUlOLo6Khs2bJFAaAUKlRI+NzGjRuVqVOnKgCUMWPGKACU1157LUdlTUtLU+Li4nL0XZ758+crAJQ1a9bk6jq21NXGjRsVAMpnn32Wo9/IzMxU0tPTc/Td7ED3kpmZadNnx44dq/nuswSVOTw83KbPV61aVQGgxMbGat6bNGmSAkAZNWpUXhczT1DXz759+5TExETN5yZMmKAAUE6cOJGt66alpSkAlNKlS+dVkXV/JyesX79eAaBMmzYt29+dOHGiAkD5888/c/TbavK6n6xevVoBoJw9ezbPrvkskJ6erqxevVrJzMxkbc/R0TFX1yyQlrqnjaOjI9LS0pCQkMBWu7ZYp+zsrD++pk2bokuXLrkuoy0kJSXh1q1b7G9yVf/222+6nx80aBD8/PwQFham+77y/92Q27dvz3ZZ/v77b/ZvSxaE3Fjq4uPj8ejRI/b3G2+8gVdeeSXb1yHOnj2L1NRUzJs3DwAQGRmJ06dPs/d79OjB3DIUyxkaGpqj33rjjTfg4eFh8TNkMeTJzMwUnlVGRobhZ63Rv39/dO7c2aqFZ//+/bh161auLXVvvPGGrkv7SWHtvsgqZUvi61WrVqFUqVJ5Ui5i5cqVuH37NjZu3Mh2r27ZsgUBAQE52jgUHR1t0+cKFSoEALpxxuSWza6l7tGjR1i7dm22vpNbUlJSEBgYiE8++UTznre3N4CsesvuNYGcb9yaN28e7t27p/teTq9JUN/JieeE2oaTk1OuygDk3j2oB/XFhw8f5vm1Cyp//PEHHBwcEBwcjL/++ou1vdwiRR0ANzc3ABBEHU2WliBR9yR3iimKgjfeeAOnTp2y+tng4GCULVuW/W2t85Mb48aNG1iwYAHq1q0rpKvIqeC6evUqOnXqxP62NEHkRtRVq1aNTVA8c+fORbdu3bJ9PapHPt6obt26up+9dOmS5rPZ4Y8//rD4/tGjR+Hm5qaJ32vYsCFKlCjB/iZXYXJyMkaPHo2rV69i165dOHv2rNUyLFq0CNu2bRPajN7EExgYiLJly+Y6LdDq1atz9f3sYm2Q5F09hw8fxsiRI9nfs2fPxpEjR9jfo0ePRkhISJ7t0lYUBW+99RZefvllvPrqqyzP3MSJE3H//v0ciXR1/LER1Gfu3LnDXpswYQJMJlOORV3fvn0RFBRk07iZEzIzMzU7mqmsN27c0Hye+rIt4ybPxYsXbf5sVFSUkCs1NTUVgwYNwrvvvqv7eT6e7e7duwCyFoelS5dGTEwMgKzNWefOndP9PvX1nIg6un5eCIcFCxawf1+9ejVbfeLevXv46aef2N+zZs1Cw4YNWV0WRFE3YsSIJ7Kbnk9tc//+ffYMcvtbUtQBLCeeNUudoihCxyRRt337dphMJoSHh0NRFNSvXx+bN2/Ok7Klpqbi999/x+DBg61+Vm1RsxQMnZqaylZtISEhGDRoEE6fPi1YCej72Z3I1BOCLaIuJ5Ol0Y7mwYMH5+j504Cnfm7379/XTA7//fef7meNuHXrlu6A2q9fPzYRhoaG4vDhwwCyRB2QtRmH5+TJk8w6+OjRI0yePBlAlrVp+vTpGDVqFNq3b4/atWvbVC4AgpDn/718+XJ07NiR/U31aOvEsHXr1nyNkVGXMzMzE5GRkexvKtulS5fQtGlTfPvtt+y9oUOHokmTJuxv6ut5tSuULMxqSy/1A5qEnwRkxeL7z+LFiwE8ruPsCnhqNzExMZg4cWKOFztGhIeHa16z9Bskis+fP5+t32nUqBEA7Xh0//59zaa44OBgtG/fnv1NYozKFRERAZPJxLwW/DhIKbbGjh2LO3fu4Pr16wgPD8eIESMwaNAgAFntlZ+HaOFrJOpOnTplaEUjSx2V7ebNm3j99ddzVE/Dhw9n/65cuTLWrFmDhw8f4ueff7b63TfffBMff/wxE93Dhg3D8ePH89xSp56rifDwcPTu3TtbiyY67ehJLVgAYODAgWwBL0VdHqBnqdPrOPPmzYOrqyuSk5MxfPhwtkNs5cqVALJWjDdu3MDJkyd1c3mlpaVl2yJFndQWtxWV/eHDh0hISDBU/n379oWTkxMbuO7cucOELfC48dL3s1tm9WRKE8T27ds1HUMt6iIiIuDl5ZXrXD0AEBYWhs8//9ywM0ZERAiDPrlN1ANd1apVUa9ePd1r2Dooli1bFn369NG8vnjxYua2atCgAZo2bQrg8are2dnZ8JoTJkxgQdMk6G/evGmxHEeOHLEooEuUKMHa/jvvvCO40cm6oHfPd+/e1UxAr7zyCpYsWQIAwqkw1gR8RkYGSpQogd27dwPIsl589tlnFr+jh7odjhgxAoULF2Z/2yI4SeyQqMuLXaGKomDo0KEAtH0rJ6KO77t8HXz11VeoUaOG8NlRo0Zh6dKlAERB6e7uDuCx9c5WF9v9+/dx5coV1sf279+PL7/8EsOHD8dHH31k8z3wpKSkaO5f7dKMjIyEv78/AP1JkCbtBw8eICIiQvN+WFgYSpUqhX///VfX06Juo0FBQXjnnXeE16nvZWRk4NatW2jQoAGAxxY1EqILFy4EoBXKiqIwa3BycjJr71WrVoWiKKhbty5at27NPk/9Tm9uysjIQL169dC7d2/2ud9//x0JCQkoVqwYCzFITk7G0qVLMXPmTKxevRoffPBBthbUemL/6tWrGDhwIAYMGICvvvrKovihxYy6H9F4p1dXOWHx4sVwdXXVGBTmzp2LP/74A//++y+ArDnZyDJK+Pj4AMATT7rOe7dygxR1sF3UUaf777//8N1337HXaQAxmUzYv38/AMDPz09YDaSnp8PR0RFvvvlmtspGjdJsNuP+/fto3rw5oqOjER0dbdgZixYtivr16xsKDoqxo3sNCQkRJga1G9KaqDt69KjwGXVHOnfuHEwmEzp16oRp06axThQTE4Pu3bsLv3HkyBHExsZa3H0WFRVlUwLcfv364fvvv2diRE2jRo1Qs2ZNoZyAVrRYslrwn7106RL27NnD/t62bRt27drFxIVRzNGhQ4cAiIMGXZffpaiubz2hz1v21C7Yq1evokmTJjh48KDFOjWyrF64cAGAKJbmz5+PcePGoWTJkujfv79wjYyMDFy+fBmbN2/Gq6++yt6zZul7+PAh7t27h++//x5AlhibOXMmrl27ZvF7atS/Q5Mr3bstoq5fv34AHos69Qp/7969ujkOLREfH88Wgrx4URSF9cnsiDoHBwcEBgYCgBBjOmHCBFy4cIG5vdeuXYtvvvmGvc+3XRoDKSbXVvFasWJFVKlShT1TWlTMmTPHpl2qenTq1IlZEwm1qPvnn38sXoOvJxJXxYoVwy+//AIAmDx5MkJCQrB9+3bde1UUBefOncPVq1cBPBYbfJuiNpGSkiJYPalf0jhKVn31OBIdHc3ETHx8PGvfhQsXxrVr13D27FkcOHCAfZ4+qzc30e9v27YNHTp0wNWrV/HGG28gMDBQiJlOTEzEu+++y1ygK1euxIULF7BkyRKrsdXA44XdW2+9JTwH6ksTJkzAn3/+yb6nFsz0t3rRkBNL3enTp/HTTz/pxjDSXK228KrH0JEjR6JWrVqsL5w+fRo//vijUG5qi7TguXz5MvOkFESkqIO+qIuJicGWLVvYZ3799VfmrlRPLjQopKenMwtTREQEHjx4wD5DE6VeXNHDhw+ZeVzd6Kjx29vbY+nSpTh48CC2b98OHx8fTJs2DTVq1MCAAQM017xy5Ypu3JbeAHb//n1B1NEzsEXU3bhxA40bNxYSvqoHLxpIAWDcuHGoVasW4uLihAGLfoNW3ZZ+c8aMGZg+fbrh+wSZs41yM9FgS5CL1drKrWjRouzf/MRYrVo1tGnThv3duXNntG/fXnD5zZw5U3O8nZ6IUltZt23bJmzMuXjxom48Ie8eHDZsmPAetc3o6GiLlpilS5cylwMPTdj8PX/00UfMBfzrr7+y10mU/Pzzz5r4Rv63MzIyNJMUWZB8fX0BPHZV6Ym6nTt3svYfHh6OcuXKsff4ch4/fpz9Lg3YvKizllCZ6kHdf1q3bo2XXnoJcXFxNm9UMHLhfvLJJ8KCxwhFUWAymbBw4UIcOHAAsbGxCAgIAKA/Kb7++uvIyMhAUFCQ8Dr/fMhSR9YnS6IuMzOT3SuJJ7LOqOPbLLm5jKwy/MKIUC/M+D6lR2JiIhvXY2NjkZqairCwMEycOBEPHz7EggUL4OjoiKNHjxr2hVq1aqFy5cpITEzUzd9H/TEpKUl47g8ePMC3337L7v3mzZuoWrWqZkHPx2jGxcWxdpGamirMHQSNYzdu3NCE2vAW+v3797O2rQ4b0WsfEREReP/99xEYGKhpm+vXr4ednR0b06ke+MXwuHHjhDpbvXo1tm7ditmzZ8PR0VGw3NH1syPq0tPTMWjQIEF03rt3D3Xr1sXHH3/MUqvMnz+fJYynTWhG7YT6My2o6Xm1atUKn3zyCRwdHZnwVYu6qlWronHjxrrXXbJkiSYsKCUlRTfuMzt8/fXX+N///mfTZ6Wog76oCw0NRZcuXRAXF4eYmBi89957bHWtnlyo8x46dIg9+Bs3bgiBkJYG6aJFi6JKlSoYP348nJyccPHiRbz66qtIS0sT3K80sNBgOHbsWFy4cMEwlmHKlCkARKGoFhB+fn6IiYmxaKmzZJ6nwf2ff/5hk7Na1OkN7Fu2bBE+R/dG/7f0m7buvqSB0dagb36nqyX4fG561tDU1FQh4JofWD777DN8+umnwufV5UtNTWWDHLXHqVOnCp+pXr06/u///s9iOflAeODxhJuUlGRR1A0bNkw3hpMmfEsuZ7IUWmrv/G/36NGDuTcuXryIe/fuaUQdDcB8e9m5cyf27t2Lnj174ueff8aBAwewZcsWQaiTVSU8PBwNGzZkr+uJOqO8bQSVoUqVKqw98xPg0aNH4ePjY1N+M6ONVfxOTUvPjybW77//Hi1atAAAtnkmIiIC169f17jt9awwvNWJ7p/uTU/URUVF4e7du5gxYwZ8fHyE+6f2qnb/U13GxcXBZDJh7969ALKER5EiRYTNKBEREUId85uE9NyvlkhMTESxYsUAZLUbGgvc3Nxw9+5dpKWloUOHDrhw4YJuX+AXlSdPnmTjUbVq1Vi/40Udb2U/e/YsRo4cyU4GiIqKwuXLl3H16lUhB+KdO3cMRR0veOk61O82bNigcdWpRQO1H3WGBj2xSHV0/fp1IV4OeBzWQeKQ2l716tU11yFWrVqFl19+mS28+X7Gizo+pILqgBd1W7Zsgclkwq5duzBv3jx89dVX7D3++VDb/eijjzBixAgAj0Wd2lJH/ZjKQe2Wt3ATJ0+eBPB4Lrp7965FS2JkZCTef/99TajO+++/r3tIgtEcpw4niIiIwPjx4zWLMiOkqMNjUZeYmKhZqURHR2sGOPXARQPhyJEj2a7IxMRE/PbbbyxLubXA4wcPHuCXX35BWloaqlevjo0bNyIkJERX1OVU9WdkZGDixInCa6VKlUJMTAx7BsDjBk+dMTMzE/369dO1CNJ9/fnnn+ysVPW96k0Qw4YNE85AVJvlLYk6vqy2YGt8EP85SyeQ8OcIU91HR0czS+6PP/4oDHrWJiByVRLz5s1jGyZo4rUWKwcAxYsXF/6+e/cus+qMGjWKtZs1a9bkOBULkDW5LF++XPdkjapVqyIkJMSi1So+Ph7h4eFo164d/vzzT8THx+Orr75C9erVUalSJVY2Ly8vAI+tQLGxsTh48CCioqLQoUMHtG7dmgniFi1a6K6QAa2l1t/fHzExMcJkYyl2ERAnR6pPfoJctmwZgKyYnVu3buHzzz8X2vB7773HdkpSW1+wYAG+/vpr9hk/Pz/2b0uijsYYPu6ULHUhISFYs2aNxvV89+5dIZ4QEMW5+vM3b95EkSJF2N89e/aEr68vSpYsyVxbvNCiyV49NpGYJLFNmw2o7dy4cQPLli2Di4sLihQpIlhaGzZsyBaK6oS7vEjVi6lLSkpiMXf9+/dnz8rNzY21h5IlSyIqKkp3fOD7x8OHD1ldhoeHY/bs2QBEUWeUxkQNzQd0D1QH//zzD6uD1NRUYcw4dOgQtm/fbjGRu/r5kJhRW6D5NkvPh8ISgKzduLygJW/Azp07ERUVhatXryIgIIAJZkuQ0F2+fDl27tzJ7g3IGgN+/PFH9lnqi7xoIjcuhTTxfZAXYXpx2tR/wsPDsWbNGrRr1w7Xrl1jv09zEj1/+n1+HKDXaD4LDw9nISh6kEs2IiJCKBPFM548eRK3bt3CBx98oKkX/lkAWW3DZDLh7NmzLLzDyDqopsCKuuHDhyMwMBBvvfWWILTS09Px7rvvGuYnyglmsxlms1mw1BFRUVEaUaJ2Gxi5Ki5evIjOnTsD0OaQWrVqFXr37i0M/FWrVhU+8/DhQ0HU0b8tNSxL7NixQ9iODmQNbMePH8exY8fYa6mpqXj06BFzJWZmZmLx4sX4+eefBStDenq6MPiQCZ63PLm4uOgKCPVrycnJOHPmDBN6loJt1aJOURSL2/xtEXVk1SQs5XNSi6eVK1fCx8eHDcrqeB9rou7cuXNCfNynn37K3AK//vorkpKSDOMCAbCdrupyJScns7Y6e/ZsNuGuXbsWb7/9tvDZ7Jw3mJKSgnfeeUewfvEcO3ZMcK2riYuLw/Lly5k4ALJicYAskUyTAbUBqr9FixahefPm6Nmzp+51Dx48qCknfx0iISEBAwYMwJdffsles2SpW7dunRCrSP2dFxYUp3rjxg2sXLkS33//vdDnf/31VwQHBwN4PLFVqFABY8aMQYsWLVCsWDGroi4mJgbx8fG6aTf8/PxgMpnQv39/XUEdEhKiGV9SUlJYv4mPj0f9+vWF9/lxjs/lR1YQEpfAY6GqFnWhoaGIiYlhdUqWKhJWTk5OmDp1Kptc1X2FYvzUoRJ8fzDaKEFhEnfu3GHtixd1xYoVQ3JystX++fDhQ0HoZGZmQlGUHIk6XljzAmb58uUsLEMt6gBg0qRJmmvRhhdA+9zIm6S2CvOWKxoveNfpiRMnYG9vjy5duuDhw4fsupMmTYKvry8OHDiAZs2aMSu6LXz22Wfo0KED3n33XXbP8fHx6Nq1K/sMCajQ0FB88sknKFq0KDv9gnI48mMkibqGDRviv//+Y58BgA8++ACLFi1i97t9+3bs3r0bvXr1Yh6Pn376CaNGjWIxk/T7/LgfFRWFf//9l22me/DgAfN+kHeBh1/k8n2H2lv9+vVRtmxZ/PLLL8jMzMT69evZZ/h4ZOCxkN2yZQtrW9YWnkSBFHWnTp1CWFgY9u3bh2rVqgmxYZs3b0aJEiWwb98+JCYmagbynOLm5obBgwejd+/ewuuPHj3SiAK1qFO7z3hXZtu2bQFoRcybb76JP/74Q1h9hIeH4/3332d/h4WFsWtv27aNCS89Uace2PgymEwm7NmzR0hMTOglVE1LSxNWQvyARsH3q1evhtlsFp6X2WyGoijYvHkzSpcujUePHsHT01PzvPhzVomkpCQhZkBt7aPnd+fOHU08XXp6umB1ULtnV61axVaj69evh8lk0kyaaoFiSdSpg7hXrFgh/K0OojUKQCYs7aratm2bkD+NIHN+0aJFmVmerDU8NCDZ29sLEy6JFGo3rVq1slhGHms7fl977TWLZ2fOnz/fYqoJsrglJydDURTWB6j968VcAdBsWDBKUQNoj4FydnbG0aNHdd2nvXr1Ev6OiYnB9OnTWfoLIKuP1KxZE6GhoUyQx8bGIi0tjf2+s7MzYmNj2ZGHjo6OsLe3R9euXZGSkiIsVtTtMyMjA97e3ihTpoyugHB2dsbYsWORnJysm/7j7t27SE1NFVLd7Ny5E2azGVeuXEF8fLzNbYDi78iazKNeXIWFhcHb25stbvm8ikBWv7d0LNvly5exevVqzThvlM6ISExMFJJ70zMzm83Muk7WJqNrOTk5wdfXVyPq4uLiEB4eLog6W4/u4uNg9VyhgNb9Cjye5HneffddVi61+9AoewD/m7wlFsiKD6UFy5YtWzB//nxNWzp48CCaN2+uK2qswYvQhIQEpKWlsXGWFiKPHj3Cjz/+yDZLAVlCE9Ba6kwmExo1aoT9+/ezPI9A1pjJ3y/NJfzGsQMHDgibhuj5qS11fJ8IDw9noo48IBRPDIiWa70+aAlnZ2e2CElKSmIC0d7ens0ftqYZypaoO3XqlLBi+u2339C8eXOULFkSLVq0EGLIcsOhQ4dYfqxOnToJHdrSezwpKSmIjY0V/rMErWjU8V+2WOrUf/OrsYYNG8LPzw9//fWX8Bm1VQXIGgDLlCnDLD0PHjxggjIpKYm9fu/ePZhMJouDIb+SOnjwINq0acPyH/HwiWwpFiA1NVWYCHlrYmRkJDIzM3VPqXBwcMC+fftw/Phx3L59Gz4+PhoBBACvvvoqO5GB4N2XgNiAr1+/juLFi2PlypVo0aKFZgC9cuWK0LmqVavG/m0ymbBq1SoMHDgQly5dYsH86olRLeIsxe2RWxDIsvCoY9fU7cGWRMCW4BOcAlk7C8lcn5GRwSYvPVFHsTDx8fFsVcpDbmb1AG+J3GaUX7ZsmbCpQg1tFkhJSUFSUpLV3dd03+rPpaSk4O7du7qLGYLap4uLCxo2bChYD4w4fvy47u7rd999FxkZGcxtdOrUKTg6OjIr171799C1a1dWn9R/nZyckJycjISEBLRt2xb169dnAvzcuXPIzMxkVorIyEhdz4CTkxNLqaHuW0DWLt07d+4I1kCa4CmuzN3dHcePHxdcwnrPnsSynqhToxanNJZQ/46NjbU4jg0fPhyvv/665nVbRB2/sKX+Hh8fz/onjcFqKyBRuXJl+Pv7C+5X4urVq+zZ0Ek+RjFP/DPnLcJGE7+epc4Iqgv1Tm6jEB3+N9UhJurk1T4+PpoyZmZmokWLFvD09LR4ohK5dnkGDx7MYnXj4+ORkpLCxtKoqCirJzTRuLN161Zs374dXl5eQigMwYcSREdH2ySG7t+/j+DgYOGz6nE7PDyctTsSofw4FhkZyRbb1LeyM1by90KGi5EjR7IsAE9E1H3wwQdsgFy0aBEGDBiABg0a4IsvvkDDhg3Rv39/YadjTomOjmYNzsvLS1iFWHqPZ+rUqfDy8mL/6VU+j5ELVS/mQh0sqV6d8mrf09MTPXv21KSz4MUUkCUiHj58CH9/f7Ru3Zod32XUKCpXrsyCpPXgV4SW4pt4AUodLC0tTbA+8vcXFRWFcePGsYmLJzU1VVNevRVdlSpVUKtWLeG1tLQ0QVDTZJCWlsYCyD/55BPdwbx3796YNm0a+5sXdWRVAIDvvvuOJSVWT/Rms1lY4VnKzs8PhsWLFzdsg8TChQtRunRpi58pUaKEEDTOw7u5gKzj3cjKkJ6ezkSd3rO2llGfvsvv6NWDF+dGFgY1hQoVgouLC0qVKsWsU7ZAYiE5OdlwIOOfZ7NmzXRP/khJSUHJkiUtHtNHgzD1WVtcHCQ6f/vtNyEEpHnz5sLnyKLIL+j27dvH/k1ixtnZGYmJidi9ezfKlSuHjh07YtWqVTh8+DBq1aqFcePGCdfVm/Dt7OxYHfEbfvr3749WrVph48aNCA0N1a3nBQsW4O7du/Dw8ED9+vWF/lOrVi1NX6f2ThZpI3e4uizA47GI2pCeqPvwww/Zv22xOOnF3yYmJgoCisawI0eOMG8I9SGjeNWqVauiSJEiuqKuZcuW7N5iY2Nx9+5dtG/fHnXq1NFckx9L+D5qJOrOnj2LJUuWaGIgCV4w0eJbPQbpLWTMZrOw4FSLOvU8ZrQLt3bt2rCzs7NoreNjB4l27drhp59+gpubG+Lj45GcnMzmnOjoaKtxelFRUYiLi8PLL7+MdevWwdfXV/d3+LkrISHBJjG0YcMGrFmzxuLY9uDBA017vH79OnumERERrO9Q3eotsIywdgzhExF1V65cYTv/5s6di1mzZuGHH37Ahx9+iJkzZ2LBggVC/rac4uPjw24gOjpasDpZeo9nzJgxiImJYf9ZW9kZoWepsxZkzlt5nJ2dERAQIFi+Hjx4oLFgpaamQlEU1rATExMxYcIEza5Hwt/f32Lmab10F3rwVicSQKmpqULH4FfbUVFRhjv8Dh8+zMQr7Ryijs/H65CgU4uY69evs3/fu3cPQ4cOhaOjI8aPHw/AOAWCWmTTJgWTySRM0pRIFnhs0ifMZrPgFp41axa2b9+umwuOf2aFCxe26l4FshI+W6JWrVoWO7V64qMBmbfU6U1uRuf+UpmoLvijwvTg25Pe/eptXvH29sa7776LH3/8UXi2RkevUTA1kZyczNqhuh74gPr27dtrkuwCxhYY4vXXX2exlCQAbMnmfv78edjZ2SEoKEhI5aNeOJL4MxL9NE7wVmIPDw8mkmhjkrq/6fW/xMREXat4uXLlhHalJ+rIbU3eCr4uL1y4oHFrk1UoJiYGnp6eukm1gay+r7bm/fHHH4iKimIxX7GxscJ4WaVKFXa9ihUrsrOc582bp3t/wGMxkpqainHjxrH8b7ylTg+yTht5mKpVq4YiRYogPDxcsFiqxQxZ7cqUKcPip3jLNy/qWrVqhdWrV6NatWqGAoKEg9HYzz/T2NhYZpHm0Rsr1XWvFnWffvop6tWrx8obFRUlWABLlSqFyZMns75oJOrmzZsnxKsCWZZDylVJoo631EVHR6NZs2aa8Cc+DnTTpk1CmV1dXQ3HEiI+Pt6mnI+WTopxdnbG1KlTcf/+fezduxcdOnQQ3qcckREREShdujRcXV1x7949zJkzx6LhRY01w9MTEXUuLi5sAr13755mN0bjxo2tDqS20KRJEzaQbN++XVgBW3qPx8nJCZ6ensJ/ljh16pRuHJDR7ihL8BOwyWTSTHj+/v7YsWOH7kRauXJlAGCrc7VZnZL1+vn5WTRXq0VdmTJldD+nJ1qioqJYYLGaX375RWPa/+mnnzBz5kwAWeZoOzs7tmKljt+mTRtmnaTX+JgkQEwVc+zYMbbLzAhKj8DvbuzSpQs7usdsNrPnp26r6jxsatFUqFAhdOzYURBwBP9aoUKFbDpxw5poqlWrlmBVVKNexVJ75i115BoxQn3MUZ8+fbB69WpcuXKFnWRhBFkNeFcSDx+/RHh5eWHu3Lno3r076wMjRozQzYEHZK3kKbaxcePG+Oeff5gYVruWadIcPnw4+vfvj+nTp2t2ZxvF3hGDBg1iE52tQchAltApXry4ps34+fmhQoUKLG6NJl8+iJuHt9QRbm5uaNCgAYYNG8ZEoZ4rjc8TBmT1AV700EkEbm5uQh93c3PDuXPn8M4772iuSZOTWgypLTj8mFS8eHF0796dLbx4fH19NWIjOTkZvr6+LGYoMjJSuN6IESPY+GBvb8/Ee/HixQ0F9549e/Dll19i27ZtmDx5MmbPnq1xvwLaEAMjkUhUrVoVlSpVwvnz54VJX734ohCgqlWrsnrhx3w+xYWHhweCgoLg6enJRN327ds1LmZXV1fDEwb4cnfq1AnOzs4WLUytW7fGwYMHNaJObQmsWbMmTpw4gQsXLqBq1aqIiooShMRbb73FMhwAMDSofPjhh0Lap7///ltYhIWHh2PcuHGCcSMtLQ1+fn6aBQtvNVYLr2rVqlkVQlFRUVYTGpcsWVIj/PjMDJ988olQh+qzfS9fvgyTyYTz58+jcOHCqFSpEr7++mu2WYPftWxp0WhJoFapUgVxcXE2zTXZEnWdO3fGvHnzAGStONTJbdesWaObjyW71K1bF/7+/ggMDMTFixfx2muvMffNK6+8gpCQEAQGBsLFxcXqZGQrderU0X2oW7duxb59++Dg4ICTJ08KO1aMUA/2RitG9UTt7OzMGj9lsuf58ssv2aRNu92MUIs6ElmWoAbTqVMnw3gZfpcsMXjwYAwdOpQFlHt6erKy0QAdEBCAAwcOWMzEnd0TA2gy5EXd1KlT2UDh4ODAJiQajJo0aYLKlSsjLCxMONdUXWc0KFsTdUYuEnW7tCbqGjduzNqJk5OTxuKtjlEhEZWens4sPZmZmdi1a5duDBIAFnNF1KhRA+7u7qhUqZJuW+JjMOk+jY7r0ls0qWMPgSzLYpMmTZgFhqAt/jt37kRsbCyKFi2Ke/fusU0Havd1xYoVAWSJYZPJhGLFimHBggW4cuUKDh06BEdHR825uTw3b95EYGAgKyMvrEhckzte7YqPjo4WJvZVq1Zhz549sLe3x7Vr13Dq1CnY2dlZPV+SLFT8b1N7pcWdGmpH1J8dHR3x6aefomfPnsJkT2MLv9sTyGpbNWrU0NTXvXv32NiinjjUG3l4T0VAQABMJpPGMgNY9xY0bdoUCxcuFDbNuLq6sr5nb2/P2nKNGjUsLmInTpzIjn66dOkSE3X//PMPK4daJFlaRAFZIq1Ro0YICwsThIF6gbFhwwZUrFgRJUqUwK+//spO0CEGDBjAAu7pN11dXdk1W7RoIZwzDGSJWKPy8Qsovo3T81E/Jzc3NzRt2pTtmqa613NdAlnjZpEiRXD//n2kp6ez56feIW409gGPx6uKFSsK5+MCYMfHhYSECGOE3sJQb9wcO3YsEhMTsWrVKphMJos77Q8fPoyIiAhDowaQZfDgLZtffPEFM1IAWc+LT1GlFy/If/ann35CXFwcTp8+ja+++kpIV2KpHH379tWdA2/dusUMTrac9JItUTd9+nTs2rULrVq1QsmSJfHdd98hMDCQNdqJEycKsU25YcaMGdi3bx9WrFgBR0dHlorDwcEBS5cuxb59+zS5XXKL3iB08uRJLF68GO7u7qhbt66wAjFCbZ41yqumFnWVK1dmkxs/QFND6NWrFwvutZarTX0vRjvbOnfuzDKdG60CqPNb2sZuZ2eHli1baspGLsESJUqgVKlSml2m69atY7nzoqOj0alTJ8FNagmaDHm3o7OzM3vdwcGBWSm6dOmCRo0aYdKkSUwg1KxZkw0kalFH19AT5GpLHU+3bt0wcuRIjWVQbxMDkDUpjxo1Cl26dIGdnR0aNGiA1atXa8ST+ndIyGVkZLBBPDMzE46OjuwZv/zyy3j11Vfx999/w8vLSzNAWouj4wdxGsBdXFx0ha6eqOOfHdUDtbFNmzaha9euaNasGd555x0m3jw9PeHh4aHZuKJecHXs2BGHDx/WpGapVKkSmjRpAhcXF4teA3oWVG7+XmfOnAk3Nzd07NgRO3fuZJYY3ivAJ35+/fXXhf5lMplYu7K08OI3ShBkMTASdTT+eHl5sRMlZs6cCRcXF6EOyFqoKIpgdddz+QKiK009uapDV+Lj49lEbWmSUreTrl27ChYQvfGWF3UODg5o0qQJFEVBuXLlrLrGKVHurl27mKhr3bo1Dh8+jO3bt2POnDnC500mk2G8pYuLCypWrKh75vMXX3whWJ4AMLehm5ubJhTAwcGBPV9e1AFZ7c/V1VVjKS5WrBi8vb3ZJpuxY8eiR48e6Nu3r0a00ZFd69atE2LOCapr2kBA98S7NtWnFfj4+LANaWSdV4s69Vz/5ptvskW/vb09pkyZonuqEd93+Dai3mXOl5XSAbVs2RKTJ0+Gi4sLew58aI+R9dVSjj8PDw/BG+fq6go/Pz9mwPLw8BA2NurNCdSvEhMT0axZM/Y3L5ydnJwMPR1AVnusUKGCZoFUunRpNlbb4oLNlqgrXrw4Tp06haZNm2Lbtm1QFAVHjx7Fjh07UKJECRw4cEDYWvysYWllSQON0erktddew969e/Hw4UPN8UxGljr1tfgVAN9x6be9vb3Zd/g8STzUgdX3wh9fRVBKBepkRgl/V65ciTZt2mhcPmrIVM67U8haod4YQvTs2RMTJkxgndvPz49ZYSgdDEGikdBzmTk7O7NnYDabERISgsjISNjb2+PIkSNo164diyOrWLEiGwDVoo4mEL1dsF5eXjh37hy2bdumqcN27dph+vTpmrJZagPTpk1jv3/s2DHmYudJTU3FL7/8wjZ6EIqiaI7TGjBgAEaOHIkNGzZg/fr1aN++vWZ3cUJCgmaSTEpKEsQJP4jzq3W9/JB6oo53+/HCk9i8eTMOHDiApUuXasQ+7/KrVKmSZgHk5eWFxo0bG070Ru3NqNx8ffXr1w/x8fEwmUxo164dfH190apVK2ESs3b4NrX79evXC7tJ+QWLnvuVBm2j8pOIcnNzQ//+/QXrKz8etGvXjv1GlSpVmKeD2oC6ffJ1Xa9ePezduxdjxoxB+/btdeORaZew+mQBfhKjcYv6c8uWLTFkyBD2vl5MltpSx2Ntd+T58+fRokULFqROfa5ChQro2LEjPDw8NDvV1f2JOHv2LJycnDQu2379+qFZs2YagwK/+94SalFHfVZdHyQievTogczMTEyaNAkbNmxguy1562a1atWQkpKCHj16wMvLi4lyqmv1/6lt8V419VF+tog69XwwefJkoT2OGTNGsyEOyFpMUt3yok5PQHfs2BGPHj1ios7SnAdAOGOax8vLi+2k5zcaPXz4UDM209/0f5PJBJPJhFmzZmH37t26Y/kXX3yBn376CYMHD4bJZMKkSZPg5ubGQoy2bt2KS5cuWV1I8787YsQIFmpGfcWWXdHZzlPn7e2NadOm4cKFC0hKSkJKSgpu3bqFFStWaNw7zxqWdt9QZzQSfn369EHLli1RuHBhTeM3sqqpG6hRAlQKYPb29mYTeNGiRXUnNDqvjsSG2WzGvHnzdMUoDSR0Tb2jX9q1a4e6deti9+7dwipD755okODdThQHYWmFAjy2ZBUpUoS5ttTWhM2bNwtuYT1RV6xYMcFS5+/vr7EwkpWmaNGihqKOUN9nxYoV4evrixo1auCll17StAeqw9GjR2P06NGsM/L30qhRI5sC8nni4uLw3nvvCSk3mjdvjoULF6J69er4999/WboAd3d3TJ8+3WJaFr2BydnZWbhffscqtX97e3tMnDhRE2dqTdTxlglbIDfDr7/+iqNHj2pSAFmLh7JkQVKX0dXV1WLyYcrz2KJFC8yZMwfr1q2zel3qs3Xq1MG4cePY5ge+H1L98M+JkszybZZvY7Tws3Tiyssvv4wGDRrg+PHjzJJJ92dkqVPTsmVLTJkyBbVr19bEJHl6erL64d3QoaGhQgJWqnOyavn5+QnjAO/WJoHg6urKykZnehJqd6Gea+6DDz7Q/D6POgaLUkPxsVvA47GMf05btmxhRzLquQotQfWl9gAYiTr+OZGo4OE/7+3tLfR1Gsvo2vyZ5VevXsXcuXNx9OhRNjfoeddKlSrFBAS1WWsbT6zt8Cfs7e3ZXKtn9efx9vaGj48P6/9GR+wRFEuqRlEUODs7w2QyCX3LxcVFc1/0LEl40kL0k08+QZs2bXSfg4+PDwYPHszuZ8SIEYiPj2fPpFOnTihbtqzFrAp8WYGseYK8ftQebMk8UCCTD+cX6lXZJ598wmJXaFDkO/m3337LViK8lc1WK42ljspDO2ddXFzQoEEDbNiwAR9//LGuMOjXrx8yMzPZJNy4cWN8+OGHupMWuWiKFi2KM2fO6J4lyu9IpGt27txZd/OInnVh2rRp+PTTT612eOq0pUuXRsmSJfH+++8LOwuBrMmEX83xdbF27VqcPXsWDg4O7DkaCbUPPvgA33zzDbp27WrofiX4TO6dO3fWnN+oFss0IHh5eWHq1Km4evUqbt26JdTtypUrWfC7reJOL2Hz/v37WSbywMBAq4fS2wKVc/HixYLLmMReenq67uYfvbQWvFipUaMGlixZIgRaW4JEQ40aNeDl5aURddYmA+pz6lMS9PDy8rJ5o8SgQYMspvAgNm7ciD///JOJS5oY+OemPl3h//7v/9jn+fujFBxz585lcW9Gu/UiIyNZ3G/9+vXZwtHIamMNPWuas7Mzs/zzsWD+/v66iz1+EcWLFRJSo0ePZlZ5V1dXmEwmKIrCwkKIjRs3Ckl4T548qTlHmbf4WjvPl0hNTcWFCxdw7NgxHDp0SIhn5fsnH+PGizr1CTJ8+WgRSmla6HlaE3XWTmzgP6+uI2prJKaprkuWLImKFSvCw8ODPacPP/wQo0aN0lyfD2ugtmjpedKxVrZCY4teP169ejU6d+6MVatWsTGN+r+lXapAVnojtXGpVatWms0NhIuLi+a+aLc69R31iTR687m1RSbBx7bTPak3LVH98Uaf7Ig67dbHFxi+UZJa3rlzJzp06KC78h8+fDgz3/NWPr1AVT3UcVbqxlWpUiU8ePAAa9euxW+//cbK16NHD6G8ZrNZWMHwqTxo8OGvPXToUAQFBWnyUfFJG52cnDTnQdJ90LVWrFghWBH0REXJkiWFoFMjaIIrXbo07OzsmJtq7969gkuQF1Q0ICxZskSIx9CzgPA4OTmxg5/JckTfWb58uTBxtGzZEpmZmVi6dKluYlq1pU69U7Bw4cIoXLiw8LqXlxd7trYMhFFRUTZbuHIL1aGjo6NQNhrI9I5vo5xg6sFJvQvcaGDVg6y9NLnRAFivXj00a9bMqqgjOnTooElfo6ZHjx6Gu+hzitqFrifqqM3Ra3ycFt+Xpk2bhlGjRqFQoUJso5HR5GYkBvh6BWzf7asn6miDmiVrIfBYrDZs2BDr169H1apVBWFE8YEuLi4at6RRWZo3b44zZ87A29sb3t7emDlzJmbOnImXXnoJGRkZwphmzbKkhsSAetMCwZed/t22bVtD6xAfB9qpUydN7C/w2Lqlrg9rJzaoLXU8tWvXxq1bt9i1bdkxqYZc5oB+iAJx8+ZNPHz40KonRg19nuqIb7dBQUGaRM5kNFEfdaemfPnyOHbsGNasWYNjx45hxowZeOeddwznAjs7O1aGgIAATJs2jY3zXbp0QdeuXTUpe3x9fWEymVCtWjV2upOt41HXrl1Rp04d/P7774Yb1PSs6S4uLvDw8JCiLi8gsWYUQ0OuL0uNmh/IFy1ahH79+iE4OBgjRozAF198wd5Ti7pz585BURQ4OTlp0n8AWXFyW7ZswYMHDzSDuVrU8R2SzhhUw0/CISEhmuOV6D7oWuqVNJC1E8yWuAE1JBbU6QLUcXQ8zs7OyMzM1HQM+lu960oPtfv17bff1gTfWxIkakud+vB4gh9UfH19DXPu6WHrKtAWatSoYfGILvXkT9AkpnfGrtEKPjeWQ7LUUbumfti4cWOrqW6ArM0OLVu2tLrDEdCmt3kS0MTKl4faXIsWLbBz505NDClhZ2en2YGoXnBZw8hiB1iOB9MLSbFVENL4UaNGDc1k1LVrV9SqVQvLly/Ha6+9hmXLlgGwvgEMgG6cFu1U1luI5BV6ljprliMjKBcdWV7zUtQtXboUU6ZMQaNGjfDzzz/btGPSEnSvevFsZcuWtbqzXw9+1/lLL71k9Rx3s9mMI0eO6IYIAWBH6FH9BwUFsXya1qzS1Kfc3NyEsd/T01M35tLZ2ZnNV4MGDcL8+fNtHqO9vLysJoT/8MMPkZycrNEcfn5+UtTlhEuXLglb9qtXr44TJ04Iq65y5cqxhvD999/jgw8+sNhw+MGFVmtt27Y13HFJWIqJArJiTvr27QsfHx+cPHlSCAKma9FAZItFiO+0esdGqUWdHmRFzC40MFjLqq3G6L5CQkJsWj1ai6mzhtpSZy2NBZD1nKlusxtbl1sOHz5sKDwBrajbs2cP5s6dy2KeeEvIihUrLIqmnIh7giYiqh9vb29s2bKF5VKzRkBAAIYMGYLVq1cDyBLf169fz1OBnB1oEuBFC1/3tLHBGtQ/1ElarWFkqevVq5dFl7iegLLVrUmiTt1HoqKimJuVJtEqVarA3d09x/Wj14+MRN2OHTusjq165KWoo1AVSn2UXVHHzzfqZ+bl5YXp06ez5O45PdaPUkLZ6qrPDtSvnZychJN8LKFn2CD03L+08LFW19RObFlQEOrfys53reHi4qKbM1eKuhxSpUoVTUCuelcOn0vG2dnZatwOX+G08iV30oEDB/DXX39hypQpNg+WhMlkYuKrbt26gvAk8ZjdgF5LUOPPTqJWW/nuu+/Qo0cPmw+KthYIn93dj0bmeWvQhNWsWTMcPHjQprNDgZyLyNzi5uZmcQCiyZ/K16pVK+b+Dg8PF8S+nqWWCA4O1s1dZiuff/45Pv/8c2HwpEPhswM/see15SY76Im6nODl5WXV7akHLdiofqndWso1BuhvRrDW/9u2bYtjx46xxYNa1OkJt1atWiEsLCxPJ0ej+lannLIVfiyla1vLCGDEzJkzMXr0aCY41GO/tXFQbfnXg557Ti11O3bsEJIB5+UCVL1DN7foXYfSSuktTHjUu12zAz2TnM4f2aF48eI2WeilqMsB1rbWA1nxGRRXQ43Fy8sLL7/8Mv79919mdm/WrBnzy+elWKJVcl5aJmyx1OXm2pbSRPCuvKtXr1oNJLYVT09PODg45HjAcnR0hIeHB8qWLWsxCabe94Cnb6mzhpH7FdC33vIcPXoUa9euxbfffosePXpke5HC89lnnxkmOs4O1PcyMzOfysBrBFl5cvNMcoN6J1+XLl1w7Ngxq3k37ezs8MYbbyAiIgJ///03AOv3sGvXLgBZKS0OHz5sczxoXgo6IO9FPH89OkHAlryleri4uAgLU/WYmp1x26hf5lbUtWnTBm3atGHhCXn5PPPS2GBEy5YtkZKSYtVSR+05J/f35ZdfwsXFRfeYwrxm9erVNs0XBU7UnThxAp9++ins7Ozg5+eHFStWCFaNPXv2oE+fPihfvjzs7e3ZAFLQ4E9eoEZVqFAhmEwmjQuJ7i8vrTctW7bEmDFjMHz48Dy7Jg26T1uIREZGCkKaD+LNLZ6enrl+7nppbKxhi8Bo2rSpxVMRngSWRJ01GjZsCE9PT3z77beavHP5BQ3UhQsXzlcBPX/+fLRv3z5bk9mUKVPy5NhFQCvqTCaTzSmoVq5ciU2bNjFRZ6vgmDRpEoYNG2bTIvhJkNeiTn0fRvFdOSG77lceo+dLcWvWFmPWGDBgANzd3Q1jPnMC9YOcuoZtxWgcO378OMu/mBtLXZEiRfLkvHtbsHX8KnCiLiAgANu3b4erqyvGjh2LjRs3auJHgoODMWPGjHwqYfYxmUyYPHmyYSoEa7s1c4KDgwM7rDyvsLQD8kmSV1Y5PYKCgqweZ2SNTp066SbO1IMGa+qgljrqwYMHc+Rqyw25EXVA1kkIT7vMlqD7oKz7+UWRIkXY8Ui2ohdXk1OoXnPbd318fNjpPrb8Zm7iKnOKnZ0dMjMz880qmhPUos7W3ZSWsLOzw/bt23OdP9bBwUH3rODckN3dsnlN/fr1WdgUtRP1KSHPKgUuT52/vz8TD2azWVforFu3DoGBgYa7OAsiY8eONTxrTx3vUlAhS11OtsgXVEqXLs3ygOWUuXPn6p7Vq+bMmTPs/EXC2g7dp21dov6Wn67KvKRevXpYu3atzfnxnlco5ii37enzzz/PteXnSUPjaF6Np3wS7icFeQvatm2Lw4cP55krumPHjk90UZxTevXqhQULFhieU/00oSTLRkfzPWsU2JH7zp072LlzJ8aNGye83qBBA7YdvHv37mjRooXuRoWUlBQhqNCWM9PyC1o9FxRRZ2SGfh5F3dNEHbAbGhqaaythXqN20z3rmEwm3TMlXzQGDBiAR48e2byR51lGnbczt8yfPx/z58/Ps+vpQTGXH3/8sebcaCMmT56c440a+Y2dnR0GDBiQ38UA8Dg/oV6C92eRfBN1YWFhuoPtpk2b4ODggD59+mDJkiWaeCd+W3m3bt1w5swZXVE3derUXO2+e5o8LVFnMpmsBi3v27dPc5QOQWbqp+1+fV7hTyEpKOSVm05SsHB2dsbEiRNzfZ2C5Fo3Yt++fezMzGcFs9mc7Wf7oluf84oGDRo8E+3aVvJN1Pn7+wuZ+4mMjAz06NED48eP1z0nLTY2lgmTffv2seNX1IwZM0bYPRcbG2soVvKbpyXqYmNjrbpfaFeuHs+bFUeihfpWfgW3SwomLVu2RPXq1TXZ9Qsi9erVsznGVSJ53ihwI/eaNWtw8OBBfP3112jdujVLHkpxDWvWrEGjRo3QrFkzBAQEGJ444OTkBE9PT+G/ggqJuicdx+Tu7p6rWA0p6p5/Ro0ahR9++MFmF5Ak+/Tp0yfbSbbzG29vb5w/f97mQ9slEkn+YFKeJ7ujBWJjY+Hl5YWYmJgCJ/AWLFiADz/8EEuWLMnW+ZhPm7S0NAQFBeH777/P0dEwEkl+ExQUhI4dO9q0sUUikUieNaSoKwBcunQJ1apVw7Vr11ChQoX8Lo5EIpFIJJJnkAK7+/VFomrVqs9VoKZEIpFIJJKnT4GLqZNIJBKJRCKRZJ8Xxv2qKAri4uLg4eFR4M7blEgkEolEIsktL4yok0gkEolEInmeke5XiUQikUgkkucAKeokEolEIpFIngOkqJNIJBKJRCJ5DpApTfB4E4VEIpFIJBJJQcXaZk8p6gBERESgaNGi+V0MiUQikUgkEkOsHaAgRR0AR0dHAEBISEiBO21CYpnY2FiULFlS1t0ziKy7ZxdZd88usu6ebTw8PCy+L0UdwEyZnp6espE/o8i6e3aRdffsIuvu2UXW3fOJ3CghkUgkEolE8hwgRZ1EIpFIJBLJc4AUdQCcnJwwYcIEODk55XdRJNlE1t2zi6y7ZxdZd88usu6eb+QxYRKJRCKRSCTPAdJSJ5FIJBKJRPIcIEWdRCKRSCQSyXOAFHUSiUQikUgkzwFS1AEYPnw4AgMD8dZbbyE1NTW/iyPR4cSJEwgMDESrVq0QFBSEtLQ0rF69Gk2bNkXbtm0REhICALh48SJatGiBpk2bYufOnflcagnPqlWrUKRIEQCQdfcMsWfPHrRr1w6tWrXC//73P1l3zwCZmZno27cvAgMD0bJlS9y4cUPW24uC8oJz8uRJ5a233lIURVEmTZqkrFixIp9LJNEjNDRUSUhIUBRFUcaMGaOsWbNGadSokZKSkqLs379f6d+/v6IoitK9e3fl6tWrSkxMjNK0adP8LLKEIyMjQ+nZs6dSt25dJTU1VdbdM0JSUpLStWtXJSUlRVEURdbdM8KJEyeU4OBgRVEUZceOHcqnn34q6+0F4YW31B06dAgdO3YEAHTq1AkHDx7M5xJJ9PD394erqysAwGw24+rVq6hevTocHR3RvHlznDt3DgAQGhqKihUrwtPTE4UKFUJERER+Flvy/1m5ciV69eoFOzs7XLt2TdbdM8LBgwfh4uKCV155Ba+++iqOHTsm6+4ZoESJEgAARVEQHR2NIkWKyHp7QXjhRV10dDQ7KsXLywuPHj3K5xJJLHHnzh3s3LkTLVq0EI64ycjIAJA1iBGyPgsGGRkZWLNmDYKDgwGIfY7eB2TdFUQePHiA//77D5s3b8aAAQMwceJEWXfPAIULF4adnR2qVq2KkSNHonXr1rLeXhBeeFHn4+OD2NhYAFmTja+vbz6XSGJEbGws+vTpgyVLlqBo0aKs3gDA3t4eAGBn97hJy/osGPz2228ICgpidcP3OUDWXUHG29sbLVq0gKOjI9q2bYtTp07JunsG2L59O1xcXHD58mWsW7cOP/zwg6y3F4QXXtQ1adIEO3bsAJDVEZo3b57PJZLokZGRgbfeegvjx49HpUqVUKFCBVy8eBGpqak4cOAAatWqBSDLTXvt2jXExsbi0aNHKFy4cD6XXHLx4kUsW7YMnTp1wrVr17Bw4UJZd88IjRo1wsWLFwEAp06dQseOHWXdPSP4+PgAyBLmERERst5eEOSJEsja/XrkyBGUKlUKS5YsgaOjY34XSaJi1apVGDJkCGrWrAkA+Oijj6AoCn744Qc4Oztj2bJlKFmyJC5evIgBAwYgIyMDX331FTp06JDPJZfwNGjQAMePH8fvv/8u6+4ZYc6cOVi9ejXs7Ozwyy+/4OjRo7LuCjgZGRno06cP7t27h5SUFHz//fe4c+eOrLcXACnqJBKJRCKRSJ4DXnj3q0QikUgkEsnzgBR1EolEIpFIJM8BUtRJJBKJRCKRPAdIUSeRSCQSiUTyHCBFnUQikUgkEslzgBR1EolEIpFIJM8BUtRJJBKJRCKRPAdIUSeRSCQSiUTyHCBFnUQikUgkEslzgBR1EolEIpFIJM8BUtRJJBKJRCKRPAf8P2XIl+R8YtO6AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 7 Axes>"
       ]
@@ -1626,7 +1626,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -1670,7 +1670,7 @@
     {
      "data": {
       "text/plain": [
-       "<tigramite.causal_effects.CausalEffects at 0x7faa77fafac0>"
+       "<tigramite.causal_effects.CausalEffects at 0x7fd35666dcd0>"
       ]
      },
      "execution_count": 33,
@@ -1699,8 +1699,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal effect for S = -1.00 is -0.66\n",
-      "Causal effect for S =  1.00 is 0.75\n"
+      "Causal effect for S = -1.00 is -0.75\n",
+      "Causal effect for S =  1.00 is 0.71\n"
      ]
     }
    ],
@@ -1771,7 +1771,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -1861,7 +1861,7 @@
     {
      "data": {
       "text/plain": [
-       "<tigramite.causal_effects.CausalEffects at 0x7faa7600a8b0>"
+       "<tigramite.causal_effects.CausalEffects at 0x7fd356591a60>"
       ]
      },
      "execution_count": 37,
@@ -1947,7 +1947,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -2221,7 +2221,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/tutorials/causal_effect_estimation/tigramite_tutorial_linear_causal_effects_mediation.ipynb b/tutorials/causal_effect_estimation/tigramite_tutorial_linear_causal_effects_mediation.ipynb
index acf8afd8..0b1bbf8b 100644
--- a/tutorials/causal_effect_estimation/tigramite_tutorial_linear_causal_effects_mediation.ipynb
+++ b/tutorials/causal_effect_estimation/tigramite_tutorial_linear_causal_effects_mediation.ipynb
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -155,14 +155,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Link coefficient (0, -1) --> 2:  -0.05929086357355118\n"
+      "Link coefficient (0, -1) --> 2:  -0.07160542160272598\n"
      ]
     }
    ],
@@ -179,24 +179,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[ 0.          0.80518115  0.          0.          0.        ]\n",
-      "  [ 0.          0.25296736  0.          0.          0.        ]\n",
-      "  [ 0.         -0.05929086  0.          0.          0.        ]]\n",
+      "[[[ 0.          0.7759527   0.          0.          0.        ]\n",
+      "  [ 0.          0.26866946  0.          0.          0.        ]\n",
+      "  [ 0.         -0.07160542  0.          0.          0.        ]]\n",
       "\n",
       " [[ 0.          0.          0.          0.          0.        ]\n",
-      "  [ 0.          0.79241943  0.          0.          0.        ]\n",
-      "  [ 0.26905519  0.          0.          0.          0.        ]]\n",
+      "  [ 0.          0.78305209  0.          0.          0.        ]\n",
+      "  [ 0.28581035  0.          0.          0.          0.        ]]\n",
       "\n",
       " [[ 0.          0.          0.          0.          0.        ]\n",
-      "  [ 0.26905519  0.          0.          0.          0.        ]\n",
-      "  [ 0.          0.80840057  0.          0.          0.        ]]]\n"
+      "  [ 0.28581035  0.          0.          0.          0.        ]\n",
+      "  [ 0.          0.79582179  0.          0.          0.        ]]]\n"
      ]
     }
    ],
@@ -214,12 +214,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -244,14 +244,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal effect (0, -2) --> 2:  0.06808702968271146\n"
+      "Causal effect (0, -2) --> 2:  0.0682760530685738\n"
      ]
     }
    ],
@@ -268,14 +268,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mediated Causal effect (0, -2) --> 2 through 1:  0.16375768313098354\n"
+      "Mediated Causal effect (0, -2) --> 2 through 1:  0.18082362756865086\n"
      ]
     }
    ],
@@ -292,12 +292,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -309,12 +309,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Contemporaneous I(1; 2)=0.269 != I(2; 1)=0.000 due to conditions, finite sample effects or masking, here edge color = larger (absolute) value.\n"
+      "Contemporaneous I(1; 2)=0.286 != I(2; 1)=0.000 due to conditions, finite sample effects or masking, here edge color = larger (absolute) value.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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HnjgbUDZOQwSAWATmnrdgbH0JJpc2JiLKOIYBGjOl6dDr50GffyZQXGl/g12tMLe9AmP7a1yfgIgog3gilsZNBUugz10KaWmAuX8zEOuxtT1pP2qNJ6iYCK1+LlSg2Nb2iIjyHcMAZURqgGF5LcxDOyGHdgI2rxkgLQ0wWg5BVU+BNnEOlC9ga3tERPmKYYAyyjp1MBdSNRnmgS2QlgabWxRr0aKm/VATplubINm5CyMRUR7imAGyhfIXQZ95sjXrIFhqf4NiQg7vslYybNgOMeL2t0lElCcYBshWqqQK+vzl0KaeAOhe+xs04jAPbrN2RjyyB2JyeWMiouEwDJDtlFLQaqZBP+FsqJrpsHVHxKR4BOa+t7lGARHRCDAMUNYojw/61OOt9QlKqrLTaDRsrVGwaTXM1sMMBUREA2AYoKxTwRJoc06DNvMUwBfMTqM9HTB3vg5j68sQLlxERJSGswnIEUopqIo6qLIayOFdMA/tBEzD/oa7WmBsewWqpBpq4myo4koou1dQJCJyOYYBcpTSdKiJc6CqplgbIDUfzEq70tEI6WgEQhXQJs6GKq1hKCCigsXTBOQKyheAPmMx9HmnA6GK7DXc1QJzxxoYm1+E2dLAMQVEVJDYM0Cuoooroc87HdJ2BOaBrUBPR3YaDrfD3PUm4A9ZPQWV9VCKWZmICgPDALmOUgqqvBaqbAKk+QDMg9uAaDg7jUe6YO55Czi4zVrNsGoylKZnp20iIocwDJBrKaWsg3FFPaRxL8yGHUA8kp3Go2GYezcCDduh1c6Eqp4KpfPtQkT5iX/dyPWUpkFNmA5VNRlyZDfMQ7sAM0vLDcci1k6MDTugTZhh1eHJwkqKRERZxDBAOUPpHmvmQc00a2fEI3ts3xkxxYjBbNgGHN4FVTMNWu0MKK8/O20TEdmMYYByjvL4oE+eD5kww9qUqHEfgCzNAjDjkMM7YRzZDVU9FVrdTKhsLZxERGQThgHKWcoXgD5tIaR2BsyD27KwXXIvYkKO7oHR+A5URT20CdOhQuXZa5+IKIMYBijnqUAx9JknQ7rbYB7YCmk/mr3GRSDNB2A0HwBC5da4goo6TkskopzCMEB5QxWVQZ9zKsyOJpgHtgBdrdktoKsV5u61wP4AtJppUNVTOK6AiHICwwDlHa2kCmreGZC2wzAPbgfC7dktINYD8+BWoGE7VGXiFEJRWXZrICIaBYYBykvWwkV1UGW1kPaj1hoFXS3ZLUJMSNN+GE37geJKKxSU1/IUAhG5DsMA5TWlFFTZBKjSGkhnM6Rhh7VBUbZ1NsPsbAZ8wWOnEDy+7NdBRDQAhgEqCEopqJIqoKQK0tUCs2EnpO1w9guJhq3xDAe3QVVNtnoLgiXZr4OIqBeGASo4KlQBffYSSLjdCgUt2dk2OY2YkMa9MBr3QpVUQU2YYfVgcBtlInIAwwAVLBUshT5zMaRnLsxDOyBNB5C1xYt6kY4mSEcT4CuCVjMVqmoSlDcw/ONEsHfvXjQ2NqKqqgrTp0+3v1giykscyUQFTwVC0Kcvgr7wHKia6YBTA/yi3TAPbIGx/u8wdrwOs/UwZIDlljds2ICLL74YNTU1mD59OpYsWYIZM2bg7rvvdqBoIsoHSkSy/1EoCyTcgfj6Z0f9OFVaA8/8ZTZURLlCYhGYh3dDjr6TvQ2RBuPxQ1VNglY9BSpQDAB473vfi507d+JjH/sYlixZgvr6etx22204fPgwXnrpJWfrJaKcxJ4Boj6U1w998nHQF74H2sS5gO7gLoXxCOTwLhhvv4D4lpdgNu5DY+NRnHrqqVi+fDnefvttnHDCCaisrESe5noiygKOGSAahPJ4oernQNXOgDTuhXl4FxCLOFdQVwvMrha8a2Y9fvjQQ3jooYcAACtXrnSuJiLKC+wZIBqG0j3QamdCP+E90GacBDi8IdE9n/9XPPvjO3Dbpz/maB1ElD/YM0A0QkrToConQaucZK1VcGQPpLkB2Z6B4PHoePcpC7H/SPriSdLTBbP1MFRZDVc5JKJRYRggGgMVqoA+owIyaT7MxncgR/cC8aizRRlRmDtfH3DQIRHRUBgGiMZB+QLQ6+dB6mZDWhpgHtkNdGd5Y6S+koMOD+8CgqXQKiZCVdZD+YucrYuIXIthgCgDlKZDVU2GqpxkDfQ7sgfScgh2nULYf7gR2/emr5zY2RXGG5t3YPG8mdC0xGmCcDvMcDtwcCtQVGYFg4qJDAZElIZhgCiDlFJAcSX04kpINAzzaOIUghHLWBt/euFVfPDGr/W7/u1de7H0ys/hwjNOwZ/+57b+D+xug9ndBhzYAoTKjwUDXzBjtRFRbuKiQ31w0SHKNDENSPMBmEf2AOGOcT/f9Xfch5e27sMjjzwCAJg/fz4aGhrQ2tqKJ554Av/1X/+F8Ct/gMejj+wJiyuOBYMRLINMRPmHPQNENlOaDlU9FapqirWN8pHdkNax75hYW1mO/ftfxi233NLvtr1796KmogyaNooNjzpbYHa2APs2QRVXWuMLyuugvP4x10hEuYVhgChL0rZRjnTDbNwHadoPxHpG9Tyf+fC/oKmtA3sO9t9tcXp5ELev+vyxMQOjJJ3NkM5mYO9GqJJqqMqJVjDw+Mb0fESUG3iaoA+eJqBsEhFI+1FI4z5I22HAlW9HBVVaDVVRZ22zzFMJRHmHPQNEDlJKQZVNAMomQGIRSPNBmI37gJ7xjy3InERgaT9qfVtUBlU2AVrZBOuyGsUpCSJyJYYBIpdQXj9U7QyoCdOtkf+N+yDNB53fObGv7jZIdxuMhu3WAkdlNVaPQWk1lJObOhHRmDEMELmMUgoIlUMPlUOmLIC0NFinETqbnS6tv3gE0rTfGvuglDUAsWyC9cXVD4lyBsMAkYslFzNC1WRIpAtm4/4xDTrMChFIRxOkownYvxnwh44Fg+JKqDEOaiQi+zEMEOUI5Q9BnzQPUj83BwYdAoh0WdMoj+wGNI91GqFsgnVagYMQiVyFYYAox6QNOoxHIU0HYDbty8iCRrYx45DWQ5DWQ9b3RWVWKCipggpVQGkjXCCJyAZiGkA8BkBgTbATKChAKUDTAd2b9wNlGQaIcpjy+I4NOuzpgNncAGk5CES6nS5taIlBiNKwA1AaECqHKqmCVlJlXWY4IBuICBCLQOIRSCwKiUeAWAQQc/gHe3zWIF+Pz7rsC+bV7ynDAFEeUEoBwVLok0oh9XOtDYqSwSAadrq8oYkJJBY7Mhq2A0qDSoQDxXBAGSCxCMxwByTcMfbZOfGo1RPX+zp/CFpRKZS/CErl9pgYLjrUBxcdonwiItY0xZaDkOYGdw48HI7SoIoroIqT4aCM4YCGJaYB6W6DGe4A4lF7G1MKKlACragMypeb42HYM0CUx9KmKU6ab22v3NIAaWmwukdzgZjHZik04Fg4SPYcFJVzpgKliIgVAjqaRtb9n5lGIeF2GOF2qEAxtJJqKE9urbnBMEBUINK2V568AOhsPhYM7P7klEm9wwGQFg4QqoAqKsu5P8SUGWZPF8z2oxndMny0pKcTRk8nVKgCWg5NqWUYICpASimgpAp6SRVkyvHWwTUZDBz8QzomfcMBYK1xECqHCpVBFZUDRaU8tZDHxIjDaDvsqoGz0tUCo7sNWnkttBxYgIthgKjAKWVtRITSasjURDBoPmhts5xrwSAp0gWJdEGaDySuUEBRCVRReSIklAOB4ryfLlYIJNoDo+UgYBpOl9KfmFbvW6gCWkmVq3/fGAaIKEUpDaq0BiitsQYfdrXAbDsCaTvi7nUMhiVAdzukux3SuNe6StOt9Q5C5daphVA54Au6+g82pTPDHTCTa1e4mHS1wIhHoVfUuXbWAcMAEQ2o9xgDTDoOEg1DEsFA2huzNzjLLqaRmtKYmlLl8UEVlVnTGRMhAR4fA4ILmV2t1viAXBHpgtF0AHplvStPWTEMENGIKF8QqmYaUDPNmrbV0ZQKB65fy2Ck4lFrq+b2o8cCgu4FgiVQwRKoQDFUsMT63uNzstKCZobbcysIJMV6YDQfhF412XUBk2GAiEZNafqxJZFFgJ5OSNsRmG1HgM4WAHm0fIkR69+DAFg9BsESa+xBIiwgUMKZDDaTWARm6xGnyxi7WA/MjkbopTVOV5KGYYCIxsVa/dA6GGp1s6xV2tqPJnoNnJ3mZat41JrB0NGUHhK8fqhACRAshgqUQAWLrddHZ0gYLzENGM0HkethU7paYXoD0IIlTpeSwjBARBmlPD6oyklA5aQ8G4Q4QrEIJBYBOhr7hIRAovcgBOUrAvxBKH8R4CuC0vmneDgiAqP10NiXE3YZs/Ww9V7x+p0uBQDDABHZqN8gxFjE6m5PrgvQ0+l0idkT64HEetLHIyR5fNZMhmQ48AcBf5EVGnwBVw44yzYJt7tqHYHxs8KNXj3VFeMHGAaIKGuU1w9VMRGomAgAhR0OeotHrdMO3W0ABugE9wYS4SAREnpdhjfgioOJnUQEZkez02VkXjwKiXRBuWBRIoYBInLMgOGgownS2QTpaC7ccNBXrMfqWUh8mxYWlAK8QcCb6HL2+K1xC15/atvd5HXQ9JwMDuPabdDlzI5mKH/I8f8uDANE5BrK64eqrAcq6wEAEuuBdPTqOYh0OVyhC4kA0W4g2p0WEgYcYqc0KxR4/VDJgOD1pS5b//oSwcHj+AEKSPQKdOZhr0BSPAKJdEMFQo6WwTBARK6lvAGGg0wS01oTIhoeWXDQvYDuAXSPNchR8yS+1wHNC6Xr1vep6/vezzPuUCE9nfk7IyXB7GyGxjBARDQyA4aDrlZIVxvQ1QrpbgWM/OxOzjoxgXjE+sLAgWHEE/y0PqFBadZufirxpWnW6Y607zWo4kpIrq90ORKxHohpODpQlGGAiHKW8gagyuuA8joAVpcyIt1WQOhuhXS1At3tub90cq4zjcRGQpHUVSMKEqYJ8QftqspVJBp2dCChO3dMICIaA6UUVCAErWoS9CnHw3PcMuiLL4A+fzm0qSdAVU0BXLTQCw1HsrIboWmaOOHMC3DL7XenXf/M86tRNO14PPqnJ22vQaI9trcxFPYMEFFeU0qzdicsKgMSK8CKEU/sYtiaOM3Qmj/7K+QRydK2xJqm4eaV/4bPfflruPnfr0VFeRneensz/t+1N+D2L34el138XttrkKizaygoEcntdR0HIeEOxNc/O+rHqdIaeOYvs6EiInIziUWsef5drZBwhzVwracLub70bU6rmJi1npx4PI4Fy8/HlR+6FJ/46Iew/OIP4eLzV+D7d3w1K+0DgF43y7EtjhkG+mAYIKIkMQ0g0gUJd1pz3Xs6IOFOzmLIlvI6oKg0a8395Fe/xVfuvAf1dbWYPnkSHv3FfdD17A3q0yfMcGxpaoaBPhgGiGg4YhpAT1eiB6EDCHda/+bVcrkukOUw0NnVhYkLl2LW9Gn45/89glBRUdbaBgC9Zrpju15yzAAR0SgpTQeKSqH6HKiskGD1Iki4M9GT0MHxCGOW3UWPbvjSKgBAU3MLdEem+Tn32ZxhgIgoQ6yQkBis2IsYcet0QyRsrRQY6bYW/ol0W70JnPo4MC17YeDWb34XT/7tebz4p0dw4Ueuxi8e/h2u+8THstY+AGuNBYcwDBAR2UzpngFDApBYGyEetdZHiHYDkXDi3+5EeAijYAcxZung+POHHsF3fvQLPPO7X2LR8fNxwzVX49s/+Bmu+dhH4PVmsdveweWfGQaIiByklDq2XwAq+t0uYgLRHkg0nB4YItZ+BIhFBnjW/KB0r+0x6Km/v4AbvnQbfv2De7D0lJMAANd/8krc86Of49eP/gGf+OhlNleQoOlcgZCIiAamlJbathglVf1uT/UsxCOQWNTa4TAehcQiVlBIXo5HgFgUOdXLYPNgujfWb8T/u/azuOPLN+PS912Qur60pBjXf+JK3H3fT/DxD1+alRkFypfdwYr92udsgnScTUBE+UpErE1/YpFeASERIpKXk6HBiGVl9b+hqKkLrXoKgFZaAy1U7lj77BkgIioQSinA4wM8PqgRLOYjYgKGAZhxawMoIw7pdTl5vfT5Pv1+hhUsxjBIUikF8Qas3o48p3zO7sHAMEBERANSSgM8GoBj3fVjHeKWFixErHBgmta/YkJSl4/dporLoaI9kHwPA0qzQpqDGAaIiMh2AwWLtNsHeZzm8cPobLatLjfQQuVWr42TNTjaOhER0RCU7hlwSmbeUArKwbECSQwDRETkalpx/ymX+UKFyh2dUpjEMEBERK6mdC9UMHt7FGSPghZyR9BhGCAiItfTSiohSsulVRKGpZVUuqJXAGAYICKiHGBCR2eoDlFvyOlSMsMfgnJJrwDAMEBERC4XjcfR1t0DEwo9/jLEdb/TJY2P7oVeXuv4DILeGAaIiMiVRATdkRg6wtFepwcUugOVMJQ7utdHT0GvmOia0wNJDANEROQ6IoLOnijC0Vj/25SG7mA1ZMxLIDlHq6iD8rqvZ4NhgIiIXMUwTbR19yAaH3xvBFPzoLuoOncGFCoFrXIStECx05UMiGGAiIhcIxo30NbVA8Mc/jAf13yIFFVnoapx0nToVVOg+Z3dmXAoDANEROQ4EUE4EkNHODKqT/sRzY94Sa1tdY2bLwi9ZporTw30xjBARESOMkXQ0RNF9wDjA0aiSzyQyimAz0WfvJUGrWwC9MpJrhssOBBuVERERI6JGyY6whGYMr6z/50xQVlFPRDrhtl21No22SGquAJaqBJKy53P2wwDRESUdSKCSCyOrkhmDtqmCDrCEZQWFUGvmQbpboPZ2QyYgw9CzDQVKIZWWg2lD7wzo5sxDBARUVYlpw0ONVtgLOKmiUgsjoDPa20AVFQGRMMwezog4U5AzIy2BwDwBqAVlUIFinPidMBgGAaIiChr4oaJzp7IiGYLjFbQ54Xfe+ywppQC/EXQ/UWQ0gmQSBekpxMS7Rn7aQRNAzx+aP4QVLA4J3sBBsIwQEREWRGJxdHZE8348yoAxUEffJ7BD2lKKahAMZCY5y8iQDwKSXzBiAEigAgEklgqWAGaDuXxWbMBPL6c/vQ/FIYBIiKylYigKxJFJJb58/eaUigN+qHroxusp5QCvH7XT/nLFoYBIiKyjWFaswXsOC3g1TUUB/3QXLThT65iGCAiIltEYnF09URtWTI46PMg6PO6aue/XMYwQEREGZXcbbAnFrfl+YsDvrSBgjR+fDWJiChjDNNEZziKuJn5aXyaUigJ+uEZ5fgAGh7DABERZUQ0bqCzJ4JxLiY4IJ9HRyjg4/gAmzAMEBHRuIgIwtEYwlF7TgsU+b0IeD0cH2AjhgEiIhozwzTR2RNF3LDntEBx0Aevnp9z+92EYYCIiEZNRBCNG7bNFvDqGooDfmgaewOygWGAiIhGxRRBlw17CyQFfV4EfTwtkE0MA0RENGKxuIHOnui4txweiFJAScAPr4enBbKNYYCIiIZl99oBHl1DScAHTeO0QScwDBAR0ZDs3GkQAAI+D4q4mqCjGAaIiGhAIoKeWBzdkTFu9zsMa7dBP3w8LeA4hgEiIurHME109UQRs2HKIADomoaSoA86Twu4AsMAERGlsXODIQAIeD0o8vO0gJswDBAREQBrymB3TxQRm6YMAtxkyK34X4SIiGydMggAumZtMsTTAu7EMEBEVMDs3lcAAPxeD0I8LeBqDANERAXKmjIYhWHDdsOAtYhQcYCzBXIBwwARUYHJRm+AV9dRHPBxb4EcwTBARFRAYoa1uZBdCwgBQMjvg9+r87RADmEYICIqAHYvJwxw7YBcxjBARJTn7J4pAABBnwdBLimcsxgGiIjylCmC7kgUkZh96wZoSqE46INX5yDBXMYwQESUh6KxODojMYiNvQF+j46igA8aewNyHsMAEVEeMU1BVySKqI2rCCoAIa4kmFf4X5KIKA+ICKJxw9Y9BQDAq2sIBThIMN8wDBAR5Ti7dxhMKvJ7EfB6OEgwDzEMEBHlKBGxdhiMxGxtR9cUigN+eHT2BuQrhgEiohxkmNZSwnGbewO43XBhYBggIsoh2VhKGEhMGQz44OW+AgWBYYCIKAckBwh2R2K2Lh4EcAGhQsQwQETkcnHDRHfE/gGCHBtQuBgGiIhcKhv7CSQV+bwI+DhToFAxDBARuUxqzQCbVxAEAI+uoZjrBhQ8hgEiIheJGya6IvbPEgCAkN8LP9cNIDAMEBG5gimCcJZOCXAVQeqLYYCIyEEigkjcQHckCpvPCEApIOT3wefR2RtAaRgGiIgcEjcMdPXEEDftPyXg8+gI+X3QNIYA6o9hgIgoy0xT0B2NIZKFUwJKKRT7vfBxh0EaAn87iIiyJLmXQHckZuvOgkl+r44ivw8aTwnQMBgGiIhsJiKIGdbqgYZpfwzgUsI0WgwDREQ2SoaAbEwVBICgz4sgFw+iUWIYICKygWGa6I7EEI0bWWnP59FR5PdyuiCNCcMAEVEGZXNwIGCdEggFrOmCRGPFMEBElAHZ2lq4N+4nQJnCMEBENA6pGQLRmO2LBiXxlABlGsMAEdEYJDcT6o7EYGYpBeiaQsjPWQKUeQwDRESjFEuEgGysHAgACkDQ70WAmwqRTRgGiIhGKG6Y6I5EEcvSNEEA8HsSCwdxGWGyEcMAEdEwDNNEOBJDJEvTBIHEKYGAD16dpwTIfgwDRESDMEwT4Wg8a9MEAeuUQJHfCz9PCVAWMQwQEfVhhYAYIrHs9QQAgN/rQZHPy1MClHUMA0RECU6cDgAAr66hyO+DR+dUQXIGwwARFTynQoCuaQj5vZwqSI5jGCCighU3rNMB2do/IElTCkV+L3weneMCyBUYBoio4DgVApSydhXkegHkNgwDRFQwnAoBABD0eRDweaExBJALMQwQUd6LGwa6o3HEHAgBfq+emCHAwYHkXgwDRJS3YoaBcCSW1RUDk7weKwRwhgDlAoYBIsorIoJY4nRA3IEQ4NE0FHGGAOUYhgEiygvJrYTD0XjWdhHsTdMUQj4rBHBwIOUahgEiymmmaaInFkdPNI7sRwBrhkCRzwe/lyGAcpcScSBCZ4iIwNi9FjAGWDfciEPajoz+ST0+qNLqAW/SKuuhVU0e/XMSUcY5OTMASEwT9HoR8HGaIOW+nO4ZUEpBeQMwj27L3JPGo5DmgwM1BjX1hMy1Q0SjZo0HMBCOxh0ZDwBwrQDKTzkdBgBAq5sN89AuwLR3VzGtZhqUv8jWNohoYE6PBwCsDx9Bn4chgPJSzocB5fVBq5sJ82AGewf6NaKg1c+17/mJaEBGYjxAxKHxAIC1dHDQ5+GWwpTXcj4MAPb3DrBXgCi7nB4PADAEUGHJizBga+8AewWIskJEEI0b6Ik5Nx4ASIYAL2cHUEHJizAA2Nc7wF4BInulTgXE4nBybpOmFIJ+L/xcJ4AKUN6EAVt6B9grQGQLt/QCAICuWT0B3E6YClnehAEg870D7BUgyizDSPQCxJ3tBQAYAoh6y6swkNHeAfYKEGWEm3oBAIYAooHkVRgAMtc7wF4BovGJGyYiybEAThcDwKtrCCZ2EWQIIEqXd2EgI70D7BUgGhMRQSRuIBKNI2463wsAAH6vB0GfB7rGrYSJBpN3YQAYf+8AewWIRieeGAsQdUkvgFIKQa8Hfp8HGnsBiIaVl2FgXL0D7BUgGhHTFETicURiBgyX9AJwPADR2ORlGADG3jvAXgGiwZkiiMYMROIjGwwoIti/bx/WrX0TO7Zvh2maOOc9K3DykiUZrcvr0RH0ejgegGiMcnoL4+EY+zaNrndAKXgWnccwQNRLcjZAJG4gNorlgdvb2vCBi9+H9evWAQCqqqqglEJbWxvWb9mGmpoJ464t4PUgwPEAROOW1+8grW42oI2884O9AkSWZADoCEfQ3BlGZ090VEEAAFb/4wWsX7cOjz76KBoaGtDY2IgXXngBsVgMu3ftGnNtmlIo8nlRURxEKOBjECDKgLw9TQCMcuwAxwpQgRMRazpg3EA0A4sCmYlxBFu2bMHvfvc7nH/++Vi6dOmYn0/XNAR9Ho4HILJBXocBYORjB9grQIVIRGCYgkgsjmjcgJnBs4ZnLF+O+QsW4I4770S4uxsLFy4c0/P4PDoCHA9AZKu8DwMj6h1grwAVGMM0EYlZPQCGac+woaqqaqx+ZQ0AYMHsGaN6rK4p+L0e+D0eaBoDAJHd8j4MAMP3DrBXgPJdsgcgGjcQjbtnKmBf7AUgckZBhIEhewfYK0B5SkQQM0zEEgEgk6cAMinVC+DlAkFETimIMAAM3jvAXgHKJ6ZI6uAfixuuWA1wMH6vDr/XA4/GXgAipxVMGBiwd4C9ApQHDNNMdf+7YVfA3tatXYun/vJndHZ2pl3/8K8eRHvTUVx22WUMAkQuUDBhAOjfO8BeAcpFIoK4eaz7364BgOPV2dmJD/zLe1EcCqGmpgbl5eXw+XyYNm0a/vHCC3jo179GIBDAJZdc4nSpRAWvoMJAWu8AewUohyTP/0fj1hRAl57+T9N05Ag6Ozrw5//7P7z73e9OXb9nzx4A1oqEW7ZsYRggcoGCCgPAsd4BrXoyewXItZKj/2OGde4/5rLu/8FoSsHnscYCLJg3G1VVVTj33HMHPBUQjUZx8sknO1AlEfWV13sT9CYigAggJswDW6GqJ0MFigHFwUvkDoZpIhY3rQBg5ManfwBQAHxeD/wevd+UwHfeeQdPPvkkDKP/UsYLFy7EmWeeyfcfkQvkZRiQWASIdEJ6uoBIFyTcAUS6ALE+XYlI+h8gjw8IFEMFSgB/CCoQsv7VdId+AioEZvKTv2EgFjddO/VvMMkeAC/XBCDKeXkTBiQahrQdhrQ0ANHuXrcoYMQTrHrfVwGl1dDK6oCSKgYDGrfe8/5jhnsH/g3F69Hh9+jcH4Aoz+R0GJB4DNJ2CNJ6CAi329eQ0oCyWmjldUCogn8EaUSSG/8kP/nHXbrq33A8uga/xwOfV+eiQER5KifDgJgmpGkf5MhuQEa3rerYJXoNgiXQJs6DKirLUruUK5IH/1QAyJFBfwPRNQ1+r9UDwC2CifJfToUBEQHaj8A8tB2IRZwtpqwWWu0sKF/Q2TrIESICM/XJ3woAbl3vf6Q0TcHv8cDvZQAgKjQ5EwakpxPmgS1AuM3pUhKU9b/qaVATZkAp/vHMZ5J24DcQN82cGe0/FF2zpgL6PB7omuIpMKIClRNhQNoOw9y/KTUbwHWCZdCmLoTy+p2uhDIgOcc/biYO/IaZk4P9BuPRtUQAYA8AEVlcHQZEBHJkN+TobqdLGZ7uhTZ9MVSwxOlKaJTM1Ll+I3XO37VvijFQsGYB+Dw6vB4OAiSi/lwbBkRM67RAa4PTpYyc0qFNWwRVXOF0JTSA3uf5DdNE3BQYRu7N7x+J5EqAXo/OdQCIaFiuDAMiAvPA5twKAikK2sxTONvAYaZYB/q4mTjwG5LzA/yGo2u9u/95/p+IRs6VYcBs2g9p2Op0GWOne6HNOQ3K48wYAhGBiDU6PN8lz+9bB/xjn/hd+GttC2/i/L+X5/+JaBxcFwakuw3mrjcw8lUD3UgBRaXQZpyc9VkGMcNAd08MPo+OoN+b1bbtlOziN8z0T/z5NLBvJDSl4NW11BgAfvonokxw1a6FEo/AfOct5HYQAAAButsgh3ZCTZyTlRYN00R3JIZo3FqEyYvc+5SY/JRvipn4tC8wEwf8fDyvPxLJwX/JAKApdv8TUea5KgyY+zcBRtzpMjJGmvZCiiuhSqrsa0ME4Wgc4Wgs7Xq3fmA2RWAmuvVNU2AkDvxmAR/w+0oe+L06z/0TUXa4JgxIdxvQ2ex0GRlnHt4Jrbgy43/QRQTRuIHuSGzAg6hTZ3+S3fnJg7vR58DP431/Hl2zAoDefwtgIqJscE0YMI/sxuh2GMwRPR1AVzNQnLnegbhhoisSRXyIte8zHQb6HuT7XpbE93n2X88Wuqbg1Tntj4jcwxVhQMIdQGeT02XYxjy8G3oGwoBpCrqjMURiw59KGUkWELEO3sKDvK00peD1WJ/8vbpeELM8iCi3uCIM5G2vQFK4DdLVAhUa22JEIoKeWBzhSGzEr5AhgnA0lppmmDywp10eUzU0HI+mwaMf++KUPyJyO8fDgBhxoOOo02XYTEFaD406DAw3LmC4x3ZHYsPfkcZFUyrtwO/R2O1PRLnH8TCAcLvTFWSBQLpaRvWIWCIExPN81bxckzzoezUNHnb5E1GecDwMSHcrsnGK4C//eBXvv/5Lg95+2fnvxm+//d/2FRANQ+IxKM/QCwHFDRPdkShiQwwOpOxILvBjBQBO8yOi/OX4CoTG7jeBUX5qHotwTwRtnV3pbRsmPvWVu7Fuyw789Wd3Y+HcmbbWoE09Eaq0ZsDb+i4aRNmllIJHU6kDv0fXuLsfERUMR3sGREyguy0rbQUDfgQDx/YKMAwDH7/lzqwFAUBBulv7hQHTtAb69YxghgBlhq5p8OjK+lezBvixu5+ICpmzpwmMOCDZ7w5PBoG/vfwG/vqzb2UhCACAALHIse8So/3DUYYAu2hKQdc1eLTEgT/xaZ9d/URE6ZwNAw4MjjMMA1clgsAzP7sbJ87LRhCwiGkcmyYYjXE1vgxRAPTEFD6PpkHXFUf1ExGNgrNhIMu9Askg8NdEEFg0b1ZW2zeVhvauHq7BP0aaUtA0BV1T0JWWCACKn/aJiMbJ2TCQxcVYkkHgmZdedyQIAIAGQWmRHz3ROMcIDEJT1sFe07TEQf/YZR7wiYjs4WwYUHpWmjEMA1f/11145qXX8fRPv4mTjpudlXb7UkqDpmkIBXwI+r2IROMIxwrvdIH1aT5xsO914OcnfCIiZzgbBnQPoHkA075PyaZp4ur/ugt/eO4l/O+3/xsTa6pwqDF9d8SaijLout3BRAH+otR3mlII+r0I+DyIxAyEo6NfZdCNlEp05ye/eh34NR7wiYhcyfl1Bt55C+hotO35X12/GcuuWDnkfRr/+QTKS4ttqyFJm74YqrhywNuSSw/3ROOuXHVw4IO8dWBPXuaBnogoNzkeBsyj70AO73CyhKzR5p8FpQ/dGSMiiBsmwtGY7asQKliL7VgHcfAgT0RUoBxfjliFygtj97xA8bBBALAOzl6Ptdd93DDRE40hMsZVCTWlEPB5Ugfz5AFfKZUKAkRERI6HAQRKrD7oPDhfPjg1pu2LPbqG4qAfQdNEJGbNQBjNy6QphaBv6L0QiIiIHN9oXWkaUD7R6TJsJlAV9WN+tK5pKPL7UBEKIuT3QR/h0rlSGH0uREQ0To6HAQDQaqY7XYKNFFBSAxUY/wBFlej2LysKoDToh9cz9AyIvO5sISKijHFFGFC+YKJ3IB/PYQu0CTMy+ozJcQWlQT/KQwEEvJ4BX7l8mKpIRET2c0UYAJK9A/l28FJAcRVUsMS2FvTEIkYVxUGE/N5+u+85PFmEiIhygGvCgPIXAeM4r+5OAq02OxshWacQvCgvCqAk6IdXt/7TMgsQEdFwnJ9N0Is2cS7M7jYg0o186CVQE+dCBUuz26ZS8Hl0+Dw6DNMEZw8SEdFwXNMzAABK06FNW5TVDYxsU1YHVTnZ0RJ0buNLREQj4LqjrvIFoU05wekyxkEB/hC0ScfxQExERDnBdWEAAFRJNVRt9rcYHj8F6FbvhtKysyMjERHReDm+N8FQzMa9kEPbnS5jhBTg9VubEfXanZCIiMjtXB0GAMBsOwzZ/7b7h8UHSqBNXwTl8TtdCRER0ai4PgwAgIQ7YL7zFhCPOF3KwCrqoU2cZy2tTERElGNyIgwAgMRjMA/vBFoOOF3KMR4f1MS50Mpqna6EiIhozHImDCRJTyfMhm1AV4tzRSgNasIMqKopHChIREQ5L+fCQJJ0NFmhINoNa0+DLP0Y5ROh1c6C8nJsABER5YecDQNAYt397lZI62FI2yHANGBLMAgUQ5VPhCqbAOUNZPa5iYiIHJbTYaA3MU2gqxlm6yGgozERDJJGEhD63MdXBFVeC1VWx6mCRESU1/ImDPQmIkA8CkS6ID2dx/6NRwExj01TVJq19LGvCCpQDARCUP5iwF/EsQBERFQw8jIMEBER0chxYjwREVGBYxggIiIqcAwDREREBY5hgIiIqMAxDBARERU4hgEiIqICxzBARERU4BgGiIiIChzDABERUYFjGCAiIipwDANEREQFjmGAiIiowDEMEBERFTiGASIiogLHMEBERFTgPE4X0Jtv8SeheXxQmg6l6dC9xy4rTTt2m65D8/igpW7T+92mNB2apqA0BV3XoPpc1jQFTVep+wx5m1LQPRp0TUHXFHyJy57U9/qx2/Rj9/P0uq8+0GWloCkFXQFeXUtd9ugadAXre03Bq6kBLlu3ezUtdVlXCkoBmgKUQuL5AQVA1xQ0wPpZNKQuawrQVe/L1nMoEUBMKDMOpF02rS9z8NuUmIBhHLtsxgHTgJgmEI9CDAMwTeu6eAxiGtblWAxIXk7eN3m/WPTYY0wDZiwOMUyIacKMxmEa1mPEMGHG4jCNY5clcdmIxSG97mdE470uGxBTYBqS+D7xeFOs2wyBGALTMGHEzMRzCoyYkXjMsceZIjBEEDUFhqDP5b7fW5dNWJcNQeK2Y5d/JHscfV9mCt/ffH/z/e3e9zd7BoiIiAocwwAREVGBYxggIiIqcAwDREREBY5hgIiIqMAxDBARERU4hgEiIqICxzBARERU4BgGiIiIChzDABERUYFjGCAiIipwDANEREQFjmGAiIiowDEMEBERFTiGASIiogLHMEBERFTgGAaIiIgKHMMAERFRgWMYICIiKnAMA0RERAWOYYCIiKjAMQwQEREVOIYBIiKiAscwQEREVOAYBoiIiAqd5Kmenh659dZbpaenx+lS+nFzbSKsbzzcXFs+cfPr7ObaRFjfeLi5tvFSIiJOBxI7tLe3o6ysDG1tbSgtLXW6nDRurg1gfePh5tryiZtfZzfXBrC+8XBzbePF0wREREQFjmGAiIiowDEMEBERFbi8DQN+vx+33nor/H6/06X04+baANY3Hm6uLZ+4+XV2c20A6xsPN9c2Xnk7gJCIiIhGJm97BoiIiGhkGAaIiIgKHMMAERFRgcu7MPCFL3wBZ555Jq644gpEo9G028LhMP7lX/4FZ511Fs477zw0Nze7qr6kO+64A0uWLHG8png8jquvvhpnnnkmPvvZz2atnpHWl5Tt16u3wWpzw+9aPuL7O3M18f09vEJ6f+dVGFi7di0OHTqE1atXY8GCBXj00UfTbn/yySdxwgkn4IUXXsCHP/xh/OpXv3JVfQDQ0dGBjRs3uqKmP/3pT5g8eTJWr16N7u5uvPTSS1mrayT1Adl/vUZam9O/a/mI7+/M1sT399hrc/p3zQ55FQZefvllnH/++QCACy+8sN8v95w5c9Dd3Q0AaG1tRU1NjavqA4Dvfe97uP76611R00jqdbI+IPuvV29D1eb071o+4vs7szXx/T20Qnt/e5wuIJNaW1tRX18PACgrK+vXdTNr1ixs3LgRJ5xwApRSePXVV11VX1tbGzZs2IAvf/nLrqiptbU1tf72QPU6XZ8Tr9dIa3P6dy0f8f2d2Zr4/h57bU7/rtkhJ3sGDh06hOXLl/f7EhG0t7cDsP5DVlZWpj3uwQcfxNlnn42NGzfitttuw6pVq1xV33e/+138+7//uy01DaaiomLQmoa6zQ31OfF69TZUbdn6XctHfH9nDt/fY1do7++cDAN1dXV48cUX+329733vwzPPPAMAePrpp7Fs2bJ+j03+By0vL0dra6ur6tuxYwe+/vWv48ILL8T27dtx55132lJfb0uXLh20pqFuy5ahanDi9RppbUB2ftfyEd/fmcP3tz21AXn4/nZu92R73HjjjbJ8+XK5/PLLJRKJiIjItddeKyIibW1t8r73vU/OOussWbZsmWzdutVV9fV2yimnOFZTsp5YLCYf//jHZfny5bJy5cqs1TPS+nrL5uvV22C1ueF3LR/x/T3+mvj+HrlCen9zOWIiIqICl5OnCYiIiChzGAaIiIgKHMMAERFRgWMYyGFbt25FXV0dOjo6bG1nz549UEph3bp1trYzlOeffx5KqXGP2j1y5Ahqampw4MCBzBRGjjv77LPxuc99LvX99OnT8d3vfnfEj8/U71ambdmyBUuXLkUgEMBJJ5006HUjceWVV+Ib3/jGuOp54IEHUF5ePq7nyIarr74aH/jAB8b9PPfeey/e//73j7+gHMEwMApXX301lFL49Kc/3e+26667DkopXH311WnXHzp0CCtXrsTMmTPh9/sxZcoUXHzxxXj22WdT95k+fTqUUv2+hptK86UvfQnXX389SkpKMvLzFYIJEybgyiuvxK233up0KWSTNWvW4Nprr3W6jHG79dZbEQqFsHXr1tTfi4GuG8769evx5z//GStXrrSz3LxzzTXXYM2aNXjxxRedLiUrGAZGacqUKfjtb3+LcDicuq6npwcPP/wwpk6dmnbfPXv24JRTTsHf//53fPOb38SGDRvw1FNP4Zxzzum3xOaqVavQ0NCQ9jXUm3f//v344x//iE984hOZ/QHzWHKjkU984hN46KGH0NLS4nBFZIeamhoUFRU5Xca47dy5E8uXL8e0adNQVVU16HXDuffee/GhD32IHxpGSEQQj8fh9/tx+eWX4/vf/77TJWUFw8AonXzyyZg6dSoef/zx1HWPP/44pkyZgsWLF6fdN9lb8Nprr+Gyyy7D3Llzcfzxx+Pzn/88XnnllbT7lpSUoK6uLu0rFAoNWscjjzyCRYsWYfLkyanrkt14Tz/9NObPn4/i4mJceOGFaGhoSN3HNE2sWrUKkydPht/vx0knnYSnnnoq7blfe+01LF68GIFAAEuWLMHatWv7tb9p0ya8733vQ3FxMWpra3HllVeisbFxyNfun//8J8466ywUFRWhoqICF1xwQeqAHIlEcMMNN2DChAkIBAJYvnw51qxZM+TzPfbYYzj++OPh9/sxffp0fPvb3067ffr06bj99ttx9dVXo6ysDNdccw0AYOHChairq8Pvf//7IZ+fclPf0wRKKfzsZz/DpZdeiqKiIsyZMwd//OMfB318OBzGRRddhKVLlw66RK9pmrjrrrswe/Zs+P1+TJ06FV//+tdTt2/YsAHvec97EAwGUVVVhWuvvRadnZ1pz3H//fdj/vz5CAQCOO644/CDH/wgreY33ngDq1atglIKX/3qVwe8bjimaeJ3v/tdv+7uaDSKm2++GZMmTUIoFMJpp52G559/Pu0+DzzwAKZOnYqioiJceumlaGpq6vf8t99+OyZMmICSkhL867/+K774xS/2O30x1M85WM3jfW17G+5vS/I00dNPP40lS5bA7/dj9erVAID3v//9eOKJJ9I+/OUth9c5yClXXXWVXHLJJXLPPffIihUrUtevWLFCvvOd78gll1wiV111lYiINDU1iVJKvvGNbwz7vNOmTZPvfOc7o6rlkksukU9/+tNp191///3i9Xrl3HPPlTVr1sgbb7wh8+fPl8svvzx1n3vuuUdKS0vl4Ycfli1btsjNN98sXq9Xtm3bJiIinZ2dUlNTIx/5yEdk48aN8qc//UlmzpwpAGTt2rUiInLw4EGprq6WW265RTZv3ixvvvmmnHfeeXLOOecMWu/atWvF7/fLZz7zGVm3bp1s3LhRvv/978vRo0dFROSGG26Q+vp6+ctf/iJvv/22XHXVVVJRUSFNTU0iIvLcc88JAGlpaRERkddff100TZNVq1bJ1q1b5f7775dgMCj3339/2utaWloqd999t2zfvl22b9+euu3DH/6wXH311aN6zcmdzjrrLPnsZz+b+r7v+wmATJ48WX7zm9/I9u3b5YYbbpDi4uIBf7daW1tl+fLlcu6550pnZ+egbd58881SUVEhDzzwgOzYsUNWr14tP/3pT0VEpKurS+rr6+WDH/ygbNiwQZ599lmZMWNG6m+DiMhPfvITmThxojz22GOya9cueeyxx6SyslIeeOABERFpaGiQ448/Xm688UZpaGiQjo6OAa8bztq1awWAHDp0KO36yy+/XM444wz5xz/+ITt27JC7775b/H5/6u/AK6+8IkopueOOO2Tr1q3yve99T8rLy6WsrCz1HL/+9a8lEAjIL37xC9m6davcdtttUlpaKosWLRrxz2nHa5v8O5000r8tJ554ojzzzDOyY8cOaWxsFBHr76FSSp5//vlhX+tcxzAwCslfsqNHj4rf75fdu3fLnj17JBAIyNGjR9PCwKuvvioA5PHHHx/2eadNmyY+n09CoVDa13PPPTfoYxYtWiSrVq1Ku+7+++8XALJjx47Udffdd5/U1tamvq+vr5evf/3raY9717veJdddd52IiPz4xz+WyspK6erqSt3+wx/+MC0MfOUrX5Hzzz8/7Tn27dsnAAZdieujH/2oLFu2bMDbOjs7xev1ykMPPZS6LhqNSn19vXzzm98Ukf5h4PLLL5fzzjsv7XluuukmWbBgQer7adOmyQc+8IEB2/yP//gPOfvsswe8jXLLSMLAl7/85dT3yT/wTz75pIgc+93asmWLLFq0SD74wQ+mVg8cSHt7u/j9/tQBqq+f/OQnUlFRkRYm/vznP4umaamD8pQpU+Q3v/lN2uO+9rWvyemnn576ftGiRXLrrbem3Weg64by+9//XnRdF9M0U9ft2LFDlFJy4MCBtPuuWLFCbrnlFhGx3q8XXnhh2u0f+chH0sLAaaedJtdff33afZYtW5YWBkbyc/aWide2dxgYzd+WJ554YsA2k8Ek3+XVroXZUl1djYsuuggPPvggRAQXXXQRqqur0+4jiYUdlVIjes6bbrqp3+DDSZMmDXr/cDiMQCDQ7/qioiLMmjUr9f3EiRNx5MgRAEB7ezsOHjzYb43tZcuW4a233gIAbN68GYsWLUo753r66aen3f+NN97Ac889h+Li4n7t79y5E3Pnzu13/bp16/ChD31owJ9l586diMViaXV5vV6ceuqp2Lx584CP2bx5My655JJ+P8d3v/tdGIYBXdcBAEuWLBnw8cFgMLUFKeW/E088MXU5FAqhpKQk9b5IOvfcc/Gud70LjzzySOr3ZyCbN29GJBLBihUrBr190aJFaaf5li1bBtM0sXXrVmiahn379uFTn/pU6tQVAMTjcZSVlY31RxxQOByG3+9P+zv05ptvQkT6vU8jkUhqHMLmzZtx6aWXpt1++umnp51S3Lp1K6677rq0+5x66qn4+9//DgA4evToqH/O8b62tbW1afcfzd+WQv9bwTAwRp/85CdTO2rdd999/W6fM2cOlFLYvHnziKa5VFdXY/bs2SNuv7q6esABcF6vN+17pVQqmPS+rjcRSV3X974DMU0TF198Me66665+t02cOHHAxwSDwUGfb7Dg1LuugR4z0P37GmzcRXNzc17sQU4jM9D7wjTNtOsuuugiPPbYY9i0aRMWLlw46HMN9bsMDP1727vdn/70pzjttNPSbh8qhIxFdXU1uru7EY1G4fP5AFjvX13X8cYbb/RrLxnwR/J3ABj4PZs0lp9zvK/tQPcfrM6+1xX63woOIByjCy+8ENFoFNFoFBdccEG/2ysrK3HBBRfgvvvuQ1dXV7/bxzunefHixdi0adOoHlNaWor6+vp+U2VeeuklzJ8/HwCwYMECvPXWW2kDZvoOdjz55JPx9ttvY/r06Zg9e3ba12BvqBNPPHHQqVCzZ8+Gz+dLqysWi+H1119P1dXXggULBvw55s6dO6I/qBs3buw34JMK25133omrrroKK1asGPK9NWfOHASDwUF/nxcsWIB169alve//+c9/QtM0zJ07F7W1tZg0aRJ27drV7/0zY8aMjP5MycF8vX+exYsXwzAMHDlypF/7dXV1qZ+h7/u+7/fz5s3Da6+9lnbd66+/nro8lp9zvK9tX2P529Lbzp070dPTUxB/KxgGxkjXdWzevBmbN28e9ODzgx/8AIZh4NRTT8Vjjz2G7du3Y/Pmzfif//mffl3vHR0dOHToUNpXci/tgVxwwQV4+eWXYRjGqOq+6aabcNddd+F///d/sXXrVnzxi1/EunXr8NnPfhYAcPnll0PTNHzqU5/Cpk2b8Je//AXf+ta30p7j+uuvR3NzMz760Y/itddew65du/DMM8/gk5/85KD13HLLLVizZg2uu+46rF+/Hlu2bMEPf/hDNDY2IhQK4TOf+QxuuukmPPXUU9i0aROuueYadHd341Of+tSAz3fjjTfi2Wefxde+9jVs27YNDz74IO6991584QtfGPY16O7uxhtvvIHzzz9/VK8d5b9vfetbuOKKK/Ce97wHW7ZsGfA+gUAA//mf/4mbb74Zv/zlL7Fz50688sor+PnPfw4AuOKKKxAIBHDVVVdh48aNeO6557By5UpceeWVqW7sr371q7jjjjvwve99D9u2bcOGDRtw//3345577hlVvStWrMC999476O01NTU4+eST0w6Gc+fOxRVXXIGPf/zjePzxx7F7926sWbMGd911F/7yl78AAG644QY89dRT+OY3v4lt27bh3nvv7TfraOXKlfj5z3+OBx98ENu3b8ftt9+O9evXp33iHu3PmYnXtrex/G3pbfXq1Zg5c2baqde8lf1hCrmr7yjVvnoPIEw6ePCgXH/99alBgpMmTZL3v//9aYMDp02bJgD6ff3bv/3boG3F43GZNGmSPPXUU6nr7r///rQBPiLWAKLe/5kNw5DbbrtNJk2aJF6vVxYtWpQaSJX08ssvy6JFi8Tn88lJJ50kjz32WNoAQhGRbdu2yaWXXirl5eUSDAbluOOOk8997nNpA5X6ev755+WMM84Qv98v5eXlcsEFF6QGBIbDYVm5cqVUV1eL3++XZcuWyWuvvZZ6bN8BhCIijz76qCxYsEC8Xq9MnTpV7r777rT2Bpul8Zvf/EbmzZs3aJ2UW0YygPD3v/992mPKyspSM08G+t1auXKlTJw4cdABsYZhyO233y7Tpk1L/f71njm0fv16OeeccyQQCEhlZaVcc801/Ub/P/TQQ3LSSSeJz+eTiooKefe735024HgkAwinTZs27IDCH/3oR7J06dK066LRqPz3f/+3TJ8+Xbxer9TV1cmll14q69evT93n5z//uUyePFmCwaBcfPHF8q1vfavf35dVq1ZJdXW1FBcXyyc/+Um54YYb+rU13M/Z13hf275/p8fytyXp/PPPlzvuuGPQWvMJtzDOYT/4wQ/whz/8AU8//bTTpeSUU089FZ/73Odw+eWXO10Kke16enowb948/Pa3v+3XI5lp5513Hurq6vCrX/3K1nayYePGjVixYgW2bduW8YGdbsQBhDns2muvRUtLCzo6Ori62AgdOXIEl112GT760Y86XQpRVgQCAfzyl78cdlGw0eru7saPfvQjXHDBBdB1HQ8//DD+9re/4a9//WtG23HKwYMH8ctf/rIgggAAsGeAiIhGLRwO4+KLL8abb76JSCSCefPm4ctf/jI++MEPOl0ajQHDABERUYHjbAIiIqICxzBARERU4BgGiIiIChzDABERUYFjGCAiIipwDANEREQFjmGAiIiowDEMEBERFbj/HwQ0KNq5yZTXAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -354,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -362,8 +362,8 @@
      "output_type": "stream",
      "text": [
       "Average Causal Effect X=0.37, Y=0.28, Z=0.00 \n",
-      "Average Causal Susceptibility X=0.00, Y=0.26, Z=0.39 \n",
-      "Average Mediated Causal Effect X=0.00, Y=0.18, Z=0.00 \n"
+      "Average Causal Susceptibility X=0.00, Y=0.25, Z=0.40 \n",
+      "Average Mediated Causal Effect X=0.00, Y=0.19, Z=0.00 \n"
      ]
     }
    ],
@@ -391,16 +391,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<tigramite.models.LinearMediation at 0x7f0e9a4a2220>"
+       "<tigramite.models.LinearMediation at 0x7f389003ef10>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -411,15 +411,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.06808702968271146\n",
-      "[0.05282617 0.08444858]\n"
+      "0.0682760530685738\n",
+      "[0.04730555 0.08479888]\n"
      ]
     }
    ],
@@ -432,16 +432,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.         0.17555924 0.        ]\n",
-      "[[0.00000000e+00 1.61331930e-01 0.00000000e+00]\n",
-      " [0.00000000e+00 1.91959437e-01 3.88578059e-16]]\n"
+      "[0.         0.18570133 0.        ]\n",
+      "[[0.00000000e+00 1.65204696e-01 0.00000000e+00]\n",
+      " [0.00000000e+00 1.98748949e-01 3.33066907e-16]]\n"
      ]
     }
    ],
diff --git a/tutorials/causal_feature_learning_and_prediction/tigramite_tutorial_prediction.ipynb b/tutorials/causal_feature_learning_and_prediction/tigramite_tutorial_prediction.ipynb
index b3276ed1..8429c7fe 100644
--- a/tutorials/causal_feature_learning_and_prediction/tigramite_tutorial_prediction.ipynb
+++ b/tutorials/causal_feature_learning_and_prediction/tigramite_tutorial_prediction.ipynb
@@ -154,7 +154,7 @@
     },
     {
      "data": {
-      "image/png": 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ECAQlbUIIIUQgKGkTQgghAkFJmxBCCBEIStqEEEKIQFDSJoQQQgSCkjYhhBAiEJS0CSGEEIGgpE0IIYQIBCVtQgghRCAoaRNCCCECQUmbEEIIEQhK2oQQQohAUNImhBBCBIKSNiGEECIQlLQJIYQQgaCkTQghhAgEJW1CCCFEIChpE0IIIQJBSZsQQggRCErahBBCiEBQ0iaEEEIEgpI2IYQQIhCUtAkhhBCBoKRNCCGECAQlbUIIIUQgKGkTQgghAkFJmxBCCBEIStqEEEKIQFDSJoQQQgSCkjYhhBAiEJS0CSGEEIGgpE0IIYQIBCVtQgghRCAoaRNCCCECQUmbEEIIEQhK2oQQQohAUNImhBBCBIKSNiGEECIQlLQJIYQQgaCkTQghhAgEJW1CCCFEIChpE0IIIQJBSZsQQggRCErahBBCiEBQ0iaEEEIEgpI2IYQQIhCUtAkhhBCBoKRNCCGECAQlbUIIIUQgKGkTQgghAkFJmxBCCBEIStqEEEKIQEjNHYChGIZBRkYG8vLyoFarAQASiQT29vZwdXWFWEz3I2XJz89HRkYGVCoV1Go1xGIxpFIpXF1dYW9vb+7wLB7LssjMzEROTk5RHRSLxUV1UCKRmDlCy6dUKpGeng6lUllUByUSCVxcXODg4ACRSGTuEC0ay7LIyspCdnY2GIYBwzCQSCSwtbWFu7s71UEdqFQqpKenQ6FQlKiDzs7OcHR0tLg6KGJZljV3EPrIy8tDamoqUlNTwTAMZxmxWAx3d3fIZDJKPm8ovNmRy+XIycnRWM7e3h4ymQxubm70wX9Dfn5+UR0sTNZvEolEcHd3h4eHByWfNxTe7MjlcmRlZWksZ2dnB5lMRsmHg1KpRGpqKuRyOVQqlcZybm5ukMlkFpl8zKnwZic1NRUZGRkay9nY2EAmk8HDwwNSqWW0cQWTtFmWRWJiIpKSkvR6n6enJ6pUqUIVFgXJJioqCgqFQuf3SCQS+Pn5wcHBwYiRCQPLskhJSUF8fLxe73N3d0f16tWp9wcFySYqKgp5eXk6v0csFsPX1xfOzs5GjEw4UlNTERcXp9d7XFxc4OPjQ3UQgFqtRnR0NLKzs3V+j0gkgo+PD1xdXY0YmY6xCCFpsyyLmJgYrXdE2lCFBXJzcxEZGamxd0IbkUgEX19fuLi4GCEyYWBZFq9fv4ZcLjfo/Y6OjvDz87PqOpifn4/IyEitLUNtvL294e7uzm9QApOYmIjExESD3mtnZ4datWpZTIvRHJRKJSIjI/VquBRXrVo1VKpUieeo9COIb5D4+HiDEzYAZGZm6t06qkgKK6ohCRsoSFjR0dF6tY4qmuTkZIMTNgDk5OQgNjYWArhHNgq1Wo2XL18anLABIDY2VusjnYouNTXV4IQNFNw0RUdHW20dZBgGL1++NDhhA8Dr16/LlYv4YPFJOysrCykpKeU+j1wuR2ZmJg8RCU9cXJzBCbtQYW+HNX7g8/LykJCQUO7zZGRkID09nYeIhOf169dQKpXlPk9MTEy567IQKRQKvHr1qtznycnJ4eX7VIgSExORn59f7vPExcWV6+azvPRO2hcuXEDPnj1RvXp1iEQiHDhwwAhh/T8+viyNcS6hyMnJ0TrYRx+Fo82tTXlaN29KSEiwuhuf/Px8pKWl8XKuwgFY1iY5OZm3epOYmGh1Nz4qlYq3mxW1Wm3WGx+9k3Z2djaaNm2K3377zRjxlJCXl4fc3FyLPZ8Q8F25rO0uXalU8nqjolQqebuJEgq+k6y11UGGYXi9hgzDWF2PT1paGq83y3K53Gw333qPSOjWrRu6detmjFhK0Wd0nz7ntKaR0Hxfw5ycHDAMYzUDqozxDDU7O9uqBvXxfZOiUCigUqmsZkBVbm4u7wkiOzsbHh4evJ7TkvFdB9VqNfLz880ypdiiv3mN0SrOyshAzJ0HUJvxmYSpqFQqozx7iXn4FBkJ+k29Eypj1MHsrGy8vH4XqnIMiBEKlmWNMoDxVcQLpMa+5v28lsgYddDaBvQZ4xqaq9fWom9VjfHcJSMpBb+3/hg2Dvao2fxt+AUGwK9VU9QKDIDMt0aFms9trOdWp5asw41N+yHzrQG/wADU+u8a1mz+NmwdK1YvhjGuYV52Nla26gWprS18mjX6rw4WXEev2jUrVB00Vhdi+LYDODPvd7hVr1L0+fVrFYCaLd6Gg2vF6sXQtIBPeVjbM21j/L3muoYWnbSN8eWVIy94lqPMzcOzi9fx7OL1omOuVTzh16ogARUmIyF/ARjry//1wwgAgDw6DvLoONzacxQAIJZIUL1xvaIv0Fqtm6Fao7qCTkLGiF2RVdDKUSkUiAy/g8jwO0XHnGTuRfXPr1UA/Nu8AycPN95jMBVj/dsnPokEAKS/SsDdAydx98DJot9XtWGdohtJv8AAeDdpCLGAV1QzxqMoIX8mDSESiXi/gTTXNbTopG1vb8/7gInkxy80HstISMa9w6dx7/BpAIBYKoV/m3fQKDgIDYODULP524L68EulUojFYt7vCBMfPON8nVGrEXv3EWLvPkLYuh0AANeqXmjYJajgGnZpD9cqXrzGYmzGeGaV9lLzalbZ8jQ8OH4eD46fBwCIxGL4tWyChsHvolFwEGoFBkBiY8N7TMYiEolga2tbrrmxXF5eucX5OsuyeP0wAq8fRuDyhj0AAKdKHmjYuR0aBr+Lhl3aQ+ZTnddYjM0YddCaxvUABdeQ70cC5loi26KTtqOjI+/nfPbPFZ3LMioVnoVdw7Owazj0zVI4erihQef2aBRckIRkvjV4j49PIpEIjo6OvA7CYPIUUObo/iwnIz4J4Vv2I3zLfgCAd9OGRQmoTvsWsLHwteGNUQejL3MnHC4swxS1xo/9uAL2Ls6o/16bomtYuY4f7/HxzdHRkd+kzTDIjNN9+mZ2Sipu7DqCG7uOAACqNqzz3434u6jXIRB2Tvz/G/PJGAnWGPXakjk6OvKatEUikdmStt7LmGZlZeHZs4KWVrNmzbBs2TJ06tQJMpkMvr6+vAbHsiyePn3Ky6IMACARi7Hi7Q/B8tTyrNqgNhoGB6FR8Luo2yEQ9s5OvJyXTxkZGYiOjubtfAlX72HPZ1/xci4bB3vU6xBYdA0ttSv9+fPnvA06EYvFWNv6Y+Rn8jOq39Pft6gnqMF7beHgZv61kd+Uk5ODFy8093DpK/NZDDb1/pyXc0ltbVG7XfOCOvjBu/Bu2sgiZ0ZER0fzOvWwQYMGVjP6HihYKyAiIoK383l4eKBGDfM02vRO2ufOnUOnTp1KvT5s2DBs3LiRr7iKGLI4viZVvLyQePsRIsPv4OW1u4gMv4PsFH7mP0psbFC7XfOiO3ifZm9ZxIefZVk8e/aMl5WAJBIJZDYOeB52regaxt17DIangTJu1asU9WI06NweLl7mXeO3UGZmJqKiong5l2elSkh98Py/+ncbL8PvICMhmZdziyUS+AUGFF3Dmi2bQmIhX8wvXrzgpaUjEolQ2dkNL8Ku/1cH7yDm9kOoebqxd/GqhAZd2v/3OCcI7tWr8HLe8uLzxkcmk6F6dWE9IuADnzc+devWhZ2dHS/n0pfFbxjCsiyioqLK3cVrIxKjbsMGJRIpy7JIjozBy/DbiLx2Fy/D7yD61n2o8svflefsKUPDLu3R8L8Pv0eNquU+p6Fyc3Px/Pnzcp/H3c4R3nX9S7ymyMlF9O0HePnfF2hk+B2kvIwt9+8CAN93GqPRB++iYXAQardtDqmtLS/nNURsbGy5V/WSisSoW79eiUTKsixSY14V1b+X1+4g6sa/UOjxCEITR3dX1H+/XVFL3NPPp9znNJRCoUBERES5BwO5SO1Qs0HdEq8p8/MRe/fRf9ev4GY8MSKyXL+nUPXG9Qtugj54F3WCWsHWwXyPc+Lj45GcXL4bPIlIBP9atWBnZd3jQMEU2IiIiHKPxq9atSo8PT15ikp/Fp+0gYKLHRkZaXBrkclTYPOHY9Bl6kh8MHOs9t+lUCDu3yd4eu4qHp64gIgL4bwk8epv1UNA7w/QanAIqtavXe7z6SstLQ2xsYYn02dHzuHKis2Yfe0gPLyraS2bkZiMF1du4dGpi3h0MoyXL1A7J0fUf68tWn7aE017BZt8ahnDMIiMjDS4m5xVqbGr/xQE9Hwf/ZbO0VpWrVLh9YOniLhwDQ9PXMCTs1d4SeKV69ZC015dEDikN7ybNCz3+fRV3h6LV1fv4u/pCzDzSiiqNayjtWy2PA2R4bfx+PQlPDwZhlf3nxj8ewtJ7WxRr0MgWnzSE836djP5zBKGYRAdHW14A4ZhcWDU16hRvzaGb15mkY+ijC0nJweRkZEG3zy6ubnB29vbrNdOEEkbMGwPVADITUzB1t7jkJ+RDZFIhNH7VqNZ7646v1+Rm4dnYdfw8GQYHp0MQ9y/j/UNvZSaLZui9ZDeaD7gQ7hWNt0dW2ZmpkG7/FxftR3ha7YDAHwCGmFa2B69nt8nR8bg0akwPDwZhsenLyI3vXwbt9g5O6FZ365oPaQP6nVsbbIR/QzDIDY2Vu8uNmVGNrb2GY/s/7rBB639GUGjB+r+/vz8gpugkwXXMPrmv3r9fi413m6AwCG90XJgL5P2AmVnZyM6Olrv1s6DnUdxdt5qAAXP8WeHH4Czp0zn96fGxePRqYLP8KNTF5GVbPiObQBgY2/33w1QHzQKDjLZiH6WZfHq1Su9lzVl8hTYOWAK5C8Kxrd89NN0dP96gjFCtHh5eXkG7Tjn5eWFypUrm/1mRzBJGyiosGlpaZDL5WW2eOxs7fBw33Gc+HY5GNX/f0HYOjpgetge+L7T2KAY0l4l4PHpi0VJPDPJ8HWQxRIJ3uraAa0GhxS0Hk3Q9Zafnw+5XI7U1FStU8FEIhHEeUrsH/cdYq7dLXGsaa8uGLN/rUHP7NUqFaJu3CtKQJFXb5frmbh7japoObAXWg/pjRpvNzD4PLpiWRYZGRlISUkp8xmtra0tIk9fxqGpP0NdrLdGLJVi0vFNaPB+O4NiyExKweMzl/DoZBgenLiA9FeGb4QjEolQ/722aDU4BO/07QZ7F2eDz6UrpVIJuVwOuVxeZvK2YYHDU+fj2elLJV6vE9QKk09tgY0BzxUZhkHsnYd4ePICHp0Mw7OLN8r1TNzFqxJafNITrQaHwK9lU5N8qWdmZiIlJaXMVreNjQ3ir/+LfZ9/W7Q+QKHPdq9C8497GDNMi6VSqYrqYFnJ29XVFTKZDM7Oxv9s6EJQSbu4vLw8pKamIjc3F2q1GizLQiqVwt7eHjKZDNHhd/DLewM5R4q7Va9S0M1bzhYGwzCIvfuwKAE9v3jD4KUp7V2c8U6/bmg1uHdB69HIg9gYhinaKlKpVBatJy6VSuHq6go3Nzes7TMG9w6d5nx/8Mwx6LPwy3LHkZuegcf/XC64hicuIDkyxuBz1WjSAIFD+qDlpx+ZpPWYn5+P1NRU5OTkFNVBiUQCe3t7eHh4QP48Cgta9uJ8vOLo7oqZV0PL/aikcF5yYQJ6ej4cylzDlg21cbBHQEgwWg3uXdB6NPIgNpZlkZmZibS0NCiVSqjVaojFYkgkkqI6uHPcN7j4xw7O97ce1hfDNiwpd5LMy8r+71HEeTw6GYb4x4aP/6hSzx+BQ3qj1aAQeNYy/hiCwl3PsrKyStRBW1tbyGQy5CbJMS+gO2fvlo29HaZd2A2/lk2NHqelYlkWWVlZSEtLg0KhgFqthkgkglQqhbOzMzw8PCxulL1gk7Yu/lmxAbsn/8B5zPedxph2YTevczTzs3MQcSG8qBVeuHKYvjy8q6HlwI8QOKQPajSuz1t8+spNz8Citn01/h1D/lyEdiP68/o7k55H4eHJC3h4MgxPzlxGXqb+z+9EIhHqv98OrYf0RkDvD0zSetTk+s5D+PPTSZzHvOr4YdbVUDhX4m/jBmVeHp5fulmUxGPuPDToPC6VPdHikw/Rekgf+DZ/22xdgsq8PPzy/iC8uHyT83jI/JnoOnscr79THh2HR6cu4uHJC3h8+hKy5WkGnadO+5ZoNTgEzft/aNZV7R6evIDfuv+Ps0fLtaoXZl87KLgFZ6xZhU7aLMtix/hvcGH1Vs7jzfp0xWd7fjdaqzY19jUengzDv0fO4P7Rswa1wr2bNkTgkD5oNbAX3KpVNkKU2iVHxmBBq16czwDFUikmn9qC+h3bGOV3q5VKRF67i4cnLuDWnqMGtYBsHOwR0PsDBA4OQcMuxm89cjnyw3Ic+X4557G6HQIx+eQWo42Mz0hIwqPTl/DvkTO4e/CUQa3wqg1qo9Xg3mg1qJdZRqBnJCZjYWCIxlkJY/atQbM+uo9T0QejViP61n08PHEBt/f9bdBNkNTWFo17dELgkN5o3L2TQV365XV+9RbsGPcN5zHvpg0x/eJei1xngpRWoZM2UPDF/1uPEXh0KozzeNcvxyHk55lGjyM7NR239hxF+JbQEuud60okFqPB++0QWNh6NOEH7NmlG1j+3kDOmw4nmTtmXg1Flbq1jBoDy7KIvnUf4Vv24/qOw8hM1H/qi0tlT7T8tCcCh/SB7zuNTdZ6ZFkWfw2ajOs7DnEebzuiP4asX2j0ePIys3An9ATCtx7A4zOXDFpkqE5QKwQO6Y3mH3eHo7vpWo+vHjzFojZ9OHtebBzsMT1sD2o2f9voccTdf4JrW0NxbdtBg3YZc/RwQ/P+PRA4pA9qt21u0h6MXZO/x9kVGzmPNfmoM8buXyuoZZqtVYVP2gCQk5aORW36aGypDdu4BG2G9TNZPMmRMbi27QDCt4Qi4an+CybYOjogcHAIOk/7DFXq+Zf9Bh6Ebw3FhiFfcB6rUs8fM6+GmqwLUK1S4dGpMIRvCcWdAycNaj1Wf6se3v9iJFoNDjFJy0eZl4dlnT5F5NXbnMf7LPoSwTPGGD2OQmmvEnB9xyGEb9mP2LuP9H6/1NYWzfv3QJcZo002fezB8XP4rccIo45T0RXDMIg4fxXhW0Jxa+/fBj3G8apdE50mDUe7kQNMspSqWqXC7x+NwoO/z3Ee7zJ9NPou5me1Q2I8VpG0gYJnpQsCQzhXQJPY2GDKmW2oG9TKpDGxLIuoG/cQviUUN3Ye1nskukgkQtOQYATPGA3/Ns2NFOX/O/TNUhz7aSXnsfrvtcWk45tMvplFXmYWbu8/jvAtoXjyz2W9p7O5VvXCe5OGI2jsYKPfdGQkJGFBqxDIo0uv8CcSiTBm/xoEhHxg1Bi4xP37GOH/tR7T4uL1fn+j4CAEzxyL+u+1NXrL8exvm7Br4necx3yavYXpYXtMvpa4IjcP9w6dQvjWA3hw/DwYPacSOXq4oeP4oeg4YajRN9TJzcjE4rZ98erBU87jg/9YgPajPjFqDKR8rCZpA0BE2DUsf38Q5/QOp0oemB1+AF61a5ohsoJu/IcnwxC+NRR3D5yEMk+/hWRqt2uBLjNGo0nPzkZ7Rs8wDNZ/MrFoK843BY3+FAPX/Gy2QUupcfG4vv0gwreE6j2f3s7ZCe1GDUDnL0YadSOYuH8fY1HbvsjPKr3egK2jA6Zf3APfZoZNRywvRq3G0/PhCN+yH7f2/s0ZozY+zd5C8IzReOfjHkYdO7Bjwrc4v2oz57GmIcEYs2+N2ZYQzkxKwY2dhxG+NRQv35gqWRapnS3aDOtr9B605JcxWNgqhLORIJZKMfnkZtTv1NZov5+Uj1UlbQC4smkvNg2fznmsaoPamHllv0mf1XHJzcgsaj0+PXtFr9Zjlfr+6DJ9NAIHhxhlBy1FTi6WdvwEUde5v5D6LZ2DzlNH8f579RV77xHCt4Ti2vaDes1jFkskaPFJTwTPGA3vpo2MEtu/R//B7x+N4uzmda9RFbOvHTT7mteKnFzcPXQK4VtC8fDEBb3m0stq1sD7X4xEu5EDjDL2Qq1SYVWP/+HhSe5xKnxNRyyv+CfPcW3bQVzbGqrXVEZT9KA9v3wTv7z3Kfd0RA83zLoaarJHb0Q/Vpe0AeDAV4twfP7vnMcadG6Picc2WMyexamxr3Htv9ajPksxulbxRKdJ/8O7n/Pf7Zv+OhELWvXiHIgjEonw+cE/0KRnZ15/p6EYtRpPzl5B+NZQ3N53XK/WY8MuQegyYzQadm7Pe+/BmV//wp4pczmP+TZ/G9Mv7Db5Uq2aZCQmF7Qet4Qi6sY9nd/n6OGGDuOGoNPEYbx3+5pjOqKhWJbF80s3EL41FDd3HUFOmu4r6hmzB+3a9oP4a9BkzmPGmI5I+GGVSZthGPzx8Tjc3n+c83jQmIEYuHqe2ZerK45lWcTefYhzq7YgfPN+naeP2Tk5ot2oAXj/i5GoVNObt3hi7jzAkvYfIz+79Kpgdk6OmHFpr9FaqobKz87B7X1/4/Sy9XoNvvIJaIQuM8ag+cfdebuZY1kWO8bNwYU12ziPN+vbDZ/tXmURO8UVF//4GS6s2YZL63dx/ttzkdrZovXQvug8bRSv6+4nvYjGwsAQs0xHNJQyPx/3Dp/BmWXr8eKK7vuqV6nvj87TPkPrIb157UE7/N0yHJ27gvNYvY6tMenEZrNu1ENKs8qkDRR8gS/tMEDjOs4fL/8W708eYeKodJMen4izKzfhwu9bdL5rF0skaD7gQwTPGA2fgLd4iePuoVNYEzKas/vew6c6ZocfMMvc8rKwLItHpy/i5KK1eHz6os7vk/nWwPtfjEC7UZ/w0u2rViqxsvv/NMbQ9avxCJk3o9y/xxiy5Wk4v3orzq7YqPP0O5FIhCa9uiB4xhjUbstPt++zi9ex/P1BZp2OaKhnl27g1OK1uHfotM6PwPjuQWNZFn9+OhE3dh3hPG6q6YhEd1abtIGCaS8LWvXiHDErEosx7tB6vN3jPTNEppu8rGxc+nMXziz7k3NEsiYNOrdH8IzRaNglqNwfxlNL/8C+6fM4j9Vs2RTTzu8y63aGZYm+fR+nlvyBm7uO6Pzc1tHdFe9+PhidJg2HW9Xy3ZSUNR1x+KalaD20b7l+hzEp8/JwdUsoTi/5Q6/pi/5tmyN4xmg0+ahLuXsTrm7Zj41Dp3IeM/V0REPEP3mO00vX4+rmfTrvKMhnD5oiNw/LOg7QOHCuz+KvEDx9dLl+B+GPVSdtAIi+dR9Lgj7m3PrQztkJMy/vM8lGFOWhVipxc88xnFq8Vq8Vm7ybNkT3byahWZ+uBidvlmWxdfSXuLR+J+fxdz7ugVE7V1pcN++bUqJicWb5X7j0x07du31tbdF6WF/0+HZSmduVamOJ0xH1xTAM7h0+jVOL1+H5pRs6v69KPX90/WocAof0KVcdOThnCf6e9xvnMXNNR9RXeXrQPvx+Srl6FNLjE7GgVQhSY16VOiYSiTAmdC0CegUbfH7CH6tP2gBw58AJrO0zlrOLSuZbA7OvHTD6/Ek+sCyLx2cu4dTitRpH1nKp074l+i2bY/DGASqFAiu7DsOTs1c4j3f/ZhI+msvdErI02anpuLB6K86u2ICMBN26fW0c7NFl+mgEzxxjcLd5xIVwLO882CKnI+rr+eWbOLl4Le4dPKVzt69Ps7fQb+nXBk81YhgG6wdMwK29xziPm3s6oj6KetB++RPyKN160CQ2Nug4YSi6fzPJ4F6F2HuPsLhdP4ucjkj+HyXt/5xYtAahsxZwHqvVuhm++GeHRXfzvin27kOcWvIHru84pHO3b+Dg3uj18wyDNg/IlqdhYeveSIyI5Dz+v63LETgoRO/zmosyLw/hWw/g1JJ1SHiiW7eva1Uv9Jo3A22G9TVoOcjLG/dg8/+4n2FXbVgHMy/vM/t0RH3EP3mOM8vW48om3bt9m/bqgj6LvjRoupEiJxdLOwzQOMK937I56PyF+acj6kqtUuHWnqM4uXgdYm4/0Ok9TjJ39PhuMjp8PtignoV7R85g9UejOG+2LGU6orWjpP0flmWxZeRMXN6wh/N4i096YuT2FYK4Uy9OHh2HM8v/wsU/duo03ak8rcaEpy+wsHVv5KSmlzomtbXFlH+2o067Fnqd09wYhsG/R87g1OJ1Oq8Z7xPQCH2XzkGD9/RvNYZ+uRAnFqzmPNawSxAmHNtglk1PyiMjIQnnftuMc6s2c9aNN4mlUnScMBQ9vpkEJ5m7Xr9LSNMRdWVID1qVev7os/hLNOnZWe/vrNO/rMfeqT9xHqvZoknBOBULmY5ojShpF6NSKPBrl8GIuHCN8/iH30/Bh99NMW1QPMlJS8eFNdvwz68bkBGfVGZ5Q1uNT85exq/BQzmXcnTxqoRZ4QdMss+wMby4egunFq/DndATOnX7NvmoM/ou/kqvViPDMFjX73PcCT3Befzdzwfj01U/Cu7mESjo9r38126cXrZep25fRw+3olajPtOOhDgdUVdFPWg7D+u0XGr9Tm3Qb9kcvWaMsCyL7WO/Qtg67n3M3+nXHaN2/Wbx41QqKkrab8hKScXCwBAkPY/iPD5y+69o+WkvE0fFH2V+PsK3hOLI98t1Wmfau2lD9Fv2jV6txovrd2LrZ7M5j1VrVBczL++Dg5urzuezNAlPX+Dvn1chfPP+MpO3WCpFh3GD0ePbyTovVJGfnYOl7/ZH9K37nMcHrPgenSYO1zNqy1HY7Xvo21+Q9OxlmeUr162FPou/RNOPuuh8s3Ln4Ems7T1G83TEawfKPfLfnOQxr3Bi4RqErdlW5uMvkUiENv/7GL1+mq7zFEy1UokVXYfhyT+XOY93+3oCev3EvbIkMS5K2hziHz/DojZ9OEdwSu1sMfXcTvi3fscMkfFHkZOLU0v/wIkFqzlHzr+pSc/O6LP4S50Xx9g342ecWrKO89hbXTtg3OE/BdfN+6boW/exd9pPeHruapllHT3c0OPbSegwbohOrcbUuHgsaNWLcwlWkViM8Uf+RONunQyK21KoFAqcW7UZx+au0Gm0dL2OrdFv2RydB0OdXLIO+2f8zHnMr1VTTD1n2dMRdRH/+Bn2z1yAe4dPl1nWzskRwbPGosu0z3Tq3s5OTcei1r01TuUbvnkZWg/po3fMpHwoaWvw+MwlrPhgKOddrEtlT8y+doDXFcbMJe1VAg7NWYIrG/fq1mr8fDB6fFd2q5FRq7GmzxjcO8T9ZdJp4nAMWPG9gVFbDpZlcffQKeyb/rNOrUavOn7ou/hLNO0VXGarUdt0RHsXZ8y4vA81Gtc3NHSLkZWSiqNzf8X537eW2eUrEonQelhf9Jo3o8wBUSzLYutns3Hpz12cx5v374GROyx/OqIuHp+5hD1Tf0TcvbI3ynGvURUh82ei1aCQMv/2hIhILGrdG9nytFLHpLa2mHJmG+q0b2lo2MQAlLS1CFu3HdvGcO8vW71xfcy4tBcOri4mjso4om/fx75p8zRO2yrO0d0V3b+dhI7jh2ptNeZlZWNJ+34alwz95Le56Dh+qMExWxKVQoHzq7fi6A+/6jTYql7H1ui3dA5839Hearwdehxr+4zlPCarWQOzrx2Ea2VPg2K2NPFPnmP/zPkab/SKs3V0wAezxqLztM+0bsWpUiiw4oOhGntDenw7CT1/EMZ0xLIwajWubNyLg3OW6DRupWaLJui3bE6ZawA8PX8Vv3YZwjkd0dlThlnhB+Dl72tw3EQ/lLTLsGfqjzjzy5+cxxp374Rxh9YbNL3HErEsi3uHT2Pf9J81Tt0qzquOH/osmo2AkA80thrlMa+woFUvzi8RsUSC8Uf/wlsfdCh37JYiW56Go3N/xblVW3hrNZ5YuBqhsxdyHvNv8w6++Ge7UXZ0M5fH/1zG3qk/6rQ+vHuNqgj5eQZaDe6tsdVY0aYjliUvMwsnF63FqSXrdNrit1nfbuizcLbWdQAub9iNzSNmch6r2rAOZl3ZL+hxKkJCSbsMjFqN1b0+w79H/+E8/t6UEej/y7cmjsq4VAoFLqzZhiPfL9ep1Vi/UxsMXr9Q4932y+t3sfTd/pxfIPauLph5ZT+qN6pb7rgtScLTF9g/cz7uHjxVZllbRwf0nDsV708ZwXkDyLIsNo+YgSsb93K+v+XAXhixdbkgR5RrwqjVuLJpHw5+vVjnVuPQDYs1Pi4oazriF2d38LYeuqWQx7zCwa8WI3xraJllJTY2+GDWWHT/ZqLG3rP9s+bj5KK1nMcaBQdh/FHhTUcUIkraOsjLzMLidv0Q9y/386KBq3/Cu2MHmzgq48uWp+HYTytxduWmMluNdk6O6Lv0awSNHsiZPG7uOYo/+o/nfK9nLR/MCj8AF69KvMRtSZ6cvYy9U3/SaXnZ2u1aYNjGJahcx6/UsbKmI/acOxU9vplU3nAtTl5W9v+3GnPztJaV2tqi59wv0GX6aM6bn8f/XC4Yp6JpOuK1A/D0E+Z0RG1eXr+LvVN/0mmdAe+mDTF88zJ4N2lY6hjDMFjbdyzuHjjJ+d4O44fi09+4t5sl/KGkraOUqFgsDAzhXNpSLJFg4vFNaNi5vRkiM76EiMiCVqOGD2txjYKDMOTPRZxrcR+b9xsOzVnC+b7a7VpgypltsLGzK3e8loZRq3F18z4c/HoJ0l8nai1r6+iAvku+wrtjB5e6+clKlmNh694apyOO2rkSLQb05C1uS6JPq9G/zTsYtmkp51rc2qYjVn+rHmZc3ldhxqkUx7Isbu37G6Ez5yM5MkZrWYmNDXr+MAVdZowp1XLOz87BkqCPNa7QNmDlD+g0YRhvcZPSKGnrITL8NpZ2GMC5JKODmwtmXQ1F1QZ1zBCZaTw5d6Wg1VjGkooObi4YsPIHBA7uXSLxsCyLjUOnavziDRzSB8M3La1Q3bzF5WVl49TitTi5uOxWY8MuQRjy58JSS8rGP36Gha17Izc9s9R7bOztMPXcTtQKbMZr3JZE11ajjYM9+iycjQ7jh5Z61r13+jycXvoH5/ve6tYR4w6tr7DdvMr8fJxduQnHflyJvIzSdai4WoEBGLZpaalpnqmxrwumI3LcgIrEYkw4+hfe6tqRz7BJMZS09XRj12Gs/2Qi5zGv2jUx62oonD1lJo7KdBiGQfiW/Tjw1WLOOcTFNQ0JxqA180pstqLMz8fy9wdp3Amq17wZ6PYVdzd6RZEa+xoHv16Mq5v3ay1n7+qC/r9+izbD+pW4kXl0+iJWdh3GOR3RtYonZl87CJlvDd7jthQsy+L2/uPYP3M+kl9Eay1bv1MbDN2wuMT0TEatxpreYzTObe40aTgG/Po9nyFbnMykFBz54dcyF2exsbdDyIJZ6DRxeImbn6gb97Dk3f6cN58VaTqiJaKkbYCjc3/F4e9+4TxWJ6gVJp/aUiG7eYvLy8zCvunzNC51WMjZU4aBa+bhnb7dil7LTErBwsAQjd10n+35Hc37dec1XksUEXYNm4ZPLzPxNOnZGYPW/VxiBa8La7Zi++dzOMvXaNIAMy7uhb2LM6/xWhplXh4OffsLTi9Zp3WNAXsXZ3z8yzdoO6J/0c1PmdMRV/2IjuOGGCVuSxJ9+z42Dp2GV/efaC1Xt0Mghm1YUmIJ4lv7/sa6fp9zlq/k541Z4QcqzHRES0JJ2wAsy+KvQZNxfcchzuOth/XFsA1LKmw3b3EPjp/D5pGzymx1txzYC5+s/KFoA4hXD55iUdu+nF10Ng72mHZ+l8FbhQpJXlY29s+cjwurt2ot5yRzx6erf0KL/h8Wvbb7i7n4Z/lfnOXf/vB9fH5gXYWZjqjNs0s3sGnYNI3P+gs17t4Jg/9YUDS9rqzpiBOObUCj4HeNErMlUebn48j3y3Fy0VqwDKOxnJ2zE/otm4P2oz4p+m47Pn8VDny1mLO8f9vm+OLMtgo1HdESUNI2kDIvD7+8NxAvrtziPN57wSx8MIv7LrSiyU5Nx+5J35c5SMitWmUMXr8Qb3cvWH7zwYnz+K37/zi/KFyrehV08xqwTagQPTx5AVtGzuLcnaq4FgM+xCerfoRzJY8ypyN2njoK/ZZyt8YrmvzsHOyftQDnV23WWs7Rww2frpqLFp98BJFIhMhrd7CswwCrmo6oyYsrN7Fx2PQy12h4q2sHDF6/EB41qoJlWWwaPk3jo55Wg0Lwvy2/WEUDxlQoaZdDRkISFgSGaNyxaMy+NWjWp6uJozKf2/uPY/vYr5GZlKK1XLtRn6Df0q/h4OqCc6s2Y+cE7nnuPgGNMC1sj95bhApVTlo6dk+Zi6ub9mkt51rVC4PXzUeTnp3LnI44aO3PCBo90BjhWqRHpy9i84iZSI15pbXcO/2649Pff4SLVyXc2H0E6wdM4CxXkacjclHk5CL0y4U4u2Kj1nKO7q4Y8NtctBrYq2A6YufBGgcHfvTjNHSfwz0OiOiPknY5xd1/gsVt+yIvM6vUMRsHe0wP24Oazd82Q2TmkZGYjB2fz8Ht/ce1lpPVrIFhGxajfqe22DnxO5z7bRNnuSYfdcbY/Wutopu30J2DJ7Ft9FfITCw9vbC4tv/7GB//8g1y0jKwoFUIZ3mxVIpJxzehwfvtjBWuxclNz8CeL37E5Q17tJZzqeyJQet+RkCvYBz7aSUOfbOUs1yd9i0x+fTWCj9OpbgnZy9j0/9mlLmFarM+XfHp6p8gFouxIDBE4/iMUbt+K/FohxiOkjYP/j12Fr/3HMnZzetWvQpmXzsIjxpVzRCZebAsi2vbD2LXhG/L3L2p06Th+OjHafij/3g8PHGBs0yXGWPQd9GXxgjVYmUly7H98zm4tfeY1nIePtUx9K9FsHN2xLKOn3BOR3R0d8XMq6E679BWUdw7cgZbP5td5opqrYf2wcfLv8Wuid/j2rYDGssM21hxpyNyyc3IxN5p83Bp/U6t5Vy8KmHgmnmo2rAOFrXRMh3x/C7UahVgpGitByVtnvyzYgN2T/6B85jvO40x7cJurRsbVESpcfHYOmoWHhw/r7Vc5bq1MHj9Auz4fA5eP4zgLDNk/UK0GznAGGFaLJZlcWPXYewY902Zy8l2mjgcNVs1wcYh3JtfeNXxK5iOqOOe3hVFVkoqdk74Fjd2HtZazr1GVQzbsBiHv1+OF5dvcpYJ+XkGun5Zsacjcvn32FlsHTWrzIWBAof0wTv9umFtn7FWOx3RFChp84RlWewY/43GUcABvT/A6L2rK8Q2gPpgWRYX1+/E3qk/IT8rW2M5Gwd79J4/E8d++g1ZyfJSx8VSKSaf2oL6HdsYM1yLlP46EVs/m61xwFmhOu1bolbrZhr3Ma/bIRCTT27RaT/viubG7iPYMe4bZKekaiwjlkrx0U/TELZmG1JexnKWGb13dYnpi9YiW56GnRO/w/XtB7WW827aEM36dcPhb5ZxHreW6YjGREmbR2qlEr/1GIFHp8I4j38w+3P0nj/LxFFZhuTIGGz633REnA/XWq7lwF64vfdvqBQc3bwebpgVfoBzecqKjmVZXN6wB3umzOUcP1HIvUZVVG9cT+OjhrYj+mPI+oVW1c1bKD0+EdtGf6VxUZVCzfp2w6OTF5CXWfom08bBHtMv7EbNFk2MFaZFu7n3GHZ8PofzxrqQUyUP1O3QCnf2n+A83qRnZ4wNta5xKnyipM2znLR0LGrTB/GPn3MeH7phMdoO/9jEUVkGhmFwduVGHJi9UOuWgTWaNEDcPe7R0FXq+WPmlf1F872tTUpULDaPmIkn/1zWWEZqZwsPn2pIesY9b7nPoi8RPGOMsUK0aCzL4urmfdg16Qety3hWbVAbCU8jucepVKtcME6FY319a5CRkIRtY7/WuheBSCxGlfr+iH/0jPN452mfod+Sr40VYoVGSdsIkp5HYUFgCGdXnMTGBlNOb0XddwPNEJlliH/yHJuGTUNk+B2NZRw93DQ+x63fqQ0mndgMiY2NkSK0bAzD4PzvW7B/5nyta5jbuzhztspFIhHG7F+DgJAPjBmmRZPHvMLmETPx+PRFjWXs3VyQxzGoCrC+6YhvYlkW4VtDsWvid5wDzwo5uLsiV8Ng1EHr5iPos0+NFWKFRUnbSCLCrmH5+4OgVipLHXOSuWNW+AHOLRithVqpxN7p87TOBxVLpRq3BG036hMMXjffKrt5C8X9+xirQ0ZrXQZVLJFwDgqydXTA9It74NussTFDtGgMw+DYjytw5PvlGsuIxGKNq4Q17dUFY/avtbpxKsUlvYjGmpDRGtcJADR/jsVSKSad2IwG77U1ZogVjvXWNiOrG9QKg/+Yz3ksW56GVR+OQHYZI4IrMomNDQb8+j2G/rVI48AobXt4X1q/E2d++dNY4QlCjbcb4Mvrh9BAy5awmjaDUOTk4veeo5BWxvKzFZlYLMaH303B2NC1sNPQYta2rOfdg6dw4MuFxgpPELz8fTHj8j400zI4T9PnmFGpsK7vWCQ8fWGs8CokStpG1GZYP3T9chznsYQnL/BH//GcLXFr0vZ//TH1/E64VatcduE37Js+r8xBRRWdk8wdE//eiM5TR+n93rS4ePz+0SgocnKNEJlwBIR8gFlXQ+FlQM/XyUVrcemv3fwHJSD2zk4Yved3fPTTdL17vnLSMvBbjxHI0jKqn5RE3eNGxjAM/vh4nMYVwoLGDMTA1fOsupsXANJeJWBt37GIvHpbr/fZOTlixqW98G7ayEiRCcfVLfux9bPZnAusaNOsbzd8tnuVVXfzAgVr6P/5yQQ8PMk9+0MTa56O+KZ7h0/jr0FTtM5w4FKvY2tMOrHZKqcj6su6P6UmIBaLMXzzMvhqWMo0bO12/LNig4mjsjzu1atg6rmdaDuiv17vy8/Owaqeo8pc+MEatB7SBzMu7tV7VPPtfX9rXMLTmjh5uGHCsY3ooufI+oJu3s+RUMZGG9agSc/OBeN19JyW+fTcVWz/fI7WLVZJAWppm0jaqwQsaNULaXHxpY6JxGKMO7Qeb/d4zwyRWRaWZXFu1WbsmTJX4/NYLjVbNsW087tg60DbAKbHJ2Jdv3F4fumGXu8bvmkpWg/ta6SohOXa9oPYMnKm1qmJb6pSzx8zr4bCycPNiJEJQ05aOv4cOBkP/j6n1/v6LP4KwdNHGyeoCoKStglF37qPJUEfcz5DtHN2wszL+1Dj7QZmiMzyPDl3BX98PF7rIg5veufjHhi1c6XVd/MCgEqhwK6J3yFs3Q6d3yOxscGUM9tQN6iVESMTjuhb97E6ZHSZO4YVV/+9tph0fJPVTkcsjlGrcXDOEpxYsFrn94hEIowJXYuAXsFGjEzY6NvNhHzfaYwR25ZzPr/Oz8rGqg9HIiNB++YG1qJ+xzb48sYh+ATo/qz61p6jWqfvWBOprS0GrZ2Pgat/glgq1ek9aqUSa3qPQdJz7kVZrI3vO43x5Y1DqKPHTcyTfy5j54RvqZsXBdMNe8+fhVE7V8JGxx4wlmXx18DJiL5938jRCRclbRMLCPkAIQu4lzKVR8dhdchoKLQsmGFNKtX0xoxL+9BigO5b+h37cQXCNezUZI3eHTsYX/yzHS6VPXUqn52SilU9RyInzXqnIxbnWtkTU05vRYdxQ3R+T9i6HTiz3LqnIxbXYkBPzLy8D7Kaum0UQtMRtaPucTNgWRZbRs7UuN9vi096YuT2FVY/orwQy7I4uWgNDny5SKcWjNTWFlP+2Y467VqYIDphkMe8wpqQ0Yi+pVsLpmGXIEw4tgESHVvp1iDsjx3YOf5bnaZpikQifH7wDzTp2dkEkQlDZlIK/ug/Hk/PXdWpfM0WTQrGqTg6GDkyYaGWthmIRCIMXDMPdd/l7na7sfMwjs791cRRWS6RSIQPZn2O8Uf/goObS5nlVQoF1vYeg+TIGBNEJwwyn+qYfnEvWg0K0an8o1Nh2DXpe+rmLSbos0/xxdkdcK1Sdq8Fy7L489NJiL370ASRCYOLVyVMPrkFnSYO16l81I172DhsGhgtC9xYI2ppm1FWSioWBoZofIY4cvuvaPlpLxNHZdkSnr7A6l6fadyQpbhqjepi5uV9cHBzNUFkwsCyLE4vW4/9M+drXe2r0IAV3+v8JWstUmNfY02fsYi6frfMsh4+1TH72gG4VdV/8aCK7PKG3dg+dg7nbn5v6vb1BPT6aboJohIGStpmFv/4GRa16YMcjkX1pXa2mHpuJ/xbv2OGyCxXbkYm1vYZi8dnLpVZ9q2uHTDu8J/UzfuG+3+fxdq+n2vdcAQomI44/sifaNytk4kiEwZlXh42DJmKW3uPlVnWr1VTTD1H0xHf9PzyTazq8T/O7743Dd+8DK2H9DFBVJaPusfNrGqDOhi9dzXn3rKqfAVW9xqNlKhYM0RmuRxcXTD+yJ94+8P3yyz74Ph57J36kwmiEpbG3Tph0onNsHdx1lqOZRisHzARcfefmCgyYbCxt8fIHSvQeljZ89pfXruLTcOpm/dNtds2x9RzO+HiVanMsltHzcazi9dNEJXlo6RtARq83w6f/v4j57HMxGSs+nAkcrXs/WuNbOztMXb/GjTv36PMsmdXbsS5VZtNEJWw1A1qhSlntsGxjMVA8jKzsOrDEchITDZRZMIgkUox9K/FOo0sv7n7KI7+sNz4QQmMd9NGmHZhF9xrVNVaTqVQFExH1LKjnbWgpG0hgkYPxPtfjOQ89ur+E/z56SS9VgizBhIbG4zcvgJthvcrs+zuyT/gwYnzJohKWPxaNi1o7ZQxJUweFYc1IaOhzKPpiMWJxWJ88ttcnZY+PTqXpiNyqdqgDqaH7YFnLR+t5bKS5Vj14QjkppfdnV6RUdK2IH0Xf6VxKdP7x85i7/R5Jo7I8oklEgz5cxE6ThimtRyjVuOP/hPw6mGEiSITDu8mDTE9bHeZa5a/uHILm0fOohHlbxCJROizcDZ6zp1aZtktI2bi+eWbJohKWDxr+WDahd2oUt9fa7n4R88KdkfUsm1vRUdJ24KIJRKM3LFC41Km/yz/CxfWbDVxVJZPLBZjwIrv8cHsz7WWy8vIxKoPRyAzKcU0gQlIlXr+mBa2G161a2otd337QRz7aaWJohIOkUiEHt9MQr+lc7SWUykUWB3yGZJf0nTEN3l4V8O0C7vh3bSh1nIPT4Zh95S5JorK8lDStjD2Ls4Yd3i9xrmgOyd8h0enL5o4KssnEonQe/4s9Jo3Q2u5lMgYrOk9Gsp83TeCsBaefgWtnaoN62gtd/jbZbix67CJohKWzlNHYdDan7UujJSVJMeqHiNonAoH18qe+OLsTtQKDNBa7vyqzTj72ybTBGVhKGlboEo1vfH5wT8gtSu9tyyjVmNdv88R//iZGSKzfN2+Go+Pf/lGa5nnl25iyyjq5uXiXr0Kpp3fBZ9mb2ktt3HoNESG67f3ubUIGj0Qwzcv45wRUuj1wwir7+bVxMnDDZNPbUXdDoFay+2e/D0eHD9nmqAsCCVtC1UrsBmGb+Le4zg3PROrPhyp1w5Y1uT9KSMx+I8FWstc23qAunk1cPGqhC/+2Q7/NprXB1ApFFjZbTjk0XEmjEw4Agf3xmd7VmndrOXhiQvYPekHE0YlHPYuzph4bCPe6tpBYxmWYbGmz1irm45ISduCtRjQU+PglqTnUVjTZyx182rQftQnGLn9V4i0bNN5+NtluLb9gOmCEhBHdzdMOrkF9Tu10VgmJzUdSzsMQF5mlgkjE45mvbti/OH1nD1mhc6v3oJTS/8wYVTCYevogLEH1qFZn64ayyhz8/BLp0+sajoiJW0L133ORLQcyL2U6bOwa9g25ivq5tWg5ae9MGb/Gq17G28YMhXPL98wYVTCYe/shPFHN2ic0QAAKS9j8cv7A2k6ogZvde2ISSc2a930Yt/0ebhz8KQJoxIOGzs7jNr1GwK1rIaWlZyKxe36Ws10REraFk4kEmHonws1dlVe3bQPJxetMXFUwhHQKxjjj/4FiS134mYZBr+8P4hG82pg62CPMfvXoFm/7hrLRF2/h/WfTjRhVMJSr0NrfHF2B+xcnDSWWdf3c9pDWgOJVIphG5cgaMxAjWWSnkXh1w+GWkUDhpK2ANjY22PsgXWo5OfNeTx09kLc3n/cxFEJR6MuQZhyehukdnacx1V5+ZjfoidyqZuXk9TWFqN2rEDLgR9pLHNrzzEc+GqRCaMSllqtAjDj4l7Yu3LvUseo1VjS/mOkvaY9pLmIxWIMXD0P700eobHMswvXsHmE9tkjFQElbYFwreyJcYf/1LhW9F+DpyDq5r8mjko46ga1wvSw3RqfL2anpOHnd3rQaF4NJFIp/rdlOQKH9NZY5vj833Hxr10mjEpYvJs0xOxrB2Dvyv0ZVuTkYl5Ad+Tn5po4MmEQiUT4+JdvEKxl9bkrG/dW+AGmlLQFpEbj+hi1ayXn4Cplbh5+/2gUUuPizRCZMPi1bIrpYXs0TsVJehaFX9771MRRCYdYLMbwTcu0DgzaNmo2Humw+5q1qlq/Nr66eQQ29ty9PpmJKZjf/EPaXEQDkUiEPou+RIfxQzWWOfTNUlzbftCEUZkWJW2BadytE/ov/5bzWPqrBKz+aBTys3NMHJVw+LVsivFH/gQ0LH7xLOw6NgyZUuI1a3hOpiuRSITP9vwO/7bNOY+zLIvfug3Dq4dPS71OClSu44fpFzXfPMY/eo4VwSU3IaHrV9InK39AQEiwxuMbhkxBRNi1Eq9VlGtISVuAOk4YpnFnoehb97FhyBdFd+pXt+ynrT3f8FbXjhj650KNx8O3HsChb5YAAF7e/BcLW4eYKDJhEIvFmHZuJyrXq8V5XK1UYUGrXkhPSAbLstj0v+k4s/wvE0dp2Wo2b4Jxh9drvHl8fOZS0fPZxOdR+CmgmynDs3gikQij961BrdbNOI+zDIvlnQch4ekLAMD+WQsQOlv72g1CIWIryu2HlVGrVFjV4394eDKM83jwzDGQ2Njg73m/YfimpWg9tOx9f63N0bm/4vB3v2g83nbkAFzduBeMWo1laffg6OZqwugsX35OLr6u1R5ZidxruTt7VUKV+v54fvE6/Nu8g5mX95s4QssX9scObBv9pcbjrQb3xs3dR6BWKPHd49OoVl/7ErPWRq1S4bsG7yH5OfeWnXbOTqjbMRD3j/yDyvVqYe6TsyaOkH+UtAUsJy0di9r2Rfwj7Uuaths5AEPWa25ZWrPNo2bi8p+7yyz3yaof0VGHfZOtTUZiMub4B0GRrX3wlK2TI1ZkPTRRVMJy6JulOg2e6jJjNPou+soEEQlLfnY2vqrZHtkpqVrLiSUS/Jb/VOvyskJA3eMC5ujuhvFH/oJTJQ+t5SIuXNN63JoNXb8I9d9vW2a5W3uPmSAa4XGt7InZ4Qe1LtcJAIrsHJoLr8FHP05Dq0EhZZZ78Pc5o8ciRHZOTvjm3nHYONhrLceo1Xh+SfgLKVHSFjgvf198fmAdpLaal0pMjIhE+utEE0YlLJNPbkXVBrW1lomm6XQaVX+rHiYc26Dx+Wyhqxv3mSgi4RmxdTlqt2+htUz8kxc0qlwD9+pVMPPyvjJb0Vc27jVRRMZDSbsCqNO+JYLGal4tCECpkZTk/4nFYkw9vwsiseakk5eRhdTY1yaMSlgadQkqs7V47/Bp0wQjUJNObNG4ch8AMEoVoq7fM2FEwuIT8BaCZ43VWubJ2SsmisZ4KGkLHMuyOLlkHc6t1L63bMT5cBNFJDxJL6LxfYP3wDLah3fc2HXERBEJz8GvF+Pa1lCtZV49eFphpt3wLTMpBd/W7Qi1Qqm1XPg27dfYmp35dQOOz/9da5mUqDjBr1FOSVvgUqJicf/oP2V+GT4Lu26iiITn7IoNyMvILrPc3UOnTBCN8KQnJCF824Eyy6nyFbQPvAYX1+1AVlLZW+0+Pk0L13DJzczCxXXbgLJuCllW8N+FlLQFztPPB1PP7sScO8fQbuQAjSstxf37GNnyNNMGJxD9l3+H5ZkP0G3OBDjJ3DWWi71No5+5uFXxws8vL+HLm4dRr1Mbrduh3txDA/q4dPt6AlZkP0TvhbPhWtVLY7nEiEjaUY2Dg4szvntwGt8/Oo3G3TtBLNX8bPv6zsMmjIx/gp7yxbIs1Go11P9VYolEAolEAlEZA2IqsqxkOS6u34nzq7aUegY77tB6NOnZucRrDMNApVKBYRiIxeKia2itWJbFg7/P4cDXixF7p3SSnh97FR41qpYoX7wOisViSKVSq66DuRmZODp3BS7+sQN5GSU3YfFrFYDZ4QdKvMYwTNE1pDpYICLsGkJnLcCLq7dLtR6/vHEINZs3Kfrv4nWQZdmi6yfWcvNU0SlycnFy8VqcXbkR2SlpJY55+vvip+cXSrwmpDooyKStUCiQmpqK1NRUqN7Y4EEqlcLDwwMeHh6w1TKiuqJTK5W4c+Akzq7YiGcXC7qDukwfjb6LC/bfzsrKglwuR2ZmZqn3Ojk5QSaTwcXFxao/+ClRsQj9chFu7/0bamXBs8ZPf/8RHT4fApVKhdTUVMjlciiVJZ9DisViyGQyeHh4wE7DzmLWgGEY3Nx9BIe/+wWJTyMBADYO9liR/QgAkJ2djdTUVKSnp5d6r4ODA2QyGdzc3Ky6DqbHJ+Lwt8sQviUUyrx8AEDwjNHos+grqFQqpKenIyUlBQqFosT7xGIx3N3d4eHhAQcHzXt5V3Qsy+L+32dx4KvFiLtbUO9EYjFW5j6GxMYGubm5kMvlSE9PL/WI0d7evqgOWlICF1TSzs/Px+vXr5GVpdsWik5OTqhevbpVf3ECBUubnl25EUnPo/G/0NVISkoqdbPDRSwWw9PTE15eXlbdclTk5uGfXzfg9LI/0OCDdxE8byoyMjJ0eq+joyOqVatm1V+cABB3/wn2z5yPiAvhmH7rCHJZVambHS6FN0CVK1e26uStUihwcf1OHP95Fao0qouP/1qAtLQ0nd5rb2+PqlWrwtmZe3cxa5H4PAoHZi/Av0f+wYSz2yGu5Ir8/Pwy3ycSieDu7o5q1apZRB0UTNLOzs5GVFSU3vMUxWIxfH19rb7CsiyLqBeRyMrVfzMRZ2dn+Pj4WNTdpjnk5OTg5cuXetdBkUgEb29vuLm5GSkyYWBZFrHR0Ujn6N0pi6OjI3x9fSEtYxGXii4/Px+RL15AZcBz7erVq0MmkxkhKuFgWRYJr14jObXsQX9vsrOzg5+fH2xsNE/LMwXz3zboIDs726AvS6Cgi+7ly5c6t84rIpZlERsba1DCBoCsrCxERUVZ9XSdvLw8g+sgy7KIiYnh7Aa2Jq9fvzYoYQMFN0yRkZFWvbiIQqHACwMTNgC8evUKcrn+yaoiSU5ONihhAwU3TC9evNCpl9KYLD5pq9VqxMTElDthxMbGmv1i80GZn48XV2/p9R5Nzw31kZOTg8TEirGqWsydB8hN1617Gyi48YuJiSl3woiLiyv17FGonp6/qtdnMiMjo9wJo/DxWEXw6mEEMpO4N1rhUnjjrS7nyPFXr14hT+DzlAtFXAjXqw7m5OQgISGhXL9TqVQiLi7OrA0YvZL2/Pnz0bJlS7i4uKBy5coICQnBkydPjBUbACAxMZGXZKtSqSpE0lHlK7CoTR+s7DZMp+StVqt5+6JLSkrS6RmQpbu9/wS+9muPoz+u0Cl5p6Sk8PJ3MwyD+Pj4cp/HEqzsOgxL3+2Px2culfkFxjAM4uLiePm9qampyMkR/n7xT/65jDm1ghA6e4FOyTstLY23v5uvfwtz2zB0Kn5u/iHuHjpVZh0svOnhQ2ZmJucAXlPRK2mfP38e48ePx9WrV3Hq1CmoVCoEBwcjO7vshSkMoVaree3OSU1NLfedqqV4cPy8Tsk7NTWV17vCitK9lpOWgcPfLiszebMsi5QU3VtEZcnIyKgwre1nF69jeedBZSbvjIwMXj93fP57mFN+dg5OLFxTZvJmWRbJycm8/d7c3Fzk5mrflU0oYm4/wOpen5WZvLOzs3n93PH576Gvcg1ES0pKQuXKlXH+/Hm8++67fMYFAEhPT0dMDL87AyliEnFphfYlPy2ZWqXG49MXS73+VtcO6PHdZPi3fqfE68+ePeO1O0wikeDCNyugyBZuayfhaSSSX5Tcf9fR3RXvTx2F9yYNh0OxfbOzs7MRGRnJbwBpWTg7bzW/5zSxhyculPqCrNO+JT78fgrqv9e2xGyDqKgo3lsmd1ZsQ2rMK17PaUopUXGlttS1c3JExwlD0XnaZ3DxqlT0en5+PiIiInj9/VKFGqe+XsbrOU3t6bmrRdPgCvk0ewsffj8FTXp2LlEH4+LikJqqfetOfTVo0MAsAyPLlbSfPXuGunXr4t9//0Xjxo35jAsAEB8fz/sdTcbzWGwO0b6ovJAVT94Mw+DhQ/5X8drRezxSnkXxfl5L8GbyTk5O5r1LW5GchnWdBvN6TktSPHkDwOPHj3nv4To1fRGenLhQdkEBejN5p6am8t6lzebmY1Wrvrye05K8mbwjIiJ4f7RXs2ZNuLi48HpOXRictFmWRa9evZCamoqwsDC+4wIAREdH6zwfVlc5r5PxV/BwXs9pid7q2gHdvp+MPCf+F5g5Mu57vAwT/r602hQm77eHfIQMnmceqLJysabNx7ye0xLVad8SPb6fAnUV/qe6XV78J25trtibZxQm7xZjPkF6Nr91kFWpsapZL17PaYkKk7e0dnXeB49Vq1YNlSpVKrsgzwwePT5hwgTcu3cPO3bs4DMewhN59Cukx5VvpKQm1rDQSl5mNlJexkKVb4TnzxX/8gEA0uLiITdaF3bFv4iKnFykvIyFIqdijPY2h4z4JMijKsbAu0IGdchPnDgRhw4dwoULF+Dt7c13TEWMMYldnSf80c/aVGtUFz2+nYR3+nUHRCKjdI9n67AbkVCJJRK0HtYX3b6eAC9/XyQmJiKH51kHjDFuBCyIZy0fdJszEa2H9IZYKsWjR494n1+dW4E3vxGJRGjevwe6fzMJ1d+qB7lcjtxXPN/8qCrGgFxN3KpVRtcvx6H9Z5/Axt4eT5480WkFPn2Ya5EVvZI2y7KYOHEiQkNDce7cOdSqVctYcQGAUZZ+FCnVqNqgNu/nNRVGzSAxovTAqOLJWlxs5TI7Ozten+WIxWI4ODsL+hpmJsmRnVJyUMqbybqQo6Mj779frGIFff0AIP7x81KvFU/WkmJfaA4ODrzPMJFCJOhrmC1PR2ZiyfE6bybrQkapg4zw62Disygwb0wHfjNZF3JyctJ52VddmWtpYr2eaY8bNw7bt2/HwYMHUb9+/aLX3dzcjPIHqFQqPH78mNdz1q9f3+zL0JVHbkYmvnB7u+i/NSXrQgkJCUhKSuLt97u7uxu1d8UUDn27DMd+XAFAc7IuxDAMHj9+zGtLsU6dOrAv9oUiRBMd6heN3NWUrAulpKTwuiiKs7Mz/Pz8eDufOZz9bRN2TfwOgOZkXYhlWTx9+pTXlqKfn5/gl3b+yq9dUde3W7XK+GD252j/2aewdSj92eJ7JpK9vT3q1KnD2/n0oVdLe/XqgmkqHTt2LPH6hg0bMHz4cL5iKiKVSuHu7s7bHZKbm5ugE3ZxZSXrQjKZjNekbY6BF8ZQVrIuKvffhhV8zWJwcnISfMIuVFayLuTu7o6EhATebnw8PT15OY+5lZWsi5erVKkSb7MY7Ozs4OTkxMu5zK2sZF3I1dUVUqmUt1UxzVkHLX7DEKVSiYiIiHJ/4MViMerUqSP47ToVuXm4d+hUmcm6uMTERF5Wg/Pw8ECNGjXKfR5ze3LuCmS+NbQm6+LUajUiIiJ4+cBXhFY2AFzdsh8tP+mpNVkXJ5fL8YqH57IuLi7w9fUV/GDIF1dvwd7FWWuyLo5hGDx//pyXR121atWqEEn72vaDCOj9gdZkXVxGRgaio6PLLlgGBwcH+Pv7m60OWnzSBvi52D4+Pla7yxLLsoiMjCzXMoi2traoU6eORWxNZw58LLJirikilqBw05TyTOGUSqWoU6eO1e70lZeXh+fPn5dr6pKXlxeqVKnCY1TCUt5NUyyh8SeIb2BXV9dyPUetUaOG1SZsoKB7rWbNmgbfXdvb26NWrVpWm7CBgm7tmjVrGnx3XbVqVatN2MD/b0/q6upadmEOtra28Pf3t9qEDZT/c+jl5YXKlSvzHJWwVKtWDR4eHga9VyqVwt/f3+y9tYJoaRfKyclBbGyszmvI2traokaNGhWiK4gPLMsiISEBKSkpOt+tu7u7o3r16ladsIvLy8tDbGyszkvDSqVS1KhRwywrJ1miwnW0k5KSdH7k5erqiho1alj9fu6FFAoFYmNjde45k0gkqFatGtzd3Y0bmICkpKToNc7C2dkZNWrUsIgxUYJK2kDBhz43NxdyuRzp6emlko9IJIKrqytkMhkcHR0F/+zLGNRqNdLT0zXuXmVrawuZTAZ3d3erbtlok5ubi9TUVI0bshTWQScnJ6qDHBiGQXp6esEcZI7NK6RSKWQyGTw8PCzii9IS5eXlFdVBruTj7OwMmUwGFxcXqoMcGIZBZmYm5HI555REiURSVAfN3bouTnBJuzi1Wo38/HwwDAOWZSGRSGBnZ0d35HrIz8+HSqUCwzAQi8VF15A+5LphGAb5+flQq9VFddDW1pZudvSgUCigVCrBMAxEIhGkUinVQT1w1UEbGxu62dGDUqmEUqmEWq2GSCSCRCKBvb29RdZBQSdtQgghxJrQg0pCCCFEIChpE0IIIQJBSZsQQggRCErahBBCiEBQ0iaEEEIEgpI2IYQQIhCUtAkhhBCBoKRNCCGECAQt20QI4YVd888gEksgEksgtrGFWCyBWGoLkVhc8JrUFiJJwXGJ1Pb/Xyv2nsKfJVJxwcpUUjFEYhHEIhHEUjHEYhFEYkAiKXi98P///2dALBZBLCkoK5WKIRGLIBGLYCsVQ/rf/0vE4qLXJKKC43bSYq8Ve0/RzxIxxCIRbCQiSESiop/FIhEkIsCm8Ph/5cUiwEYshkSM/14v+FkiFkEMEUQiFBwr/FmEonOJRAXv5/q5sJxIBIgYNUQsA7AMUPgzo/rvvxmIGBXAsgBb+PN//88wAMsWvQaWAatSAowarFJRcJxRg1UpwTLq/15XFr2GwtcK36NWA/+VZZQqsGoGLMOAUajAqEu+pla8cZxhwCiUYBkGrPq/40U/q8EybMH/q1kwahZqZeHPDBgFA0bNQK0sKM/+d5xRswU///c+JcNAzQJqloWCYYv9zPUaCwYlXy/4/4Kf17Avzfo5o5Y2IYQQIhCUtAkhhBCBoKRNCCGECAQlbUIIIUQgKGkTQgghAkFJmxBCCBEIStqEEEKIQFDSJoQQQgSCkjYhhBAiEJS0CSGEEIGgpE0IIYQIBCVtQgghRCAoaRNCCCECQUmbEEIIEQhK2oQQQohAiFiWZc0dBCGEAEB+fj7mz5+PL7/8EnZ2duYOB4BlxgRYZlwUk/FR0iaEWIyMjAy4ubkhPT0drq6u5g4HgGXGBFhmXBST8VH3OCGEECIQlLQJIYQQgaCkTQghhAgEJW1CiMWws7PDd999Z1EDhiwxJsAy46KYjI8GohFCCCECQS1tQgghRCAoaRNCCCECQUmbEEIIEQhK2oQQQohAUNImhFiE6dOnIygoCIMGDYJCoShxLDc3Fx9++CE6dOiALl26QC6Xmz2mQvPnz0eLFi3MFotKpcLw4cMRFBSEyZMnGz0OXWIqZKpro0tM5qpDfKOkTQgxu9u3byM+Ph5hYWFo1KgR9u7dW+L433//jcaNG+P8+fPo378/tmzZYvaYACAzMxP37983ayyHDx+Gt7c3wsLCkJOTg8uXLxs9nrJiAkx3bXSNyRx1yBgoaRNCzO7KlSsIDg4GAHTt2rVU4qlbty5ycnIAAGlpafDy8jJ7TADw66+/Yvz48WaNRZc4TR0TYLpro2tM5qhDxiA1dwCEEJKWlobq1asDANzc3Ep1XdauXRv3799H48aNIRKJEB4ebvaY0tPT8e+//2LOnDlmjSUtLa1oIwyuOM0Rkymvja4xmaMOGQO1tAkhJhMfH4/27duX+h/LssjIyABQ8MUrk8lKvG/Tpk3o2LEj7t+/jx9++AFz5841e0zLly/HhAkTeItDGw8PD42xaDtmrphMeW10jcmYdciUKGkTQkymatWquHjxYqn/de/eHSdPngQAnDhxAu3atSv13sIvYHd3d6SlpZk9pmfPnmHevHno2rUrIiIisGDBAt5ielPr1q01xqLtmDFp+72mvDa6xgQYrw6ZFEsIIRZg2rRpbPv27dmBAwey+fn5LMuy7OjRo1mWZdn09HS2e/fubIcOHdh27dqxT548MXtMxTVv3tzksRTGoVQq2aFDh7Lt27dnJ06caPQ4dImpOFNcG11iMlcd4hutPU4IIYQIBHWPE0IIIQJBSZsQQggRCErahBBCiEBQ0iaEEEIEgpI2IYQQIhCUtAkhhBCBoKRNCCGECAQlbUIIIUQgKGkTQgghAkFJmxBCCBGI/wN0M2bKoUH6zwAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "<Figure size 600x300 with 1 Axes>"
       ]
@@ -245,7 +245,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1800x500 with 1 Axes>"
       ]
@@ -298,7 +298,7 @@
     {
      "data": {
       "text/plain": [
-       "<tigramite.models.Prediction at 0x7f157c03a400>"
+       "<tigramite.models.Prediction at 0x7fad057d7820>"
       ]
      },
      "execution_count": 6,
@@ -336,7 +336,7 @@
     },
     {
      "data": {
-      "image/png": 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ERORAYmJi4O/vL6slJydj//79Fn0frU4gbk8yjE00Vdbi9iTX6VQUp5+IiIgcgBACLi6GYxXWWgx8IjXPYIRG9r4ANPklOJGap9/sz9o4UkNERGTnfvrpJ4NA8/bbb1v17qbswqoDTU2uswSO1BAREdmx5s2b4/r167JaUVERGjRoYNX39fdWVX+RGddZAkdqiIiI7FBRUREkSZIFmpCQEAghrB5oACAqzBeBahUM76+qIKHiLqioMF+r91KJoYaIiMjOrFixAt7e3rLa0aNHkZaWVmc9uLpIiB0eAQAGwaby+9jhEXB1qSr2WB6nn4iIiOyIsb1nlNoZeEiHQKydEGmwT02AQvvUMNQQERHZgV9//RUdOnSQ1ebMmYOVK1cq1FGFIR0CMSgiACdS85BdWAJ/74opp7ocoanEUENERGTj+vTpgyNHjshqubm58POrm1ulq+PqItXZbdv3w1BDRERko+7evQt3d3eDuqMdRGkpXChMRERkgz799FODQPP1118z0NwHR2qIiIhsjLHFwFqt1uiOwfQ//NMhIiKyEVeuXDEINE8//XSVRyCQHEdqiIiIbEDXrl2RmJgoq6WlpSEkJEShjuwPQw0REZGCdDodXF1dDepcO2M+jmUREREpZNu2bQaBZunSpQw0NcSRGiIiIgX0798fBw8elNVKSkrg4eGhTEMOgKGGiIioDhUUFECtVstqbm5uKCsrU6gjx8HpJyIiojry0UcfGQSalJQUBhoL4UgNERFRHbj3Vm0XFxdotVqFunFMHKkhIiKyogsXLhgEmm3btjHQWAFHaoiIiKzk2WefxWeffSarFRcXo379+lU+R6sTNnHitT2yq1Bz6NAhrFixAqdPn4ZGo8GuXbswcuRIpdsiIiKSKS0thUqlktWefPJJ7Ny5877PS0jSIG5PMjT5JfpaoFqF2OERGNIh0Cq9OhK7mn4qLi5G586d8cEHHyjdChERkVFffvmlQaA5d+6cSYEmOj5RFmgAICu/BNHxiUhI0li8V0djVyM1Q4cOxdChQ5Vug4iIyCi1Wo2CggJZTafTGT2g8s+0OoG4PckwtuWeACABiNuTjEERAZyKug+7GqkxV2lpKQoKCmRfRERElpaRkQFJkmSfM6tWrYIQotpAAwAnUvMMRmj+TADQ5JfgRGqeJdp1WA4dapYtWwa1Wq3/Cg4OVrolIiJyMHPnzkWLFi1ktZs3b2LWrFkmv0Z2YdWBpibXOSuHDjULFixAfn6+/isjI0PploiIyEFotVpIkoS3335bX3vwwQchhEDDhg3Nei1/b1X1F5lxnbNy6FDj4eEBHx8f2RcREVFt7d+/H/XqyZelHj58GCdOnKjR60WF+SJQrUJVE1USKu6CigrzrdHrOwuHDjVERESW1qFDBzzyyCOymlarxUMPPVTj13R1kRA7PAIADIJN5fexwyO4SLgadhVqioqKcPbsWZw9exYAkJqairNnzyI9PV3ZxoiIyOHl5uZCkiT8+uuv+trChQshhICLS+0/Tod0CMTaCZEIUMunmALUKqydEMl9akwgCSGM3UFmkw4ePIj+/fsb1J977jls3ry52udXnoyan5/PqSgiIjLZihUr8OKLL8pq169fR1BQkMXfizsKGzL189uu9qnp168f7CiDERGRnTM2ChMQEACNxnob4bm6SOgZ7me113dkdjX9REREVFdOnz5tEGh2795dbaDR6gR+TrmBr85ex88pN6DV8R/jdcWuRmqIiIjqwtChQ5GQkCCrlZaWwt3d/b7P49lNyuJIDRER0f8pKiqCJEmyQDN16lQIIUwKNDy7SVkMNURERAA2bdoEb29vWe3y5cv4+OOPq31udWc3ARVnN3Eqyro4/URERE7P2PlM5tyYYs7ZTVwEbD0cqSEiIqd1+fJlg0CzefNms++05dlNtoEjNURE5JSmTJmCTZs2yWpFRUVo0KCB2a/Fs5tsA0MNERE5lbKyMnh4eMhqQ4cOxd69e2v8mpVnN2XllxhdVyOhYmdgnt1kXZx+IiIip7F7926DQJOYmFirQAPw7CZbwZEaIiJyCv7+/sjJyZHVdDqd0UXCNVF5dtO9+9QEcJ+aOsNQQ0REDu369eto3ry5rLZy5UrMmTPH4u81pEMgBkUE8OwmhTDUEBGRw3r55ZexbNkyWS03Nxd+fta7rZpnNymHoYaIiByOTqeDq6urrNapUyecO3dOoY6oLnChMBER2QVTD4o8dOiQQaA5cOAAA40T4EgNERHZPFMPiuzatSsSExNlzy0vLzcIOeSYOFJDREQ2zZSDIm/evAlJkmSBZt68eRBCMNA4EY7UEBGRzaruoEgJwIyXXsNv36yVPZaRkWFwxxM5PoYaIiKyWfc7KFIIgd/eGi6rNWzYEDdv3qz1+2p1grdl2yGGGiIisllVHQBZln0Nmk2zZLWdO3fiySefrPV7mrp+h2wPQw0REdksYwdAZn/5Gu5cPS6rHfz1OvpGBNX6/SrX79w73VW5fmfthEgGGxvGhcJERGSzKg+KlADo7pbgt+WPywJNg/b90WPp93iobe2DRnXrdwAgbk9ylbeSk/IYaoiIyGZVHhRZ9OsBZLzzlOyxoGlr0eTxORY7KPJ+63eAimCjyS/BidS8Wr8XWQenn4iIyKYN7Wg4rRQy/2uLr3Opav1OTa+jusdQQ0RENiklJQWtWrWS1ea//g56DhtrlTuSjK3fqc11VPcYaoiIyObMmDED69evl9UKCgrg7e1ttfesXL+TlV9idF2NBCBAXRGmyDZxTQ0REdmMu3fvQpIkWaAZMGAAhBBWDTTA/9bvABUB5s8qv7fU+h2yDoYaIiKyCXv37oW7u7usduLECfzwww911sOQDoFYOyESAWr5FFOAWsXbue0Ap5+IiEhxLVq0QEZGhqym0+kgSXU/KjKkQyAGRQRwR2E7xJEaIiJSTFZWFiRJkgWapUuXQgihSKCp5OoioWe4H0Z0aYae4X4MNHaCoYaIiBSxZMkSBAbKp3Oys7OxYMEChToie8fpJyIiqlM6nQ6urq6yWps2bXDp0iWFOiJHwZEaIiKqM0ePHjUINPv27WOgIYvgSA0REdWJXr164eeff5bV7t69i3r1+FFElsGRGiIisqr8/HxIkiQLNLNmzYIQgoGGLIqhhoiIrGbNmjVo2LChrJaWloZVq1Yp0xA5NEZkIiKyOCEEXFzk/2728PBASQkPgyTr4UgNERFZVFJSkkGg2b59OwMNWR1HaoiIyGLGjh2LL774Qla7c+cOVCrbP9laqxPcRdjOMdQQEVGt3blzB/Xr15fVxowZgx07dijUkXkSkjSI25MMTf7/RpMC1SrEDo/geU92pMah5vbt20hPT0dZWZms3qlTp1o3RURE9mP79u0YN26crJaUlIT27dsr1JF5EpI0iI5PhLinnpVfguj4RB5kaUfMDjU5OTmYPHkyvv32W6OPa7XaWjdFRET2wcPDw+Aft0odRFkTWp1A3J5kg0ADAAKABCBuTzIGRQRwKsoOmL1QOCYmBjdv3sSxY8fg6emJhIQEbNmyBa1bt8bu3but0SMREdmYtLQ0SJIkCzQffvih4gdRmutEap5syuleAoAmvwQnUvPqrimqMbNHavbv34+vvvoKDz74IFxcXBASEoJBgwbBx8cHy5Ytw7Bhw6zRJxER2YgXXngB77//vqx269YtqNVqhTqquexC0+7IMvU6UpbZoaa4uBj+/v4AAF9fX+Tk5KBNmzbo2LEjEhMTLd4gERHZhvLycri5uclqvXv3xpEjRxTqqPb8vU27K8vU60hZZk8//eUvf9EfPNalSxesX78e169fx7p16wyOkCciIsewb98+g0Bz9OhRuw40ABAV5otAtQpVTZhJqLgLKirMty7bohoye6QmJiYGGo0GABAbG4vBgwdj27ZtcHd3x+bNmy3dHxERKaxNmza4cuWKrKbVag022LNHri4SYodHIDo+ERIgWzBcGXRih0dwkbCdkIQQxhZ9m+z27du4ePEiWrRogcaNG1uqL6soKCiAWq1Gfn4+fHx8lG6HiMim5eTk6JcbVFqyZAliY2MV6sh6uE+NbTP189vsUPPqq69i7ty5Bpss3blzBytWrMDixYtr1nEdYKghIjLN0qVLsXDhQllNo9EgICBAoY6sjzsK2y6rhRpXV1doNBqD9H7jxg34+/vb9D41DDVERPdn7CDKFi1a4LffflOoIyLTP7/NnhCtag+Cc+fOwdeXC6mIiOzViRMnDALN3r17GWjIbpi8ULhRo0aQJAmSJKFNmzayYKPValFUVIQZM2ZYpUkiIrKuAQMG4MCBA7JaWVmZwR1PRLbM5FDz3nvvQQiBKVOmIC4uTrbJkru7O0JDQ9GzZ0+rNElERNZRWFhoMJwfHR2NNWvWKNQRUc2ZHGqee+45AEBYWBh69erF9E5EZOc2bNiAv/3tb7JaSkoKWrZsqVBHRLVj9j41ffv21f/6zp07uHv3ruxxLsAlInvg7He6GFsbWcsdPogUZ3aouX37Nl588UXs2LEDN27cMHjclu9+IiICnHtPkosXL6Jdu3ayWnx8PJ599lmFOiKyHLPvfpo3bx7279+PNWvWwMPDAx9//DHi4uIQFBSETz/91Bo9EhFZTEKSBtHxiQYnM2fllyA6PhEJSRqFOrO+v/71rwaBpri4mIGGHIbZoWbPnj1Ys2YNnnrqKdSrVw99+vTBokWLsHTpUmzbts0aPRIRWYRWJxC3JxnGJlkqa3F7kqHVOdY0TGlpKSRJQnx8vL42YsQICCEMNlIlsmdmh5q8vDyEhYUBqFg/k5eXBwB46KGHcOjQIct2R0RkQSdS8wxGaP5MANDkl+BEal7dNWVlO3fuhEolP2H67Nmz+M9//qNMQ0RWZPaampYtWyItLQ0hISGIiIjAjh07EBUVhT179qBhw4ZWaJGIyDKyC6sONDW5ztY1bNgQ+fn5sppOpzO6SJjIEZg9UjN58mScO3cOALBgwQL92pp//vOfmDdvnsUbJCKyFH9vVfUXmXGdrcrIyIAkSbJAU7nXGAMNOTKzR2r++c9/6n/dv39/XLx4EadOnUJ4eDg6d+5s0eaIiCwpKswXgWoVsvJLjK6rkQAEqCtu77ZX8+bNw8qVK2W1vLw8NGrUSKGOiOqO2aHmXi1atECLFi0s0QsRkVW5ukiIHR6B6PhESIAs2FSOX8QOj7DL/Wq0Wi3q1ZP/SO/WrRtOnjypUEdEdc+kUPP++++b/IKzZs2qcTNERNY2pEMg1k6INNinJsCO96k5cOAABgwYIKsdOnQIffr0UagjImVIwoQtJCvvdqqUk5OD27dv6xcG37p1C/Xr14e/vz+uXbtmlUYrrVmzBitWrIBGo0H79u3x3nvvmfw/rqlHlxOR43OUHYU7duyIpKQkWU2r1Rqctk1kz0z9/Dbpv/rU1FT91xtvvIEuXbrgwoULyMvLQ15eHi5cuIDIyEi89tprFvsNGLN9+3bExMRg4cKFOHPmDPr06YOhQ4ciPT3dqu9LRI7H1UVCz3A/jOjSDD3D/ewu0Ny4cQOSJMkCzcsvvwwhBAMNOS2TRmr+LDw8HP/+97/xwAMPyOqnT5/GU089hdTUVIs2+Gfdu3dHZGQk1q5dq6+1a9cOI0eOxLJly6p9PkdqiMgRrFy50uBu0+vXryMoKEihjoisy9TPb7MXCms0GoNDLIGK4c4//vjD3JczWVlZGU6fPo2XXnpJVn/00Udx9OhRo88pLS1FaWmp/vuCggKr9UdEZG3GRmH8/f2t+rOXyJ6YPUb5yCOPYPr06Th16pT+RNdTp07h//2//4eBAwdavMFKubm50Gq1aNq0qazetGlTZGVlGX3OsmXLoFar9V/BwcFW64+IyJoSExMNAs1XX33FQEP0J2aHmo0bN6JZs2aIioqCSqWCh4cHunfvjsDAQHz88cfW6FHm3o2j7reZ1IIFC5Cfn6//ysjIsHp/RESW9thjj6Fr166yWmlpKZ544gmFOiKyTWZPPzVp0gR79+7FlStXcOHCBQgh0K5dO7Rp08Ya/ek1btwYrq6uBqMy2dnZBqM3lTw8PODh4WHVvoiIrKW4uBheXl6y2uTJk7Fx40aFOiKybTXefK9169Zo3bq1JXu5L3d3d3Tt2hXfffcdnnzySX39u+++w4gRI+qsDyIy5Ci3R9uSzZs3Y/LkybLa5cuX6/TnLpG9qfWOwnVp9uzZ+Otf/4pu3bqhZ8+e+Oijj5Ceno4ZM2Yo3RqR00pI0hhsZBdoxxvZ2QJjU+pm3qhK5JTsajODp59+Gu+99x5effVVdOnSBYcOHcLevXsREhKidGtETikhSYPo+ERZoAGArPwSRMcnIiFJo1Bn9unKlSsGgWbjxo0MNEQmMnufGnvGfWqILEerE3ho+X6DQFOp8nDII/MHcCrKBNOmTcMnn3wiqxUWFhqsqSFyRhbdUfjP0tPTjf6rQQjBnX2JnMiJ1LwqAw1QcVikJr8EJ1Lz6q4pO1RWVgZJkmSBZsiQIRBCMNAQmcnsUBMWFoacnByDel5ensEZUUTkuLILqw40NbnOGe3evdvgDs1Tp07h22+/VagjIvtm9kLhqvaFKSoqgkqlskhTRGT7/L1N+//d1OucTUBAgMHGeTqdrsp9t4ioeiaHmtmzZwOoWJX/yiuvoH79+vrHtFotjh8/ji5duli8QSKyTVFhvghUq5CVXwJjC/Mq19REhfnWdWs2LTMzE82aNZPV3nrrLYOznIjIfCaHmjNnzgCoGKk5f/483N3d9Y+5u7ujc+fOmDt3ruU7JCKb5OoiIXZ4BKLjEyEBsmBTOdYQOzyCi4T/ZOHChVi6dKmslpubCz8/P4U6InIsZt/9NHnyZKxatcou7x7i3U9Elsd9aqqn0+ng6uoqq7Vv3x5JSUkKdURkX0z9/K71Ld0FBQXYv38/2rZti7Zt29bmpayOoYbIOrijcNUOHz6Mhx9+WFb74YcfMGDAAIU6IrI/pn5+m71QeOzYsXj44Yfx97//HXfu3EG3bt2QlpYGIQT+9a9/YfTo0bVqnIjsj6uLhJ7hnEK514MPPohTp07JauXl5QajNkRkGWbf0n3o0CH06dMHALBr1y4IIXDr1i28//77eP311y3eIBGRvbl58yYkSZIFmrlz50IIwUBDZEVmh5r8/Hz4+lbczZCQkIDRo0ejfv36GDZsGK5cuWLxBomI7Mk//vEP/c/ISunp6VixYoVCHRE5D7Onn4KDg/Hzzz/D19cXCQkJ+Ne//gWg4l8m3KeGiJyVEAIuLvJ/J3p7e6OgoEChjoicj9kjNTExMXj22WfRvHlzBAYGol+/fgAqpqU6duxo6f6IiGze4cOHDQLNqlWrGGiI6pjZIzUzZ85EVFQUMjIyMGjQIP3/yC1btuSaGiJyOv7+/gZHxxQXF8s2KCWiulHjW7rLysqQmpqK8PBw1KtndjZSBG/pJiJLKSwsNPg5Eh4ejqtXryrUEZHjstop3bdv38bUqVNRv359tG/fXn8y96xZs/Dmm2/WvGMiIjvx5ptvGvxgPX78OAMNkcLMDjULFizAuXPncPDgQdnC4IEDB2L79u0WbY6IyNZIkoQFCxbIakIIREVFKdQREVUyO9T85z//wQcffICHHnpIdppsREQEUlJSLNocEZGtOH/+vMEJ2vPnz0ctN2UnIgsyezFMTk4O/P39DerFxcUG/8MTETmCnj174tixY7LajRs3DPajISJlmT1S8+CDD+Kbb77Rf18ZZDZs2ICePXtarjMiIoWVlZVBkiSDQCOEYKAhskFmj9QsW7YMQ4YMQXJyMsrLy7Fq1Sr8+uuv+Pnnn/Hjjz9ao0ciojq3adMmTJkyRVbbu3cvhg4dqlBHRFQds0NNr1698NNPP2HlypUIDw/Hvn37EBkZiZ9//pmb7xGRQzA2la7Vag022CMi21LjfWrsEfepIaL7SU9PR0hIiKz27LPPIj4+XqGOiAiw4j41rq6uyM7ONqjfuHGDp88Skd16+umnDQJNeno6Aw2RHTF7+qmqgZ3S0lK4u7vXuiEiorqk0+mM/oPMiQaxiRyGyaHm/fffB1Ax1/zxxx/Dy8tL/5hWq8WhQ4fQtm1by3dIRGQl33zzDR5//HFZbcuWLZg4caJCHRFRbZgcat59910AFf96WbdunexfNu7u7ggNDcW6dess3yERkRUYWwxcVlYGNzc3BbohIkswOdSkpqYCAPr374+dO3eiUaNGVmuKiMhabty4gcaNG8tqDz30EA4fPqxQR0RkKWYvFD5w4AADDRHZpXnz5hkEmqSkJAYaIgdh9kJhIiJ7I4QwuscMFwMTORbuJEVEDu3nn382CDRvvfUWAw2RA+JIDRE5rLCwMKSlpclqhYWFsrs3ichxMNQQkcMpLi42CC7NmjXD77//rlBHRFQXTAo1v/zyi8kv2KlTpxo3Q0RUW++88w7mzJkjqx05cgS9e/dWqCMiqismhZouXbpAkiQIIYzu7fBnWq3WIo0REZnL2M8nnU5X7c8tInIMJi0UTk1NxbVr15Camoovv/wSYWFhWLNmDc6cOYMzZ85gzZo1CA8Px5dffmntfomIDFy4cMEguMTExJj0DzEichwmjdT8+ZC3MWPG4P3338djjz2mr3Xq1AnBwcF45ZVXMHLkSIs3SURUlQEDBuDAgQOyWnZ2Npo0aaJQR0SkFLMXCp8/fx5hYWEG9bCwMCQnJ1ukKSKi6ty9e9foIbq8VZvIeZm9T027du3w+uuvo6SkRF8rLS3F66+/jnbt2lm0OSIiY7Zt22YQaL766isGGiInZ/ZIzbp16zB8+HAEBwejc+fOAIBz585BkiR8/fXXFm+QiGyTVidwIjUP2YUl8PdWISrMF64u1l+/YmyNjFarNbpjMBE5F0nU4J82t2/fRnx8PC5evAghBCIiIjB+/Hg0aNDAGj1aTEFBAdRqNfLz8+Hj46N0O0R2KyFJg7g9ydDk/2/ENlCtQuzwCAzpEGiV97x+/TqaN28uq40aNYo3KBA5AVM/v2sUauwVQw1R7SUkaRAdn4h7f3BUjp+snRBpEGxqO6ozadIkbNmyRVa7du2a0fV9ROR4TP38rtGOwlu3bsX69etx7do1/PzzzwgJCcG7776Lli1bYsSIETVumohsm1YnELcn2SDQAIBARbCJ25OMQREB+tBSm1EdHkRJROYwexJ67dq1mD17NoYOHYqbN2/qN9tr1KgR3nvvPUv3R0Q25ERqniyc3EsA0OSX4ERqHoD/jerc+5ys/BJExyciIUlT5Wvt27fPINBs2LCBgYaIqmR2qFm9ejU2bNiAhQsXol69/w30dOvWDefPn7doc0RkW7ILqw40915X3agOUDGqo9UZXuHh4YHBgwfLaiUlJZg2bZqZHRORMzE71KSmpuKBBx4wqHt4eKC4uNgiTRGRbfL3Vpl8nbmjOgBw8+ZNSJKEsrIyfS0yMhJCCHh4eNS4byJyDmaHmrCwMJw9e9ag/u233yIiIsISPRGRjYoK80WgWoWqlvhKqFgvExXma9aoDgAsWrQIvr6+ssfOnj2L06dP16JjInImZi8UnjdvHp5//nmUlJRACIETJ07g888/x7Jly/Dxxx9bo0cishGuLhJih0cgOj4REiCbWqoMOrHDI+DqIpk1qmNs7xl7XDuj1N49RFTB7FAzefJklJeX48UXX8Tt27cxfvx4NGvWDKtWrcK4ceOs0SMR2ZAhHQKxdkIkluz+FVkFpfp6Ux8PLHmivf6OpqgwXzSs74Zbt+9W+VoeN1PRq9Xjstprr72GRYsWWad5K1Ji7x4ikqvRLd3Tp0/H9OnTkZubC51OB39/f0v3RUQ2794RCPNGJDI/nom7N9JlNXvdQ6qqvXsq7/IytncPEVme2WtqBgwYgFu3bgEAGjdurA80BQUFGDBggEWbIyLbU/kBnlUgXzPzR4H8Nu0TqXlGR2l0d0vx2/LHZYGmYcOGEELYZaCpzV1eRGRZZoeagwcPyu5MqFRSUoLDhw9bpCkisk3mfIAbWyhcmPg1Mt4ZLau9tmEHbt68aflm60hN7vIiIuswefrpl19+0f86OTkZWVlZ+u+1Wi0SEhLQrFkzy3ZHRDbFnA/wexcK/7b8cYPrW7y4B4/072npNuuUuXd5EZH1mBxqunTpAkmSIEmS0WkmT09PrF692qLNEZFtMecD/PFOQQhUq/DbhXPQbJ0je9yry1A0Hvw8Av7v9m97Zs5dXkRkXSaHmtTUVAgh0LJlS5w4cQJNmjTRP+bu7g5/f3+4urpapUkisg3mfIC7ukg49vJAg8eaP78V9bwaAfjf7d/2rHLvnqz8EqPTchLgEOGNyB6YHGpCQkIAADqdzmrNEJFtM/UD/IHm3kb3ngmZ/zXwf9c4yq3O5uzdQ0TWZfZC4WXLlmHjxo0G9Y0bN2L58uUWaYqIbFPlBzhQ9Q3drX7/Fp4q+ZEGS+LicPRqLlaN64LPp/fAkfkDHCLQVKrcuydALR/JClCr6ux2bq1O4OeUG/jq7HX8nHKDd1uRU5KEmdt2hoaG4rPPPkOvXr1k9ePHj2PcuHFITU21aIOWVFBQALVabbd7YRDZiqo2mjM23VRWVgY3N7e6bE8xSu0ozI3/yNGZ+vltdqhRqVS4cOECwsLCZPVr164hIiICJSW2u8KfoYbIcv78Aa4tyMHovoYH3drjUQf2pqqN/yqjFDf+I0dg6ue32dNPwcHB+OmnnwzqP/30E4KCgsx9OSKyU64uEnqG+yFh/esGgeb48eMMNHWAG/8RyZl9TMK0adMQExODu3fv6m/t/uGHH/Diiy9izpw51TybiByFEAIuLob/LmKYqTvm7BvUM9yv7hojUojZoebFF19EXl4eZs6cqd9ZWKVSYf78+ViwYIHFGyQi25OWlmYwBf3hhx9i5syZCnXknLjxH5Gc2aFGkiQsX74cr7zyCi5cuABPT0+0bt0aHh4e1T+ZiOze8uXL8dJLL8lqt2/fhqenp0IdOS9u/EckV6NTugHAy8sLDz74oCV7ISIbVlpaCpVK/uHI0RllceM/IjmTQs2oUaOwefNm+Pj4YNSoUfe9dufOnRZp7F5vvPEGvvnmG5w9exbu7u76k8KJyPoOHTqEvn37ymrXr1/nzQEK48Z/RHIm3f2kVqv1u4Oq1er7fllLWVkZxowZg+joaKu9BxEZGj16tCzQPP744xBCMNDYCFvY+I/IVpi9T43SNm/ejJiYmBqN1HCfGiLTZWVlITBQ/oH4ww8/GD3QlpSn1MZ/RHXB1M/vGq+pISLHtX79esyYMUNWu3PnjsGaGrIdlfsGETkzk0LNAw88YPRwOmMSExNr1ZAllZaWorS0VP99QUGBgt0Q2b7y8nI0a9YM2dnZ+tprr72GRYsWKdgVEZFpTFpTM3LkSIwYMQIjRozA4MGDkZKSAg8PD/Tr1w/9+vWDSqVCSkoKBg8ebNabL1myBJIk3ffr1KlTNfqNARWHb/55vU9wcHCNX4vI0SUmJsLNzU0WaK5evcpAQ0R2w+w1NdOmTUNgYCBee+01WT02NhYZGRlGT/CuSm5uLnJzc+97TWhoqGzI25w1NcZGaoKDg7mmhugeM2bMwPr16/Xf9+jRA0ePHjV5hJaIyJqstqbmiy++MDp6MmHCBHTr1s2sUNO4cWM0btzY3BZM5uHhwU0Bie7j5s2b8PWV72Gya9cujBw5UpmGiIhqwexQ4+npiSNHjqB169ay+pEjR6y6iDA9PR15eXlIT0+HVqvF2bNnAQCtWrWCl5eX1d6XyFHt2LEDTz/9tKzGUUwismdmh5qYmBhER0fj9OnT6NGjBwDg2LFj2LhxIxYvXmzxBistXrwYW7Zs0X//wAMVpwIfOHAA/fr1s9r7EjkaIQQ6deqEpKQkfS0mJgbvvvuugl0REdVejfap2bFjB1atWoULFy4AANq1a4cXXngBY8eOtXiDlsR9asjZXbp0CW3btpXVfvnlF3Ts2FGhjoiIqmfq57fdbb5XGww15MwWLlyIpUuX6r8PCwvDlStX4OrqqmBXRETVs+rme7du3cK///1vXLt2DXPnzoWvry8SExPRtGlTNGvWrMZNE5HlFRcXG6w727JlCyZOnKhQR0RE1mF2qPnll18wcOBAqNVqpKWlYdq0afD19cWuXbvw22+/4dNPP7VGn0RUAwkJCRg6dKislpOTY9W7DomIlGLS5nt/Nnv2bEyaNAlXrlyR3e00dOhQHDp0yKLNEVHNCCEwYMAAWaCZMGEChBAMNETksMweqTl58qRsk65KzZo1Q1ZWlkWaIqKaS09PR0hIiKx29OhR9OzZU6GOiIjqhtkjNSqVyugZSpcuXUKTJk0s0hQR1cw777wjCzQNGjRAWVkZAw0ROQWzQ82IESPw6quv4u7duwAASZKQnp6Ol156CaNHj7Z4g0RUvbKyMri7u2POnDn62rvvvouioiK4ubkp2BkRUd0xO9SsXLkSOTk58Pf3x507d9C3b1+0atUK3t7eeOONN6zRIxHdx08//QQPDw/9PzQAICMjAzExMco1RUSkALPX1Pj4+ODIkSPYv38/EhMTodPpEBkZiYEDB1qjPyK6j3HjxmH79u367wcPHoyEhAQFOyIiUo5Zoaa8vBwqlQpnz57FgAEDMGDAAGv1RUT3kZ2djaZNm8pq+/btw6BBgxTqiIhIeWZNP9WrVw8hISHQarXW6oeIqrFx40aDQFNcXMxAQ0ROz+w1NYsWLcKCBQuQl5dnjX6IqAparRbBwcGYOnWqvrZ48WIIIVC/fn0FOyMisg1mr6l5//33cfXqVQQFBSEkJAQNGjSQPZ6YmGix5oiowrlz59ClSxdZ7fLly2jdurUyDRER2SCzQ82IESMgSZI1eiEiI/7xj3/ggw8+0H//wAMP4PTp0/z/kIjoHjylm8hG5efno2HDhrLaF198gaeeekqZhoiIFGLq57fJa2pu376N559/Hs2aNYO/vz/Gjx+P3NxcizRLRHI7d+40CDS3bt1ioCEiug+TQ01sbCw2b96MYcOGYdy4cfjuu+8QHR1tzd6InI4QApGRkbLduWfOnAkhBNRqtYKdERHZPpPX1OzcuROffPIJxo0bB6DixN/evXtDq9XC1dXVag0SOYsrV66gTZs2stqZM2cMFggTEZFxJo/UZGRkoE+fPvrvo6KiUK9ePWRmZlqlMSJnEhcXJws0zZo1Q3l5OQMNEZEZTB6p0Wq1cHd3lz+5Xj2Ul5dbvCmiuqTVCZxIzUN2YQn8vVWICvOFq0vd3Fl0584dgz1mNmzYgGnTptXJ+xMRORKTQ40QApMmTYKHh4e+VlJSghkzZsj2qtm5c6dlOySyooQkDeL2JEOTX6KvBapViB0egSEdAq363t9//73BLsB//PEH/P39rfq+RESOyuRQ89xzzxnUJkyYYNFmiOpSQpIG0fGJuHdPg6z8EkTHJ2LthEirBZuhQ4fKDp4cO3as7GBKIiIyn8mhZtOmTdbsg6hOaXUCcXuSDQINAAgAEoC4PckYFBFg0amo69evo3nz5rLa4cOH8dBDD1nsPYiInJXZZz8ROYITqXmyKad7CQCa/BKcSLXcGWfvv/++LNC4ubmhtLSUgYaIyEIYasgpZRdWHWhqct393L17F97e3njhhRf0tRUrVqCsrMxg8T0REdWc2Wc/ETkCf2+VRa+ryrFjx9CzZ09ZLS0tDSEhIbV6XSIiMsSRGnJKUWG+CFSrUNVqGQkVd0FFhfnW+D0mTpwoCzT9+/eHTqdjoCEishKGGnJKri4SYodHAIBBsKn8PnZ4RI0WCefm5kKSJGzdulVf27t3L/bv38+TtYmIrIihhpzWkA6BWDshEgFq+RRTgFpV49u5t27diiZNmshqRUVFGDp0aK16JSKi6nFNDTm1IR0CMSgioNY7Cut0OrRq1Qqpqan62ksvvYRly5ZZumUiIqoCQw05PVcXCT3D/Wr8/KSkJHTs2FFWu3DhAtq2bVvb1oiIyAycfiKqhTlz5sgCTUREBLRaLQMNEZECOFJDVAOFhYXw8fGR1T7//HOMGzdOoY6IiIihhshMu3fvxogRI2S1vLw8NGrUSKGOlKfkSedERJUYaohMJIRAz549cfz4cX1t+vTp+OijjxTsSnlKnnRORPRnDDVEJrh27RrCw8NltZMnT6Jbt24KdWQblDzpnIjoXlwoTFSNpUuXygJNkyZNcPfuXacPNNWddA5UnHSu1Rm7gojI8hhqiKpQUlICSZKwcOFCfW3t2rXIzs5GvXoc5FTipHMiovvhT2YiIw4cOIABAwbIahqNBgEBAQp1ZHvq8qRzIiJTcKSG6B4jRoyQBZqRI0dCCMFAc4+6OumciMhUHKkh+j8ajQZBQUGy2oEDB9CvXz9lGrJxlSedZ+WXGF1XI6HiHK3anHRORGQOjtQQoWKtzL2B5s6dOww092HNk86JiGqCoYacWnl5Ofz8/DBz5kx9benSpRBCQKXitEl1rHHSORFRTXH6qZa4k6r9OnXqFB588EFZ7dq1awgLC1OoI/tkqZPOiYhqi6GmFriTqv2aNm0aPvnkE/33vXv3xuHDhyFJ/CCuidqedE5EZAmcfqqhyp1U792no3In1YQkjUKd0f3k5eVBkiRZoNm9ezeOHDnCQENEZOcYamqAO6nap88//xx+fvLRhIKCAgwfPlyhjoiIyJIYamqAO6naF51Oh7Zt22L8+PH62pw5cyCEgLe3t4KdERGRJXFNTQ1wJ1X7ceHCBURERMhqSUlJaN++vUIdERGRtXCkpga4k6p9eOmll2SBpnXr1tBqtQw0REQOiiM1NcCdVG1bUVGRwbTS1q1bMWHCBIU6IiKiusCRmhrgTqq265tvvjEINLm5uQw0REROgKGmhriTqm0RQuDhhx/G448/rq9NmjQJQgiDO56IiMgxcfqpFriTqm347bffEBoaKqsdO3YM3bt3V6YhIiJSBENNLXEnVWW99dZbmD9/vv57tVqNnJwcuLm5KdgVEREpgaGG7FJpaSnq168PnU6nr61evRp///vfFeyKiIiUxFBDdufw4cN4+OGHZbXff/8dzZo1U6gjIiKyBVwoTHZlzJgxskDz2GOPQQjBQENERBypsSVaneCi4yr88ccfCAgIkNW+//57PPLIIwp1REREtoahxkYkJGkQtydZdqZUoFqF2OERTn97+IYNG/C3v/1NVrt9+zY8PT0V6oiIiGwRp59sQEKSBtHxiQaHZGbllyA6PhEJSRqFOlOWVqtFQECALNDExcVBCMFAQ0REBjhSozCtTiBuT7LR4xYEKnYojtuTjEERAU41FXXmzBlERkbKalevXkV4eLhCHRERka3jSI3CTqTmGYzQ/JkAoMkvwYnUvLprSmEzZ86UBZoHH3wQOp2OgYaIiO6LIzUKyy6sOtDU5Dp7duvWLTRq1EhW+/LLLzFq1CiFOiIiIntiFyM1aWlpmDp1KsLCwuDp6Ynw8HDExsairKxM6dZqzd9bVf1FZlxnr7744guDQJOfn89AQ0REJrOLkZqLFy9Cp9Nh/fr1aNWqFZKSkjB9+nQUFxdj5cqVSrdXK1FhvghUq5CVX2J0XY2EikMyo8J867q1OiGEQOfOnXH+/Hl9bdasWVi1apWCXRERkT2ShBDGPktt3ooVK7B27Vpcu3bN5OcUFBRArVYjPz8fPj4+VuzOPJV3PwGQBZvKZcGOeur3pUuX0LZtW1nt3Llz6NSpk0IdERGRLTL189supp+Myc/Ph6/v/UcvSktLUVBQIPuyRUM6BGLthEgEqOVTTAFqlcMGmkWLFskCTUhICMrLyxloiIioxuxi+uleKSkpWL16Nd5+++37Xrds2TLExcXVUVe1M6RDIAZFBDj8jsK3b99GgwYNZLWNGzdi8uTJCnVERESOQtHppyVLllQbOk6ePIlu3brpv8/MzETfvn3Rt29ffPzxx/d9bmlpKUpLS/XfFxQUIDg42Oamn5zFf//7XwwZMkRWy87ORpMmTRTqiIiI7IGp00+Khprc3Fzk5ube95rQ0FCoVBXTMpmZmejfvz+6d++OzZs3w8XFvNkzW11T4+iEEHj00Ufx/fff62vjx4/Htm3bFOyKiIjshamf34pOPzVu3BiNGzc26drr16+jf//+6Nq1KzZt2mR2oCFlZGRkoEWLFrLaTz/9hF69einUEREROSq7SAaZmZno168fgoODsXLlSuTk5CArKwtZWVlKt0b38e6778oCjUqlQmlpKQMNERFZhV0sFN63bx+uXr2Kq1evonnz5rLH7PSOdIdWVlYGHx8f2Xqmd955B//85z8V7IqIiByd3e5TUxNcU2N9R48eRe/evWW19PR0BAcHK9QRERHZO4ffp4Zsz/jx42WBZuDAgdDpdAw0RERUJ+xi+olsW05ODvz9/WW1hIQEDB48WKGOiIjIGXGkhmpl06ZNBoGmuLiYgYaIiOocQw3ViFarRYsWLTBlyhR9bdGiRRBCoH79+gp2RkREzorTT2S2X375BZ07d5bVLl26hDZt2ijUEREREUdqyEwvvPCCLNB07twZOp2OgYaIiBTHkRoySeXtdH+2Y8cOjBkzRqGOiIiI5BhqqFq7du3CqFGjZLWbN2+iYcOGyjRERERkBKefqEpCCHTr1k0WaKKjoyGEYKAhIiKbw5EaMurq1ato3bq1rJaYmIgHHnhAoY6IiIjujyM1ZODVV1+VBZrAwECUl5cz0BARkU3jSA3p3blzx2CPmY8++gjTp09XqCMiIiLTMdQQAOCHH37AwIEDZbWsrCw0bdpUoY6IiIjMw+knwrBhw2SB5qmnnoIQgoGGiIjsCkdqnNj169fRvHlzWe3HH3/Eww8/rFBHRERENceRGie1evVqWaBxcXFBSUkJAw0REdkthhonc/fuXfj4+GDWrFn62vLly6HVauHh4aFgZ0RERLXD6Scncvz4cfTo0UNWS01NRWhoqDINERERWRBHapzEpEmTZIGmb9++0Ol0DDREROQwOFLj4G7cuIHGjRvLat988w0ee+wxhToiIiKyDo7UOLCtW7caBJrCwkIGGiIickgMNQ5Ip9OhVatWmDhxor42f/58CCHg5eWlYGdERETWw+knB/Prr7+iQ4cOslpycjLatWunUEdERER1gyM1DmTu3LmyQNOuXTtotVoGGiIicgocqXEAhYWF8PHxkdU+++wzPPPMMwp1REREVPcYauzcnj178MQTT8hqN27cgK+vr0IdERERKYPTT3ZKCIHevXvLAs3UqVMhhGCgISIip8SRGjuUmpqKli1bymonT55Et27dFOqIiIhIeRypsTPLli2TBRo/Pz/cvXuXgYaIiJweR2rsRElJCTw9PWW1Dz/8EDNnzlSoIyIiItvCUGMHDh48iP79+8tqmZmZCAwMVKgjIiIi28PpJxs3cuRIWaB54oknIIRgoCEiIroHR2pslEajQVBQkKy2f/9+gxEbIiIiqsCRGhu0bt06g0Bz584dBhoiIqL7YKixIeXl5WjcuDGio6P1tTfeeANCCKhUKgU7IyIisn2cfrIRp0+fNrgtOyUlxWA/GiIiIjKOIzU2YPr06bJA06NHD+h0OgYaIiIiM3CkRkE3b940ONLgq6++MjjLiYiIiKrHkRqF/Otf/zIINAUFBQw0RERENcRQU8d0Oh3at2+PZ555Rl+bPXs2hBDw9vZWsDMiIiL7xumnOnTx4kW0a9dOVjt//jw6dOigUEdERESOgyM1dWTBggWyQBMeHg6tVstAQ0REZCEcqbGy4uJieHl5yWpbtmzBxIkTFeqIiIjIMTHUWNG3336Lxx57TFbLyclB48aNFeqIiIjIcXH6yQqEEOjfv78s0EycOBFCCAYaIiIiK+FIjYX99ttvCA0NldWOHTuG7t27K9MQERGRk+BIjQWtXLlSFmi8vb1RVlbGQENERFQHOFJjAaWlpWjQoAG0Wq2+tmrVKsyaNUvBroiIiJwLQ40F3HuC9u+//45mzZop1A0REZFz4vSTBVQeRjlkyBAIIRhoiIiIFMCRGgs4efKk0i0QERE5PY7UEBERkUNgqCEiIiKHwFBDREREDoGhhoiIiBwCQw0RERE5BIYaIiIicggMNUREROQQGGqIiIjIITDUEBERkUNgqCEiIiKHwFBDREREDoGhhoiIiBwCQw0RERE5BIYaIiIicgj1lG6gLgkhAAAFBQUKd0JERESmqvzcrvwcr4pThZrCwkIAQHBwsMKdEBERkbkKCwuhVqurfFwS1cUeB6LT6ZCZmQlvb29IkqR0O06voKAAwcHByMjIgI+Pj9Lt0J/w78Z28e/GtvHvxzqEECgsLERQUBBcXKpeOeNUIzUuLi5o3ry50m3QPXx8fPg/v43i343t4t+NbePfj+Xdb4SmEhcKExERkUNgqCEiIiKHwFBDivHw8EBsbCw8PDyUboXuwb8b28W/G9vGvx9lOdVCYSIiInJcHKkhIiIih8BQQ0RERA6BoYaIiIgcAkMNEREROQSGGlJcWloapk6dirCwMHh6eiI8PByxsbEoKytTujUC8MYbb6BXr16oX78+GjZsqHQ7Tm/NmjUICwuDSqVC165dcfjwYaVbIgCHDh3C8OHDERQUBEmS8J///EfplpwSQw0p7uLFi9DpdFi/fj1+/fVXvPvuu1i3bh1efvllpVsjAGVlZRgzZgyio6OVbsXpbd++HTExMVi4cCHOnDmDPn36YOjQoUhPT1e6NadXXFyMzp0744MPPlC6FafGW7rJJq1YsQJr167FtWvXlG6F/s/mzZsRExODW7duKd2K0+revTsiIyOxdu1afa1du3YYOXIkli1bpmBn9GeSJGHXrl0YOXKk0q04HY7UkE3Kz8+Hr6+v0m0Q2YyysjKcPn0ajz76qKz+6KOP4ujRowp1RWRbGGrI5qSkpGD16tWYMWOG0q0Q2Yzc3FxotVo0bdpUVm/atCmysrIU6orItjDUkNUsWbIEkiTd9+vUqVOy52RmZmLIkCEYM2YMpk2bplDnjq8mfzdkGyRJkn0vhDCoETmreko3QI7r73//O8aNG3ffa0JDQ/W/zszMRP/+/dGzZ0989NFHVu7OuZn7d0PKa9y4MVxdXQ1GZbKzsw1Gb4icFUMNWU3jxo3RuHFjk669fv06+vfvj65du2LTpk1wceEgojWZ83dDtsHd3R1du3bFd999hyeffFJf/+677zBixAgFOyOyHQw1pLjMzEz069cPLVq0wMqVK5GTk6N/LCAgQMHOCADS09ORl5eH9PR0aLVanD17FgDQqlUreHl5Kduck5k9ezb++te/olu3bvoRzfT0dK4/swFFRUW4evWq/vvU1FScPXsWvr6+aNGihYKdORfe0k2K27x5MyZPnmz0Mf7nqbxJkyZhy5YtBvUDBw6gX79+dd+Qk1uzZg3eeustaDQadOjQAe+++y4efvhhpdtyegcPHkT//v0N6s899xw2b95c9w05KYYaIiIicghcuEBEREQOgaGGiIiIHAJDDRERETkEhhoiIiJyCAw1RERE5BAYaoiIiMghMNQQERGRQ2CoISKqRr9+/RATE6N0G0RUDYYaIgKAak/tnjRpUp31Yo0QMWnSJIwcOdKir1mVgwcPQpIk3Lp1q07ej4gq8OwnIgIAaDQa/a+3b9+OxYsX49KlS/qap6en7Pq7d+/Czc2tzvojIqoOR2qICEDF4aGVX2q1GpIk6b8vKSlBw4YNsWPHDvTr1w8qlQrx8fFYsmQJunTpInud9957D6GhobLapk2b0K5dO6hUKrRt2xZr1qypso9Jkybhxx9/xKpVq/SjRGlpaQCA5ORkPPbYY/Dy8kLTpk3x17/+Fbm5ufrn/vvf/0bHjh3h6ekJPz8/DBw4EMXFxViyZAm2bNmCr776Sv+aBw8eNPr+xcXFmDhxIry8vBAYGIi3337b4Jr4+Hh069YN3t7eCAgIwPjx45GdnQ0ASEtL058B1KhRI9koV0JCAh566CE0bNgQfn5+ePzxx5GSknKfvxUiMgdDDRGZbP78+Zg1axYuXLiAwYMHm/ScDRs2YOHChXjjjTdw4cIFLF26FK+88orRQzIBYNWqVejZsyemT58OjUYDjUaD4OBgaDQa9O3bF126dMGpU6eQkJCAP/74A2PHjgVQMdL0zDPPYMqUKbhw4QIOHjyIUaNGQQiBuXPnYuzYsRgyZIj+NXv16mX0/efNm4cDBw5g165d2LdvHw4ePIjTp0/LrikrK8Nrr72Gc+fO4T//+Q9SU1P1wSU4OBhffvklAODSpUvQaDRYtWoVgIrANHv2bJw8eRI//PADXFxc8OSTT0Kn05n0Z0lE98fpJyIyWUxMDEaNGmXWc1577TW8/fbb+ueFhYUhOTkZ69evx3PPPWdwvVqthru7O+rXr4+AgAB9fe3atYiMjMTSpUv1tY0bNyI4OBiXL19GUVERysvLMWrUKISEhAAAOnbsqL/W09MTpaWlste8V1FRET755BN8+umnGDRoEABgy5YtaN68uey6KVOm6H/dsmVLvP/++4iKikJRURG8vLzg6+sLAPD390fDhg31144ePVr2Op988gn8/f2RnJyMDh06VNkXEZmGIzVEZLJu3bqZdX1OTg4yMjIwdepUeHl56b9ef/11s6ddTp8+jQMHDshep23btgCAlJQUdO7cGY888gg6duyIMWPGYMOGDbh586ZZ75GSkoKysjL07NlTX/P19cVf/vIX2XVnzpzBiBEjEBISAm9vb/Tr1w8AkJ6eXu3rjx8/Hi1btoSPjw/CwsJMeh4RmYYjNURksgYNGsi+d3FxgRBCVrt7967+15XTKhs2bED37t1l17m6upr13jqdDsOHD8fy5csNHgsMDISrqyu+++47HD16FPv27cPq1auxcOFCHD9+XB8eqnPv78WY4uJiPProo3j00UcRHx+PJk2aID09HYMHD0ZZWdl9nzt8+HAEBwdjw4YNCAoKgk6nQ4cOHap9HhGZhqGGiGqsSZMmyMrKghACkiQBAM6ePat/vGnTpmjWrBmuXbuGZ5991uTXdXd3h1arldUiIyPx5ZdfIjQ0FPXqGf/RJUkSevfujd69e2Px4sUICQnBrl27MHv2bKOvea9WrVrBzc0Nx44dQ4sWLQAAN2/exOXLl9G3b18AwMWLF5Gbm4s333wTwcHBAIBTp04Z9A9A9n43btzAhQsXsH79evTp0wcAcOTIEVP/SIjIBJx+IqIa69evH3JycvDWW28hJSUFH374Ib799lvZNUuWLMGyZcuwatUqXL58GefPn8emTZvwzjvvVPm6oaGhOH78ONLS0pCbmwudTofnn38eeXl5eOaZZ3DixAlcu3YN+/btw5QpU6DVanH8+HEsXboUp06dQnp6Onbu3ImcnBy0a9dO/5q//PILLl26hNzcXNmIUiUvLy9MnToV8+bNww8//ICkpCRMmjQJLi7/+1HZokULuLu7Y/Xq1bh27Rp2796N1157TfY6ISEhkCQJX3/9NXJyclBUVIRGjRrBz88PH330Ea5evYr9+/dj9uzZtfnjJ6J7CSKie2zatEmo1Wr996mpqQKAOHPmjMG1a9euFcHBwaJBgwZi4sSJ4o033hAhISGya7Zt2ya6dOki3N3dRaNGjcTDDz8sdu7cWeX7X7p0SfTo0UN4enoKACI1NVUIIcTly5fFk08+KRo2bCg8PT1F27ZtRUxMjNDpdCI5OVkMHjxYNGnSRHh4eIg2bdqI1atX618zOztbDBo0SHh5eQkA4sCBA0bfu7CwUEyYMEHUr19fNG3aVLz11luib9++4oUXXtBf89lnn4nQ0FDh4eEhevbsKXbv3m3w5/Pqq6+KgIAAIUmSeO6554QQQnz33XeiXbt2wsPDQ3Tq1EkcPHhQABC7du2q8s+CiEwnCWHCJDIRERGRjeP0ExERETkEhhoiIiJyCAw1RERE5BAYaoiIiMghMNQQERGRQ2CoISIiIofAUENEREQOgaGGiIiIHAJDDRERETkEhhoiIiJyCAw1RERE5BAYaoiIiMgh/H/MhCdaY3IWWQAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -347,7 +347,7 @@
    ],
    "source": [
     "predicted = pred.predict(target)\n",
-    "true_data = pred.get_test_array()[0]\n",
+    "true_data = pred.get_test_array(j=target)[0]\n",
     "\n",
     "plt.scatter(true_data, predicted)\n",
     "plt.title(r\"NRMSE = %.2f\" % (np.abs(true_data - predicted).mean()/true_data.std()))\n",
@@ -381,7 +381,7 @@
     },
     {
      "data": {
-      "image/png": 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dV9UxqSEiIrJT/tVUuDr3SVFbWL8Z8KrdQtxWBWrQSMGkhoiIyA4dOHAAnR99VNQWNXYTXDy8Na+rUg0aKZjUEBER2Znhw4dj5cqVmtc+D3RC9SffFq2AsqcaNPay6opJDRERkZ3Iz8+Hn5+fqO23335DcVgjrTo19lKDRlcNnQgbxaYQBKHibuVOKzc3FwEBAcjJyYG/v7+twyEiItL47bff0KVLF1FbXl4efH19AdhPb0h5umroAPd7kRYPSTBLYiP1+c0l3URERDY2ePBgUUIzYsQICIKgSWiA+zVoejeriTZ1Qmye0FSsoVOeum3Gj+egVFmv74TDT0RERDaSk5ODwMBAUduBAwfQrl072wQkQ/kaOroIANJzCnE0OdtqBQHZU0NERGQDu3bt0kpoCgoKHCKhAaTXxrFmDR0mNURERFbWp08fPPHEE5rXr7/+OgRBgJeXlw2jkkdqbRxr1tDh8BMREZGVZGdnIyREPBRz5MgRtGrVykYRma5VXDAiAjyRkVOoc16NLWrosKeGiIjICrZv366V0BQWFjpkQgOUTVye1isewP3VTmq2qqHDpIaIiMjCunXrhj59+mheT5gwAYIgwMPDw3ZBmUH3xhFYPCQB4QHiIabwAE+zLeeWg8NPREREFnLr1i2EhYWJ2k6cOIHmzZvbKCLz6944Al3jw+2ihg6TGiIiIgvYtGkTBgwYoHnt4uKCe/fuwd3d3YZRWYa6ho6tcfiJiIjIjARBQPv27UUJzZQpU6BUKp0yobEnDpXU7N+/H7169UJkZCQUCgW2bdtm65CIiIg0MjIy4OLigoMHD2razpw5gw8++MCGUVUdDpXU3L17F02bNsWCBQtsHQoREZHIunXrEBFxf2Ksr68vSkpK0LhxYxtGVbU41JyaHj16oEePHrYOg4iISEMQBLRs2RInTpzQtM2ePRuTJk2yYVRVk0MlNXIVFRWhqKhI8zo3N9eG0RARkbO5fv06oqKiRG3nz59Hw4YNtY61x122nY1TJzVz5szBjBkzbB0GERH9P2d6sK9YsQIjRozQvA4LC0NaWhpcXV21jk08m44ZP54TbQAZEeCJab3irV7LxZkpBEGw3p7gZqRQKLB161ZRMaOKdPXUREVFIScnB/7+/laIkoiI1JzlwS4IAuLj43HhwgVN22effYZx48bpPD7xbDpGrTuhtZWAOpWzRZE6R5Obm4uAgACjz2+n7qnx8PBw+GqNRETOQN+DPSOnEKPWnXCYB/vVq1cRGxsrart8+TLq1Kmj83ilSsCMH8/p3BtJQFliM+PHc+gaH+6wPVb2xKFWPxERkeMx9mAHyh7sSpXhgQOlSsChpCxsP3UDh5KyjB5vbgsXLhQlNLGxsVAqlXoTGgA4mpwt6pmqSACQnlOIo8nZZoy06nKonpr8/HxcvnxZ8zo5ORmnTp1CcHAwoqOjbRgZERHpI+fBrq8qrS2HrlQqFeLi4pCamqppW7hwIUaPHm303Jt5+j+3ruOcac6RLThUUnP8+HF06tRJ81o9fvniiy9i9erVNoqKiIgMkftgr8iWQ1eXL19GvXr1RG0pKSmIiYmRdH5K5l1Jx4X5eTrNnCNbcqjhp44dO0IQBK0fJjRERPYrzM/T+EF6jjPX0JUpPv/8c1FCEx8fD5VKJTmhUaoErD+aavS4iABP3L5bhFHrTmj1aKkTt8Sz6fKCr6IcKqkhIiLH0yImCMZGUFwUZcdVZIs5KUqlEtWrV8fbb7+taVu+fDn++ecfKBTSh4KOJmcjI7fI6HEDWkZh5s/nbZK4ORsmNUREZFF/Xb0NY89jlVB2XEWVHbqS68KFC6hWrRoyMzM1bdevX8fw4cNlX0tqTKUqFScTmwmTGiIisiipD/c/Lmdq9UZUZuhKrtmzZ6NRo0aa1y1btoRKpULNmjVNup70mKT1/pgrcXNmTGqIiMiipD7cF+y9jHZz94jmj7SKC0ZEgKfex74CZXNSWsUFmxxfaWkpfHx8MHnyZE3b2rVrcezYMVnDTRVJjV3fiq+KzJG4OTsmNUREZFHqh7sUFSfGurooMK1XPADt/gz162m94k1e9nzmzBm4ubmhoKBA05aeno4hQ4YYPE9KzRypsbeuHWLxxK2qYFJDREQW5eqiwFNNpS1J1jUxtnvjCCwekoDwColReIBnpZZzT506FQ8++KDm9aOPPgqVSoXw8HCD5yWeTUe7uXswaNlhvLnhFAYtO6zVw6QmJXZLJ25VicPu/WQKqXtHEBGR6SoWkGsRE4QOn+w1OBlWl/UjW4uGZsxVmK64uFhrC51NmzahX79+Rs81dR8nKbGzTo1+3PuJiIhMZmoCoevBHOzjhuy7JbJjqDgx1tVFIXn+iT4nTpxAixYtRG23bt1CaGio0XMrs4+TlNi7N45A1/hw2d87qxDfx6SGiIhETO0x0NeLYUpCA5h/YuyECRPwySefaF5369YN//vf/ySfb47tHoyRm7ixd0eMc2qIiEhDnZjIrWxrqBdDLnNPjC0sLIRCoRAlNNu2bZOV0ADWr5ljjKl/V86MSQ0REQGo3JYExnoxpDL3xNgjR47Ay8tL1JaVlYXevXvLvpY1a+YYY8vtI+wZkxoiIgJQuS0JzNU7UdkVTeW9/vrraN26teZ1nz59IAgCgoNN6wGyRs0cqWyxfYQj4JwaIiICULnhFVN7J8L9PTCoVTRiQ33MNsm1oKAAPj4+orZdu3ahe/fulbqueun1qHUnoABEvSTWXnptb0Nh9oJJDRGRHbLFipbKDK+0igtGoJcb7twzPil4TKe6qFfD1yKf6+DBg2jfvr2o7c6dOwgICDDL9dV1ZypOzg238uRcexoKsydMaoiI7IytVrS0iAkyuPxagbKHt67hFVcXBV5qG4t5v/5r9D5t64ZWemm2LiNHjsTy5cs1rwcPHoxvv/3W7Pcpv/Q6I+ceMvOLcaegGGdv5MDPww2t64RYPAFVD4Vl5BTqnFdj6O/KmTGpISKn50h1PPQti1avaDHXfBNd953x4zmDCQ1geHhlTOd6WPVnCu4UyE+KypP795Wfnw8/Pz9R26+//orHHnvM4H0qw9VFgZx7xZjx0znR512wNwmB3m74qG8Tiyag9jQUZk9YUZiInJoj1fFQqgS0m7tH7wRQdVJwcGJnsz6s9CVS5Un9zkytuFv+fDl/X3v27NFKXvLy8uDr62swzspKPJuOV9edMHjMEgsloBXjcJT/vitD6vObSQ0ROa3KPmCt7VBSFgYtO2z0uIrbB1SGsUQKAEJ83HFo0mNwryZtway5i/fp+/saMmSIaHhp+PDhouEnS1GqBLT9aA8ycg1Pwg3398Af7z5m8d4SR+qJNBW3SSCiKq0yJe1txRYrWqTUl8m6W4y/rt6WnEiZUu5fzt9Xfl4uAgMDRcfs378f7du3t8oD/mhyttGEBgAycosqVV1YKnNsH+EsmNQQkVOyRkl7c7PFihZLJVJyH7SHr2RJ+vv6avX3GDd8gOi9u3fvwtvb22pDMXK+i6q2pNrWWHyPiJySI9bxsEVxN2slUkqVgENJWdh+6gYOJWWJKt0mnk3Ha98anp8CADe3fihKaMaMGQNBEDQJjbW2DJDzXVS1JdW2xp4aInJKjljHwxYrWqyxNNhQDwoAo5OUlYX5uP7lQFHb4cOH8fDDD5e9b+WhxlZxwQj395Q0p6aqLam2NfbUEJFTsqeS9nKoi7uFB4iTLXNuH1CeOpECoPVdmSORMtaD8u6WMwYTmoJ/j2glNPfu3dMkNID1twxwdVFg+lPxRo+b/tQDdjNfq6pgTw0ROSVHruNhykRbQ4xNnrVUlVwpmy7qq2kDAP9tmobC5L80r58Z+ip+WLVY6zhbDDV2bxyBJUMS8O6WM1qfwRp1akg3JjVE5LTspaS9Kcy1omXn6XS8v/0ssu8Wa9p0TZ4VVcnNLUR2fhGCfdwR4OUOpUowKaEydeduZUEOrs9/TtQ2f2MixvR/XOfxthpqVH9nh69k4VBSFgABbWqHWqWiMOnGpIaInJq5ez0cyZyd57B0f7JWe7qe6sTqKrkfJ14wywoiU3pGcg5/jzu/rxG1PTjlR4x6tpvec2y5ZYCriwJt64aibd1Qs1+b5OOcGiJyeupej97NaqJNFfkteufpNJ0JjZqAssmzFVchmXMFkZyeEUEQcHXuk6KEJqDNAMRM/Ak5xQqD82EsPS+IHAeTGiIiJ6NUCXh/+1mjx5WfPCtl/kvFJMiYVnHBCPR20/u+AoCPuytKbqcj9eNeovciXvoKgY8+r3ltrNfH0ATrhYMTEODlrnM5OTkXDj8REdmAOSrf6rvG0eRsvRtTVvTruQy0qRNikWKFu89lGJwILACIuLQF5zYuE7VHv7MVCldxMiSl10fXUOPtu0WY+bPz741EZZjUEBFZmTkq3xq6RlGpSnIsK/5IwUNxwZLPkTpPRt3zo48gCEj9uBeulmvzjGuBGv1naB0rZ+l9+QnWiWfT8dp3J62+4znZDoefiIisyBzzVoxdIyWzQFZMM348h1AfD0nHSp0nY6jnpyTrmtZwU/hzcxGuI6EBgHslSuw+lyHpvmqWGE4j+8ekhojISszxoJVyjQ3HUhHuLy1JAcqGlaCAWYsV6uvRyd69BGnLR4na9l/IwKp3n0eAnvk3OQUlsicqW7sgH9kHJjVERFZijget1GsMeChKb4KiS2Z+kVlXEFXs0VGvbso78ZOmzbv+I4iZ+BOy75Wic8MaeuM1pWfFEff+ospjUkNEZCXmeNBKvcaaP6/i5UfjEOzjLun4MD9Ps27R0CouGME+ZT0vxTeTtYebnv8M1Z9+DwCQknkXref8httGJhXL6VlxxL2/qPI4UZiIyErM8aCVeo0790rw9f5kfDWoOabtOKt3NVTFwnTmKlbo6qLArN6N0X/Ii7h75lfRe9Hjt0Ph4goACPJ2w7xf/5V8XalJnS0L8pHtsKeGiMhKzLHJprFrVDR753nM6t0YCkgfVjJHsUKVSoWeTWuKEhqfxo8hZuJPmoSm4p5cUmTmFUkagmJBvqqJSQ0RkZWY40Fb/hrGqIdsgnw8rLrz9/Hjx+Hq6ipqazB6MUJ7vqV5HRHgibFd6husY6PLzJ/Po93cPZImDVt7x3OyPYUgCFVmPVtubi4CAgKQk5MDf39/W4dDRFWUuerUvLv5DO7cM54UfDmwGXo3q2mWgn/GDBgwAJs2bRK1KZVKCFBo3fun02l4c8Mp2fdQRyw1MbHG5ybLkvr85pwaIiIzkvIANce8le6NI+Dn6Ybnlh8xeqx6Ho65dv7WpbS0FG5u4iXZo0ePxsKFCzWvK97b1Em6AsoSmxk/nkPX+HCj35slPzfZFyY1RERmIqcHxtQHbfmkKdTXA+H+Hvgvt8imk2H//PNPtG3bVtT2zz//ID7e8DCZscm8hpiybQM5PyY1RERmoK7ya8mS/LqSpkBvN03PRfl7W2MyrFIloP1j3XBon3h1k0qlgkJh/J7q+UGj1p0wadIwwDozJMaJwkRElWSNkvz6tkbI+f+JthWr8RqaDKtUCTiUlCXatVpXmyE/nryKaq4uooQmon0/7DqTJimhUVNP5tVXTdgYS9WZkft9kH1gTw0RUSVZYofr8owlTQoAXm6uWDg8AZl3iwzO0dHX2wNAtBLJ0MTlj1Z8j0kj+ovaIl/+Gu5BkSb3SsldBWXJoTVzTOQm22BPDRFRJVm6JL/UpMnFRWGwtoy+3p47BSVaSYW+DTY7dOyoldBET/gRbkGRJvVKGdvNWxdLDq2ZY8NRsh321BARVZKcSsGmLC82R9JkqLdHF/Vx724+Az9PNzSL9IGvj7fomIBHBiGw/XNa56l7pVrFBRv9rMYSNl3CLdRrIqVHTOqKK7INJjVERJUktST/7btFaDd3j+xhDXNsr2BK8gCUbbfQd9JC3Pxhuqi95qsrUS0gTO95v57LwLhNp4x+VqkJ25hOdVCvhp9F68xYehiRLI/DT0RElSSlUvBTTSPw2ncnTRrWMMf2CqYOfaWvflMroYmZ+JPBhAYAVvyRIumzSk3Y2tatXqltG6Tgzt6Oz+SkpqCgABcuXMDp06dFP0REVZGhkvwLBydgx9/pJq+OMsf2CnJXCalKCnF17pMo/i9J0xbUYShaz/4V4f4eBhMsfWHo+qzmSNjMhTt7Oz7Zw0+3bt3CSy+9hF27dul8X6lUVjooIiJrMlcZfX2Vgs0xrKFOmqbvOIeM3PvXkjq/RE6hu4JLh3Br64eitpqjV6OaXyjScwrxVpd6+OLXf3XWxhEAGNp8p+JnLV+rRh9rbTzJnb0dn+yemrFjx+L27ds4fPgwvLy8kJiYiDVr1qBevXrYsWOHJWIkIrKYxLPpaDd3DwYtO4w3N5zCoGWHJW+YqIuuHa6lDldk5NyTUBtF3KZv+76KdVYA6O3tKe/Gsle0EpqYiT+hml+o5nXuvRK9vVLD2sYauPp95b+T7o0j8PKjcVo9PC4K4OVH46y2jJo7ezs+2RtaRkREYPv27WjVqhX8/f1x/Phx1K9fHzt27MDHH3+MgwcPWirWSuOGlkRUnr4qwHI3TDTmUFIWBi07bPS4YB93ZN8t1rwuP7FWTqyG6qwA0FmnRllUgLOznxZdO6jLK/Bv0UtHnG44NrkrAOjslZLyWdePbK3plbLW34NUrFNjfyy2oeXdu3cRFlY2QSw4OBi3bt1C/fr10aRJE5w4ob/7kIjInlhz+a7UoZ/yCQ1wf2LtwsEJmPmztFh3n8swul3DwYmdRcnIlcP/w5Ah4qXZtcasg6tPoJ44SzTDRxWHy+QO4djjMmpzbDhKtiF7+KlBgwa4ePEiAKBZs2ZYunQpbty4gSVLliAighksETkGOfNcKsvQsIYh6gf9lO1nJcV6+EqWwQRBAPDe1jPY8XcaAODJByPRt11jUULjWq0aYib+pDehUdM3pCZ3CMeafw9y6BpGJPtn0pya9PSyseZp06YhMTER0dHR+OqrrzB79myzB0hEZAnWXr6rb3VUsI/hPY8EAFkVenD0OZSUZbQWTfbdEry18RT6z/8V1VxdkJGRoXnv66+/xoELGQbOvs/QCiBDK8EqDiVxGTWZk+zhp+eeu5/RN2/eHCkpKbhw4QKio6MRGhpq4EwiIvthyvLdyq6S0jWskZFbiLc2npIbvk7XbxdIOi7/9G5k7fpS1JZ4/F90aV4HQNn8EX3JkbEVQOrvqKhUhU+fbQoogMx8/ftRcRk1mZPspOaDDz7AO++8A2/vsnLZ3t7eSEhIwL179/DBBx9g6tSpZg+SiMjc5M79MNfkUfWwhpp6ZZIxQd5uuFNQYnBOzrZTaUavk/r5sxBK7n8GFy9/RL3xHV75/iIifrmKab3iRUusKy7ZBvSvADL0Helbqs5l1GROsoefZsyYgfz8fK32goICzJgxwyxBERFZmpy5H/o2OUzPKcSr605g52nTNzk0VnxOTSUImomzplAW5ODq3CdFCU3Ik28j6o3vNK/Vk4kBSB4+UjN1I0guoyZzkp3UCIIAhUL7P66///4bwcGWz6QXLVqEuLg4eHp6okWLFjhw4IDF70lEzknK3A+lSsC7W84Y7CEZs/4Edp423kuii9RJxLn3SgEAAd6G5+DoknfiZ1yfL17dFDV2I3wf6CRqK1/xt2t8OA5O7Iz1I1vjy4HNsH5kaxyc2FlnQmNsBZP6mvoqJsuZg0NkiOThp6CgICgUCigUCtSvX1+U2CiVSuTn5+PVV1+1SJBqGzduxNixY7Fo0SK0bdsWS5cuRY8ePXDu3DlER0db9N5E5JyMLd9dsOdf3CkoMXgNlQCM/u4klrgoTHoA66sWXJ66l8bLzRWvPVEXH+48L+naV+c+KXrtGlADtV5doff4ihV/pWzcaK6KyVxGTZUlOan54osvIAgChg0bhhkzZiAgIEDznru7O2JjY9GmTRuLBKn2+eefY/jw4RgxYoQmpv/9739YvHgx5syZY9F7E5HzqjjPRU2pErDqjxTJ16lMPZXujSPg5+GG51Yc0XuMOjnIuWc4yQKA0vxs3Fj4gqgttPe78GnYTlI8clYbmWsFk76/ByKpJCc1L774IgAgLi4OjzzyCNzc5HeBVkZxcTH++usvvPvuu6L2bt264c8//9R5TlFREYqKijSvc3NzLRojETmXo8nZuCMhgVBLzynEvN0X0bZudUm9DBVXU0lPJAwXgs89ugW3964UtUW99T1c3L0kXh9IyZS2kgrgCiayH7JXP3Xo0EHz53v37qGkRPw/vKW2H8jMzIRSqUSNGjVE7TVq1BDVWShvzpw5nLxMRCYzpTbKgr1JWLA3yejKKF0rhYJ93CXdo03tUGw+cUPniqGKw01u1WMROWyBrM8AABuOpWJM57qSep24gonsheyJwgUFBRgzZgzCwsLg6+uLoKAg0Y+lVZykrG/iMgBMmjQJOTk5mp9r165ZPD4ich6V6VkwtOpH30qh20aK7ClQtkS6dZ0QzeRitdLcW1oJTfVnppqU0ADyqviWn+ysD1cwkTXITmrGjx+PPXv2YNGiRfDw8MDy5csxY8YMREZG4ptvvrFEjACA0NBQuLq6avXK3Lx5U6v3Rs3DwwP+/v6iHyKiiiruaK1epSN1ubUu6m0J3t18Bn9cztRcU8pKIV0qLm9WTy4O9nHDnT/W48bil0THR43bDO+6rUyI/L5dZ9MN7BYuZi87bVPVJnuX7ujoaHzzzTfo2LEj/P39ceLECdStWxdr167F+vXrsXPnTkvFiocffhgtWrTAokWLNG3x8fHo3bu3pInC3KWbiCoyVlRP3asCGJvJYpj6mgFe7pJ2sfbzdEVeoVJnTOVV7Kn2qNkI4UM+qUSk2qQUGbS3nbbJuUh9fsvuqcnOzkZcXByAsvkz2dll3ZPt2rXD/v37TQxXmnHjxmH58uVYuXIlzp8/j7feegupqakWX0pORM5JSsE4fTVU5FJf89dz0vZWyitUws+zGoY+EqOzRsyVK1e0Epqw/jONJjTqMwK93ST3QBkroFfZOjVE5iJ7onDt2rWRkpKCmJgYxMfHY9OmTWjVqhV+/PFHBAYGWiDE+wYMGICsrCx88MEHSE9PR+PGjbFz507ExMRY9L5E5HyMPYgVuL9Eu2INlVAfD0BRtsXBgr2XJd1Pfc2Nx6XP7csrLMXqP6/Co5qLaKnzpEmT8NFHH4mOjX57KxTVjK9KDf//XhcAGLXuBBQw3gNV8fuoODfGHHVqiMxBdlLz0ksv4e+//0aHDh0wadIk9OzZE/Pnz0dpaSk+//xzS8QoMnr0aIwePdri9yEi+1XZjSUB+Q9iXTVUWtcOweYT1/Wu+tF1zfwipdHjKlq6PxlNawWhR5NwuLiIO9g9Y5ujxoCZkq4zplMdvNW1gea7WjwkQWvozVDs+hIT7rRN9kJ2UvPWW29p/typUydcuHABx48fR506ddC0aVOzBkdEVJG5NpY0x4NYvepHao9HZYxfsQs9vxoualu0/kfMPSU9mWtbt7oo+SvfA7XrbDq+OXTV6DV0fR+sU0P2Qvacmoqio6PRt29fJjREZHGmbpqoi7kexOaac2NI9q9Lca5CQlNcXIyX+/eUvDor4v/rxFRc6QUAbeqEoIfEhFDX92FslZh6KTrr1JClSeqp+eqrryRf8I033jA5GCIifeTMgTFHwThA+oNY3eNxOCkLr313QlYVYkMEQUDqx71EbX379sXmzZs1Q3BPNA7HCiNbOShQthR897kMvb1cXePDTS6gZ6jHijttkzVJWtKtXu2kduvWLRQUFGgmBt+5cwfe3t4ICwvDlStXLBKoOXBJN5HjOpSUJWkp9JhOddG2bqikeTb6liGrvfJoHCY9YbionNxrSlV8KwXpK8eI2pb98D+MeKabziE4F0XZxpoVRVSYGGxoybX6GEB3YmJsWba5hgaJKpL6/JZdp+a7777DokWLsGLFCjRo0AAAcPHiRYwcORKvvPIKnnvuOSNXsB0mNUSOa/upG3hzwynJx0cEeGJKz3gE+bgbnFA8Z+c5LN2frPMaCphWX8XQNaXI2vUV8k//ImqLnbAdC59rCRcXhd7kRADwUttY1Ar0QrCPe9lQkQK4mVeEmT/9g+y7unuQ1L0wByd2NtibI+V7MDaJ2xyTvKnqsVhSU6dOHfzwww9o3ry5qP2vv/7Cs88+i+Rk0/9HtjQmNUSOS2pPjSEVH85KlYB2c/foXf1T/mHv6qKQ9EA2dk19vN1dcbeoBKkfPyVq93mgE0KffFvzOtDbDXcKTEtOjFk/sjXa1AmRlXjIOZY9OWQqqc9v2auf0tPTtTaxBAClUon//vtP7uWIiCSRMgfGGPWEYnXvi9Rl3YevZCGvsETSA9nYNSsK9HbDwkEJcLuTgodbPSR6L2Lol3CvUUfUpi+hKR/vgj3/4otf/5X9PalXNulavq6LnCRF37Bcxb8TosqQvfrpsccew8iRI3H8+HGoO3mOHz+OV155BV26dDF7gEREgHjTRFMHKypWt5W6rHvkN8fxqsRVV3JqsSgAfNS3CRZMfV0roYmesEMroZFq1R8pJiV+cpZcy1mJxorDZC2yk5qVK1eiZs2aaNWqFTw9PeHh4YGHH34YERERWL58uSViJCICYJ7l0+WLyEl9iBcU6y6Yp+uBLPWaIT7uWDDwQfRoEokNGzZo2n2bdUfMxJ+gUJhecUPu6iu5S67lJilyCh0SVYbs4afq1atj586d+Pfff3H+/HkIgoBGjRqhfv36loiPiEikfMG4Py7fwoK9SSZd52ZeIZ58MLLSQ1oVK+1KGSYL9nHDvI7e6NA8WtS+budBTP79jomRlCUnAQbm3Og7BwCm9IyXPDdGbjVmVhwma5Gd1KjVq1cP9erVM2csRESSqOd8tIoLxndHryH7brHsa4T5eYrqq1RW+fkoxmq2uP32CTpM/VV0vkqlgkoAFpzaY7BWTIC3G3L+P2nRde2XHonDvF8vSY47PMATTzWNwMyfpU/glZuk2GPFYa7Cck6VrihMRGQpFavfVpxz4eqiQJ9mkbKuWXGopXvjCLz8aJzhkyQo/0DWN0wW5lsNKXOfxNHf7yc048aNgyAIUCgUBucNqV9/1LeJzmuHB3hi8ZAEjOlc12h13xAfd8zr3xTrR7bGlJ6N8PX+ZFlVmuUmKfZWcTjxbDrazd2DQcsO480NpzBo2WG0m7tHVkVqsk+yl3Q7Mi7pJnIcUlfWyFnqrauInKlLsMtfs/yy7/LK9wZcO3sMrz//tOj9S5cu6ezxlvLZDfU0qCfxAoaL6Mld0l7+c7Wba7hHqeJ5UmOyNH2rsKwdB8ljsTo1joxJDZFjkPPgkZOUVDYpqkjqg7BTp07Yt2+fqE2lUkGh0D/cUdnhESmJkdTPrq5fU/H6cpMUW9epMTWJI9uzWJ2a1NRUREVFaf3PKAgCrl27hujoaD1nEhEZJ3ePp4rzYio+YAUAw9rGomt8uM7EoDKTU8ONPJALCwvh5eUlapsyZQo++OADo9eWWitGn/ITqvUlRpWZwKseYquYpBj6TqTEZElyJziT45Gd1MTFxSE9PR1hYWGi9uzsbMTFxUGp1L30kYhIClMePKY8YNXkTE5VAAj2ccf7PRshPMDLcPXcxET06NFD1JacnIzY2FjJ96ssY4lRZSfwmpKkVDZZqwyuwnJ+spMa9YS2ivLz8+Hpab2Z60TkPMoPtfz7X56kcyo+eMo/YDNyC5GdX4RgH3cEeLlDqRL0PmjlVCoWAGTdLUZ4gJfBB3OrVq1w7Ngx8bl2ONJv7LMb2plbzZZJilz2uAqLzEtyUjNu3DgAgEKhwJQpU+Dt7a15T6lU4siRI2jWrJnZAyQi+2GJZbC65llIoevB4+qiQM69YnyceEHyvA1TlnXr+02+oKAAPj4+orbZs2dj0qRJkq9tTVKWn0/rFe8080vMkcSRfZOc1Jw8eRJA2W8bZ86cgbu7u+Y9d3d3NG3aFO+88475IyQiu2CJSZ76JgQbYujBY+r+Qurhq/e2ntG7k3V5mXlF2H7qhiix2759O/r06SM67vr166hZs6bR61mzZkrFe3WNDzd56M7RVLUkriqSvfrppZdewpdffumQq4e4+onINJZYBmvKUmpdS5LVD+hQHw+8/f3fyMg1fWVLcakKref8ZrCYn4sCKF8uJyLAE9e/fgXXU8SVjaX806pUCViw5zJW/ZEs2trAUiuCDCWmtpzAa222XoVF8lltSXdubi727NmDhg0bomHDhpW5lMUxqSGSz1LLYE1ZSl3+wWPqsJWu5cnl6VuqrIuqqADXvugvaps3bx7Gjh1rNI7Es+l4d8sZnVsaWKJmCuuziLGisGOx2JLu/v3749FHH8WYMWNw7949tGzZEikpKRAEARs2bMAzzzxTqcCJyL5Yahms1BUmYzrVRb0avqIHjynDVmoZOfdwKClL78NM30qqij00d8/vR+aOj0XXvpGWjsiIcKMxJJ5Nx6sG5vDoWrpeGXKXyVcFjjTBmaSTndTs378fkydPBgBs3boVgiDgzp07WLNmDWbNmsWkhsjJyFkGK+e3X6krTNrWDRU9fAw9oKWYuuMf5BWWal7rGnaouFQ5M68IM38+r3n/+sIXoczPun9RhQtiJuzA1QI3GNu0QR2/MeasmcL6LFRVyE5qcnJyEBxcNkEvMTERzzzzDLy9vdGzZ0+MHz/e7AESkW1JTT5SMgu0hqkMzVMwdSWKsQe0MeUTGqDsYa5rEnH53+S3n7oBAFAV5uPalwNF5wc/PgZ+zboDkJYAyo1fyjWNJZOsz0JVheykJioqCocOHUJwcDASExOxYcMGAMDt27dZp4bICUlJPgK83fDFr5dkrToydSWKJR68AgwPv4T5eSL/zK/I2vmFqL3WG9/B1ctfdJwxcuM3dk0pk15Zn4WqCtm7dI8dOxbPPfccatWqhYiICHTs2BFA2bBUkyZNzB0fEdmYsZ2j1cmIvvkaQFnCUHGHbeD+DtkV63kqFMDLj8bp7OEx5cFrYIslDfXwiy6PN48TJTQunr6ImfiTJqGRs8t0SmaBlJABCddUzy0ytsO2OjE15raBVV9EjkB2UjN69GgcOnQIK1euxB9//AEXl7JL1K5dG7NmzTJ7gERke+rJs+EVHozhAZ54q0s9nSt41MrP16go8Ww6vt6fjIr5jkoAvt6frHkol6d+QEuZzjqsbSym9GwEqWs8K/aiZGZmQqFQIC/vfpXj0J7jEPXmBs1rOfVNEs+m44tfL0mKRWHkmsYm/wL3k0lXFwWm9Gxk9J4zf9adfBI5CtlJDQC0bNkSPXv2xI0bN1BaWjY+3bNnT7Rt29aswRGR/ejeOAIHJ3bG+pGt8eXAZlg/sjUOTuyM2FAf4ydDO2GQMuFXVw+PuufI2KNXAWDX2QwE+7gbOfK+lMy7mj8vWbIE1atXF73/w58XULftE6K28ABPScuh5UxwDvJ2M3pNOZN/ASDIx8PofQ31VhE5AtlzagoKCvD6669jzZo1AIBLly6hdu3aeOONNxAZGYl3333X7EESkX3QtQzW1PkalVmR071xBN7qUg/zfv3X6PmGCulVtP5oKsZ0rodqruLf96Kjo3H16lUAQJ+H65tU30TqBOFnE2pi7rNNjV5T7uRfThamqkB2T82kSZPw999/Y9++faKJwV26dMHGjRvNGhwR2T9jw0H65ptU9iErtYco2NdD0nwSALiRlqGV0GzatEmT0AD3E7vezWqiTZ0QTfKhVAk4lJSF7adu4FBSllYPk9TP275+dUlJktxkkpOFqSqQ3VOzbds2bNy4Ea1btxbt1h0fH4+kpCQDZxKRMzJ1FVNlH7JSzw/3L1sJZKjYHQDkHtuG23uWi9ry8vLg6+tr9B62WIEkd0k8N3OkqkB2T82tW7cQFham1X737l1RkkNEVYe+icTBPu5YOFj33BBTe3hMOb9suKq+3vivzn1SlNDE1G0AQRAkJzRyViCZ+nkrMrYqDRAnk3KPJ3JEspOahx56CD///LPmtTqRWbZsGdq0aWO+yIjIoXRvHIEpPRsh2MdN05Z1txgzfz6ncxVTZR+ycs8f07kuwv3Fk2VLczNxde6TorawZ6YgYthC0fCRvqEluSuQzJ1UGFqVpmuisdzjiRyN7A0t//zzT3Tv3h3PPfccVq9ejVdeeQX//PMPDh06hN9//x0tWrSwVKyVxg0tiSxHzoaJ5SvgpmTexfqjqcjILdKco68Ssa7KubvPZUjecbn8nks5f27EnQNrRe9HjdsMF7eyxEe98aWhoaUAL3dJm3KW30TTEjtEy92ckZs5kqOx6C7dZ86cwaeffoq//voLKpUKCQkJmDhxot0X32NSQ2QZcnby1pWEhPt7YlCraMSGeut9yBpKBsrv02TsIT3zx38w9anGojb3yAaIeP4zUduYTnUQH+GP1747qTdRe6ltLFb+kaL3e1H7cmAz9G5WU/OaSQWRPBZNahwVkxoiMXM9XA8lZUnqsXirS32d2yno6s0pT04vkCHJycmoXbu2qC2s/wfwikvQeXzFnbkr3jvYxx1ZEpaMl++pISL5pD6/Za9+cnV1RXp6utZk4aysLISFhUGpVMqPloisTs4wiLk2TFz1R7Le+ScK6N5/ydi8FX3nVTR58mTMnj1b1Bb99lYoqrnpOUN/QqO+d9bdYgT7uCH7rv6KyoDzbz/AnieyF7KTGn0dO0VFRXB3l165k4hsR1/Ph64NKM25XPnOPWnbKZTv1TiclGVykT6g7N8s9XYuas0eboc7Hd/VnF8ZvZtGYtWfVw0eM/Pnc3i8seGky1FZYo4QkakkJzVfffUVgLLVTsuXLxctdVQqldi/fz8aNmxo/giJyKzk9HzsPpchKfmRtJO3l5vBpEatfK9P4tl0vLv5jKTPpau36OLFi1r/Lu3btw8dOnTQ+TA2Ra0gb6PHGEq6HJmc5JjIGiQnNfPmzQNQ9lvPkiVL4OrqqnnP3d0dsbGxWLJkifkjJCKzkro9weGkLMnJDwAMfChK57YF5SfVGtrWQE3d66PvgWnsPLW33noLX3zxhaituLgYbm5lw03dG0ega3w45u2+hAV7L0u8y33qyc9S95Zytu0HzDUsSGROkpOa5ORkAECnTp2wZcsWBAUFWSwooqpI17wEAGafqyD14XroSqak5GfBnsvYcCxV77Hh5VYobTh2TVJFWzmbP1ashKtruKl3797Ytm2b1rmuLgq0rRsqO6kpX1cmwEtaUuNs2w9UZu8uIkuRPadm7969loiDqErTNRQS6F3Wo3Cn4P6QjTnmKkh/uEpLnub9eknve291qY8xnetqEjGp2ykcMjKPpiL1eWfPntUqLXHo0CG0bt1a77nGhs4A7VVQ4eX+HpQqoUpuP8ANMskeya4oTETmpa/M/p2CElFCA2iX3jeF1HL9lf3tWgFgw7FUUZvUirZSH4SBXm6a815++WWthKa0tFRnQlO+QvDR5GxM6am/0q8CwIJBCVg/sjW+HNgM60e2xsGJnTWxVtXtB7hBJtkj2T01RGQ+coZZAPPMVZC6AWXr2iFGezCMxapr+EE9l8XQsJrUB+HC5xLQpnaw1r5zHZ54GnO+WgYotH9v07da5+VH47Dj73RxUUCJPWPqZE2rqKDMnjVHWhrNDTLJHjGpIbIhY/MSdDHHXAWpD2FDyY/UREdXr4uri8Jg7FIfmO53rsLVtbrovfAXv0BKeF0MWnYYEQGemNKzEYJ8PDRbMuiarJyRU4iv9ydj4eDmmmPlJhVSkjVDHG1ptKm7sxNZEisKE9nQ9lM38OaGUyadW7H0vimk9Azoe9jqW+1UkanVdNXDcoDuB2bsmRXYt3Or6JzoCTug0NE7I0X5rRz0PYgt1ZNirGLy2C71DW4hYUuOloyRYzJrReHTp09LvvGDDz4o+Viiqq4y8w3MMVfBWI8JoL8HAoDk1Uym0NebVMPPDUfefxwp5Y4Ne6gnvDqPMuk+asZ6wCz18Jay03f5ydj2ljBUtoeKyJwk9dS4uLhAoVBAEAStseuK7HmbBPbUkL1RbwQpZ96KlB4FazHWm6KexFuZHo7y5/737xm80r+H6P21Ow/g/d9zzPBpyujqAdt5Og2jvzupdazcvad0kbpvljnvSeRopD6/JfXTJicn48qVK0hOTsbmzZsRFxeHRYsW4eTJkzh58iQWLVqEOnXqYPPmzWb7AERVgaGVM7rY21wFKauZEs+mo93cPRi07DDe3HAKg5YdRru5eySv4FL3Jq2cNloroVGpVEgqNW/NrIo9YDtPp2PMeu2EBrifyM348RyUhjaKMkDukmdz3JPIWUkafoqJidH8uV+/fvjqq6/wxBNPaNoefPBBREVFYcqUKejTp4/ZgyRyZvqGWYK83SBAXKdG7moaazA0/LDzdDpGf3dC6xw5ZfRLSkq09pUbO3Ys5s2bh52n07D6zxSzfA5dw2WJZ3XHX15lJ26bMozIwnZEusle/XTmzBnExcVptcfFxeHcuXNmCYqoqjE0b8XYsI09LAPWNTdn5+k0oz0c7245Az8PN7SuE6Iz5t9//x0dO3YUtV28eBH169f//4RD9/Xl0tUDpp7rIpWpReakFP8z9z2JnJXspKZRo0aYNWsWVqxYAU/Pst8wioqKMGvWLDRq1MjsARJVFfom7Rr6TdxeV55ITTjuFJTguRVHdMb8WJcu2PPbb6LjVSoVFAqF7ITDGF09YHKX25s6cdvQ0mhL3ZPIWclOapYsWYJevXohKioKTZs2BQD8/fffUCgU+Omnn8weIBHpZq87JJuScJSPuVO9YM0vTGr+bQYgvtdI/O+fDHRvHGFSfR81qcuk5fSCRFSyyJy+IUh9WNiOSDfZSU2rVq2QnJyMdevW4cKFCxAEAQMGDMDgwYPh4+NjiRiJqAJ73iHZ1IKCCgBvfbYGF1ZPEr0X+cpyuAWGixKfolKV5Gsb2rfJEDm9IOaYuF1xCFJdKJCF7YikM6misLe3N15++WVzx0JEEtnzDsmmzvNI+2YcitPFm2PGTLzf+1s+Wfv02aaSr7tgUAKCfNxlzzmSutHlgkHNzdYjVnEIskG4X6W3XiCqSkxKatauXYulS5fiypUrOHToEGJiYjBv3jzUrl0bvXv3NneMRFSBPe+QLHeeh6qkENc+f1bUFvjoCwho01/rWHWyBgUkJhwJeOJB0x7+Uua6VOb6UrCwHZE8suuJL168GOPGjUOPHj1w+/ZtTbG9oKAgfPHFF+aOj4h0sOcdko3tAl5ewb9HtBKamqNW60xoysvMLzJa32fBoOaVTjj01eGJCPDEkiGWTWjU1L03vZvVRBs9q8SIqIzsvZ/i4+Mxe/Zs9OnTB35+fvj7779Ru3ZtnD17Fh07dkRmZqZFAv3www/x888/49SpU3B3d8edO3dkX4MVhclZGKtEbOuqw/oqDZeXtnwUSrKuidrKDzcZot5Pylqrv+xh2TxRVWbWvZ/KS05ORvPmzbXaPTw8cPfuXbmXk6y4uBj9+vVDmzZtsGLFCovdh8gR2PsOyfpW8wR6uyH7Ti6uzesnOj6o8wj4P9RH0rXLrzSy1vCMlD2yiMj2ZCc1cXFxOHXqlKjKMADs2rUL8fHxZgusohkzZgAAVq9ebbF7EDkSfYmDuSeSmtpLoSvhSD66G88NHiQ6rtZra+HqK32rg4rJWsWEQ6kScCgpi70qRFWQ7KRm/PjxeO2111BYWAhBEHD06FGsX78ec+bMwfLlyy0RIxHpYemeisoO77i6KNAqLhhHk7PxVLsHkZmRJnpf6nCT2ltd6hu8r70WIyQi65Cd1Lz00ksoLS3FhAkTUFBQgMGDB6NmzZr48ssvMXDgQEvEaLKioiIUFRVpXufm5towGiLjTOkVsdTQiDmK+yWeTceUTUdxfGYfUXtwt9Hwa/6E7pP0CPf3wJjOdS0aLxE5NpOWdI8cORIjR45EZmYmVCoVwsLCTLr59OnTNcNK+hw7dgwtW7Y06fpz5swxen0ie2FPvQzmKO6XeDYdz7/7CTJ/nidqr/X6t3D1DpAci/rq0596QO+97LkYIRFZj+wl3Z07d9asPAoNDdUkNLm5uejcubOsa40ZMwbnz583+NO4cWO5IWpMmjQJOTk5mp9r164ZP4nIBtS9DBUL6ql7GRLPpls1HjnF/XRRqgQ8+VA9UUKj8PBBzMSfZCU0QNnkYmO9LJWNl4icg+yemn379qG4uFirvbCwEAcOHJB1rdDQUISGhsoNQTIPDw94eHhY7PpE5mCPvQyVKe6XlZWl9f91yBNvwbfJYybF4lHNBV3jw2XHUZnjiMgxSU5qTp8+rfnzuXPnkJGRoXmtVCqRmJiImjVrmje6clJTU5GdnY3U1FQolUqcOnUKAFC3bl34+vpa7L5ElmaPWx6YWtxv6dKlePXVV0VtUW9ugIun6f+PZuQWGf3sUuP99798HErK4oooIiclOalp1qwZFAoFFAqFzmEmLy8vzJ8/36zBlTd16lSsWbNG81pdK2fv3r3o2LGjxe5LZGn22MtgbN8jXbtEu7i4oHwtT1e/UNQavdrgfZ5uFomtp9IMHgMY/+xS9mkCgAV7L2PB3stcEUXkpCTPqUlOTkZSUpJmGXdycrLm58aNG8jNzcWwYcMsFujq1ashCILWDxMacnSW2vJAXa9l+6kbOJSUBaVKevFwdXE/fWcIuF8v5r///oNCoRAlNOvXb8BDkzbo3cJAgbJJ0M+2jJIUj7HPro5XfW1jbDVXiYgsS3JPjbrYnkqlslgwRFWRKb0ixlhrJdUXX3yBt956S9SWm5sLPz8/BJbbKqEiAcBTTSPQunaI2T67vmKE+u7PFVFEzkf26qc5c+Zg5cqVWu0rV67E3LlzzRIUUVViqJfBlC0P9K2kSpfRO6GevKyPAkCPJpGihKZRo0YoVapw9mYxtp+6gQAvd4xoH6v3Gl/vT8bucxlm/ezdG0fg4MTOWD+yNcZ0qmPwWK6IInI+spOapUuXomHDhlrtDzzwAJYsWWKWoIiqGn27QYcHeMoqGmdoJRVQ9iCftOWM0aEoQ5OXS3MzkTL3SVHbtm3b8Pmm39Bu7h4MWnYYb244hUHLDmPFwRSD91H3lJjjs6upixHWq+En6XiuiCJyHrKXdGdkZCAiQvsfmerVqyM9nePTRKbqGh8OPw83HLqSCaDswdy6doisoRFjK6kA4HZBCRbs+Rdvdqmv9xh9D/qcQ5twZ/83ora7d+9i/5UcndV8DeVO5XtKLLHdg5y5StyFm8g5yE5qoqKi8McffyAuLk7U/scffyAyMtJsgRFVJbrmwGw+cV32HBipvQ6r/kjBmM719D64dSUEVyv0zrhH1MO+A4fg4emFGT8eMrjqyBB1zOoeFnWC8dPptEolGFLnKt2+W4R2c/fYRSVnIqoc2UnNiBEjMHbsWJSUlGiWdv/222+YMGEC3n77bbMHSOTsEs+m41UdE2pN2bNIau/EnXslBmu/lE8ISnL+w40lw0Xv1+g3A3HN22o2qzTWOyQ1ZnNOcFbPVRq17gQUgCixUadITzWNwGvfneR+UUROQvacmgkTJmD48OEYPXo0ateujdq1a+P111/HG2+8gUmTJlkiRiKnpVQJeHfLGZ3vqR+0M348J3k5dqu4YAR6uUk61lCvjjohuL1/rVZCE/P2VnjVbqGZwGvqnBT1sm71yiZLbBVhaK7SwsEJ2PF3ut5KzoC8756IbE92T41CocDcuXMxZcoUnD9/Hl5eXqhXrx63IyAywYI9l3GnoETv+3KrCbu6KPBS2zjM+/WS0WMN9eoIgoAeTcTDyZ4xD6LGwNlaPSdy6+cA2iubLLlVhL75OvZYyZmIKsekXboBwNfXFw899JA5YyGqUpQqAav+SJZ0rJzekDGd62LVn8l6kyVjtV8uXbqEBg0aiNpqDJoNz+gHAUBUZA+QVs3XRSGeNBxeITGydIKhnq9Tnj1WciaiypGU1PTt2xerV6+Gv78/+vbta/DYLVu2mCUwImd3NDkbd+7p76UpT05viKuLAh/1baJzno6x2i/jxo3DvHnzRG3R72yDwvX+PxX/5RaJ5ptImbuyYFACgnzc9a4uskWCYalKzkRkO5KSmoCAACgUCs2fiajyfj2XYfwgAIFebrKqCQNlQy5LdFTXVfeQdI0Px6GkLE2S8VBsENyquYquEdSwDfx7T9a6tq7hIH3VfCv2yOhjiwTDEpWcici2FELFvmQnlpubi4CAAOTk5MDf39/W4VAVpm/Fky5vdalnsKaMIbrqr+w+lyFKPopvXUX6ytdE5y3dtAuz/1Iavf76ka1Fwzqm1ntRqgS0m7vHaIJxcGJns9aPSSy3lYOuHiaufiKyD1Kf3ybPqSEi0xjbgqC8IG83jOlcz+R7VZxLon6Iqx/gWf9bgPxTiaJzSkpK8PPZ/4C/Thm9fsXhIF1zV6TGaWwIS852CVJVtoeJiOyLpKSmefPmmuEnY06ckPbbJ1FVUr4HIzOvSHJdlzl9m5jtQV5+hZEgqJD68VOi930adUCT56dA4eJqk+EgWyUYlqhmTES2ISmp6dOnj+bPhYWFWLRoEeLj49GmTRsAwOHDh/HPP/9g9OjRFgmSyJHpKignxbC2sWZ9kKtXGBX/l4T01W+K3gt/8Qt4hNfVrDCy1XwTWyUYpvYwEZF9kZTUTJs2TfPnESNG4I033sDMmTO1jrl27Zp5o6Mqy1n24qk43CNH1/hws8ZyM68QmT99hrv/7BW1R0/YAYXCRXScrYaDACYYRGQ62ROFAwICcPz4cdSrJx7n//fff9GyZUvk5OSYNUBz4kRhx2DOUvm2pJ78KreHxhKTYpVKJapVE/8O4/tgN4T0eEPr2PKTf53l74KIHJvFJgp7eXnh4MGDWknNwYMH4enJeg5UOfp6NuxxLx5jvUmm7IlkiV6Qw4cPa4aK1SKGLYB79Vite1ccUuJ8EyJyJLKTmrFjx2LUqFH466+/0Lp1awBl/2iuXLkSU6dONXuAVHVYslS+uUnpwTClUJw5J8UqVQI6d38S+3fvFLXHTPgRCoVC8pASh4OIyFHITmreffdd1K5dG19++SW+++47AECjRo2wevVq9O/f3+wBUtXhKHvxSO1NkroyaErPRgj18zBrL8hPJ1PRKyFG1Bb+SF+sWroAALiEmYickkl1avr3788EhszOEfbikdObJHUF0dC2cWbtefpk9VZMeEm8nUnkiCVwD6mlSboOTuxs9SElS0/+dpbJ5URkOpOSmjt37uCHH37AlStX8M477yA4OBgnTpxAjRo1ULNmTXPHSFWEI+zFI7c3ydoriLp27YZff90taosuN9xUPumyZm+XpSccc0IzEQGAi/FDxE6fPo369etj7ty5+OSTT3Dnzh0AwNatWzFp0iRzx0dViLpnQ98jXoGyB5Ut9+KR25ukLigXHiBOxMIDPM066bmoqAgKhUKU0Pi37oeYiT+JCmeWT7qsRT1cVzEZVA/XJZ5Nt+vrE5HjkN1TM27cOAwdOhQff/wx/Pz8NO09evTA4MGDzRocVS22rI0ilSm9SZZeQbR7925069ZN1Bb5ynK4Beqvc2OtITxLT/52pMnlRGR5sntqjh07hldeeUWrvWbNmsjIkLbrMJE+1urZMJWpvUnqFUS9m9VEmzohZnvAtmnTRiuhiZn4k8GEBrDeEJ6c4Tp7vD4RORbZPTWenp7Izc3Var948SKqV69ulqCoarPn2ij20pt07949eHt7i9pmzpyJSe9NlrTbtbWG8Cw9+dsRJpcTkfXI7qnp3bs3PvjgA5SUlAAAFAoFUlNT8e677+KZZ54xe4BUNVmqZ8McbN2b9OOPP2olNKmpqXj//fc1SRcArd4kWwzhWXrytyNMLici65G9TUJubi6eeOIJ/PPPP8jLy0NkZCQyMjLQpk0b7Ny5Ez4+PpaKtdK4TQKZky2WEDdp0gRnz54Vten6X9heVgOpt4ow1nNk6pYQlr4+EdkHqc9v2UmN2p49e3DixAmoVCokJCSgS5cuJgdrLUxqyFHl5+eLJuYDwGeffYZx48bpPcde6raoVycBuofrKtu7ZenrE5HtWSSpKS0thaenJ06dOoXGjRubJVBrYlJDjuj777/XKnaZnp6O8HDz7uJtSaxTQ0SVYZENLatVq4aYmBgolcpKB0hExsXGxuLq1auiNhM7V23K0pO/7XlyORFZj+zhp1WrVuH777/HunXrEBxsuyJopmBPDTmKnJwcBAYGitoWLVqEUaNG2SYgIiIbskhPDQB89dVXuHz5MiIjIxETE6M1MfjEiRPyoyUijbVr1+KFF14Qtd26dQuhoaE2ioiIyDHITmp69+4tKrtOJIe9TF61V3379sXWrVs1r319fZGXl2fDiIiIHIfJq58cEYefbIuTOfXLy8vT+m9y1apVGDp0qG0CIiKyI1Kf35KL7xUUFOC1115DzZo1ERYWhsGDByMzM9MswZLz46aD+v36669a/5NmZ2czoSEikklyUjNt2jSsXr0aPXv2xMCBA7F7925OWiRJjG06CJRtOqhUVZlOQ42BAweia9eumtcvv/wyBEFAUFCQDaMiInJMkufUbNmyBStWrMDAgQMBAEOGDEHbtm2hVCrh6upqsQDJ8cnZdLBNnRDrBWZDulY3HTx4EG3btrVNQERETkByT821a9fQvn17zetWrVqhWrVqSEtLs0hg5Dyq0qaDSpWAQ0lZ2H7qBg4lZensfdq5c6dWQlNQUMCEhoiokiT31CiVSri7u4tPrlYNpaWlZg+KnEtV2XRQykTo3r17Y8eOHZr333zzTXzxxRfWDpWIyClJTmoEQcDQoUPh4eGhaSssLMSrr74qqlWzZcsW80ZIDq9VXDAiAjyNbjrYKs6xijmWp54IXfHzqSdCf9wrDv3bPSB67+jRo3jooYesFyQRkZOTnNS8+OKLWm1DhgwxazDknFxdFJjWKx6j1p2AAro3HZzWK95h69UYmwhdcOkQ+rd7UtReWFgo+gWBiIgqT3JSs2rVKkvGQU6ue+MILB6SoDU8E+4EdWoMTYT+b8P7KLx6SvP63XffxZw5c6wUGRFR1SK7ojCRqZx100FdE5yVBTm4Pv85UdvnG/6HtwZ0s1ZYRERVDpMasipXF4XJy7btdYuFihOc754/gMwdc+83uLgietxmtG7ZwsqRERFVLUxqyCHY8xYL6onQ6XfuIePbCSi6cV7zXkDbwQhqN9jhJ0ITETkCJjVk94ytLFo8JMGmiY2riwKvtw7Bc52bi9ojhi2ER/UYAI49EZqIyFFILr5HZAuOsMXCN998I0poFO7eiB6/He7VYxAe4GnzpIuIqKpgTw3ZJfX8mT8u37LbLRYEQUBCQgJOnTqlaftw9mx06v+y3c37ISKqCpjUkN3RNX/GGGtvsXD9+nVERUWJ2i5cuIAGDRpYNQ4iIrqPw09kV9TzZ+QkNIB1t1hYtmyZKKEJqR6GzcevIrtaaJXcaZyIyF6wp4bshqH5M/pYc4sFQRDQsGFDXLp0SdMW03MU0Lgnxn1/BoD9rMgiIqqK2FNDdsNQZV5drLnFQkpKClxcXEQJTc2XlwGNe4qOU6/ISjybbtF4iIhIG5Mashty58VYa2XRggULEBcXp3ldu3ZtPDzrF1QL0r6vvazIIiKqijj8RHZD6ryYMZ3qom3dUIuvLFKpVIiJicH169c1bYsWLUKzbv0xaNlhvefZckUWEVFVxqSG7Ia6Mm9GTqHOeTXq+TNvda1v8eGmy5cvo169eqK2q1evIjo6GttP3ZB0DWuvyCIiquo4/ER2w9VFgWm94gHcny+jZs35M59++qkooWncuDFUKhWio6MBSO9RsuaKLCIiYlJDdqZ74wgsHpKA8ABxQmCN+TNKpRIhISEYP368pm3FihU4c+YMFIr7iZS6R0lfaqVA2SooU1ZkKVUCDiVlYfupGziUlMV5OUREMjjE8FNKSgpmzpyJPXv2ICMjA5GRkRgyZAgmT54Md3d3W4dHZta9cQS6xodbdUfu8+fPIz4+XtR248YNREZGah2r7lEate4EFIBoqKwyPUr2vGknEZEjcIiemgsXLkClUmHp0qX4559/MG/ePCxZsgTvvfeerUMjC3F1UaBNnRD0blYTbeqEWDShmTVrliihadWqFVQqlc6ERs3cPUr6ig5yiTgRkXQKQRAcsn/7k08+weLFi3HlyhXJ5+Tm5iIgIAA5OTnw9/e3YHTkCEpKSuDv74/CwvuJxLfffovBgwdLvoZ6j6qMnHvIvluMYF8PhPvL61lSqgS0m7tHb40e9QTpgxM7cx8pIqqSpD6/HWL4SZecnBwEBxues1BUVISioiLN69zcXEuHRQ7i9OnTaNq0qagtIyMDNWrUkHUdVxcFcu4V4+P/XTR52MhY0UEuESciksYhhp8qSkpKwvz58/Hqq68aPG7OnDkICAjQ/FTcgJCqpilTpogSmo4dO0IQBNkJDWCeYSOpS7+5RJyIyDCbJjXTp0+HQqEw+HP8+HHROWlpaejevTv69euHESNGGLz+pEmTkJOTo/m5du2aJT8O2bni4mIoFArMmjVL0/b9999j7969Jl3P0F5VcioLc4k4EZF52HT4acyYMRg4cKDBY2JjYzV/TktLQ6dOndCmTRt8/fXXRq/v4eEBDw+PyoZpkHpOhbVW6ZBp/vrrL7Rs2VLUduvWLYSGhpp8TXMNG0ktOmiNTTuJiByZTZOa0NBQyQ+VGzduoFOnTmjRogVWrVoFFxfbj5xxCa5jeOedd/DZZ59pXvfo0QM7d+6s9HXNNWxkqSXiRERVje0zAwnS0tLQsWNHREVF4dNPP8WtW7eQkZGBjIwMm8XEJbj2r7CwEAqFQpTQbN++3SwJDWDeYSNbFh0kInIWDrH66ZdffsHly5dx+fJl1KpVS/SeLVakG5tLoUDZXIqu8eH87dpGDh8+jDZt2ojasrOzERQUZLZ7mHvYyBZFB4mInIlD9NQMHToUgiDo/LEFOXMpyPrGjBkjSmj69u0LQRDMmtAAltmryppFB4mInI1D9NTYGy7BtU8FBQXw8fERtSUmJuLxxx+32D3Vw0YV51aFc24VEZHVMakxAZfg2p8DBw7g0UcfFbVZq3I0h42IiOyDQww/2RtL7tJM8g0fPlyU0AwZMgSCIFh1KwwOGxER2R57akzgaEtwnbWWTn5+Pvz8/ERte/bsQadOnWwUERER2RKTGhM5ylwKZ62l89tvv6FLly6itry8PPj6+tooIiIisjWH3aXbFJbYpduee0HUtXQq/gWro3PU+ieDBw/G+vXrNa9HjhwpqcI0ERE5JqffpdteqOdS2BtnrKWTk5ODwMBAUduBAwfQrl07s97HnhNVIiLSj0mNkzLXvkT2YteuXXjiiSdEbQUFBfDy8jLrfZx1uI6IqCrg6icn5Uy1dPr06SNKaF5//XUIgmCRhIZbXxAROS721DgpZ6ilk52djZAQcS/SkSNH0KpVK7PfyxmH64iIqhr21DgpR6+ls337dq2EprCw0CIJDcCtL4iInAGTGidliX2JrKVbt27o06eP5vWECRMgCAI8PDwsdk9nGq4jIqqqOPzkxByllo7arVu3EBYWJmo7ceIEmjdvbvF7O8NwHRFRVcekxsk5yr5EmzZtwoABAzSvXV1dUVBQAHd3d6vcXz1cl5FTqHNejQJlyaC9DtcRERGHn6oEe96XSBAEtG/fXpTQTJs2DaWlpVZLaADHHq4jIqIy7Kkhm8nIyEBEhHgI7MyZM2jcuLFN4nG04ToiIhJjUkM2sXbtWrzwwgua176+vrh9+zaqVbPtf5KOMlxHRETamNSQVQmCgJYtW+LEiROattmzZ2PSpEk2jErMXre+ICIiw5jUkNVcv34dUVFRorYLFy6gQYMGNoqIiIicCScKk1UsX75clNCEhYWhtLSUCQ0REZkNkxqyKEEQ0KhRI4wcOVLT9vnnn+O///6Dq6urDSMjIiJnw+EnspirV68iNjZW1Hb58mXUqVPHNgHZkFIlcPIxEZGFMakhi1i4cCHGjBmjeR0XF4fLly/DxaXqdQ4mnk3XWiYewWXiRERmV/WeMGRRKpUKMTExooRm0aJFuHLlSpVNaEatO6G1WWZGTiFGrTuBxLPpNoqMiMj5sKeGzOby5cuoV6+eqC0lJQUxMTE2isi2lCoBM348p3PbBQFllYpn/HgOXePDORRFRGQGVe9XZ7KIzz77TJTQxMfHa3ptqqqjydlaPTTlCQDScwpxNDnbekERETkx9tRQpSiVSoSHhyMzM1PTtmLFCgwbNsyGUdmHm3n6ExpTjiMiIsOY1JDJzp8/j/j4eFHb9evXUbNmTRtFZF/C/DzNehwRERnG4ScyyezZs0UJTcuWLaFSqZjQlNMqLhgRAZ5au36rKVC2CqpVXLA1wyIiclpMakiW0tJS+Pj4YPLkyZq2tWvX4tixY1AoONm1PFcXBab1Kkv8Kn4z6tfTesVzkjARkZkwqSHJzpw5Azc3NxQUFGja0tPTMWTIEBtGZd+6N47A4iEJCA8QDzGFB3hi8ZAE1qkhIjIjzqkhSaZMmYJZs2ZpXj/66KPYt28fe2ck6N44Al3jw1lRmIjIwpjUkEHFxcXw8PAQtW3atAn9+vWzUUSOydVFgTZ1QmwdBhGRU2NSQ3qdOHECLVq0ELXdunULoaGhNoqIiIhIP86pIZ3Gjx8vSmgef/xxCILAhIaIiOwWe2pIpLCwEF5eXqK2bdu2oXfv3jaKiIiISBomNaRx5MgRtG7dWtSWnZ2NoKAgG0VEREQkHYefCADw+uuvixKaPn36QBAEJjREROQw2FNTxRUUFMDHx0fUtmvXLnTv3t1GEREREZmGSU0VdvDgQbRv317UdufOHQQEBNgoIiIiItNx+KmKGjFihCihGTx4MARBYEJDREQOiz01VUx+fj78/PxEbb/99hs6d+5so4iIiIjMgz01VciePXu0Epq8vDwmNERE5BSY1FQRQ4YMwWOPPaZ5PXz4cAiCAF9fXxtGRUREZD4cfnJyOTk5CAwMFLXt379fa4IwERGRo2NPjRNLTEzUSmgKCgqY0BARkVNiUuOknn76afTo0UPzesyYMRAEQWsLBCIiImfB4Scnc/v2bQQHB4vaDh8+jIcffthGEREREVkHe2qcyI4dO7QSmnv37jGhISKiKoFJjZPo3r27aCft8ePHQxAEeHp62jAqIiIi6+Hwk4PLzMxE9erVRW1//fUXEhISbBQRERGRbbCnxoF9//33WglNUVERExoiIqqSmNQ4IEEQ0KFDB/Tv31/TNmXKFAiCAHd3dxtGRkREZDscfnIwGRkZiIiIELWdPn0aTZo0sVFERERE9oE9NQ5k3bp1ooTG19cXJSUlTGiIiIjApMYhCIKAli1b4vnnn9e0ffjhh8jLy0O1auxsIyIiAjj8ZPdu3LiBWrVqidrOnz+Phg0b2igiIiIi++QwPTVPPfUUoqOj4enpiYiICDz//PNIS0uzdVgWtXLlSlFCU716dZSWljKhISIi0sFhkppOnTph06ZNuHjxIjZv3oykpCQ8++yztg7LIgRBQHx8PIYPH65p++yzz3Dz5k24urraMDIiIiL7pRAEQbB1EKbYsWMH+vTpg6KiIri5uUk6Jzc3FwEBAcjJyYG/v7+FIzTN1atXERsbK2r7999/UbduXdsEREREZGNSn98O01NTXnZ2Nr799ls88sgjBhOaoqIi5Obmin7s2aJFi0QJTXR0NJRKJRMaIiIiCRwqqZk4cSJ8fHwQEhKC1NRUbN++3eDxc+bMQUBAgOYnKirKSpHKo1KpEBcXh9dee03TtmDBAly9ehUuLg71V0RERGQzNn1iTp8+HQqFwuDP8ePHNcePHz8eJ0+exC+//AJXV1e88MILMDR6NmnSJOTk5Gh+rl27Zo2PJUtSUhJcXV2RkpKiaUtOThYlOERERGScTefUZGZmIjMz0+AxsbGxOneavn79OqKiovDnn3+iTZs2ku5nb3Nq5s2bh3HjxmleN2rUCP/88w8UCoUNoyIiIrIvUp/fNq1TExoaitDQUJPOVediRUVF5gzJKpRKJSIjI3Hz5k1N27JlyzBixAgbRkVEROTYHKL43tGjR3H06FG0a9cOQUFBuHLlCqZOnYo6depI7qWxFxcuXECjRo1EbdeuXdMqsEdERETyOMQsVC8vL2zZsgWPPfYYGjRogGHDhqFx48b4/fff4eHhYevwJJszZ44ooWnRogVUKhUTGiIiIjNwiJ6aJk2aYM+ePbYOw2SlpaUIDAzE3bt3NW1r167FkCFDbBgVERGRc3GIpMaRnT17VmsX7fT0dISHh9soIiIiIufkEMNPjmratGmihKZdu3ZQqVRMaIiIiCyAPTUWUFJSAk9PT6hUKk3bxo0b0b9/fxtGRURE5NyY1JjZyZMnkZCQIGq7efMmqlevbqOIiIiIqgYOP5nRxIkTRQlN165dIQgCExoiIiIrYE+NGRQVFWlVPd62bRt69+5to4iIiIiqHiY1ZjBmzBjR66ysLAQHB9soGiIioqqJw09m0KpVKwBA7969IQgCExoiIiIbsOmGltZmbxtaEhERkXFSn9/sqSEiIiKnwKSGiIiInAKTGiIiInIKTGqIiIjIKTCpISIiIqfApIaIiIicApMaIiIicgpMaoiIiMgpMKkhIiIip8CkhoiIiJwCkxoiIiJyCkxqiIiIyCkwqSEiIiKnwKSGiIiInEI1WwdgTYIgACjbwpyIiIgcg/q5rX6O61Olkpq8vDwAQFRUlI0jISIiIrny8vIQEBCg932FYCztcSIqlQppaWnw8/ODQqGwdTg65ebmIioqCteuXYO/v7+tw3Fq/K6ti9+3dfH7th5+15YnCALy8vIQGRkJFxf9M2eqVE+Ni4sLatWqZeswJPH39+f/HFbC79q6+H1bF79v6+F3bVmGemjUOFGYiIiInAKTGiIiInIKTGrsjIeHB6ZNmwYPDw9bh+L0+F1bF79v6+L3bT38ru1HlZooTERERM6LPTVERETkFJjUEBERkVNgUkNEREROgUkNEREROQUmNXbsqaeeQnR0NDw9PREREYHnn38eaWlptg7LKaWkpGD48OGIi4uDl5cX6tSpg2nTpqG4uNjWoTmlDz/8EI888gi8vb0RGBho63CczqJFixAXFwdPT0+0aNECBw4csHVITmn//v3o1asXIiMjoVAosG3bNluHVOUxqbFjnTp1wqZNm3Dx4kVs3rwZSUlJePbZZ20dllO6cOECVCoVli5din/++Qfz5s3DkiVL8N5779k6NKdUXFyMfv36YdSoUbYOxels3LgRY8eOxeTJk3Hy5Em0b98ePXr0QGpqqq1Dczp3795F06ZNsWDBAluHQv+PS7odyI4dO9CnTx8UFRXBzc3N1uE4vU8++QSLFy/GlStXbB2K01q9ejXGjh2LO3fu2DoUp/Hwww8jISEBixcv1rQ1atQIffr0wZw5c2wYmXNTKBTYunUr+vTpY+tQqjT21DiI7OxsfPvtt3jkkUeY0FhJTk4OgoODbR0GkWTFxcX466+/0K1bN1F7t27d8Oeff9ooKiLrYVJj5yZOnAgfHx+EhIQgNTUV27dvt3VIVUJSUhLmz5+PV1991dahEEmWmZkJpVKJGjVqiNpr1KiBjIwMG0VFZD1Maqxs+vTpUCgUBn+OHz+uOX78+PE4efIkfvnlF7i6uuKFF14ARwylk/t9A0BaWhq6d++Ofv36YcSIETaK3PGY8l2TZSgUCtFrQRC02oicUTVbB1DVjBkzBgMHDjR4TGxsrObPoaGhCA0NRf369dGoUSNERUXh8OHDaNOmjYUjdQ5yv++0tDR06tQJbdq0wddff23h6JyL3O+azC80NBSurq5avTI3b97U6r0hckZMaqxMnaSYQt1DU1RUZM6QnJqc7/vGjRvo1KkTWrRogVWrVsHFhR2ZclTmv20yD3d3d7Ro0QK7d+/G008/rWnfvXs3evfubcPIiKyDSY2dOnr0KI4ePYp27dohKCgIV65cwdSpU1GnTh320lhAWloaOnbsiOjoaHz66ae4deuW5r3w8HAbRuacUlNTkZ2djdTUVCiVSpw6dQoAULduXfj6+to2OAc3btw4PP/882jZsqWmxzE1NZXzwywgPz8fly9f1rxOTk7GqVOnEBwcjOjoaBtGVoUJZJdOnz4tdOrUSQgODhY8PDyE2NhY4dVXXxWuX79u69Cc0qpVqwQAOn/I/F588UWd3/XevXttHZpTWLhwoRATEyO4u7sLCQkJwu+//27rkJzS3r17df53/OKLL9o6tCqLdWqIiIjIKXDSABERETkFJjVERETkFJjUEBERkVNgUkNEREROgUkNEREROQUmNUREROQUmNQQERGRU2BSQ0RkRMeOHTF27Fhbh0FERjCpISIAMLrD9tChQ60WiyWSiKFDh6JPnz5mvaY++/btg0KhwJ07d6xyPyIqw72fiAgAkJ6ervnzxo0bMXXqVFy8eFHT5uXlJTq+pKQEbm5uVouPiMgY9tQQEYCyjTvVPwEBAVAoFJrXhYWFCAwMxKZNm9CxY0d4enpi3bp1mD59Opo1aya6zhdffIHY2FhR26pVq9CoUSN4enqiYcOGWLRokd44hg4dit9//x1ffvmlppcoJSUFAHDu3Dk88cQT8PX1RY0aNfD8888jMzNTc+4PP/yAJk2awMvLCyEhIejSpQvu3r2L6dOnY82aNdi+fbvmmvv27dN5/7t37+KFF16Ar68vIiIi8Nlnn2kds27dOrRs2RJ+fn4IDw/H4MGDcfPmTQBASkoKOnXqBAAICgoS9XIlJiaiXbt2CAwMREhICJ588kkkJSUZ+FshIjmY1BCRZBMnTsQbb7yB8+fP4/HHH5d0zrJlyzB58mR8+OGHOH/+PGbPno0pU6ZgzZo1Oo//8ssv0aZNG4wcORLp6elIT09HVFQU0tPT0aFDBzRr1gzHjx9HYmIi/vvvP/Tv3x9AWU/ToEGDMGzYMJw/fx779u1D3759IQgC3nnnHfTv3x/du3fXXPORRx7Ref/x48dj79692Lp1K3755Rfs27cPf/31l+iY4uJizJw5E3///Te2bduG5ORkTeISFRWFzZs3AwAuXryI9PR0fPnllwDKEqZx48bh2LFj+O233+Di4oKnn34aKpVK0ndJRIZx+ImIJBs7diz69u0r65yZM2fis88+05wXFxeHc+fOYenSpXjxxRe1jg8ICIC7uzu8vb0RHh6uaV+8eDESEhIwe/ZsTdvKlSsRFRWFS5cuIT8/H6Wlpejbty9iYmIAAE2aNNEc6+XlhaKiItE1K8rPz8eKFSvwzTffoGvXrgCANWvWoFatWqLjhg0bpvlz7dq18dVXX6FVq1bIz8+Hr68vgoODAQBhYWEIDAzUHPvMM8+IrrNixQqEhYXh3LlzaNy4sd64iEga9tQQkWQtW7aUdfytW7dw7do1DB8+HL6+vpqfWbNmyR52+euvv7B3717RdRo2bAgASEpKQtOmTfHYY4+hSZMm6NevH5YtW4bbt2/LukdSUhKKi4vRpk0bTVtwcDAaNGggOu7kyZPo3bs3YmJi4Ofnh44dOwIAUlNTjV5/8ODBqF27Nvz9/REXFyfpPCKShj01RCSZj4+P6LWLiwsEQRC1lZSUaP6sHlZZtmwZHn74YdFxrq6usu6tUqnQq1cvzJ07V+u9iIgIuLq6Yvfu3fjzzz/xyy+/YP78+Zg8eTKOHDmiSR6MqfhZdLl79y66deuGbt26Yd26dahevTpSU1Px+OOPo7i42OC5vXr1QlRUFJYtW4bIyEioVCo0btzY6HlEJA2TGiIyWfXq1ZGRkQFBEKBQKAAAp06d0rxfo0YN1KxZE1euXMFzzz0n+bru7u5QKpWitoSEBGzevBmxsbGoVk33P10KhQJt27ZF27ZtMXXqVMTExGDr1q0YN26czmtWVLduXbi5ueHw4cOIjo4GANy+fRuXLl1Chw4dAAAXLlxAZmYmPvroI0RFRQEAjh8/rhU/ANH9srKycP78eSxduhTt27cHABw8eFDqV0JEEnD4iYhM1rFjR9y6dQsff/wxkpKSsHDhQuzatUt0zPTp0zFnzhx8+eWXuHTpEs6cOYNVq1bh888/13vd2NhYHDlyBCkpKcjMzIRKpcJrr72G7OxsDBo0CEePHsWVK1fwyy+/YNiwYVAqlThy5Ahmz56N48ePIzU1FVu2bMGtW7fQqFEjzTVPnz6NixcvIjMzU9SjpObr64vhw4dj/Pjx+O2333D27FkMHToULi73/6mMjo6Gu7s75s+fjytXrmDHjh2YOXOm6DoxMTFQKBT46aefcOvWLeTn5yMoKAghISH4+uuvcfnyZezZswfjxo2rzNdPRBUJREQVrFq1SggICNC8Tk5OFgAIJ0+e1Dp28eLFQlRUlODj4yO88MILwocffijExMSIjvn222+FZs2aCe7u7kJQUJDw6KOPClu2bNF7/4sXLwqtW7cWvLy8BABCcnKyIAiCcOnSJeHpp58WAgMDBS8vL6Fhw4bC2LFjBZVKJZw7d054/PHHherVqwseHh5C/fr1hfnz52uuefPmTaFr166Cr6+vAEDYu3evznvn5eUJQ4YMEby9vYUaNWoIH3/8sdChQwfhzTff1Bzz3XffCbGxsYKHh4fQpk0bYceOHVrfzwcffCCEh4cLCoVCePHFFwVBEITdu3cLjRo1Ejw8PIQHH3xQ2LdvnwBA2Lp1q97vgoikUwiChEFkIiIiIjvH4SciIiJyCkxqiIiIyCkwqSEiIiKnwKSGiIiInAKTGiIiInIKTGqIiIjIKTCpISIiIqfApIaIiIicApMaIiIicgpMaoiIiMgpMKkhIiIip8CkhoiIiJzC/wFRevzLu1ns8gAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -393,7 +393,7 @@
    "source": [
     "new_data = pp.DataFrame(toys.var_process(links_coeffs, T=200)[0])\n",
     "predicted = pred.predict(target, new_data=new_data)\n",
-    "true_data = pred.get_test_array()[0]\n",
+    "true_data = pred.get_test_array(j=target)[0]\n",
     "\n",
     "plt.scatter(true_data, predicted)\n",
     "plt.title(r\"NRMSE = %.2f\" % (np.abs(true_data - predicted).mean()/true_data.std()))\n",
@@ -427,7 +427,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -445,7 +445,7 @@
     "# new_data = pp.DataFrame(toys.var_process(links_coeffs, T=100)[0])\n",
     "predicted = pred.predict(target, new_data=new_data)\n",
     "# predicted = pred.predict(target)\n",
-    "true_data = pred.get_test_array()[0]\n",
+    "true_data = pred.get_test_array(j=target)[0]\n",
     "\n",
     "plt.scatter(true_data, predicted)\n",
     "plt.plot(true_data, true_data, 'k-')\n",
@@ -479,7 +479,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -502,7 +502,7 @@
     "                    tau_max=tau_max)\n",
     "predicted = pred.predict(target)\n",
     "# predicted = pred.predict(target)\n",
-    "true_data = pred.get_test_array()[0]\n",
+    "true_data = pred.get_test_array(j=target)[0]\n",
     "\n",
     "plt.scatter(true_data, predicted)\n",
     "plt.plot(true_data, true_data, 'k-')\n",
@@ -575,12 +575,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/jakobrunge/anaconda3/envs/py39/lib/python3.9/site-packages/tigramite-5.2.0.1-py3.9.egg/tigramite/models.py:1693: UserWarning: test_indices - maxlag(predictors) [or tau_max] overlaps with train_indices: Choose test_indices such that there is a gap of max_lag to train_indices!\n"
+      "/home/jakobrunge/anaconda3/envs/py39/lib/python3.9/site-packages/tigramite-5.2.1.23-py3.9.egg/tigramite/models.py:1728: UserWarning: test_indices - maxlag(predictors) [or tau_max] overlaps with train_indices: Choose test_indices such that there is a gap of max_lag to train_indices!\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -618,7 +618,7 @@
     "                    tau_max=tau_max)\n",
     "predicted = pred.predict(target)\n",
     "# predicted = pred.predict(target)\n",
-    "true_data = pred.get_test_array()[0]\n",
+    "true_data = pred.get_test_array(j=target)[0]\n",
     "\n",
     "plt.scatter(true_data, predicted)\n",
     "plt.plot(true_data, true_data, 'k-')\n",
diff --git a/tutorials/dataset_challenges/tigramite_tutorial_missing_masking.ipynb b/tutorials/dataset_challenges/tigramite_tutorial_missing_masking.ipynb
index 062d66ee..86eddf94 100644
--- a/tutorials/dataset_challenges/tigramite_tutorial_missing_masking.ipynb
+++ b/tutorials/dataset_challenges/tigramite_tutorial_missing_masking.ipynb
@@ -145,7 +145,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.688 | pval = 0.00000 \n",
+      "        val =  0.688 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.688\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -156,7 +156,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.449 | pval = 0.00000 \n",
+      "        val =  0.449 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.449\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -167,7 +167,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.355 | pval = 0.00039 \n",
+      "        val =  0.355 | pval = 0.00039 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00039 / val =  0.355\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -178,7 +178,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.248 | pval = 0.01470 \n",
+      "        val =  0.248 | pval = 0.01470 | dependent = True \n",
       "    Subset 0: () gives pval = 0.01470 / val =  0.248\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -189,7 +189,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.152 | pval = 0.16123 \n",
+      "        val =  0.152 | pval = 0.16123 | dependent = True \n",
       "    Subset 0: () gives pval = 0.16123 / val =  0.152\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -200,7 +200,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.129 | pval = 0.23578 \n",
+      "        val =  0.129 | pval = 0.23578 | dependent = False \n",
       "    Subset 0: () gives pval = 0.23578 / val =  0.129\n",
       "    Non-significance detected.\n",
       "\n",
@@ -225,7 +225,7 @@
       "            Z = [(0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.585 | pval = 0.00000 \n",
+      "        val =  0.585 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^0$ -2)  gives pval = 0.00000 / val =  0.585\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -236,7 +236,7 @@
       "            Z = [(0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.052 | pval = 0.61676 \n",
+      "        val = -0.052 | pval = 0.61676 | dependent = False \n",
       "    Subset 0: ($X^0$ -1)  gives pval = 0.61676 / val = -0.052\n",
       "    Non-significance detected.\n",
       "\n",
@@ -247,7 +247,7 @@
       "            Z = [(0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.018 | pval = 0.86238 \n",
+      "        val =  0.018 | pval = 0.86238 | dependent = False \n",
       "    Subset 0: ($X^0$ -1)  gives pval = 0.86238 / val =  0.018\n",
       "    Non-significance detected.\n",
       "\n",
@@ -258,7 +258,7 @@
       "            Z = [(0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.006 | pval = 0.95598 \n",
+      "        val =  0.006 | pval = 0.95598 | dependent = False \n",
       "    Subset 0: ($X^0$ -1)  gives pval = 0.95598 / val =  0.006\n",
       "    Non-significance detected.\n",
       "\n",
@@ -269,7 +269,7 @@
       "            Z = [(0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.001 | pval = 0.99188 \n",
+      "        val = -0.001 | pval = 0.99188 | dependent = False \n",
       "    Subset 0: ($X^0$ -1)  gives pval = 0.99188 / val = -0.001\n",
       "    Non-significance detected.\n",
       "\n",
@@ -298,7 +298,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.725 | pval = 0.00000 \n",
+      "        val =  0.725 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.725\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -309,7 +309,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.790 | pval = 0.00000 \n",
+      "        val =  0.790 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.790\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -320,7 +320,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.903 | pval = 0.00000 \n",
+      "        val =  0.903 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.903\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -331,7 +331,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.776 | pval = 0.00000 \n",
+      "        val =  0.776 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.776\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -342,7 +342,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.534 | pval = 0.00000 \n",
+      "        val =  0.534 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.534\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -353,7 +353,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.431 | pval = 0.00003 \n",
+      "        val =  0.431 | pval = 0.00003 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00003 / val =  0.431\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -379,7 +379,7 @@
       "            Z = [(0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.780 | pval = 0.00000 \n",
+      "        val =  0.780 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^0$ -2)  gives pval = 0.00000 / val =  0.780\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -390,7 +390,7 @@
       "            Z = [(1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.451 | pval = 0.00000 \n",
+      "        val =  0.451 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^1$ -1)  gives pval = 0.00000 / val =  0.451\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -401,7 +401,7 @@
       "            Z = [(1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.191 | pval = 0.06359 \n",
+      "        val = -0.191 | pval = 0.06359 | dependent = True \n",
       "    Subset 0: ($X^1$ -1)  gives pval = 0.06359 / val = -0.191\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -412,7 +412,7 @@
       "            Z = [(1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.736 | pval = 0.00000 \n",
+      "        val =  0.736 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^1$ -1)  gives pval = 0.00000 / val =  0.736\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -423,7 +423,7 @@
       "            Z = [(1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.181 | pval = 0.09800 \n",
+      "        val = -0.181 | pval = 0.09800 | dependent = True \n",
       "    Subset 0: ($X^1$ -1)  gives pval = 0.09800 / val = -0.181\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -434,7 +434,7 @@
       "            Z = [(1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.113 | pval = 0.30104 \n",
+      "        val = -0.113 | pval = 0.30104 | dependent = False \n",
       "    Subset 0: ($X^1$ -1)  gives pval = 0.30104 / val = -0.113\n",
       "    Non-significance detected.\n",
       "\n",
@@ -459,7 +459,7 @@
       "            Z = [(0, -1), (0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.856 | pval = 0.00000 \n",
+      "        val =  0.856 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^0$ -1) ($X^0$ -2)  gives pval = 0.00000 / val =  0.856\n",
       "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
       "\n",
@@ -470,7 +470,7 @@
       "            Z = [(1, -1), (0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.654 | pval = 0.00000 \n",
+      "        val =  0.654 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^1$ -1) ($X^0$ -2)  gives pval = 0.00000 / val =  0.654\n",
       "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
       "\n",
@@ -481,7 +481,7 @@
       "            Z = [(1, -1), (0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.078 | pval = 0.45596 \n",
+      "        val =  0.078 | pval = 0.45596 | dependent = False \n",
       "    Subset 0: ($X^1$ -1) ($X^0$ -1)  gives pval = 0.45596 / val =  0.078\n",
       "    Non-significance detected.\n",
       "\n",
@@ -492,7 +492,7 @@
       "            Z = [(1, -1), (0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.013 | pval = 0.90014 \n",
+      "        val = -0.013 | pval = 0.90014 | dependent = False \n",
       "    Subset 0: ($X^1$ -1) ($X^0$ -1)  gives pval = 0.90014 / val = -0.013\n",
       "    Non-significance detected.\n",
       "\n",
@@ -503,7 +503,7 @@
       "            Z = [(1, -1), (0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.051 | pval = 0.64715 \n",
+      "        val = -0.051 | pval = 0.64715 | dependent = False \n",
       "    Subset 0: ($X^1$ -1) ($X^0$ -1)  gives pval = 0.64715 / val = -0.051\n",
       "    Non-significance detected.\n",
       "\n",
@@ -533,7 +533,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.277 | pval = 0.00994 \n",
+      "        val =  0.277 | pval = 0.00994 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00994 / val =  0.277\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -544,7 +544,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.401 | pval = 0.00013 \n",
+      "        val =  0.401 | pval = 0.00013 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00013 / val =  0.401\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -555,7 +555,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.782 | pval = 0.00000 \n",
+      "        val =  0.782 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.782\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -566,7 +566,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.864 | pval = 0.00000 \n",
+      "        val =  0.864 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.864\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -577,7 +577,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.979 | pval = 0.00000 \n",
+      "        val =  0.979 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.979\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -588,7 +588,7 @@
       "            Z = []\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.936 | pval = 0.00000 \n",
+      "        val =  0.936 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: () gives pval = 0.00000 / val =  0.936\n",
       "    No conditions of dimension 0 left.\n",
       "\n",
@@ -614,7 +614,7 @@
       "            Z = [(2, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.872 | pval = 0.00000 \n",
+      "        val =  0.872 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^2$ -2)  gives pval = 0.00000 / val =  0.872\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -625,7 +625,7 @@
       "            Z = [(2, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.538 | pval = 0.00000 \n",
+      "        val = -0.538 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^2$ -1)  gives pval = 0.00000 / val = -0.538\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -636,7 +636,7 @@
       "            Z = [(2, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.746 | pval = 0.00000 \n",
+      "        val =  0.746 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^2$ -1)  gives pval = 0.00000 / val =  0.746\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -647,7 +647,7 @@
       "            Z = [(2, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.915 | pval = 0.00000 \n",
+      "        val =  0.915 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^2$ -1)  gives pval = 0.00000 / val =  0.915\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -658,7 +658,7 @@
       "            Z = [(2, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.707 | pval = 0.00000 \n",
+      "        val =  0.707 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^2$ -1)  gives pval = 0.00000 / val =  0.707\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -669,7 +669,7 @@
       "            Z = [(2, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.461 | pval = 0.00003 \n",
+      "        val =  0.461 | pval = 0.00003 | dependent = True \n",
       "    Subset 0: ($X^2$ -1)  gives pval = 0.00003 / val =  0.461\n",
       "    No conditions of dimension 1 left.\n",
       "\n",
@@ -695,7 +695,7 @@
       "            Z = [(1, -1), (1, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.986 | pval = 0.00000 \n",
+      "        val =  0.986 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^1$ -1) ($X^1$ -2)  gives pval = 0.00000 / val =  0.986\n",
       "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
       "\n",
@@ -706,7 +706,7 @@
       "            Z = [(2, -1), (1, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.795 | pval = 0.00000 \n",
+      "        val =  0.795 | pval = 0.00000 | dependent = True \n",
       "    Subset 0: ($X^2$ -1) ($X^1$ -2)  gives pval = 0.00000 / val =  0.795\n",
       "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
       "\n",
@@ -717,7 +717,7 @@
       "            Z = [(2, -1), (1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.015 | pval = 0.90125 \n",
+      "        val =  0.015 | pval = 0.90125 | dependent = False \n",
       "    Subset 0: ($X^2$ -1) ($X^1$ -1)  gives pval = 0.90125 / val =  0.015\n",
       "    Non-significance detected.\n",
       "\n",
@@ -728,7 +728,7 @@
       "            Z = [(2, -1), (1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.342 | pval = 0.00463 \n",
+      "        val =  0.342 | pval = 0.00463 | dependent = True \n",
       "    Subset 0: ($X^2$ -1) ($X^1$ -1)  gives pval = 0.00463 / val =  0.342\n",
       "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
       "\n",
@@ -739,7 +739,7 @@
       "            Z = [(2, -1), (1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.013 | pval = 0.91292 \n",
+      "        val = -0.013 | pval = 0.91292 | dependent = False \n",
       "    Subset 0: ($X^2$ -1) ($X^1$ -1)  gives pval = 0.91292 / val = -0.013\n",
       "    Non-significance detected.\n",
       "\n",
@@ -750,7 +750,7 @@
       "            Z = [(2, -1), (1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.150 | pval = 0.20014 \n",
+      "        val = -0.150 | pval = 0.20014 | dependent = False \n",
       "    Subset 0: ($X^2$ -1) ($X^1$ -1)  gives pval = 0.20014 / val = -0.150\n",
       "    Non-significance detected.\n",
       "\n",
@@ -801,7 +801,7 @@
       "            Z = [(0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.585 | pval = 0.00000 [cached]\n",
+      "        val =  0.585 | pval = 0.00000 | dependent = True [cached]\n",
       "\n",
       "        link ($X^0$ -2) -?> $X^0$ (2/8):\n",
       "        with conds_y = [ ($X^0$ -1) ]\n",
@@ -812,7 +812,7 @@
       "            Z = [(0, -1), (0, -3)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.048 | pval = 0.64516 \n",
+      "        val = -0.048 | pval = 0.64516 | dependent = False \n",
       "\n",
       "        link ($X^1$  0) o?o $X^0$ (3/8):\n",
       "        with conds_y = [ ($X^0$ -1) ]\n",
@@ -823,7 +823,7 @@
       "            Z = [(0, -1), (1, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.072 | pval = 0.48950 \n",
+      "        val = -0.072 | pval = 0.48950 | dependent = False \n",
       "\n",
       "        link ($X^1$ -1) -?> $X^0$ (4/8):\n",
       "        with conds_y = [ ($X^0$ -1) ]\n",
@@ -834,7 +834,7 @@
       "            Z = [(0, -1), (1, -2), (0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.091 | pval = 0.38447 \n",
+      "        val =  0.091 | pval = 0.38447 | dependent = False \n",
       "\n",
       "        link ($X^1$ -2) -?> $X^0$ (5/8):\n",
       "        with conds_y = [ ($X^0$ -1) ]\n",
@@ -845,7 +845,7 @@
       "            Z = [(0, -1), (1, -3), (0, -3)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.048 | pval = 0.64957 \n",
+      "        val =  0.048 | pval = 0.64957 | dependent = False \n",
       "\n",
       "        link ($X^2$  0) o?o $X^0$ (6/8):\n",
       "        with conds_y = [ ($X^0$ -1) ]\n",
@@ -856,7 +856,7 @@
       "            Z = [(0, -1), (2, -1), (1, -1), (2, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.068 | pval = 0.59118 \n",
+      "        val = -0.068 | pval = 0.59118 | dependent = False \n",
       "\n",
       "        link ($X^2$ -1) -?> $X^0$ (7/8):\n",
       "        with conds_y = [ ($X^0$ -1) ]\n",
@@ -867,7 +867,7 @@
       "            Z = [(0, -1), (2, -2), (1, -2), (2, -3)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.118 | pval = 0.34583 \n",
+      "        val = -0.118 | pval = 0.34583 | dependent = False \n",
       "\n",
       "        link ($X^2$ -2) -?> $X^0$ (8/8):\n",
       "        with conds_y = [ ($X^0$ -1) ]\n",
@@ -878,7 +878,7 @@
       "            Z = [(0, -1), (2, -3), (1, -3), (2, -4)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.007 | pval = 0.95741 \n",
+      "        val =  0.007 | pval = 0.95741 | dependent = False \n",
       "\n",
       "        link ($X^0$  0) o?o $X^1$ (1/8):\n",
       "        with conds_y = [ ($X^1$ -1) ($X^0$ -1) ]\n",
@@ -889,7 +889,7 @@
       "            Z = [(1, -1), (0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.072 | pval = 0.48950 [cached]\n",
+      "        val = -0.072 | pval = 0.48950 | dependent = False [cached]\n",
       "\n",
       "        link ($X^0$ -1) -?> $X^1$ (2/8):\n",
       "        with conds_y = [ ($X^1$ -1) ]\n",
@@ -900,7 +900,7 @@
       "            Z = [(1, -1), (0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.654 | pval = 0.00000 [cached]\n",
+      "        val =  0.654 | pval = 0.00000 | dependent = True [cached]\n",
       "\n",
       "        link ($X^0$ -2) -?> $X^1$ (3/8):\n",
       "        with conds_y = [ ($X^1$ -1) ($X^0$ -1) ]\n",
@@ -911,7 +911,7 @@
       "            Z = [(1, -1), (0, -1), (0, -3)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.098 | pval = 0.34941 \n",
+      "        val =  0.098 | pval = 0.34941 | dependent = False \n",
       "\n",
       "        link ($X^1$ -1) -?> $X^1$ (4/8):\n",
       "        with conds_y = [ ($X^0$ -1) ]\n",
@@ -922,7 +922,7 @@
       "            Z = [(0, -1), (1, -2), (0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.560 | pval = 0.00000 \n",
+      "        val =  0.560 | pval = 0.00000 | dependent = True \n",
       "\n",
       "        link ($X^1$ -2) -?> $X^1$ (5/8):\n",
       "        with conds_y = [ ($X^1$ -1) ($X^0$ -1) ]\n",
@@ -933,7 +933,7 @@
       "            Z = [(1, -1), (0, -1), (1, -3), (0, -3)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.151 | pval = 0.15061 \n",
+      "        val =  0.151 | pval = 0.15061 | dependent = False \n",
       "\n",
       "        link ($X^2$  0) o?o $X^1$ (6/8):\n",
       "        with conds_y = [ ($X^1$ -1) ($X^0$ -1) ]\n",
@@ -944,7 +944,7 @@
       "            Z = [(1, -1), (0, -1), (2, -1), (2, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.057 | pval = 0.65168 \n",
+      "        val =  0.057 | pval = 0.65168 | dependent = False \n",
       "\n",
       "        link ($X^2$ -1) -?> $X^1$ (7/8):\n",
       "        with conds_y = [ ($X^1$ -1) ($X^0$ -1) ]\n",
@@ -955,7 +955,7 @@
       "            Z = [(1, -1), (0, -1), (2, -2), (1, -2), (2, -3)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.250 | pval = 0.04498 \n",
+      "        val = -0.250 | pval = 0.04498 | dependent = False \n",
       "\n",
       "        link ($X^2$ -2) -?> $X^1$ (8/8):\n",
       "        with conds_y = [ ($X^1$ -1) ($X^0$ -1) ]\n",
@@ -966,7 +966,7 @@
       "            Z = [(1, -1), (0, -1), (2, -3), (1, -3), (2, -4)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.057 | pval = 0.64695 \n",
+      "        val =  0.057 | pval = 0.64695 | dependent = False \n",
       "\n",
       "        link ($X^0$  0) o?o $X^2$ (1/8):\n",
       "        with conds_y = [ ($X^2$ -1) ($X^1$ -1) ($X^2$ -2) ]\n",
@@ -977,7 +977,7 @@
       "            Z = [(2, -1), (1, -1), (2, -2), (0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.068 | pval = 0.59118 [cached]\n",
+      "        val = -0.068 | pval = 0.59118 | dependent = False [cached]\n",
       "\n",
       "        link ($X^0$ -1) -?> $X^2$ (2/8):\n",
       "        with conds_y = [ ($X^2$ -1) ($X^1$ -1) ($X^2$ -2) ]\n",
@@ -988,7 +988,7 @@
       "            Z = [(2, -1), (1, -1), (2, -2), (0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.135 | pval = 0.28206 \n",
+      "        val = -0.135 | pval = 0.28206 | dependent = False \n",
       "\n",
       "        link ($X^0$ -2) -?> $X^2$ (3/8):\n",
       "        with conds_y = [ ($X^2$ -1) ($X^1$ -1) ($X^2$ -2) ]\n",
@@ -999,7 +999,7 @@
       "            Z = [(2, -1), (1, -1), (2, -2), (0, -3)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val = -0.188 | pval = 0.13451 \n",
+      "        val = -0.188 | pval = 0.13451 | dependent = False \n",
       "\n",
       "        link ($X^1$  0) o?o $X^2$ (4/8):\n",
       "        with conds_y = [ ($X^2$ -1) ($X^1$ -1) ($X^2$ -2) ]\n",
@@ -1010,7 +1010,7 @@
       "            Z = [(2, -1), (1, -1), (2, -2), (0, -1)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.057 | pval = 0.65168 [cached]\n",
+      "        val =  0.057 | pval = 0.65168 | dependent = False [cached]\n",
       "\n",
       "        link ($X^1$ -1) -?> $X^2$ (5/8):\n",
       "        with conds_y = [ ($X^2$ -1) ($X^2$ -2) ]\n",
@@ -1021,7 +1021,7 @@
       "            Z = [(2, -1), (2, -2), (1, -2), (0, -2)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.731 | pval = 0.00000 \n",
+      "        val =  0.731 | pval = 0.00000 | dependent = True \n",
       "\n",
       "        link ($X^1$ -2) -?> $X^2$ (6/8):\n",
       "        with conds_y = [ ($X^2$ -1) ($X^1$ -1) ($X^2$ -2) ]\n",
@@ -1032,7 +1032,7 @@
       "            Z = [(2, -1), (1, -1), (2, -2), (1, -3), (0, -3)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.152 | pval = 0.23139 \n",
+      "        val =  0.152 | pval = 0.23139 | dependent = False \n",
       "\n",
       "        link ($X^2$ -1) -?> $X^2$ (7/8):\n",
       "        with conds_y = [ ($X^1$ -1) ($X^2$ -2) ]\n",
@@ -1043,7 +1043,7 @@
       "            Z = [(1, -1), (2, -2), (1, -2), (2, -3)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.415 | pval = 0.00108 \n",
+      "        val =  0.415 | pval = 0.00108 | dependent = True \n",
       "\n",
       "        link ($X^2$ -2) -?> $X^2$ (8/8):\n",
       "        with conds_y = [ ($X^2$ -1) ($X^1$ -1) ]\n",
@@ -1054,7 +1054,7 @@
       "            Z = [(2, -1), (1, -1), (2, -3), (1, -3), (2, -4)]\n",
       "            extraZ = []\n",
       "            with missing values = 999.0 removed\n",
-      "        val =  0.238 | pval = 0.08553 \n",
+      "        val =  0.238 | pval = 0.08553 | dependent = False \n",
       "\n",
       "## Significant links at alpha = 0.01:\n",
       "\n",
@@ -1263,15 +1263,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note, however, that the failure to detect the link on the whole sample occurs only for partial correlation because the positive and negative dependencies cancel out. Using CMIknn recovers the link.\n",
-    "\n",
-    "To make CMIknn faster, here we use the option ``significance='fixed_thres'`` and choose a threshold ``fixed_thres`` $I^*$ in any conditional independence test. Then no hypothesis testing on conditional independence is conducted which avoids computationally expensive permutation testing. The criterion for conditional independence for a test statistic $I(X;Y|Z)$ is then \n",
-    "\n",
-    "$$\n",
-    "I(X;Y|Z) < I^*\n",
-    "$$\n",
-    "\n",
-    "$I^*$ should then be regarded as a hyperparameter. Note that picking $I^*$ for CMIknn is tricky because the value of $I(X;Y|Z)$ depends on the dimensionality of the variables due to an estimation bias for finite samples. This option only makes sense for conditional independence tests that rely on permutation testing, these are CMIknn and CMIsymb."
+    "Note, however, that the failure to detect the link on the whole sample occurs only for partial correlation because the positive and negative dependencies cancel out. As shown below, using CMIknn recovers the link. To make CMIknn faster, here we use the option ``significance='fixed_thres'``, then ``pc_alpha`` and ``alpha_level`` are interpreted as a threshold $I^*$ in any conditional independence test. See the tutorial on conditional independence tests for further details."
    ]
   },
   {
@@ -1280,10 +1272,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cmi_knn = CMIknn(significance='fixed_thres', fixed_thres=0.01, mask_type=None)\n",
+    "cmi_knn = CMIknn(significance='fixed_thres', mask_type=None)\n",
     "\n",
+    "fixed_thres = 0.01\n",
     "pcmci = PCMCI(dataframe=dataframe, cond_ind_test=cmi_knn)\n",
-    "results = pcmci.run_pcmci(tau_max=2,pc_alpha=0.05, alpha_level=0.01)\n"
+    "results = pcmci.run_pcmci(tau_max=2, pc_alpha=fixed_thres, alpha_level=fixed_thres)\n"
    ]
   },
   {
diff --git a/tutorials/dataset_challenges/tigramite_tutorial_multiple_datasets.ipynb b/tutorials/dataset_challenges/tigramite_tutorial_multiple_datasets.ipynb
index cb18859f..d0ece194 100644
--- a/tutorials/dataset_challenges/tigramite_tutorial_multiple_datasets.ipynb
+++ b/tutorials/dataset_challenges/tigramite_tutorial_multiple_datasets.ipynb
@@ -221,7 +221,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -231,7 +231,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -241,7 +241,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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79uyhX375hZYvX067d++muLg4hZJVNS7q2OrVq8nAwIAA0JYtWxSOc+jQIQJAN27cIFtbW5o0aZKAWWrWwoULqUyZMiSVSjWdChERde3alTp16qTSPgICAsjQ0JCePn2q0n4K0rFjR+rYsaNKYkul0rwvHg0aNFBJH/rqxo0bZGxsTMOHDxfk70JYWBgZGBjQTz/9JEB28jlw4AABoFmzZuV778yZM2RhYUEtWrSg9+/fqz03Jp958+aRRCKhO3fuyNRerqJu48aNVKtWLZo0aRItXbqUli5dSt999x3Vrl2bNm7cqFDCqsRFHevbty+1a9eOOnXqRC1atFA4TpcuXahly5ZERPT999+TjY0NZWZmCpWmRg0YMICcnZ01nUaexYsXk5mZGWVkZKgkflxcHJUuXZrGjx+vkvjFWbt2LUkkEvr06ZPgscPDwwkAffvttwSAHj58KHgf+uj9+/dUuXJlatGihVIj+v+1ePFiAkDHjx8XLGZxrl27RqamptSvX79Ci9ObN2+SnZ0d1apVix4/fqy23Jh87t+/T0ZGRnKNustV1NWuXZuSkpLy/TwxMZFq1aolTyi5JSYmUsuWLcnc3FzmipWLupJNKpWSra0t+fv70+HDhwkAXbt2Te44z549I5FIlPfFJSoqigDQ4cOHhU5ZIxwdHWn06NGaTiPPtWvXCACFh4erJL6/vz+ZmZnR27dvVRK/OK9fvyYAtHXrVsFjf/3111StWjVKSkoiExMTWrJkieB96JusrCzy8PAgGxsbevHihaCxc3JyyMvLi6ytrQWPXZDnz5+Tvb09tW7dmlJTU4ts+/jxY6pVqxbZ2dlRRESEynNj8snJySEXFxeqVauWXF805NooIRKJkJycnO/nycnJKj/fytTUFEePHoWvr69K+2H64+HDh3j//j1cXV3RrVs3VK5cWaFt/Zs2bYKFhQX69esHAHB0dESTJk00cvG90LKzsxETE6MVmyRyOTk5oVSpUio5r+7OnTtYtmwZpk6dqtAieCFUqFABbdq0EXwXbGJiIvbu3Yvhw4fDwsICnTt3RlBQkKB96KO5c+fiwoUL2LdvHxwcHASNLRaLsW3bNpiamqJ///4q3aCQlJQEb29vGBsbIygoCKampkW2r169OsLDw1G5cmW4urri7NmzKsuNye/PP//EpUuXsH79epiYmMj+oDyV45EjR6h27drUu3dvmjBhAk2YMIF69epFtWvXpiNHjshdiSpi2LBhPFLHZLJ+/XoyMDCgxMREIiJatGgRmZiY0MePH2WOkZWVRRUqVKAxY8Z88fNff/2VDA0N5Yqlje7fv08AKCQkRNOpfKFbt27UoUMHQWNmZWVRs2bNqH79+pSeni5obHktWbKETExMBF2s/vvvv5OBgUHehpfNmzeTSCTS2IikLrh58yYZGBjQ//73P5X2Ex4eTgYGBvTjjz+qJH52djZ169aNSpUqJfPnY67k5GTq2rUrGRoa0u7du1WSH5PP27dvqXTp0vT111/L/azcGyWys7Pp8uXLdODAAdq/fz9dvnyZsrOz5e5YUUUVdenp6ZSQkJD3evnyZYku6vRlzZeiBg4c+MU6uvfv35ORkZFcU1K507Y3b9784ufv378niURCq1evFixfTdi/fz8B0LoF00uXLiVTU1NBi6+AgAASi8UKTcEL7dGjRwSADh48KFjMZs2aUY8ePfL+/ePHjyQWi2n9+vWC9aFPsrKyqGnTptSoUSOVrd/8tyVLlhAAOnbsmOCxv/vuOxKLxXTixAmFns/MzKShQ4eSRCKhy5cvC5wdk5efnx/Z2NgoNGigc0eaFFXUzZkzhwDke5XEom7Pnj1kZmZGO3bs0HQqGiGVSqlixYo0derUL34+ePBgqlatmkxbw4n+GTFq3rx5ge/17Nmz0Pd0xdy5c8nGxkbTaeRz48YNAkAXL14UJN7du3fJyMiIfvjhB0HiCaFRo0Y0aNAgQWLdvHmTAFBwcPAXP3dzc6OuXbsK0oe+WbJkCYlEIvrrr7/U0l9OTg5169aNypYtK+j6urVr1xIA+u2335SKk5mZSc7OzlSpUiWt+5JXkhw5coQA0M6dOxV6XuGi7sCBA4o+qhQeqStadnY2/fDDDwSATExMyNvbW9MpacTjx48L/JC7cuWKzN+WX7x4UeRIR+4xJ9HR0YLkrAm+vr7k7u6u6TTyyc7OJisrK0GmxbKzs6lVq1ZUt25dQXc2KmvOnDlkZWUlyCjR2LFjqUKFCvnORPv111/JyMioRP0OlMWjR4/IxMRE7UcTffz4kSpVqkRt27YVZIbr0qVLZGBgQBMnThQgu3828dja2lLHjh3VOgPH/pGUlEQODg7UuXNnhY/VUbioMzIyouXLlxfZRuhzr7p27Urly5en1q1b0+bNm4ttX9LW1MXFxVHnzp1JLBbTL7/8Qj///DOZmZlp1QeZuuSuJ/rvGYpSqZScnJzIy8ur2Bhz584lc3PzvDV5/5WRkUHW1tY0bdo0QXLWhHr16mnsaI/ieHt7C1JwLl26lEQikdZNK+Xuoj558qRScZKTk8nS0rLAYw+ePXtGAGjPnj1K9aFPpFIpeXh4UNWqVTVyAG9oaCgBoKNHjyodq0ePHuTo6ChoARYSEkJisbjAM+6Yak2aNIlMTU3pyZMnCsdQuKg7efIkWVpa0oQJE/IVb9nZ2bR582aqU6eOwokJoaii7tGjR/Ts2TMNZKUa0dHRVKNGDSpbtiydOXOGiIju3LlDAOjUqVMazk79vvrqK2rcuHGB723cuJFEIlGR5zNlZ2dTpUqVaOTIkUX2M2HCBLK3t9fYqfHKSE9PJ4lEovTp+aqyfPlyMjExUepLSUxMDBkbG9PkyZMFzEwYUqmUqlevTqNGjVIqzpYtWwhAoR8ETk5O1L9/f6X60Cd//vmnIMW0oqRSKTVt2pS6d++uVJyXL1+SWCym33//XaDM/s/ChQvVfr5eSXft2jUSi8W0dOlSpeIotaYuMjKSKlWqRD4+PpSamkoZGRm0du1aqlq1KpUpU4Zmz56tVHLKKqqo8/PzIzMzM1q8eLHObyg4ePAgmZubU6NGjb4oVKRSKTk4ONB3332nueQ0pHr16oVOSaSkpFCZMmXyrbf7t6NHjxIAun79epH95K790sVffrlXqF26dEnTqRQod53Y+fPnFXo+Ozub2rZtSzVr1qSUlBSBsxPG1KlTyc7OTqmRlnbt2pGnp2eh78+bN49KlSql8R2/2uDt27dUpkwZGjJkiEbzWL9+PYnFYnr+/LnCMebNm0dmZmYqmYnKycmh7t27U9myZVU2+PHhwwdavXq1WjapaLvMzExq3LgxNWnSROkBAqU3Srx69YocHR3J0dGRKlSoQDY2NrRw4cJCp6zUqaiiLjExkSZNmkRisZgaNmyosoNOVSknJ4dmzZpFAKhv374FTiWMHj1a5QdDa5vctZRFrfucPHkylS1bttADOnv06EFOTk7FLiGQSqXUsGFD8vPzUypnTdi9ezcB0Npr/nJycqhMmTI0d+5chZ5fvnw5iUQiwTZbqELuDRCKFtZ3794lALR3795C2+QW74rujNQnfn5+VK5cOfrw4YNG80hMTKRSpUopPMWZnZ1NDg4ONHz4cIEz+z+fPn2iqlWrUosWLQT/QnD79m2qVq2a0tc36oslS5aQWCwudhBBFkoVdfHx8fS///2PrK2tydTUlMzMzOj27dtKJyUUWdbU3bx5k1q0aEEAaOTIkSq5ukcV4uPjqXv37iQSiWjRokWFFh+5R3KUpOuCdu7cWewxHQ8fPiQABa7NfPXqlVzTGkuXLiVjY2OtLY4KM2PGDKpQoYKm0yiSj48Pubq6yv3co0ePyNTUVLAF5KqSk5ND9vb2Ck8PT548mcqVK1fkh65Q07y6Lvd34a5duzSdChERjRkzhsqXL6/QTFHuTIKqj+e5fv06GRkZ0bfffitYzMDAQDI3NydHR0dq3bo1tW7dWrDYuuj169dkampK33//vSDxFC7qfvrpJ7KysqLq1avT+vXrKTk5mYYNG0a2trZacQ4UkewbJbKzs+m3334jS0tLsrGxoW3btmnN5eYFef78OdWpU4esrKyK3cWZnJxMRkZG9Ouvv6opO80bNWoU1atXr9h2nTt3LvBIkvnz58s1rfHmzRsyMDDQ2rVphfHx8Sly2k4b5O7eLO7Ko3/LvV6nevXqGlkIL68xY8ZQ1apV5f6dk56eTtbW1jRlypRi206ZMoXs7OxkPspH38THx1OFChXIy8tLa36337p1iwDQoUOH5H62R48e1KRJE7X8WdatW6fUERu5pFIpzZ8/nwBQnz59KCkpKe8EgVu3bgmTrA6aMWMGWVhYUHx8vCDxFC7q6tatS1u3bs23FmTmzJlkbm5OQUFBSienLHl3v75584b69+9PAMjd3Z1iYmJUnKFivvrqKypfvjw9ePBApvadOnWiTp06qTgr7VG3bt18N0AUJDg4mAB8cU5VdnY2ValShb755hu5+vTy8tK5b5w1a9YU7NuhquTuEJXnxotVq1YRAAoNDVVdYgI6ffq0Qh9se/bsIQB07969YtteunSJAGjdDmB1GTt2LFlYWCi1hk0VWrVqRZ07d5brGVVukCiIVCqlIUOGkJmZmcLHNyUnJ1Pfvn0JAM2dOzfvy0VmZmaBN/aUFGlpaVSuXDmaMGGCYDEVLuqK+oawYcMGMjY21vhp+4oeaXLq1CmqUaMGGRkZ0Zw5c7Tq2+379+/J2NiYFi9eLPMzuaMdujBqoazY2FgCINN1N7kF3LBhw/J+duLECQJAV69elavfvXv3EgCt/SLwX6mpqSQSiWjjxo2aTqVIOTk5ZG1tLfPao8ePH5OZmZmg00WqlpmZSaVLl5Z7fVXHjh3J2dlZprbZ2dlkY2Oj08fvKCq3oNX051FB/vzzz2J34v/XvHnzyNzcXK1HdSUnJ1ODBg2oTp06cq+Xf/78OTVp0oTMzMwKvEFl9uzZZGFhoRXr8NVt06ZNJBKJBF0epbIbJY4fP06lSpVSVXiZKHNOXWpqKvn7+xMAOn36tAqyU8zChQvlvr80d/3Yfw/i1Ue5117l3n9ZnICAADI2Ns5bON2rVy9ydHSUe1ojLS2NSpcuTf7+/jK1z8nJocjISI0dhRIREaFQ8aoJvXv3JhcXl2LbJSUlkZubG1WpUoWSkpLUkJlwhgwZQg0bNpS5fe7h2lu3bpX5mREjRlDNmjW1ZvpRHdLS0qhu3brUpk0brTxMNyUlhaysrOinn36SqX3uBokRI0aoOLP8YmJiqFSpUtSvXz+Z1wFeunSJbGxsqEqVKhQZGVlgm+IOeddXUqmUHB0dlT7a5r9Uek1YRESEKsMXS9nDh6VSKdnb28v8F07VsrKyqFKlSgrteKpZs2aJGOIeP3481axZU+b2uffBLl68OG9t3Jo1axTqe8yYMVSpUqViPzxCQ0PJycmJAFCLFi3kvoBbCNu2bSMAOvHteNWqVWRoaFjgsSSJiYm0a9cu6tWrF5mYmJCBgUHeOY26JDAwUOapVCKi6dOnk5WVlVxHteQurtflG1DkNXPmTDI0NNTqP/OECRPI1tZWpqM91LVBojC5X5oNDAyoWrVq5OnpSWPGjKFly5ZRUFAQ3blzJ++/yQ0bNpChoSG1b9++2GvHvL29ZTptQJ/kHkIt9O8rnbv7VR5C3CgxYMAAatmypYBZKS73L1Rh33iKMnHiRKpcubLe/6VxdHSUez3ckCFDqGrVqjR//nwyNTWlz58/K9T31atXi/xL+vDhQ/Lx8SEA1KpVK9q2bRvVq1ePDA0NacGCBWo9L/GHH36gKlWqqK0/ZeQeon327Fkiyl/I5RbHS5YsoadPn2o2WQWlpKSQra0tWVhY0NSpU4scac7KyqLy5cvLPcWclpZGFhYWtGDBAmXT1Ql//PEHGRgY0Jw5czSdSpGio6OLPZYmlzYUP9euXaP169fTtGnTqHfv3uTo6Ejm5uZf3LduZ2dHAGj06NEyFavHjh3Lt75Z3/Xs2ZMaNGgg+P+WXNQV448//iCxWCzYzhRluLi4UPv27RV69uTJk3r/Lf3Tp08kEonkmpIi+r9izMTE5Iv1dfKSSqVUp06dfJe0x8XF0aRJk0gikVDlypVp165deX+R09LSyN/fnwwMDMjJyUmhgl0R3bp1k+mqNG0glUqpXLly1L17d/Lx8SFjY2MCQC1btqSlS5fqbCH3X7GxseTv70+WlpZkZGREI0eOpEePHuVrl3s0hyI7Bvv27Vvgjm99kpOTQ9OmTSMA9O233+rEbS/Ozs7k4eFRZBt1b5CQh1Qqpbdv39KlS5doy5YtNHPmTLl2yyq6QS3XiRMnqH79+rRv3z6dGLh4/PgxiUQilUw5c1FXjL///psA0JEjRwTMTH6529/379+v0PNpaWlkamoq1wYLXRMUFEQA5P6Qz722B4DSh1AHBASQqakpJSQkUGZmJq1cuZLKli1LFhYWtHDhwkKP5rh+/To1bNiQJBIJzZkzR+WnrFetWlWnFs0PHDgwr5BbtmyZXl3x91/x8fEUEBBAtra2JBaLqV+/fl8UcN27d6dmzZopFDv3DMcXL14IlO0/Hj16RO/evRM0piJSUlKod+/eJBKJ6Ndff9WJD3giou3btxOAIk800MQGCXVasGCBQjMl8fHxVLFiRbKxsSEA1LNnT3r16pVqkhTI999/T2XLllXJTTdc1BUj96otTd8d+c0331ClSpWU+tbZvXt3hQ5yVTepVEqjRo2iVatWyfXc5MmTqXLlygr1efToURo8eLDSHwIvX74kkUhEI0eOpDp16pBIJKIRI0bQ27dvi302IyODZs+eTRKJhBwdHVW2JjUpKUnuRfaalpiYqPW/qIWWmppKa9euzTt5v0uXLrR//34Si8UKn4kYHx9PhoaGguwETUxMpA0bNlCrVq0IABkbG9OoUaM0dtD527dvqUWLFmRmZkaHDx/WSA6KSktLo7JlyxZ65qAmN0ioy5s3b0gikcj9e3/s2LFkbm5Oz58/p4MHD5K9vT1ZWlrS+vXrterkilyJiYlkaWlJP/74o0ric1Eng2HDhlGTJk0Eykp+Hz9+JBMTE1q0aJFScdauXUsGBgZaMZVclA0bNhAAMjQ0lHnhOBFRs2bNaPDgwSrMTDadOnUiANShQweFplNv3bpFjRs3JgMDA5o+fbrgV/T89ddfBIBu3LghaFymGllZWbRjxw5q2LAhAVD6vs9OnTpRhw4dFHpWKpVSeHg4ffPNN2Rubk4ikYi6dOlCe/fupYULF5KtrS2JRCLq06ePWhfz37lzhypXrkzly5fX2f+uJ0+eTNbW1pSWlpbvPVnvotZ1vr6+VL9+fZm/XF+8eJEAfFEIxsXF0TfffEMAyM3NrcAlDJq0evVqMjAwEHy0PJdOFXVTpkwhZ2dnGjhwoEzTU0IVdVu2bCGRSCTXMSJC+vnnn784dkNRz549U2oKVx2ePn1KFhYWNGTIEKpVqxa5urrK9Bc8ISGBxGIxbdiwQQ1ZFu3p06cUEhKi1KhfZmYmzZs3jwwNDcne3p5++OEHun//viD55Z6NpK2X3LOCSaVSOnr0KB09elSpOLlf7uS5EvH9+/e0bNkyqlevHgGgKlWq0Lx58/Id5puWlkbr1q2jmjVr5n2oHj9+XKXToKdOnSJLS0tydHRU2QelOsTExBAA2rFjR773tGGDhDqcPXtW5ruQ09LSqE6dOoUeV3P27FmqXr06mZiY0OLFi7VibWVOTg7VqlVLpXeF60xRd/PmzbwF6AsWLJBpEaZQRd3z588JQIEHJ6paVlYWOTg40Ndffy1IvAYNGggWS2g5OTnk7u5OlStXpoSEBDpz5ozM04THjx/Xyztu7927R+PHj6cyZcoQAGrbti1t2LBBqf+mJ0+eTDVq1BAwS6ZLXr9+TQBo27ZtRbbLycmhU6dOUZ8+fcjQ0JCMjIyoX79+dPr06WKntbKzs+nAgQN592o3atSItm/fLvgO7/Xr15OBgQF5eXnpxPE8xXF3d893JmPuBgldu4ZQEblFz383mxVkxowZxR5Xk5ycTJMnTyaxWExNmzbV+HVkubt8lV27XRSdKep+++23vA/3Gzdu0Lhx4/K1SU9Pp4SEhLzXy5cvBSnqiIhq1KhB48ePVzqOvA4ePEgA6ObNm4LEmzZtGtnb22vlWoPVq1d/cXQF0T9HypQrV67YUYWffvqJ7O3t9fabbFpaGu3Zs4c6d+5MIpGIzMzMaOjQoXT+/Hm5/8ydOnWinj17qiZRphNatWpFvXr1KvC92NhYCggIoOrVqxMAatiwIf36668KzVRIpVIKDQ2lrl27EgCqXbu2IMs/cnJyaOrUqQSAxo8frxWjMELIvZnm34XK3Llz9XqDxH8tXbqUjIyMipyZioqKIolEQnPnzpUp5l9//UUNGzYkAwMDmj17tsb+e+nUqRM1b95cpZ9TOlPULVy4kAIDA4non51WAwYMyNdmzpw5X5yVk/sS4i/DyJEjqX79+krHkZerq6vMVwHJIvfAQ00fDP1fDx8+JFNT03zF+tu3b8nKyopGjhxZ5PNt2rShfv36qTJFrfHixQtasGAB1ahRgwBQ9erVKSAgQOYT8ytWrEjTp09XcZZMm/38889kamqaNwWfk5NDZ86cIV9fX5JIJGRiYkJDhw6l8PBwwT6AIiIiyNzcnL777julY/n7+5NIJKKVK1cqn5gWycjIIFtbW5o4cSIR/TPiWalSpWJ//+mTDx8+kJGRES1btqzA97Ozs6lFixbUoEEDuU4JyMjIoDlz5pBYLKY2bdrQkydPhEpZJnfv3iUAtH37dpX2ozNF3dq1a/NG6q5fv672kbpdu3YRAIqNjVU6lqxyLzOX5VBKWWVmZlKpUqVo/vz5gsVUVnZ2NrVt25Zq1KhR4P20v/32W5FD1ikpKSSRSOi3335TdapaRSqV0oULF+irr74iAwMD+vbbb4v9AP78+TMBoF27dqkpS6aNctdvbdy4kRYvXpy3Bq5evXr066+/yrXeTh6LFy8mAwMDun37tsIxIiMjycDAQG8PUf7xxx+pdOnSlJKSUmI2SPzXoEGDqFatWgXOKC1fvpxEIhFdvnxZodjh4eFUtWpVsrS0LHD9oqqMHj2a7O3tVX5clc4Udf9dUyfLh5JQa+qI/tluDYD27NmjdCxZjRw5kipWrCj4OpQ+ffpQ69atBY2pjKVLl5JIJCp0cWzuN7NGjRoV+P+L3MW1+nywcnH++OMPAkBLliwpsl1YWBgBoKioKDVlxrRV3bp1844iGTRoEF28eFHlyxcyMjKobt265OLiolBf2dnZ1LJlS7lHaXRJ7r2+mzdvLjEbJP7r0qVL+ZbiEBE9efKEzMzM8kYyFRUfH0+DBg0iADR48GCVT21/+vSJTE1Nad68eSrth0iHijoize1+zVWvXj0aNWqUILGKk/sfgSq+jebuflR2N60Q7t69S8bGxsWeAxgREUFisbjAIfnZs2eTtbW1Vq4TVKcZM2YU+8Vj3bp1ZGBgIPgxKUz3nDlzhlauXKn2Xf2nT58udJdncXLX3apyobk26NSpE9WtW7fEbJD4L6lUSvXr1ydfX98vftaxY0eqXLkyJSUlCdLPjh07qFSpUlStWjWFR/5ksXjxYjIyMlLLTJ9OFXXyErqo+/bbb6lWrVqCxCrOkiVLyMjISCWntOfuflPn0HNBsrKyqHnz5lS3bt1Cb1r4t++++y7vkMl/c3V1LXTRd0kilUpp8ODBZGRkRBcvXiywzYQJE6hu3bpqzoyxL/Xp04fs7e3l+t388uVLsrCwoLFjx6owM+1w6NAhAlCiNkj816pVq0gikeQd3L5lyxYCQMePHxe0nydPnlCbNm3IwMCA5s2bJ/gmitwTLL766itB4xaGizo5HDhwgADQy5cvBYlXmNx78IYOHaqyPpycnGjgwIEqiy+L+fPnk1gslvkS54SEBKpQoQL5+Pjk/Sw9PZ2MjY1pxYoVKspSt2RkZJC7uzuVKVOmwHPt3N3dqU+fPhrIjLH/8/z5czIzMyv0BoWC+Pj4UPny5bX+8HQhZGZmkoODA40ZM0bTqWjM58+fydTUlBYuXEixsbFUpkwZmY46UURWVhbNnj2bxGIxtWvXTtBrCPfv36/wXc2K4KJODh8+fJDpfCdlBQYGqvzE/5kzZ1LZsmVl3jEptFu3bpFEIpF7F+a+ffsIQN41QLknigt15Is++Pz5MzVo0ICqVq2ab7jf1taW5syZo5nEGPuXhQsXkkQiobt37xbbNnfkSpsPThfa+/fvZZrB0GfffPMNValShfr27UvlypVT+ZKhS5cuUZUqVahUqVK0YsUKQUbtnJ2dqX379gJkJxsu6uTUuHFjlQ+jenh4UJs2bVTax+XLlwmAStcRFCY9PZ0cHR3J0dFR7rVdUqmUunTpQpUrV6bk5GRasGABWVlZaaw41VbPnz+n8uXLU/PmzfN2FL9//54A0L59+zScHWP//B6oVasWubu7F7kRICEhgSpWrEjdu3cvcRsGSrpr167lHU0my4UDQoiPj6dx48aRSCSixo0bK/UZGRERofaLC7iok9OkSZOocuXKKvvlcufOHbUcOZGdnU1ly5almTNnqrSfgkyfPp0kEonCw9F///03mZiY0A8//ECenp7UrVs3YRPUEzdv3iQLCwvq3r07ZWVl5Z1RKM99uoyp0okTJ4rd3DN+/PgC19Iy/SeVSsnZ2Zl69uyp9oL++vXr1Lx5cwJAI0aMkGtDUXR0NE2YMIGsrKyoevXqaj3smIs6OQUHBxMAevz4sWAx/2306NFUvnx5tWzXHzhwIDk5Oam8n3/bs2cPicVi+t///qdUnAULFuQdklrcMR4l2cmTJ8nAwIDGjh1Lq1evJkNDQ8GPyGFMGT4+PlSxYsUCdzRevXqVRCIRr5ktwdLT0zV2skF2djatXbuWrKysyNramjZu3FhoLunp6bRz505ycXEhAGRra0v+/v706tUrtebMRZ2c4uPjSSwW08aNGwWLmSs9PZ1KlSpFs2bNEjx2QXbs2EEA6PXr1yrvSyqV5t34MXDgQKULi/T09Lxztq5evSpQlvpp48aNBIAqVKhAjRo10nQ6jH3h6dOneSPv/5aZmUmNGjWiZs2a8fIKplGxsbE0dOhQAkBt2rShyMjIvPf+/vtv+uGHH6hcuXIEgNzc3GjPnj0aO0eRizoFNG/eXCU7R0+dOkUAvvgPRpU+fPhAIpGINm3apNJ+UlNTyc/PjwDQggULBBtGv3z5Mnl7e/PIkwxmzZpFAKh///6aToWxfObNm0eGhoZf7Nj++eefycDAgDdBMa1x/vx5ql+/PonFYho9ejR16tSJAFDp0qXpu+++K/DEAXXjok4BP/zwA5UvX17wOf5x48apdL1eQVq3bk29e/dWWfzXr19TixYtyNTUlA4cOKCyfljRpFIpzZ07N98J7Yxpg7S0NKpevTp5enqSVCqlx48fk6mpqVxHnjCmDhkZGbR48WKysLCgli1b0ubNm/PuUNYGIiIi6KnExERYWVkhISEBlpaWgsU9efIkunbtivv376Nu3bqCxCQiVKlSBT179sTq1asFiSmLhQsXIiAgAB8+fICpqamgsW/evIkePXoAAIKDg9G0aVNB4zPG9MfRo0fh7e2NAwcO4I8//sCDBw9w9+5dmJubazo1xvIhIohEIk2nkY9Y0wnoImdnZ0gkEoSGhgoWMyoqCi9fvkTPnj0FiymLvn37IiUlBcePHxc07sGDB+Hs7IwKFSrg2rVrXNAxxorUvXt3dO/eHcOGDcPp06exdu1aLuiY1tLGgg7gok4hFhYWaNmyJUJCQgSLGRwcDEtLS7Rv316wmLKoXbs2mjRpgn379gkSj4iwcOFC+Pr6okePHrhw4QIqVKggSGzGmH5buXIlsrOz0a9fP3h5eWk6HcZ0Dhd1CvLw8MD58+chlUoFiRccHIyuXbvCyMhIkHjy8PPzw9GjR5GSkqJUnPT0dAwZMgQzZ87E3LlzsXv3bsGndBlj+qt69eq4d+8etm7dqulUGNNJOlHUJSUloVWrVrCwsEB0dLSm0wEAuLu74+PHj4Lk8+rVK0REROStP1O3vn37IjU1Vekp2NGjR+PgwYPYs2cP5syZo7XD04wx7VW9enUYGxtrOg3GdJJOFHWmpqY4evQofH19NZ1KnjZt2sDY2FiQdXVHjhyBgYEBunbtKkBm8qtZsyaaNm2q1BTs+/fvsXv3bixatAj9+vUTMDvGGGOMyUInijqJRAIbG5ti22VkZCAxMfGLl6qYmpqiTZs2gqyrCw4ORvv27VGmTBkBMlNMv379cOzYMSQnJyv0/JYtWyAWizFs2DCBM2OMMcaYLHSiqJNVQEAArKys8l4ODg4q7c/d3R0XLlxATk6OwjGSkpIQEhKi9l2v/9W3b1+kpaXh6NGjcj8rlUqxYcMG9O3bF2XLllVBdowxxhgrjlYVdbGxsXB2ds73iouLk+l5f39/JCQk5L1evnyp0nw9PDyQkJCAyMhIhWOcPn0amZmZ8Pb2Fi4xBVSrVg0tWrRQaAo2NDQUf//9N0aPHq2CzBhjjDEmC4mmE/g3e3t7hIWFKfy8sbGxWhfYtmzZEmZmZggJCUGzZs0UihEcHIyGDRuievXqAmcnPz8/P8ycORNJSUkoVaqUzM+tX78e9erVQ7t27VSYHWOMMcaKolUjdUXx8vLC6dOnMXLkSGzZskXT6QAAjIyM4OzsrPBmiezsbBw7dkxju17/q2/fvsjIyMCRI0dkfubdu3cIDAzE6NGjebcrY4wxpkFaNVJXFKFvPBCKu7s7FixYgKysLBgaGsr17JUrV/Dp0yetKeqqVKmC1q1bY9++fRg4cKBMz2zZsgUGBgYYMmSIirNjjDHGWFF0ZqROW3l4eCAlJQU3btyQ+9ng4GDY29ujRYsWKshMMX5+fjhx4gQSEhKKbZu7QcLPz483SDDGGGMaxkWdkpo2bYpSpUrh3Llzcj1HRDh8+DC8vb0hFmvP/wy+vr7IzMxEcHBwsW1DQkLw+PFjjBo1Sg2ZMcYYY6wo2lNN6CiJRILu3bvjt99+w6dPn2R+7sGDB3j06JHWTL3mcnBwQNu2bWXaBfvHH3+gfv36vEGCMcYY0wJc1Alg2bJlyMjIwIQJE2R+Jjg4GKampujQoYMKM1NMv379cOrUKcTHxxfaJneDxKhRo3iDBGOMMaYFuKgTQIUKFbB69Wrs3r0bBw8elOmZ4OBgdOrUSSsvvO/Tpw+ys7Nx+PDhQtts2bIFEomEN0gwxhhjWoKLOoEMHDgQvXr1wpgxY/D+/fsi23748AGXL1/WuqnXXBUrVoSzszP27t1b4PtSqRR//PEH3yDBGGOMaREu6gQiEomwbt06AMDYsWNBRIW2PXbsGACgW7duaslNEX5+fjhz5kyBt3mcO3cOT5484RskGGOMMS3CRZ2AbG1t8fvvv+PQoUPYvXt3oe2Cg4PRpk0b2NnZqTE7+fTp0wc5OTkICgrK917uBom2bduqPzHGGGOMFYiLOoH5+vqif//+GD9+PN68eZPv/bS0NJw6dUprp15zlS9fHq6urvl2wcbGxiIoKIhvkGCMMca0DBd1KrBmzRoYGxtj1KhR+aZhQ0JCkJqaqvVFHfDPFOzZs2e/OKqFN0gwxhhj2omLOhWwtrbGH3/8gWPHjuW7pzY4OBg1a9ZE3bp1NZOcHHr37g0iQmBgIIAvb5AoU6aMhrNjjDHG2L9xUaci3t7eGDZsGCZNmoSXL18C+KcoOnLkCHr06KETU5d2dnZwc3PL2wWbu0GCb5BgjDHGtI9OFHURERFwcXGBq6sr/Pz8kJWVpemUZPLrr7+iVKlSGD58OIgIERERePv2rU5Mveby8/NDSEgIPnz4gPXr16NBgwa8QYIxxhjTQjpR1FWsWBGnTp3ChQsXULNmzQJ3ZGqj0qVLY9OmTThz5gzWr1+P4OBglC1bVqeu1erduzdEIhHWrl2Lw4cP8w0SjDHGmJaSaDoBWdjb2+f9s6GhISSSgtPOyMhARkZG3r8nJiaqPLfidO7cGaNGjcLUqVNhY2ODbt26FZq/NrKxsYGHhwfmz58PQ0ND3iDBGGOMaSmdGKnL9eLFC5w9exbdu3cv8P2AgABYWVnlvRwcHNScYcGWLVuGcuXK4dmzZzo19ZrLz88POTk5vEGCMcYY02IiKurqAzWLjY2Fr69vvp8HBwdDIpHA29sbGzZsQO3atQt8vqCROgcHByQkJMDS0lJlecvi4sWL+PHHH3H69GmUKlVKo7nIKy4uDp6enti0aROaNGmi6XQYY4wxVgCtKuoKk5OTAx8fH0yaNAkdOnSQ+bnExERYWVlpRVHHGGOMMaZKOjH9um/fPly+fBnz58//4ogNxhhjjDH2D50YqVMUj9QxxhhjrKTQiZE6xhhjjDFWNC7qGGOMMcb0ABd1jDHGGGN6QK/X1BERkpKSUKpUKb4FgTHGGGN6Ta+LOsYYY4yxkoKnXxljjDHG9AAXdYwxxhhjeoCLOsYYY4wxPcBFHWOMMcaYHuCijjHGGGNMD3BRxxhjjDGmB7ioY4wxxhjTA1zUMcYYY4zpAS7qGGOMMcb0ABd1jDHGGGN6gIs6xhhjjDE9oNdFHREhMTERfL0tY4wxxvSdXhd1SUlJsLKyQlJSkqZTYYwxxhhTKb0u6hhjjDHGSgou6hhjjDHG9AAXdYwxxhhjekBnirro6Gi0a9cOrq6u6NatG5KTkzWdEmOMMcaY1tCZoq5OnToIDw/HhQsX0LJlSwQGBmo6JcYY02tpaWlYu3YtsrOzNZ0KY0wGOlPUGRoa5v1zamoq6tatm69NRkYGEhMTv3gxxhhTzL59+zBu3DicPn1a06kwxmSgM0UdAJw5cwZOTk4IDQ1FjRo18r0fEBAAKyurvJeDg4MGsmSMMf1w7NgxAMCBAwc0nAljTBYi0sGTeZcsWQKpVIqffvrpi59nZGQgIyMj798TExPh4OCAhIQEWFpaqjtNxhjTWVlZWShXrhwMDAwAAO/evftixoQxpn10ZqTu38WalZUVzM3N87UxNjaGpaXlFy/GGGPyu3z5MhITE7FkyRJ8/vwZoaGhmk6JMVYMnSnqzpw5A1dXV7i7u+PcuXMYPny4plNirECxsbG4ffu2ptNgTCnHjh2DnZ0dvvnmG9SoUYOnYBnTATpT1HXv3h0XLlxAaGgo9u3bBzMzM02nxFiBpk+fjiZNmmDhwoWQSqWaTocxhRw7dgxdu3aFWCyGr68vAgMDeRcs01mZmZlwcnLCunXrNJ2KSulMUceYrrh79y4qVaqEWbNmoUePHvj8+bOmU2JMLs+ePcO9e/fQrVs3AICvry8+fvyIixcvajgzxhSzefNmREZGYteuXZpORaW4qGNMQESE+/fvY9y4cTh+/DiuXLmCpk2b4ubNm5pOTWcREZYsWYI1a9ZoOpUS4/jx45BIJPD09AQANGvWDFWqVOEpWKaT0tPTMX/+fFhbW+Py5cuIj4/XdEoqw0UdYwJ6+/YtkpKSULduXXTp0gU3b95EuXLl0LZtW2zatEnT6emc9PR0DBo0CD/++COWLl2q6XRKjGPHjsHZ2RlWVlYAAJFIBF9fXxw6dAg5OTkazo4x+axbtw6xsbHYt28fcnJycObMGU2npDJc1DEmoJiYGABAvXr1AABVqlRBWFgYvv76a4wYMQLffPMN0tLSNJmizvj48SM8PT1x6NAhDB06FC9evMCHDx80nZbeS0tLQ0hISN7Uay5fX1+8e/cO4eHhGsqMMfklJycjICAAX331FTw8PNCwYUMcP35c02mpDBd1jAkoJiYGhoaGqFatWt7PjI2N8fvvv2Pr1q3Ys2cP2rZti8ePH2swS+336NEjtGnTBg8ePEBoaCjmzJkDAIiIiNBwZvovNDQU6enp+Yq6li1bolKlSjwFq4M2bNiA58+fazoNjVizZg3i4+Mxe/ZsAEDXrl1x4sQJvd3ExkUdYwKKiYlBzZo1CzykdejQobh69SqSk5PRrFkzBAcHayBD7RcWFobWrVvDwMAAV69eRZs2bVCtWjWUKVMGN27c0HR6eu/YsWOoWrVqvqsYxWIx+vTpg4MHD+rtB6I+evr0KUaNGoXFixdrOhW1i4+Px5IlSzBq1ChUrlwZAODl5YV3794hMjJSs8mpCBd1jAkoJiamwHuJczk6OuL69etwc3NDz549cerUKTVmp/12796NDh06oFGjRrh8+TKqV68O4J81Xc2aNeOROhUjIhw/fhzdunWDSCTK976vry/evHmDq1evaiA7pojDhw8DAPbv31/ijqRZsWIF0tPTMX369LyftWvXDqVKldLbKVgu6hgT0P3794ss6gCgdOnSCAwMhLu7OyZMmPDFbSklFRFh4cKFGDhwIPr164fTp0+jbNmyX7Rp1qwZj9Sp2P379/Hs2bN8U6+52rZti/Lly/MUrA4JDAxE9erV8fHjR4SEhGg6HbX5+PEjVqxYgfHjx6N8+fJ5Pzc0NISnpydOnDihwexUh4s6xgSSlJSEV69eFVvUAf+MPK1atQpPnjzBr7/+qvrktFhWVhaGDx+OmTNnYu7cudi6dSuMjIzytWvevDlevXqFd+/eaSDLkuHYsWMwNTWFm5tbge+LxWL07t0bBw4cgA5eG17ifPjwAWFhYfD390etWrWwZ88eTaekNkuWLAER4Ycffsj3npeXF65evYpPnz5pIDPV4qKOMYE8fPgQwP/tfC1Ow4YNMX78eMyfPx+vX79WZWpaKyMjA15eXtixYwe2b9+OOXPmFDjtB/wzUgfwZglVOn78ODw8PGBqalpoG19fX7x8+RLXr19XY2ZMEUeOHAEA9OjRA/3798ehQ4dKxMzA27dvsWbNGnz//fcoV65cvve7dOkCqVSK06dPayA71eKijjGB5B5nUqdOHZmfmTt3LszMzAr8NlkSzJo1CxcuXMCpU6cwePDgIttWrVoVZcqU4aJORRISEhAWFlbo1GsuFxcX2NjY8BSsDggMDES7du1ga2uL/v37IyEhoUSs4w0ICICJiQkmT55c4PsVK1ZE48aN9XIKVmeKuoiICLi4uMDV1RV+fn7IysrSdEqMfSEmJgYVKlSApaWlzM+ULl0aixcvxq5du0rcFUyhoaFYtmwZFi5cCHd392Lbi0QiNG/enNfVqcjp06eRnZ0NLy+vItsZGBjwFKwOSEpKwpkzZ+Dj4wMAqF+/Pho1aqT3U7AvXrzA+vXrMW3aNJQuXbrQdl5eXnp5tInOFHUVK1bEqVOncOHCBdSsWRNBQUGaTomxLxS387Uww4YNQ6tWrTBhwoQSszvt8+fPGDp0KNzc3DBlyhSZn+MdsKpz/PhxNGjQAFWqVCm2ra+vL54+fYpbt26pITOmiFOnTiEjIwO9evXK+1n//v1x+PBhpKSkaDAz1Zo/fz5Kly6NCRMmFNmua9eu+Pjxo959SdSZos7e3h5mZmYA/tm9IpFI8rXJyMhAYmLiFy/G1EXRok4sFmP16tW4c+cO1q1bp4LMtAsRYezYsUhOTsbWrVshFsv+a6h58+Z4/fo1YmNjVZhhySOVSvOOMpGFq6srrK2teQpWiwUGBqJx48ZfHITer18/pKam4tixYxrMTHX+/vtvbN68Gf7+/rCwsCiybZs2bWBlZaV3U7A6U9TlevHiBc6ePYvu3bvney8gIABWVlZ5LwcHBw1kyEqi7OxsPHz4UKGiDgBatGiB4cOHY9asWXJfhbV161a0bt0aCQkJCvWtbjt37sTevXuxbt06uf+O8mYJ1YiIiMD79++LnXrNZWhoCB8fH+zfv5+nYLVQZmYmjh07ljf1mqtGjRpo0aKF3k7Bzp07F/b29hgzZkyxbSUSCTp16qR/59WRDklISKD27dvTgwcPCnw/PT2dEhIS8l4vX74kAJSQkKDmTFlJ8+jRIwJAp0+fVjjG+/fvqXTp0jRy5EiZ2mdnZ9OUKVMIAAGgo0ePKty3ujx9+pQsLS1p8ODBCj0vlUrJ2tqa5s6dK3BmJducOXPIysqKMjMzZX7mxIkTBICioqJUmBlTxMmTJwkARUZG5nvvl19+IWNjY4qPj9dAZqoTHR1NIpGIfv/9d5mf2bx5M4lEInr//r0KM1MvnRmpy8nJwaBBgzB79mzUrl27wDbGxsawtLT84sWYOuTufJX1OJOC2NjYYP78+di4cWOx6zwSEhLQo0cPrFixAitWrICdnR0uX76scN/qkJOTg6FDh6JMmTJYs2aNQjH0+WaJLVu2aOwMvuPHj6Nz584FXm9XGA8PD5QuXZqnYLVQUFAQqlWrBkdHx3zv+fn5ISMjI++mCX0xZ84cVK1aFd98843Mz3Tp0gVEpFc7gnWmqNu3bx8uX76M+fPnw83NDXv37tV0SozliYmJgbm5OSpWrKhUnDFjxuSdX1fYrqzHjx+jTZs2CA8Px/HjxzFp0iS0bdsWV65cUapvVVuyZAnCwsKwfft2WFlZKRxHH3fAnj59Gl9//TWWLVum9r7fvXuH69evy7yeLpeRkRF69uwpV1FHPFWrclKpFIcPH4aPj0+BZz5WqlQJLi4uejUFe/PmTRw8eBBz5swp8ODywtjb26Np06b6NQWr6aFCVUpISODpV6YWw4cPp2bNmgkS6/z58wSA/vzzz3zvhYSEUNmyZalWrVoUExOT9/MlS5aQmZkZZWVlCZKD0G7cuEESiYT8/f2VjnXw4EECQK9fvxYgM83Lzs6mxo0bEwCqVKkS5eTkqLX/3Cmod+/eyf3skSNHCADdvXu30DY5OTl0+PBhcnZ2pnLlylFKSooy6bJiXL58mQDQxYsXC23z22+/kUQioQ8fPqgxM9Xp3Lkz1a1bV6HffzNnzqSyZctSdna2CjJTP50ZqWNMmym687Ugrq6uGDBgAH766acvNj+sW7cOnTp1QtOmTfHXX399cchx27ZtkZqaijt37giSg5BSU1MxaNAgNG7cGHPnzlU6nr5tlti5cyeioqKwZMkSvHr1CuHh4Wrt//jx42jRogVsbW3lftbT0xOlSpUqcLQuPT0dGzduRIMGDdCzZ08kJibi48ePuHr1qhBps0IEBQXBxsYGbdu2LbSNr68vpFIpDh06pMbMVCM0NBSnTp3CokWLCjwVozhdu3ZFXFwcrl27poLs1I+LOsYEIGRRBwBLly5FSkoK5s6di6ysLIwfPx5jx47F2LFjceLECZQpU+aL9s2aNYOhoaFWrqubOnUqXrx4gR07dsg1NVKYypUro1y5cnpR1KWlpWHmzJno06cPpkyZAgcHB+zevVtt/WdlZeHUqVNyT73mMjY2Ro8ePb4o6j5//oxFixahatWqGDVqFOrUqYOwsDDcunULZcuWLXGHbKsTESEwMBA9evSAgYFBoe1sbW3RoUMHnZ+CJSL89NNPaNmyZb6dvrJq1aoVypYtqz9Hm2h6qFCVePqVqcP79+8JAO3fv1/QuAEBAWRgYEBt27YliURC69evL7J9q1ataNCgQYLmoKzc6Tl5dqTJonPnztStWzdBY2rC4sWLSSKR5O3onzZtGpUrV06uXajKCA0NJQB0/fp1hWMEBgbm7fyeNGkSmZubk7GxMY0aNeqLJQJERD179iR3d3dl02aFiI6Olnkn/KZNm0gkEtGbN2/UkJlqHDp0iABQSEiIUnH69+8v2PIZTeOijjElXbx4kQBQdHS0oHHT09OpVq1aVLZsWQoNDS22/aRJk6hatWqC5qCM2NhYsrGxoe7du5NUKhU09owZM8je3l7QmOr28eNHsrKyonHjxuX97ObNmwSATpw4oZYcpk2bRnZ2dkqt40tNTSVzc3MCQGXKlKGZM2dSbGxsgW1/+eUXMjExoYyMDIX7Y4VbsGABWVhYUFpaWrFt4+LiyNDQkFauXKmGzISXlZVF9erVo06dOikda9u2bQSA3r59K0BmmsXTr4wpKSYmBmKxGDVr1hQ0rrGxMcLCwhATEwM3N7di27dp0wZPnz7VmtsWFi9eDKlUio0bNxa4C08ZzZo1Q2xsLN68eSNoXHVauHAhcnJyMHv27LyfNWnSBHXq1FHbFOyxY8fg5eUl160e/2VqaorffvsNq1evxsuXLzF//nzY2dkV2LZ9+/ZIT0/Xu93L2iIwMBBeXl4wMTEptm2ZMmXQpUsXnZ2C3b59O+7fv4+AgAClY3Xu3BkikUgvjjbhoo4xJcXExKB69eowNjYWPLatrS1sbGxkapu7MFobjjYhIhw8eBADBgwo9ANeGc2bNwcAnS0Onj59ijVr1uDHH3/8YoOCSCTCgAEDEBgYiLS0NIVix8fHw8vLC4MGDUJAQACOHDmCp0+f5jsi59mzZ7h3757Mt0gUZdiwYRg/fjzMzc2LbNekSRNYWFioZV2dVCrF9OnTcfjw4RJxlMqLFy8QEREh19qy/v3748qVK3j27JnK8lKF9PR0zJkzB/369UPTpk2Vjmdra4vmzZvrxdEmXNQxpiShN0koqlKlSqhUqZJWFHU3b97EixcvvrhMXEiVKlWCjY2Nzm6WmDFjBsqVK4fvv/8+33sDBgxAUlKSwh8wq1atQmhoKJ4/f47FixejR48eqF69OiwtLdGqVSsMHz4879BqiUQCT09PZf84MpNIJGjXrp1airrnz58jICAAPj4+cHNz05vdjYUJCgqCoaGhXEV6jx49YGpqin379qkwM+GtXbsWb968wfz58wWL6eXlhdOnTyM7O1uwmJrARR1jStKWog6A1hxCHBgYiLJly6J9+/YqiS8SiXT2EOKIiAjs3r0b//vf/woc2apduzaaNm2q0BRsUlISfv31V4waNQphYWH4/PkzXr58iRMnTmDu3LmoV68ebt++jRkzZmDVqlVwd3dX6iBoRbRv3x5hYWHIyclRaT+5t7xs2LABcXFxaNWqFQYMGICnT5+qtF9NCQoKQocOHeT639PCwgLdu3fXqSnYhIQELFq0CCNGjECtWrUEi9u1a1fEx8fr/pE7ml7Up0q8UYKpWlpaGolEItq4caOmUyEiohUrVpCxsbHGF6LXr1+fhg0bptI+Zs6cSXZ2doJvwlAlqVRK7u7uVL9+/SIPSl26dKlC93P+/PPPZGRkRC9fviyyXXZ2Nv3999/0+fNnueIL4dKlSwSAIiIiVNrP8uXLydTUlHJycig7O5s2bdpEFSpUICMjI5o8eTJ9+vRJpf2r08ePH8nAwIDWrVsn97O5h3n/d6eytpo1axaZmJgIfvh4dnY2lStXTpAD0jWJR+oYU8LDhw9BRFo1UpeRkYHIyEiN5fDgwQPcu3cPvXv3Vmk/zZs3x7t37/D69WuV9iOkkydPIjQ0FIsXLy7yoNR+/fohIyMDQUFBMsdOTU3FL7/8gq+//hqVKlUqsq2BgQFq1KiB0qVLyxxfKC1atICxsbHKp2BjYmJQp04diMViGBgY4JtvvsHDhw8xa9Ys/PHHH6hZsyZ++eUXZGRkqDQPdThy5AikUil69uwp97Ndu3ZFqVKldOLqzXfv3mH58uX47rvvUKFCBUFjGxgYoEuXLjp/Xh0XdYwpIXeKR1uKuiZNmsDExESjhxAHBgbC3Nxc5Wu1dO1miZycHPzwww9wdXUt9rBfBwcHtG/fXq4p2D/++ANxcXH48ccflU1VpYyNjdG6dWuVF3UPHjzI9/fS3NwcM2fOxN9//43+/fvjxx9/RN26dbF69Wpcu3ZN4c0pmhYUFIQ2bdrA3t5e7mdNTU3h4+OD3bt3C7ah5OnTp7h69argG1QWLFgAQ0NDlf033rVrV0RGRurUF8X/0pmiLikpCa1atYKFhQWio6M1nQ5jAP4p6mxsbGBtba3pVAD8c8l6s2bNNLquLjAwEF26dIGpqalK+6lYsSJsbW11Zl3dtm3bEB0djSVLlsh0xMuAAQNw9uxZfPjwodi26enpWLp0KYYMGYJq1aoJka5KtW/fHhcvXlTprtSi1rra2dlh7dq1iI6ORpMmTTB58mS0atUKpUqVgqOjI77++musXr0a4eHhSE5OVlmOQkhJScGpU6cUvlEB+GcXbExMjCDXDD58+BCtW7dGmzZtULduXfz888+CHD305MkTrF+/Hj/99FO+G3WEknu0ycmTJ1USXx10pqgzNTXF0aNH4evrq+lUGMujTZskcrVt21ZjI3WvXr3CtWvXVD71CvzfZgldGKlLTU3FrFmz0K9fP7Rs2VKmZ3x9fSESibB///5i227evBmxsbHw9/dXNlW1aN++PT59+oT79++rJP7nz5/x7t27L+5HLkjdunURGBiIpKQkXL9+HWvXrkXbtm1x9+5dTJs2Dc7OzrC0tES9evUwcOBALFq0CIGBgXjw4IHW7JI8deoU0tPTldpp3rFjR5QtW1bpDRMvX75Ex44dYW1tjSNHjqBly5aYN28eHBwc0L17dxw6dAiZmZkKxZ4zZw7KlSuHCRMmKJVjUaytrdGqVSvdnoLV7JI++Q0bNozu3LlT4Hvp6emUkJCQ93r58iVvlGAq5eTkRCNHjtR0Gl/IvTrnxYsXau979erVZGhoKPcCf0XNmjWLbGxstH6zxKJFi8jQ0JD+/vtvuZ7r2rUrOTs7F9kmMzOTKleuTAMGDFAmRbVKTk4miUQi+PVxua5cuUIA6NatWwrHyMzMpMjISPrzzz9p/Pjx1K5dOypdujQBIABkaGhIDRo0oL59+9KcOXNoz549dPv2bUGueJNKpXTmzBk6duwYvX79usj/vocMGUINGzZUus/Ro0dTmTJl6OLFiwo9/+7dO6pTpw5VrVqVXr16lffzz58/07p166hly5YEgMqVK0eTJk2i27dvyxw7KiqKRCKRQhtB5LVo0SIyMjKi4OBglfelCnpV1M2ZMyfvL9y/X1zUMVXIyckhMzMz+uWXXzSdyhfevn1LAGjv3r1q79vDw4M6d+6stv4OHz6ssQJWVu/fv6dSpUrRd999J/ezudcXPX/+vNA2mzZtIgCF/l7UVq1atVJZIbp582YCQCkpKYLGlUql9PbtWzp37hytXr2avv32W3JzcyNbW9u8zxsHBwf6/fffKT09XaE+zp8/T61bt/7iM8zGxoY8PT3phx9+oF27dtG9e/coOzubMjMzqXTp0jRz5kyl/2yfPn0iNzc3MjQ0pD///FOuZ+Pj48nJyYns7Ozo0aNHhbaLjo6mKVOmkI2NDQEgJycnGjduHP3xxx907do1Sk1NLfC5bt26Ua1atdRyJ3JaWhr16dOHDAwMaNOmTSrvT2h6VdTxSB1Tp+fPnxMAOnbsmKZTyadatWo0adIktfapzLEKinr16hUBoEOHDqmtT3nNnj2bLCws6MOHD3I/m5iYSCYmJrRkyZIC38/KyqKaNWtS7969lU1T7aZNm0YVK1ZUySjrjz/+SFWqVBE8blE+fvxI58+fpwEDBpBIJKJKlSrR2rVrZS7ubt68SV26dCEA1KxZMzp9+jQ9ffqUAgMDac6cOdSzZ0+qUqVKXqFnampKDRo0EPR4mIyMDBo1ahQBoMmTJ1N2dnaxz6SkpJCzszOVKVNG5tG3zMxMCgwMpIEDB1K9evVILBYTABKLxVS3bl3q168fLVq0iI4fP06BgYFq/5KanZ1NY8aMIQC0cOFCrZ8J+De9Kur+i8+pY6p08uRJAkBPnjzRdCr5DBo0iFq2bKnWPjdv3kwikUitl2JLpVKyt7enGTNmqK1PeeTk5FDVqlVpxIgRCsfw9fUlJyenAt/bsWMHAaCbN28qHF9Tjhw5QgDo8ePHgsfu2bOnWkeM/+vevXs0cODAvOLut99+K7S4e/ToEfXv358AUO3atWn//v1FFhGfPn2ikJAQWr58OQ0dOpS++uorQYsOqVRKK1euJLFYTF5eXkV+fmZkZFDXrl3J3Nycrly5onCfqampdP36ddqwYQONHz+eXFxcyNLSMq+Abdq0KeXk5CgcXxFSqZTmzZtHAGjChAlq719ROlXUde3alcqXL0+tW7emzZs3F9ueizqmSr/++iuZmJjI9G1W3dasWUOGhoaUlpamtj579OhB7dq1U1t/ubp166bRD/CinD9/ngAovE6JqPDDYXNycqhevXrUrVs3ZdPUiLi4OBKJRDL9LpdX3bp1FZruFlpucScWi6lSpUq0Zs2avL+Tr1+/pjFjxpBEIqGKFSvShg0bijyQWt1OnjxJVlZW1KBBgwIL7+zsbOrXrx8ZGRnRmTNnBO9fKpXSkydPKCgoiJ4+fSp4fFmtW7eOxGIx+fn5KTylrk46VdTJi4s6pkpjxowhR0dHTadRoIiICAJAYWFhaukvKSmJTExMaNmyZWrp79/mzJlD5cqV08opkm+++YaqVaumVG5paWlkaWlJc+bM+eLn+/fvJwBKjZBoWuPGjenrr78WNGZmZqZKN2Eo4v79+zRo0CASi8VUsWJFGj58OJmamlKZMmVo6dKlha4l07T79+9TjRo1yNrams6fP5/3c6lUSiNHjiSxWKzVSx+EcujQITI2NiYPDw+trye4qGNMQW5ubuTn56fpNAqUlZVFZmZmha7FElpugaGKqbTiBAcHEwB69uyZ2vsuSkpKCpUqVSpfMaaIYcOGUa1atfKKQ6lUSo0bN6aOHTsqHVuTJkyYQDVr1hQ0ZkxMDAGgkJAQQeMKISYmhgYPHky2trY0Y8YMjVzTJq+PHz+Su7s7SSQS2rhxI0mlUpo2bRoBoC1btmg6PbW5cOECWVlZkZOTk1qXmMiLizrGFGRvb0+zZ8/WdBqFcnNzo169eqmlr4EDB1Ljxo3V0td/vX79mgDQwYMHNdJ/YXbu3EkA5D7GpCC56zdv3LhBRP9XyF64cEHp2JqU+2VAyHs8g4KCCAC9efNGsJglXWZmJo0ePZoAUPv27QkA/frrr5pOS+2ioqKofPnyVL169SJ3+WqSzhw+zJg2iY+PR2xsrNYdPPxvuYcQkwpP7QeAzMxMHD16VKnDT5VRoUIFlC9fXutulti2bRucnZ1Ro0YNpWN16NABNjY2eVc5zZ8/Hy4uLmjfvr0AmWqOi4sLAODSpUuCxYyJiYGlpaVCV2axghkaGuL333/Pu2Vj3rx5+O677zSdlto5Ojri8uXLkEgkaNeuXd7VfNqEizrGFPDgwQMA2nPna0HatGmDd+/e4dmzZyrtJyQkBImJiRor6oB/7oHVppsl3rx5gzNnzmDo0KGCxJNIJPDz88PevXtx6tQpXL9+HbNmzRIktibZ2dmhTp06gt4Dm3vLiyxXsTHZiUQijB8/HvHx8Zg9e7am09GYqlWrIiwsDC1btsTYsWNhb2+PHj16YM+ePUhJSdF0elzUMaaI3OuNiruGSJNat24NACq/MiwwMBA1atRAo0aNVNpPUZo3b44bN26ofFRSVjt37oShoSH8/PwEizlgwAC8evUK33zzDVq2bImOHTsKFluTcu+BFYo2Xt2nTywsLDSdgsbZ2NjgyJEjeP36NX755Rd8+PABAwYMgJ2dHQYNGoRjx44hKytLI7lxUceYAmJiYlClShWYmZlpOpVClStXDrVr18aVK1dU1kdOTg4OHz6MXr16aXRkpFmzZoiLi8Pz5881lkMuIsLWrVvh4+MDKysrweK2adMGlStXxtu3bzFr1iy9GYlq3749oqOj8enTJ6VjEREXdUxt7O3tMWHCBFy5cgWPHz/G9OnTERkZie7du8Pe3h5jxozBvXv31JoTF3WMKUBXPjhy19WpypUrV/Du3TuNTr0C/xR1ALRiXd2tW7dw9+5dwaZec4nFYowdOxZubm7o1q2boLE1KXddYFhYmNKxPnz4gPj4eK0eQWf6qXr16pg+fTqio6MRFRWFUaNG4ejRo/D29lZrHiW2qMvJyYFUKtV0GkxH6UpR16ZNG9y+fRvJyckqiR8YGAh7e/u8qV5NKV++PCpUqKAV6+q2bdsGOzs7dOrUSfDYP/30E0JDQ/VmlA4AKleujCpVqggyBRsTEwNAu9e6Mv0mEong6OiIgIAArF27Fk+ePMHTp0/V1r9gRZ02/DKVx/Tp09G7d2+Vfdgx3fLmzRscOXJEprZZWVl4/PixTnxwtGnTBjk5Obh+/brgsYkIgYGB8PHxgVis+e+HuevqNCkrKwu7du3CoEGDIJFINJqLLhFqXV1MTAwMDAwE2XHMmLLat28PsViMkJAQtfUp2G9iTU+/yMvFxQXnzp1Du3bt8OLFC02nwzRs1apV6NGjB4KCgopt+/jxY2RnZ+tEUVe/fn1YWlqqZF3d7du38fTpU635u5+7A1aTmyVOnjyJDx8+CD71qu/at2+PmzdvIikpSak4MTExqF69OoyNjQXKjDHFlS5dGs2aNVNrUSfXV8nCdnIRkdad1VKc7t2748qVK/D29kaLFi0QFBSENm3aaDotpiGRkZEAgGHDhiEiIgI1a9YstK0uTfEYGBigVatWKinqDh06BCsrK7i5uQkeWxHNmjXD58+f8eTJE42N1GzduhWNGzdG48aNNdK/rmrfvj2kUikuX76Mzp07KxxHV5ZFsJLDw8MDW7duBRGpZdmEXCN1Z8+exbBhwzBu3Lh8L3Nzc1XlmGfq1KlwcXHBoEGDkJmZqXS8hg0b4tq1a6hduzbc3NywY8cOuWNIpVIcOXIkryhguikqKgoTJ06EnZ0dfH19kZaWVmjb+/fvo3Tp0rCzs1Njhopr27Ytrly5IvgIVmBgILy9vWFkZCRoXEW1adMGIpEI58+f10j/cXFxOHLkCI/SKaBWrVqws7NTegqWizqmbTw8PBAbG5s3GKBqchV1bm5usLCwgKur6xcvNzc3ODk5qSpHAP/sKIuNjcWlS5dQv359HDhwQJC4NjY2OHv2LAYNGoQhQ4Zg+vTpMm2gyMrKwtatW9GgQQP06NEDnTp1QmxsrCA5MfV69+4dYmNj4eLigoMHD+Lhw4cYN25coe117XDTNm3a4NOnT3j06JFgMf/++2/cuXNHa6ZeAaBs2bJo0aIFTp8+rZH+9+3bh5ycHAwcOFAj/esykUik9Lq6tLQ0PHv2jIs6plXatWsHQ0NDtU3BylXUHTp0CK6urgW+d/LkSUESKsyVK1fydpN16dKlwGMaMjIykJiY+MVLFsbGxti0aROWLl2Kn3/+GX369Cl0A0VaWhrWrFmDmjVr4quvvkKdOnVw/PhxiMViDBs2jHfU6qCoqCgAQJMmTdCoUSOsW7cOmzdvxqZNmwpsr2ujAa1atYJIJBL0aJPAwECYmJgoNVWmCp6enjh79ixycnLU3vfWrVvRuXNnvp5KQe3bt8e1a9eKHCUvyt9//w0i4uNMmFYxNzdH69attbOo06T4+HhYWloCAKysrApcwxcQEAArK6u8l4ODg8zxRSIRpk6diuDgYJw9exbOzs5fbKBISEjAzz//jKpVq+K7776Di4sL7ty5g6CgIHTt2hXbtm3D6dOnsWLFCuX/sEytoqKiYGFhgerVqwMAhg4dilGjRmHcuHG4devWF2118XDT0qVLo379+oKuqwsMDESXLl3UsuxCHp06dUJcXFy+/91U7eHDh7h69SpPvSqhffv2yMzMxLVr1xR6XpfWurKSxcPDA6GhoWoZ9FG4qDt48KCQeRSrTJkyeSNv8fHxKFu2bL42/v7+SEhIyHu9fPlS7n66d++Oy5cvIz4+Hi1btsSJEycwc+ZMVKlSBXPmzEGvXr3w8OFD7NixAw0bNsx7rlOnTpg6dSr8/f117niXki4qKgqNGjX64liOlStXokGDBvD19UV8fHzez2NjY5GYmKhzHxxt2rQRbKTu8ePHuHLlCnx9fQWJJ6TWrVvD3NwcZ86cUWu/27dvh5WVFXr06KHWfvVJw4YNUbp0aYWnYGNiYlCuXDlYW1sLnBljyvHw8MDnz5/zZoVUSeGibuDAgcWOSgm5MLt169Z5a2VOnTqFdu3a5WtjbGwMS0vLL16KaNSoEa5du4aaNWvCy8sLv/76K0aMGIGnT59i3bp1he6sW7hwIRwdHTFgwAA+/06HREVF5dutaGJiggMHDiAuLu6LaXVdHQ1o27Yt7t69i4SEBKVj/fnnn7CyskLv3r0FyExYRkZGcHd3V+u6OqlUim3btsHPzw+mpqZq61ffiMViuLi4KFXU6drfS1YytGrVCqampmqZglW4qAsODsbcuXMxceLEfMVbTk4OtmzZgnr16imdYC4nJyfY29vDxcUF9+7dQ58+fQSLXRBbW1ucO3cOu3btwvPnz7Fs2TJUqFChyGeMjIywe/duvHnzBhMmTFBpfkwY6enpuH//foFHUFSrVg3bt29HcHAwli5dCuCfna+GhoZ5U7W6ok2bNiAi/PXXX0rFyc7OxubNmzFo0CCtLWA8PT0RHh6OlJQUtfR38eJFvHjxgqdeBdC+fXtcvnxZocvQuahj2srY2BjOzs7qWVdHSoiMjKRKlSqRj48PpaamUkZGBq1du5aqVq1KZcqUodmzZysTXmkJCQkEgBISEtTa7+bNmwkA7d69W639MvlFREQQALpy5Uqhbfz9/UksFlNoaChNmDCB6tWrp8YMhZGTk0Nly5alGTNmKBUnODiYANDNmzcFykx49+/fJwB0/PhxtfT31VdfUfXq1UkqlaqlP332119/EQC6evWqXM/l5OSQmZkZLVu2TEWZMaacgIAAsrCwoMzMTJX2o9RGicaNG+Pq1at48uQJWrdujWrVqmHOnDkYOXIknj9/jnnz5ilfdeqgYcOGYcCAARg9erRa73xj8ouKioJIJEKjRo0KbfO///0Prq6u6N+/P8LCwnRyNEAsFqN3797YtGkTMjIyFI6zadMmNG3aVOVHGCmjTp06qFSpklqmYFNSUnDgwAEMHTpUZ4640WZOTk6wsLDAuXPn5Hru9evXSE1N1cm/m6xk8PDwQHJyssqvMlSqqEtISMCff/6J169f49GjR4iPj8e5c+cwffp0lCpVSqgcdY5IJMLvv/+OsmXLYuDAgQpNJTD1iIqKQs2aNYvcxSmRSLB7926IxWLcunVLZz84pk6dinfv3il0yDYAvH37FkePHsWIESMEzkxYIpEInTp1UstmiaCgICQnJ2PIkCEq76skMDQ0hJeXl9znkOaudeXjTJi2atq0KSwtLVU+BatwUefv748qVapgy5YtWLRoET58+IC+ffuiY8eOKrk8XNdYWVlh9+7duH79eokdsdQFBW2SKIidnR327dsHiUSCJk2aqD4xFahTpw569uyJpUuXKrS1fuvWrTA0NMSAAQNUkJ2wPD09cffuXbx+/Vql/WzduhUuLi46t8ZSm/Xr1w+3bt2S67DsBw8ewMjICFWrVlVdYowpQSKRwNXVVXuLuqCgIKxatQoPHz7EqFGjYG5uji1btmDUqFFwd3fH4cOHhcxTJ7Vu3Rr/+9//sGjRIo1dXcQKR0SIjIyUuUhzdnbGq1evtPIoD1lNmzYNDx48wNGjR+V6joiwceNG9O3bF6VLl1ZNcgLq2LEjRCIRzp49q7I+YmJicObMGXzzzTcq66Mk6tq1K8zNzbF//36Zn4mJiUGtWrUgkch1nTljauXh4YHw8HCkp6errA+Fi7p79+5h6NChMDAw+OLn8+fPx6+//op+/fphzZo1Sieo63788Ue4urpi8ODB+PTpk6bTYf/y8uVLxMfHy3X5up2d3Rfn2ematm3bol27dliyZIlcz124cAGPHz/W+qnXXOXKlYOTk5NK19WtXLkSdnZ2OjFyqUtMTU3Ro0cP7Nu3T+ZneOcr0wUeHh7IyMgQ9CD4/1L406moRcEjRoxAYGAgpk+frmh4vWFgYIAdO3YgLS0NI0aMEPxSdaa43IMg5Snq9MG0adMQHh4u12HEmzZtQq1ateDi4qLCzITVqVMnnD17ViWnuH/69Albt27FuHHjYGxsLHj8ks7Pzw9RUVF48OCBTO25qGO6oGHDhihXrpxKp2BVNuTQtWtXnnL8/ypWrIiVK1ciKCgIjx8/1nQ67P+LiopCmTJlUKlSJU2nolbe3t6oU6dO3tl7xfn8+TMOHDiA4cOH69QOT09PT7x//x63b98WPPb69etBRBgzZozgsdk/93tbWFjINFqXlJSE169fc1HHtJ5YLIa7u7tuFnXAP7s92D86deoEAGq/k5IVLjIyEo0bN9apQkUIYrEYU6dOxeHDh2UaCdm1axeysrIwbNgwNWQnnHbt2sHU1FTwXbCZmZlYs2YNhgwZAhsbG0Fjs3+YmJigZ8+eMhV1uf8Nc1HHdIGHhweuXbuGpKQklcTX3cVBOsbW1hYVKlTgok6LREVF6exOVmUNHjwYdnZ2+OWXX4psR0TYsGEDvL29YW9vr6bshGFsbAxXV1fB19Xt3bsXb9++xaRJkwSNy77k5+eH6Oho3Lt3r8h2uceZ1K5dWx1pMaYUDw8PZGdnIywsTCXxuahTIycnJy7qtERycjIeP35c4tbT5TIxMcHEiROxdetWxMbGFtru5s2biIqK0pkNEv/VqVMnXLp0CWlpaYLEIyIsX74cXbp0Qf369QWJyQrWuXNnWFpaFrsL9sGDB6hQoYLCd30zpk61atVCxYoVVTYFy0WdGnFRpz3u3LkDIiqxRR0AjBkzBkZGRli9enWhbTZt2oSKFSuic+fOasxMOJ6ensjIyMClS5cEiXf+/HlERkZi8uTJgsRjhTM2NoaPj0+xU7C8SYLpEpFIBA8Pj5Jd1CUlJaFVq1awsLBAdHS0ptNRmJOTE969e4e3b99qOhW5EBHOnTun1PVS2iYqKgoSiaREj7aUKVMGI0eOxNq1awtc35GamoqdO3fiq6++0tnzvxo0aIDy5csLtq5uxYoVaNiwITp27ChIPFY0Pz8/3Lt3D3fv3i20DRd1TNd4eHjg1q1biIuLEzy2ThR1pqamOHr0qE4f+gog775MXRutW7NmDTp27IgJEyZoOhXBREZGol69eiX+OIpJkyYhOTkZmzZtyvfegQMHkJiYqNOH64pEInh6egqyru7hw4c4cuQIvv/++xK3uUZTPD09YWVlhb179xb4fk5ODh4+fMhFHdMp7u7uICJcuHBB8Ng6UdRJJBKZdpllZGQgMTHxi5c2qVq1KkqXLq1TRV1ERASmTp2KFi1aYMOGDdi5c6emUxKErNeD6bvKlSujf//+WL58eb47ijdu3IgOHTro/BVYnTp1wu3bt4tcOyiLlStXwtbWFgMHDhQoM1YcIyMj9OrVC/v27SvwjM9nz54hMzOTizqmU6pUqYIaNWqoZApWJ4o6WQUEBMDKyirv5eDgoOmUviASidCkSROdKeoSEhLg5+cHR0dHXLp0CYMHD8bo0aNlPhBUW0mlUty5c4eLuv9v2rRpePny5RejIQ8ePMClS5d0doPEv+VOlSpzZVhcXBy2bNmCb7/9FiYmJkKlxmTg5+eHBw8e4M6dO/ney935ykUd0zWqWlenVUVdbGwsnJ2d871knXf29/dHQkJC3uvly5cqzlh+urJZgogwcuRIfPz4EXv37oWxsTF+//13ODg4oG/fvkhNTdV0igp7/PgxUlJSuKj7/xwdHdGlSxcsXbo0bzTkzz//RNmyZeHj46PZ5ARgZ2eHxo0bK7Wu7o8//kBOTg7Gjh0rYGZMFh07dkSZMmUK3DARExMDMzMzVKxYUQOZMaY4Dw8P3Lt3T+kZhP/SqqLO3t4eYWFh+V5ly5aV6XljY2NYWlp+8dI2Tk5OePLkCRISEjSdSpHWrVuH/fv3Y9OmTXnTbxYWFti/fz/+/vtvfPfddxrOUHGRkZEASt71YEWZNm0abt++jdOnTyMrKwtbtmzB4MGD9WZUytPTE2fOnFHomr7MzEysXr0agwcPhq2trQqyY0UxNDRE7969C5yCjYmJQZ06dXT6PmZWMrm7uwMAQkNDBY2rM38TvLy8cPr0aYwcORJbtmzRdDoKy90skVtYaKPIyEh8//33+Pbbb/NtTmnYsCHWrFmDjRs3YseOHRrKUDlRUVEoX748f0D/i7u7O5o1a4alS5fi6NGjeP/+PYYPH67ptATTqVMnvH37tshdlIXZv38/3rx5w4cNa5Cfnx8ePXqUd19zrgcPHvDUK9NJdnZ2aNCggeBTsDpzTsHx48c1nYIg6tatCxMTE9y6dQuurq6aTiefpKQk+Pn5oV69eoXeNvD111/jwoULGDNmDJo3b65zv1R5k0R+IpEI06ZNQ//+/fHq1Su0bNkSjo6Omk5LMM7OzjA2Nsbp06fRsGFDmZ/LPWy4U6dOcj3HhOXu7g5ra2vs3bv3i1tgYmJi+HgZprM8PDxw7NgxQWPqzEidvpBIJGjUqJFWrqsjIowePRpv377Fvn37Cp16E4lEWLt2LSpXrqyT6+u4qCtYnz59UK1aNTx48EAvNkj8m6mpKdq3by/3urpLly7h5s2b+P7771WUGZNFQVOwnz59wocPH3TuSyVjuTw8PPDkyRM8e/ZMsJhc1GmAtm6W2LhxI3bv3o0NGzagVq1aRbY1NzfHvn378PjxY0ycOFEt+UmlUmRmZiI1NRWJiYmIi4tDSkqKXDHi4uLw8uVLLuoKIJFIMH36dFhbW6Nfv36aTkdwnp6euHDhAtLT02V+Zvny5ahfv77O3qihT/z8/PDkyRPcvHkTAPJ24XNRx3SVq6srRCKRoOvquKjTACcnJ9y7d0+uDxdVu337NiZOnIhRo0ahf//+Mj3TsGFDrF27Fps2bcL27dsFyyU5ORleXl6wtLSEmZkZjIyMIBaLYWBgAGNjY5ibm8PKygrW1tawt7eX64aO3DU5/57CYf9nxIgRePPmjVZuMlJWp06dkJaWhsuXL8vU/u+//0ZwcDAmTZrEhw1rATc3N9jY2OTtgo2JiYFIJCr2Cyhj2qpMmTJo2rSpoOvqdGZNnT5xcnJCTk4OoqOj0bx5c02ng+TkZPj5+aF27dr49ddf5Xr2q6++wvnz5/PW19WrV0+pXFJTU+Ht7Y2IiAhMnz4dZmZmkEgk+V6GhoYQiUQYPnw4VqxYgSVLlsgUPyoqCiYmJvxBUAQjIyNNp6ASjRo1gq2tLU6fPg0PD49i269cuRLW1tYYPHiwGrJjxZFIJOjTpw/27duHn3/+GTExMahSpQpMTU01nRpjCvPw8MDOnTtBRMJ8eSQ9lpCQQAAoISFB06l8ISUlhcRiMW3YsEHTqZBUKqUhQ4aQubk5xcTEKBQjOTmZ6tevTw0aNKCUlBSFc0lLSyNPT08yNzensLAwmZ7x9/cnCwsL+vTpk0ztv/rqK2revLnCOTLdNmjQIGratGmRbaRSKUVGRpK5uTnNmjVLTZkxWYSEhBAAunbtGnl7e1OXLl00nRJjSjlx4gQBoHPnzgkSj6dfNcDMzAx169bVinV1u3btwvbt27Fu3TrUqVNHoRjm5ubYv38/nj59igEDBiA+Pl7uGJmZmfD19cWlS5dw5MgRtGvXTqbnJk2ahOzsbKxZs0am9rxJomTz9PTEzZs38eHDh7yf5eTk4NatW1i5ciV69+4NW1tbNGnSBKampvj22281mC37r/bt28PW1hZ79+5FTEwMr6djOs/NzQ1t27ZFly5dsHbtWoXO0vw3Luo0RBs2S2RmZmL69Onw9fVVeoqpfv362LNnDy5cuIBGjRrJtUYgKysL/fr1w5kzZ3D48OG8QxllYWtri5EjR2LlypVITk4utp+7d+/yeroSzNPTEwDw+++/Y+nSpfD29oa1tTWaNm2KH3/8EXFxcRg7dizOnj2LZ8+ewd7eXsMZs38zMDCAr68v9u7diydPnnBRx3SeiYkJQkNDMWbMGIwbNw5DhgyRewPgFwQZ79NS2jr9SkS0bNkyMjMzo+zsbI3lsH79ehKJRHT37l3BYj5//pzc3NwIAE2ePJnS0tKKbJ+VlUV+fn5kaGhIR48eVbhPiURCv/zyS5Htbt++TQDo4sWLCvXD9EOjRo0IAJmZmVHHjh3pf//7H124cKHY/1aZdjh//jwBIAB0/vx5TafDmGB2795N5ubm1KBBA4WXQ3FRpyHnzp0jAHT//n2N9J+RkUGVK1em/v37Cx47JyeHli1bRkZGRtSoUSOKiooqsF12djYNHjyYDAwM6NChQ0r1+fXXX1P58uWL/GDevn07AaD4+Hil+mK67e+//6bLly9TRkaGplNhCsjOzqby5csTAIqNjdV0OowJ6u7du1S3bl2ysLCg/fv3y/08T79qSO4UoKamYDdv3oyXL19i1qxZgscWi8WYMmUKrl+/DiJCixYtsGzZMkil0rw2UqkUo0aNwq5du7Br1y706tVLqT5//PFHxMbGYuvWrYW2iYqKQtWqVWFlZaVUX0y31ahRA23atNHbXb76zsDAAH379oW1tTVf9cf0Tv369XHt2jV069YNffv2xeTJk5GVlSV7AOHrTO2hzSN1RERVqlShadOmqb3fjIwMcnBwUMko3X+lpaXRlClTSCQSkZubGz1//pykUimNHTuWRCIRbd++XbC+/Pz8qFq1apSVlVXg+56entSzZ0/B+mOMaUZCQgLduXNH02kwpjJSqZRWrVpFEomE2rVrR69evZLpOZ0YqYuIiICLiwtcXV3h5+cnX9WqxTS1WWLz5s149eqVSkbp/svExATLli3DuXPn8PjxYzg6OqJ37974/fffsWHDBkHPAPP398fTp0+xd+/efO8RESIjI3mTBGN6wNLSku/iZXpNJBJhwoQJuHDhAp49e4amTZvKdPOEThR1FStWxKlTp3DhwgXUrFkTQUFBmk5JELlFHSm5hVkemZmZWLhwIfr164f69eurrV93d3fcvn0b3bp1Q1BQEH777TcMHz5c0D6aNGkCLy8vBAQEfDHVCwCxsbH48OEDH2fCGGNMZ7Rt2xY3b97Mu8GpODpR1Nnb28PMzAzAPxc7SyT6cRGGk5MTPn36hFevXqmtz9xRutmzZ6utz1ylS5fGzp078eHDB5Wd/zV9+nTcvXsXR44c+eLnudeDcVHHGGNMl+TehLNly5Zi2+pEUZfrxYsXOHv2LLp3717g+xkZGUhMTPzipc3UvVkid5Suf//+Sl/npYxy5cqpLHa7du3Qvn17LFy48IsR0KioKJQqVQpVq1ZVWd+MMcaYKhgYGMDc3LzYdlpV1MXGxsLZ2TnfKy4uDomJiRgyZAg2b94MQ0PDAp8PCAiAlZVV3svBwUHNfwL5VKpUCdbW1mor6tS5lk6TZsyYgevXr+PcuXN5P4uMjETjxo0hFmvVf/KMMcaYYESkzgVdCsrJyYGPjw8mTZqEDh06FNouIyMDGRkZef+emJgIBwcHJCQkwNLSUh2pys3T0xPm5uYqXyeYkZGBWrVqwdnZGbt27VJpX5pG//8YFUtLy7ybLerXrw8PDw+ZrxNjjDHGdI1ODFvs27cPly9fxvz58+Hm5lbg7kYAMDY2hqWl5RcvbaeuHbAlZZQO+GfX0PTp0xEaGoorV64gLS0NDx484PV0jDHG9JpOjNQpKjExEVZWVlo9Urd7924MHDgQHz9+hLW1tUr6KEmjdLmkUikaNGiAWrVqYfbs2WjRogX++usvtGzZUtOpMcYYYyqhEyN1+szJyQnAP2u+VEWTO141RSwWw9/fH0eOHMH27dshFov5XCvGGGN6jYs6DatVqxbMzMxUNgWbkZGBRYsWYcCAAahbt65K+tBWAwYMQNWqVbF69WrUrl0771gcxhhjTB9xUadhBgYGaNy4scqKupK0lu6/DA0N8cMPP4CIeD0dY4wxvcdFnRZQ1WaJkjxKl+vrr79G1apV0b59e02nwhhjjKmUflzNoOOcnJywbt06pKamCjpFWJJH6XKZmJjg0aNHenMLCWOMMVYYHqnTAk5OTpBKpbh9+7ZgMRMSErBw4cISPUqXiws6xhhjJQEXdVqgYcOGkEgkgk3BEhG++eYbJCUlYcGCBYLEZIwxxph24yEMLWBsbIz69esLVtStWrUKhw4dQmBgIKpVqyZITMYYY4xpNx6p0xJCbZb466+/MHXqVEyePBk+Pj7KJ8YYY4wxncBFnZZwcnLCnTt3kJWVpXCMuLg4+Pn5oXnz5vj5558FzI4xxhhj2o6LOi3h5OSEjIwMPHjwQKHnpVIphg0bhuTkZOzduxeGhoYCZ8gYY4wxbaYTRV10dDTatWsHV1dXdOvWDcnJyZpOSXBNmjQBAIWnYJctW4ajR49i+/btqFy5soCZMcYYY0wX6ERRV6dOHYSHh+PChQto2bIlAgMDNZ2S4CwtLVGjRg2FirqwsDBMnz4dP/30E7y8vFSQHWOMMca0nU4Udf+eSkxNTdXbc9cU2Szx4cMH9OvXD23btsX8+fNVlBljjDHGtJ1OFHUAcObMGTg5OSE0NBQ1atQosE1GRgYSExO/eOkSJycnREZGgohkai+VSjF48GBkZWVhz549fMguY4wxVoJpVVEXGxsLZ2fnfK+4uDh4enri1q1b8PX1xR9//FHg8wEBAbCyssp7OTg4qPlPoBwnJyfEx8fj2bNnMrVftGgRzpw5g507d6JChQqqTY4xxhhjWk2rhnbs7e0RFhaW7+cZGRl5/2xlZYXMzMwCn/f398fkyZPz/j0xMVGnCjsnJycAwI0bN4o9NDgkJARz5szB7Nmz4enpqY70GGOMMabFRCTrXJ8GHT16FEuXLoVYLIaNjQ22bNki08X3iYmJsLKyQkJCAiwtLdWQqfIqVaqE169fw9TUFNbW1ihbtiysra2/+OcyZcrgl19+QcOGDXHq1CkYGBhoOm3GGGOMaZhOFHWK0sWi7u7du7h16xY+ffqEuLi4Qv9v+fLlcf78edjZ2Wk6ZcYYY4xpAS7qGGOMMcb0gFZtlGCMMcYYY4rhoo4xxhhjTA9wUccYY4wxpge4qGOMMcYY0wNc1DHGGGOM6QG93v1KREhKSkKpUqUgEok0nQ5jjDHGmMrodVHHGGOMMVZS8PQrY4wxxpge4KKOMcYYY0wPcFHHGGOMMaYHuKhjjDHGGNMDXNQxxhhjjOkBLuoYY4wxxvQAF3WMMcYYY3qAizrGGGOMMT3ARR1jjDHGmB7goo4xxhhjTA9wUccYY4wxpgf0uqgjIiQmJoKvt2WMMcaYvtProi4pKQlWVlZISkrSdCqMMcYYYyql10UdY4wxxlhJwUUdY4wxxpge4KKOMcYYY0wPcFHHGGOMMaYHuKgr4R48eIDQ0FBNp8EYY4wxJXFRV4IFBQWhWbNm8PT0xMWLF5WOl5ycjISEBAEyY4wxxpi8uKgrgYgICxYsQK9evdClSxc4OzujX79+ePv2rcIxP3/+jBYtWqB+/fp48OCBgNkyxhhjTBZc1JUwKSkp6NevH2bNmoV58+Zh37592LNnD0QiEfr164esrCy5Y2ZmZqJ379748OEDLC0t4erqirt376oge8YYY4wVhou6EuTFixdwcXHB8ePHcejQIcyePRtisRj29vbYt28fLl++jOnTp8sVk4gwcuRIXL58GUFBQbhw4QLs7Ozg5uaGqKgoFf1JGGOMMfZfXNSVEOHh4WjRogXi4uJw+fJl9OrV64v3nZ2dsXTpUixbtgyHDh2SOe6CBQuwbds2bNmyBc7OzrC1tUVISAgqV64Md3d3RERECP1HYYwxxlgBuKgrAf7880+4u7ujbt26uH79OhwdHQtsN2nSJPj6+uKrr77Cw4cPi427c+dOzJ49G/Pnz8eAAQPyfm5tbY1z586hdu3a6NChA65evSrYn4UxxhhjBRORHt92n5iYCCsrKyQkJMDS0lLT6ahddnY2pk6dipUrV2L06NFYtWoVjIyMinwmKSkJLVq0gKGhIa5evQpzc/MC2126dAkdO3bEwIED8eeff0IkEuVrk5iYiG7duiEyMhLHjx+Hi4uLIH8uxhhjjOXHI3V6iojQs2dPrFmzBmvXrsW6deuKLegAoFSpUjh06BCePn2KMWPGoKCa/+HDh/Dx8UG7du2wfv36Ags6ALC0tMTJkyfRokULdOnSBSEhIUr/uRhjjDFWMC7q9NSZM2dw/PhxHDx4EGPHjpXr2fr162PDhg3YsWMH1q1b98V7Hz9+RLdu3WBra4uDBw8WWyiam5vj2LFjcHFxQbdu3XDq1Cm5/yyMMcYYKx5Pv+opLy8vvHv3Djdu3Ch0JK04EydOxLp16xAWFoaWLVsiPT0dHTt2xMOHD/HXX3+hWrVqMsdKT09H3759cfr0aRw8eBDdu3dXKCfGGGOMFYyLOj0UExODevXqYdu2bRgyZIjCcTIzM+Hq6orXr18jIiICEydORFBQEEJDQ9G6dWuF4g0YMCDvJgsPDw94eHigXbt2ha7dY4wxxphsuKjTQ99++y0OHTqE58+fw9jYWKlYr169gpOTEyQSCWJjY7F//374+voqHC8rKws7d+7EmTNnEBISgtjYWBgaGqJ169Z5RV6rVq2Uzpsxxhgrabio0zNxcXFwcHDAjz/+iNmzZwsS89y5c+jatSsWLlyIadOmCRIT+GczR0xMDEJCQhASEoLQ0FB8/vwZpqamaNeuHebPn6/QiCBjjDFWEnFRp2eWLFmCWbNm4eXLl7C1tRUsbkpKisqnSKVSKW7fvo2QkBCsW7cO5cuXx4ULF1TaJ2OMMaYvJJpOgAknKysLq1evxqBBgwQt6ACoZc2bWCxGkyZN0KRJE1hYWODbb79FXFwcypYtq/K+GWOMMV3HR5rokcDAQLx69QrfffedplNRWvfu3ZGTk4OTJ09qOhXGGGNMJ/D0qx5p27YtTExM9OaQ3+bNm6NWrVrYvXu3plNhjDHGtB6P1OmJv/76C1euXMGkSZM0nYpgvL29ceLECWRlZWk6FcYYY0zr6UxRFxERARcXF7i6usLPz48/6P9j5cqVqFGjBrp166bpVATj7e2NhIQEhIWFaToVVsK8e/cOU6dOxcWLFwu8Ko8xxrSRzhR1FStWxKlTp3DhwgXUrFkTQUFBmk5Ja7x69Qr79+/HxIkTYWBgoOl0BOPk5IQKFSrgyJEjmk6FlTCBgYH45Zdf4Orqinr16mH58uX4+PGjptNijLEi6UxRZ29vDzMzMwCAoaEhJBLeuJtr7dq1MDMzw9dff63pVAQlEonQvXt3HDlyhEdLmFrduXMH9erVQ2hoKJycnODv74+KFSti4MCBOH/+PP/3yBjTSjpT1OV68eIFzp49W+DdoRkZGUhMTPzipe9SU1Oxfv16DB8+HKVKldJ0OoLz9vbG33//jQcPHmg6FfYvYWFhSEpK0nQaKhMdHQ1HR0e4ublh9+7dePXqFRYtWoSIiAi4u7ujbt26WLZsGT58+KDpVBljLI9OFXWJiYkYMmQINm/eDENDw3zvBwQEwMrKKu/l4OCggSzVa8eOHYiPj8eECRM0nYpKdOjQAaampjwFq0VOnjwJFxcXTJ48WdOpqAQRITo6Gg0bNsz7mY2NDaZMmYKYmBicP38ezZs3x4wZM1CxYkX4+Phg586dJeJLJGNMu+nMkSY5OTnw8fHBpEmT0KFDhwLbZGRkICMjI+/fExMT4eDgoLdHmhARGjRogLp16+LQoUOaTkdlevTogfj4eFy8eFHTqZR47969g6OjI7KyspCcnIwnT56gUqVKmk5LUG/fvkWFChUQGBgIHx+fQtt9+vQJ27dvx969e3H16lUYGxujc+fO8PX1RY8ePWBlZaW+pBljDDo0Urdv3z5cvnwZ8+fPh5ubG/bu3ZuvjbGxMSwtLb946bMzZ87g/v37enWMSUG8vb0RHh6OT58+aTqVEk0qlWLYsGEQiUS4du0azM3NsXz5ck2nJbjo6GgA+GKkriDW1taYNGkSrly5gufPnyMgIAAfPnzA0KFDYWtrC29vb2zbtg3x8fFqyJoxxnRopE4R+n74sJeXF2JjYxEREQGRSKTpdFQmd+Rk+/btGDx4sKbTKbGWL1+OKVOm4OTJk+jcuTNmzZqFFStW4Pnz57C2ttZ0eoJZsWIFZsyYgaSkJIV2k798+RIHDx7E/v37cfnyZRgaGmLEiBFYu3atCrJljLH/ozMjdexLMTExOHHiBCZNmqTXBR0AlC9fHs2bN8fRo0c1nUqJdfPmTfz000+YPHkyOnfuDACYOHEipFIpVq9ereHshHXnzh00aNBA4eOBHBwcMGnSJISHh+PVq1cYO3Ysfv/9dx5pZoypHBd1OmrVqlWws7NDv379NJ2KWnh7e+PkyZN86LQGJCcnY8CAAWjUqBEWLVqU93MbGxuMHDkSq1at0qudsP/dJKGMihUrYuzYsQCAyMhIQWIyxlhhuKjTQXFxcdi6dSu+/fZbGBsbazodtci9XeLSpUuaTqXEmThxIl6/fo3du3fn++9typQpSEpKwh9//KGh7IQllUpx9+5dwYo6AKhVqxbMzMxw69YtwWIyxlhBuKjTQTt27EBWVhZGjx6t6VTUpkmTJqhYsSIfbaJme/fuxebNm7F69WrUrl073/uVK1fG4MGDsXz58i92nuuqZ8+eITU1VdCizsDAAI0bN+aijjGmclzU6aCgoCB07NgRdnZ2mk5Fbfh2CfV79uwZRo0ahf79++Orr74qtN2PP/6It2/fYtu2bepLTkVk3fkqr6ZNm3JRxxhTOS7qdMznz59x8eJF9OjRQ9OpqJ23tzceP36MmJgYwWO/evVKr8/6k1d2djYGDhyIsmXLYt26dUVuxqlbty569+6NxYsXIzs7W41ZCi86OhqlS5dGhQoVBI3r5OSEBw8eIDU1VdC4jDH2b1zU6ZgTJ04gJyenwGvS9J2Hh4dKbpcgIgwYMAB9+vTB4sWLBY2tq+bNm4dr165h165dMh2i6+/vj8ePH+PAgQNqyE51cjdJCL2j3MnJCVKpFLdv3xY0LmOM/RsXdTrmyJEjaNasmd6d4i8LU1NTeHp6Cl7U7dy5E2FhYejTpw9++uknvTuiQ14XLlzAwoULMXfuXLRp00amZ5o1awZPT0/8/PPPOj09fufOHTRq1EjwuA0aNIBEIsHNmzcFj80YY7m4qNMhmZmZOHHiRImces3l7e2Ny5cvC3bmV2JiIqZNmwZfX1/s378fU6ZMwcSJE/Hnn38KEl/XxMXFYfDgwWjfvj38/f3lenb69OmIiorCiRMnVJSdamVmZiImJkbw9XTAP7fdNGjQgNfVMcZUios6HXLp0iUkJCTA29tb06loTLdu3SCVSnH8+HFB4s2bNw+JiYlYvnw5RCIRli5dirFjx2LEiBHYvXu3IH3oks2bN+PTp0/YsWOH3Ifvurq6onXr1ggICFBRdqr16NEjZGdnq6SoA/6ZguWijjGmSlzU6ZDg4GBUqlQJTZo00XQqGlO+fHm0aNFCkCnYu3fvYuXKlZg5cyYcHBwA/LPLds2aNRg6dCiGDBmCoKAgpfvRJWFhYWjdurVC0/sikQj+/v4ICwvTyfMEc3e+NmjQQCXxnZyccOfOHT5AmzGmMlzU6QgiQnBwMHr06KH314IVJ/d2iczMTIVjEBHGjx+P6tWrY/LkyV+8JxaLsXHjRvTu3Rv9+vXDqVOnlE1ZJxARwsPD0a5dO4VjdO/eHQ0aNNDJ0bro6GiUL19eZffYOjk5ITMzE/fv31dJfMYY46JOR0RHR+PZs2clej1dLm9vbyQlJSk1GrRv3z6cP38eq1atKvBWDolEgh07dqBTp07w8fHBhQsXlElZJzx69AgfPnxQqqgTi8Xw9/fHiRMndO5aLCGvBytI7gg7T8EyxlSFizodERwcDAsLC7i5uWk6FY1r3LgxHBwcFJ6CTU5OxpQpU+Dj44MuXboU2s7IyAj79++Hs7MzunfvjqtXryqask4IDw+HSCSSecdrYfr164dq1arh559/Figz9VDVztdcpUqVQq1atbioY4ypDBd1OiI4OBhdunQpMXe9FkXZ2yXmz5+PT58+YcWKFcW2NTExQVBQEJo0aYKuXbvq3OiTPMLDw9GoUSOZzqUrikQiwbRp07B//348evRIoOxUKyUlBU+ePFHpSB3wzxQsH2vCGFMVLup0wNu3b3Ht2jWeev2X7t2748mTJ3KvT4qJicGKFSvg7++PqlWryvSMubk5jh07hpo1a8LT0xNPnjxRIGPtp+x6un/7+uuvYWNjgyVLlggST9Xu378PIlJLURcZGQmpVKrSfhhjJRMXdWpy8+ZNNGnSBO/fv5f72aNHj0IsFsPLy0sFmekmDw8PmJmZyTUFS0SYOHEiHBwc8MMPP8jVn6WlJU6dOgWJRIJffvlF3nS13sePHxETEyNYUWdiYoJx48Zh9+7dSm1oUZfcna/169dXaT9OTk5ISkrS2y8GjDHN4qJOTXbt2oWoqCgsWrRI7mePHDkCZ2dnle3K00UmJiZy3y5x6NAhnDlzBitXroSJiYncfZYtWxbDhw/Hjh07kJKSIvfz2uzy5csAIFhRB/yzoSUlJQXh4eGCxVSV6OhoVK9eHebm5irtx8nJCQBvlmCMqQYXdWpy7NgxWFpa4vfff8fz589lfi41NRVnzpwp0QcOF8bX1xfh4eFo1qwZVq1ahY8fPxbaNiUlBd9//z26deum1L25I0aMQFJSEvbt26dwDG0UHh6OChUqoEqVKoLFdHR0hJ2dHU6ePClYTFVR9c7XXLa2tqhQoQIXdYwxleCiTg2ePHmCmJgYrFmzBqVLl8bcuXNlfvbs2bNIT0/n9XQFGDRoEA4fPowqVapgypQpqFChAnr16oXDhw/nO+A1ICAA79+/x8qVK5Xqs2rVqujcuTPWr1+vVBxtEx4eDmdnZ0HPQBSLxejcubNOnPOn6p2v/8Y3SzDGVIWLOjU4ceIEDA0N0bNnT8yaNQvbtm3D3bt3ZXo2ODgYderUQe3atVWcpe4RiUTo0aMHDh06hLdv3+KXX37Bixcv4OPjgwoVKmDSpEmIjIzEo0ePsHTpUvzwww+oUaOG0v2OGjUKf/31F6KiogT4U2heeno6rl+/LujUa64uXbogKioKb9++FTy2UOLi4vDmzRu1jNQBQNOmTXHz5k2Fdm4zxlhRuKhTg+PHj8PFxQWWlpYYNWoUKleujJkzZxb7nFQqxZEjR3iUTgblypXDhAkTEBERgdu3b2PYsGHYs2cPnJyc0LRpU5QvXx4//fSTIH11794d9vb22LBhgyDxNC0iIgKZmZkqKeo8PT0hEolw+vRpwWMLJfcLlrqKOicnJ7x//16rC13GmG7iok7F0tLSEBISkrdz1cjICP/73/8QFBSEv/76q8hnr127hvfv33NRJ6dGjRph2bJlePXqFY4ePYrevXtj8+bNMDMzEyS+oaEhvvnmG2zfvh2pqamCxNSk8PBwmJubo3HjxoLHLleuHJo3b67V6+qio6MhkUjUNhrOmyUYY6rCRZ2KhYaGIj09/YvjSAYOHIgGDRrA39+/yCmY4OBgWFtbK33Cf0klkUjQrVs3bN26Fe7u7oLGzt0wsXfvXkHjakJ4eDhatWoFiUSikvidO3fGmTNnkJOTo5L4yoqOjkadOnVgZGSklv6qVKmCMmXKcFHHGBMcF3Uqdvz4cVSrVg1169bN+5mBgQEWLVqE0NBQnD17ttBng4OD0b17dxgYGKgjVSaHatWqoVOnTvjjjz80nYpSiAiXL19WydRrri5duuDTp0+IiIhQWR/KUNfO11wikQhNmjThoo4xJjgu6lSIiHDs2DF4eXnl21Xo7e2NNm3awN/fv8DT5R8/foy7d+/y1KsWGzVqFK5evYrbt29rOhXEx8cr9NzDhw/x8eNHODs7C5vQv7Rq1QpWVlZaOQVLRGov6gDeAcsYUw25i7q0tDS8fv06389l3c1Zkjx48ADPnj0r8CYIkUiEgIAARERE4ODBg/neP3LkCIyMjNCpUyd1pMoU4O3tDTs7O41vmLhw4QJsbW0RExMj97NhYWEQi8Vo3bq1CjL7h0QiQceOHbXyaJO3b98iLi5ObceZ5HJycsLTp08VLsYZY6wgchV1Bw4cQO3ateHl5QVHR8cvFvoPGTJE8OT+a+rUqXBxccGgQYOUvnro7t27CAsLEyizgh0/fhwmJiZwc3Mr8H1XV1d06dIFM2fORHZ29hfvHTlyBB4eHrCwsFBpjkxx2rJh4syZM8jKylKouAwPD0ejRo1gaWmpgsz+T+fOnXH16lV8/vxZpf3IK/d6MHWP1DVt2hQAEBkZqdZ+GWP6Ta6ibsGCBbh58yaioqLw559/4ptvvsGuXbsAQOVnLt26dQux/6+9+w6rum7/AP4+rMNGpgM1wIWLBFEBQTE1UFAcuFHJTFtmylOKuSrNNEdqy8xUVBB3aubo5IjhgoOimHuLCxVENuf+/eHvkMQ6+8u4X9fF9dA5n3E/X/Vw85n37+Pvv/9GmzZtsG3bNrXaW7BggdLXTCnr999/R48ePSrddfnVV1/h0qVLWLduXclrT58+xdGjR3nqtQZ45513kJmZKegNE/JfTtatW4e8vDyl6sbHx2t1PZ1cQEAAZDIZJBKJ1vtSxrlz52BiYgJnZ2ed9tuqVSuYmJggOTlZp/0yxmo3pZK6wsJC2NvbAwA8PT1x7NgxrFq1Cl988YVGT6IvT2JiYslUZGBgYMldla/Kz89HVlZWqa+K/PLLL+jTpw8GDhyIqKgojceblZWFv//+G0FBQZWWc3d3x7Bhw/D5558jNzcXALB//34UFxfz1WA1gNAbJgoLC3Hy5ElMnDgRT548wY4dOxSu++jRI1y6dEknSV3Tpk3Rpk2bareu7ty5c2jbti309HS7vFhfXx9ubm68ro4xplFKfZI5ODiUWhRua2uLQ4cO4cKFC1pfLP7s2bOSKSIrKys8efKkTJkFCxbAysqq5KtJkyYVtmdsbIwtW7YgPDwcY8eOxdKlSzUar0QiQWFhIfr06VNl2S+//BLp6en44YcfALzc9erh4YHGjRtrNCamHRMmTEBiYiJSU1N13rdUKkVubi7eeust+Pv7K5Vcyn8x0uYmiVcFBARg//791eomBSE2ScgJvVniyJEj2LVrl2D9M1bbJScn47ffftNpn0oldRs2bICDg0Op14yMjBATE4OjR49qNLD/sra2Lhl5e/bsGWxsbMqUiYyMRGZmZsnX7du3K23TwMAAq1evxvTp0xEREVHluXHK2LdvH1xdXeHi4lJl2RYtWuDtt9/GV199hcePH+OPP/7gqdcapH///oJtmIiLi4OxsTHc3d0xYcIEHD16VOENE3FxcWjcuDGaNm2q5ShfCgwMxN27d5GWlqaT/qoik8lw/vx5QZO6f/75p2SEXpeuXr2Kfv36YeDAgRg5cmS1W+tYm+Xk5CAzM1PoMJgOTJ8+He+9955O+1QqqWvcuDEaNGhQ7nvansLx8vIquWrowIED5fYnFothaWlZ6qsq8l2oS5Yswddff40JEyaU2bSgLCLCvn37yt31WpHZs2cjJycHoaGhyMzM5KSuBjE0NMRbb72FqKgonW+YkB8cbGRkhEGDBsHW1lbh5FJX6+nk/Pz8YGxsXG2mYK9fv46cnByd73yVc3d3R3Fxsc5HeIuKijB69Gg4ODjgl19+wb59++Dm5obDhw/rNI7a6O7du9i0aROWLVuGyMhIvP322+jXrx+6dOkCFxcXmJubw8zMDLa2tpg9e7baG/5Y9fX06VMcPnwY6enpur0SkFS0bds2VauqLCIignx9fWnkyJGUn59fZfnMzEwCQJmZmQq1v379etLX16eBAwdSbm6uynFKpVICQBKJRKl6n376KQGgxo0bk0wmU7l/pntXr14lALRu3Tqd9SmTyah+/fo0Y8aMktciIiLI1ta2yr+/ubm5ZGRkRCtWrNB2mKUEBgZS7969ddpnRXbt2kUA6O7du4L0n5ubS/r6+vTTTz/ptN+5c+eSvr4+JSQkEBHRzZs3yd/fn0QiEUVERFBeXp5O46ktXrx4Qa+99hoBIDMzM3J2dqYuXbpQcHAwjRs3jqZPn05Lly6ljRs30qxZs8jAwIDc3d3p3LlzQofOtGDjxo0EgADQ3r17ddavykmdkZERLV26tNIyQicmyiZ1RES7d+8mY2Nj6tGjh1L1XjV//nwyNzdXKPF8VUZGBtWrV48+/PBDlfplwurduzf5+PjorL/Lly8TANq3b1/Ja//88w8BoE2bNlVa9++//yYAlJSUpO0wS1m2bBmJxWJ68eKFTvstz7x588ja2lrQz6l27drRxIkTddZfYmIi6evr05w5c0q9XlxcTN988w0ZGRmRm5sbnT17Vmcx1RYzZ84kIyMjunDhgkLlT58+TW3atCGxWEyLFy+moqIiLUfIdGnw4MHUqVMnsrGxoS+++EJn/aqc1O3fv58sLS1p0qRJZT4Ui4qKaO3atdSqVSu1A1SHKkkdEdGxY8fIysqKPDw86MGDB0r327VrVxo4cKDS9YiIrl27RllZWSrVZcLaunUrAaDU1FSd9Ldu3ToSiUT09OnTUq/7+/tT9+7dK6379ddfk5mZGRUWFmovwHJcuHChTCIqlOHDh5Ofn5+gMYwZM4Y6deqkk76ysrLIxcWFvLy8KvxzT0lJobZt25JYLKalS5dScXGxTmKr6S5dukRGRkY0a9Ysperl5uZSREQEiUQi8vPzo2vXrmkpQqZLOTk5ZGZmRl999RX16tWLQkJCdNa3ykkd0csPgMaNG9OAAQMoJyeH8vPz6YcffiAnJyeytram2bNnaypOlaia1BG9/P/WoEEDatGiBd2/f1/hehkZGaSnp0erV69Wuk9Ws+Xn55ODgwNNmjRJJ/2NHz+e2rVrV+b16OhoAkD//PNPhXWDg4OpV69e2gyvXDKZjF577TX66KOPdN73f7Vr147ee+89QWNYtmwZGRsb6yS5fuutt8jc3JyuXLlSabnc3FyaMmUKAaCePXvS7du3tR5bTSaTySggIICcnJxUHoE+cuQIOTk5kbm5Oa1evVrjo8dnz55VeuaIqW7Pnj0EgNLS0mjatGnUpEkTnfWtVlJHRHTnzh1yc3MjNzc3atSoEdnb29P8+fOrxWiTOkkd0ct1Ug0aNKAePXoo/KEbExNDAOjOnTsq9clqtmnTplG9evV0Mr3YunVrevfdd8u8npubS7a2tjR16tRy6xUXF5ONjU2ZKThdmTBhArVs2VKQvuXy8/PJwMCAvv/+e0HjOHLkiE5Gd7dt20YA6Ndff1W4zqFDh8jR0ZFsbGzo3r17WoyuZtuxYwcBoN9++02tdrKysmj8+PEEgIKCgig9PV3t2IqLi2n27NkEgL755hu122OKGTduXMlMZWxsLAGghw8f6qRvtZK6Z8+e0RdffEG2trZkYmJCpqam1WothrpJHdHLD109Pb1Si9ErExYWRh06dFC5P1azXblyhQDQ+vXrtdrP48ePCQBt2LCh3PenTp1a4YaJtLQ0AkAHDx7UaowVkf8QFHKqKTU1lQDQ0aNHBYuB6OVnKACKiorSWh937twha2trGjx4sNIjQA8ePCBjY2P6+uuvtRRdzZadnU1NmzaloKAgjY2u7dmzh+rXr0+2tra0efNmldt98eIFDR06lACQvb09BQcHayQ+VrmioiKys7OjadOmEdG/a5/379+vk/5VTuqmT59OVlZW5OLiQqtWraLs7GwaO3YsOTg40MmTJzUZo8o0kdQRvVx/BID27NlTaTn5H6aiCSCrnXr16kWenp5aXYC/e/fuShMj+YaJ6OjoMu+tXr2a9PT0BBtNf/bsGenr69OPP/4oSP9E/46oZ2RkCBaDnIuLC02ZMkUrbRcXF1PPnj2pUaNG9PjxY5XaGDFiBLVu3VrwjW/V0YwZM0gsFlc5pa2sR48e0ZAhQwgADRw4UOlRu3v37lGnTp3I1NSUtm/fTp9//jlZW1vzGkkdOHr0KAGg48ePE9HL6XkrKyuaP3++TvpXOalzdXWl9evXl9mxM3PmTDIzM6Ndu3apHZy6NJXUFRcXU//+/alevXqVji4cP36cAFBcXJxa/bGa7fDhwxUmVJoybdo0atSoUaU/aLt3717uhonw8HDBR5P9/PxowIABgvX/2WefUaNGjQTr/1WDBw8mf39/rbS9ZMkSAkCHDh1SuY0DBw4QADpx4oQGI6v5Ll68SIaGhlpdxrB161ayt7cnGxsb2rhxo0KJdXJyMjk6OpKjo2PJ7naJREIA+PgUHZgyZQo1bNiwVALt7+9PgwcP1kn/Kid1lf3lWr16NYnFYlq5cqWqzWuEppI6IqInT56Qs7MzdezYscIzwGbPnk3W1tY631HIqp8BAwZQ06ZNKScnRyvtd+3alYYMGVJpmU2bNpW7YaJ58+aCH5szb948srCwEGzxdkhICL355puC9P1f8+bNIysrK42PhKWkpJCRkVGFaysVVVRURI6OjvT+++9rKLKaTyaT0ZtvvknOzs5a+zcu9+jRIxoxYgQBoH79+lV6ruKOHTvI1NSUPD09S5XLzs4W5EzEukYmk5GTk1OZDVgRERHk5OSkkxjU3ihRkX379pGFhYW2mleIJpM6IqKkpCQSi8XlLk4nIvL09KThw4drpC9Ws126dIkMDQ21MuSel5dHYrGYli9fXmk5+YaJiIiIktfu379PACgmJkbjcSnj9OnTBICOHDkiSP/NmjVTO9nRlN9//50A0NWrVzXWZk5ODrVt25bc3Nw0cpjwtGnTyNramg8m/n/yjSdVLcnRpF27dlGDBg3IysqKfv3111K/BMhkMlqwYAEBoCFDhpS7UatTp04UFhams3jropSUFAJABw4cKPW6/BdsdZZ7TJs2rWSdXmW0ltQRkc4PNv0vTSd1REQ///xzuQvU09PTK124zuqeKVOmkLm5uUZ2sb0qPj6eANDp06erLPvfDRPyTQq3bt3SaEzKKi4uJnt7e4qMjNR539nZ2SQSiZTaCapN8s8OTd7S89FHH5GxsbHGptvkm2u2bt2qkfZ0IT8/n3r16qXxX2Cys7OpcePG1K9fP422q4iMjAwaM2YMAaDAwEC6desW5eXllbw2e/bsCtfNTZkyRWejRXXVnDlzyMrKqswMhHyN859//qly287Ozgodl6XVpE5o2kjqZDIZjRkzhkxNTUsdQ7B27VoSiUQ627bMqr8nT56QjY0NjR8/XqPtLly4UOGDg+WH/crX90VEROj0zKTKjBo1itzd3XXe76lTpwhAtdnQRUTUoEED+uyzzzTSljwB+/bbbzXSnlznzp0pKChIo21qU1JSEgEgPT09io2N1Vi706dPJ2NjY0F3b+/du5caNWpEFhYW5O7uTmKxuMo1vNu3bycAfO6gFr3++us0cuTIMq8XFxeTubk5LVy4UKV2r127RgAU2qugp9KFsXWYSCTCjz/+iGbNmiE0NBTPnz8HAOzbtw+dO3eGvb29wBGy6sLa2hpz587FmjVrcObMGY21Gx8fDy8vLxgYGFRZ1tXVFd27d8fPP/9cUrdr164ai0UdgYGBkEqlePDggU77TU1NhUgkQps2bXTab2Xc3d0hlUo10tamTZtQr149vPvuuxppTy48PBz79+9X+c/rzJkzGD9+PGQymUbjqohUKoVIJEJoaChGjhyJHTt2qN3mP//8gyVLliAyMhLOzs4aiFI1QUFBOH/+PIYOHYqnT5/iyJEjGDFiRKV15P/u4+PjdRFinXP9+nWcOXMGAwcOLPOenp4eOnTogOTkZJXalkgk0NPTQ/fu3asurFLaWENoY6RO7uLFi2RhYUFDhw6lgoICsrKyos8//1zj/bCaraCggFq1akU9e/bUyEJ4mUxGtra2St3WIl/PIZVKydDQUPANTHIPHjzQ+hlt5Zk6dSo1a9ZMp31WZcaMGdSwYUO125HJZOTs7Kzx0WGil1N/RkZGtGTJEpXi8vHxIQB0/vx5jcdWng8//JBatWpFRUVFNHz4cDIwMFDrgGCZTEa9evUiFxeXCjfLVXctWrQQfJNUbbV06VISi8X0/Pnzct+fPHkyNW/eXKW2hw8fTp07d1aoLI/Uqahly5b49ddfsWXLFowZMwaZmZkICgoSOixWzRgaGmLx4sWQSCT4/fff1W7v4sWLyMjIUGq0bdCgQbC1tcW7776LwsJC+Pr6qh2HJjg4OMDDwwMHDhzQab8pKSlo27atTvusiru7O9LT09UetTxx4gSuX7+OkSNHaiiyf9nY2CAkJATr1q0DESlVd+fOnUhISACAkv/VNqlUCnd3d+jr62PDhg0YMGAAQkNDsW/fPpXa27ZtG/7880+sWLECxsbGGo5WN3x9fREXFyd0GBqXl5eHnTt3oqioSLAYdu3ahd69e8Pc3Lzc9z08PHDlyhVkZmYq1S4R4a+//kLPnj0VrlBraXOkTu7jjz8mAFS/fn0+2JGVSyaTUc+ePalVq1ZUUFCgVlu//PIL6enpKf13eurUqQSALCwsypwtKaTIyEiys7PT2b+dR48ekYGBAa1YsUIn/SlKfhPJvn371Gpn0qRJ1KhRI639Ge/du5cAUHJyssJ1CgoKqEWLFhQQEEAdOnSgcePGaSW2V5W3hqmgoIAGDBhAYrG4zO7Eqhw/fpwcHR11ejG7NqxZs4b09PTo2bNnQoeiUT/++CMBoG7dugmyZvDhw4ekp6dHv/zyS4Vlzp07RwDo8OHDSrV99uxZpTZZ8EidmhYuXIjAwECEh4dDT48fJytLJBJh6dKluHTpEn766Se12oqLi4ObmxssLS2VqvfOO+8AALy8vKCvr69WDJoUGBiIx48fIykpSSf9bdu2DUSEoUOH6qQ/Rbm4uKBx48bYvn27ym0UFRUhNjYWw4cP19qfcUBAAOrXr49169YpXGfVqlW4cuUKFi1aBG9vb52M1F25cgXZ2dlwd3cvec3Q0BCxsbHo3bs3QkJCIJFIKm1DJpNh79696NatG7y8vGBubo7ly5drO3St8vX1hUwmw/Hjx4UORaMSEhLw2muv4dq1a3j99dexZ88enfYv769fv34VlmnVqhVMTEyUXlcnkUggFovh4+OjWAWlUsYaRhcjdXJ8hQ6ryvjx48nGxoaePHmichvqrIl57733tHrLhSoKCgrIzs6OPvnkE530161bNwoICNBJX8qaP38+GRsbq3ydl/zmB0WOulFHREQE2dnZKXRw9LNnz8jOzo7eeustIiLasGGDTq5nq+wS9by8PAoMDCQTE5Nyz0nMy8ujX3/9lVq3bk0AyMvLi3bs2FGtRrhVJZPJyN7enmbOnCl0KBolP1D98ePH1L9/fwJAkydP1tm5isHBweTn51dlOW9v73J3x1bV9htvvKFweU7qGNOR9PR0Mjc3V/meT/nGAqEPDta0Dz74gBwdHbX+Q/PWrVsEgNavX6/VflT18OFDMjIyUvnYg7Fjx1LLli21/gumfDpo586dVZaNjIwkExMTunPnDhH9O838+++/azXG6dOnk6OjY4Xv5+TkUK9evcjMzIz+/vtvInqZgC5cuJAaNmxIAKh///70999/17pf2AcOHKi1a+mE8PDhQwJAmzZtIqKXievy5cvJyMiIPDw86NKlS1rt//nz5yQWixXaQPTBBx+Qq6urwm0XFhaShYWFUofYc1LHmA7Nnz+fDA0NVfqgqS4HB2taYmIiASCJRKLVfhYtWkTGxsbV+vNgzJgx9Nprrymd4Obk5JCFhYXOduB7eHhUeXfvrVu3yNjYuNT5e7oaKQoICKDg4OBKy7x48YJ69OhB5ubm9N5775GFhQUZGRnR22+/TWlpaVqNT0hLliwhExMTwa7o07Tdu3cTgDLnBiYlJVHz5s3J3NycNm7cqLX+t27dqvCNMGvWrCGRSFThDtn/SkhIIACUmJiocDyc1DGmQzk5OdS0aVOVFlxHRERQ06ZNNR+UwGQyGTVr1qxkik5bOnToQKGhoVrtQ13yg5EVGQV71ZYtWwiA1kcl5JYvX04GBgaVHrYeHh5O9vb2ZT5/Q0JClJpOUpZMJiMHBweaNWtWlWWzs7OpW7duZGVlRdOmTav0XtXa4sSJEwSAjh8/LnQoGhEZGUn169cvd0Q1KyuLRo0aRQAoPDycsrOzNd7/qFGjyM3NTaGy8mvE5KPDVZk3bx5ZWloqdZ88J3WM6Vh0dDQBoL/++kupel26dKERI0ZoKSphzZkzhywsLLR2Obr8loUdO3ZopX1N8vLyop49eypVZ8CAAdSpUyctRVTWo0ePyNDQsML7h1NSUkgkEtH3339f5r2vv/6azM3NlfpBpYx79+4RANq+fbtC5QsLC2vsuXOqKCgoIBMTE1q8eLHQoWiEv79/paPGMpmM1q1bR6amptSqVSu6ePGixvrOz88nKysrmjNnjkLlCwoKFLq3W65Hjx5KX0fH2zUZ07Hhw4fDy8sLU6dORXFxsUJ1cnNzkZycXG1ug9C0sLAwPH/+HLt379ZK+zExMbCyskKfPn200r4mTZo0CRKJBGlpaQqVf/r0Kfbt26eVs+kqYmdnh6CgIKxfv77c9z/99FO0aNGiZNf1q3x8fJCdnY1z585pJTb5zRyv7nytjIGBQY09d04VhoaG6NKli1bPqyMiFBYWaq19uaKiIpw8ebLSnaEikQhjx45FUlIS8vPz8b///U9j/R89ehSZmZkYMGCAQuUNDQ3h5uam0G7/3NxcJCQkKH4+3f/jpI4xHZMfcXLmzBnMnDlToTqnTp2qVgcHa1rz5s3h5eWFjRs3arxtIkJ0dDQGDx5cI354h4aGokGDBvjuu+8UKr9jxw4UFhZi2LBhWo6stPDwcCQnJyM1NbXU6wcPHsTBgwexcOFCGBoalqnn6ekJAwMDJCYmaiUuqVQKKysrODk5aaX92kB+CDEpeYi0IvLy8uDv748hQ4ZovO3/Sk1NRU5ODry9vass6+rqilmzZmHPnj24dOmSRvrftWsXnJyc8Prrrytcx8PDQ6FjTeLj45Gfn89JHWM1gbe3NxYvXoyvv/4aq1atqrJ8XFwcLC0t0a5dOx1EJ4ywsDDs378fjx490mi7p06dwtWrV6u8G7O6MDIywsSJExEVFaXQ6fPR0dF444030LBhQx1E968+ffrAzs6u1GhdcXExPvnkE/j6+iIkJKTceiYmJnB3d9faeXVSqRQdOnSASCTSSvu1ga+vLx4/fqyx5EaOiDB+/HgcO3YMv//+O7KysjTa/n8lJibCwMAAHTt2VKj8yJEj4eDggGXLlqndt0wmw65duzBgwACl/q517NgRaWlpyMnJqbScRCJB/fr1lb79hpM6xgQyZcoUfPjhh3j//fervLooPj4e3t7e1ergYE2THwi8ZcsWjbYbExOD+vXro0ePHhptV5smTJiA/Px8rF27ttJy9+7dw+HDh3U69SpnZGSEUaNGYePGjSXXM23YsAFnz57FN998U+kPOm9vb62N1KWkpCg89VpXeXt7Q09PT+NTsPPnz8emTZuwcOFCFBUVVXnAs7oSEhLg7u4OExMThcobGxvjww8/xPr16/H48WO1+j59+jTu3buHgQMHKlXPw8MDMpkMZ8+erbScRCLBG2+8ofQvJ5zUMSYQkUiEb7/9FsHBwRg6dGiF6yxkMhkSEhJq7Xo6OXt7ewQGBmp0Cra4uBibN2/W6i0L2tCoUSOEhobi+++/h0wmq7BcbGwsDA0NMWjQIB1G96+xY8fiwYMHOHDgAHJycjBz5kwMGTIEXl5eldbz8fHB1atX8fDhQ43Gk5mZiatXr3JSVwVLS0u4ublpNKnbunUrZs2ahS+++AKffvopXF1dVb5nV1GJiYkKTb2+6t133wURKTRDUpmdO3fCzs5O8Zse/l+7du1gaGhY6bq6Z8+eISkpSempV4CTOsYEpa+vj5iYGLRt2xbBwcG4efNmmTJpaWl49uxZrU/qAGD06NE4fvw4rly5opH2jhw5gvv37wsykqWuSZMm4cqVKzhw4ECFZaKjoxEUFIR69erpLrBXdOjQAW5ubli3bh2WL1+Ohw8fYsGCBVXWk/8g1vRo3ZkzZwAovkmiLpOvq9OEU6dOYcyYMRg5cmTJOuG+ffvijz/+0Mq6PQB4+PAhrl27pnRSZ29vjzFjxuC7775Dfn6+Sn0TEXbu3Il+/frBwMBAqbpisRjt2rWrdF3dkSNHIJPJam9Sl5SUBD8/P3Tv3h1Dhw7Vya4axnTF1NQUe/bsgYmJCfr06YOnT5+Wej8+Ph76+vro0qWLQBHqTr9+/WBhYaGx0bro6Gg0a9YMnTp10kh7uuTt7Q0PDw+sXLmy3PcvXbqE06dPC5qwyncW7t69GwsWLMD777+PZs2aVVmvSZMmcHR01Pi6upSUFIjFYri6umq03drI19cXV65cwf3799Vq5/bt2+jfvz/c3d2xZs2akunCvn374u7du2U20miK/BcCZZM64OXSl/v372Pz5s0q9R0TE4OLFy8iLCxMpfpVbZaQSCRwcXFRabNPjUjqHB0dceDAARw9ehTNmzfHrl27hA6JMY1ycHDAH3/8gQcPHmDQoEGlfoOMi4uDu7s7zMzMBIxQN0xMTBAaGoqNGzeq/Rt+fn4+tm/fjhEjRtTIRfMikQiTJk3CH3/8gcuXL5d5PyYmBhYWFggKChIgun+NGjUKxcXF0NPTw6xZsxSqIxKJtLKuTiqVlkxvscrJR/7j4+NVbiM7Oxv9+/eHkZERdu7cWWp3ua+vL8zNzbU2BZuYmIiGDRuiadOmStd1dXVFUFAQlixZovTnzNOnTzFlyhSEhobijTfeULpv4OVmiXPnziEvL6/c9yUSiUqjdEANSeoaNGgAU1NTAC/PeVF2uJOxmqBVq1b47bffkJiYiLfffrvkwyY+Pr5OTL3KhYWF4erVqzhx4oRa7fzxxx/IzMyskVOvcsOGDYOtrS2+//77Uq+/ekyLoovEtaV+/fqYMWMGVq5cCVtbW4Xr+fj44NSpUygoKNBYLFKplKdeFdS4cWM4OTmpPAUrk8kQFhaGK1euYO/evahfv36p98ViMXr27KnVpM7b21vlX9imTp2K1NRUpTdzREZGIjc3F8uXL1epX+DlSF1RUVG5ZzXeu3cPFy5cUDmpq1E3Sty8eZN8fHyooKCg3Pfz8vIoMzOz5Ov27dt8owSrcWJjYwkAffbZZ3T37l0CQFu3bhU6LJ0pKioiR0dH+uCDD9RqZ8iQIdShQwcNRSWc6dOnk6WlZan7Ik+fPk0A6ODBgwJGph75nb8nT57USHt5eXlkYGBQ7i0WrHxhYWHk6empUt1PP/2U9PT0aO/evRWWWbVqFenr69PTp09VjLB8mrgVQyaT0euvv059+vRRuI78LtYVK1ao3C/Ry+si9fX16aeffirz3oYNGwgAPXjwQKW2q1VSl56eTl27di3zlZGRQZmZmdStW7dKr/iYM2cOASjzxUkdq2m++eYbAkD9+/cnAHTv3j2hQ9KpTz75hGxtbSv8Ba4qmZmZZGxsTIsWLdJwZLp38+ZN0tPTox9++KHktalTp1L9+vW1dtWWLuTl5ZFYLKZvv/1WI+0lJycTAIqPj9dIe3XBTz/9RPr6+gpfMC/366+/EgBatmxZpeVu3bpFAGjLli1qRFmW/Jcadf+so6KiCACdP3++yrIFBQXUvn178vT0pKKiIrX6JSJq3749TZgwoczr4eHh1L59e5XbrVZJXUWKioooODiY/vzzz0rL8Ugdqy1kMhl98MEHBIBcXFyEDkfnzp49SwBoz549KtWXf1jfvHlTw5EJY9CgQdSmTRuSyWRUVFREjRo1oo8++kjosNTm4+NDw4YN00hba9asIZFIpHSCUpedO3eOAFT5s/VVR44cIUNDQ5o4cSLJZLIqy7dr147Cw8PVCbOMlStXkqGhodp39ubn51PDhg1p/PjxVZZdtGgR6enpUVJSklp9yoWHh1PHjh1LvSaTyahp06b08ccfq9xujVhTt2XLFiQkJODLL7+Ev78/YmNjyy0nFothaWlZ6ouxmkgkEmH58uUYN24cxo0bJ3Q4Ote+fXu4ubmpvAs2Ojoafn5+Ki2iro4mTZqEtLQ0/PXXXzh27Bju3btXo9cKyvn4+GhsB6xUKkXLli1hbm6ukfbqgtatW8Pa2lrhdXVpaWkYNGgQunXrhpUrVyq0nk1+tEll5y0qKzExER4eHmpf+2dkZIRJkyZhw4YNlZ6ZePPmTcydOxeTJk2Ch4eHWn3KeXh4IDU1tdSa0qtXr+LWrVuqr6cDataaOmVlZmbySB1jNdSiRYvI2NiYnj17plS9Bw8ekL6+Pv34449aikz3ZDIZtWvXjkJCQujtt98mFxcXhUZJqrvt27cTALp9+7babXXt2lVjo351SXBwMPXq1avKcpcvX6aGDRuSm5sbPXnyROH2jxw5QgA0NsJFROTk5KTWaNarMjIyyNTUlObOnVvu+zKZjIKDg8nR0ZGysrI00icRUVxcHAGg5OTkktfk0+Hq5Cw1YqSOMVb3jBgxAvn5+dixY4dS9bZu3QqRSITQ0FAtRaZ7IpEIH374Ifbs2YPY2FiMHDmyRh7T8l+aOoRYJpPhzJkzvPNVBb6+vkhMTCy56q088tEjS0tLHDp0CNbW1gq37+PjA0tLS43tgr1//z5u3Lih0vl05bGxscFbb72F77//vtwjRnbu3Im9e/dixYoVsLCw0EifAEruJ371vDqJRILOnTurNcvISR1jrFpq3LgxevToofQUbExMDAICAmBnZ6elyIQRFhYGS0tLZGdn14qpVwBo2LAhnJyc1E7qrl69iuzsbE7qVODr64sXL15UeBfp/fv30atXL+jr60MikcDBwUGp9g0NDdG7d2+NJXXqHDpckcmTJ+Px48fYtGlTqdefP3+Ojz76CMHBwUrf8VoVMzMzuLq6liR1MpkMf/31l3pTr+CkjjFWjY0ePRqHDx/GnTt3FCp/48YNxMfHY8SIEVqOTPfMzMwwefJk9OjRA61btxY6HI3RxLo6qVQK4OXoB1OOp6cnxGJxuevqMjIy0KtXL+Tk5EAikcDR0VGlPvr27YsTJ04gIyND3XCRmJgIR0dHNGnSRO225Fq0aIH+/ftj6dKlpQ4jnjVrFp4+fYrvvvtOKyPjHTt2LLkD9uzZs8jIyOCkjjFWew0aNAhisRgxMTEKld+8eTNMTEwQEhKi5ciEMXfuXPz1119Ch6FR3t7eSE5OrvB0fUWkpKSgUaNGSo8isZcbDDt16lQmqcvMzERAQAAePXoEiUQCZ2dnlfsIDAyETCbDwYMH1Q235NBhTZs6dSrS0tJK7lpOTk7GypUr8fnnn+O1117TeH/Ay80SZ86cQVFRESQSCUxMTNT+/8ZJHWOs2rK0tERISIjCU7DR0dEICQnhHZA1iI+PDwoLC0tGLFTBN0mox9fXF3FxcSWjVC9evEDfvn1x7do1HDp0CK1atVKr/UaNGqFDhw5qT8EWFBTg9OnT8PHxUaud8vj5+cHT0xNLly5FcXExJk6ciHbt2mHy5Mka70uuY8eOyMvLw4ULFyCRSODr6wuxWKxWm5zUMcaqtbCwMJw9e7bUmp/nz5/j1KlTiIqKQmRkJAYMGIBWrVohNTW11qw3qyvc3Nxgamqq1ro6TurU07VrV6Snp+P69evIy8tDSEgIzp49i/3798PNzU0jffTt2xf79+9X62iTM2fOIC8vTysjdSKRCFOnTsWhQ4fwwQcfICkpCatWrdLqPcLy5QLHjx/HsWPH1J56BQARkZq3ZldjWVlZsLKyQmZmJp9Zx1gNVVhYiIYNG6JFixawsLDAhQsXSq2xa9KkCVq3bo3WrVujc+fOGDFiRK3YGVqX9OjRA9bW1krvdAaA9PR0NGrUCNu3b8egQYO0EF3t9+TJE9ja2mL16tX47bffIJFIsH//fnTr1k1jfcTHx8PX1xcnTpxA586dVWpjxYoV+OSTT5CVlaX2iFZ5CgsL4eLigjt37uDdd9/Fjz/+qPE+/qtly5awsLBAcnIyTp06BU9PT7XaM9BQXIwxphWGhoaIiIhAVFQUGjRogNGjR5ckca6urjzVWgt4e3tj7dq1ICKlE/KUlBQAvElCHTY2Nmjbti0mT56MoqIi7N69W6MJHQB06dIF9erVw759+1RO6hITE9GxY0etJHTAy8+aWbNmYfHixViwYIFW+vivjh07YvPmzahXr55GRpt5+pUxVu1FRkbiwoUL2LlzJ7766iuMHj0anp6enNDVEj4+PiXnjylLKpXCyspKrYX8DOjWrRvy8/MRGxuLgIAAjbdvYGCAgIAAtdbVJSQkaGXq9VUTJkzAP//8g3r16mm1Hzn5DRX+/v7Q19dXuz1O6hhjjAnKy8sLgGqHEEul0pKDXJnq5s+fj+TkZAwYMEBrffTt2xenT5+u9Equity7dw+3bt3SelIHAHp6ukuNOnbsCAAaWU8HcFLHGGNMYHZ2dmjZsqVK59XxJgnNsLa21timiIoEBASAiEqODVGGNg4drg68vLwQHh6usRtwOKljjDEmOG9vb6VH6rKysnD16lVeT1dD1K9fH56enipNwSYmJqJJkyYqH4BcXZmammLt2rVo0KCBRtrjpI4xxpjgfHx8cObMGbx48ULhOmfOnAEAHqmrQfr27YsDBw6guLhYqXraOnS4tuGkjjHGmOC8vb1RXFyMU6dOKVxHKpXCyMioVl2bVtv16dMHT58+xYkTJxSuU1BQgKSkJE7qFMBJHWOMMcG1adMGlpaWSq2rk0qlaNeunVYPiGWa1alTJ9ja2io1BSuVSpGfn6+VmyRqG07qGGOMCU5fXx9dunRRal1dSkoKT73WMPr6+ggMDFQqqUtMTISxsTGvnVQAJ3WMMcaqBR8fHyQmJkKRi44KCgpw/vx5TupqoL59+0IqlSI9PV2h8vJDh42MjLQcWc3HSR1jjLFqwdvbGxkZGbh8+XKVZc+fP4/CwkJO6mqgN998EyKRCPv371eoPG+SUBwndYwxxqqFLl26QCQSYc2aNSgqKqq0bEpKCkQikdbPVmOaZ2dnhy5duig0BXvnzh3cvn2bkzoFcVLHGGOsWqhXrx4+/vhjLFq0CB4eHpBIJBWWlUqlaNGiBV8VV0P17dsXBw8exMmTJ/Ho0aMKp9xr66HD2iIiRRYv1FBZWVmwsrJCZmYmLC0thQ6HMcaYAk6dOoWPP/4YCQkJ6N+/PxYvXowWLVqUKuPn5wdHR0ds3rxZoCiZOlJTU+Hu7l5yXp2ZmRmcnZ3h5OQEZ2fnku937tyJY8eOqXQvcF1kIHQAjDHG2Ks6deqEuLg4xMbG4tNPP0Xbtm3x0UcfYebMmahXrx5kMhlSUlIQHBwsdKhMRe3bt8fjx49x7do1XL9+HdevX8eNGzdw/fp1/Pnnn7hx4wZyc3MBAGFhYQJHW3PwSB1jjLFqKzc3F0uWLMGCBQtgamqKefPmoXv37mjdujX279+PgIAAoUNkWkBEePjwIW7cuIGWLVvC2tpa6JBqBE7qGGOMVXt3797FjBkzEBUVBVtbW2RkZODBgwdwcHAQOjTGqg3eKMEYY6zac3R0xPr163Hy5Em4urqiffv2nNAx9h88UscYY6zGISKIRCKhw2CsWuGROsYYYzUOJ3SMlVWjkrqYmBjY29sLHQZjjDHGWLVTY5I6mUyGbdu2oUmTJkKHwhhjjDFW7dSYpC46OhqhoaHQ06s45Pz8fGRlZZX6YowxxhirC2pEUldcXIwtW7Zg2LBhlZZbsGABrKysSr54VI8xxhhjdUW12v16//59hIaGlnn9nXfegb6+PsLCwuDp6YnTp0+XWz8/Px/5+fkl/01EKCgogJ2dHS+qZYwxxlitVq2SuopMmzYNUqkUenp6SExMxLhx47Bs2TKhw2KMMcYYqzZqRFL3qspG6hhjjDHG6qoal9QxxhhjjLGyasRGCcYYY4wxVjkDoQMQChHh+fPnQofBGGOMMaYQCwuLSjd+1tmk7vHjx3wZNGOMMcZqjKrusq+zSZ2RkREA4Pbt25U+IKZZWVlZaNKkCT93AfCzFwY/d+HwsxcGP3ftsbCwqPT9OpvUyYcvLS0t+S+dAPi5C4efvTD4uQuHn70w+LnrHm+UYIwxxhirBTipY4wxxhirBepsUicWizFnzhyIxWKhQ6lT+LkLh5+9MPi5C4efvTD4uQuHDx9mjDHGGKsF6uxIHWOMMcZYbcJJHWOMMcZYLcBJHWOMMcZYLVBnk7r//e9/8PPzw6hRo1BQUCB0OLXa8+fP0aVLF5ibm+PcuXMAgNjYWHh7e+ONN97A7du3BY6wdkpKSoKfnx+6d++OoUOHorCwkJ+7jpw7dw5du3ZF9+7dERQUhOzsbH72OhQTEwN7e3sA/FmjCzdu3IC9vT38/f3h7++PR48e8XMXCtVBycnJNGrUKCIimjdvHm3atEngiGq3wsJCevjwIY0dO5ZSU1OpoKCAOnfuTPn5+RQXF0fvvPOO0CHWSunp6fTixQsiIoqMjKQtW7bwc9eRgoKCku/nzp1LUVFR/Ox1pLi4mAYNGkTu7u78WaMj169fp8GDB5f8Nz934dTJkbrExES8+eabAIDAwEAkJCQIHFHtZmBgUPJbMwBcvnwZbdu2hZGREbp27YrU1FQBo6u9GjRoAFNTUwCAoaEhLl26xM9dRwwNDUu+z8nJQdOmTfnZ60h0dDRCQ0Ohp6fHnzU6FB8fDz8/P8yYMYM/awRUJ5O6Z8+elVxdYmVlhSdPnggcUd3y6vMHgOLiYgGjqf1u3bqFP//8E76+vvzcdejQoUNwd3fH4cOHYWhoyM9eB4qLi7FlyxYMGzYMAH/W6ErDhg1x5coVHDt2DA8fPsRvv/3Gz10gdTKps7a2RlZWFoCX/+htbGwEjqhuefX5A4C+vr6A0dRuWVlZGD16NNauXQsHBwd+7jrUu3dvSKVShIaG4ujRo/zsdWDjxo0YOnQo9PRe/mjjzxrdEIvFMDMzg0gkwuDBgyGVSvm5C6ROJnVeXl44ePAgAODAgQPo2rWrwBHVLc2bN0daWhoKCgoQHx8PNzc3oUOqlYqLizFq1CjMnj0bLVu25OeuQ/n5+SXfW1lZwdzcnJ+9DqSlpSEqKgqBgYG4fPkyfv75Z37uOvD8+fOS748dO4bg4GB+7gIxEDoAIbi7u6NBgwbw8/ND06ZN8cknnwgdUq3Xt29fpKSk4OLFi5g4cSI+/vhjdO/eHcbGxoiKihI6vFppy5YtSEhIwPPnz/Hll1/ivffe4+euI4cOHcI333wDPT092NvbY926dbC3t+dnr2ULFy4s+d7T0xPLli3D5s2b+blrWVxcHGbOnAlTU1M4Ozvjyy+/hFgs5ucuAL4mjDHGGGOsFqiT06+MMcYYY7UNJ3WMMcYYY7UAJ3WMMcYYY7UAJ3WMMcYYY7UAJ3WMMcYYY7UAJ3WMMcYYY7UAJ3WMMcYYY7UAJ3WMMcYYY7UAJ3WMMcYYY7UAJ3WMMcYYY7UAJ3WMMcYYY7XA/wEQtQ63gud7lQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -251,7 +251,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -261,7 +261,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -290,7 +290,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -394,7 +394,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -404,7 +404,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -414,7 +414,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -424,7 +424,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -434,7 +434,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -513,7 +513,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -523,7 +523,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]

From 690f9deb01b3eca4b2ad5f177222dfa00394b2c4 Mon Sep 17 00:00:00 2001
From: jakobrunge <jakobrunge@gmail.com>
Date: Tue, 27 Jun 2023 17:11:06 +0200
Subject: [PATCH 5/5] revised docs

---
 README.md                                     |     2 +
 docs/Pipfile                                  |    12 +
 docs/Pipfile.lock                             |   313 +
 docs/_build/.buildinfo                        |     2 +-
 docs/_build/.doctrees/environment.pickle      |   Bin 4233118 -> 1199935 bytes
 docs/_build/.doctrees/index.doctree           |   Bin 2245147 -> 2197135 bytes
 ...43f6fc8c8fa5c1bcbe7ac1e6cb52ba65f14e0a.png |   Bin 518 -> 0 bytes
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 ...d96ac57b370ce811ab9d31f0c7f2b1efa18711.png |   Bin 403 -> 0 bytes
 docs/_build/_modules/abc.html                 |    20 +-
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diff --git a/README.md b/README.md
index b548260f..3e77cbe7 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,8 @@ Further, Tigramite provides several causal discovery methods that can be used un
 
 Tigramite is a causal inference for time series python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
 
+- Overview: Runge, J., Gerhardus, A., Varando, G. et al. Causal inference for time series. Nat Rev Earth Environ (2023). https://doi.org/10.1038/s43017-023-00431-y
+
 - PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019). https://advances.sciencemag.org/content/5/11/eaau4996
 - PCMCI+: J. Runge (2020): Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020,Toronto, Canada, 2019, AUAI Press, 2020. http://auai.org/uai2020/proceedings/579_main_paper.pdf
 - LPCMCI: Gerhardus, A. & Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders Advances in Neural Information Processing Systems, 2020, 33. https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html
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diff --git a/docs/_build/.buildinfo b/docs/_build/.buildinfo
index 8f04c1fb..36e6eca9 100644
--- a/docs/_build/.buildinfo
+++ b/docs/_build/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 921d6c7e445f291d7dc8f19863452108
+config: 76a64b56702803c95ff1395bf9f343be
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_build/.doctrees/environment.pickle b/docs/_build/.doctrees/environment.pickle
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diff --git a/docs/_build/.doctrees/index.doctree b/docs/_build/.doctrees/index.doctree
index 79dd26cdac05358a24a11f9712faf28edce7fd34..fc4d5cb51137ab9559d8d51f2314e827d43e7772 100644
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diff --git a/docs/_build/_modules/abc.html b/docs/_build/_modules/abc.html
index 14a1d545..78f38697 100644
--- a/docs/_build/_modules/abc.html
+++ b/docs/_build/_modules/abc.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../_static/alabaster.css" />
     <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+    <script src="../_static/jquery.js"></script>
+    <script src="../_static/underscore.js"></script>
+    <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
    
@@ -72,9 +74,9 @@ <h1>Source code for abc</h1><div class="highlight"><pre>
 
     <span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callable</span><span class="p">):</span>
-        <span class="n">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">callable</span><span class="p">)</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">callable</span><span class="p">):</span>
+        <span class="nb">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="nb">callable</span><span class="p">)</span>
 
 
 <span class="k">class</span> <span class="nc">abstractstaticmethod</span><span class="p">(</span><span class="nb">staticmethod</span><span class="p">):</span>
@@ -92,9 +94,9 @@ <h1>Source code for abc</h1><div class="highlight"><pre>
 
     <span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callable</span><span class="p">):</span>
-        <span class="n">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">callable</span><span class="p">)</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">callable</span><span class="p">):</span>
+        <span class="nb">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="nb">callable</span><span class="p">)</span>
 
 
 <span class="k">class</span> <span class="nc">abstractproperty</span><span class="p">(</span><span class="nb">property</span><span class="p">):</span>
@@ -234,8 +236,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/index.html b/docs/_build/_modules/index.html
index 9b896027..fca319d3 100644
--- a/docs/_build/_modules/index.html
+++ b/docs/_build/_modules/index.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../_static/alabaster.css" />
     <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+    <script src="../_static/jquery.js"></script>
+    <script src="../_static/underscore.js"></script>
+    <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
    
@@ -103,8 +105,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/causal_effects.html b/docs/_build/_modules/tigramite/causal_effects.html
index 1b695d3f..7840b799 100644
--- a/docs/_build/_modules/tigramite/causal_effects.html
+++ b/docs/_build/_modules/tigramite/causal_effects.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -2516,46 +2518,94 @@ <h1>Source code for tigramite.causal_effects</h1><div class="highlight"><pre>
     <span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
 
 
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-    <span class="n">coeff</span> <span class="o">=</span> <span class="mf">.5</span>
+    <span class="c1"># def lin_f(x): return x</span>
+    <span class="c1"># coeff = .5</span>
  
-    <span class="n">links_coeffs</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">1</span><span class="p">:</span> <span class="p">[((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">2</span><span class="p">:</span> <span class="p">[((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)]</span>
-             <span class="p">}</span>
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    <span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">structural_causal_process</span><span class="p">(</span>
-        <span class="n">links_coeffs</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
-    <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">CausalEffects</span><span class="o">.</span><span class="n">get_graph_from_dict</span><span class="p">(</span><span class="n">links_coeffs</span><span class="p">)</span>
-
-    <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)]</span>
-    <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-
-    <span class="c1"># Initialize class as `stationary_dag`</span>
-    <span class="n">causal_effects</span> <span class="o">=</span> <span class="n">CausalEffects</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">graph_type</span><span class="o">=</span><span class="s1">&#39;stationary_dag&#39;</span><span class="p">,</span> 
+    <span class="c1"># links_coeffs = {0: [((0, -1), 0.5, lin_f)],</span>
+    <span class="c1">#          1: [((1, -1), 0.5, lin_f), ((0, -1), 0.5, lin_f)],</span>
+    <span class="c1">#          2: [((2, -1), 0.5, lin_f), ((1, 0), 0.5, lin_f)]</span>
+    <span class="c1">#          }</span>
+    <span class="c1"># T = 1000</span>
+    <span class="c1"># data, nonstat = toys.structural_causal_process(</span>
+    <span class="c1">#     links_coeffs, T=T, noises=None, seed=7)</span>
+    <span class="c1"># dataframe = pp.DataFrame(data)</span>
+
+    <span class="c1"># graph = CausalEffects.get_graph_from_dict(links_coeffs)</span>
+
+    <span class="n">original_graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">]],</span>
+
+       <span class="p">[[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">]],</span>
+
+       <span class="p">[[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">]],</span>
+
+       <span class="p">[[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
+    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">original_graph</span><span class="p">)</span>
+
+    <span class="c1"># Add T &lt;-&gt; Reco and T </span>
+    <span class="n">graph</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;+-&gt;&#39;</span> <span class="p">;</span> <span class="n">graph</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;-+&#39;</span>
+    <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;-&gt;&#39;</span> <span class="c1">#; graph[2,1,0] = &#39;&lt;--&#39;</span>
+
+    <span class="n">added</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
+    <span class="n">added</span><span class="p">[:]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">added</span> <span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+
+    <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+    <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+
+    <span class="c1"># # Initialize class as `stationary_dag`</span>
+    <span class="n">causal_effects</span> <span class="o">=</span> <span class="n">CausalEffects</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">graph_type</span><span class="o">=</span><span class="s1">&#39;stationary_admg&#39;</span><span class="p">,</span> 
                                 <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">S</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
                                 <span class="n">hidden_variables</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
                                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 
-    <span class="n">causal_effects</span><span class="o">.</span><span class="n">fit_wright_effect</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
-                            <span class="c1"># links_coeffs = links_coeffs,</span>
-                            <span class="c1"># mediation = [(1, 0), (1, -1), (1, -2)]</span>
-                            <span class="p">)</span>
-
-    <span class="n">intervention_data</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="n">y1</span> <span class="o">=</span> <span class="n">causal_effects</span><span class="o">.</span><span class="n">predict_wright_effect</span><span class="p">(</span> 
-            <span class="n">intervention_data</span><span class="o">=</span><span class="n">intervention_data</span><span class="p">,</span>
-            <span class="p">)</span>
-
-    <span class="n">intervention_data</span> <span class="o">=</span> <span class="mf">0.</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="n">y2</span> <span class="o">=</span> <span class="n">causal_effects</span><span class="o">.</span><span class="n">predict_wright_effect</span><span class="p">(</span> 
-            <span class="n">intervention_data</span><span class="o">=</span><span class="n">intervention_data</span><span class="p">,</span>
-            <span class="p">)</span>
-
-    <span class="n">beta</span> <span class="o">=</span> <span class="p">(</span><span class="n">y1</span> <span class="o">-</span> <span class="n">y2</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Causal effect is </span><span class="si">%.5f</span><span class="s2">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">beta</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">causal_effects</span><span class="o">.</span><span class="n">get_optimal_set</span><span class="p">())</span>
+
+    <span class="n">tp</span><span class="o">.</span><span class="n">plot_time_series_graph</span><span class="p">(</span>
+        <span class="n">graph</span> <span class="o">=</span> <span class="n">graph</span><span class="p">,</span>
+        <span class="n">save_name</span><span class="o">=</span><span class="s1">&#39;Example_graph_in.pdf&#39;</span><span class="p">,</span>
+        <span class="c1"># special_nodes=special_nodes,</span>
+        <span class="c1"># var_names=var_names,</span>
+        <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+        <span class="p">)</span>
+
+    <span class="n">tp</span><span class="o">.</span><span class="n">plot_time_series_graph</span><span class="p">(</span>
+        <span class="n">graph</span> <span class="o">=</span> <span class="n">causal_effects</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span>
+        <span class="n">save_name</span><span class="o">=</span><span class="s1">&#39;Example_graph_out.pdf&#39;</span><span class="p">,</span>
+        <span class="c1"># special_nodes=special_nodes,</span>
+        <span class="c1"># var_names=var_names,</span>
+        <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+        <span class="p">)</span>
+
+    <span class="c1"># causal_effects.fit_wright_effect(dataframe=dataframe, </span>
+    <span class="c1">#                         # links_coeffs = links_coeffs,</span>
+    <span class="c1">#                         # mediation = [(1, 0), (1, -1), (1, -2)]</span>
+    <span class="c1">#                         )</span>
+
+    <span class="c1"># intervention_data = 1.*np.ones((1, 1))</span>
+    <span class="c1"># y1 = causal_effects.predict_wright_effect( </span>
+    <span class="c1">#         intervention_data=intervention_data,</span>
+    <span class="c1">#         )</span>
+
+    <span class="c1"># intervention_data = 0.*np.ones((1, 1))</span>
+    <span class="c1"># y2 = causal_effects.predict_wright_effect( </span>
+    <span class="c1">#         intervention_data=intervention_data,</span>
+    <span class="c1">#         )</span>
+
+    <span class="c1"># beta = (y1 - y2)</span>
+    <span class="c1"># print(&quot;Causal effect is %.5f&quot; %(beta))</span>
 
     <span class="c1"># tp.plot_time_series_graph(</span>
     <span class="c1">#     graph = causal_effects.graph,</span>
@@ -2722,8 +2772,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/data_processing.html b/docs/_build/_modules/tigramite/data_processing.html
index ab48e1d7..fe5e4e3f 100644
--- a/docs/_build/_modules/tigramite/data_processing.html
+++ b/docs/_build/_modules/tigramite/data_processing.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -50,7 +52,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="DataFrame"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.DataFrame">[docs]</a><span class="k">class</span> <span class="nc">DataFrame</span><span class="p">():</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Data object containing single or multiple time series arrays and optional </span>
-<span class="sd">    mask.</span>
+<span class="sd">    mask, as well as variable definitions.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -169,7 +171,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 <span class="sd">            1D numpy array holding all specified reference_points, less those</span>
 <span class="sd">            smaller than 0 and larger than self.largest_time_step-1</span>
 <span class="sd">        If reference_points is None:</span>
-<span class="sd">            Is np.array(range(self.largest_time_step))</span>
+<span class="sd">            Is np.array(self.largest_time_step)</span>
 <span class="sd">    self.time_offsets : dictionary</span>
 <span class="sd">        If time_offsets is not None:</span>
 <span class="sd">            Is time_offsets</span>
@@ -332,6 +334,11 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Ndata</span><span class="p">),</span> <span class="p">[[(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> 
                                 <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Ndata</span><span class="p">)]))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">has_vector_data</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">has_vector_data</span> <span class="o">=</span> <span class="kc">True</span>
+
+
         <span class="c1"># TODO: check vector_vars!</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span><span class="p">)</span>
 
@@ -530,7 +537,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">reference_points</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="c1"># If no reference point is specified, use as many reference points</span>
             <span class="c1"># as possible</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">largest_time_step</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">largest_time_step</span><span class="p">)</span>
 
         <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reference_points</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
             <span class="c1"># If a single reference point is specified as an int, convert it to</span>
@@ -677,7 +684,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 <span class="sd">        array, xyz [,XYZ], data_type : Tuple of data array of shape (dim, n_samples),</span>
 <span class="sd">            xyz identifier array of shape (dim,) identifying which row in array</span>
 <span class="sd">            corresponds to X, Y, and Z, and the type mask that indicates which samples</span>
-<span class="sd">            are continuous or discrete. For example:: X = [(0, -1)],</span>
+<span class="sd">            are continuous or discrete. For example: X = [(0, -1)],</span>
 <span class="sd">            Y = [(1, 0)], Z = [(1, -1), (0, -2)] yields an array of shape</span>
 <span class="sd">            (4, n_samples) and xyz is xyz = numpy.array([0,1,2,2]). If</span>
 <span class="sd">            return_cleaned_xyz is True, also outputs the cleaned XYZ lists.</span>
@@ -1631,8 +1638,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/cmiknn.html b/docs/_build/_modules/tigramite/independence_tests/cmiknn.html
index 8fc38410..dce8d776 100644
--- a/docs/_build/_modules/tigramite/independence_tests/cmiknn.html
+++ b/docs/_build/_modules/tigramite/independence_tests/cmiknn.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -114,6 +116,9 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
 <span class="sd">        Number of workers to use for parallel processing. If -1 is given</span>
 <span class="sd">        all processors are used. Default: -1.</span>
 
+<span class="sd">    model_selection_folds : int</span>
+<span class="sd">        Number of folds in cross-validation used in model selection.</span>
+
 <span class="sd">    significance : str, optional (default: &#39;shuffle_test&#39;)</span>
 <span class="sd">        Type of significance test to use. For CMIknn only &#39;fixed_thres&#39; and</span>
 <span class="sd">        &#39;shuffle_test&#39; are available.</span>
@@ -134,6 +139,7 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
                  <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
                  <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;ranks&#39;</span><span class="p">,</span>
                  <span class="n">workers</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                 <span class="n">model_selection_folds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="c1"># Set the member variables</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">=</span> <span class="n">knn</span>
@@ -144,6 +150,7 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
         <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">workers</span> <span class="o">=</span> <span class="n">workers</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_selection_folds</span> <span class="o">=</span> <span class="n">model_selection_folds</span>
         <span class="c1"># Call the parent constructor</span>
         <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">significance</span><span class="o">=</span><span class="n">significance</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="c1"># Print some information about construction</span>
@@ -202,7 +209,7 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># FIXME: If the time series is constant, return nan rather than</span>
             <span class="c1"># raising Exception</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
                 <span class="c1"># raise ValueError(&quot;nans after standardizing, &quot;</span>
                 <span class="c1">#                  &quot;possibly constant array!&quot;)</span>
@@ -421,7 +428,7 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># FIXME: If the time series is constant, return nan rather than</span>
             <span class="c1"># raising Exception</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
             <span class="c1">#     raise ValueError(&quot;nans after standardizing, &quot;</span>
@@ -483,8 +490,80 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
             <span class="n">restricted_permutation</span><span class="p">[</span><span class="n">sample_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">use</span>
             <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">used</span><span class="p">,</span> <span class="n">use</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">restricted_permutation</span></div>
+        <span class="k">return</span> <span class="n">restricted_permutation</span>
+
+<div class="viewcode-block" id="CMIknn.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknn.CMIknn.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a cross-validation-based score for nearest-neighbor estimates.</span>
+
+<span class="sd">        Fits a nearest-neighbor model of the parents to variable j and returns</span>
+<span class="sd">        the score. The lower, the better the fit. Here used to determine</span>
+<span class="sd">        optimal hyperparameters in PCMCI(pc_alpha or fixed thres).</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        j : int</span>
+<span class="sd">            Index of target variable in data array.</span>
+
+<span class="sd">        parents : list</span>
+<span class="sd">            List of form [(0, -1), (3, -2), ...] containing parents.</span>
+
+<span class="sd">        tau_max : int, optional (default: 0)</span>
+<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
+<span class="sd">            different lags in X, Z, all have the same sample size.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">        score : float</span>
+<span class="sd">            Model score.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="kn">import</span> <span class="nn">sklearn</span>
+        <span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsRegressor</span>
+        <span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
+
+        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>   <span class="c1"># dummy variable here</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="n">parents</span>
+        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
+                                                    <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
+                                                    <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
+                                                    <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                    <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                                    <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
+        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="c1"># Standardize</span>
+        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+        <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
+                <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
+            <span class="c1"># raise ValueError(&quot;nans after standardizing, &quot;</span>
+            <span class="c1">#                  &quot;possibly constant array!&quot;)</span>
+
+        <span class="n">predictor_indices</span> <span class="o">=</span>  <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">predictor_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">predictor_indices</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span>
+        <span class="c1"># Target is only first entry of Y, ie [y]</span>
+        <span class="n">target_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="p">:]</span>
+
+        <span class="k">if</span> <span class="n">predictor_array</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="c1"># Regressing on ones if empty parents</span>
+            <span class="n">predictor_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">knn_here</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">knn_here</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">))</span>
 
+        <span class="n">knn_model</span> <span class="o">=</span> <span class="n">KNeighborsRegressor</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="n">knn_here</span><span class="p">)</span>
+        
+        <span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">knn_model</span><span class="p">,</span> 
+            <span class="n">X</span><span class="o">=</span><span class="n">predictor_array</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">target_array</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_selection_folds</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">)</span>
+        
+        <span class="c1"># print(scores)</span>
+        <span class="k">return</span> <span class="o">-</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div></div>
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
     
@@ -495,8 +574,8 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
 
     <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
     <span class="n">cmi</span> <span class="o">=</span> <span class="n">CMIknn</span><span class="p">(</span><span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                   <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
-                   <span class="n">fixed_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                   <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;fixed_thres&#39;</span><span class="p">,</span>
+                   <span class="n">fixed_thres</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
                    <span class="n">sig_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
                    <span class="n">sig_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                    <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">,</span>
@@ -506,15 +585,28 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
     <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
     <span class="n">dimz</span> <span class="o">=</span> <span class="mi">1</span>
 
-    <span class="c1"># Continuous data</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.8</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.8</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="c1"># # Continuous data</span>
+    <span class="c1"># z = random_state.standard_normal((T, dimz))</span>
+    <span class="c1"># x = (1.*z[:,0] + random_state.standard_normal(T)).reshape(T, 1)</span>
+    <span class="c1"># y = (1.*z[:,0] + random_state.standard_normal(T)).reshape(T, 1)</span>
 
     <span class="c1"># print(&#39;X _|_ Y&#39;)</span>
     <span class="c1"># print(cmi.run_test_raw(x, y, z=None))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;X _|_ Y | Z&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">))</span>
+    <span class="c1"># print(&#39;X _|_ Y | Z&#39;)</span>
+    <span class="c1"># print(cmi.run_test_raw(x, y, z=z))</span>
+
+    <span class="c1"># Continuous data</span>
+    <span class="n">z</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mf">1.</span><span class="o">*</span><span class="n">x</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>
+    <span class="nb">print</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">dataframe</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">cmi</span><span class="o">.</span><span class="n">set_dataframe</span><span class="p">(</span><span class="n">dataframe</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span><span class="n">j</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="p">[],</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">folds</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span><span class="n">j</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">folds</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span><span class="n">j</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">folds</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
 </pre></div>
 
           </div>
@@ -569,8 +661,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/cmisymb.html b/docs/_build/_modules/tigramite/independence_tests/cmisymb.html
index 4fa07b11..07a3d4fc 100644
--- a/docs/_build/_modules/tigramite/independence_tests/cmisymb.html
+++ b/docs/_build/_modules/tigramite/independence_tests/cmisymb.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -368,8 +370,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/gpdc.html b/docs/_build/_modules/tigramite/independence_tests/gpdc.html
index 1d87821d..7c24f23e 100644
--- a/docs/_build/_modules/tigramite/independence_tests/gpdc.html
+++ b/docs/_build/_modules/tigramite/independence_tests/gpdc.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -244,7 +246,7 @@ <h1>Source code for tigramite.independence_tests.gpdc</h1><div class="highlight"
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -743,8 +745,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/gpdc_torch.html b/docs/_build/_modules/tigramite/independence_tests/gpdc_torch.html
index fd219826..dbcd744c 100644
--- a/docs/_build/_modules/tigramite/independence_tests/gpdc_torch.html
+++ b/docs/_build/_modules/tigramite/independence_tests/gpdc_torch.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -251,7 +253,7 @@ <h1>Source code for tigramite.independence_tests.gpdc_torch</h1><div class="high
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).any():</span>
@@ -899,8 +901,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/gsquared.html b/docs/_build/_modules/tigramite/independence_tests/gsquared.html
index 3b702b6c..10f89b76 100644
--- a/docs/_build/_modules/tigramite/independence_tests/gsquared.html
+++ b/docs/_build/_modules/tigramite/independence_tests/gsquared.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -273,8 +275,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/independence_tests_base.html b/docs/_build/_modules/tigramite/independence_tests/independence_tests_base.html
index 47fb21c1..4e1a0582 100644
--- a/docs/_build/_modules/tigramite/independence_tests/independence_tests_base.html
+++ b/docs/_build/_modules/tigramite/independence_tests/independence_tests_base.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -69,8 +71,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">        &#39;fixed_thres&#39; and &#39;shuffle_test&#39; are available.</span>
 
 <span class="sd">    fixed_thres : float, optional (default: 0.1)</span>
-<span class="sd">        If significance is &#39;fixed_thres&#39;, this specifies the threshold for the</span>
-<span class="sd">        absolute value of the dependence measure.</span>
+<span class="sd">        Deprecated.</span>
 
 <span class="sd">    sig_samples : int, optional (default: 500)</span>
 <span class="sd">        Number of samples for shuffle significance test.</span>
@@ -120,7 +121,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
                  <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
                  <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;analytic&#39;</span><span class="p">,</span>
-                 <span class="n">fixed_thres</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
+                 <span class="n">fixed_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">sig_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
                  <span class="n">sig_blocklength</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">confidence</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@@ -136,9 +137,11 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">=</span> <span class="n">significance</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span> <span class="o">=</span> <span class="n">sig_samples</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span> <span class="o">=</span> <span class="n">sig_blocklength</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span> <span class="o">=</span> <span class="n">fixed_thres</span>
+        <span class="k">if</span> <span class="n">fixed_thres</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;fixed_thres is replaced by providing alpha_or_thres in run_test&quot;</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ci_results</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="c1"># If we recycle residuals, then set up a residual cache</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="n">recycle_residuals</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span><span class="p">:</span>
@@ -190,9 +193,9 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;shuffle_test&#39;</span><span class="p">:</span>
             <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sig_samples = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span>
             <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sig_blocklength = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span>
-        <span class="c1"># Check if we are using a fixed threshold</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">fixed_thres = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span>
+        <span class="c1"># # Check if we are using a fixed threshold</span>
+        <span class="c1"># elif self.significance == &#39;fixed_thres&#39;:</span>
+        <span class="c1">#     info_str += &quot;\nfixed_thres = %s&quot; % self.fixed_thres</span>
         <span class="c1"># Check if we have a confidence type</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span><span class="p">:</span>
             <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">confidence = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span>
@@ -355,7 +358,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="k">return</span> <span class="n">combined_hash</span>
 
 
-<div class="viewcode-block" id="CondIndTest.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">):</span>
+<div class="viewcode-block" id="CondIndTest.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test.</span>
 
 <span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
@@ -369,11 +372,9 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">        X, Y, Z : list of tuples</span>
 <span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
 <span class="sd">            variable index and tau the time lag.</span>
-
 <span class="sd">        tau_max : int, optional (default: 0)</span>
 <span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
 <span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
 <span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;}</span>
 <span class="sd">            How many samples to cutoff at the beginning. The default is</span>
 <span class="sd">            &#39;2xtau_max&#39;, which guarantees that MCI tests are all conducted on</span>
@@ -381,11 +382,16 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">            which uses the maximum of tau_max and the conditions, which is</span>
 <span class="sd">            useful to compare multiple models on the same sample.  Last,</span>
 <span class="sd">            &#39;max_lag&#39; uses as much samples as possible.</span>
+<span class="sd">        alpha_or_thres : float (optional)</span>
+<span class="sd">            Significance level (if significance=&#39;analytic&#39; or &#39;shuffle_test&#39;) or</span>
+<span class="sd">            threshold (if significance=&#39;fixed_thres&#39;). If given, run_test returns</span>
+<span class="sd">            the test decision dependent=True/False.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-<span class="sd">            The test statistic value and the p-value.</span>
+<span class="sd">        val, pval, [dependent] : Tuple of floats and bool</span>
+<span class="sd">            The test statistic value and the p-value. If alpha_or_thres is</span>
+<span class="sd">            given, run_test also returns the test decision dependent=True/False.      </span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
         <span class="c1"># Get the array to test on</span>
@@ -394,12 +400,14 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         
         <span class="c1"># Record the dimensions</span>
         <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
+
         <span class="c1"># Ensure it is a valid array</span>
         <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
 
         <span class="n">combined_hash</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array_hash</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">)</span>
 
+        <span class="c1"># Get test statistic value and p-value [cached if possible]</span>
         <span class="k">if</span> <span class="n">combined_hash</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
             <span class="n">cached</span> <span class="o">=</span> <span class="kc">True</span>
             <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span>
@@ -407,17 +415,39 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
             <span class="n">cached</span> <span class="o">=</span> <span class="kc">False</span>
             <span class="c1"># Get the dependence measure, reycling residuals if need be</span>
             <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dependence_measure_recycle</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">data_type</span><span class="p">)</span>
-            <span class="c1"># Get the p-value</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
+            <span class="c1"># Get the p-value (None if significance = &#39;fixed_thres&#39;)</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_p_value</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">)</span>
 
+        <span class="c1"># Make test decision</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;significance == &#39;fixed_thres&#39; requires setting alpha_or_thres&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">alpha_or_thres</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">val</span> <span class="o">&gt;=</span> <span class="n">alpha_or_thres</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span> <span class="k">if</span> <span class="n">dependent</span> <span class="k">else</span> <span class="mf">1.</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">pval</span> <span class="o">&lt;=</span> <span class="n">alpha_or_thres</span>
+                
+        <span class="bp">self</span><span class="o">.</span><span class="n">ci_results</span><span class="p">[(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">Y</span><span class="p">),</span><span class="nb">tuple</span><span class="p">(</span><span class="n">Z</span><span class="p">))]</span> <span class="o">=</span> <span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span><span class="p">)</span>
+
+        <span class="c1"># Return the calculated value(s)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="n">cached</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="n">cached</span><span class="p">,</span> <span class="n">dependent</span><span class="o">=</span><span class="n">dependent</span><span class="p">,</span>
                                          <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-        <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
 
-<div class="viewcode-block" id="CondIndTest.run_test_raw"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw">[docs]</a>    <span class="k">def</span> <span class="nf">run_test_raw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">y_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span>
+        <span class="k">else</span><span class="p">:</span>              
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span></div>
+
+
+<div class="viewcode-block" id="CondIndTest.run_test_raw"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw">[docs]</a>    <span class="k">def</span> <span class="nf">run_test_raw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">y_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test directly on input arrays x, y, z.</span>
 
 <span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
@@ -434,11 +464,16 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">            are continuous or discrete: 0s for continuous variables and</span>
 <span class="sd">            1s for discrete variables</span>
 
+<span class="sd">        alpha_or_thres : float (optional)</span>
+<span class="sd">            Significance level (if significance=&#39;analytic&#39; or &#39;shuffle_test&#39;) or</span>
+<span class="sd">            threshold (if significance=&#39;fixed_thres&#39;). If given, run_test returns</span>
+<span class="sd">            the test decision dependent=True/False.</span>
+<span class="sd">        </span>
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-
-<span class="sd">            The test statistic value and the p-value.</span>
+<span class="sd">        val, pval, [dependent] : Tuple of floats and bool</span>
+<span class="sd">            The test statistic value and the p-value. If alpha_or_thres is</span>
+<span class="sd">            given, run_test also returns the test decision dependent=True/False.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
         <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
@@ -496,13 +531,30 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 
         <span class="c1"># Get the p-value</span>
         <span class="k">if</span> <span class="n">has_data_type</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
+            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_p_value</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
                     <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">data_type</span><span class="o">=</span><span class="n">data_type</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
-                    <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span>            
+            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_p_value</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
+                    <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span>    
+
+        <span class="c1"># Make test decision</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">alpha_or_thres</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">val</span> <span class="o">&gt;=</span> <span class="n">alpha_or_thres</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span> <span class="k">if</span> <span class="n">dependent</span> <span class="k">else</span> <span class="mf">1.</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">pval</span> <span class="o">&lt;=</span> <span class="n">alpha_or_thres</span>
+
         <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
+        <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span>
+        <span class="k">else</span><span class="p">:</span>              
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span></div>
 
     <span class="k">def</span> <span class="nf">_get_dependence_measure_recycle</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the dependence_measure, optionally recycling residuals</span>
@@ -587,12 +639,12 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="c1"># Return these residuals</span>
         <span class="k">return</span> <span class="n">x_resid</span>
 
-<div class="viewcode-block" id="CondIndTest.get_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span>
+    <span class="k">def</span> <span class="nf">_get_p_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span>
                          <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                          <span class="n">sig_override</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the p-value from whichever significance function is specified</span>
-<span class="sd">        for this test.  If an override is used, then it will call a different</span>
+<span class="sd">        for this test. If an override is used, then it will call a different</span>
 <span class="sd">        function then specified by self.significance</span>
 
 <span class="sd">        Parameters</span>
@@ -639,13 +691,26 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
                                                  <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
         <span class="c1"># Check if we are using the fixed_thres significance</span>
         <span class="k">elif</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_fixed_thres_significance</span><span class="p">(</span>
-                    <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">,</span>
-                    <span class="n">fixed_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span><span class="p">)</span>
+            <span class="c1"># Determined outside then</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="c1"># if self.two_sided:</span>
+            <span class="c1">#     dependent = np.abs(val) &gt;= np.abs(alpha_or_thres)</span>
+            <span class="c1"># else:</span>
+            <span class="c1">#     dependent = val &gt;= alpha_or_thres</span>
+            <span class="c1"># pval = 0. if dependent else 1.</span>
+            <span class="c1"># # pval = self.get_fixed_thres_significance(</span>
+            <span class="c1"># #         value=val,</span>
+            <span class="c1"># #         fixed_thres=self.fixed_thres)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> not known.&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span><span class="p">)</span>
-        <span class="c1"># Return the calculated value</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
+
+        <span class="c1"># # Return the calculated value(s)</span>
+        <span class="c1"># if alpha_or_thres is not None:</span>
+        <span class="c1">#     if use_sig != &#39;fixed_thres&#39;: </span>
+        <span class="c1">#         dependent = pval &lt;= alpha_or_thres </span>
+        <span class="c1">#     return pval, dependent </span>
+        <span class="c1"># else:</span>
+        <span class="k">return</span> <span class="n">pval</span>
 
 <div class="viewcode-block" id="CondIndTest.get_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                     <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@@ -758,7 +823,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="c1"># Return the confidence interval</span>
         <span class="k">return</span> <span class="p">(</span><span class="n">conf_lower</span><span class="p">,</span> <span class="n">conf_upper</span><span class="p">)</span></div>
 
-    <span class="k">def</span> <span class="nf">_print_cond_ind_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_print_cond_ind_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dependent</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Print results from conditional independence test.</span>
 
 <span class="sd">        Parameters</span>
@@ -769,12 +834,17 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">        pval : float, optional (default: None)</span>
 <span class="sd">            p-value</span>
 
+<span class="sd">        dependent : bool</span>
+<span class="sd">            Test decision.</span>
+
 <span class="sd">        conf : tuple of floats, optional (default: None)</span>
 <span class="sd">            Confidence bounds.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">printstr</span> <span class="o">=</span> <span class="s2">&quot;        val = </span><span class="si">% .3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">val</span><span class="p">)</span>      
         <span class="k">if</span> <span class="n">pval</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | pval = </span><span class="si">%.5f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">pval</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">dependent</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | dependent = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">dependent</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">conf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | conf bounds = (</span><span class="si">%.3f</span><span class="s2">, </span><span class="si">%.3f</span><span class="s2">)&quot;</span> <span class="o">%</span> <span class="p">(</span>
                 <span class="n">conf</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conf</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
@@ -967,7 +1037,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
                     <span class="n">ydata</span><span class="o">=</span><span class="n">hilbert</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">phi</span> <span class="o">=</span> <span class="n">popt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="c1"># Formula of Peifer (2005) assuming non-overlapping blocks</span>
+                <span class="c1"># Formula assuming non-overlapping blocks</span>
                 <span class="n">l_opt</span> <span class="o">=</span> <span class="p">(</span><span class="mf">4.</span> <span class="o">*</span> <span class="n">T</span> <span class="o">*</span> <span class="p">(</span><span class="n">phi</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">)</span> <span class="o">+</span> <span class="n">phi</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
                          <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">phi</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span>
                 <span class="n">block_len</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">block_len</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">l_opt</span><span class="p">))</span>
@@ -1067,31 +1137,15 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="k">return</span> <span class="n">null_dist</span>
 
 <div class="viewcode-block" id="CondIndTest.get_fixed_thres_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_fixed_thres_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">fixed_thres</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns signficance for thresholding test.</span>
-
-<span class="sd">        Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1 else.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-
-<span class="sd">        fixed_thres : number</span>
-<span class="sd">            Fixed threshold, is made positive.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : bool</span>
-<span class="sd">            Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1</span>
-<span class="sd">            else.</span>
-
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;DEPRECATED Returns signficance for thresholding test.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">fixed_thres</span><span class="p">):</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">1.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;fixed_thres is replaced by alpha_or_thres in run_test.&quot;</span><span class="p">)</span></div>
+        <span class="c1"># if np.abs(value) &lt; np.abs(fixed_thres):</span>
+        <span class="c1">#     pval = 1.</span>
+        <span class="c1"># else:</span>
+        <span class="c1">#     pval = 0.</span>
 
-        <span class="k">return</span> <span class="n">pval</span></div>
+        <span class="c1"># return pval</span>
 
     <span class="k">def</span> <span class="nf">_trafo2uniform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Transforms input array to uniform marginals.</span>
@@ -1175,8 +1229,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/oracle_conditional_independence.html b/docs/_build/_modules/tigramite/independence_tests/oracle_conditional_independence.html
index 703a759f..bb511c49 100644
--- a/docs/_build/_modules/tigramite/independence_tests/oracle_conditional_independence.html
+++ b/docs/_build/_modules/tigramite/independence_tests/oracle_conditional_independence.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -1081,7 +1083,7 @@ <h1>Source code for tigramite.independence_tests.oracle_conditional_independence
 
         <span class="k">return</span> <span class="n">any_path_observed</span></div>
 
-<div class="viewcode-block" id="OracleCI.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span>
+<div class="viewcode-block" id="OracleCI.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform oracle conditional independence test.</span>
 
@@ -1096,6 +1098,8 @@ <h1>Source code for tigramite.independence_tests.oracle_conditional_independence
 <span class="sd">            Not used here.</span>
 <span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;}</span>
 <span class="sd">            Not used here.</span>
+<span class="sd">        alpha_or_thres : float</span>
+<span class="sd">            Not used here.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -1120,15 +1124,20 @@ <h1>Source code for tigramite.independence_tests.oracle_conditional_independence
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dsepsets</span><span class="p">[</span><span class="nb">str</span><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))]:</span>
             <span class="n">val</span> <span class="o">=</span> <span class="mf">0.</span>
             <span class="n">pval</span> <span class="o">=</span> <span class="mf">1.</span>
+            <span class="n">dependent</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span>
             <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
+            <span class="n">dependent</span> <span class="o">=</span> <span class="kc">True</span>
 
         <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                          <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
         <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
+        <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span></div>
 
 <div class="viewcode-block" id="OracleCI.get_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns dependence measure.</span>
@@ -1650,8 +1659,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/parcorr.html b/docs/_build/_modules/tigramite/independence_tests/parcorr.html
index 5ed4ec16..030bb8bc 100644
--- a/docs/_build/_modules/tigramite/independence_tests/parcorr.html
+++ b/docs/_build/_modules/tigramite/independence_tests/parcorr.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -128,7 +130,7 @@ <h1>Source code for tigramite.independence_tests.parcorr</h1><div class="highlig
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -340,6 +342,7 @@ <h1>Source code for tigramite.independence_tests.parcorr</h1><div class="highlig
             <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span> <span class="o">+</span> <span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">T</span> <span class="o">-</span> <span class="n">p</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span>
+
         <span class="k">return</span> <span class="n">score</span></div></div>
 </pre></div>
 
@@ -395,8 +398,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/parcorr_mult.html b/docs/_build/_modules/tigramite/independence_tests/parcorr_mult.html
index 390a8fcd..2206ba02 100644
--- a/docs/_build/_modules/tigramite/independence_tests/parcorr_mult.html
+++ b/docs/_build/_modules/tigramite/independence_tests/parcorr_mult.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -133,7 +135,7 @@ <h1>Source code for tigramite.independence_tests.parcorr_mult</h1><div class="hi
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -222,7 +224,7 @@ <h1>Source code for tigramite.independence_tests.parcorr_mult</h1><div class="hi
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -460,8 +462,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/parcorr_wls.html b/docs/_build/_modules/tigramite/independence_tests/parcorr_wls.html
index e8d607d7..bfeb64da 100644
--- a/docs/_build/_modules/tigramite/independence_tests/parcorr_wls.html
+++ b/docs/_build/_modules/tigramite/independence_tests/parcorr_wls.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -356,7 +358,7 @@ <h1>Source code for tigramite.independence_tests.parcorr_wls</h1><div class="hig
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="n">x_vals_sum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
             <span class="n">x_vals_has_nan</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">x_vals_sum</span><span class="p">)</span>
@@ -499,8 +501,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/regressionCI.html b/docs/_build/_modules/tigramite/independence_tests/regressionCI.html
index 6708cb05..228502c3 100644
--- a/docs/_build/_modules/tigramite/independence_tests/regressionCI.html
+++ b/docs/_build/_modules/tigramite/independence_tests/regressionCI.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -453,8 +455,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/independence_tests/robust_parcorr.html b/docs/_build/_modules/tigramite/independence_tests/robust_parcorr.html
index b051d63c..432b3e3d 100644
--- a/docs/_build/_modules/tigramite/independence_tests/robust_parcorr.html
+++ b/docs/_build/_modules/tigramite/independence_tests/robust_parcorr.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -198,7 +200,7 @@ <h1>Source code for tigramite.independence_tests.robust_parcorr</h1><div class="
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -482,8 +484,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
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-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
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+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/lpcmci.html b/docs/_build/_modules/tigramite/lpcmci.html
index 986199b3..f78d771c 100644
--- a/docs/_build/_modules/tigramite/lpcmci.html
+++ b/docs/_build/_modules/tigramite/lpcmci.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -39,9 +41,11 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="LPCMCI"><a class="viewcode-back" href="../../index.html#tigramite.lpcmci.LPCMCI">[docs]</a><span class="k">class</span> <span class="nc">LPCMCI</span><span class="p">(</span><span class="n">PCMCIbase</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot; LPCMCI is an algorithm for causal discovery in large-scale times series that allows for latent confounders and</span>
 <span class="sd">    learns lag-specific causal relationships. The algorithm is introduced and explained in:</span>
+
 <span class="sd">    [1] Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders.</span>
 <span class="sd">    Advances in Neural Information Processing Systems, 2020, 33.</span>
 <span class="sd">    https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</span>
+<span class="sd">    </span>
 <span class="sd">    NOTE: This method is still EXPERIMENTAL since the default settings of hyperparameters are still being fine-tuned.</span>
 <span class="sd">    We actually invite feedback on which work best in applications and numerical experiments.</span>
 <span class="sd">    The main function, which applies the algorithm, is &#39;run_lpcmci&#39;.</span>
@@ -371,6 +375,8 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">remember_only_parents</span> <span class="o">=</span> <span class="n">remember_only_parents</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">no_apr</span> <span class="o">=</span> <span class="n">no_apr</span>
 
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pc_alpha</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;pc_alpha must be single float in LPCMCI.&quot;</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">pc_alpha</span> <span class="o">&lt;</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="n">pc_alpha</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Choose 0 &lt;= pc_alpha &lt;= 1&quot;</span><span class="p">)</span>
             
@@ -858,7 +864,8 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_YX</span><span class="p">)</span>
 
                             <span class="c1"># Test conditional independence of X and Y given Z</span>
-                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                                <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ANC(Y):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -869,7 +876,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                             <span class="c1"># Check whether test result was significant</span>
-                            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                            <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1">#pval &gt; self.pc_alpha:</span>
 
                                 <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                                 <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -897,7 +904,8 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_XY</span><span class="p">)</span>
 
                             <span class="c1"># Test conditional independence of X and Y given Z</span>
-                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                                <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ANC(X):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -908,7 +916,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                             <span class="c1"># Check whether test result was significant</span>
-                            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                            <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                                 <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                                 <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -1184,7 +1192,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                         <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_YX</span><span class="p">)</span>
 
                         <span class="c1"># Test conditional independence of X and Y given Z</span>
-                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                        <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                            <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Non-ANC(Y):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -1195,7 +1205,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                         <span class="c1"># Check whether test result was significant</span>
-                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                             <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                             <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -1227,7 +1237,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_XY</span><span class="p">)</span>
 
                             <span class="c1"># Test conditional independence of X and Y given Z</span>
-                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                            <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                                <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Non-ANC(X):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -1238,7 +1250,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                             <span class="c1"># Check whether test result was significant</span>
-                            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                            <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                                 <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                                 <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -1906,7 +1918,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="n">Z_A</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">A</span><span class="p">]</span>
 
                 <span class="c1"># Run the conditional independence test</span>
-                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">Z_A</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = Z_A, tau_max = self.tau_max)</span>
+                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">Z_A</span><span class="p">,</span> 
+                    <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MakeMin:    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_A = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -1917,7 +1931,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_A</span><span class="p">))</span>
 
                 <span class="c1"># Check whether the test result was significant</span>
-                <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                     <span class="n">new_sepsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z_A</span><span class="p">))</span>
                     <span class="n">val_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
 
@@ -2002,7 +2016,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
 
                 <span class="c1"># Test conditional independence of X and Y given Z</span>
-                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                    <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;BnotinSepSetAC(A):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_add = </span><span class="si">%s</span><span class="s2">, Z = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -2013,7 +2029,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                 <span class="c1"># Check whether test result was significant</span>
-                <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                     <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
         <span class="c1"># Test for independence given all subsets of non-future adjacencies of C</span>
@@ -2031,7 +2047,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
 
                 <span class="c1"># Test conditional independence of X and Y given Z</span>
-                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                    <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                     <span class="c1"># print(&quot;BnotinSepSetAC(C):    %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f&quot; %</span>
@@ -2044,7 +2062,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                 <span class="c1"># Check whether test result was significant</span>
-                <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                     <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
         <span class="c1"># Append the already known sepset</span>
@@ -2128,8 +2146,10 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
+                    
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;BinSepSetAC(A):    %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f&quot; %</span>
                         <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z)]), val, pval))</span>
@@ -2141,7 +2161,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                         <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
             <span class="c1"># Test for independence given all subsets of non-future adjacencies of C</span>
@@ -2159,8 +2179,10 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
+                    
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;BinSepSetAC(C):     %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f&quot; %</span>
                         <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z)]), val, pval))</span>
@@ -2172,7 +2194,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                         <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
             <span class="c1"># Append the already known sepset</span>
@@ -2794,7 +2816,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z_add2</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;ER00a(part1):    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f&quot; %</span>
@@ -2807,7 +2831,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_test</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                         <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                         <span class="n">remove_AB</span> <span class="o">=</span> <span class="kc">True</span>
@@ -2851,7 +2875,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z_add2</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;ER00a(part2):    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f&quot; %</span>
@@ -2864,7 +2890,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_test</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                         
                         <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                         <span class="n">remove_CB</span> <span class="o">=</span> <span class="kc">True</span>
@@ -2959,7 +2985,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z_add2</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;ER00b:    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f&quot; %</span>
@@ -2972,7 +3000,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_test</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                         <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                         <span class="n">remove_AB</span> <span class="o">=</span> <span class="kc">True</span>
@@ -3582,7 +3610,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
 
-    <span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span>
+    <span class="kn">from</span> <span class="nn">tigramite.independence_tests.parcorr</span> <span class="kn">import</span> <span class="n">ParCorr</span>
     <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
     <span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
     <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
@@ -3609,18 +3637,18 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
     <span class="c1"># Data must be array of shape (time, variables)</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
     <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-    <span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
+    <span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">(</span><span class="n">significance</span><span class="o">=</span><span class="s1">&#39;fixed_thres&#39;</span><span class="p">)</span>
     <span class="n">lpcmci</span> <span class="o">=</span> <span class="n">LPCMCI</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">)</span>
-    <span class="c1"># results = pcmci.run_lpcmci(tau_max=2, pc_alpha=0.01)</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">lpcmci</span><span class="o">.</span><span class="n">run_lpcmci</span><span class="p">(</span><span class="n">tau_max</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
 
     <span class="c1"># # For a proper causal interpretation of the graph see the paper!</span>
     <span class="c1"># print(results[&#39;graph&#39;])</span>
     <span class="c1"># tp.plot_graph(graph=results[&#39;graph&#39;], val_matrix=results[&#39;val_matrix&#39;])</span>
     <span class="c1"># plt.show()</span>
 
-    <span class="n">results</span> <span class="o">=</span> <span class="n">lpcmci</span><span class="o">.</span><span class="n">run_sliding_window_of</span><span class="p">(</span>
-        <span class="n">window_step</span><span class="o">=</span><span class="mi">499</span><span class="p">,</span> <span class="n">window_length</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-        <span class="n">method</span><span class="o">=</span><span class="s1">&#39;run_lpcmci&#39;</span><span class="p">,</span> <span class="n">method_args</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;tau_max&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">})</span>
+    <span class="c1"># results = lpcmci.run_sliding_window_of(</span>
+    <span class="c1">#     window_step=499, window_length=500,</span>
+    <span class="c1">#     method=&#39;run_lpcmci&#39;, method_args={&#39;tau_max&#39;:1})</span>
 </pre></div>
 
           </div>
@@ -3675,8 +3703,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
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-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/models.html b/docs/_build/_modules/tigramite/models.html
index 8d3fbdb8..63bc8646 100644
--- a/docs/_build/_modules/tigramite/models.html
+++ b/docs/_build/_modules/tigramite/models.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -869,9 +871,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
         <span class="n">psi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))</span>
 
         <span class="n">psi</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="o">-</span> <span class="n">phi</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
         <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="c1"># psi[tau] = np.matmul(psi[0], np.matmul(phi[tau], psi[0]))</span>
             <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
                 <span class="n">psi</span><span class="p">[</span><span class="n">tau</span><span class="p">]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">psi</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">phi</span><span class="p">[</span><span class="n">s</span><span class="p">],</span> <span class="n">psi</span><span class="p">[</span><span class="n">tau</span> <span class="o">-</span> <span class="n">s</span><span class="p">])</span> <span class="p">)</span> 
 
@@ -1601,6 +1601,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
             <span class="n">mask</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)}</span>
         <span class="c1"># Get the dataframe shape</span>
         <span class="n">T</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
         <span class="c1"># Have the default dataframe be the training data frame</span>
         <span class="n">train_mask</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
         <span class="n">train_mask</span><span class="p">[</span><span class="mi">0</span><span class="p">][[</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> <span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">train_indices</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -1688,6 +1689,14 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
 <span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
 <span class="sd">            containing estimated predictors.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">link_assumptions</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">selected_links</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">):</span><span class="s2">&quot;-?&gt;&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">+</span><span class="mi">1</span><span class="p">)}</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">link_assumptions</span> <span class="o">=</span> <span class="kc">None</span>
+
         <span class="c1"># Ensure an independence model is given</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No cond_ind_test given!&quot;</span><span class="p">)</span>
@@ -1695,7 +1704,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">selected_targets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span> <span class="o">=</span> <span class="n">selected_targets</span>
-        <span class="n">predictors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">selected_links</span><span class="o">=</span><span class="n">selected_links</span><span class="p">,</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
                                         <span class="n">tau_min</span><span class="o">=</span><span class="n">steps_ahead</span><span class="p">,</span>
                                         <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
                                         <span class="n">save_iterations</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
@@ -2078,8 +2087,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/pcmci.html b/docs/_build/_modules/tigramite/pcmci.html
index 93e61480..6a562a71 100644
--- a/docs/_build/_modules/tigramite/pcmci.html
+++ b/docs/_build/_modules/tigramite/pcmci.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -446,12 +448,13 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                     <span class="k">if</span> <span class="n">link_assumptions_j</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">:</span>
                         <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span>
                         <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
+                        <span class="n">dependent</span> <span class="o">=</span> <span class="kc">True</span>
                     <span class="k">else</span><span class="p">:</span>
-                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[</span><span class="n">parent</span><span class="p">],</span>
+                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[</span><span class="n">parent</span><span class="p">],</span>
                                                     <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span>
                                                     <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
                                                     <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                    <span class="c1"># verbosity=self.verbosity</span>
+                                                    <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
                                                     <span class="p">)</span>
                     <span class="c1"># Print some information if needed</span>
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
@@ -473,7 +476,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                         <span class="n">a_iter</span><span class="p">[</span><span class="n">comb_index</span><span class="p">][</span><span class="s1">&#39;val&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
                         <span class="n">a_iter</span><span class="p">[</span><span class="n">comb_index</span><span class="p">][</span><span class="s1">&#39;pval&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
                     <span class="c1"># Delete link later and break while-loop if non-significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1">#pval &gt; pc_alpha:</span>
                         <span class="n">nonsig_parents</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="n">parent</span><span class="p">))</span>
                         <span class="n">nonsig</span> <span class="o">=</span> <span class="kc">True</span>
                         <span class="k">break</span>
@@ -1108,15 +1111,14 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
             <span class="k">if</span> <span class="n">val_only</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
                 <span class="c1"># Run the independence tests and record the results</span>
-                <span class="k">if</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> 
-                     <span class="ow">and</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o-o&#39;</span><span class="p">]):</span>
+                <span class="k">if</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> 
+                     <span class="ow">and</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o-o&#39;</span><span class="p">]):</span>
                     <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span> 
                     <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
                 <span class="k">else</span><span class="p">:</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
                                                         <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                        <span class="c1"># verbosity=</span>
-                                                        <span class="c1"># self.verbosity</span>
+                                                        <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">,</span>
                                                         <span class="p">)</span>
                 <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">val</span>
                 <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">pval</span>
@@ -1139,13 +1141,21 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
         <span class="c1"># Correct the p_matrix if there is a fdr_method</span>
         <span class="k">if</span> <span class="n">fdr_method</span> <span class="o">!=</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;FDR-correction not compatible with significance == &#39;fixed_thres&#39;&quot;</span><span class="p">)</span>
             <span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_corrected_pvalues</span><span class="p">(</span><span class="n">p_matrix</span><span class="o">=</span><span class="n">p_matrix</span><span class="p">,</span> <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
                                                   <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
                                                   <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span>
                                                   <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
 
-        <span class="c1"># Threshold p_matrix to get graph</span>
-        <span class="n">final_graph</span> <span class="o">=</span> <span class="n">p_matrix</span> <span class="o">&lt;=</span> <span class="n">alpha_level</span>
+        <span class="c1"># Threshold p_matrix to get graph (or val_matrix for significance == &#39;fixed_thres&#39;)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">two_sided</span><span class="p">:</span>
+                <span class="n">final_graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">alpha_level</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">final_graph</span> <span class="o">=</span> <span class="n">val_matrix</span> <span class="o">&gt;=</span> <span class="n">alpha_level</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">final_graph</span> <span class="o">=</span> <span class="n">p_matrix</span> <span class="o">&lt;=</span> <span class="n">alpha_level</span>
 
         <span class="c1"># Convert to string graph representation</span>
         <span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_to_string_graph</span><span class="p">(</span><span class="n">final_graph</span><span class="p">)</span>
@@ -2159,7 +2169,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
 <span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
 <span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        ambiguous_triples : list</span>
 <span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
@@ -2187,31 +2197,31 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                         <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
                         <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
 
-        <span class="c1"># else:</span>
-        <span class="c1">#     raise ValueError(&quot;pc_alpha=None not supported in PCMCIplus, choose&quot;</span>
-        <span class="c1">#                      &quot; 0 &lt; pc_alpha &lt; 1 (e.g., 0.01)&quot;)</span>
-
-        <span class="k">if</span> <span class="n">pc_alpha</span> <span class="o">&lt;</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="n">pc_alpha</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">elif</span> <span class="n">pc_alpha</span> <span class="o">&lt;</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="n">pc_alpha</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Choose 0 &lt;= pc_alpha &lt;= 1&quot;</span><span class="p">)</span>
 
         <span class="c1"># Check the limits on tau</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_check_tau_limits</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="c1"># Set the selected links</span>
-        <span class="c1"># _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)</span>
+        <span class="c1"># Set the link assumption</span>
         <span class="n">_int_link_assumptions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_link_assumptions</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
 
-        <span class="c1"># Step 1: Get a superset of lagged parents from run_pc_stable</span>
-        <span class="n">lagged_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-                                            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-                                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
-                                            <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                            <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">)</span>
 
+        <span class="c1">#</span>
+        <span class="c1"># Phase 1: Get a superset of lagged parents from run_pc_stable</span>
+        <span class="c1">#</span>
+        <span class="n">lagged_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
+                            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
+                            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
+                            <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
+                            <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">)</span>
+        <span class="c1"># Extract p- and val-matrix</span>
         <span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p_matrix</span>
         <span class="n">val_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_matrix</span>
 
-        <span class="c1"># Step 2+3+4: PC algorithm with contemp. conditions and MCI tests</span>
+        <span class="c1">#</span>
+        <span class="c1"># Phase 2: PC algorithm with contemp. conditions and MCI tests</span>
+        <span class="c1">#</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Step 2: PC algorithm with contemp. conditions &quot;</span>
                   <span class="s2">&quot;and MCI tests</span><span class="se">\n</span><span class="s2">##&quot;</span>
@@ -2232,6 +2242,180 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                   <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">fdr_method = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">fdr_method</span>
                   <span class="p">)</span>
 
+        <span class="n">skeleton_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcmciplus_mci_skeleton_phase</span><span class="p">(</span>
+                            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span> 
+                            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span> 
+                            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
+                            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
+                            <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span> 
+                            <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">,</span> 
+                            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
+                            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span> 
+                            <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span> 
+                            <span class="n">reset_lagged_links</span><span class="o">=</span><span class="n">reset_lagged_links</span><span class="p">,</span> 
+                            <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">,</span>
+                            <span class="n">p_matrix</span><span class="o">=</span><span class="n">p_matrix</span><span class="p">,</span> 
+                            <span class="n">val_matrix</span><span class="o">=</span><span class="n">val_matrix</span><span class="p">,</span>
+                            <span class="p">)</span>
+
+        <span class="c1">#</span>
+        <span class="c1"># Phase 3: Collider orientations (with MCI tests for default majority collider rule)</span>
+        <span class="c1">#</span>
+        <span class="n">colliders_step_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcmciplus_collider_phase</span><span class="p">(</span>
+                            <span class="n">skeleton_graph</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">],</span> 
+                            <span class="n">sepsets</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">],</span> 
+                            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span> 
+                            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span> 
+                            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
+                            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span> 
+                            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span> 
+                            <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
+                            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span> 
+                            <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">)</span>
+        
+        <span class="c1">#</span>
+        <span class="c1"># Phase 4: Meek rule orientations</span>
+        <span class="c1">#</span>
+        <span class="n">final_graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcmciplus_rule_orientation_phase</span><span class="p">(</span>
+                            <span class="n">collider_graph</span><span class="o">=</span><span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">],</span>
+                            <span class="n">ambiguous_triples</span><span class="o">=</span><span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">],</span> 
+                            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">)</span>
+
+        <span class="c1"># Store the parents in the pcmci member</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">all_lagged_parents</span> <span class="o">=</span> <span class="n">lagged_parents</span>
+
+        <span class="n">return_dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">final_graph</span><span class="p">,</span>
+            <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;sepsets&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">],</span>
+            <span class="p">}</span>
+
+        <span class="c1"># No confidence interval estimation here</span>
+        <span class="n">return_dict</span><span class="p">[</span><span class="s1">&#39;conf_matrix&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># Print the results</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">return_dict</span><span class="p">,</span> <span class="n">alpha_level</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">)</span>
+        
+        <span class="c1"># Return the dictionary</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="o">=</span> <span class="n">return_dict</span>
+        
+        <span class="k">return</span> <span class="n">return_dict</span></div>
+
+    
+        <span class="c1"># # Set the maximum condition dimension for Y and X</span>
+        <span class="c1"># max_conds_py = self._set_max_condition_dim(max_conds_py,</span>
+        <span class="c1">#                                            tau_min, tau_max)</span>
+        <span class="c1"># max_conds_px = self._set_max_condition_dim(max_conds_px,</span>
+        <span class="c1">#                                            tau_min, tau_max)</span>
+
+        <span class="c1"># if reset_lagged_links:</span>
+        <span class="c1">#     # Run PCalg on full graph, ignoring that some lagged links</span>
+        <span class="c1">#     # were determined as non-significant in PC1 step</span>
+        <span class="c1">#     links_for_pc = deepcopy(_int_link_assumptions)</span>
+        <span class="c1"># else:</span>
+        <span class="c1">#     # Run PCalg only on lagged parents found with PC1 </span>
+        <span class="c1">#     # plus all contemporaneous links</span>
+        <span class="c1">#     links_for_pc = {}  #deepcopy(lagged_parents)</span>
+        <span class="c1">#     for j in range(self.N):</span>
+        <span class="c1">#         links_for_pc[j] = {}</span>
+        <span class="c1">#         for parent in lagged_parents[j]:</span>
+        <span class="c1">#             if _int_link_assumptions[j][parent] in [&#39;-?&gt;&#39;, &#39;--&gt;&#39;]:</span>
+        <span class="c1">#                 links_for_pc[j][parent] = _int_link_assumptions[j][parent]</span>
+
+        <span class="c1">#         # Add contemporaneous links</span>
+        <span class="c1">#         for link in _int_link_assumptions[j]:</span>
+        <span class="c1">#             i, tau = link</span>
+        <span class="c1">#             link_type = _int_link_assumptions[j][link]</span>
+        <span class="c1">#             if abs(tau) == 0:</span>
+        <span class="c1">#                 links_for_pc[j][(i, 0)] = link_type</span>
+
+        <span class="c1"># results = self.run_pcalg(</span>
+        <span class="c1">#     link_assumptions=links_for_pc,</span>
+        <span class="c1">#     pc_alpha=pc_alpha,</span>
+        <span class="c1">#     tau_min=tau_min,</span>
+        <span class="c1">#     tau_max=tau_max,</span>
+        <span class="c1">#     max_conds_dim=max_conds_dim,</span>
+        <span class="c1">#     max_combinations=max_combinations,</span>
+        <span class="c1">#     lagged_parents=lagged_parents,</span>
+        <span class="c1">#     max_conds_py=max_conds_py,</span>
+        <span class="c1">#     max_conds_px=max_conds_px,</span>
+        <span class="c1">#     max_conds_px_lagged=max_conds_px_lagged,</span>
+        <span class="c1">#     mode=&#39;contemp_conds&#39;,</span>
+        <span class="c1">#     contemp_collider_rule=contemp_collider_rule,</span>
+        <span class="c1">#     conflict_resolution=conflict_resolution)</span>
+
+        <span class="c1"># graph = results[&#39;graph&#39;]</span>
+
+        <span class="c1"># # Update p_matrix and val_matrix with values from links_for_pc</span>
+        <span class="c1"># for j in range(self.N):</span>
+        <span class="c1">#     for link in links_for_pc[j]:</span>
+        <span class="c1">#         i, tau = link</span>
+        <span class="c1">#         if links_for_pc[j][link] not in [&#39;&lt;--&#39;, &#39;&lt;?-&#39;]:</span>
+        <span class="c1">#             p_matrix[i, j, abs(tau)] = results[&#39;p_matrix&#39;][i, j, abs(tau)]</span>
+        <span class="c1">#             val_matrix[i, j, abs(tau)] = results[&#39;val_matrix&#39;][i, j, </span>
+        <span class="c1">#                                                                abs(tau)]</span>
+
+        <span class="c1"># # Update p_matrix and val_matrix for indices of symmetrical links</span>
+        <span class="c1"># p_matrix[:, :, 0] = results[&#39;p_matrix&#39;][:, :, 0]</span>
+        <span class="c1"># val_matrix[:, :, 0] = results[&#39;val_matrix&#39;][:, :, 0]</span>
+
+        <span class="c1"># ambiguous = results[&#39;ambiguous_triples&#39;]</span>
+
+        <span class="c1"># conf_matrix = None</span>
+        <span class="c1"># TODO: implement confidence estimation, but how?</span>
+        <span class="c1"># if self.cond_ind_test.confidence is not False:</span>
+        <span class="c1">#     conf_matrix = results[&#39;conf_matrix&#39;]</span>
+
+        <span class="c1"># # Correct the p_matrix if there is a fdr_method</span>
+        <span class="c1"># if fdr_method != &#39;none&#39;:</span>
+        <span class="c1">#     p_matrix = self.get_corrected_pvalues(p_matrix=p_matrix, tau_min=tau_min, </span>
+        <span class="c1">#                                           tau_max=tau_max, </span>
+        <span class="c1">#                                           link_assumptions=_int_link_assumptions,</span>
+        <span class="c1">#                                           fdr_method=fdr_method)</span>
+
+        <span class="c1"># # Store the parents in the pcmci member</span>
+        <span class="c1"># self.all_lagged_parents = lagged_parents</span>
+
+        <span class="c1"># # p_matrix=results[&#39;p_matrix&#39;]</span>
+        <span class="c1"># # val_matrix=results[&#39;val_matrix&#39;]</span>
+
+        <span class="c1"># Cache the resulting values in the return dictionary</span>
+        <span class="c1"># return_dict = {&#39;graph&#39;: graph,</span>
+        <span class="c1">#                &#39;val_matrix&#39;: val_matrix,</span>
+        <span class="c1">#                &#39;p_matrix&#39;: p_matrix,</span>
+        <span class="c1">#                &#39;ambiguous_triples&#39;: ambiguous,</span>
+        <span class="c1">#                &#39;conf_matrix&#39;: conf_matrix}</span>
+
+        <span class="c1"># # Print the results</span>
+        <span class="c1"># if self.verbosity &gt; 0:</span>
+        <span class="c1">#     self.print_results(return_dict, alpha_level=pc_alpha)</span>
+        <span class="c1"># # Return the dictionary</span>
+        <span class="c1"># self.results = return_dict</span>
+        <span class="c1"># return return_dict</span>
+
+    <span class="k">def</span> <span class="nf">_pcmciplus_mci_skeleton_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+            <span class="n">lagged_parents</span><span class="p">,</span> 
+            <span class="n">link_assumptions</span><span class="p">,</span> 
+            <span class="n">pc_alpha</span><span class="p">,</span>
+            <span class="n">tau_min</span><span class="p">,</span> 
+            <span class="n">tau_max</span><span class="p">,</span> 
+            <span class="n">max_conds_dim</span><span class="p">,</span> 
+            <span class="n">max_combinations</span><span class="p">,</span> 
+            <span class="n">max_conds_py</span><span class="p">,</span> 
+            <span class="n">max_conds_px</span><span class="p">,</span> 
+            <span class="n">max_conds_px_lagged</span><span class="p">,</span> 
+            <span class="n">reset_lagged_links</span><span class="p">,</span>
+            <span class="n">fdr_method</span><span class="p">,</span>
+            <span class="n">p_matrix</span><span class="p">,</span> 
+            <span class="n">val_matrix</span><span class="p">,</span>
+            <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI Skeleton phase.&quot;&quot;&quot;</span>
+
         <span class="c1"># Set the maximum condition dimension for Y and X</span>
         <span class="n">max_conds_py</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_py</span><span class="p">,</span>
                                                    <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
@@ -2241,7 +2425,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">reset_lagged_links</span><span class="p">:</span>
             <span class="c1"># Run PCalg on full graph, ignoring that some lagged links</span>
             <span class="c1"># were determined as non-significant in PC1 step</span>
-            <span class="n">links_for_pc</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">_int_link_assumptions</span><span class="p">)</span>
+            <span class="n">links_for_pc</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="c1"># Run PCalg only on lagged parents found with PC1 </span>
             <span class="c1"># plus all contemporaneous links</span>
@@ -2249,75 +2433,120 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
             <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
                 <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
                 <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="k">if</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">]:</span>
-                        <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">]:</span>
+                        <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span>
 
                 <span class="c1"># Add contemporaneous links</span>
-                <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
+                <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
                     <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link</span>
-                    <span class="n">link_type</span> <span class="o">=</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">link</span><span class="p">]</span>
+                    <span class="n">link_type</span> <span class="o">=</span> <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">link</span><span class="p">]</span>
                     <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                         <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">=</span> <span class="n">link_type</span>
 
-        <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pcalg</span><span class="p">(</span>
-            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">links_for_pc</span><span class="p">,</span>
+
+        <span class="k">if</span> <span class="n">max_conds_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">max_conds_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span>
+
+        <span class="k">if</span> <span class="n">max_combinations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">max_combinations</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+
+        <span class="n">initial_graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict_to_graph</span><span class="p">(</span><span class="n">links_for_pc</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
+
+        <span class="n">skeleton_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_skeleton</span><span class="p">(</span>
+            <span class="n">initial_graph</span><span class="o">=</span><span class="n">initial_graph</span><span class="p">,</span>
+            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
+            <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;contemp_conds&#39;</span><span class="p">,</span>
             <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
             <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
             <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
             <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
             <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">,</span>
-            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
             <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
             <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
             <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
-            <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;contemp_conds&#39;</span><span class="p">,</span>
-            <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">,</span>
-            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">)</span>
+            <span class="p">)</span>
 
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span>
+        <span class="c1"># Symmetrize p_matrix and val_matrix coming from skeleton</span>
+        <span class="n">symmetrized_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symmetrize_p_and_val_matrix</span><span class="p">(</span>
+                            <span class="n">p_matrix</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span> 
+                            <span class="n">val_matrix</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span> 
+                            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">links_for_pc</span><span class="p">,</span>
+                            <span class="n">conf_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 
-        <span class="c1"># Update p_matrix and val_matrix with values from links_for_pc</span>
+        <span class="c1"># Update p_matrix and val_matrix with values from skeleton phase</span>
+        <span class="c1"># Contemporaneous entries (not filled in run_pc_stable lagged phase)</span>
+        <span class="n">p_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">val_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
+
+        <span class="c1"># Update all entries computed in the MCI step </span>
+        <span class="c1"># (these are in links_for_pc); values for entries</span>
+        <span class="c1"># that were removed in the lagged-condition phase are kept from before</span>
         <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
             <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
                 <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link</span>
                 <span class="k">if</span> <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">link</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;?-&#39;</span><span class="p">]:</span>
-                    <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
-                    <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> 
-                                                                       <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
-
-        <span class="c1"># Update p_matrix and val_matrix for indices of symmetrical links</span>
-        <span class="n">p_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
-        <span class="n">val_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">ambiguous</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span>
+                    <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
+                    <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> 
+                                                                 <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
 
-        <span class="n">conf_matrix</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="c1"># TODO: implement confidence estimation, but how?</span>
-        <span class="c1"># if self.cond_ind_test.confidence is not False:</span>
-        <span class="c1">#     conf_matrix = results[&#39;conf_matrix&#39;]</span>
-
-        <span class="c1"># Correct the p_matrix if there is a fdr_method</span>
+        <span class="c1"># Optionally correct the p_matrix</span>
         <span class="k">if</span> <span class="n">fdr_method</span> <span class="o">!=</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
             <span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_corrected_pvalues</span><span class="p">(</span><span class="n">p_matrix</span><span class="o">=</span><span class="n">p_matrix</span><span class="p">,</span> <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
                                                   <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
-                                                  <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span>
+                                                  <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
                                                   <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
 
-        <span class="c1"># Store the parents in the pcmci member</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_lagged_parents</span> <span class="o">=</span> <span class="n">lagged_parents</span>
+        <span class="c1"># Update matrices</span>
+        <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">p_matrix</span>
+        <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span>
+
+        <span class="k">return</span> <span class="n">skeleton_results</span>
+
+
+    <span class="k">def</span> <span class="nf">_pcmciplus_collider_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skeleton_graph</span><span class="p">,</span> <span class="n">sepsets</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span>
+        <span class="n">pc_alpha</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span> <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span>
+        <span class="n">conflict_resolution</span><span class="p">,</span> <span class="n">contemp_collider_rule</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI collider phase.&quot;&quot;&quot;</span>    
+
+        <span class="c1"># Set the maximum condition dimension for Y and X</span>
+        <span class="n">max_conds_py</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_py</span><span class="p">,</span>
+                                                   <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+        <span class="n">max_conds_px</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_px</span><span class="p">,</span>
+                                                   <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+
+        <span class="c1"># Now change assumed links marks</span>
+        <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;o?o&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;o-o&#39;</span>
+        <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;--&gt;&#39;</span>
+        <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;&lt;?-&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;--&#39;</span>
+
+        <span class="n">colliders_step_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_colliders</span><span class="p">(</span>
+            <span class="n">graph</span><span class="o">=</span><span class="n">skeleton_graph</span><span class="p">,</span>
+            <span class="n">sepsets</span><span class="o">=</span><span class="n">sepsets</span><span class="p">,</span>
+            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
+            <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;contemp_conds&#39;</span><span class="p">,</span>
+            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
+            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
+            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
+            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
+            <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
+            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span>
+            <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">colliders_step_results</span>
+
+    <span class="k">def</span> <span class="nf">_pcmciplus_rule_orientation_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">collider_graph</span><span class="p">,</span>
+         <span class="n">ambiguous_triples</span><span class="p">,</span> <span class="n">conflict_resolution</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI rule orientation phase.&quot;&quot;&quot;</span>  
+
+        <span class="n">final_graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_rules_timeseries</span><span class="p">(</span>
+            <span class="n">graph</span><span class="o">=</span><span class="n">collider_graph</span><span class="p">,</span>
+            <span class="n">ambiguous_triples</span><span class="o">=</span><span class="n">ambiguous_triples</span><span class="p">,</span>
+            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">final_graph</span>
 
-        <span class="c1"># Cache the resulting values in the return dictionary</span>
-        <span class="n">return_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-                       <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">val_matrix</span><span class="p">,</span>
-                       <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">p_matrix</span><span class="p">,</span>
-                       <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">ambiguous</span><span class="p">,</span>
-                       <span class="s1">&#39;conf_matrix&#39;</span><span class="p">:</span> <span class="n">conf_matrix</span><span class="p">}</span>
-        <span class="c1"># Print the results</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">return_dict</span><span class="p">,</span> <span class="n">alpha_level</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">)</span>
-        <span class="c1"># Return the dictionary</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="o">=</span> <span class="n">return_dict</span>
-        <span class="k">return</span> <span class="n">return_dict</span></div>
 
 <div class="viewcode-block" id="PCMCI.run_pcalg"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_pcalg">[docs]</a>    <span class="k">def</span> <span class="nf">run_pcalg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
                     <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
@@ -2405,7 +2634,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
 <span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
 <span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        ambiguous_triples : list</span>
 <span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
@@ -2458,16 +2687,16 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
         <span class="p">)</span>
 
         <span class="n">skeleton_graph</span> <span class="o">=</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span>
-        <span class="n">sepset</span> <span class="o">=</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">]</span>
+        <span class="n">sepsets</span> <span class="o">=</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">]</span>
 
-        <span class="c1"># Now change assumed links mark</span>
+        <span class="c1"># Now change assumed links marks</span>
         <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;o?o&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;o-o&#39;</span>
         <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;--&gt;&#39;</span>
         <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;&lt;?-&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;--&#39;</span>
 
         <span class="n">colliders_step_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_colliders</span><span class="p">(</span>
             <span class="n">graph</span><span class="o">=</span><span class="n">skeleton_graph</span><span class="p">,</span>
-            <span class="n">sepset</span><span class="o">=</span><span class="n">sepset</span><span class="p">,</span>
+            <span class="n">sepsets</span><span class="o">=</span><span class="n">sepsets</span><span class="p">,</span>
             <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
             <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span>
             <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
@@ -2502,7 +2731,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
             <span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph_str</span><span class="p">,</span>
             <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span>
             <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span>
-            <span class="s1">&#39;sepset&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;sepsets&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">],</span>
             <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">],</span>
         <span class="p">}</span>
 
@@ -2550,7 +2779,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
 <span class="sd">        p_matrix : array of shape [N, N, 1]</span>
 <span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        ambiguous_triples : list</span>
 <span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
@@ -2563,16 +2792,13 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                   <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">)</span>
 
         <span class="c1"># Remove tau-dimension</span>
-        <span class="c1"># results[&#39;graph&#39;] = results[&#39;graph&#39;].squeeze()</span>
-        <span class="c1"># results[&#39;val_matrix&#39;] = results[&#39;val_matrix&#39;].squeeze()</span>
-        <span class="c1"># results[&#39;p_matrix&#39;] = results[&#39;p_matrix&#39;].squeeze()</span>
-        <span class="n">old_sepsets</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">old_sepset</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">:</span>
-           <span class="n">new_sepset</span> <span class="o">=</span> <span class="p">(</span><span class="n">old_sepset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">old_sepset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-           <span class="n">conds</span> <span class="o">=</span> <span class="p">[</span><span class="n">cond</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">cond</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">[</span><span class="n">old_sepset</span><span class="p">]]</span>
+        <span class="n">old_sepsets</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">old_sepsets</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">:</span>
+           <span class="n">new_sepsets</span> <span class="o">=</span> <span class="p">(</span><span class="n">old_sepsets</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">old_sepsets</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+           <span class="n">conds</span> <span class="o">=</span> <span class="p">[</span><span class="n">cond</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">cond</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">[</span><span class="n">old_sepsets</span><span class="p">]]</span>
 
-           <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">][</span><span class="n">new_sepset</span><span class="p">]</span> <span class="o">=</span> <span class="n">conds</span>
+           <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">][</span><span class="n">new_sepsets</span><span class="p">]</span> <span class="o">=</span> <span class="n">conds</span>
 
         <span class="n">ambiguous_triples</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
         <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
@@ -2586,7 +2812,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
 
     <span class="k">def</span> <span class="nf">_run_pcalg_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span>
-                        <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
+                        <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI conditional independence tests within PCMCIplus or PC algorithm.</span>
 
 <span class="sd">        Parameters</span>
@@ -2612,15 +2838,16 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            tests. If None is passed, this number is equal to max_conds_px.</span>
 <span class="sd">        tau_max : int</span>
 <span class="sd">            Maximum time lag.</span>
+<span class="sd">        alpha_or_thres : float</span>
+<span class="sd">            Significance level (if significance=&#39;analytic&#39; or &#39;shuffle_test&#39;) or</span>
+<span class="sd">            threshold (if significance=&#39;fixed_thres&#39;). If given, run_test returns</span>
+<span class="sd">            the test decision dependent=True/False.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Test statistic value.</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            Test statistic p-value.</span>
-<span class="sd">        Z : list</span>
-<span class="sd">            List of conditions.</span>
+<span class="sd">        val, pval, Z, [dependent] : Tuple of floats, list, and bool</span>
+<span class="sd">            The test statistic value and the p-value and list of conditions. If alpha_or_thres is</span>
+<span class="sd">            given, run_test also returns the test decision dependent=True/False.             </span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
         <span class="c1"># Perform independence test adding lagged parents</span>
@@ -2650,13 +2877,15 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">abstau</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span> <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">abstau</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;-&#39;</span><span class="p">:</span>
             <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span> 
             <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
+            <span class="n">dependent</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span>
+            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span>
                                                 <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
+                                                <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">alpha_or_thres</span><span class="p">,</span>
                                                 <span class="c1"># verbosity=self.verbosity</span>
                                                 <span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span>
+        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">dependent</span>
 
     <span class="k">def</span> <span class="nf">_print_triple_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">triple</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">n_triples</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Print info about the current triple being tested.</span>
@@ -2768,7 +2997,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
 <span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
 <span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span>
@@ -2791,23 +3020,25 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
             <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
 
         <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+        
         <span class="n">val_min</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
         <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
             <span class="n">val_min</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
                           <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">))}</span>
 
         <span class="c1"># Initialize p-values. Set to 1 if there&#39;s no link in the initial graph</span>
-        <span class="n">pvalues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">pvalues</span><span class="p">[</span><span class="n">graph</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+        <span class="n">p_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="n">p_matrix</span><span class="p">[</span><span class="n">graph</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+
         <span class="n">pval_max</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
         <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
             <span class="n">pval_max</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="mf">0.</span>
                            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">))}</span>
 
-        <span class="c1"># TODO: Remove sepset alltogether?</span>
+        <span class="c1"># TODO: Remove sepsets alltogether?</span>
         <span class="c1"># Intialize sepsets that store the conditions that make i and j</span>
         <span class="c1"># independent</span>
-        <span class="n">sepset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepset</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+        <span class="n">sepsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepsets</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">--------------------------&quot;</span><span class="p">)</span>
@@ -2857,9 +3088,11 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                             <span class="k">break</span>
 
                         <span class="c1"># Run MCI test</span>
-                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                            <span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span>
-                            <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span>
+                            <span class="n">i</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="o">=</span><span class="n">abstau</span><span class="p">,</span> <span class="n">j</span><span class="o">=</span><span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="o">=</span><span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span> 
+                            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
+                            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                         <span class="c1"># Store minimum test statistic value for sorting adjt</span>
                         <span class="c1"># (only internally used)</span>
@@ -2872,8 +3105,8 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                                             <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)))</span>
 
                         <span class="c1"># Store max. p-value and corresponding value to return</span>
-                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;=</span> <span class="n">pvalues</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]:</span>
-                            <span class="n">pvalues</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
+                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;=</span> <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]:</span>
+                            <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
                             <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
 
                         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
@@ -2881,16 +3114,18 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                                   <span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
 
                         <span class="c1"># If conditional independence is found, remove link</span>
-                        <span class="c1"># from graph and store sepset</span>
-                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pc_alpha</span><span class="p">:</span>
+                        <span class="c1"># from graph and store sepsets</span>
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; pc_alpha:</span>
                             <span class="n">nonsig</span> <span class="o">=</span> <span class="kc">True</span>
                             <span class="k">if</span> <span class="n">abstau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                                 <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="n">sepset</span><span class="p">[</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="n">sepsets</span><span class="p">[</span>
                                     <span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
+                                <span class="c1"># Also store p-value in other contemp. entry</span>
+                                <span class="n">p_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
                             <span class="k">else</span><span class="p">:</span>
                                 <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
                             <span class="k">break</span>
 
                     <span class="c1"># Print the results if needed</span>
@@ -2929,13 +3164,13 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                     <span class="s2">&quot; reached.&quot;</span> <span class="o">%</span> <span class="n">max_conds_dim</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-                <span class="s1">&#39;sepset&#39;</span><span class="p">:</span> <span class="n">sepset</span><span class="p">,</span>
-                <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">pvalues</span><span class="p">,</span>
+                <span class="s1">&#39;sepsets&#39;</span><span class="p">:</span> <span class="n">sepsets</span><span class="p">,</span>
+                <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">p_matrix</span><span class="p">,</span>
                 <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">val_matrix</span><span class="p">,</span>
                 <span class="p">}</span>
 
-    <span class="k">def</span> <span class="nf">_get_sepset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns initial sepset.</span>
+    <span class="k">def</span> <span class="nf">_get_sepsets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns initial sepsets.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
@@ -2946,15 +3181,15 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        sepset : dict</span>
-<span class="sd">            Initialized sepset.</span>
+<span class="sd">        sepsets : dict</span>
+<span class="sd">            Initialized sepsets.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">sepset</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">),</span> <span class="p">[])</span>
+        <span class="n">sepsets</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">),</span> <span class="p">[])</span>
                        <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
                        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
                        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)])</span>
 
-        <span class="k">return</span> <span class="n">sepset</span>
+        <span class="k">return</span> <span class="n">sepsets</span>
 
     <span class="k">def</span> <span class="nf">_find_unshielded_triples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Find unshielded triples i_tau o-(&gt;) k_t o-o j_t with i_tau -/- j_t.</span>
@@ -2998,7 +3233,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
     <span class="k">def</span> <span class="nf">_pcalg_colliders</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                         <span class="n">graph</span><span class="p">,</span>
-                        <span class="n">sepset</span><span class="p">,</span>
+                        <span class="n">sepsets</span><span class="p">,</span>
                         <span class="n">lagged_parents</span><span class="p">,</span>
                         <span class="n">mode</span><span class="p">,</span>
                         <span class="n">pc_alpha</span><span class="p">,</span>
@@ -3016,7 +3251,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">        ----------</span>
 <span class="sd">        graph : array of shape (N, N, tau_max+1)</span>
 <span class="sd">            Current graph.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        lagged_parents : dictionary</span>
 <span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing</span>
@@ -3052,7 +3287,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">        -------</span>
 <span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
 <span class="sd">            Resulting causal graph, see description above for interpretation.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        ambiguous_triples : list</span>
 <span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
@@ -3079,11 +3314,11 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
         <span class="k">if</span> <span class="n">contemp_collider_rule</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">contemp_collider_rule</span> <span class="o">==</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
             <span class="c1"># Standard collider orientation rule of PC algorithm</span>
-            <span class="c1"># If k_t not in sepset(i_tau, j_t), then orient</span>
+            <span class="c1"># If k_t not in sepsets(i_tau, j_t), then orient</span>
             <span class="c1"># as i_tau --&gt; k_t &lt;-- j_t</span>
             <span class="k">for</span> <span class="n">itaukj</span> <span class="ow">in</span> <span class="n">triples</span><span class="p">:</span>
                 <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">itaukj</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
                     <span class="n">v_structures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itaukj</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="c1"># Apply &#39;majority&#39; or &#39;conservative&#39; rule to orient colliders          </span>
@@ -3142,15 +3377,17 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                 <span class="c1"># Test which neighbor subsets separate i and j</span>
                 <span class="n">neighbor_sepsets</span> <span class="o">=</span> <span class="p">[]</span>
                 <span class="k">for</span> <span class="n">iss</span><span class="p">,</span> <span class="n">S</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">neighbor_subsets</span><span class="p">):</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span>
-                        <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span>
+                            <span class="n">i</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="o">=</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="o">=</span><span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="o">=</span><span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span> 
+                            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
+                            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_info</span><span class="p">(</span><span class="n">Z</span><span class="o">=</span><span class="n">S</span><span class="p">,</span> <span class="n">comb_index</span><span class="o">=</span><span class="n">iss</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span>
                                               <span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
 
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1">#pval &gt; pc_alpha:</span>
                         <span class="n">neighbor_sepsets</span> <span class="o">+=</span> <span class="p">[</span><span class="n">S</span><span class="p">]</span>
 
                 <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbor_sepsets</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
@@ -3178,12 +3415,12 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                     <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
                                     <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is = 0 --&gt; collider &quot;</span>
                                     <span class="s2">&quot;found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also delete (k, 0) from sepset (if present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                            <span class="c1"># Also delete (k, 0) from sepsets (if present)</span>
+                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                             <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
+                                    <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                         <span class="k">elif</span> <span class="n">fraction</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                             <span class="c1"># If (k, 0) is in all of the neighbor_sepsets,</span>
                             <span class="c1"># leave unoriented</span>
@@ -3192,12 +3429,12 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                     <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
                                     <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is = 1 --&gt; &quot;</span>
                                     <span class="s2">&quot;non-collider found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also add (k, 0) to sepset (if not present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                            <span class="c1"># Also add (k, 0) to sepsets (if not present)</span>
+                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                             <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
+                                    <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                         <span class="k">else</span><span class="p">:</span>
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span>
@@ -3230,12 +3467,12 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                     <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
                                     <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is &lt; 0.5 &quot;</span>
                                     <span class="s2">&quot;--&gt; collider found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also delete (k, 0) from sepset (if present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                            <span class="c1"># Also delete (k, 0) from sepsets (if present)</span>
+                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                             <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
+                                    <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                         <span class="k">elif</span> <span class="n">fraction</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">:</span>
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span>
@@ -3243,12 +3480,12 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                     <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is &gt; 0.5 &quot;</span>
                                     <span class="s2">&quot;--&gt; non-collider found&quot;</span> <span class="o">%</span>
                                     <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also add (k, 0) to sepset (if not present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                            <span class="c1"># Also add (k, 0) to sepsets (if not present)</span>
+                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                             <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
+                                    <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
 
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">v_structures</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Orienting links among colliders:&quot;</span><span class="p">)</span>
@@ -3319,7 +3556,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
             <span class="bp">self</span><span class="o">.</span><span class="n">_print_parents</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">adjt</span><span class="p">,</span> <span class="n">val_min</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pval_max</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-                <span class="s1">&#39;sepset&#39;</span><span class="p">:</span> <span class="n">sepset</span><span class="p">,</span>
+                <span class="s1">&#39;sepsets&#39;</span><span class="p">:</span> <span class="n">sepsets</span><span class="p">,</span>
                 <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">ambiguous_triples</span><span class="p">,</span>
                 <span class="p">}</span>
 
@@ -3689,9 +3926,9 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                 <span class="n">parents</span> <span class="o">=</span> <span class="p">[]</span>
                 <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">dag</span><span class="p">[:,</span><span class="n">j</span><span class="p">,:]</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">)):</span>
                     <span class="n">parents</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]</span> <span class="o">+=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span>
+                <span class="n">score_j</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span>
                         <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+                <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]</span> <span class="o">+=</span> <span class="n">score_j</span>
             <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]</span> <span class="o">/=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
 
         <span class="c1"># Record the optimal alpha value</span>
@@ -3715,6 +3952,8 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
     <span class="kn">from</span> <span class="nn">tigramite.independence_tests.parcorr</span> <span class="kn">import</span> <span class="n">ParCorr</span>
+    <span class="kn">from</span> <span class="nn">tigramite.independence_tests.cmiknn</span> <span class="kn">import</span> <span class="n">CMIknn</span>
+
     <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
     <span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
     <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
@@ -3727,54 +3966,62 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
     <span class="k">def</span> <span class="nf">nonlin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mf">5.</span> <span class="o">*</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mf">20.</span><span class="p">))</span>
 
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">20000</span>
+    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
     <span class="n">data</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
     <span class="c1"># Simple sun</span>
-    <span class="n">data</span><span class="p">[:,</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">*</span><span class="mi">20</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.1</span><span class="o">*</span><span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">))</span>
+    <span class="n">data</span><span class="p">[:,</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">))</span> <span class="c1"># np.sin(np.arange(T)*20/np.pi) + 0.1*random_state.standard_normal((T))</span>
     <span class="n">c</span> <span class="o">=</span> <span class="mf">0.8</span>
     <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span>
         <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.4</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.4</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.6</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.3</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
+        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
+        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.6</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.3</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="c1">#+ c*data[t-1,3]</span>
     <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">var_names</span><span class="o">=</span><span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$X^0$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$X^1$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$X^2$&#39;</span><span class="p">,</span> <span class="s1">&#39;Sun&#39;</span><span class="p">])</span>
     <span class="c1"># tp.plot_timeseries(dataframe); plt.show()</span>
 
-    <span class="n">parcorr</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
+    <span class="n">ci_test</span> <span class="o">=</span> <span class="n">CMIknn</span><span class="p">(</span><span class="n">significance</span><span class="o">=</span><span class="s2">&quot;fixed_thres&quot;</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>   <span class="c1">#</span>
+    <span class="c1"># ci_test = ParCorr() #significance=&quot;fixed_thres&quot;)   #</span>
     <span class="c1"># dataframe_nosun = pp.DataFrame(data[:,[0,1,2]], var_names=[r&#39;$X^0$&#39;, r&#39;$X^1$&#39;, r&#39;$X^2$&#39;])</span>
     <span class="c1"># pcmci_parcorr = PCMCI(</span>
     <span class="c1">#     dataframe=dataframe_nosun, </span>
     <span class="c1">#     cond_ind_test=parcorr,</span>
     <span class="c1">#     verbosity=0)</span>
-    <span class="n">tau_max</span> <span class="o">=</span> <span class="mi">2</span>
+    <span class="n">tau_max</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1">#2</span>
     <span class="c1"># results = pcmci_parcorr.run_pcmci(tau_max=tau_max, pc_alpha=0.2, alpha_level = 0.01)</span>
     <span class="c1"># Remove parents of variable 3</span>
     <span class="c1"># Only estimate parents of variables 0, 1, 2</span>
-    <span class="n">link_assumptions</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">j</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]:</span>
-            <span class="c1"># Directed lagged links</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span><span class="p">):</span> <span class="s1">&#39;-?&gt;&#39;</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-                             <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)}</span>
-            <span class="c1"># Unoriented contemporaneous links</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span> <span class="s1">&#39;o?o&#39;</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">var</span> <span class="o">!=</span> <span class="n">j</span><span class="p">})</span>
-            <span class="c1"># Directed lagged and contemporaneous links from the sun (3)</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span><span class="p">):</span> <span class="s1">&#39;-?&gt;&#39;</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-                             <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)})</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">)</span>
+    <span class="n">link_assumptions</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1">#{}</span>
+    <span class="c1"># for j in range(4):</span>
+    <span class="c1">#     if j in [0, 1, 2]:</span>
+    <span class="c1">#         # Directed lagged links</span>
+    <span class="c1">#         link_assumptions[j] = {(var, -lag): &#39;-?&gt;&#39; for var in [0, 1, 2]</span>
+    <span class="c1">#                          for lag in range(1, tau_max + 1)}</span>
+    <span class="c1">#         # Unoriented contemporaneous links</span>
+    <span class="c1">#         link_assumptions[j].update({(var, 0): &#39;o?o&#39; for var in [0, 1, 2] if var != j})</span>
+    <span class="c1">#         # Directed lagged and contemporaneous links from the sun (3)</span>
+    <span class="c1">#         link_assumptions[j].update({(var, -lag): &#39;-?&gt;&#39; for var in [3]</span>
+    <span class="c1">#                          for lag in range(0, tau_max + 1)})</span>
+    <span class="c1">#     else:</span>
+    <span class="c1">#         link_assumptions[j] = {}</span>
+
+    <span class="c1"># for j in link_assumptions:</span>
+    <span class="c1">#     print(link_assumptions[j])</span>
     <span class="n">pcmci_parcorr</span> <span class="o">=</span> <span class="n">PCMCI</span><span class="p">(</span>
         <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
-        <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">parcorr</span><span class="p">,</span>
-        <span class="n">verbosity</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">results</span> <span class="o">=</span> <span class="n">pcmci_parcorr</span><span class="o">.</span><span class="n">run_pcmciplus</span><span class="p">(</span><span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> 
-                                      <span class="c1"># link_assumptions=link_assumptions</span>
-                                      <span class="p">)</span> <span class="c1">#, alpha_level = 0.01)</span>
+        <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">ci_test</span><span class="p">,</span>
+        <span class="n">verbosity</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">pcmci_parcorr</span><span class="o">.</span><span class="n">run_pcmciplus</span><span class="p">(</span><span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
+                    <span class="n">pc_alpha</span><span class="o">=</span><span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">],</span> 
+                    <span class="n">reset_lagged_links</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span>
+                    <span class="p">)</span> <span class="c1">#, alpha_level = 0.01)</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">][:,</span><span class="mi">3</span><span class="p">,:])</span>
+    <span class="c1"># print(results[&#39;graph&#39;][:,3,:])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][:,:,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][:,:,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">][:,:,</span><span class="mi">0</span><span class="p">])</span>
+
     <span class="c1"># Plot time series graph</span>
-    <span class="c1"># tp.plot_time_series_graph(</span>
+    <span class="c1"># tp.plot_graph(</span>
     <span class="c1">#     val_matrix=results[&#39;val_matrix&#39;],</span>
     <span class="c1">#     graph=results[&#39;graph&#39;],</span>
     <span class="c1">#     var_names=[r&#39;$X^0$&#39;, r&#39;$X^1$&#39;, r&#39;$X^2$&#39;, &#39;Sun&#39;],</span>
@@ -3880,8 +4127,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/plotting.html b/docs/_build/_modules/tigramite/plotting.html
index 1d1acc26..e7527d0a 100644
--- a/docs/_build/_modules/tigramite/plotting.html
+++ b/docs/_build/_modules/tigramite/plotting.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -4725,8 +4727,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/rpcmci.html b/docs/_build/_modules/tigramite/rpcmci.html
index d298a605..5c943291 100644
--- a/docs/_build/_modules/tigramite/rpcmci.html
+++ b/docs/_build/_modules/tigramite/rpcmci.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -32,7 +34,7 @@
   <h1>Source code for tigramite.rpcmci</h1><div class="highlight"><pre>
 <span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
 
-<span class="c1"># Authors: Elena Saggioro, Matthias Bruhns, Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
+<span class="c1"># Authors: Elena Saggioro, Sagar Simha, Matthias Bruhns, Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
 <span class="c1">#</span>
 <span class="c1"># License: GNU General Public License v3.0</span>
 
@@ -111,7 +113,11 @@ <h1>Source code for tigramite.rpcmci</h1><div class="highlight"><pre>
 
         <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">!=</span> <span class="s1">&#39;single&#39;</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only single time series data allowed for RPCMCI.&quot;</span><span class="p">)</span>
+   
+        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">has_vector_data</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only scalar data allowed for RPCMCI.&quot;</span><span class="p">)</span>
         
+               
         <span class="c1"># Masking is not available in RPCMCI, but missing values can be specified</span>
         <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)}</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">missing_flag</span>
@@ -547,8 +553,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_modules/tigramite/toymodels/structural_causal_processes.html b/docs/_build/_modules/tigramite/toymodels/structural_causal_processes.html
index 8ecaa4a2..76b23dc3 100644
--- a/docs/_build/_modules/tigramite/toymodels/structural_causal_processes.html
+++ b/docs/_build/_modules/tigramite/toymodels/structural_causal_processes.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -1233,8 +1235,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_build/_sources/index.rst.txt b/docs/_build/_sources/index.rst.txt
index af04214a..6ed3fa3a 100644
--- a/docs/_build/_sources/index.rst.txt
+++ b/docs/_build/_sources/index.rst.txt
@@ -31,6 +31,7 @@ TIGRAMITE
 
 Tigramite is a causal time series analysis python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use these graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
 
+- Overview: Runge, J., Gerhardus, A., Varando, G. et al. Causal inference for time series. Nat Rev Earth Environ (2023). https://doi.org/10.1038/s43017-023-00431-y
 
 - PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019). https://advances.sciencemag.org/content/5/11/eaau4996
 
@@ -40,7 +41,6 @@ Tigramite is a causal time series analysis python package. It allows to efficien
 
 - RPCMCI: Elena Saggioro, Jana de Wiljes, Marlene Kretschmer, Jakob Runge; Reconstructing regime-dependent causal relationships from observational time series. Chaos 1 November 2020; 30 (11): 113115. https://doi.org/10.1063/5.0020538
 
-
 - Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310. https://aip.scitation.org/doi/10.1063/1.5025050
 
 - Nature Communications Perspective paper: https://www.nature.com/articles/s41467-019-10105-3
diff --git a/docs/_build/_static/ajax-loader.gif b/docs/_build/_static/ajax-loader.gif
new file mode 100644
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diff --git a/docs/_build/_static/alabaster.css b/docs/_build/_static/alabaster.css
index 0eddaeb0..bc420a48 100644
--- a/docs/_build/_static/alabaster.css
+++ b/docs/_build/_static/alabaster.css
@@ -1,17 +1,33 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 @import url("basic.css");
 
 /* -- page layout ----------------------------------------------------------- */
 
 body {
-    font-family: Georgia, serif;
+    font-family: 'goudy old style', 'minion pro', 'bell mt', Georgia, 'Hiragino Mincho Pro', serif;
     font-size: 17px;
-    background-color: #fff;
+    background-color: white;
     color: #000;
     margin: 0;
     padding: 0;
 }
 
-
 div.document {
     width: 940px;
     margin: 30px auto 0 auto;
@@ -28,8 +44,6 @@ div.bodywrapper {
 
 div.sphinxsidebar {
     width: 220px;
-    font-size: 14px;
-    line-height: 1.5;
 }
 
 hr {
@@ -37,7 +51,7 @@ hr {
 }
 
 div.body {
-    background-color: #fff;
+    background-color: #ffffff;
     color: #3E4349;
     padding: 0 30px 0 30px;
 }
@@ -58,11 +72,6 @@ div.footer a {
     color: #888;
 }
 
-p.caption {
-    font-family: inherit;
-    font-size: inherit;
-}
-
 
 div.relations {
     display: none;
@@ -79,6 +88,11 @@ div.sphinxsidebar a:hover {
     border-bottom: 1px solid #999;
 }
 
+div.sphinxsidebar {
+    font-size: 14px;
+    line-height: 1.5;
+}
+
 div.sphinxsidebarwrapper {
     padding: 18px 10px;
 }
@@ -107,7 +121,7 @@ div.sphinxsidebarwrapper p.blurb {
 
 div.sphinxsidebar h3,
 div.sphinxsidebar h4 {
-    font-family: Georgia, serif;
+    font-family: 'Garamond', 'Georgia', serif;
     color: #444;
     font-size: 24px;
     font-weight: normal;
@@ -151,7 +165,7 @@ div.sphinxsidebar ul li.toctree-l2 > a {
 
 div.sphinxsidebar input {
     border: 1px solid #CCC;
-    font-family: Georgia, serif;
+    font-family: 'goudy old style', 'minion pro', 'bell mt', Georgia, 'Hiragino Mincho Pro', serif;
     font-size: 1em;
 }
 
@@ -166,19 +180,6 @@ div.sphinxsidebar hr {
     width: 50%;
 }
 
-div.sphinxsidebar .badge {
-    border-bottom: none;
-}
-
-div.sphinxsidebar .badge:hover {
-    border-bottom: none;
-}
-
-/* To address an issue with donation coming after search */
-div.sphinxsidebar h3.donation {
-    margin-top: 10px;
-}
-
 /* -- body styles ----------------------------------------------------------- */
 
 a {
@@ -197,7 +198,7 @@ div.body h3,
 div.body h4,
 div.body h5,
 div.body h6 {
-    font-family: Georgia, serif;
+    font-family: 'Garamond', 'Georgia', serif;
     font-weight: normal;
     margin: 30px 0px 10px 0px;
     padding: 0;
@@ -228,17 +229,21 @@ div.body p, div.body dd, div.body li {
 div.admonition {
     margin: 20px 0px;
     padding: 10px 30px;
-    background-color: #EEE;
-    border: 1px solid #CCC;
+    background-color: #FCC;
+    border: 1px solid #FAA;
 }
 
-div.admonition tt.xref, div.admonition code.xref, div.admonition a tt {
-    background-color: #FBFBFB;
+div.admonition tt.xref, div.admonition a tt {
     border-bottom: 1px solid #fafafa;
 }
 
+dd div.admonition {
+    margin-left: -60px;
+    padding-left: 60px;
+}
+
 div.admonition p.admonition-title {
-    font-family: Georgia, serif;
+    font-family: 'Garamond', 'Georgia', serif;
     font-weight: normal;
     font-size: 24px;
     margin: 0 0 10px 0;
@@ -251,71 +256,25 @@ div.admonition p.last {
 }
 
 div.highlight {
-    background-color: #fff;
+    background-color: white;
 }
 
 dt:target, .highlight {
     background: #FAF3E8;
 }
 
-div.warning {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-}
-
-div.danger {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-    -moz-box-shadow: 2px 2px 4px #D52C2C;
-    -webkit-box-shadow: 2px 2px 4px #D52C2C;
-    box-shadow: 2px 2px 4px #D52C2C;
-}
-
-div.error {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-    -moz-box-shadow: 2px 2px 4px #D52C2C;
-    -webkit-box-shadow: 2px 2px 4px #D52C2C;
-    box-shadow: 2px 2px 4px #D52C2C;
-}
-
-div.caution {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-}
-
-div.attention {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-}
-
-div.important {
-    background-color: #EEE;
-    border: 1px solid #CCC;
-}
-
 div.note {
     background-color: #EEE;
     border: 1px solid #CCC;
 }
 
-div.tip {
-    background-color: #EEE;
-    border: 1px solid #CCC;
-}
-
-div.hint {
-    background-color: #EEE;
-    border: 1px solid #CCC;
-}
-
 div.seealso {
     background-color: #EEE;
     border: 1px solid #CCC;
 }
 
 div.topic {
-    background-color: #EEE;
+    background-color: #eee;
 }
 
 p.admonition-title {
@@ -327,7 +286,7 @@ p.admonition-title:after {
 }
 
 pre, tt, code {
-    font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+    font-family: 'Consolas', 'Menlo', 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
     font-size: 0.9em;
 }
 
@@ -350,16 +309,16 @@ tt.descname, code.descname {
 }
 
 img.screenshot {
-    -moz-box-shadow: 2px 2px 4px #EEE;
-    -webkit-box-shadow: 2px 2px 4px #EEE;
-    box-shadow: 2px 2px 4px #EEE;
+    -moz-box-shadow: 2px 2px 4px #eee;
+    -webkit-box-shadow: 2px 2px 4px #eee;
+    box-shadow: 2px 2px 4px #eee;
 }
 
 table.docutils {
     border: 1px solid #888;
-    -moz-box-shadow: 2px 2px 4px #EEE;
-    -webkit-box-shadow: 2px 2px 4px #EEE;
-    box-shadow: 2px 2px 4px #EEE;
+    -moz-box-shadow: 2px 2px 4px #eee;
+    -webkit-box-shadow: 2px 2px 4px #eee;
+    box-shadow: 2px 2px 4px #eee;
 }
 
 table.docutils td, table.docutils th {
@@ -399,18 +358,8 @@ table.field-list p {
     margin-bottom: 0.8em;
 }
 
-/* Cloned from
- * https://github.com/sphinx-doc/sphinx/commit/ef60dbfce09286b20b7385333d63a60321784e68
- */
-.field-name {
-    -moz-hyphens: manual;
-    -ms-hyphens: manual;
-    -webkit-hyphens: manual;
-    hyphens: manual;
-}
-
 table.footnote td.label {
-    width: .1px;
+    width: 0px;
     padding: 0.3em 0 0.3em 0.5em;
 }
 
@@ -433,7 +382,6 @@ blockquote {
 }
 
 ul, ol {
-    /* Matches the 30px from the narrow-screen "li > ul" selector below */
     margin: 10px 0 10px 30px;
     padding: 0;
 }
@@ -445,15 +393,16 @@ pre {
     line-height: 1.3em;
 }
 
-div.viewcode-block:target {
-    background: #ffd;
-}
-
 dl pre, blockquote pre, li pre {
     margin-left: 0;
     padding-left: 30px;
 }
 
+dl dl pre {
+    margin-left: -90px;
+    padding-left: 90px;
+}
+
 tt, code {
     background-color: #ecf0f3;
     color: #222;
@@ -462,7 +411,7 @@ tt, code {
 
 tt.xref, code.xref, a tt {
     background-color: #FBFBFB;
-    border-bottom: 1px solid #fff;
+    border-bottom: 1px solid white;
 }
 
 a.reference {
@@ -470,11 +419,6 @@ a.reference {
     border-bottom: 1px dotted #004B6B;
 }
 
-/* Don't put an underline on images */
-a.image-reference, a.image-reference:hover {
-    border-bottom: none;
-}
-
 a.reference:hover {
     border-bottom: 1px solid #6D4100;
 }
@@ -524,11 +468,6 @@ a:hover tt, a:hover code {
     	margin-left: 0;
     }
 
-	li > ul {
-        /* Matches the 30px from the "ul, ol" selector above */
-		margin-left: 30px;
-	}
-
     .document {
     	width: auto;
     }
@@ -564,7 +503,7 @@ a:hover tt, a:hover code {
 
     div.documentwrapper {
         float: none;
-        background: #fff;
+        background: white;
     }
 
     div.sphinxsidebar {
@@ -579,7 +518,7 @@ a:hover tt, a:hover code {
 
     div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p,
     div.sphinxsidebar h3 a {
-        color: #fff;
+        color: white;
     }
 
     div.sphinxsidebar a {
@@ -651,51 +590,4 @@ table.docutils.citation, table.docutils.citation td, table.docutils.citation th
   -moz-box-shadow: none;
   -webkit-box-shadow: none;
   box-shadow: none;
-}
-
-
-/* relbar */
-
-.related {
-    line-height: 30px;
-    width: 100%;
-    font-size: 0.9rem;
-}
-
-.related.top {
-    border-bottom: 1px solid #EEE;
-    margin-bottom: 20px;
-}
-
-.related.bottom {
-    border-top: 1px solid #EEE;
-}
-
-.related ul {
-    padding: 0;
-    margin: 0;
-    list-style: none;
-}
-
-.related li {
-    display: inline;
-}
-
-nav#rellinks {
-    float: right;
-}
-
-nav#rellinks li+li:before {
-    content: "|";
-}
-
-nav#breadcrumbs li+li:before {
-    content: "\00BB";
-}
-
-/* Hide certain items when printing */
-@media print {
-    div.related {
-        display: none;
-    }
 }
\ No newline at end of file
diff --git a/docs/_build/_static/basic.css b/docs/_build/_static/basic.css
index 08896771..dc88b5a2 100644
--- a/docs/_build/_static/basic.css
+++ b/docs/_build/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -15,12 +15,6 @@ div.clearer {
     clear: both;
 }
 
-div.section::after {
-    display: block;
-    content: '';
-    clear: left;
-}
-
 /* -- relbar ---------------------------------------------------------------- */
 
 div.related {
@@ -87,26 +81,10 @@ div.sphinxsidebar input {
     font-size: 1em;
 }
 
-div.sphinxsidebar #searchbox form.search {
-    overflow: hidden;
-}
-
 div.sphinxsidebar #searchbox input[type="text"] {
-    float: left;
-    width: 80%;
-    padding: 0.25em;
-    box-sizing: border-box;
+    width: 170px;
 }
 
-div.sphinxsidebar #searchbox input[type="submit"] {
-    float: left;
-    width: 20%;
-    border-left: none;
-    padding: 0.25em;
-    box-sizing: border-box;
-}
-
-
 img {
     border: 0;
     max-width: 100%;
@@ -130,7 +108,7 @@ ul.search li a {
     font-weight: bold;
 }
 
-ul.search li p.context {
+ul.search li div.context {
     color: #888;
     margin: 2px 0 0 30px;
     text-align: left;
@@ -221,11 +199,6 @@ table.modindextable td {
 
 /* -- general body styles --------------------------------------------------- */
 
-div.body {
-    min-width: 360px;
-    max-width: 800px;
-}
-
 div.body p, div.body dd, div.body li, div.body blockquote {
     -moz-hyphens: auto;
     -ms-hyphens: auto;
@@ -267,25 +240,19 @@ p.rubric {
     font-weight: bold;
 }
 
-img.align-left, figure.align-left, .figure.align-left, object.align-left {
+img.align-left, .figure.align-left, object.align-left {
     clear: left;
     float: left;
     margin-right: 1em;
 }
 
-img.align-right, figure.align-right, .figure.align-right, object.align-right {
+img.align-right, .figure.align-right, object.align-right {
     clear: right;
     float: right;
     margin-left: 1em;
 }
 
-img.align-center, figure.align-center, .figure.align-center, object.align-center {
-  display: block;
-  margin-left: auto;
-  margin-right: auto;
-}
-
-img.align-default, figure.align-default, .figure.align-default {
+img.align-center, .figure.align-center, object.align-center {
   display: block;
   margin-left: auto;
   margin-right: auto;
@@ -299,45 +266,30 @@ img.align-default, figure.align-default, .figure.align-default {
     text-align: center;
 }
 
-.align-default {
-    text-align: center;
-}
-
 .align-right {
     text-align: right;
 }
 
 /* -- sidebars -------------------------------------------------------------- */
 
-div.sidebar,
-aside.sidebar {
+div.sidebar {
     margin: 0 0 0.5em 1em;
     border: 1px solid #ddb;
-    padding: 7px;
+    padding: 7px 7px 0 7px;
     background-color: #ffe;
     width: 40%;
     float: right;
-    clear: right;
-    overflow-x: auto;
 }
 
 p.sidebar-title {
     font-weight: bold;
 }
-nav.contents,
-aside.topic,
-
-div.admonition, div.topic, blockquote {
-    clear: left;
-}
 
 /* -- topics ---------------------------------------------------------------- */
-nav.contents,
-aside.topic,
 
 div.topic {
     border: 1px solid #ccc;
-    padding: 7px;
+    padding: 7px 7px 0 7px;
     margin: 10px 0 10px 0;
 }
 
@@ -359,6 +311,10 @@ div.admonition dt {
     font-weight: bold;
 }
 
+div.admonition dl {
+    margin-bottom: 0;
+}
+
 p.admonition-title {
     margin: 0px 10px 5px 0px;
     font-weight: bold;
@@ -369,50 +325,13 @@ div.body p.centered {
     margin-top: 25px;
 }
 
-/* -- content of sidebars/topics/admonitions -------------------------------- */
-
-div.sidebar > :last-child,
-aside.sidebar > :last-child,
-nav.contents > :last-child,
-aside.topic > :last-child,
-
-div.topic > :last-child,
-div.admonition > :last-child {
-    margin-bottom: 0;
-}
-
-div.sidebar::after,
-aside.sidebar::after,
-nav.contents::after,
-aside.topic::after,
-
-div.topic::after,
-div.admonition::after,
-blockquote::after {
-    display: block;
-    content: '';
-    clear: both;
-}
-
 /* -- tables ---------------------------------------------------------------- */
 
 table.docutils {
-    margin-top: 10px;
-    margin-bottom: 10px;
     border: 0;
     border-collapse: collapse;
 }
 
-table.align-center {
-    margin-left: auto;
-    margin-right: auto;
-}
-
-table.align-default {
-    margin-left: auto;
-    margin-right: auto;
-}
-
 table caption span.caption-number {
     font-style: italic;
 }
@@ -428,6 +347,10 @@ table.docutils td, table.docutils th {
     border-bottom: 1px solid #aaa;
 }
 
+table.footnote td, table.footnote th {
+    border: 0 !important;
+}
+
 th {
     text-align: left;
     padding-right: 5px;
@@ -442,34 +365,22 @@ table.citation td {
     border-bottom: none;
 }
 
-th > :first-child,
-td > :first-child {
-    margin-top: 0px;
-}
-
-th > :last-child,
-td > :last-child {
-    margin-bottom: 0px;
-}
-
 /* -- figures --------------------------------------------------------------- */
 
-div.figure, figure {
+div.figure {
     margin: 0.5em;
     padding: 0.5em;
 }
 
-div.figure p.caption, figcaption {
+div.figure p.caption {
     padding: 0.3em;
 }
 
-div.figure p.caption span.caption-number,
-figcaption span.caption-number {
+div.figure p.caption span.caption-number {
     font-style: italic;
 }
 
-div.figure p.caption span.caption-text,
-figcaption span.caption-text {
+div.figure p.caption span.caption-text {
 }
 
 /* -- field list styles ----------------------------------------------------- */
@@ -487,81 +398,6 @@ table.field-list td, table.field-list th {
     margin: 0;
 }
 
-.field-name {
-    -moz-hyphens: manual;
-    -ms-hyphens: manual;
-    -webkit-hyphens: manual;
-    hyphens: manual;
-}
-
-/* -- hlist styles ---------------------------------------------------------- */
-
-table.hlist {
-    margin: 1em 0;
-}
-
-table.hlist td {
-    vertical-align: top;
-}
-
-/* -- object description styles --------------------------------------------- */
-
-.sig {
-	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
-}
-
-.sig-name, code.descname {
-    background-color: transparent;
-    font-weight: bold;
-}
-
-.sig-name {
-	font-size: 1.1em;
-}
-
-code.descname {
-    font-size: 1.2em;
-}
-
-.sig-prename, code.descclassname {
-    background-color: transparent;
-}
-
-.optional {
-    font-size: 1.3em;
-}
-
-.sig-paren {
-    font-size: larger;
-}
-
-.sig-param.n {
-	font-style: italic;
-}
-
-/* C++ specific styling */
-
-.sig-inline.c-texpr,
-.sig-inline.cpp-texpr {
-	font-family: unset;
-}
-
-.sig.c   .k, .sig.c   .kt,
-.sig.cpp .k, .sig.cpp .kt {
-	color: #0033B3;
-}
-
-.sig.c   .m,
-.sig.cpp .m {
-	color: #1750EB;
-}
-
-.sig.c   .s, .sig.c   .sc,
-.sig.cpp .s, .sig.cpp .sc {
-	color: #067D17;
-}
-
-
 /* -- other body styles ----------------------------------------------------- */
 
 ol.arabic {
@@ -584,106 +420,11 @@ ol.upperroman {
     list-style: upper-roman;
 }
 
-:not(li) > ol > li:first-child > :first-child,
-:not(li) > ul > li:first-child > :first-child {
-    margin-top: 0px;
-}
-
-:not(li) > ol > li:last-child > :last-child,
-:not(li) > ul > li:last-child > :last-child {
-    margin-bottom: 0px;
-}
-
-ol.simple ol p,
-ol.simple ul p,
-ul.simple ol p,
-ul.simple ul p {
-    margin-top: 0;
-}
-
-ol.simple > li:not(:first-child) > p,
-ul.simple > li:not(:first-child) > p {
-    margin-top: 0;
-}
-
-ol.simple p,
-ul.simple p {
-    margin-bottom: 0;
-}
-
-/* Docutils 0.17 and older (footnotes & citations) */
-dl.footnote > dt,
-dl.citation > dt {
-    float: left;
-    margin-right: 0.5em;
-}
-
-dl.footnote > dd,
-dl.citation > dd {
-    margin-bottom: 0em;
-}
-
-dl.footnote > dd:after,
-dl.citation > dd:after {
-    content: "";
-    clear: both;
-}
-
-/* Docutils 0.18+ (footnotes & citations) */
-aside.footnote > span,
-div.citation > span {
-    float: left;
-}
-aside.footnote > span:last-of-type,
-div.citation > span:last-of-type {
-  padding-right: 0.5em;
-}
-aside.footnote > p {
-  margin-left: 2em;
-}
-div.citation > p {
-  margin-left: 4em;
-}
-aside.footnote > p:last-of-type,
-div.citation > p:last-of-type {
-    margin-bottom: 0em;
-}
-aside.footnote > p:last-of-type:after,
-div.citation > p:last-of-type:after {
-    content: "";
-    clear: both;
-}
-
-/* Footnotes & citations ends */
-
-dl.field-list {
-    display: grid;
-    grid-template-columns: fit-content(30%) auto;
-}
-
-dl.field-list > dt {
-    font-weight: bold;
-    word-break: break-word;
-    padding-left: 0.5em;
-    padding-right: 5px;
-}
-
-dl.field-list > dt:after {
-    content: ":";
-}
-
-dl.field-list > dd {
-    padding-left: 0.5em;
-    margin-top: 0em;
-    margin-left: 0em;
-    margin-bottom: 0em;
-}
-
 dl {
     margin-bottom: 15px;
 }
 
-dd > :first-child {
+dd p {
     margin-top: 0px;
 }
 
@@ -697,24 +438,23 @@ dd {
     margin-left: 30px;
 }
 
-dl > dd:last-child,
-dl > dd:last-child > :last-child {
-    margin-bottom: 0;
-}
-
-dt:target, span.highlighted {
+dt:target, .highlighted {
     background-color: #fbe54e;
 }
 
-rect.highlighted {
-    fill: #fbe54e;
-}
-
 dl.glossary dt {
     font-weight: bold;
     font-size: 1.1em;
 }
 
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
 .versionmodified {
     font-style: italic;
 }
@@ -753,13 +493,6 @@ dl.glossary dt {
     font-style: oblique;
 }
 
-.classifier:before {
-    font-style: normal;
-    margin: 0 0.5em;
-    content: ":";
-    display: inline-block;
-}
-
 abbr, acronym {
     border-bottom: dotted 1px;
     cursor: help;
@@ -772,69 +505,29 @@ pre {
     overflow-y: hidden;  /* fixes display issues on Chrome browsers */
 }
 
-pre, div[class*="highlight-"] {
-    clear: both;
-}
-
 span.pre {
     -moz-hyphens: none;
     -ms-hyphens: none;
     -webkit-hyphens: none;
     hyphens: none;
-    white-space: nowrap;
-}
-
-div[class*="highlight-"] {
-    margin: 1em 0;
 }
 
 td.linenos pre {
+    padding: 5px 0px;
     border: 0;
     background-color: transparent;
     color: #aaa;
 }
 
 table.highlighttable {
-    display: block;
-}
-
-table.highlighttable tbody {
-    display: block;
-}
-
-table.highlighttable tr {
-    display: flex;
+    margin-left: 0.5em;
 }
 
 table.highlighttable td {
-    margin: 0;
-    padding: 0;
-}
-
-table.highlighttable td.linenos {
-    padding-right: 0.5em;
-}
-
-table.highlighttable td.code {
-    flex: 1;
-    overflow: hidden;
-}
-
-.highlight .hll {
-    display: block;
-}
-
-div.highlight pre,
-table.highlighttable pre {
-    margin: 0;
-}
-
-div.code-block-caption + div {
-    margin-top: 0;
+    padding: 0 0.5em 0 0.5em;
 }
 
 div.code-block-caption {
-    margin-top: 1em;
     padding: 2px 5px;
     font-size: small;
 }
@@ -843,14 +536,8 @@ div.code-block-caption code {
     background-color: transparent;
 }
 
-table.highlighttable td.linenos,
-span.linenos,
-div.highlight span.gp {  /* gp: Generic.Prompt */
-  user-select: none;
-  -webkit-user-select: text; /* Safari fallback only */
-  -webkit-user-select: none; /* Chrome/Safari */
-  -moz-user-select: none; /* Firefox */
-  -ms-user-select: none; /* IE10+ */
+div.code-block-caption + div > div.highlight > pre {
+    margin-top: 0;
 }
 
 div.code-block-caption span.caption-number {
@@ -862,7 +549,21 @@ div.code-block-caption span.caption-text {
 }
 
 div.literal-block-wrapper {
-    margin: 1em 0;
+    padding: 1em 1em 0;
+}
+
+div.literal-block-wrapper div.highlight {
+    margin: 0;
+}
+
+code.descname {
+    background-color: transparent;
+    font-weight: bold;
+    font-size: 1.2em;
+}
+
+code.descclassname {
+    background-color: transparent;
 }
 
 code.xref, a code {
@@ -903,7 +604,8 @@ span.eqno {
 }
 
 span.eqno a.headerlink {
-    position: absolute;
+    position: relative;
+    left: 0px;
     z-index: 1;
 }
 
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GIT binary patch
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diff --git a/docs/_build/_static/doctools.js b/docs/_build/_static/doctools.js
index c3db08d1..56549772 100644
--- a/docs/_build/_static/doctools.js
+++ b/docs/_build/_static/doctools.js
@@ -2,263 +2,286 @@
  * doctools.js
  * ~~~~~~~~~~~
  *
- * Base JavaScript utilities for all Sphinx HTML documentation.
+ * Sphinx JavaScript utilities for all documentation.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
-"use strict";
 
-const _ready = (callback) => {
-  if (document.readyState !== "loading") {
-    callback();
-  } else {
-    document.addEventListener("DOMContentLoaded", callback);
-  }
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+/**
+ * make the code below compatible with browsers without
+ * an installed firebug like debugger
+if (!window.console || !console.firebug) {
+  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
+    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
+    "profile", "profileEnd"];
+  window.console = {};
+  for (var i = 0; i < names.length; ++i)
+    window.console[names[i]] = function() {};
+}
+ */
+
+/**
+ * small helper function to urldecode strings
+ */
+jQuery.urldecode = function(x) {
+  return decodeURIComponent(x).replace(/\+/g, ' ');
 };
 
 /**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
+ * small helper function to urlencode strings
  */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
+jQuery.urlencode = encodeURIComponent;
 
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+  if (typeof s == 'undefined')
+    s = document.location.search;
+  var parts = s.substr(s.indexOf('?') + 1).split('&');
+  var result = {};
+  for (var i = 0; i < parts.length; i++) {
+    var tmp = parts[i].split('=', 2);
+    var key = jQuery.urldecode(tmp[0]);
+    var value = jQuery.urldecode(tmp[1]);
+    if (key in result)
+      result[key].push(value);
+    else
+      result[key] = [value];
+  }
+  return result;
+};
 
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+  function highlight(node) {
+    if (node.nodeType == 3) {
+      var val = node.nodeValue;
+      var pos = val.toLowerCase().indexOf(text);
+      if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) {
+        var span = document.createElement("span");
+        span.className = className;
+        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
           document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
+          node.nextSibling));
+        node.nodeValue = val.substr(0, pos);
       }
     }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+    else if (!jQuery(node).is("button, select, textarea")) {
+      jQuery.each(node.childNodes, function() {
+        highlight(this);
+      });
+    }
   }
+  return this.each(function() {
+    highlight(this);
+  });
 };
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+  jQuery.uaMatch = function(ua) {
+    ua = ua.toLowerCase();
+
+    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+      /(msie) ([\w.]+)/.exec(ua) ||
+      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+      [];
+
+    return {
+      browser: match[ 1 ] || "",
+      version: match[ 2 ] || "0"
+    };
+  };
+  jQuery.browser = {};
+  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
 
 /**
  * Small JavaScript module for the documentation.
  */
-const Documentation = {
-  init: () => {
-    Documentation.highlightSearchWords();
-    Documentation.initDomainIndexTable();
-    Documentation.initOnKeyListeners();
+var Documentation = {
+
+  init : function() {
+    this.fixFirefoxAnchorBug();
+    this.highlightSearchWords();
+    this.initIndexTable();
+    
   },
 
   /**
    * i18n support
    */
-  TRANSLATIONS: {},
-  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
-  LOCALE: "unknown",
+  TRANSLATIONS : {},
+  PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; },
+  LOCALE : 'unknown',
 
   // gettext and ngettext don't access this so that the functions
   // can safely bound to a different name (_ = Documentation.gettext)
-  gettext: (string) => {
-    const translated = Documentation.TRANSLATIONS[string];
-    switch (typeof translated) {
-      case "undefined":
-        return string; // no translation
-      case "string":
-        return translated; // translation exists
-      default:
-        return translated[0]; // (singular, plural) translation tuple exists
-    }
+  gettext : function(string) {
+    var translated = Documentation.TRANSLATIONS[string];
+    if (typeof translated == 'undefined')
+      return string;
+    return (typeof translated == 'string') ? translated : translated[0];
   },
 
-  ngettext: (singular, plural, n) => {
-    const translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated !== "undefined")
-      return translated[Documentation.PLURAL_EXPR(n)];
-    return n === 1 ? singular : plural;
+  ngettext : function(singular, plural, n) {
+    var translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated == 'undefined')
+      return (n == 1) ? singular : plural;
+    return translated[Documentation.PLURALEXPR(n)];
   },
 
-  addTranslations: (catalog) => {
-    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
-    Documentation.PLURAL_EXPR = new Function(
-      "n",
-      `return (${catalog.plural_expr})`
-    );
-    Documentation.LOCALE = catalog.locale;
+  addTranslations : function(catalog) {
+    for (var key in catalog.messages)
+      this.TRANSLATIONS[key] = catalog.messages[key];
+    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
+    this.LOCALE = catalog.locale;
   },
 
   /**
-   * highlight the search words provided in the url in the text
+   * add context elements like header anchor links
    */
-  highlightSearchWords: () => {
-    const highlight =
-      new URLSearchParams(window.location.search).get("highlight") || "";
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:Documentation.hideSearchWords()">' +
-            Documentation.gettext("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
+  addContextElements : function() {
+    $('div[id] > :header:first').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this headline')).
+      appendTo(this);
+    });
+    $('dt[id]').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this definition')).
+      appendTo(this);
+    });
   },
 
   /**
-   * helper function to hide the search marks again
+   * workaround a firefox stupidity
+   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
    */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    const url = new URL(window.location);
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
+  fixFirefoxAnchorBug : function() {
+    if (document.location.hash)
+      window.setTimeout(function() {
+        document.location.href += '';
+      }, 10);
   },
 
   /**
-   * helper function to focus on search bar
+   * highlight the search words provided in the url in the text
    */
-  focusSearchBar: () => {
-    document.querySelectorAll("input[name=q]")[0]?.focus();
+  highlightSearchWords : function() {
+    var params = $.getQueryParameters();
+    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
+    if (terms.length) {
+      var body = $('div.body');
+      if (!body.length) {
+        body = $('body');
+      }
+      window.setTimeout(function() {
+        $.each(terms, function() {
+          body.highlightText(this.toLowerCase(), 'highlighted');
+        });
+      }, 10);
+      $('<p class="highlight-link"><a href="javascript:Documentation.' +
+        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
+          .appendTo($('#searchbox'));
+    }
   },
 
   /**
-   * Initialise the domain index toggle buttons
+   * init the domain index toggle buttons
    */
-  initDomainIndexTable: () => {
-    const toggler = (el) => {
-      const idNumber = el.id.substr(7);
-      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
-      if (el.src.substr(-9) === "minus.png") {
-        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
-        toggledRows.forEach((el) => (el.style.display = "none"));
-      } else {
-        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
-        toggledRows.forEach((el) => (el.style.display = ""));
-      }
-    };
-
-    const togglerElements = document.querySelectorAll("img.toggler");
-    togglerElements.forEach((el) =>
-      el.addEventListener("click", (event) => toggler(event.currentTarget))
-    );
-    togglerElements.forEach((el) => (el.style.display = ""));
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  initIndexTable : function() {
+    var togglers = $('img.toggler').click(function() {
+      var src = $(this).attr('src');
+      var idnum = $(this).attr('id').substr(7);
+      $('tr.cg-' + idnum).toggle();
+      if (src.substr(-9) == 'minus.png')
+        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
+      else
+        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
+    }).css('display', '');
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
+        togglers.click();
+    }
   },
 
-  initOnKeyListeners: () => {
-    // only install a listener if it is really needed
-    if (
-      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
-      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
-    )
-      return;
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords : function() {
+    $('#searchbox .highlight-link').fadeOut(300);
+    $('span.highlighted').removeClass('highlighted');
+  },
 
-    const blacklistedElements = new Set([
-      "TEXTAREA",
-      "INPUT",
-      "SELECT",
-      "BUTTON",
-    ]);
-    document.addEventListener("keydown", (event) => {
-      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
-      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
+  /**
+   * make the url absolute
+   */
+  makeURL : function(relativeURL) {
+    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
+  },
 
-      if (!event.shiftKey) {
-        switch (event.key) {
-          case "ArrowLeft":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+  /**
+   * get the current relative url
+   */
+  getCurrentURL : function() {
+    var path = document.location.pathname;
+    var parts = path.split(/\//);
+    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
+      if (this == '..')
+        parts.pop();
+    });
+    var url = parts.join('/');
+    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+  },
 
-            const prevLink = document.querySelector('link[rel="prev"]');
-            if (prevLink && prevLink.href) {
-              window.location.href = prevLink.href;
-              event.preventDefault();
+  initOnKeyListeners: function() {
+    $(document).keyup(function(event) {
+      var activeElementType = document.activeElement.tagName;
+      // don't navigate when in search box or textarea
+      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
+        switch (event.keyCode) {
+          case 37: // left
+            var prevHref = $('link[rel="prev"]').prop('href');
+            if (prevHref) {
+              window.location.href = prevHref;
+              return false;
             }
-            break;
-          case "ArrowRight":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const nextLink = document.querySelector('link[rel="next"]');
-            if (nextLink && nextLink.href) {
-              window.location.href = nextLink.href;
-              event.preventDefault();
+          case 39: // right
+            var nextHref = $('link[rel="next"]').prop('href');
+            if (nextHref) {
+              window.location.href = nextHref;
+              return false;
             }
-            break;
-          case "Escape":
-            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-            Documentation.hideSearchWords();
-            event.preventDefault();
         }
       }
-
-      // some keyboard layouts may need Shift to get /
-      switch (event.key) {
-        case "/":
-          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-          Documentation.focusSearchBar();
-          event.preventDefault();
-      }
     });
-  },
+  }
 };
 
 // quick alias for translations
-const _ = Documentation.gettext;
+_ = Documentation.gettext;
 
-_ready(Documentation.init);
+$(document).ready(function() {
+  Documentation.init();
+});
\ No newline at end of file
diff --git a/docs/_build/_static/documentation_options.js b/docs/_build/_static/documentation_options.js
index b5bf05a2..8a5f4b08 100644
--- a/docs/_build/_static/documentation_options.js
+++ b/docs/_build/_static/documentation_options.js
@@ -1,14 +1,9 @@
 var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '5.2',
-    LANGUAGE: 'en',
+    URL_ROOT: '',
+    VERSION: '4.0',
+    LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
-    BUILDER: 'html',
     FILE_SUFFIX: '.html',
-    LINK_SUFFIX: '.html',
     HAS_SOURCE: true,
-    SOURCELINK_SUFFIX: '.txt',
-    NAVIGATION_WITH_KEYS: false,
-    SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: false,
+    SOURCELINK_SUFFIX: '.txt'
 };
\ No newline at end of file
diff --git a/docs/_build/_static/down-pressed.png b/docs/_build/_static/down-pressed.png
new file mode 100644
index 0000000000000000000000000000000000000000..5756c8cad8854722893dc70b9eb4bb0400343a39
GIT binary patch
literal 222
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z%OS_REiHONoJ6{+Ks@6k3590|7k9F+ddB6!zw3#&!aw#S`x}3V3&=A(a#84O-&F7T
z^k3tZB;&iR9siw0|F|E|DAL<8r-F4!1H-;1{e*~yAKZN5f0|Ei6yUmR#Is)EM(Po_
zi`qJR6|P<~+)N+kSDgL7AjdIC_!O7Q?eGb+L+qOjm{~LLinM4NHn7U%HcK%uoMYO5
VJ~8zD2B3o(JYD@<);T3K0RV0%P>BEl

literal 0
HcmV?d00001

diff --git a/docs/_build/_static/down.png b/docs/_build/_static/down.png
new file mode 100644
index 0000000000000000000000000000000000000000..1b3bdad2ceffae91cee61b32f3295f9bbe646e48
GIT binary patch
literal 202
zcmeAS@N?(olHy`uVBq!ia0vp^0wB!60wlNoGJgf6CVIL!hEy=F?b*7pIY7kW{q%Rg
zx!yQ<9v8bmJwa`TQk7YSw}WVQ()mRdQ;TC;*<T*?<?OIJF4)K~*`+^EdvW@{fRyJ(
z?weB&PpGe(dw+-5<oNH&JW|0z@kgId=L!~9O|3`?{k1Umt465Dp9i1pCaQ@{=WpF|
zklotk)=&P-c9F*_Q}bpRPq*XX{4Fk5IYl~|@#V*s^(ig4#DI=t@O1TaS?83{1OQnY
BO{f3>

literal 0
HcmV?d00001

diff --git a/docs/_build/_static/jquery-3.1.0.js b/docs/_build/_static/jquery-3.1.0.js
new file mode 100644
index 00000000..f2fc2747
--- /dev/null
+++ b/docs/_build/_static/jquery-3.1.0.js
@@ -0,0 +1,10074 @@
+/*eslint-disable no-unused-vars*/
+/*!
+ * jQuery JavaScript Library v3.1.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright jQuery Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2016-07-07T21:44Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var document = window.document;
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var concat = arr.concat;
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+
+
+	function DOMEval( code, doc ) {
+		doc = doc || document;
+
+		var script = doc.createElement( "script" );
+
+		script.text = code;
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+/* global Symbol */
+// Defining this global in .eslintrc would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.1.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	},
+
+	// Support: Android <=4.0 only
+	// Make sure we trim BOM and NBSP
+	rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,
+
+	// Matches dashed string for camelizing
+	rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g,
+
+	// Used by jQuery.camelCase as callback to replace()
+	fcamelCase = function( all, letter ) {
+		return letter.toUpperCase();
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+		return num != null ?
+
+			// Return just the one element from the set
+			( num < 0 ? this[ num + this.length ] : this[ num ] ) :
+
+			// Return all the elements in a clean array
+			slice.call( this );
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !jQuery.isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				src = target[ name ];
+				copy = options[ name ];
+
+				// Prevent never-ending loop
+				if ( target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = jQuery.isArray( copy ) ) ) ) {
+
+					if ( copyIsArray ) {
+						copyIsArray = false;
+						clone = src && jQuery.isArray( src ) ? src : [];
+
+					} else {
+						clone = src && jQuery.isPlainObject( src ) ? src : {};
+					}
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isFunction: function( obj ) {
+		return jQuery.type( obj ) === "function";
+	},
+
+	isArray: Array.isArray,
+
+	isWindow: function( obj ) {
+		return obj != null && obj === obj.window;
+	},
+
+	isNumeric: function( obj ) {
+
+		// As of jQuery 3.0, isNumeric is limited to
+		// strings and numbers (primitives or objects)
+		// that can be coerced to finite numbers (gh-2662)
+		var type = jQuery.type( obj );
+		return ( type === "number" || type === "string" ) &&
+
+			// parseFloat NaNs numeric-cast false positives ("")
+			// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+			// subtraction forces infinities to NaN
+			!isNaN( obj - parseFloat( obj ) );
+	},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+
+		/* eslint-disable no-unused-vars */
+		// See https://github.com/eslint/eslint/issues/6125
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	type: function( obj ) {
+		if ( obj == null ) {
+			return obj + "";
+		}
+
+		// Support: Android <=2.3 only (functionish RegExp)
+		return typeof obj === "object" || typeof obj === "function" ?
+			class2type[ toString.call( obj ) ] || "object" :
+			typeof obj;
+	},
+
+	// Evaluates a script in a global context
+	globalEval: function( code ) {
+		DOMEval( code );
+	},
+
+	// Convert dashed to camelCase; used by the css and data modules
+	// Support: IE <=9 - 11, Edge 12 - 13
+	// Microsoft forgot to hump their vendor prefix (#9572)
+	camelCase: function( string ) {
+		return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+	},
+
+	nodeName: function( elem, name ) {
+		return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// Support: Android <=4.0 only
+	trim: function( text ) {
+		return text == null ?
+			"" :
+			( text + "" ).replace( rtrim, "" );
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return concat.apply( [], ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// Bind a function to a context, optionally partially applying any
+	// arguments.
+	proxy: function( fn, context ) {
+		var tmp, args, proxy;
+
+		if ( typeof context === "string" ) {
+			tmp = fn[ context ];
+			context = fn;
+			fn = tmp;
+		}
+
+		// Quick check to determine if target is callable, in the spec
+		// this throws a TypeError, but we will just return undefined.
+		if ( !jQuery.isFunction( fn ) ) {
+			return undefined;
+		}
+
+		// Simulated bind
+		args = slice.call( arguments, 2 );
+		proxy = function() {
+			return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+		};
+
+		// Set the guid of unique handler to the same of original handler, so it can be removed
+		proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+		return proxy;
+	},
+
+	now: Date.now,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = jQuery.type( obj );
+
+	if ( type === "function" || jQuery.isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.0
+ * https://sizzlejs.com/
+ *
+ * Copyright jQuery Foundation and other contributors
+ * Released under the MIT license
+ * http://jquery.org/license
+ *
+ * Date: 2016-01-04
+ */
+(function( window ) {
+
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ({}).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	push_native = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[i] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
+	identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+		// "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
+		"*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+
+	rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
+			"*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
+			"*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" +
+			whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
+	funescape = function( _, escaped, escapedWhitespace ) {
+		var high = "0x" + escaped - 0x10000;
+		// NaN means non-codepoint
+		// Support: Firefox<24
+		// Workaround erroneous numeric interpretation of +"0x"
+		return high !== high || escapedWhitespace ?
+			escaped :
+			high < 0 ?
+				// BMP codepoint
+				String.fromCharCode( high + 0x10000 ) :
+				// Supplemental Plane codepoint (surrogate pair)
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\x80-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	disabledAncestor = addCombinator(
+		function( elem ) {
+			return elem.disabled === true;
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		(arr = slice.call( preferredDoc.childNodes )),
+		preferredDoc.childNodes
+	);
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			push_native.apply( target, slice.call(els) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+			// Can't trust NodeList.length
+			while ( (target[j++] = els[i++]) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+
+		if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
+			setDocument( context );
+		}
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) {
+
+				// ID selector
+				if ( (m = match[1]) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( (elem = context.getElementById( m )) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && (elem = newContext.getElementById( m )) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[2] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( (m = match[3]) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!compilerCache[ selector + " " ] &&
+				(!rbuggyQSA || !rbuggyQSA.test( selector )) ) {
+
+				if ( nodeType !== 1 ) {
+					newContext = context;
+					newSelector = selector;
+
+				// qSA looks outside Element context, which is not what we want
+				// Thanks to Andrew Dupont for this workaround technique
+				// Support: IE <=8
+				// Exclude object elements
+				} else if ( context.nodeName.toLowerCase() !== "object" ) {
+
+					// Capture the context ID, setting it first if necessary
+					if ( (nid = context.getAttribute( "id" )) ) {
+						nid = nid.replace( rcssescape, fcssescape );
+					} else {
+						context.setAttribute( "id", (nid = expando) );
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[i] = "#" + nid + " " + toSelector( groups[i] );
+					}
+					newSelector = groups.join( "," );
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+				}
+
+				if ( newSelector ) {
+					try {
+						push.apply( results,
+							newContext.querySelectorAll( newSelector )
+						);
+						return results;
+					} catch ( qsaError ) {
+					} finally {
+						if ( nid === expando ) {
+							context.removeAttribute( "id" );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return (cache[ key + " " ] = value);
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement("fieldset");
+
+	try {
+		return !!fn( el );
+	} catch (e) {
+		return false;
+	} finally {
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split("|"),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[i] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( (cur = cur.nextSibling) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return (name === "input" || name === "button") && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+	// Known :disabled false positives:
+	// IE: *[disabled]:not(button, input, select, textarea, optgroup, option, menuitem, fieldset)
+	// not IE: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Check form elements and option elements for explicit disabling
+		return "label" in elem && elem.disabled === disabled ||
+			"form" in elem && elem.disabled === disabled ||
+
+			// Check non-disabled form elements for fieldset[disabled] ancestors
+			"form" in elem && elem.disabled === false && (
+				// Support: IE6-11+
+				// Ancestry is covered for us
+				elem.isDisabled === disabled ||
+
+				// Otherwise, assume any non-<option> under fieldset[disabled] is disabled
+				/* jshint -W018 */
+				elem.isDisabled !== !disabled &&
+					("label" in elem || !disabledAncestor( elem )) !== disabled
+			);
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction(function( argument ) {
+		argument = +argument;
+		return markFunction(function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ (j = matchIndexes[i]) ] ) {
+					seed[j] = !(matches[j] = seed[j]);
+				}
+			}
+		});
+	});
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	// documentElement is verified for cases where it doesn't yet exist
+	// (such as loading iframes in IE - #4833)
+	var documentElement = elem && (elem.ownerDocument || elem).documentElement;
+	return documentElement ? documentElement.nodeName !== "HTML" : false;
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9-11, Edge
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	if ( preferredDoc !== document &&
+		(subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert(function( el ) {
+		el.className = "i";
+		return !el.getAttribute("className");
+	});
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert(function( el ) {
+		el.appendChild( document.createComment("") );
+		return !el.getElementsByTagName("*").length;
+	});
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert(function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	});
+
+	// ID find and filter
+	if ( support.getById ) {
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var m = context.getElementById( id );
+				return m ? [ m ] : [];
+			}
+		};
+		Expr.filter["ID"] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute("id") === attrId;
+			};
+		};
+	} else {
+		// Support: IE6/7
+		// getElementById is not reliable as a find shortcut
+		delete Expr.find["ID"];
+
+		Expr.filter["ID"] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode("id");
+				return node && node.value === attrId;
+			};
+		};
+	}
+
+	// Tag
+	Expr.find["TAG"] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( (elem = results[i++]) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert(function( el ) {
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll("[selected]").length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push("~=");
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll(":checked").length ) {
+				rbuggyQSA.push(":checked");
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push(".#.+[+~]");
+			}
+		});
+
+		assert(function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement("input");
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll("[name=d]").length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll(":enabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll(":disabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll("*,:x");
+			rbuggyQSA.push(",.*:");
+		});
+	}
+
+	if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector) )) ) {
+
+		assert(function( el ) {
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		});
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			));
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( (b = b.parentNode) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			(!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+
+			// Choose the first element that is related to our preferred document
+			if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+				return -1;
+			}
+			if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+			return a === document ? -1 :
+				b === document ? 1 :
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( (cur = cur.parentNode) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( (cur = cur.parentNode) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[i] === bp[i] ) {
+			i++;
+		}
+
+		return i ?
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[i], bp[i] ) :
+
+			// Otherwise nodes in our document sort first
+			ap[i] === preferredDoc ? -1 :
+			bp[i] === preferredDoc ? 1 :
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	// Make sure that attribute selectors are quoted
+	expr = expr.replace( rattributeQuotes, "='$1']" );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!compilerCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+					// As well, disconnected nodes are said to be in a document
+					// fragment in IE 9
+					elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch (e) {}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+	// Set document vars if needed
+	if ( ( context.ownerDocument || context ) !== document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			(val = elem.getAttributeNode(name)) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return (sel + "").replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( (elem = results[i++]) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+		// If no nodeType, this is expected to be an array
+		while ( (node = elem[i++]) ) {
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[1] = match[1].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape );
+
+			if ( match[2] === "~=" ) {
+				match[3] = " " + match[3] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[1] = match[1].toLowerCase();
+
+			if ( match[1].slice( 0, 3 ) === "nth" ) {
+				// nth-* requires argument
+				if ( !match[3] ) {
+					Sizzle.error( match[0] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) );
+				match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" );
+
+			// other types prohibit arguments
+			} else if ( match[3] ) {
+				Sizzle.error( match[0] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[6] && match[2];
+
+			if ( matchExpr["CHILD"].test( match[0] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[3] ) {
+				match[2] = match[4] || match[5] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+				// Get excess from tokenize (recursively)
+				(excess = tokenize( unquoted, true )) &&
+				// advance to the next closing parenthesis
+				(excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+
+				// excess is a negative index
+				match[0] = match[0].slice( 0, excess );
+				match[2] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() { return true; } :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				(pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) &&
+				classCache( className, function( elem ) {
+					return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" );
+				});
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+			};
+		},
+
+		"CHILD": function( type, what, argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( (node = node[ dir ]) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || (node[ expando ] = {});
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								(outerCache[ node.uniqueID ] = {});
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( (node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								(diff = nodeIndex = 0) || start.pop()) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+							// Use previously-cached element index if available
+							if ( useCache ) {
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || (node[ expando ] = {});
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									(outerCache[ node.uniqueID ] = {});
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+								// Use the same loop as above to seek `elem` from the start
+								while ( (node = ++nodeIndex && node && node[ dir ] ||
+									(diff = nodeIndex = 0) || start.pop()) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] || (node[ expando ] = {});
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												(outerCache[ node.uniqueID ] = {});
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction(function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[i] );
+							seed[ idx ] = !( matches[ idx ] = matched[i] );
+						}
+					}) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+		// Potentially complex pseudos
+		"not": markFunction(function( selector ) {
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction(function( seed, matches, context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( (elem = unmatched[i]) ) {
+							seed[i] = !(matches[i] = elem);
+						}
+					}
+				}) :
+				function( elem, context, xml ) {
+					input[0] = elem;
+					matcher( input, null, xml, results );
+					// Don't keep the element (issue #299)
+					input[0] = null;
+					return !results.pop();
+				};
+		}),
+
+		"has": markFunction(function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		}),
+
+		"contains": markFunction(function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1;
+			};
+		}),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+			// lang value must be a valid identifier
+			if ( !ridentifier.test(lang || "") ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( (elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( (elem = elem.parentNode) && elem.nodeType === 1 );
+				return false;
+			};
+		}),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected);
+		},
+
+		"selected": function( elem ) {
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos["empty"]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo(function() {
+			return [ 0 ];
+		}),
+
+		"last": createPositionalPseudo(function( matchIndexes, length ) {
+			return [ length - 1 ];
+		}),
+
+		"eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		}),
+
+		"even": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"odd": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		})
+	}
+};
+
+Expr.pseudos["nth"] = Expr.pseudos["eq"];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || (match = rcomma.exec( soFar )) ) {
+			if ( match ) {
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[0].length ) || soFar;
+			}
+			groups.push( (tokens = []) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( (match = rcombinators.exec( soFar )) ) {
+			matched = match.shift();
+			tokens.push({
+				value: matched,
+				// Cast descendant combinators to space
+				type: match[0].replace( rtrim, " " )
+			});
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] ||
+				(match = preFilters[ type ]( match ))) ) {
+				matched = match.shift();
+				tokens.push({
+					value: matched,
+					type: type,
+					matches: match
+				});
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[i].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( (elem = elem[ dir ]) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || (elem[ expando ] = {});
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( (oldCache = uniqueCache[ key ]) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return (newCache[ 2 ] = oldCache[ 2 ]);
+						} else {
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[i]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[0];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[i], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( (elem = unmatched[i]) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction(function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( (elem = temp[i]) ) {
+					matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem);
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( (elem = matcherOut[i]) ) {
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( (matcherIn[i] = elem) );
+						}
+					}
+					postFinder( null, (matcherOut = []), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( (elem = matcherOut[i]) &&
+						(temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) {
+
+						seed[temp] = !(results[temp] = elem);
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	});
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[0].type ],
+		implicitRelative = leadingRelative || Expr.relative[" "],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				(checkContext = context).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( (matcher = Expr.relative[ tokens[i].type ]) ) {
+			matchers = [ addCombinator(elementMatcher( matchers ), matcher) ];
+		} else {
+			matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[j].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+						// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+						tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" })
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( (tokens = tokens.slice( j )) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1),
+				len = elems.length;
+
+			if ( outermost ) {
+				outermostContext = context === document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && (elem = elems[i]) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+					if ( !context && elem.ownerDocument !== document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( (matcher = elementMatchers[j++]) ) {
+						if ( matcher( elem, context || document, xml) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+					// They will have gone through all possible matchers
+					if ( (elem = !matcher && elem) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( (matcher = setMatchers[j++]) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !(unmatched[i] || setMatched[i]) ) {
+								setMatched[i] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[i] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( (selector = compiled.selector || selector) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[0] = match[0].slice( 0 );
+		if ( tokens.length > 2 && (token = tokens[0]).type === "ID" &&
+				support.getById && context.nodeType === 9 && documentIsHTML &&
+				Expr.relative[ tokens[1].type ] ) {
+
+			context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[i];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ (type = token.type) ] ) {
+				break;
+			}
+			if ( (find = Expr.find[ type ]) ) {
+				// Search, expanding context for leading sibling combinators
+				if ( (seed = find(
+					token.matches[0].replace( runescape, funescape ),
+					rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context
+				)) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert(function( el ) {
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
+});
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert(function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute("href") === "#" ;
+}) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	});
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert(function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+}) ) {
+	addHandle( "value", function( elem, name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	});
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert(function( el ) {
+	return el.getAttribute("disabled") == null;
+}) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+					(val = elem.getAttributeNode( name )) && val.specified ?
+					val.value :
+				null;
+		}
+	});
+}
+
+return Sizzle;
+
+})( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+var risSimple = /^.[^:#\[\.,]*$/;
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( jQuery.isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+
+	}
+
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+
+	}
+
+	if ( typeof qualifier === "string" ) {
+		if ( risSimple.test( qualifier ) ) {
+			return jQuery.filter( qualifier, elements, not );
+		}
+
+		qualifier = jQuery.filter( qualifier, elements );
+	}
+
+	return jQuery.grep( elements, function( elem ) {
+		return ( indexOf.call( qualifier, elem ) > -1 ) !== not && elem.nodeType === 1;
+	} );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	return elems.length === 1 && elem.nodeType === 1 ?
+		jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [] :
+		jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+			return elem.nodeType === 1;
+		} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( jQuery.isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( jQuery.isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		return elem.contentDocument || jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnotwhite = ( /\S+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnotwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( jQuery.isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && jQuery.type( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && jQuery.isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && jQuery.isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Support: Android 4.0 only
+			// Strict mode functions invoked without .call/.apply get global-object context
+			resolve.call( undefined, value );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.call( undefined, value );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = jQuery.isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && jQuery.isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( jQuery.isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				jQuery.isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Hold (or release) the ready event
+	holdReady: function( hold ) {
+		if ( hold ) {
+			jQuery.readyWait++;
+		} else {
+			jQuery.ready( true );
+		}
+	},
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( jQuery.type( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !jQuery.isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	return chainable ?
+		elems :
+
+		// Gets
+		bulk ?
+			fn.call( elems ) :
+			len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ jQuery.camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ jQuery.camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ jQuery.camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( jQuery.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( jQuery.camelCase );
+			} else {
+				key = jQuery.camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnotwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = data === "true" ? true :
+					data === "false" ? false :
+					data === "null" ? null :
+
+					// Only convert to a number if it doesn't change the string
+					+data + "" === data ? +data :
+					rbrace.test( data ) ? JSON.parse( data ) :
+					data;
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = jQuery.camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || jQuery.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			jQuery.contains( elem.ownerDocument, elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+var swap = function( elem, options, callback, args ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.apply( elem, args || [] );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted,
+		scale = 1,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		do {
+
+			// If previous iteration zeroed out, double until we get *something*.
+			// Use string for doubling so we don't accidentally see scale as unchanged below
+			scale = scale || ".5";
+
+			// Adjust and apply
+			initialInUnit = initialInUnit / scale;
+			jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Update scale, tolerating zero or NaN from tween.cur()
+		// Break the loop if scale is unchanged or perfect, or if we've just had enough.
+		} while (
+			scale !== ( scale = currentValue() / initial ) && scale !== 1 && --maxIterations
+		);
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) ),
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]+)/i );
+
+var rscriptType = ( /^$|\/(?:java|ecma)script/i );
+
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// Support: IE <=9 only
+	option: [ 1, "<select multiple='multiple'>", "</select>" ],
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+// Support: IE <=9 only
+wrapMap.optgroup = wrapMap.option;
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret = typeof context.getElementsByTagName !== "undefined" ?
+			context.getElementsByTagName( tag || "*" ) :
+			typeof context.querySelectorAll !== "undefined" ?
+				context.querySelectorAll( tag || "*" ) :
+			[];
+
+	return tag === undefined || tag && jQuery.nodeName( context, tag ) ?
+		jQuery.merge( [ context ], ret ) :
+		ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, contains, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( jQuery.type( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		contains = jQuery.contains( elem.ownerDocument, elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( contains ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+} )();
+var documentElement = document.documentElement;
+
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 only
+// See #13393 for more info
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Don't attach events to noData or text/comment nodes (but allow plain objects)
+		if ( !elemData ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = {};
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnotwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnotwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		// Make a writable jQuery.Event from the native event object
+		var event = jQuery.event.fix( nativeEvent );
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+			handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// Triggered event must either 1) have no namespace, or 2) have namespace(s)
+				// a subset or equal to those in the bound event (both can have no namespace).
+				if ( !event.rnamespace || event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, matches, sel, handleObj,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Support: IE <=9
+		// Find delegate handlers
+		// Black-hole SVG <use> instance trees (#13180)
+		//
+		// Support: Firefox <=42
+		// Avoid non-left-click in FF but don't block IE radio events (#3861, gh-2343)
+		if ( delegateCount && cur.nodeType &&
+			( event.type !== "click" || isNaN( event.button ) || event.button < 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && ( cur.disabled !== true || event.type !== "click" ) ) {
+					matches = [];
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matches[ sel ] === undefined ) {
+							matches[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matches[ sel ] ) {
+							matches.push( handleObj );
+						}
+					}
+					if ( matches.length ) {
+						handlerQueue.push( { elem: cur, handlers: matches } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: this, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: jQuery.isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		focus: {
+
+			// Fire native event if possible so blur/focus sequence is correct
+			trigger: function() {
+				if ( this !== safeActiveElement() && this.focus ) {
+					this.focus();
+					return false;
+				}
+			},
+			delegateType: "focusin"
+		},
+		blur: {
+			trigger: function() {
+				if ( this === safeActiveElement() && this.blur ) {
+					this.blur();
+					return false;
+				}
+			},
+			delegateType: "focusout"
+		},
+		click: {
+
+			// For checkbox, fire native event so checked state will be right
+			trigger: function() {
+				if ( this.type === "checkbox" && this.click && jQuery.nodeName( this, "input" ) ) {
+					this.click();
+					return false;
+				}
+			},
+
+			// For cross-browser consistency, don't fire native .click() on links
+			_default: function( event ) {
+				return jQuery.nodeName( event.target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || jQuery.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			return ( button & 1 ? 1 : ( button & 2 ? 3 : ( button & 4 ? 2 : 0 ) ) );
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	/* eslint-disable max-len */
+
+	// See https://github.com/eslint/eslint/issues/3229
+	rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi,
+
+	/* eslint-enable */
+
+	// Support: IE <=10 - 11, Edge 12 - 13
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rscriptTypeMasked = /^true\/(.*)/,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+function manipulationTarget( elem, content ) {
+	if ( jQuery.nodeName( elem, "table" ) &&
+		jQuery.nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return elem.getElementsByTagName( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	var match = rscriptTypeMasked.exec( elem.type );
+
+	if ( match ) {
+		elem.type = match[ 1 ];
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.access( src );
+		pdataCur = dataPriv.set( dest, pdataOld );
+		events = pdataOld.events;
+
+		if ( events ) {
+			delete pdataCur.handle;
+			pdataCur.events = {};
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = concat.apply( [], args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		isFunction = jQuery.isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( isFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( isFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl ) {
+								jQuery._evalUrl( node.src );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && jQuery.contains( node.ownerDocument, node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html.replace( rxhtmlTag, "<$1></$2>" );
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = jQuery.contains( elem.ownerDocument, elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rmargin = ( /^margin/ );
+
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		div.style.cssText =
+			"box-sizing:border-box;" +
+			"position:relative;display:block;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"top:1%;width:50%";
+		div.innerHTML = "";
+		documentElement.appendChild( container );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = divStyle.marginLeft === "2px";
+		boxSizingReliableVal = divStyle.width === "4px";
+
+		// Support: Android 4.0 - 4.3 only
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.marginRight = "50%";
+		pixelMarginRightVal = divStyle.marginRight === "4px";
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, pixelMarginRightVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	container.style.cssText = "border:0;width:8px;height:0;top:0;left:-9999px;" +
+		"padding:0;margin-top:1px;position:absolute";
+	container.appendChild( div );
+
+	jQuery.extend( support, {
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelMarginRight: function() {
+			computeStyleTests();
+			return pixelMarginRightVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// Support: IE <=9 only
+	// getPropertyValue is only needed for .css('filter') (#12537)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !jQuery.contains( elem.ownerDocument, elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelMarginRight() && rnumnonpx.test( ret ) && rmargin.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	},
+
+	cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style;
+
+// Return a css property mapped to a potentially vendor prefixed property
+function vendorPropName( name ) {
+
+	// Shortcut for names that are not vendor prefixed
+	if ( name in emptyStyle ) {
+		return name;
+	}
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+function setPositiveNumber( elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function augmentWidthOrHeight( elem, name, extra, isBorderBox, styles ) {
+	var i = extra === ( isBorderBox ? "border" : "content" ) ?
+
+		// If we already have the right measurement, avoid augmentation
+		4 :
+
+		// Otherwise initialize for horizontal or vertical properties
+		name === "width" ? 1 : 0,
+
+		val = 0;
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin, so add it if we want it
+		if ( extra === "margin" ) {
+			val += jQuery.css( elem, extra + cssExpand[ i ], true, styles );
+		}
+
+		if ( isBorderBox ) {
+
+			// border-box includes padding, so remove it if we want content
+			if ( extra === "content" ) {
+				val -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// At this point, extra isn't border nor margin, so remove border
+			if ( extra !== "margin" ) {
+				val -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		} else {
+
+			// At this point, extra isn't content, so add padding
+			val += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// At this point, extra isn't content nor padding, so add border
+			if ( extra !== "padding" ) {
+				val += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	return val;
+}
+
+function getWidthOrHeight( elem, name, extra ) {
+
+	// Start with offset property, which is equivalent to the border-box value
+	var val,
+		valueIsBorderBox = true,
+		styles = getStyles( elem ),
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+	// Support: IE <=11 only
+	// Running getBoundingClientRect on a disconnected node
+	// in IE throws an error.
+	if ( elem.getClientRects().length ) {
+		val = elem.getBoundingClientRect()[ name ];
+	}
+
+	// Some non-html elements return undefined for offsetWidth, so check for null/undefined
+	// svg - https://bugzilla.mozilla.org/show_bug.cgi?id=649285
+	// MathML - https://bugzilla.mozilla.org/show_bug.cgi?id=491668
+	if ( val <= 0 || val == null ) {
+
+		// Fall back to computed then uncomputed css if necessary
+		val = curCSS( elem, name, styles );
+		if ( val < 0 || val == null ) {
+			val = elem.style[ name ];
+		}
+
+		// Computed unit is not pixels. Stop here and return.
+		if ( rnumnonpx.test( val ) ) {
+			return val;
+		}
+
+		// Check for style in case a browser which returns unreliable values
+		// for getComputedStyle silently falls back to the reliable elem.style
+		valueIsBorderBox = isBorderBox &&
+			( support.boxSizingReliable() || val === elem.style[ name ] );
+
+		// Normalize "", auto, and prepare for extra
+		val = parseFloat( val ) || 0;
+	}
+
+	// Use the active box-sizing model to add/subtract irrelevant styles
+	return ( val +
+		augmentWidthOrHeight(
+			elem,
+			name,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {
+		"float": "cssFloat"
+	},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = jQuery.camelCase( name ),
+			style = elem.style;
+
+		name = jQuery.cssProps[ origName ] ||
+			( jQuery.cssProps[ origName ] = vendorPropName( origName ) || origName );
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			if ( type === "number" ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				style[ name ] = value;
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = jQuery.camelCase( name );
+
+		// Make sure that we're working with the right name
+		name = jQuery.cssProps[ origName ] ||
+			( jQuery.cssProps[ origName ] = vendorPropName( origName ) || origName );
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( i, name ) {
+	jQuery.cssHooks[ name ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, name, extra );
+						} ) :
+						getWidthOrHeight( elem, name, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = extra && getStyles( elem ),
+				subtract = extra && augmentWidthOrHeight(
+					elem,
+					name,
+					extra,
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+					styles
+				);
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ name ] = value;
+				value = jQuery.css( elem, name );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( !rmargin.test( prefix ) ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( jQuery.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 &&
+				( tween.elem.style[ jQuery.cssProps[ tween.prop ] ] != null ||
+					jQuery.cssHooks[ tween.prop ] ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, timerId,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function raf() {
+	if ( timerId ) {
+		window.requestAnimationFrame( raf );
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = jQuery.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 13
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = jQuery.camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( jQuery.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			if ( percent < 1 && length ) {
+				return remaining;
+			} else {
+				deferred.resolveWith( elem, [ animation ] );
+				return false;
+			}
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( jQuery.isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					jQuery.proxy( result.stop, result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( jQuery.isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	// attach callbacks from options
+	return animation.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( jQuery.isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnotwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			jQuery.isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !jQuery.isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off or if document is hidden
+	if ( jQuery.fx.off || document.hidden ) {
+		opt.duration = 0;
+
+	} else {
+		opt.duration = typeof opt.duration === "number" ?
+			opt.duration : opt.duration in jQuery.fx.speeds ?
+				jQuery.fx.speeds[ opt.duration ] : jQuery.fx.speeds._default;
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( jQuery.isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue && type !== false ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = jQuery.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Checks the timer has not already been removed
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	if ( timer() ) {
+		jQuery.fx.start();
+	} else {
+		jQuery.timers.pop();
+	}
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( !timerId ) {
+		timerId = window.requestAnimationFrame ?
+			window.requestAnimationFrame( raf ) :
+			window.setInterval( jQuery.fx.tick, jQuery.fx.interval );
+	}
+};
+
+jQuery.fx.stop = function() {
+	if ( window.cancelAnimationFrame ) {
+		window.cancelAnimationFrame( timerId );
+	} else {
+		window.clearInterval( timerId );
+	}
+
+	timerId = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					jQuery.nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+			attrNames = value && value.match( rnotwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				return tabindex ?
+					parseInt( tabindex, 10 ) :
+					rfocusable.test( elem.nodeName ) ||
+						rclickable.test( elem.nodeName ) && elem.href ?
+							0 :
+							-1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+var rclass = /[\t\r\n\f]/g;
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( typeof value === "string" && value ) {
+			classes = value.match( rnotwhite ) || [];
+
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 &&
+					( " " + curValue + " " ).replace( rclass, " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = jQuery.trim( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		if ( typeof value === "string" && value ) {
+			classes = value.match( rnotwhite ) || [];
+
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 &&
+					( " " + curValue + " " ).replace( rclass, " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = jQuery.trim( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value;
+
+		if ( typeof stateVal === "boolean" && type === "string" ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( type === "string" ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = value.match( rnotwhite ) || [];
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + getClass( elem ) + " " ).replace( rclass, " " )
+					.indexOf( className ) > -1
+			) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g,
+	rspaces = /[\x20\t\r\n\f]+/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, isFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				return typeof ret === "string" ?
+
+					// Handle most common string cases
+					ret.replace( rreturn, "" ) :
+
+					// Handle cases where value is null/undef or number
+					ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		isFunction = jQuery.isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( isFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( jQuery.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					jQuery.trim( jQuery.text( elem ) ).replace( rspaces, " " );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length,
+					i = index < 0 ?
+						max :
+						one ? index : 0;
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!jQuery.nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( jQuery.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/;
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && jQuery.isFunction( elem[ type ] ) && !jQuery.isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+					elem[ type ]();
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( i, name ) {
+
+	// Handle event binding
+	jQuery.fn[ name ] = function( data, fn ) {
+		return arguments.length > 0 ?
+			this.on( name, null, data, fn ) :
+			this.trigger( name );
+	};
+} );
+
+jQuery.fn.extend( {
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+
+
+
+support.focusin = "onfocusin" in window;
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = jQuery.now();
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( jQuery.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && jQuery.type( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = jQuery.isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( jQuery.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( i, elem ) {
+			var val = jQuery( this ).val();
+
+			return val == null ?
+				null :
+				jQuery.isArray( val ) ?
+					jQuery.map( val, function( val ) {
+						return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+					} ) :
+					{ name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rts = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnotwhite ) || [];
+
+		if ( jQuery.isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() ] = match[ 2 ];
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() ];
+					}
+					return match == null ? null : match;
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnotwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 13
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available, append data to url
+			if ( s.data ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add anti-cache in uncached url if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rts, "" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( jQuery.isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+
+jQuery._evalUrl = function( url ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+		"throws": true
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( jQuery.isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( jQuery.isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var isFunction = jQuery.isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( isFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain requests
+	if ( s.crossDomain ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" ).prop( {
+					charset: s.scriptCharset,
+					src: s.url
+				} ).on(
+					"load error",
+					callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					}
+				);
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = jQuery.isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && jQuery.isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = jQuery.trim( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( jQuery.isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+// Attach a bunch of functions for handling common AJAX events
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+/**
+ * Gets a window from an element
+ */
+function getWindow( elem ) {
+	return jQuery.isWindow( elem ) ? elem : elem.nodeType === 9 && elem.defaultView;
+}
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( jQuery.isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var docElem, win, rect, doc,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		rect = elem.getBoundingClientRect();
+
+		// Make sure element is not hidden (display: none)
+		if ( rect.width || rect.height ) {
+			doc = elem.ownerDocument;
+			win = getWindow( doc );
+			docElem = doc.documentElement;
+
+			return {
+				top: rect.top + win.pageYOffset - docElem.clientTop,
+				left: rect.left + win.pageXOffset - docElem.clientLeft
+			};
+		}
+
+		// Return zeros for disconnected and hidden elements (gh-2310)
+		return rect;
+	},
+
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// Fixed elements are offset from window (parentOffset = {top:0, left: 0},
+		// because it is its only offset parent
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume getBoundingClientRect is there when computed position is fixed
+			offset = elem.getBoundingClientRect();
+
+		} else {
+
+			// Get *real* offsetParent
+			offsetParent = this.offsetParent();
+
+			// Get correct offsets
+			offset = this.offset();
+			if ( !jQuery.nodeName( offsetParent[ 0 ], "html" ) ) {
+				parentOffset = offsetParent.offset();
+			}
+
+			// Add offsetParent borders
+			parentOffset = {
+				top: parentOffset.top + jQuery.css( offsetParent[ 0 ], "borderTopWidth", true ),
+				left: parentOffset.left + jQuery.css( offsetParent[ 0 ], "borderLeftWidth", true )
+			};
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+			var win = getWindow( elem );
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( jQuery.isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	}
+} );
+
+jQuery.parseJSON = JSON.parse;
+
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( !noGlobal ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+return jQuery;
+} );
diff --git a/docs/_build/_static/jquery-3.2.1.js b/docs/_build/_static/jquery-3.2.1.js
new file mode 100644
index 00000000..d2d8ca47
--- /dev/null
+++ b/docs/_build/_static/jquery-3.2.1.js
@@ -0,0 +1,10253 @@
+/*!
+ * jQuery JavaScript Library v3.2.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2017-03-20T18:59Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var document = window.document;
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var concat = arr.concat;
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+
+
+	function DOMEval( code, doc ) {
+		doc = doc || document;
+
+		var script = doc.createElement( "script" );
+
+		script.text = code;
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.2.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	},
+
+	// Support: Android <=4.0 only
+	// Make sure we trim BOM and NBSP
+	rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,
+
+	// Matches dashed string for camelizing
+	rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g,
+
+	// Used by jQuery.camelCase as callback to replace()
+	fcamelCase = function( all, letter ) {
+		return letter.toUpperCase();
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !jQuery.isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				src = target[ name ];
+				copy = options[ name ];
+
+				// Prevent never-ending loop
+				if ( target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+
+					if ( copyIsArray ) {
+						copyIsArray = false;
+						clone = src && Array.isArray( src ) ? src : [];
+
+					} else {
+						clone = src && jQuery.isPlainObject( src ) ? src : {};
+					}
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isFunction: function( obj ) {
+		return jQuery.type( obj ) === "function";
+	},
+
+	isWindow: function( obj ) {
+		return obj != null && obj === obj.window;
+	},
+
+	isNumeric: function( obj ) {
+
+		// As of jQuery 3.0, isNumeric is limited to
+		// strings and numbers (primitives or objects)
+		// that can be coerced to finite numbers (gh-2662)
+		var type = jQuery.type( obj );
+		return ( type === "number" || type === "string" ) &&
+
+			// parseFloat NaNs numeric-cast false positives ("")
+			// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+			// subtraction forces infinities to NaN
+			!isNaN( obj - parseFloat( obj ) );
+	},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+
+		/* eslint-disable no-unused-vars */
+		// See https://github.com/eslint/eslint/issues/6125
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	type: function( obj ) {
+		if ( obj == null ) {
+			return obj + "";
+		}
+
+		// Support: Android <=2.3 only (functionish RegExp)
+		return typeof obj === "object" || typeof obj === "function" ?
+			class2type[ toString.call( obj ) ] || "object" :
+			typeof obj;
+	},
+
+	// Evaluates a script in a global context
+	globalEval: function( code ) {
+		DOMEval( code );
+	},
+
+	// Convert dashed to camelCase; used by the css and data modules
+	// Support: IE <=9 - 11, Edge 12 - 13
+	// Microsoft forgot to hump their vendor prefix (#9572)
+	camelCase: function( string ) {
+		return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// Support: Android <=4.0 only
+	trim: function( text ) {
+		return text == null ?
+			"" :
+			( text + "" ).replace( rtrim, "" );
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return concat.apply( [], ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// Bind a function to a context, optionally partially applying any
+	// arguments.
+	proxy: function( fn, context ) {
+		var tmp, args, proxy;
+
+		if ( typeof context === "string" ) {
+			tmp = fn[ context ];
+			context = fn;
+			fn = tmp;
+		}
+
+		// Quick check to determine if target is callable, in the spec
+		// this throws a TypeError, but we will just return undefined.
+		if ( !jQuery.isFunction( fn ) ) {
+			return undefined;
+		}
+
+		// Simulated bind
+		args = slice.call( arguments, 2 );
+		proxy = function() {
+			return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+		};
+
+		// Set the guid of unique handler to the same of original handler, so it can be removed
+		proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+		return proxy;
+	},
+
+	now: Date.now,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = jQuery.type( obj );
+
+	if ( type === "function" || jQuery.isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.3
+ * https://sizzlejs.com/
+ *
+ * Copyright jQuery Foundation and other contributors
+ * Released under the MIT license
+ * http://jquery.org/license
+ *
+ * Date: 2016-08-08
+ */
+(function( window ) {
+
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ({}).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	push_native = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[i] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
+	identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+		// "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
+		"*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+
+	rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
+			"*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
+			"*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" +
+			whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
+	funescape = function( _, escaped, escapedWhitespace ) {
+		var high = "0x" + escaped - 0x10000;
+		// NaN means non-codepoint
+		// Support: Firefox<24
+		// Workaround erroneous numeric interpretation of +"0x"
+		return high !== high || escapedWhitespace ?
+			escaped :
+			high < 0 ?
+				// BMP codepoint
+				String.fromCharCode( high + 0x10000 ) :
+				// Supplemental Plane codepoint (surrogate pair)
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	disabledAncestor = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && ("form" in elem || "label" in elem);
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		(arr = slice.call( preferredDoc.childNodes )),
+		preferredDoc.childNodes
+	);
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			push_native.apply( target, slice.call(els) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+			// Can't trust NodeList.length
+			while ( (target[j++] = els[i++]) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+
+		if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
+			setDocument( context );
+		}
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) {
+
+				// ID selector
+				if ( (m = match[1]) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( (elem = context.getElementById( m )) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && (elem = newContext.getElementById( m )) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[2] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( (m = match[3]) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!compilerCache[ selector + " " ] &&
+				(!rbuggyQSA || !rbuggyQSA.test( selector )) ) {
+
+				if ( nodeType !== 1 ) {
+					newContext = context;
+					newSelector = selector;
+
+				// qSA looks outside Element context, which is not what we want
+				// Thanks to Andrew Dupont for this workaround technique
+				// Support: IE <=8
+				// Exclude object elements
+				} else if ( context.nodeName.toLowerCase() !== "object" ) {
+
+					// Capture the context ID, setting it first if necessary
+					if ( (nid = context.getAttribute( "id" )) ) {
+						nid = nid.replace( rcssescape, fcssescape );
+					} else {
+						context.setAttribute( "id", (nid = expando) );
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[i] = "#" + nid + " " + toSelector( groups[i] );
+					}
+					newSelector = groups.join( "," );
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+				}
+
+				if ( newSelector ) {
+					try {
+						push.apply( results,
+							newContext.querySelectorAll( newSelector )
+						);
+						return results;
+					} catch ( qsaError ) {
+					} finally {
+						if ( nid === expando ) {
+							context.removeAttribute( "id" );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return (cache[ key + " " ] = value);
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement("fieldset");
+
+	try {
+		return !!fn( el );
+	} catch (e) {
+		return false;
+	} finally {
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split("|"),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[i] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( (cur = cur.nextSibling) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return (name === "input" || name === "button") && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+						disabledAncestor( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction(function( argument ) {
+		argument = +argument;
+		return markFunction(function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ (j = matchIndexes[i]) ] ) {
+					seed[j] = !(matches[j] = seed[j]);
+				}
+			}
+		});
+	});
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	// documentElement is verified for cases where it doesn't yet exist
+	// (such as loading iframes in IE - #4833)
+	var documentElement = elem && (elem.ownerDocument || elem).documentElement;
+	return documentElement ? documentElement.nodeName !== "HTML" : false;
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9-11, Edge
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	if ( preferredDoc !== document &&
+		(subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert(function( el ) {
+		el.className = "i";
+		return !el.getAttribute("className");
+	});
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert(function( el ) {
+		el.appendChild( document.createComment("") );
+		return !el.getElementsByTagName("*").length;
+	});
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert(function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	});
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter["ID"] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute("id") === attrId;
+			};
+		};
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter["ID"] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode("id");
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode("id");
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( (elem = elems[i++]) ) {
+						node = elem.getAttributeNode("id");
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find["TAG"] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( (elem = results[i++]) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert(function( el ) {
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll("[selected]").length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push("~=");
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll(":checked").length ) {
+				rbuggyQSA.push(":checked");
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push(".#.+[+~]");
+			}
+		});
+
+		assert(function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement("input");
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll("[name=d]").length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll(":enabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll(":disabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll("*,:x");
+			rbuggyQSA.push(",.*:");
+		});
+	}
+
+	if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector) )) ) {
+
+		assert(function( el ) {
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		});
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			));
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( (b = b.parentNode) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			(!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+
+			// Choose the first element that is related to our preferred document
+			if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+				return -1;
+			}
+			if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+			return a === document ? -1 :
+				b === document ? 1 :
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( (cur = cur.parentNode) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( (cur = cur.parentNode) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[i] === bp[i] ) {
+			i++;
+		}
+
+		return i ?
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[i], bp[i] ) :
+
+			// Otherwise nodes in our document sort first
+			ap[i] === preferredDoc ? -1 :
+			bp[i] === preferredDoc ? 1 :
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	// Make sure that attribute selectors are quoted
+	expr = expr.replace( rattributeQuotes, "='$1']" );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!compilerCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+					// As well, disconnected nodes are said to be in a document
+					// fragment in IE 9
+					elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch (e) {}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+	// Set document vars if needed
+	if ( ( context.ownerDocument || context ) !== document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			(val = elem.getAttributeNode(name)) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return (sel + "").replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( (elem = results[i++]) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+		// If no nodeType, this is expected to be an array
+		while ( (node = elem[i++]) ) {
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[1] = match[1].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape );
+
+			if ( match[2] === "~=" ) {
+				match[3] = " " + match[3] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[1] = match[1].toLowerCase();
+
+			if ( match[1].slice( 0, 3 ) === "nth" ) {
+				// nth-* requires argument
+				if ( !match[3] ) {
+					Sizzle.error( match[0] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) );
+				match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" );
+
+			// other types prohibit arguments
+			} else if ( match[3] ) {
+				Sizzle.error( match[0] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[6] && match[2];
+
+			if ( matchExpr["CHILD"].test( match[0] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[3] ) {
+				match[2] = match[4] || match[5] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+				// Get excess from tokenize (recursively)
+				(excess = tokenize( unquoted, true )) &&
+				// advance to the next closing parenthesis
+				(excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+
+				// excess is a negative index
+				match[0] = match[0].slice( 0, excess );
+				match[2] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() { return true; } :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				(pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) &&
+				classCache( className, function( elem ) {
+					return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" );
+				});
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+			};
+		},
+
+		"CHILD": function( type, what, argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( (node = node[ dir ]) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || (node[ expando ] = {});
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								(outerCache[ node.uniqueID ] = {});
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( (node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								(diff = nodeIndex = 0) || start.pop()) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+							// Use previously-cached element index if available
+							if ( useCache ) {
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || (node[ expando ] = {});
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									(outerCache[ node.uniqueID ] = {});
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+								// Use the same loop as above to seek `elem` from the start
+								while ( (node = ++nodeIndex && node && node[ dir ] ||
+									(diff = nodeIndex = 0) || start.pop()) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] || (node[ expando ] = {});
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												(outerCache[ node.uniqueID ] = {});
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction(function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[i] );
+							seed[ idx ] = !( matches[ idx ] = matched[i] );
+						}
+					}) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+		// Potentially complex pseudos
+		"not": markFunction(function( selector ) {
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction(function( seed, matches, context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( (elem = unmatched[i]) ) {
+							seed[i] = !(matches[i] = elem);
+						}
+					}
+				}) :
+				function( elem, context, xml ) {
+					input[0] = elem;
+					matcher( input, null, xml, results );
+					// Don't keep the element (issue #299)
+					input[0] = null;
+					return !results.pop();
+				};
+		}),
+
+		"has": markFunction(function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		}),
+
+		"contains": markFunction(function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1;
+			};
+		}),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+			// lang value must be a valid identifier
+			if ( !ridentifier.test(lang || "") ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( (elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( (elem = elem.parentNode) && elem.nodeType === 1 );
+				return false;
+			};
+		}),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected);
+		},
+
+		"selected": function( elem ) {
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos["empty"]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo(function() {
+			return [ 0 ];
+		}),
+
+		"last": createPositionalPseudo(function( matchIndexes, length ) {
+			return [ length - 1 ];
+		}),
+
+		"eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		}),
+
+		"even": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"odd": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		})
+	}
+};
+
+Expr.pseudos["nth"] = Expr.pseudos["eq"];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || (match = rcomma.exec( soFar )) ) {
+			if ( match ) {
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[0].length ) || soFar;
+			}
+			groups.push( (tokens = []) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( (match = rcombinators.exec( soFar )) ) {
+			matched = match.shift();
+			tokens.push({
+				value: matched,
+				// Cast descendant combinators to space
+				type: match[0].replace( rtrim, " " )
+			});
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] ||
+				(match = preFilters[ type ]( match ))) ) {
+				matched = match.shift();
+				tokens.push({
+					value: matched,
+					type: type,
+					matches: match
+				});
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[i].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( (elem = elem[ dir ]) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || (elem[ expando ] = {});
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( (oldCache = uniqueCache[ key ]) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return (newCache[ 2 ] = oldCache[ 2 ]);
+						} else {
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[i]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[0];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[i], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( (elem = unmatched[i]) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction(function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( (elem = temp[i]) ) {
+					matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem);
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( (elem = matcherOut[i]) ) {
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( (matcherIn[i] = elem) );
+						}
+					}
+					postFinder( null, (matcherOut = []), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( (elem = matcherOut[i]) &&
+						(temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) {
+
+						seed[temp] = !(results[temp] = elem);
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	});
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[0].type ],
+		implicitRelative = leadingRelative || Expr.relative[" "],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				(checkContext = context).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( (matcher = Expr.relative[ tokens[i].type ]) ) {
+			matchers = [ addCombinator(elementMatcher( matchers ), matcher) ];
+		} else {
+			matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[j].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+						// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+						tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" })
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( (tokens = tokens.slice( j )) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1),
+				len = elems.length;
+
+			if ( outermost ) {
+				outermostContext = context === document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && (elem = elems[i]) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+					if ( !context && elem.ownerDocument !== document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( (matcher = elementMatchers[j++]) ) {
+						if ( matcher( elem, context || document, xml) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+					// They will have gone through all possible matchers
+					if ( (elem = !matcher && elem) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( (matcher = setMatchers[j++]) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !(unmatched[i] || setMatched[i]) ) {
+								setMatched[i] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[i] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( (selector = compiled.selector || selector) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[0] = match[0].slice( 0 );
+		if ( tokens.length > 2 && (token = tokens[0]).type === "ID" &&
+				context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) {
+
+			context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[i];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ (type = token.type) ] ) {
+				break;
+			}
+			if ( (find = Expr.find[ type ]) ) {
+				// Search, expanding context for leading sibling combinators
+				if ( (seed = find(
+					token.matches[0].replace( runescape, funescape ),
+					rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context
+				)) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert(function( el ) {
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
+});
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert(function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute("href") === "#" ;
+}) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	});
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert(function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+}) ) {
+	addHandle( "value", function( elem, name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	});
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert(function( el ) {
+	return el.getAttribute("disabled") == null;
+}) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+					(val = elem.getAttributeNode( name )) && val.specified ?
+					val.value :
+				null;
+		}
+	});
+}
+
+return Sizzle;
+
+})( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+var risSimple = /^.[^:#\[\.,]*$/;
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( jQuery.isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Simple selector that can be filtered directly, removing non-Elements
+	if ( risSimple.test( qualifier ) ) {
+		return jQuery.filter( qualifier, elements, not );
+	}
+
+	// Complex selector, compare the two sets, removing non-Elements
+	qualifier = jQuery.filter( qualifier, elements );
+	return jQuery.grep( elements, function( elem ) {
+		return ( indexOf.call( qualifier, elem ) > -1 ) !== not && elem.nodeType === 1;
+	} );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( jQuery.isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( jQuery.isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+        if ( nodeName( elem, "iframe" ) ) {
+            return elem.contentDocument;
+        }
+
+        // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+        // Treat the template element as a regular one in browsers that
+        // don't support it.
+        if ( nodeName( elem, "template" ) ) {
+            elem = elem.content || elem;
+        }
+
+        return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( jQuery.isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && jQuery.type( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && jQuery.isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && jQuery.isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = jQuery.isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && jQuery.isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( jQuery.isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				jQuery.isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( jQuery.type( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !jQuery.isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ jQuery.camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ jQuery.camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ jQuery.camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( jQuery.camelCase );
+			} else {
+				key = jQuery.camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = jQuery.camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			jQuery.contains( elem.ownerDocument, elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+var swap = function( elem, options, callback, args ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.apply( elem, args || [] );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted,
+		scale = 1,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		do {
+
+			// If previous iteration zeroed out, double until we get *something*.
+			// Use string for doubling so we don't accidentally see scale as unchanged below
+			scale = scale || ".5";
+
+			// Adjust and apply
+			initialInUnit = initialInUnit / scale;
+			jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Update scale, tolerating zero or NaN from tween.cur()
+		// Break the loop if scale is unchanged or perfect, or if we've just had enough.
+		} while (
+			scale !== ( scale = currentValue() / initial ) && scale !== 1 && --maxIterations
+		);
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]+)/i );
+
+var rscriptType = ( /^$|\/(?:java|ecma)script/i );
+
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// Support: IE <=9 only
+	option: [ 1, "<select multiple='multiple'>", "</select>" ],
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+// Support: IE <=9 only
+wrapMap.optgroup = wrapMap.option;
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, contains, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( jQuery.type( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		contains = jQuery.contains( elem.ownerDocument, elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( contains ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+} )();
+var documentElement = document.documentElement;
+
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 only
+// See #13393 for more info
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Don't attach events to noData or text/comment nodes (but allow plain objects)
+		if ( !elemData ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = {};
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		// Make a writable jQuery.Event from the native event object
+		var event = jQuery.event.fix( nativeEvent );
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+			handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// Triggered event must either 1) have no namespace, or 2) have namespace(s)
+				// a subset or equal to those in the bound event (both can have no namespace).
+				if ( !event.rnamespace || event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: jQuery.isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		focus: {
+
+			// Fire native event if possible so blur/focus sequence is correct
+			trigger: function() {
+				if ( this !== safeActiveElement() && this.focus ) {
+					this.focus();
+					return false;
+				}
+			},
+			delegateType: "focusin"
+		},
+		blur: {
+			trigger: function() {
+				if ( this === safeActiveElement() && this.blur ) {
+					this.blur();
+					return false;
+				}
+			},
+			delegateType: "focusout"
+		},
+		click: {
+
+			// For checkbox, fire native event so checked state will be right
+			trigger: function() {
+				if ( this.type === "checkbox" && this.click && nodeName( this, "input" ) ) {
+					this.click();
+					return false;
+				}
+			},
+
+			// For cross-browser consistency, don't fire native .click() on links
+			_default: function( event ) {
+				return nodeName( event.target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || jQuery.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	/* eslint-disable max-len */
+
+	// See https://github.com/eslint/eslint/issues/3229
+	rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi,
+
+	/* eslint-enable */
+
+	// Support: IE <=10 - 11, Edge 12 - 13
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rscriptTypeMasked = /^true\/(.*)/,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( ">tbody", elem )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	var match = rscriptTypeMasked.exec( elem.type );
+
+	if ( match ) {
+		elem.type = match[ 1 ];
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.access( src );
+		pdataCur = dataPriv.set( dest, pdataOld );
+		events = pdataOld.events;
+
+		if ( events ) {
+			delete pdataCur.handle;
+			pdataCur.events = {};
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = concat.apply( [], args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		isFunction = jQuery.isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( isFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( isFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl ) {
+								jQuery._evalUrl( node.src );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && jQuery.contains( node.ownerDocument, node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html.replace( rxhtmlTag, "<$1></$2>" );
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = jQuery.contains( elem.ownerDocument, elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rmargin = ( /^margin/ );
+
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		div.style.cssText =
+			"box-sizing:border-box;" +
+			"position:relative;display:block;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"top:1%;width:50%";
+		div.innerHTML = "";
+		documentElement.appendChild( container );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = divStyle.marginLeft === "2px";
+		boxSizingReliableVal = divStyle.width === "4px";
+
+		// Support: Android 4.0 - 4.3 only
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.marginRight = "50%";
+		pixelMarginRightVal = divStyle.marginRight === "4px";
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, pixelMarginRightVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	container.style.cssText = "border:0;width:8px;height:0;top:0;left:-9999px;" +
+		"padding:0;margin-top:1px;position:absolute";
+	container.appendChild( div );
+
+	jQuery.extend( support, {
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelMarginRight: function() {
+			computeStyleTests();
+			return pixelMarginRightVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !jQuery.contains( elem.ownerDocument, elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelMarginRight() && rnumnonpx.test( ret ) && rmargin.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	},
+
+	cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style;
+
+// Return a css property mapped to a potentially vendor prefixed property
+function vendorPropName( name ) {
+
+	// Shortcut for names that are not vendor prefixed
+	if ( name in emptyStyle ) {
+		return name;
+	}
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a property mapped along what jQuery.cssProps suggests or to
+// a vendor prefixed property.
+function finalPropName( name ) {
+	var ret = jQuery.cssProps[ name ];
+	if ( !ret ) {
+		ret = jQuery.cssProps[ name ] = vendorPropName( name ) || name;
+	}
+	return ret;
+}
+
+function setPositiveNumber( elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function augmentWidthOrHeight( elem, name, extra, isBorderBox, styles ) {
+	var i,
+		val = 0;
+
+	// If we already have the right measurement, avoid augmentation
+	if ( extra === ( isBorderBox ? "border" : "content" ) ) {
+		i = 4;
+
+	// Otherwise initialize for horizontal or vertical properties
+	} else {
+		i = name === "width" ? 1 : 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin, so add it if we want it
+		if ( extra === "margin" ) {
+			val += jQuery.css( elem, extra + cssExpand[ i ], true, styles );
+		}
+
+		if ( isBorderBox ) {
+
+			// border-box includes padding, so remove it if we want content
+			if ( extra === "content" ) {
+				val -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// At this point, extra isn't border nor margin, so remove border
+			if ( extra !== "margin" ) {
+				val -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		} else {
+
+			// At this point, extra isn't content, so add padding
+			val += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// At this point, extra isn't content nor padding, so add border
+			if ( extra !== "padding" ) {
+				val += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	return val;
+}
+
+function getWidthOrHeight( elem, name, extra ) {
+
+	// Start with computed style
+	var valueIsBorderBox,
+		styles = getStyles( elem ),
+		val = curCSS( elem, name, styles ),
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+	// Computed unit is not pixels. Stop here and return.
+	if ( rnumnonpx.test( val ) ) {
+		return val;
+	}
+
+	// Check for style in case a browser which returns unreliable values
+	// for getComputedStyle silently falls back to the reliable elem.style
+	valueIsBorderBox = isBorderBox &&
+		( support.boxSizingReliable() || val === elem.style[ name ] );
+
+	// Fall back to offsetWidth/Height when value is "auto"
+	// This happens for inline elements with no explicit setting (gh-3571)
+	if ( val === "auto" ) {
+		val = elem[ "offset" + name[ 0 ].toUpperCase() + name.slice( 1 ) ];
+	}
+
+	// Normalize "", auto, and prepare for extra
+	val = parseFloat( val ) || 0;
+
+	// Use the active box-sizing model to add/subtract irrelevant styles
+	return ( val +
+		augmentWidthOrHeight(
+			elem,
+			name,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {
+		"float": "cssFloat"
+	},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = jQuery.camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			if ( type === "number" ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = jQuery.camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( i, name ) {
+	jQuery.cssHooks[ name ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, name, extra );
+						} ) :
+						getWidthOrHeight( elem, name, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = extra && getStyles( elem ),
+				subtract = extra && augmentWidthOrHeight(
+					elem,
+					name,
+					extra,
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+					styles
+				);
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ name ] = value;
+				value = jQuery.css( elem, name );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( !rmargin.test( prefix ) ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 &&
+				( tween.elem.style[ jQuery.cssProps[ tween.prop ] ] != null ||
+					jQuery.cssHooks[ tween.prop ] ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = jQuery.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 13
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = jQuery.camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( jQuery.isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					jQuery.proxy( result.stop, result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( jQuery.isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( jQuery.isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			jQuery.isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !jQuery.isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( jQuery.isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue && type !== false ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = jQuery.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://html.spec.whatwg.org/multipage/infrastructure.html#strip-and-collapse-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( typeof value === "string" && value ) {
+			classes = value.match( rnothtmlwhite ) || [];
+
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		if ( typeof value === "string" && value ) {
+			classes = value.match( rnothtmlwhite ) || [];
+
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value;
+
+		if ( typeof stateVal === "boolean" && type === "string" ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( type === "string" ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = value.match( rnothtmlwhite ) || [];
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, isFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		isFunction = jQuery.isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( isFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/;
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && jQuery.isFunction( elem[ type ] ) && !jQuery.isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+					elem[ type ]();
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( i, name ) {
+
+	// Handle event binding
+	jQuery.fn[ name ] = function( data, fn ) {
+		return arguments.length > 0 ?
+			this.on( name, null, data, fn ) :
+			this.trigger( name );
+	};
+} );
+
+jQuery.fn.extend( {
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+
+
+
+support.focusin = "onfocusin" in window;
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = jQuery.now();
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && jQuery.type( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = jQuery.isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( jQuery.isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() ] = match[ 2 ];
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() ];
+					}
+					return match == null ? null : match;
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 13
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available, append data to url
+			if ( s.data ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( jQuery.isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+
+jQuery._evalUrl = function( url ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+		"throws": true
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( jQuery.isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( jQuery.isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var isFunction = jQuery.isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( isFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain requests
+	if ( s.crossDomain ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" ).prop( {
+					charset: s.scriptCharset,
+					src: s.url
+				} ).on(
+					"load error",
+					callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					}
+				);
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = jQuery.isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && jQuery.isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( jQuery.isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+// Attach a bunch of functions for handling common AJAX events
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( jQuery.isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var doc, docElem, rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		rect = elem.getBoundingClientRect();
+
+		doc = elem.ownerDocument;
+		docElem = doc.documentElement;
+		win = doc.defaultView;
+
+		return {
+			top: rect.top + win.pageYOffset - docElem.clientTop,
+			left: rect.left + win.pageXOffset - docElem.clientLeft
+		};
+	},
+
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// Fixed elements are offset from window (parentOffset = {top:0, left: 0},
+		// because it is its only offset parent
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume getBoundingClientRect is there when computed position is fixed
+			offset = elem.getBoundingClientRect();
+
+		} else {
+
+			// Get *real* offsetParent
+			offsetParent = this.offsetParent();
+
+			// Get correct offsets
+			offset = this.offset();
+			if ( !nodeName( offsetParent[ 0 ], "html" ) ) {
+				parentOffset = offsetParent.offset();
+			}
+
+			// Add offsetParent borders
+			parentOffset = {
+				top: parentOffset.top + jQuery.css( offsetParent[ 0 ], "borderTopWidth", true ),
+				left: parentOffset.left + jQuery.css( offsetParent[ 0 ], "borderLeftWidth", true )
+			};
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( jQuery.isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( jQuery.isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	}
+} );
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( !noGlobal ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/_build/_static/jquery-3.4.1.js b/docs/_build/_static/jquery-3.4.1.js
new file mode 100644
index 00000000..773ad95c
--- /dev/null
+++ b/docs/_build/_static/jquery-3.4.1.js
@@ -0,0 +1,10598 @@
+/*!
+ * jQuery JavaScript Library v3.4.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2019-05-01T21:04Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var document = window.document;
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var concat = arr.concat;
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.4.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	},
+
+	// Support: Android <=4.0 only
+	// Make sure we trim BOM and NBSP
+	rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a global context
+	globalEval: function( code, options ) {
+		DOMEval( code, { nonce: options && options.nonce } );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// Support: Android <=4.0 only
+	trim: function( text ) {
+		return text == null ?
+			"" :
+			( text + "" ).replace( rtrim, "" );
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return concat.apply( [], ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.4
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2019-04-08
+ */
+(function( window ) {
+
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ({}).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	push_native = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[i] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
+	identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+		// "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
+		"*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
+			"*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
+			"*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" +
+			whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
+	funescape = function( _, escaped, escapedWhitespace ) {
+		var high = "0x" + escaped - 0x10000;
+		// NaN means non-codepoint
+		// Support: Firefox<24
+		// Workaround erroneous numeric interpretation of +"0x"
+		return high !== high || escapedWhitespace ?
+			escaped :
+			high < 0 ?
+				// BMP codepoint
+				String.fromCharCode( high + 0x10000 ) :
+				// Supplemental Plane codepoint (surrogate pair)
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		(arr = slice.call( preferredDoc.childNodes )),
+		preferredDoc.childNodes
+	);
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			push_native.apply( target, slice.call(els) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+			// Can't trust NodeList.length
+			while ( (target[j++] = els[i++]) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+
+		if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
+			setDocument( context );
+		}
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) {
+
+				// ID selector
+				if ( (m = match[1]) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( (elem = context.getElementById( m )) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && (elem = newContext.getElementById( m )) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[2] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( (m = match[3]) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				(!rbuggyQSA || !rbuggyQSA.test( selector )) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				(nodeType !== 1 || context.nodeName.toLowerCase() !== "object") ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 && rdescend.test( selector ) ) {
+
+					// Capture the context ID, setting it first if necessary
+					if ( (nid = context.getAttribute( "id" )) ) {
+						nid = nid.replace( rcssescape, fcssescape );
+					} else {
+						context.setAttribute( "id", (nid = expando) );
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[i] = "#" + nid + " " + toSelector( groups[i] );
+					}
+					newSelector = groups.join( "," );
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return (cache[ key + " " ] = value);
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement("fieldset");
+
+	try {
+		return !!fn( el );
+	} catch (e) {
+		return false;
+	} finally {
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split("|"),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[i] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( (cur = cur.nextSibling) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return (name === "input" || name === "button") && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+						inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction(function( argument ) {
+		argument = +argument;
+		return markFunction(function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ (j = matchIndexes[i]) ] ) {
+					seed[j] = !(matches[j] = seed[j]);
+				}
+			}
+		});
+	});
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem.namespaceURI,
+		docElem = (elem.ownerDocument || elem).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9-11, Edge
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	if ( preferredDoc !== document &&
+		(subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert(function( el ) {
+		el.className = "i";
+		return !el.getAttribute("className");
+	});
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert(function( el ) {
+		el.appendChild( document.createComment("") );
+		return !el.getElementsByTagName("*").length;
+	});
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert(function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	});
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter["ID"] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute("id") === attrId;
+			};
+		};
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter["ID"] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode("id");
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode("id");
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( (elem = elems[i++]) ) {
+						node = elem.getAttributeNode("id");
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find["TAG"] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( (elem = results[i++]) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert(function( el ) {
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll("[selected]").length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push("~=");
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll(":checked").length ) {
+				rbuggyQSA.push(":checked");
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push(".#.+[+~]");
+			}
+		});
+
+		assert(function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement("input");
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll("[name=d]").length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll(":enabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll(":disabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll("*,:x");
+			rbuggyQSA.push(",.*:");
+		});
+	}
+
+	if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector) )) ) {
+
+		assert(function( el ) {
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		});
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			));
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( (b = b.parentNode) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			(!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+
+			// Choose the first element that is related to our preferred document
+			if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+				return -1;
+			}
+			if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+			return a === document ? -1 :
+				b === document ? 1 :
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( (cur = cur.parentNode) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( (cur = cur.parentNode) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[i] === bp[i] ) {
+			i++;
+		}
+
+		return i ?
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[i], bp[i] ) :
+
+			// Otherwise nodes in our document sort first
+			ap[i] === preferredDoc ? -1 :
+			bp[i] === preferredDoc ? 1 :
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+					// As well, disconnected nodes are said to be in a document
+					// fragment in IE 9
+					elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch (e) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+	// Set document vars if needed
+	if ( ( context.ownerDocument || context ) !== document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			(val = elem.getAttributeNode(name)) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return (sel + "").replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( (elem = results[i++]) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+		// If no nodeType, this is expected to be an array
+		while ( (node = elem[i++]) ) {
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[1] = match[1].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape );
+
+			if ( match[2] === "~=" ) {
+				match[3] = " " + match[3] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[1] = match[1].toLowerCase();
+
+			if ( match[1].slice( 0, 3 ) === "nth" ) {
+				// nth-* requires argument
+				if ( !match[3] ) {
+					Sizzle.error( match[0] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) );
+				match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" );
+
+			// other types prohibit arguments
+			} else if ( match[3] ) {
+				Sizzle.error( match[0] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[6] && match[2];
+
+			if ( matchExpr["CHILD"].test( match[0] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[3] ) {
+				match[2] = match[4] || match[5] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+				// Get excess from tokenize (recursively)
+				(excess = tokenize( unquoted, true )) &&
+				// advance to the next closing parenthesis
+				(excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+
+				// excess is a negative index
+				match[0] = match[0].slice( 0, excess );
+				match[2] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() { return true; } :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				(pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) &&
+				classCache( className, function( elem ) {
+					return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" );
+				});
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+			};
+		},
+
+		"CHILD": function( type, what, argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( (node = node[ dir ]) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || (node[ expando ] = {});
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								(outerCache[ node.uniqueID ] = {});
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( (node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								(diff = nodeIndex = 0) || start.pop()) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+							// Use previously-cached element index if available
+							if ( useCache ) {
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || (node[ expando ] = {});
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									(outerCache[ node.uniqueID ] = {});
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+								// Use the same loop as above to seek `elem` from the start
+								while ( (node = ++nodeIndex && node && node[ dir ] ||
+									(diff = nodeIndex = 0) || start.pop()) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] || (node[ expando ] = {});
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												(outerCache[ node.uniqueID ] = {});
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction(function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[i] );
+							seed[ idx ] = !( matches[ idx ] = matched[i] );
+						}
+					}) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+		// Potentially complex pseudos
+		"not": markFunction(function( selector ) {
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction(function( seed, matches, context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( (elem = unmatched[i]) ) {
+							seed[i] = !(matches[i] = elem);
+						}
+					}
+				}) :
+				function( elem, context, xml ) {
+					input[0] = elem;
+					matcher( input, null, xml, results );
+					// Don't keep the element (issue #299)
+					input[0] = null;
+					return !results.pop();
+				};
+		}),
+
+		"has": markFunction(function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		}),
+
+		"contains": markFunction(function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		}),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+			// lang value must be a valid identifier
+			if ( !ridentifier.test(lang || "") ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( (elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( (elem = elem.parentNode) && elem.nodeType === 1 );
+				return false;
+			};
+		}),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected);
+		},
+
+		"selected": function( elem ) {
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos["empty"]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo(function() {
+			return [ 0 ];
+		}),
+
+		"last": createPositionalPseudo(function( matchIndexes, length ) {
+			return [ length - 1 ];
+		}),
+
+		"eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		}),
+
+		"even": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"odd": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		})
+	}
+};
+
+Expr.pseudos["nth"] = Expr.pseudos["eq"];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || (match = rcomma.exec( soFar )) ) {
+			if ( match ) {
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[0].length ) || soFar;
+			}
+			groups.push( (tokens = []) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( (match = rcombinators.exec( soFar )) ) {
+			matched = match.shift();
+			tokens.push({
+				value: matched,
+				// Cast descendant combinators to space
+				type: match[0].replace( rtrim, " " )
+			});
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] ||
+				(match = preFilters[ type ]( match ))) ) {
+				matched = match.shift();
+				tokens.push({
+					value: matched,
+					type: type,
+					matches: match
+				});
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[i].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( (elem = elem[ dir ]) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || (elem[ expando ] = {});
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( (oldCache = uniqueCache[ key ]) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return (newCache[ 2 ] = oldCache[ 2 ]);
+						} else {
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[i]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[0];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[i], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( (elem = unmatched[i]) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction(function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( (elem = temp[i]) ) {
+					matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem);
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( (elem = matcherOut[i]) ) {
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( (matcherIn[i] = elem) );
+						}
+					}
+					postFinder( null, (matcherOut = []), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( (elem = matcherOut[i]) &&
+						(temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) {
+
+						seed[temp] = !(results[temp] = elem);
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	});
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[0].type ],
+		implicitRelative = leadingRelative || Expr.relative[" "],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				(checkContext = context).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( (matcher = Expr.relative[ tokens[i].type ]) ) {
+			matchers = [ addCombinator(elementMatcher( matchers ), matcher) ];
+		} else {
+			matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[j].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+						// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+						tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" })
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( (tokens = tokens.slice( j )) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1),
+				len = elems.length;
+
+			if ( outermost ) {
+				outermostContext = context === document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && (elem = elems[i]) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+					if ( !context && elem.ownerDocument !== document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( (matcher = elementMatchers[j++]) ) {
+						if ( matcher( elem, context || document, xml) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+					// They will have gone through all possible matchers
+					if ( (elem = !matcher && elem) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( (matcher = setMatchers[j++]) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !(unmatched[i] || setMatched[i]) ) {
+								setMatched[i] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[i] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( (selector = compiled.selector || selector) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[0] = match[0].slice( 0 );
+		if ( tokens.length > 2 && (token = tokens[0]).type === "ID" &&
+				context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) {
+
+			context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[i];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ (type = token.type) ] ) {
+				break;
+			}
+			if ( (find = Expr.find[ type ]) ) {
+				// Search, expanding context for leading sibling combinators
+				if ( (seed = find(
+					token.matches[0].replace( runescape, funescape ),
+					rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context
+				)) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert(function( el ) {
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
+});
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert(function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute("href") === "#" ;
+}) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	});
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert(function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+}) ) {
+	addHandle( "value", function( elem, name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	});
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert(function( el ) {
+	return el.getAttribute("disabled") == null;
+}) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+					(val = elem.getAttributeNode( name )) && val.specified ?
+					val.value :
+				null;
+		}
+	});
+}
+
+return Sizzle;
+
+})( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( typeof elem.contentDocument !== "undefined" ) {
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+var swap = function( elem, options, callback, args ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.apply( elem, args || [] );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// Support: IE <=9 only
+	option: [ 1, "<select multiple='multiple'>", "</select>" ],
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+// Support: IE <=9 only
+wrapMap.optgroup = wrapMap.option;
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+} )();
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Don't attach events to noData or text/comment nodes (but allow plain objects)
+		if ( !elemData ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = {};
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		// Make a writable jQuery.Event from the native event object
+		var event = jQuery.event.fix( nativeEvent );
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+			handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+						return result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	/* eslint-disable max-len */
+
+	// See https://github.com/eslint/eslint/issues/3229
+	rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi,
+
+	/* eslint-enable */
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.access( src );
+		pdataCur = dataPriv.set( dest, pdataOld );
+		events = pdataOld.events;
+
+		if ( events ) {
+			delete pdataCur.handle;
+			pdataCur.events = {};
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = concat.apply( [], args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								} );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html.replace( rxhtmlTag, "<$1></$2>" );
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Fall back to offsetWidth/offsetHeight when value is "auto"
+	// This happens for inline elements with no explicit setting (gh-3571)
+	// Support: Android <=4.1 - 4.3 only
+	// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+	// Support: IE 9-11 only
+	// Also use offsetWidth/offsetHeight for when box sizing is unreliable
+	// We use getClientRects() to check for hidden/disconnected.
+	// In those cases, the computed value can be trusted to be border-box
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+		val === "auto" ||
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+					jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue && type !== false ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = Date.now();
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+
+jQuery._evalUrl = function( url, options ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+// Attach a bunch of functions for handling common AJAX events
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( i, name ) {
+
+	// Handle event binding
+	jQuery.fn[ name ] = function( data, fn ) {
+		return arguments.length > 0 ?
+			this.on( name, null, data, fn ) :
+			this.trigger( name );
+	};
+} );
+
+jQuery.fn.extend( {
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	}
+} );
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( !noGlobal ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/_build/html/_static/jquery-3.6.0.js b/docs/_build/_static/jquery-3.5.1.js
similarity index 98%
rename from docs/_build/html/_static/jquery-3.6.0.js
rename to docs/_build/_static/jquery-3.5.1.js
index fc6c299b..50937333 100644
--- a/docs/_build/html/_static/jquery-3.6.0.js
+++ b/docs/_build/_static/jquery-3.5.1.js
@@ -1,15 +1,15 @@
 /*!
- * jQuery JavaScript Library v3.6.0
+ * jQuery JavaScript Library v3.5.1
  * https://jquery.com/
  *
  * Includes Sizzle.js
  * https://sizzlejs.com/
  *
- * Copyright OpenJS Foundation and other contributors
+ * Copyright JS Foundation and other contributors
  * Released under the MIT license
  * https://jquery.org/license
  *
- * Date: 2021-03-02T17:08Z
+ * Date: 2020-05-04T22:49Z
  */
 ( function( global, factory ) {
 
@@ -76,16 +76,12 @@ var support = {};
 
 var isFunction = function isFunction( obj ) {
 
-		// Support: Chrome <=57, Firefox <=52
-		// In some browsers, typeof returns "function" for HTML <object> elements
-		// (i.e., `typeof document.createElement( "object" ) === "function"`).
-		// We don't want to classify *any* DOM node as a function.
-		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
-		// Plus for old WebKit, typeof returns "function" for HTML collections
-		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
-		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
-			typeof obj.item !== "function";
-	};
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
 
 
 var isWindow = function isWindow( obj ) {
@@ -151,7 +147,7 @@ function toType( obj ) {
 
 
 var
-	version = "3.6.0",
+	version = "3.5.1",
 
 	// Define a local copy of jQuery
 	jQuery = function( selector, context ) {
@@ -405,7 +401,7 @@ jQuery.extend( {
 			if ( isArrayLike( Object( arr ) ) ) {
 				jQuery.merge( ret,
 					typeof arr === "string" ?
-						[ arr ] : arr
+					[ arr ] : arr
 				);
 			} else {
 				push.call( ret, arr );
@@ -500,9 +496,9 @@ if ( typeof Symbol === "function" ) {
 
 // Populate the class2type map
 jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-	function( _i, name ) {
-		class2type[ "[object " + name + "]" ] = name.toLowerCase();
-	} );
+function( _i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
 
 function isArrayLike( obj ) {
 
@@ -522,14 +518,14 @@ function isArrayLike( obj ) {
 }
 var Sizzle =
 /*!
- * Sizzle CSS Selector Engine v2.3.6
+ * Sizzle CSS Selector Engine v2.3.5
  * https://sizzlejs.com/
  *
  * Copyright JS Foundation and other contributors
  * Released under the MIT license
  * https://js.foundation/
  *
- * Date: 2021-02-16
+ * Date: 2020-03-14
  */
 ( function( window ) {
 var i,
@@ -1112,8 +1108,8 @@ support = Sizzle.support = {};
  * @returns {Boolean} True iff elem is a non-HTML XML node
  */
 isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem && elem.namespaceURI,
-		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+	var namespace = elem.namespaceURI,
+		docElem = ( elem.ownerDocument || elem ).documentElement;
 
 	// Support: IE <=8
 	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
@@ -3028,9 +3024,9 @@ var rneedsContext = jQuery.expr.match.needsContext;
 
 function nodeName( elem, name ) {
 
-	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
 
-}
+};
 var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
 
 
@@ -4001,8 +3997,8 @@ jQuery.extend( {
 			resolveContexts = Array( i ),
 			resolveValues = slice.call( arguments ),
 
-			// the primary Deferred
-			primary = jQuery.Deferred(),
+			// the master Deferred
+			master = jQuery.Deferred(),
 
 			// subordinate callback factory
 			updateFunc = function( i ) {
@@ -4010,30 +4006,30 @@ jQuery.extend( {
 					resolveContexts[ i ] = this;
 					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
 					if ( !( --remaining ) ) {
-						primary.resolveWith( resolveContexts, resolveValues );
+						master.resolveWith( resolveContexts, resolveValues );
 					}
 				};
 			};
 
 		// Single- and empty arguments are adopted like Promise.resolve
 		if ( remaining <= 1 ) {
-			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
 				!remaining );
 
 			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( primary.state() === "pending" ||
+			if ( master.state() === "pending" ||
 				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
 
-				return primary.then();
+				return master.then();
 			}
 		}
 
 		// Multiple arguments are aggregated like Promise.all array elements
 		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
 		}
 
-		return primary.promise();
+		return master.promise();
 	}
 } );
 
@@ -4184,8 +4180,8 @@ var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
 			for ( ; i < len; i++ ) {
 				fn(
 					elems[ i ], key, raw ?
-						value :
-						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
 				);
 			}
 		}
@@ -5093,7 +5089,10 @@ function buildFragment( elems, context, scripts, selection, ignored ) {
 }
 
 
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
 
 function returnTrue() {
 	return true;
@@ -5388,8 +5387,8 @@ jQuery.event = {
 			event = jQuery.event.fix( nativeEvent ),
 
 			handlers = (
-				dataPriv.get( this, "events" ) || Object.create( null )
-			)[ event.type ] || [],
+					dataPriv.get( this, "events" ) || Object.create( null )
+				)[ event.type ] || [],
 			special = jQuery.event.special[ event.type ] || {};
 
 		// Use the fix-ed jQuery.Event rather than the (read-only) native event
@@ -5513,12 +5512,12 @@ jQuery.event = {
 			get: isFunction( hook ) ?
 				function() {
 					if ( this.originalEvent ) {
-						return hook( this.originalEvent );
+							return hook( this.originalEvent );
 					}
 				} :
 				function() {
 					if ( this.originalEvent ) {
-						return this.originalEvent[ name ];
+							return this.originalEvent[ name ];
 					}
 				},
 
@@ -5657,13 +5656,7 @@ function leverageNative( el, type, expectSync ) {
 						// Cancel the outer synthetic event
 						event.stopImmediatePropagation();
 						event.preventDefault();
-
-						// Support: Chrome 86+
-						// In Chrome, if an element having a focusout handler is blurred by
-						// clicking outside of it, it invokes the handler synchronously. If
-						// that handler calls `.remove()` on the element, the data is cleared,
-						// leaving `result` undefined. We need to guard against this.
-						return result && result.value;
+						return result.value;
 					}
 
 				// If this is an inner synthetic event for an event with a bubbling surrogate
@@ -5828,7 +5821,34 @@ jQuery.each( {
 	targetTouches: true,
 	toElement: true,
 	touches: true,
-	which: true
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
 }, jQuery.event.addProp );
 
 jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
@@ -5854,12 +5874,6 @@ jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateTyp
 			return true;
 		},
 
-		// Suppress native focus or blur as it's already being fired
-		// in leverageNative.
-		_default: function() {
-			return true;
-		},
-
 		delegateType: delegateType
 	};
 } );
@@ -6527,10 +6541,6 @@ var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
 		// set in CSS while `offset*` properties report correct values.
 		// Behavior in IE 9 is more subtle than in newer versions & it passes
 		// some versions of this test; make sure not to make it pass there!
-		//
-		// Support: Firefox 70+
-		// Only Firefox includes border widths
-		// in computed dimensions. (gh-4529)
 		reliableTrDimensions: function() {
 			var table, tr, trChild, trStyle;
 			if ( reliableTrDimensionsVal == null ) {
@@ -6538,32 +6548,17 @@ var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
 				tr = document.createElement( "tr" );
 				trChild = document.createElement( "div" );
 
-				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
-				tr.style.cssText = "border:1px solid";
-
-				// Support: Chrome 86+
-				// Height set through cssText does not get applied.
-				// Computed height then comes back as 0.
+				table.style.cssText = "position:absolute;left:-11111px";
 				tr.style.height = "1px";
 				trChild.style.height = "9px";
 
-				// Support: Android 8 Chrome 86+
-				// In our bodyBackground.html iframe,
-				// display for all div elements is set to "inline",
-				// which causes a problem only in Android 8 Chrome 86.
-				// Ensuring the div is display: block
-				// gets around this issue.
-				trChild.style.display = "block";
-
 				documentElement
 					.appendChild( table )
 					.appendChild( tr )
 					.appendChild( trChild );
 
 				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
-					parseInt( trStyle.borderTopWidth, 10 ) +
-					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
 
 				documentElement.removeChild( table );
 			}
@@ -7027,10 +7022,10 @@ jQuery.each( [ "height", "width" ], function( _i, dimension ) {
 					// Running getBoundingClientRect on a disconnected node
 					// in IE throws an error.
 					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-					swap( elem, cssShow, function() {
-						return getWidthOrHeight( elem, dimension, extra );
-					} ) :
-					getWidthOrHeight( elem, dimension, extra );
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
 			}
 		},
 
@@ -7089,7 +7084,7 @@ jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
 					swap( elem, { marginLeft: 0 }, function() {
 						return elem.getBoundingClientRect().left;
 					} )
-			) + "px";
+				) + "px";
 		}
 	}
 );
@@ -7228,7 +7223,7 @@ Tween.propHooks = {
 			if ( jQuery.fx.step[ tween.prop ] ) {
 				jQuery.fx.step[ tween.prop ]( tween );
 			} else if ( tween.elem.nodeType === 1 && (
-				jQuery.cssHooks[ tween.prop ] ||
+					jQuery.cssHooks[ tween.prop ] ||
 					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
 				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
 			} else {
@@ -7473,7 +7468,7 @@ function defaultPrefilter( elem, props, opts ) {
 
 			anim.done( function() {
 
-				/* eslint-enable no-loop-func */
+			/* eslint-enable no-loop-func */
 
 				// The final step of a "hide" animation is actually hiding the element
 				if ( !hidden ) {
@@ -7593,7 +7588,7 @@ function Animation( elem, properties, options ) {
 			tweens: [],
 			createTween: function( prop, end ) {
 				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
 				animation.tweens.push( tween );
 				return tween;
 			},
@@ -7766,8 +7761,7 @@ jQuery.fn.extend( {
 					anim.stop( true );
 				}
 			};
-
-		doAnimation.finish = doAnimation;
+			doAnimation.finish = doAnimation;
 
 		return empty || optall.queue === false ?
 			this.each( doAnimation ) :
@@ -8407,8 +8401,8 @@ jQuery.fn.extend( {
 				if ( this.setAttribute ) {
 					this.setAttribute( "class",
 						className || value === false ?
-							"" :
-							dataPriv.get( this, "__className__" ) || ""
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
 					);
 				}
 			}
@@ -8423,7 +8417,7 @@ jQuery.fn.extend( {
 		while ( ( elem = this[ i++ ] ) ) {
 			if ( elem.nodeType === 1 &&
 				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-				return true;
+					return true;
 			}
 		}
 
@@ -8713,7 +8707,9 @@ jQuery.extend( jQuery.event, {
 				special.bindType || type;
 
 			// jQuery handler
-			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+			handle = (
+					dataPriv.get( cur, "events" ) || Object.create( null )
+				)[ event.type ] &&
 				dataPriv.get( cur, "handle" );
 			if ( handle ) {
 				handle.apply( cur, data );
@@ -8860,7 +8856,7 @@ var rquery = ( /\?/ );
 
 // Cross-browser xml parsing
 jQuery.parseXML = function( data ) {
-	var xml, parserErrorElem;
+	var xml;
 	if ( !data || typeof data !== "string" ) {
 		return null;
 	}
@@ -8869,17 +8865,12 @@ jQuery.parseXML = function( data ) {
 	// IE throws on parseFromString with invalid input.
 	try {
 		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {}
+	} catch ( e ) {
+		xml = undefined;
+	}
 
-	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
-	if ( !xml || parserErrorElem ) {
-		jQuery.error( "Invalid XML: " + (
-			parserErrorElem ?
-				jQuery.map( parserErrorElem.childNodes, function( el ) {
-					return el.textContent;
-				} ).join( "\n" ) :
-				data
-		) );
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
 	}
 	return xml;
 };
@@ -8980,14 +8971,16 @@ jQuery.fn.extend( {
 			// Can add propHook for "elements" to filter or add form elements
 			var elements = jQuery.prop( this, "elements" );
 			return elements ? jQuery.makeArray( elements ) : this;
-		} ).filter( function() {
+		} )
+		.filter( function() {
 			var type = this.type;
 
 			// Use .is( ":disabled" ) so that fieldset[disabled] works
 			return this.name && !jQuery( this ).is( ":disabled" ) &&
 				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
 				( this.checked || !rcheckableType.test( type ) );
-		} ).map( function( _i, elem ) {
+		} )
+		.map( function( _i, elem ) {
 			var val = jQuery( this ).val();
 
 			if ( val == null ) {
@@ -9040,8 +9033,7 @@ var
 
 	// Anchor tag for parsing the document origin
 	originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
+	originAnchor.href = location.href;
 
 // Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
 function addToPrefiltersOrTransports( structure ) {
@@ -9422,8 +9414,8 @@ jQuery.extend( {
 			// Context for global events is callbackContext if it is a DOM node or jQuery collection
 			globalEventContext = s.context &&
 				( callbackContext.nodeType || callbackContext.jquery ) ?
-				jQuery( callbackContext ) :
-				jQuery.event,
+					jQuery( callbackContext ) :
+					jQuery.event,
 
 			// Deferreds
 			deferred = jQuery.Deferred(),
@@ -9735,10 +9727,8 @@ jQuery.extend( {
 				response = ajaxHandleResponses( s, jqXHR, responses );
 			}
 
-			// Use a noop converter for missing script but not if jsonp
-			if ( !isSuccess &&
-				jQuery.inArray( "script", s.dataTypes ) > -1 &&
-				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+			// Use a noop converter for missing script
+			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
 				s.converters[ "text script" ] = function() {};
 			}
 
@@ -10476,6 +10466,12 @@ jQuery.offset = {
 			options.using.call( elem, props );
 
 		} else {
+			if ( typeof props.top === "number" ) {
+				props.top += "px";
+			}
+			if ( typeof props.left === "number" ) {
+				props.left += "px";
+			}
 			curElem.css( props );
 		}
 	}
@@ -10644,11 +10640,8 @@ jQuery.each( [ "top", "left" ], function( _i, prop ) {
 
 // Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
 jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( {
-		padding: "inner" + name,
-		content: type,
-		"": "outer" + name
-	}, function( defaultExtra, funcName ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
 
 		// Margin is only for outerHeight, outerWidth
 		jQuery.fn[ funcName ] = function( margin, value ) {
@@ -10733,8 +10726,7 @@ jQuery.fn.extend( {
 	}
 } );
 
-jQuery.each(
-	( "blur focus focusin focusout resize scroll click dblclick " +
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
 	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
 	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
 	function( _i, name ) {
@@ -10745,8 +10737,7 @@ jQuery.each(
 				this.on( name, null, data, fn ) :
 				this.trigger( name );
 		};
-	}
-);
+	} );
 
 
 
diff --git a/docs/_build/_static/jquery.js b/docs/_build/_static/jquery.js
index c4c6022f..f6a6a99e 100644
--- a/docs/_build/_static/jquery.js
+++ b/docs/_build/_static/jquery.js
@@ -1,2 +1,4 @@
-/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return 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diff --git a/docs/_build/_static/language_data.js b/docs/_build/_static/language_data.js
index 2e22b06a..5266fb19 100644
--- a/docs/_build/_static/language_data.js
+++ b/docs/_build/_static/language_data.js
@@ -5,16 +5,15 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
 
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
 
 
-/* Non-minified version is copied as a separate JS file, is available */
-
+/* Non-minified version JS is _stemmer.js if file is provided */ 
 /**
  * Porter Stemmer
  */
@@ -197,3 +196,102 @@ var Stemmer = function() {
   }
 }
 
+
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
+}
+
+
diff --git a/docs/_build/_static/navigation.png b/docs/_build/_static/navigation.png
new file mode 100644
index 0000000000000000000000000000000000000000..fda6cd29ede1be6168ce8b5e02d099ea9055ccfb
GIT binary patch
literal 120
zcmeAS@N?(olHy`uVBq!ia0vp^j6iI|!2~2VCcCc%QudxMjv*C{TfG{24>0g7{2zat
zIm@i@&@2&y-PR1xY$qjFM{J&XU;25XW1{_&{(Ldr+IaofPcAOcp2zd*+4*{gj8%&M
U)(><J1C3(vboFyt=akR{0L>{V3jhEB

literal 0
HcmV?d00001

diff --git a/docs/_build/_static/pygments.css b/docs/_build/_static/pygments.css
index 87f8bd12..8213e90b 100644
--- a/docs/_build/_static/pygments.css
+++ b/docs/_build/_static/pygments.css
@@ -1,82 +1,65 @@
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-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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-.highlight { background: #f8f8f8; }
-.highlight .c { color: #8f5902; font-style: italic } /* Comment */
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+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
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-.highlight .bp { color: #3465a4 } /* Name.Builtin.Pseudo */
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+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
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+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
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+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
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diff --git a/docs/_build/_static/searchtools.js b/docs/_build/_static/searchtools.js
index ac4d5861..c8215738 100644
--- a/docs/_build/_static/searchtools.js
+++ b/docs/_build/_static/searchtools.js
@@ -1,497 +1,727 @@
 /*
- * searchtools.js
+ * searchtools.js_t
  * ~~~~~~~~~~~~~~~~
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
-"use strict";
 
+
+/* Non-minified version JS is _stemmer.js if file is provided */ 
 /**
- * Simple result scoring code.
+ * Porter Stemmer
  */
-if (typeof Scorer === "undefined") {
-  var Scorer = {
-    // Implement the following function to further tweak the score for each result
-    // The function takes a result array [docname, title, anchor, descr, score, filename]
-    // and returns the new score.
-    /*
-    score: result => {
-      const [docname, title, anchor, descr, score, filename] = result
-      return score
-    },
-    */
-
-    // query matches the full name of an object
-    objNameMatch: 11,
-    // or matches in the last dotted part of the object name
-    objPartialMatch: 6,
-    // Additive scores depending on the priority of the object
-    objPrio: {
-      0: 15, // used to be importantResults
-      1: 5, // used to be objectResults
-      2: -5, // used to be unimportantResults
-    },
-    //  Used when the priority is not in the mapping.
-    objPrioDefault: 0,
-
-    // query found in title
-    title: 15,
-    partialTitle: 7,
-    // query found in terms
-    term: 5,
-    partialTerm: 2,
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
   };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
 }
 
-const _removeChildren = (element) => {
-  while (element && element.lastChild) element.removeChild(element.lastChild);
-};
+
 
 /**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ * Simple result scoring code.
  */
-const _escapeRegExp = (string) =>
-  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, highlightTerms, searchTerms) => {
-  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
-  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-
-  const [docName, title, anchor, descr] = item;
-
-  let listItem = document.createElement("li");
-  let requestUrl;
-  let linkUrl;
-  if (docBuilder === "dirhtml") {
-    // dirhtml builder
-    let dirname = docName + "/";
-    if (dirname.match(/\/index\/$/))
-      dirname = dirname.substring(0, dirname.length - 6);
-    else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
-    linkUrl = requestUrl;
-  } else {
-    // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
-    linkUrl = docName + docLinkSuffix;
-  }
-  const params = new URLSearchParams();
-  params.set("highlight", [...highlightTerms].join(" "));
-  let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + "?" + params.toString() + anchor;
-  linkEl.innerHTML = title;
-  if (descr)
-    listItem.appendChild(document.createElement("span")).innerText =
-      " (" + descr + ")";
-  else if (showSearchSummary)
-    fetch(requestUrl)
-      .then((responseData) => responseData.text())
-      .then((data) => {
-        if (data)
-          listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, highlightTerms)
-          );
-      });
-  Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
-  Search.stopPulse();
-  Search.title.innerText = _("Search Results");
-  if (!resultCount)
-    Search.status.innerText = Documentation.gettext(
-      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
-    );
-  else
-    Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
-};
-const _displayNextItem = (
-  results,
-  resultCount,
-  highlightTerms,
-  searchTerms
-) => {
-  // results left, load the summary and display it
-  // this is intended to be dynamic (don't sub resultsCount)
-  if (results.length) {
-    _displayItem(results.pop(), highlightTerms, searchTerms);
-    setTimeout(
-      () => _displayNextItem(results, resultCount, highlightTerms, searchTerms),
-      5
-    );
-  }
-  // search finished, update title and status message
-  else _finishSearch(resultCount);
+var Scorer = {
+  // Implement the following function to further tweak the score for each result
+  // The function takes a result array [filename, title, anchor, descr, score]
+  // and returns the new score.
+  /*
+  score: function(result) {
+    return result[4];
+  },
+  */
+
+  // query matches the full name of an object
+  objNameMatch: 11,
+  // or matches in the last dotted part of the object name
+  objPartialMatch: 6,
+  // Additive scores depending on the priority of the object
+  objPrio: {0:  15,   // used to be importantResults
+            1:  5,   // used to be objectResults
+            2: -5},  // used to be unimportantResults
+  //  Used when the priority is not in the mapping.
+  objPrioDefault: 0,
+
+  // query found in title
+  title: 15,
+  // query found in terms
+  term: 5
 };
 
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
-  var splitQuery = (query) => query
-      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
-      .filter(term => term)  // remove remaining empty strings
+
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
 }
 
+
+
+
 /**
  * Search Module
  */
-const Search = {
-  _index: null,
-  _queued_query: null,
-  _pulse_status: -1,
-
-  htmlToText: (htmlString) => {
-    const htmlElement = document
-      .createRange()
-      .createContextualFragment(htmlString);
-    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
-    const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
-    console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
-    );
-    return "";
+var Search = {
+
+  _index : null,
+  _queued_query : null,
+  _pulse_status : -1,
+
+  init : function() {
+      var params = $.getQueryParameters();
+      if (params.q) {
+          var query = params.q[0];
+          $('input[name="q"]')[0].value = query;
+          this.performSearch(query);
+      }
   },
 
-  init: () => {
-    const query = new URLSearchParams(window.location.search).get("q");
-    document
-      .querySelectorAll('input[name="q"]')
-      .forEach((el) => (el.value = query));
-    if (query) Search.performSearch(query);
+  loadIndex : function(url) {
+    $.ajax({type: "GET", url: url, data: null,
+            dataType: "script", cache: true,
+            complete: function(jqxhr, textstatus) {
+              if (textstatus != "success") {
+                document.getElementById("searchindexloader").src = url;
+              }
+            }});
   },
 
-  loadIndex: (url) =>
-    (document.body.appendChild(document.createElement("script")).src = url),
-
-  setIndex: (index) => {
-    Search._index = index;
-    if (Search._queued_query !== null) {
-      const query = Search._queued_query;
-      Search._queued_query = null;
-      Search.query(query);
+  setIndex : function(index) {
+    var q;
+    this._index = index;
+    if ((q = this._queued_query) !== null) {
+      this._queued_query = null;
+      Search.query(q);
     }
   },
 
-  hasIndex: () => Search._index !== null,
-
-  deferQuery: (query) => (Search._queued_query = query),
+  hasIndex : function() {
+      return this._index !== null;
+  },
 
-  stopPulse: () => (Search._pulse_status = -1),
+  deferQuery : function(query) {
+      this._queued_query = query;
+  },
 
-  startPulse: () => {
-    if (Search._pulse_status >= 0) return;
+  stopPulse : function() {
+      this._pulse_status = 0;
+  },
 
-    const pulse = () => {
+  startPulse : function() {
+    if (this._pulse_status >= 0)
+        return;
+    function pulse() {
+      var i;
       Search._pulse_status = (Search._pulse_status + 1) % 4;
-      Search.dots.innerText = ".".repeat(Search._pulse_status);
-      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
-    };
+      var dotString = '';
+      for (i = 0; i < Search._pulse_status; i++)
+        dotString += '.';
+      Search.dots.text(dotString);
+      if (Search._pulse_status > -1)
+        window.setTimeout(pulse, 500);
+    }
     pulse();
   },
 
   /**
    * perform a search for something (or wait until index is loaded)
    */
-  performSearch: (query) => {
+  performSearch : function(query) {
     // create the required interface elements
-    const searchText = document.createElement("h2");
-    searchText.textContent = _("Searching");
-    const searchSummary = document.createElement("p");
-    searchSummary.classList.add("search-summary");
-    searchSummary.innerText = "";
-    const searchList = document.createElement("ul");
-    searchList.classList.add("search");
-
-    const out = document.getElementById("search-results");
-    Search.title = out.appendChild(searchText);
-    Search.dots = Search.title.appendChild(document.createElement("span"));
-    Search.status = out.appendChild(searchSummary);
-    Search.output = out.appendChild(searchList);
-
-    const searchProgress = document.getElementById("search-progress");
-    // Some themes don't use the search progress node
-    if (searchProgress) {
-      searchProgress.innerText = _("Preparing search...");
-    }
-    Search.startPulse();
+    this.out = $('#search-results');
+    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
+    this.dots = $('<span></span>').appendTo(this.title);
+    this.status = $('<p style="display: none"></p>').appendTo(this.out);
+    this.output = $('<ul class="search"/>').appendTo(this.out);
+
+    $('#search-progress').text(_('Preparing search...'));
+    this.startPulse();
 
     // index already loaded, the browser was quick!
-    if (Search.hasIndex()) Search.query(query);
-    else Search.deferQuery(query);
+    if (this.hasIndex())
+      this.query(query);
+    else
+      this.deferQuery(query);
   },
 
   /**
    * execute search (requires search index to be loaded)
    */
-  query: (query) => {
-    // stem the search terms and add them to the correct list
-    const stemmer = new Stemmer();
-    const searchTerms = new Set();
-    const excludedTerms = new Set();
-    const highlightTerms = new Set();
-    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
-    splitQuery(query.trim()).forEach((queryTerm) => {
-      const queryTermLower = queryTerm.toLowerCase();
-
-      // maybe skip this "word"
-      // stopwords array is from language_data.js
-      if (
-        stopwords.indexOf(queryTermLower) !== -1 ||
-        queryTerm.match(/^\d+$/)
-      )
-        return;
+  query : function(query) {
+    var i;
+    var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+
+    // stem the searchterms and add them to the correct list
+    var stemmer = new Stemmer();
+    var searchterms = [];
+    var excluded = [];
+    var hlterms = [];
+    var tmp = splitQuery(query);
+    var objectterms = [];
+    for (i = 0; i < tmp.length; i++) {
+      if (tmp[i] !== "") {
+          objectterms.push(tmp[i].toLowerCase());
+      }
 
+      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
+          tmp[i] === "") {
+        // skip this "word"
+        continue;
+      }
       // stem the word
-      let word = stemmer.stemWord(queryTermLower);
+      var word = stemmer.stemWord(tmp[i].toLowerCase());
+      // prevent stemmer from cutting word smaller than two chars
+      if(word.length < 3 && tmp[i].length >= 3) {
+        word = tmp[i];
+      }
+      var toAppend;
       // select the correct list
-      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      if (word[0] == '-') {
+        toAppend = excluded;
+        word = word.substr(1);
+      }
       else {
-        searchTerms.add(word);
-        highlightTerms.add(queryTermLower);
+        toAppend = searchterms;
+        hlterms.push(tmp[i].toLowerCase());
       }
-    });
+      // only add if not already in the list
+      if (!$u.contains(toAppend, word))
+        toAppend.push(word);
+    }
+    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
+
+    // console.debug('SEARCH: searching for:');
+    // console.info('required: ', searchterms);
+    // console.info('excluded: ', excluded);
 
-    // console.debug("SEARCH: searching for:");
-    // console.info("required: ", [...searchTerms]);
-    // console.info("excluded: ", [...excludedTerms]);
+    // prepare search
+    var terms = this._index.terms;
+    var titleterms = this._index.titleterms;
 
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
-    _removeChildren(document.getElementById("search-progress"));
+    // array of [filename, title, anchor, descr, score]
+    var results = [];
+    $('#search-progress').empty();
 
     // lookup as object
-    objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
-    );
+    for (i = 0; i < objectterms.length; i++) {
+      var others = [].concat(objectterms.slice(0, i),
+                             objectterms.slice(i+1, objectterms.length));
+      results = results.concat(this.performObjectSearch(objectterms[i], others));
+    }
 
     // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+    if (Scorer.score) {
+      for (i = 0; i < results.length; i++)
+        results[i][4] = Scorer.score(results[i]);
+    }
 
     // now sort the results by score (in opposite order of appearance, since the
     // display function below uses pop() to retrieve items) and then
     // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
+    results.sort(function(a, b) {
+      var left = a[4];
+      var right = b[4];
+      if (left > right) {
+        return 1;
+      } else if (left < right) {
+        return -1;
+      } else {
         // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+        left = a[1].toLowerCase();
+        right = b[1].toLowerCase();
+        return (left > right) ? -1 : ((left < right) ? 1 : 0);
       }
-      return leftScore > rightScore ? 1 : -1;
     });
 
-    // remove duplicate search results
-    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
-    let seen = new Set();
-    results = results.reverse().reduce((acc, result) => {
-      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
-      if (!seen.has(resultStr)) {
-        acc.push(result);
-        seen.add(resultStr);
-      }
-      return acc;
-    }, []);
-
-    results = results.reverse();
-
     // for debugging
     //Search.lastresults = results.slice();  // a copy
-    // console.info("search results:", Search.lastresults);
+    //console.info('search results:', Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, highlightTerms, searchTerms);
+    var resultCount = results.length;
+    function displayNextItem() {
+      // results left, load the summary and display it
+      if (results.length) {
+        var item = results.pop();
+        var listItem = $('<li style="display:none"></li>');
+        if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === '') {
+          // dirhtml builder
+          var dirname = item[0] + '/';
+          if (dirname.match(/\/index\/$/)) {
+            dirname = dirname.substring(0, dirname.length-6);
+          } else if (dirname == 'index/') {
+            dirname = '';
+          }
+          listItem.append($('<a/>').attr('href',
+            DOCUMENTATION_OPTIONS.URL_ROOT + dirname +
+            highlightstring + item[2]).html(item[1]));
+        } else {
+          // normal html builders
+          listItem.append($('<a/>').attr('href',
+            item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX +
+            highlightstring + item[2]).html(item[1]));
+        }
+        if (item[3]) {
+          listItem.append($('<span> (' + item[3] + ')</span>'));
+          Search.output.append(listItem);
+          listItem.slideDown(5, function() {
+            displayNextItem();
+          });
+        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
+          var suffix = DOCUMENTATION_OPTIONS.SOURCELINK_SUFFIX;
+          $.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[5] + (item[5].slice(-suffix.length) === suffix ? '' : suffix),
+                  dataType: "text",
+                  complete: function(jqxhr, textstatus) {
+                    var data = jqxhr.responseText;
+                    if (data !== '' && data !== undefined) {
+                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
+                    }
+                    Search.output.append(listItem);
+                    listItem.slideDown(5, function() {
+                      displayNextItem();
+                    });
+                  }});
+        } else {
+          // no source available, just display title
+          Search.output.append(listItem);
+          listItem.slideDown(5, function() {
+            displayNextItem();
+          });
+        }
+      }
+      // search finished, update title and status message
+      else {
+        Search.stopPulse();
+        Search.title.text(_('Search Results'));
+        if (!resultCount)
+          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
+        else
+            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
+        Search.status.fadeIn(500);
+      }
+    }
+    displayNextItem();
   },
 
   /**
    * search for object names
    */
-  performObjectSearch: (object, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const objects = Search._index.objects;
-    const objNames = Search._index.objnames;
-    const titles = Search._index.titles;
-
-    const results = [];
-
-    const objectSearchCallback = (prefix, match) => {
-      const name = match[4]
-      const fullname = (prefix ? prefix + "." : "") + name;
-      const fullnameLower = fullname.toLowerCase();
-      if (fullnameLower.indexOf(object) < 0) return;
-
-      let score = 0;
-      const parts = fullnameLower.split(".");
-
-      // check for different match types: exact matches of full name or
-      // "last name" (i.e. last dotted part)
-      if (fullnameLower === object || parts.slice(-1)[0] === object)
-        score += Scorer.objNameMatch;
-      else if (parts.slice(-1)[0].indexOf(object) > -1)
-        score += Scorer.objPartialMatch; // matches in last name
-
-      const objName = objNames[match[1]][2];
-      const title = titles[match[0]];
-
-      // If more than one term searched for, we require other words to be
-      // found in the name/title/description
-      const otherTerms = new Set(objectTerms);
-      otherTerms.delete(object);
-      if (otherTerms.size > 0) {
-        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
-        if (
-          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
-        )
-          return;
+  performObjectSearch : function(object, otherterms) {
+    var filenames = this._index.filenames;
+    var docnames = this._index.docnames;
+    var objects = this._index.objects;
+    var objnames = this._index.objnames;
+    var titles = this._index.titles;
+
+    var i;
+    var results = [];
+
+    for (var prefix in objects) {
+      for (var name in objects[prefix]) {
+        var fullname = (prefix ? prefix + '.' : '') + name;
+        if (fullname.toLowerCase().indexOf(object) > -1) {
+          var score = 0;
+          var parts = fullname.split('.');
+          // check for different match types: exact matches of full name or
+          // "last name" (i.e. last dotted part)
+          if (fullname == object || parts[parts.length - 1] == object) {
+            score += Scorer.objNameMatch;
+          // matches in last name
+          } else if (parts[parts.length - 1].indexOf(object) > -1) {
+            score += Scorer.objPartialMatch;
+          }
+          var match = objects[prefix][name];
+          var objname = objnames[match[1]][2];
+          var title = titles[match[0]];
+          // If more than one term searched for, we require other words to be
+          // found in the name/title/description
+          if (otherterms.length > 0) {
+            var haystack = (prefix + ' ' + name + ' ' +
+                            objname + ' ' + title).toLowerCase();
+            var allfound = true;
+            for (i = 0; i < otherterms.length; i++) {
+              if (haystack.indexOf(otherterms[i]) == -1) {
+                allfound = false;
+                break;
+              }
+            }
+            if (!allfound) {
+              continue;
+            }
+          }
+          var descr = objname + _(', in ') + title;
+
+          var anchor = match[3];
+          if (anchor === '')
+            anchor = fullname;
+          else if (anchor == '-')
+            anchor = objnames[match[1]][1] + '-' + fullname;
+          // add custom score for some objects according to scorer
+          if (Scorer.objPrio.hasOwnProperty(match[2])) {
+            score += Scorer.objPrio[match[2]];
+          } else {
+            score += Scorer.objPrioDefault;
+          }
+          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
+        }
       }
+    }
 
-      let anchor = match[3];
-      if (anchor === "") anchor = fullname;
-      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
-      const descr = objName + _(", in ") + title;
-
-      // add custom score for some objects according to scorer
-      if (Scorer.objPrio.hasOwnProperty(match[2]))
-        score += Scorer.objPrio[match[2]];
-      else score += Scorer.objPrioDefault;
-
-      results.push([
-        docNames[match[0]],
-        fullname,
-        "#" + anchor,
-        descr,
-        score,
-        filenames[match[0]],
-      ]);
-    };
-    Object.keys(objects).forEach((prefix) =>
-      objects[prefix].forEach((array) =>
-        objectSearchCallback(prefix, array)
-      )
-    );
     return results;
   },
 
   /**
    * search for full-text terms in the index
    */
-  performTermsSearch: (searchTerms, excludedTerms) => {
-    // prepare search
-    const terms = Search._index.terms;
-    const titleTerms = Search._index.titleterms;
-    const docNames = Search._index.docnames;
-    const filenames = Search._index.filenames;
-    const titles = Search._index.titles;
+  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
+    var docnames = this._index.docnames;
+    var filenames = this._index.filenames;
+    var titles = this._index.titles;
 
-    const scoreMap = new Map();
-    const fileMap = new Map();
+    var i, j, file;
+    var fileMap = {};
+    var scoreMap = {};
+    var results = [];
 
     // perform the search on the required terms
-    searchTerms.forEach((word) => {
-      const files = [];
-      const arr = [
-        { files: terms[word], score: Scorer.term },
-        { files: titleTerms[word], score: Scorer.title },
+    for (i = 0; i < searchterms.length; i++) {
+      var word = searchterms[i];
+      var files = [];
+      var _o = [
+        {files: terms[word], score: Scorer.term},
+        {files: titleterms[word], score: Scorer.title}
       ];
-      // add support for partial matches
-      if (word.length > 2) {
-        const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
-      }
 
       // no match but word was a required one
-      if (arr.every((record) => record.files === undefined)) return;
-
+      if ($u.every(_o, function(o){return o.files === undefined;})) {
+        break;
+      }
       // found search word in contents
-      arr.forEach((record) => {
-        if (record.files === undefined) return;
-
-        let recordFiles = record.files;
-        if (recordFiles.length === undefined) recordFiles = [recordFiles];
-        files.push(...recordFiles);
-
-        // set score for the word in each file
-        recordFiles.forEach((file) => {
-          if (!scoreMap.has(file)) scoreMap.set(file, {});
-          scoreMap.get(file)[word] = record.score;
-        });
+      $u.each(_o, function(o) {
+        var _files = o.files;
+        if (_files === undefined)
+          return
+
+        if (_files.length === undefined)
+          _files = [_files];
+        files = files.concat(_files);
+
+        // set score for the word in each file to Scorer.term
+        for (j = 0; j < _files.length; j++) {
+          file = _files[j];
+          if (!(file in scoreMap))
+            scoreMap[file] = {}
+          scoreMap[file][word] = o.score;
+        }
       });
 
       // create the mapping
-      files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
-      });
-    });
+      for (j = 0; j < files.length; j++) {
+        file = files[j];
+        if (file in fileMap)
+          fileMap[file].push(word);
+        else
+          fileMap[file] = [word];
+      }
+    }
 
     // now check if the files don't contain excluded terms
-    const results = [];
-    for (const [file, wordList] of fileMap) {
-      // check if all requirements are matched
+    for (file in fileMap) {
+      var valid = true;
 
-      // as search terms with length < 3 are discarded
-      const filteredTermCount = [...searchTerms].filter(
-        (term) => term.length > 2
-      ).length;
-      if (
-        wordList.length !== searchTerms.size &&
-        wordList.length !== filteredTermCount
-      )
-        continue;
+      // check if all requirements are matched
+      if (fileMap[file].length != searchterms.length)
+          continue;
 
       // ensure that none of the excluded terms is in the search result
-      if (
-        [...excludedTerms].some(
-          (term) =>
-            terms[term] === file ||
-            titleTerms[term] === file ||
-            (terms[term] || []).includes(file) ||
-            (titleTerms[term] || []).includes(file)
-        )
-      )
-        break;
+      for (i = 0; i < excluded.length; i++) {
+        if (terms[excluded[i]] == file ||
+            titleterms[excluded[i]] == file ||
+            $u.contains(terms[excluded[i]] || [], file) ||
+            $u.contains(titleterms[excluded[i]] || [], file)) {
+          valid = false;
+          break;
+        }
+      }
 
-      // select one (max) score for the file.
-      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
-      // add result to the result list
-      results.push([
-        docNames[file],
-        titles[file],
-        "",
-        null,
-        score,
-        filenames[file],
-      ]);
+      // if we have still a valid result we can add it to the result list
+      if (valid) {
+        // select one (max) score for the file.
+        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
+        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
+        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
+      }
     }
     return results;
   },
@@ -499,33 +729,30 @@ const Search = {
   /**
    * helper function to return a node containing the
    * search summary for a given text. keywords is a list
-   * of stemmed words, highlightWords is the list of normal, unstemmed
+   * of stemmed words, hlwords is the list of normal, unstemmed
    * words. the first one is used to find the occurrence, the
    * latter for highlighting it.
    */
-  makeSearchSummary: (htmlText, keywords, highlightWords) => {
-    const text = Search.htmlToText(htmlText).toLowerCase();
-    if (text === "") return null;
-
-    const actualStartPosition = [...keywords]
-      .map((k) => text.indexOf(k.toLowerCase()))
-      .filter((i) => i > -1)
-      .slice(-1)[0];
-    const startWithContext = Math.max(actualStartPosition - 120, 0);
-
-    const top = startWithContext === 0 ? "" : "...";
-    const tail = startWithContext + 240 < text.length ? "..." : "";
-
-    let summary = document.createElement("div");
-    summary.classList.add("context");
-    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
-
-    highlightWords.forEach((highlightWord) =>
-      _highlightText(summary, highlightWord, "highlighted")
-    );
-
-    return summary;
-  },
+  makeSearchSummary : function(text, keywords, hlwords) {
+    var textLower = text.toLowerCase();
+    var start = 0;
+    $.each(keywords, function() {
+      var i = textLower.indexOf(this.toLowerCase());
+      if (i > -1)
+        start = i;
+    });
+    start = Math.max(start - 120, 0);
+    var excerpt = ((start > 0) ? '...' : '') +
+      $.trim(text.substr(start, 240)) +
+      ((start + 240 - text.length) ? '...' : '');
+    var rv = $('<div class="context"></div>').text(excerpt);
+    $.each(hlwords, function() {
+      rv = rv.highlightText(this, 'highlighted');
+    });
+    return rv;
+  }
 };
 
-_ready(Search.init);
+$(document).ready(function() {
+  Search.init();
+});
\ No newline at end of file
diff --git a/docs/_build/_static/sphinxdoc.css b/docs/_build/_static/sphinxdoc.css
new file mode 100644
index 00000000..88767732
--- /dev/null
+++ b/docs/_build/_static/sphinxdoc.css
@@ -0,0 +1,345 @@
+/*
+ * sphinxdoc.css_t
+ * ~~~~~~~~~~~~~~~
+ *
+ * Sphinx stylesheet -- sphinxdoc theme.  Originally created by
+ * Armin Ronacher for Werkzeug.
+ *
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+@import url("basic.css");
+
+/* -- page layout ----------------------------------------------------------- */
+
+body {
+    font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
+                 'Verdana', sans-serif;
+    font-size: 14px;
+    letter-spacing: -0.01em;
+    line-height: 150%;
+    text-align: center;
+    background-color: #BFD1D4;
+    color: black;
+    padding: 0;
+    border: 1px solid #aaa;
+
+    margin: 0px 80px 0px 80px;
+    min-width: 740px;
+}
+
+div.document {
+    background-color: white;
+    text-align: left;
+    background-image: url(contents.png);
+    background-repeat: repeat-x;
+}
+
+div.bodywrapper {
+    margin: 0 240px 0 0;
+    border-right: 1px solid #ccc;
+}
+
+div.body {
+    margin: 0;
+    padding: 0.5em 20px 20px 20px;
+}
+
+div.related {
+    font-size: 1em;
+}
+
+div.related ul {
+    background-image: url(navigation.png);
+    height: 2em;
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+}
+
+div.related ul li {
+    margin: 0;
+    padding: 0;
+    height: 2em;
+    float: left;
+}
+
+div.related ul li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+div.related ul li a {
+    margin: 0;
+    padding: 0 5px 0 5px;
+    line-height: 1.75em;
+    color: #EE9816;
+}
+
+div.related ul li a:hover {
+    color: #3CA8E7;
+}
+
+div.sphinxsidebarwrapper {
+    padding: 0;
+}
+
+div.sphinxsidebar {
+    margin: 0;
+    padding: 0.5em 15px 15px 0;
+    width: 210px;
+    float: right;
+    font-size: 1em;
+    text-align: left;
+}
+
+div.sphinxsidebar h3, div.sphinxsidebar h4 {
+    margin: 1em 0 0.5em 0;
+    font-size: 1em;
+    padding: 0.1em 0 0.1em 0.5em;
+    color: white;
+    border: 1px solid #86989B;
+    background-color: #AFC1C4;
+}
+
+div.sphinxsidebar h3 a {
+    color: white;
+}
+
+div.sphinxsidebar ul {
+    padding-left: 1.5em;
+    margin-top: 7px;
+    padding: 0;
+    line-height: 130%;
+}
+
+div.sphinxsidebar ul ul {
+    margin-left: 20px;
+}
+
+div.footer {
+    background-color: #E3EFF1;
+    color: #86989B;
+    padding: 3px 8px 3px 0;
+    clear: both;
+    font-size: 0.8em;
+    text-align: right;
+}
+
+div.footer a {
+    color: #86989B;
+    text-decoration: underline;
+}
+
+/* -- body styles ----------------------------------------------------------- */
+
+p {    
+    margin: 0.8em 0 0.5em 0;
+}
+
+a {
+    color: #CA7900;
+    text-decoration: none;
+}
+
+a:hover {
+    color: #2491CF;
+}
+
+div.body a {
+    text-decoration: underline;
+}
+
+h1 {
+    margin: 0;
+    padding: 0.7em 0 0.3em 0;
+    font-size: 1.5em;
+    color: #11557C;
+}
+
+h2 {
+    margin: 1.3em 0 0.2em 0;
+    font-size: 1.35em;
+    padding: 0;
+}
+
+h3 {
+    margin: 1em 0 -0.3em 0;
+    font-size: 1.2em;
+}
+
+div.body h1 a, div.body h2 a, div.body h3 a, div.body h4 a, div.body h5 a, div.body h6 a {
+    color: black!important;
+}
+
+h1 a.anchor, h2 a.anchor, h3 a.anchor, h4 a.anchor, h5 a.anchor, h6 a.anchor {
+    display: none;
+    margin: 0 0 0 0.3em;
+    padding: 0 0.2em 0 0.2em;
+    color: #aaa!important;
+}
+
+h1:hover a.anchor, h2:hover a.anchor, h3:hover a.anchor, h4:hover a.anchor,
+h5:hover a.anchor, h6:hover a.anchor {
+    display: inline;
+}
+
+h1 a.anchor:hover, h2 a.anchor:hover, h3 a.anchor:hover, h4 a.anchor:hover,
+h5 a.anchor:hover, h6 a.anchor:hover {
+    color: #777;
+    background-color: #eee;
+}
+
+a.headerlink {
+    color: #c60f0f!important;
+    font-size: 1em;
+    margin-left: 6px;
+    padding: 0 4px 0 4px;
+    text-decoration: none!important;
+}
+
+a.headerlink:hover {
+    background-color: #ccc;
+    color: white!important;
+}
+
+cite, code, code {
+    font-family: 'Consolas', 'Deja Vu Sans Mono',
+                 'Bitstream Vera Sans Mono', monospace;
+    font-size: 0.95em;
+    letter-spacing: 0.01em;
+}
+
+code {
+    background-color: #f2f2f2;
+    border-bottom: 1px solid #ddd;
+    color: #333;
+}
+
+code.descname, code.descclassname, code.xref {
+    border: 0;
+}
+
+hr {
+    border: 1px solid #abc;
+    margin: 2em;
+}
+
+a code {
+    border: 0;
+    color: #CA7900;
+}
+
+a code:hover {
+    color: #2491CF;
+}
+
+pre {
+    font-family: 'Consolas', 'Deja Vu Sans Mono',
+                 'Bitstream Vera Sans Mono', monospace;
+    font-size: 0.95em;
+    letter-spacing: 0.015em;
+    line-height: 120%;
+    padding: 0.5em;
+    border: 1px solid #ccc;
+    background-color: #f8f8f8;
+}
+
+pre a {
+    color: inherit;
+    text-decoration: underline;
+}
+
+td.linenos pre {
+    padding: 0.5em 0;
+}
+
+div.quotebar {
+    background-color: #f8f8f8;
+    max-width: 250px;
+    float: right;
+    padding: 2px 7px;
+    border: 1px solid #ccc;
+}
+
+div.topic {
+    background-color: #f8f8f8;
+}
+
+table {
+    border-collapse: collapse;
+    margin: 0 -0.5em 0 -0.5em;
+}
+
+table td, table th {
+    padding: 0.2em 0.5em 0.2em 0.5em;
+}
+
+div.admonition, div.warning {
+    font-size: 0.9em;
+    margin: 1em 0 1em 0;
+    border: 1px solid #86989B;
+    background-color: #f7f7f7;
+    padding: 0;
+}
+
+div.admonition p, div.warning p {
+    margin: 0.5em 1em 0.5em 1em;
+    padding: 0;
+}
+
+div.admonition pre, div.warning pre {
+    margin: 0.4em 1em 0.4em 1em;
+}
+
+div.admonition p.admonition-title,
+div.warning p.admonition-title {
+    margin: 0;
+    padding: 0.1em 0 0.1em 0.5em;
+    color: white;
+    border-bottom: 1px solid #86989B;
+    font-weight: bold;
+    background-color: #AFC1C4;
+}
+
+div.warning {
+    border: 1px solid #940000;
+}
+
+div.warning p.admonition-title {
+    background-color: #CF0000;
+    border-bottom-color: #940000;
+}
+
+div.admonition ul, div.admonition ol,
+div.warning ul, div.warning ol {
+    margin: 0.1em 0.5em 0.5em 3em;
+    padding: 0;
+}
+
+div.versioninfo {
+    margin: 1em 0 0 0;
+    border: 1px solid #ccc;
+    background-color: #DDEAF0;
+    padding: 8px;
+    line-height: 1.3em;
+    font-size: 0.9em;
+}
+
+.viewcode-back {
+    font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
+                 'Verdana', sans-serif;
+}
+
+div.viewcode-block:target {
+    background-color: #f4debf;
+    border-top: 1px solid #ac9;
+    border-bottom: 1px solid #ac9;
+}
+
+div.code-block-caption {
+    background-color: #ddd;
+    color: #222;
+    border: 1px solid #ccc;
+}
\ No newline at end of file
diff --git a/docs/_build/_static/underscore-1.3.1.js b/docs/_build/_static/underscore-1.3.1.js
new file mode 100644
index 00000000..208d4cd8
--- /dev/null
+++ b/docs/_build/_static/underscore-1.3.1.js
@@ -0,0 +1,999 @@
+//     Underscore.js 1.3.1
+//     (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
+//     Underscore is freely distributable under the MIT license.
+//     Portions of Underscore are inspired or borrowed from Prototype,
+//     Oliver Steele's Functional, and John Resig's Micro-Templating.
+//     For all details and documentation:
+//     http://documentcloud.github.com/underscore
+
+(function() {
+
+  // Baseline setup
+  // --------------
+
+  // Establish the root object, `window` in the browser, or `global` on the server.
+  var root = this;
+
+  // Save the previous value of the `_` variable.
+  var previousUnderscore = root._;
+
+  // Establish the object that gets returned to break out of a loop iteration.
+  var breaker = {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var slice            = ArrayProto.slice,
+      unshift          = ArrayProto.unshift,
+      toString         = ObjProto.toString,
+      hasOwnProperty   = ObjProto.hasOwnProperty;
+
+  // All **ECMAScript 5** native function implementations that we hope to use
+  // are declared here.
+  var
+    nativeForEach      = ArrayProto.forEach,
+    nativeMap          = ArrayProto.map,
+    nativeReduce       = ArrayProto.reduce,
+    nativeReduceRight  = ArrayProto.reduceRight,
+    nativeFilter       = ArrayProto.filter,
+    nativeEvery        = ArrayProto.every,
+    nativeSome         = ArrayProto.some,
+    nativeIndexOf      = ArrayProto.indexOf,
+    nativeLastIndexOf  = ArrayProto.lastIndexOf,
+    nativeIsArray      = Array.isArray,
+    nativeKeys         = Object.keys,
+    nativeBind         = FuncProto.bind;
+
+  // Create a safe reference to the Underscore object for use below.
+  var _ = function(obj) { return new wrapper(obj); };
+
+  // Export the Underscore object for **Node.js**, with
+  // backwards-compatibility for the old `require()` API. If we're in
+  // the browser, add `_` as a global object via a string identifier,
+  // for Closure Compiler "advanced" mode.
+  if (typeof exports !== 'undefined') {
+    if (typeof module !== 'undefined' && module.exports) {
+      exports = module.exports = _;
+    }
+    exports._ = _;
+  } else {
+    root['_'] = _;
+  }
+
+  // Current version.
+  _.VERSION = '1.3.1';
+
+  // Collection Functions
+  // --------------------
+
+  // The cornerstone, an `each` implementation, aka `forEach`.
+  // Handles objects with the built-in `forEach`, arrays, and raw objects.
+  // Delegates to **ECMAScript 5**'s native `forEach` if available.
+  var each = _.each = _.forEach = function(obj, iterator, context) {
+    if (obj == null) return;
+    if (nativeForEach && obj.forEach === nativeForEach) {
+      obj.forEach(iterator, context);
+    } else if (obj.length === +obj.length) {
+      for (var i = 0, l = obj.length; i < l; i++) {
+        if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
+      }
+    } else {
+      for (var key in obj) {
+        if (_.has(obj, key)) {
+          if (iterator.call(context, obj[key], key, obj) === breaker) return;
+        }
+      }
+    }
+  };
+
+  // Return the results of applying the iterator to each element.
+  // Delegates to **ECMAScript 5**'s native `map` if available.
+  _.map = _.collect = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
+    each(obj, function(value, index, list) {
+      results[results.length] = iterator.call(context, value, index, list);
+    });
+    if (obj.length === +obj.length) results.length = obj.length;
+    return results;
+  };
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
+  _.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
+    var initial = arguments.length > 2;
+    if (obj == null) obj = [];
+    if (nativeReduce && obj.reduce === nativeReduce) {
+      if (context) iterator = _.bind(iterator, context);
+      return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
+    }
+    each(obj, function(value, index, list) {
+      if (!initial) {
+        memo = value;
+        initial = true;
+      } else {
+        memo = iterator.call(context, memo, value, index, list);
+      }
+    });
+    if (!initial) throw new TypeError('Reduce of empty array with no initial value');
+    return memo;
+  };
+
+  // The right-associative version of reduce, also known as `foldr`.
+  // Delegates to **ECMAScript 5**'s native `reduceRight` if available.
+  _.reduceRight = _.foldr = function(obj, iterator, memo, context) {
+    var initial = arguments.length > 2;
+    if (obj == null) obj = [];
+    if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
+      if (context) iterator = _.bind(iterator, context);
+      return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
+    }
+    var reversed = _.toArray(obj).reverse();
+    if (context && !initial) iterator = _.bind(iterator, context);
+    return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
+  };
+
+  // Return the first value which passes a truth test. Aliased as `detect`.
+  _.find = _.detect = function(obj, iterator, context) {
+    var result;
+    any(obj, function(value, index, list) {
+      if (iterator.call(context, value, index, list)) {
+        result = value;
+        return true;
+      }
+    });
+    return result;
+  };
+
+  // Return all the elements that pass a truth test.
+  // Delegates to **ECMAScript 5**'s native `filter` if available.
+  // Aliased as `select`.
+  _.filter = _.select = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
+    each(obj, function(value, index, list) {
+      if (iterator.call(context, value, index, list)) results[results.length] = value;
+    });
+    return results;
+  };
+
+  // Return all the elements for which a truth test fails.
+  _.reject = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    each(obj, function(value, index, list) {
+      if (!iterator.call(context, value, index, list)) results[results.length] = value;
+    });
+    return results;
+  };
+
+  // Determine whether all of the elements match a truth test.
+  // Delegates to **ECMAScript 5**'s native `every` if available.
+  // Aliased as `all`.
+  _.every = _.all = function(obj, iterator, context) {
+    var result = true;
+    if (obj == null) return result;
+    if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
+    each(obj, function(value, index, list) {
+      if (!(result = result && iterator.call(context, value, index, list))) return breaker;
+    });
+    return result;
+  };
+
+  // Determine if at least one element in the object matches a truth test.
+  // Delegates to **ECMAScript 5**'s native `some` if available.
+  // Aliased as `any`.
+  var any = _.some = _.any = function(obj, iterator, context) {
+    iterator || (iterator = _.identity);
+    var result = false;
+    if (obj == null) return result;
+    if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
+    each(obj, function(value, index, list) {
+      if (result || (result = iterator.call(context, value, index, list))) return breaker;
+    });
+    return !!result;
+  };
+
+  // Determine if a given value is included in the array or object using `===`.
+  // Aliased as `contains`.
+  _.include = _.contains = function(obj, target) {
+    var found = false;
+    if (obj == null) return found;
+    if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
+    found = any(obj, function(value) {
+      return value === target;
+    });
+    return found;
+  };
+
+  // Invoke a method (with arguments) on every item in a collection.
+  _.invoke = function(obj, method) {
+    var args = slice.call(arguments, 2);
+    return _.map(obj, function(value) {
+      return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
+    });
+  };
+
+  // Convenience version of a common use case of `map`: fetching a property.
+  _.pluck = function(obj, key) {
+    return _.map(obj, function(value){ return value[key]; });
+  };
+
+  // Return the maximum element or (element-based computation).
+  _.max = function(obj, iterator, context) {
+    if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
+    if (!iterator && _.isEmpty(obj)) return -Infinity;
+    var result = {computed : -Infinity};
+    each(obj, function(value, index, list) {
+      var computed = iterator ? iterator.call(context, value, index, list) : value;
+      computed >= result.computed && (result = {value : value, computed : computed});
+    });
+    return result.value;
+  };
+
+  // Return the minimum element (or element-based computation).
+  _.min = function(obj, iterator, context) {
+    if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
+    if (!iterator && _.isEmpty(obj)) return Infinity;
+    var result = {computed : Infinity};
+    each(obj, function(value, index, list) {
+      var computed = iterator ? iterator.call(context, value, index, list) : value;
+      computed < result.computed && (result = {value : value, computed : computed});
+    });
+    return result.value;
+  };
+
+  // Shuffle an array.
+  _.shuffle = function(obj) {
+    var shuffled = [], rand;
+    each(obj, function(value, index, list) {
+      if (index == 0) {
+        shuffled[0] = value;
+      } else {
+        rand = Math.floor(Math.random() * (index + 1));
+        shuffled[index] = shuffled[rand];
+        shuffled[rand] = value;
+      }
+    });
+    return shuffled;
+  };
+
+  // Sort the object's values by a criterion produced by an iterator.
+  _.sortBy = function(obj, iterator, context) {
+    return _.pluck(_.map(obj, function(value, index, list) {
+      return {
+        value : value,
+        criteria : iterator.call(context, value, index, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria, b = right.criteria;
+      return a < b ? -1 : a > b ? 1 : 0;
+    }), 'value');
+  };
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  _.groupBy = function(obj, val) {
+    var result = {};
+    var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
+    each(obj, function(value, index) {
+      var key = iterator(value, index);
+      (result[key] || (result[key] = [])).push(value);
+    });
+    return result;
+  };
+
+  // Use a comparator function to figure out at what index an object should
+  // be inserted so as to maintain order. Uses binary search.
+  _.sortedIndex = function(array, obj, iterator) {
+    iterator || (iterator = _.identity);
+    var low = 0, high = array.length;
+    while (low < high) {
+      var mid = (low + high) >> 1;
+      iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
+    }
+    return low;
+  };
+
+  // Safely convert anything iterable into a real, live array.
+  _.toArray = function(iterable) {
+    if (!iterable)                return [];
+    if (iterable.toArray)         return iterable.toArray();
+    if (_.isArray(iterable))      return slice.call(iterable);
+    if (_.isArguments(iterable))  return slice.call(iterable);
+    return _.values(iterable);
+  };
+
+  // Return the number of elements in an object.
+  _.size = function(obj) {
+    return _.toArray(obj).length;
+  };
+
+  // Array Functions
+  // ---------------
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. Aliased as `head`. The **guard** check allows it to work
+  // with `_.map`.
+  _.first = _.head = function(array, n, guard) {
+    return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
+  };
+
+  // Returns everything but the last entry of the array. Especcialy useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N. The **guard** check allows it to work with
+  // `_.map`.
+  _.initial = function(array, n, guard) {
+    return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
+  };
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  _.last = function(array, n, guard) {
+    if ((n != null) && !guard) {
+      return slice.call(array, Math.max(array.length - n, 0));
+    } else {
+      return array[array.length - 1];
+    }
+  };
+
+  // Returns everything but the first entry of the array. Aliased as `tail`.
+  // Especially useful on the arguments object. Passing an **index** will return
+  // the rest of the values in the array from that index onward. The **guard**
+  // check allows it to work with `_.map`.
+  _.rest = _.tail = function(array, index, guard) {
+    return slice.call(array, (index == null) || guard ? 1 : index);
+  };
+
+  // Trim out all falsy values from an array.
+  _.compact = function(array) {
+    return _.filter(array, function(value){ return !!value; });
+  };
+
+  // Return a completely flattened version of an array.
+  _.flatten = function(array, shallow) {
+    return _.reduce(array, function(memo, value) {
+      if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
+      memo[memo.length] = value;
+      return memo;
+    }, []);
+  };
+
+  // Return a version of the array that does not contain the specified value(s).
+  _.without = function(array) {
+    return _.difference(array, slice.call(arguments, 1));
+  };
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // Aliased as `unique`.
+  _.uniq = _.unique = function(array, isSorted, iterator) {
+    var initial = iterator ? _.map(array, iterator) : array;
+    var result = [];
+    _.reduce(initial, function(memo, el, i) {
+      if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
+        memo[memo.length] = el;
+        result[result.length] = array[i];
+      }
+      return memo;
+    }, []);
+    return result;
+  };
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  _.union = function() {
+    return _.uniq(_.flatten(arguments, true));
+  };
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays. (Aliased as "intersect" for back-compat.)
+  _.intersection = _.intersect = function(array) {
+    var rest = slice.call(arguments, 1);
+    return _.filter(_.uniq(array), function(item) {
+      return _.every(rest, function(other) {
+        return _.indexOf(other, item) >= 0;
+      });
+    });
+  };
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  _.difference = function(array) {
+    var rest = _.flatten(slice.call(arguments, 1));
+    return _.filter(array, function(value){ return !_.include(rest, value); });
+  };
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  _.zip = function() {
+    var args = slice.call(arguments);
+    var length = _.max(_.pluck(args, 'length'));
+    var results = new Array(length);
+    for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
+    return results;
+  };
+
+  // If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
+  // we need this function. Return the position of the first occurrence of an
+  // item in an array, or -1 if the item is not included in the array.
+  // Delegates to **ECMAScript 5**'s native `indexOf` if available.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  _.indexOf = function(array, item, isSorted) {
+    if (array == null) return -1;
+    var i, l;
+    if (isSorted) {
+      i = _.sortedIndex(array, item);
+      return array[i] === item ? i : -1;
+    }
+    if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
+    for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
+    return -1;
+  };
+
+  // Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
+  _.lastIndexOf = function(array, item) {
+    if (array == null) return -1;
+    if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
+    var i = array.length;
+    while (i--) if (i in array && array[i] === item) return i;
+    return -1;
+  };
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](http://docs.python.org/library/functions.html#range).
+  _.range = function(start, stop, step) {
+    if (arguments.length <= 1) {
+      stop = start || 0;
+      start = 0;
+    }
+    step = arguments[2] || 1;
+
+    var len = Math.max(Math.ceil((stop - start) / step), 0);
+    var idx = 0;
+    var range = new Array(len);
+
+    while(idx < len) {
+      range[idx++] = start;
+      start += step;
+    }
+
+    return range;
+  };
+
+  // Function (ahem) Functions
+  // ------------------
+
+  // Reusable constructor function for prototype setting.
+  var ctor = function(){};
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally). Binding with arguments is also known as `curry`.
+  // Delegates to **ECMAScript 5**'s native `Function.bind` if available.
+  // We check for `func.bind` first, to fail fast when `func` is undefined.
+  _.bind = function bind(func, context) {
+    var bound, args;
+    if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
+    if (!_.isFunction(func)) throw new TypeError;
+    args = slice.call(arguments, 2);
+    return bound = function() {
+      if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
+      ctor.prototype = func.prototype;
+      var self = new ctor;
+      var result = func.apply(self, args.concat(slice.call(arguments)));
+      if (Object(result) === result) return result;
+      return self;
+    };
+  };
+
+  // Bind all of an object's methods to that object. Useful for ensuring that
+  // all callbacks defined on an object belong to it.
+  _.bindAll = function(obj) {
+    var funcs = slice.call(arguments, 1);
+    if (funcs.length == 0) funcs = _.functions(obj);
+    each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
+    return obj;
+  };
+
+  // Memoize an expensive function by storing its results.
+  _.memoize = function(func, hasher) {
+    var memo = {};
+    hasher || (hasher = _.identity);
+    return function() {
+      var key = hasher.apply(this, arguments);
+      return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
+    };
+  };
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  _.delay = function(func, wait) {
+    var args = slice.call(arguments, 2);
+    return setTimeout(function(){ return func.apply(func, args); }, wait);
+  };
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  _.defer = function(func) {
+    return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
+  };
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time.
+  _.throttle = function(func, wait) {
+    var context, args, timeout, throttling, more;
+    var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
+    return function() {
+      context = this; args = arguments;
+      var later = function() {
+        timeout = null;
+        if (more) func.apply(context, args);
+        whenDone();
+      };
+      if (!timeout) timeout = setTimeout(later, wait);
+      if (throttling) {
+        more = true;
+      } else {
+        func.apply(context, args);
+      }
+      whenDone();
+      throttling = true;
+    };
+  };
+
+  // Returns a function, that, as long as it continues to be invoked, will not
+  // be triggered. The function will be called after it stops being called for
+  // N milliseconds.
+  _.debounce = function(func, wait) {
+    var timeout;
+    return function() {
+      var context = this, args = arguments;
+      var later = function() {
+        timeout = null;
+        func.apply(context, args);
+      };
+      clearTimeout(timeout);
+      timeout = setTimeout(later, wait);
+    };
+  };
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  _.once = function(func) {
+    var ran = false, memo;
+    return function() {
+      if (ran) return memo;
+      ran = true;
+      return memo = func.apply(this, arguments);
+    };
+  };
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  _.wrap = function(func, wrapper) {
+    return function() {
+      var args = [func].concat(slice.call(arguments, 0));
+      return wrapper.apply(this, args);
+    };
+  };
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  _.compose = function() {
+    var funcs = arguments;
+    return function() {
+      var args = arguments;
+      for (var i = funcs.length - 1; i >= 0; i--) {
+        args = [funcs[i].apply(this, args)];
+      }
+      return args[0];
+    };
+  };
+
+  // Returns a function that will only be executed after being called N times.
+  _.after = function(times, func) {
+    if (times <= 0) return func();
+    return function() {
+      if (--times < 1) { return func.apply(this, arguments); }
+    };
+  };
+
+  // Object Functions
+  // ----------------
+
+  // Retrieve the names of an object's properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`
+  _.keys = nativeKeys || function(obj) {
+    if (obj !== Object(obj)) throw new TypeError('Invalid object');
+    var keys = [];
+    for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
+    return keys;
+  };
+
+  // Retrieve the values of an object's properties.
+  _.values = function(obj) {
+    return _.map(obj, _.identity);
+  };
+
+  // Return a sorted list of the function names available on the object.
+  // Aliased as `methods`
+  _.functions = _.methods = function(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (_.isFunction(obj[key])) names.push(key);
+    }
+    return names.sort();
+  };
+
+  // Extend a given object with all the properties in passed-in object(s).
+  _.extend = function(obj) {
+    each(slice.call(arguments, 1), function(source) {
+      for (var prop in source) {
+        obj[prop] = source[prop];
+      }
+    });
+    return obj;
+  };
+
+  // Fill in a given object with default properties.
+  _.defaults = function(obj) {
+    each(slice.call(arguments, 1), function(source) {
+      for (var prop in source) {
+        if (obj[prop] == null) obj[prop] = source[prop];
+      }
+    });
+    return obj;
+  };
+
+  // Create a (shallow-cloned) duplicate of an object.
+  _.clone = function(obj) {
+    if (!_.isObject(obj)) return obj;
+    return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
+  };
+
+  // Invokes interceptor with the obj, and then returns obj.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  _.tap = function(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  };
+
+  // Internal recursive comparison function.
+  function eq(a, b, stack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
+    if (a === b) return a !== 0 || 1 / a == 1 / b;
+    // A strict comparison is necessary because `null == undefined`.
+    if (a == null || b == null) return a === b;
+    // Unwrap any wrapped objects.
+    if (a._chain) a = a._wrapped;
+    if (b._chain) b = b._wrapped;
+    // Invoke a custom `isEqual` method if one is provided.
+    if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
+    if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className != toString.call(b)) return false;
+    switch (className) {
+      // Strings, numbers, dates, and booleans are compared by value.
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return a == String(b);
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
+        // other numeric values.
+        return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a == +b;
+      // RegExps are compared by their source patterns and flags.
+      case '[object RegExp]':
+        return a.source == b.source &&
+               a.global == b.global &&
+               a.multiline == b.multiline &&
+               a.ignoreCase == b.ignoreCase;
+    }
+    if (typeof a != 'object' || typeof b != 'object') return false;
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+    var length = stack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (stack[length] == a) return true;
+    }
+    // Add the first object to the stack of traversed objects.
+    stack.push(a);
+    var size = 0, result = true;
+    // Recursively compare objects and arrays.
+    if (className == '[object Array]') {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      size = a.length;
+      result = size == b.length;
+      if (result) {
+        // Deep compare the contents, ignoring non-numeric properties.
+        while (size--) {
+          // Ensure commutative equality for sparse arrays.
+          if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
+        }
+      }
+    } else {
+      // Objects with different constructors are not equivalent.
+      if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
+      // Deep compare objects.
+      for (var key in a) {
+        if (_.has(a, key)) {
+          // Count the expected number of properties.
+          size++;
+          // Deep compare each member.
+          if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
+        }
+      }
+      // Ensure that both objects contain the same number of properties.
+      if (result) {
+        for (key in b) {
+          if (_.has(b, key) && !(size--)) break;
+        }
+        result = !size;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    stack.pop();
+    return result;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  _.isEqual = function(a, b) {
+    return eq(a, b, []);
+  };
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  _.isEmpty = function(obj) {
+    if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
+    for (var key in obj) if (_.has(obj, key)) return false;
+    return true;
+  };
+
+  // Is a given value a DOM element?
+  _.isElement = function(obj) {
+    return !!(obj && obj.nodeType == 1);
+  };
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native Array.isArray
+  _.isArray = nativeIsArray || function(obj) {
+    return toString.call(obj) == '[object Array]';
+  };
+
+  // Is a given variable an object?
+  _.isObject = function(obj) {
+    return obj === Object(obj);
+  };
+
+  // Is a given variable an arguments object?
+  _.isArguments = function(obj) {
+    return toString.call(obj) == '[object Arguments]';
+  };
+  if (!_.isArguments(arguments)) {
+    _.isArguments = function(obj) {
+      return !!(obj && _.has(obj, 'callee'));
+    };
+  }
+
+  // Is a given value a function?
+  _.isFunction = function(obj) {
+    return toString.call(obj) == '[object Function]';
+  };
+
+  // Is a given value a string?
+  _.isString = function(obj) {
+    return toString.call(obj) == '[object String]';
+  };
+
+  // Is a given value a number?
+  _.isNumber = function(obj) {
+    return toString.call(obj) == '[object Number]';
+  };
+
+  // Is the given value `NaN`?
+  _.isNaN = function(obj) {
+    // `NaN` is the only value for which `===` is not reflexive.
+    return obj !== obj;
+  };
+
+  // Is a given value a boolean?
+  _.isBoolean = function(obj) {
+    return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
+  };
+
+  // Is a given value a date?
+  _.isDate = function(obj) {
+    return toString.call(obj) == '[object Date]';
+  };
+
+  // Is the given value a regular expression?
+  _.isRegExp = function(obj) {
+    return toString.call(obj) == '[object RegExp]';
+  };
+
+  // Is a given value equal to null?
+  _.isNull = function(obj) {
+    return obj === null;
+  };
+
+  // Is a given variable undefined?
+  _.isUndefined = function(obj) {
+    return obj === void 0;
+  };
+
+  // Has own property?
+  _.has = function(obj, key) {
+    return hasOwnProperty.call(obj, key);
+  };
+
+  // Utility Functions
+  // -----------------
+
+  // Run Underscore.js in *noConflict* mode, returning the `_` variable to its
+  // previous owner. Returns a reference to the Underscore object.
+  _.noConflict = function() {
+    root._ = previousUnderscore;
+    return this;
+  };
+
+  // Keep the identity function around for default iterators.
+  _.identity = function(value) {
+    return value;
+  };
+
+  // Run a function **n** times.
+  _.times = function (n, iterator, context) {
+    for (var i = 0; i < n; i++) iterator.call(context, i);
+  };
+
+  // Escape a string for HTML interpolation.
+  _.escape = function(string) {
+    return (''+string).replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#x27;').replace(/\//g,'&#x2F;');
+  };
+
+  // Add your own custom functions to the Underscore object, ensuring that
+  // they're correctly added to the OOP wrapper as well.
+  _.mixin = function(obj) {
+    each(_.functions(obj), function(name){
+      addToWrapper(name, _[name] = obj[name]);
+    });
+  };
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  _.uniqueId = function(prefix) {
+    var id = idCounter++;
+    return prefix ? prefix + id : id;
+  };
+
+  // By default, Underscore uses ERB-style template delimiters, change the
+  // following template settings to use alternative delimiters.
+  _.templateSettings = {
+    evaluate    : /<%([\s\S]+?)%>/g,
+    interpolate : /<%=([\s\S]+?)%>/g,
+    escape      : /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /.^/;
+
+  // Within an interpolation, evaluation, or escaping, remove HTML escaping
+  // that had been previously added.
+  var unescape = function(code) {
+    return code.replace(/\\\\/g, '\\').replace(/\\'/g, "'");
+  };
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  _.template = function(str, data) {
+    var c  = _.templateSettings;
+    var tmpl = 'var __p=[],print=function(){__p.push.apply(__p,arguments);};' +
+      'with(obj||{}){__p.push(\'' +
+      str.replace(/\\/g, '\\\\')
+         .replace(/'/g, "\\'")
+         .replace(c.escape || noMatch, function(match, code) {
+           return "',_.escape(" + unescape(code) + "),'";
+         })
+         .replace(c.interpolate || noMatch, function(match, code) {
+           return "'," + unescape(code) + ",'";
+         })
+         .replace(c.evaluate || noMatch, function(match, code) {
+           return "');" + unescape(code).replace(/[\r\n\t]/g, ' ') + ";__p.push('";
+         })
+         .replace(/\r/g, '\\r')
+         .replace(/\n/g, '\\n')
+         .replace(/\t/g, '\\t')
+         + "');}return __p.join('');";
+    var func = new Function('obj', '_', tmpl);
+    if (data) return func(data, _);
+    return function(data) {
+      return func.call(this, data, _);
+    };
+  };
+
+  // Add a "chain" function, which will delegate to the wrapper.
+  _.chain = function(obj) {
+    return _(obj).chain();
+  };
+
+  // The OOP Wrapper
+  // ---------------
+
+  // If Underscore is called as a function, it returns a wrapped object that
+  // can be used OO-style. This wrapper holds altered versions of all the
+  // underscore functions. Wrapped objects may be chained.
+  var wrapper = function(obj) { this._wrapped = obj; };
+
+  // Expose `wrapper.prototype` as `_.prototype`
+  _.prototype = wrapper.prototype;
+
+  // Helper function to continue chaining intermediate results.
+  var result = function(obj, chain) {
+    return chain ? _(obj).chain() : obj;
+  };
+
+  // A method to easily add functions to the OOP wrapper.
+  var addToWrapper = function(name, func) {
+    wrapper.prototype[name] = function() {
+      var args = slice.call(arguments);
+      unshift.call(args, this._wrapped);
+      return result(func.apply(_, args), this._chain);
+    };
+  };
+
+  // Add all of the Underscore functions to the wrapper object.
+  _.mixin(_);
+
+  // Add all mutator Array functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    wrapper.prototype[name] = function() {
+      var wrapped = this._wrapped;
+      method.apply(wrapped, arguments);
+      var length = wrapped.length;
+      if ((name == 'shift' || name == 'splice') && length === 0) delete wrapped[0];
+      return result(wrapped, this._chain);
+    };
+  });
+
+  // Add all accessor Array functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    wrapper.prototype[name] = function() {
+      return result(method.apply(this._wrapped, arguments), this._chain);
+    };
+  });
+
+  // Start chaining a wrapped Underscore object.
+  wrapper.prototype.chain = function() {
+    this._chain = true;
+    return this;
+  };
+
+  // Extracts the result from a wrapped and chained object.
+  wrapper.prototype.value = function() {
+    return this._wrapped;
+  };
+
+}).call(this);
diff --git a/docs/_build/_static/underscore.js b/docs/_build/_static/underscore.js
index cf177d42..5b55f32b 100644
--- a/docs/_build/_static/underscore.js
+++ b/docs/_build/_static/underscore.js
@@ -1,6 +1,31 @@
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-//     Underscore.js 1.13.1
-//     https://underscorejs.org
-//     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
-//     Underscore may be freely distributed under the MIT license.
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diff --git a/docs/_build/_static/websupport.js b/docs/_build/_static/websupport.js
new file mode 100644
index 00000000..53f6a452
--- /dev/null
+++ b/docs/_build/_static/websupport.js
@@ -0,0 +1,808 @@
+/*
+ * websupport.js
+ * ~~~~~~~~~~~~~
+ *
+ * sphinx.websupport utilities for all documentation.
+ *
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+(function($) {
+  $.fn.autogrow = function() {
+    return this.each(function() {
+    var textarea = this;
+
+    $.fn.autogrow.resize(textarea);
+
+    $(textarea)
+      .focus(function() {
+        textarea.interval = setInterval(function() {
+          $.fn.autogrow.resize(textarea);
+        }, 500);
+      })
+      .blur(function() {
+        clearInterval(textarea.interval);
+      });
+    });
+  };
+
+  $.fn.autogrow.resize = function(textarea) {
+    var lineHeight = parseInt($(textarea).css('line-height'), 10);
+    var lines = textarea.value.split('\n');
+    var columns = textarea.cols;
+    var lineCount = 0;
+    $.each(lines, function() {
+      lineCount += Math.ceil(this.length / columns) || 1;
+    });
+    var height = lineHeight * (lineCount + 1);
+    $(textarea).css('height', height);
+  };
+})(jQuery);
+
+(function($) {
+  var comp, by;
+
+  function init() {
+    initEvents();
+    initComparator();
+  }
+
+  function initEvents() {
+    $(document).on("click", 'a.comment-close', function(event) {
+      event.preventDefault();
+      hide($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.vote', function(event) {
+      event.preventDefault();
+      handleVote($(this));
+    });
+    $(document).on("click", 'a.reply', function(event) {
+      event.preventDefault();
+      openReply($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.close-reply', function(event) {
+      event.preventDefault();
+      closeReply($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.sort-option', function(event) {
+      event.preventDefault();
+      handleReSort($(this));
+    });
+    $(document).on("click", 'a.show-proposal', function(event) {
+      event.preventDefault();
+      showProposal($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.hide-proposal', function(event) {
+      event.preventDefault();
+      hideProposal($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.show-propose-change', function(event) {
+      event.preventDefault();
+      showProposeChange($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.hide-propose-change', function(event) {
+      event.preventDefault();
+      hideProposeChange($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.accept-comment', function(event) {
+      event.preventDefault();
+      acceptComment($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.delete-comment', function(event) {
+      event.preventDefault();
+      deleteComment($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.comment-markup', function(event) {
+      event.preventDefault();
+      toggleCommentMarkupBox($(this).attr('id').substring(2));
+    });
+  }
+
+  /**
+   * Set comp, which is a comparator function used for sorting and
+   * inserting comments into the list.
+   */
+  function setComparator() {
+    // If the first three letters are "asc", sort in ascending order
+    // and remove the prefix.
+    if (by.substring(0,3) == 'asc') {
+      var i = by.substring(3);
+      comp = function(a, b) { return a[i] - b[i]; };
+    } else {
+      // Otherwise sort in descending order.
+      comp = function(a, b) { return b[by] - a[by]; };
+    }
+
+    // Reset link styles and format the selected sort option.
+    $('a.sel').attr('href', '#').removeClass('sel');
+    $('a.by' + by).removeAttr('href').addClass('sel');
+  }
+
+  /**
+   * Create a comp function. If the user has preferences stored in
+   * the sortBy cookie, use those, otherwise use the default.
+   */
+  function initComparator() {
+    by = 'rating'; // Default to sort by rating.
+    // If the sortBy cookie is set, use that instead.
+    if (document.cookie.length > 0) {
+      var start = document.cookie.indexOf('sortBy=');
+      if (start != -1) {
+        start = start + 7;
+        var end = document.cookie.indexOf(";", start);
+        if (end == -1) {
+          end = document.cookie.length;
+          by = unescape(document.cookie.substring(start, end));
+        }
+      }
+    }
+    setComparator();
+  }
+
+  /**
+   * Show a comment div.
+   */
+  function show(id) {
+    $('#ao' + id).hide();
+    $('#ah' + id).show();
+    var context = $.extend({id: id}, opts);
+    var popup = $(renderTemplate(popupTemplate, context)).hide();
+    popup.find('textarea[name="proposal"]').hide();
+    popup.find('a.by' + by).addClass('sel');
+    var form = popup.find('#cf' + id);
+    form.submit(function(event) {
+      event.preventDefault();
+      addComment(form);
+    });
+    $('#s' + id).after(popup);
+    popup.slideDown('fast', function() {
+      getComments(id);
+    });
+  }
+
+  /**
+   * Hide a comment div.
+   */
+  function hide(id) {
+    $('#ah' + id).hide();
+    $('#ao' + id).show();
+    var div = $('#sc' + id);
+    div.slideUp('fast', function() {
+      div.remove();
+    });
+  }
+
+  /**
+   * Perform an ajax request to get comments for a node
+   * and insert the comments into the comments tree.
+   */
+  function getComments(id) {
+    $.ajax({
+     type: 'GET',
+     url: opts.getCommentsURL,
+     data: {node: id},
+     success: function(data, textStatus, request) {
+       var ul = $('#cl' + id);
+       var speed = 100;
+       $('#cf' + id)
+         .find('textarea[name="proposal"]')
+         .data('source', data.source);
+
+       if (data.comments.length === 0) {
+         ul.html('<li>No comments yet.</li>');
+         ul.data('empty', true);
+       } else {
+         // If there are comments, sort them and put them in the list.
+         var comments = sortComments(data.comments);
+         speed = data.comments.length * 100;
+         appendComments(comments, ul);
+         ul.data('empty', false);
+       }
+       $('#cn' + id).slideUp(speed + 200);
+       ul.slideDown(speed);
+     },
+     error: function(request, textStatus, error) {
+       showError('Oops, there was a problem retrieving the comments.');
+     },
+     dataType: 'json'
+    });
+  }
+
+  /**
+   * Add a comment via ajax and insert the comment into the comment tree.
+   */
+  function addComment(form) {
+    var node_id = form.find('input[name="node"]').val();
+    var parent_id = form.find('input[name="parent"]').val();
+    var text = form.find('textarea[name="comment"]').val();
+    var proposal = form.find('textarea[name="proposal"]').val();
+
+    if (text == '') {
+      showError('Please enter a comment.');
+      return;
+    }
+
+    // Disable the form that is being submitted.
+    form.find('textarea,input').attr('disabled', 'disabled');
+
+    // Send the comment to the server.
+    $.ajax({
+      type: "POST",
+      url: opts.addCommentURL,
+      dataType: 'json',
+      data: {
+        node: node_id,
+        parent: parent_id,
+        text: text,
+        proposal: proposal
+      },
+      success: function(data, textStatus, error) {
+        // Reset the form.
+        if (node_id) {
+          hideProposeChange(node_id);
+        }
+        form.find('textarea')
+          .val('')
+          .add(form.find('input'))
+          .removeAttr('disabled');
+	var ul = $('#cl' + (node_id || parent_id));
+        if (ul.data('empty')) {
+          $(ul).empty();
+          ul.data('empty', false);
+        }
+        insertComment(data.comment);
+        var ao = $('#ao' + node_id);
+        ao.find('img').attr({'src': opts.commentBrightImage});
+        if (node_id) {
+          // if this was a "root" comment, remove the commenting box
+          // (the user can get it back by reopening the comment popup)
+          $('#ca' + node_id).slideUp();
+        }
+      },
+      error: function(request, textStatus, error) {
+        form.find('textarea,input').removeAttr('disabled');
+        showError('Oops, there was a problem adding the comment.');
+      }
+    });
+  }
+
+  /**
+   * Recursively append comments to the main comment list and children
+   * lists, creating the comment tree.
+   */
+  function appendComments(comments, ul) {
+    $.each(comments, function() {
+      var div = createCommentDiv(this);
+      ul.append($(document.createElement('li')).html(div));
+      appendComments(this.children, div.find('ul.comment-children'));
+      // To avoid stagnating data, don't store the comments children in data.
+      this.children = null;
+      div.data('comment', this);
+    });
+  }
+
+  /**
+   * After adding a new comment, it must be inserted in the correct
+   * location in the comment tree.
+   */
+  function insertComment(comment) {
+    var div = createCommentDiv(comment);
+
+    // To avoid stagnating data, don't store the comments children in data.
+    comment.children = null;
+    div.data('comment', comment);
+
+    var ul = $('#cl' + (comment.node || comment.parent));
+    var siblings = getChildren(ul);
+
+    var li = $(document.createElement('li'));
+    li.hide();
+
+    // Determine where in the parents children list to insert this comment.
+    for(i=0; i < siblings.length; i++) {
+      if (comp(comment, siblings[i]) <= 0) {
+        $('#cd' + siblings[i].id)
+          .parent()
+          .before(li.html(div));
+        li.slideDown('fast');
+        return;
+      }
+    }
+
+    // If we get here, this comment rates lower than all the others,
+    // or it is the only comment in the list.
+    ul.append(li.html(div));
+    li.slideDown('fast');
+  }
+
+  function acceptComment(id) {
+    $.ajax({
+      type: 'POST',
+      url: opts.acceptCommentURL,
+      data: {id: id},
+      success: function(data, textStatus, request) {
+        $('#cm' + id).fadeOut('fast');
+        $('#cd' + id).removeClass('moderate');
+      },
+      error: function(request, textStatus, error) {
+        showError('Oops, there was a problem accepting the comment.');
+      }
+    });
+  }
+
+  function deleteComment(id) {
+    $.ajax({
+      type: 'POST',
+      url: opts.deleteCommentURL,
+      data: {id: id},
+      success: function(data, textStatus, request) {
+        var div = $('#cd' + id);
+        if (data == 'delete') {
+          // Moderator mode: remove the comment and all children immediately
+          div.slideUp('fast', function() {
+            div.remove();
+          });
+          return;
+        }
+        // User mode: only mark the comment as deleted
+        div
+          .find('span.user-id:first')
+          .text('[deleted]').end()
+          .find('div.comment-text:first')
+          .text('[deleted]').end()
+          .find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id +
+                ', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id)
+          .remove();
+        var comment = div.data('comment');
+        comment.username = '[deleted]';
+        comment.text = '[deleted]';
+        div.data('comment', comment);
+      },
+      error: function(request, textStatus, error) {
+        showError('Oops, there was a problem deleting the comment.');
+      }
+    });
+  }
+
+  function showProposal(id) {
+    $('#sp' + id).hide();
+    $('#hp' + id).show();
+    $('#pr' + id).slideDown('fast');
+  }
+
+  function hideProposal(id) {
+    $('#hp' + id).hide();
+    $('#sp' + id).show();
+    $('#pr' + id).slideUp('fast');
+  }
+
+  function showProposeChange(id) {
+    $('#pc' + id).hide();
+    $('#hc' + id).show();
+    var textarea = $('#pt' + id);
+    textarea.val(textarea.data('source'));
+    $.fn.autogrow.resize(textarea[0]);
+    textarea.slideDown('fast');
+  }
+
+  function hideProposeChange(id) {
+    $('#hc' + id).hide();
+    $('#pc' + id).show();
+    var textarea = $('#pt' + id);
+    textarea.val('').removeAttr('disabled');
+    textarea.slideUp('fast');
+  }
+
+  function toggleCommentMarkupBox(id) {
+    $('#mb' + id).toggle();
+  }
+
+  /** Handle when the user clicks on a sort by link. */
+  function handleReSort(link) {
+    var classes = link.attr('class').split(/\s+/);
+    for (var i=0; i<classes.length; i++) {
+      if (classes[i] != 'sort-option') {
+	by = classes[i].substring(2);
+      }
+    }
+    setComparator();
+    // Save/update the sortBy cookie.
+    var expiration = new Date();
+    expiration.setDate(expiration.getDate() + 365);
+    document.cookie= 'sortBy=' + escape(by) +
+                     ';expires=' + expiration.toUTCString();
+    $('ul.comment-ul').each(function(index, ul) {
+      var comments = getChildren($(ul), true);
+      comments = sortComments(comments);
+      appendComments(comments, $(ul).empty());
+    });
+  }
+
+  /**
+   * Function to process a vote when a user clicks an arrow.
+   */
+  function handleVote(link) {
+    if (!opts.voting) {
+      showError("You'll need to login to vote.");
+      return;
+    }
+
+    var id = link.attr('id');
+    if (!id) {
+      // Didn't click on one of the voting arrows.
+      return;
+    }
+    // If it is an unvote, the new vote value is 0,
+    // Otherwise it's 1 for an upvote, or -1 for a downvote.
+    var value = 0;
+    if (id.charAt(1) != 'u') {
+      value = id.charAt(0) == 'u' ? 1 : -1;
+    }
+    // The data to be sent to the server.
+    var d = {
+      comment_id: id.substring(2),
+      value: value
+    };
+
+    // Swap the vote and unvote links.
+    link.hide();
+    $('#' + id.charAt(0) + (id.charAt(1) == 'u' ? 'v' : 'u') + d.comment_id)
+      .show();
+
+    // The div the comment is displayed in.
+    var div = $('div#cd' + d.comment_id);
+    var data = div.data('comment');
+
+    // If this is not an unvote, and the other vote arrow has
+    // already been pressed, unpress it.
+    if ((d.value !== 0) && (data.vote === d.value * -1)) {
+      $('#' + (d.value == 1 ? 'd' : 'u') + 'u' + d.comment_id).hide();
+      $('#' + (d.value == 1 ? 'd' : 'u') + 'v' + d.comment_id).show();
+    }
+
+    // Update the comments rating in the local data.
+    data.rating += (data.vote === 0) ? d.value : (d.value - data.vote);
+    data.vote = d.value;
+    div.data('comment', data);
+
+    // Change the rating text.
+    div.find('.rating:first')
+      .text(data.rating + ' point' + (data.rating == 1 ? '' : 's'));
+
+    // Send the vote information to the server.
+    $.ajax({
+      type: "POST",
+      url: opts.processVoteURL,
+      data: d,
+      error: function(request, textStatus, error) {
+        showError('Oops, there was a problem casting that vote.');
+      }
+    });
+  }
+
+  /**
+   * Open a reply form used to reply to an existing comment.
+   */
+  function openReply(id) {
+    // Swap out the reply link for the hide link
+    $('#rl' + id).hide();
+    $('#cr' + id).show();
+
+    // Add the reply li to the children ul.
+    var div = $(renderTemplate(replyTemplate, {id: id})).hide();
+    $('#cl' + id)
+      .prepend(div)
+      // Setup the submit handler for the reply form.
+      .find('#rf' + id)
+      .submit(function(event) {
+        event.preventDefault();
+        addComment($('#rf' + id));
+        closeReply(id);
+      })
+      .find('input[type=button]')
+      .click(function() {
+        closeReply(id);
+      });
+    div.slideDown('fast', function() {
+      $('#rf' + id).find('textarea').focus();
+    });
+  }
+
+  /**
+   * Close the reply form opened with openReply.
+   */
+  function closeReply(id) {
+    // Remove the reply div from the DOM.
+    $('#rd' + id).slideUp('fast', function() {
+      $(this).remove();
+    });
+
+    // Swap out the hide link for the reply link
+    $('#cr' + id).hide();
+    $('#rl' + id).show();
+  }
+
+  /**
+   * Recursively sort a tree of comments using the comp comparator.
+   */
+  function sortComments(comments) {
+    comments.sort(comp);
+    $.each(comments, function() {
+      this.children = sortComments(this.children);
+    });
+    return comments;
+  }
+
+  /**
+   * Get the children comments from a ul. If recursive is true,
+   * recursively include childrens' children.
+   */
+  function getChildren(ul, recursive) {
+    var children = [];
+    ul.children().children("[id^='cd']")
+      .each(function() {
+        var comment = $(this).data('comment');
+        if (recursive)
+          comment.children = getChildren($(this).find('#cl' + comment.id), true);
+        children.push(comment);
+      });
+    return children;
+  }
+
+  /** Create a div to display a comment in. */
+  function createCommentDiv(comment) {
+    if (!comment.displayed && !opts.moderator) {
+      return $('<div class="moderate">Thank you!  Your comment will show up '
+               + 'once it is has been approved by a moderator.</div>');
+    }
+    // Prettify the comment rating.
+    comment.pretty_rating = comment.rating + ' point' +
+      (comment.rating == 1 ? '' : 's');
+    // Make a class (for displaying not yet moderated comments differently)
+    comment.css_class = comment.displayed ? '' : ' moderate';
+    // Create a div for this comment.
+    var context = $.extend({}, opts, comment);
+    var div = $(renderTemplate(commentTemplate, context));
+
+    // If the user has voted on this comment, highlight the correct arrow.
+    if (comment.vote) {
+      var direction = (comment.vote == 1) ? 'u' : 'd';
+      div.find('#' + direction + 'v' + comment.id).hide();
+      div.find('#' + direction + 'u' + comment.id).show();
+    }
+
+    if (opts.moderator || comment.text != '[deleted]') {
+      div.find('a.reply').show();
+      if (comment.proposal_diff)
+        div.find('#sp' + comment.id).show();
+      if (opts.moderator && !comment.displayed)
+        div.find('#cm' + comment.id).show();
+      if (opts.moderator || (opts.username == comment.username))
+        div.find('#dc' + comment.id).show();
+    }
+    return div;
+  }
+
+  /**
+   * A simple template renderer. Placeholders such as <%id%> are replaced
+   * by context['id'] with items being escaped. Placeholders such as <#id#>
+   * are not escaped.
+   */
+  function renderTemplate(template, context) {
+    var esc = $(document.createElement('div'));
+
+    function handle(ph, escape) {
+      var cur = context;
+      $.each(ph.split('.'), function() {
+        cur = cur[this];
+      });
+      return escape ? esc.text(cur || "").html() : cur;
+    }
+
+    return template.replace(/<([%#])([\w\.]*)\1>/g, function() {
+      return handle(arguments[2], arguments[1] == '%' ? true : false);
+    });
+  }
+
+  /** Flash an error message briefly. */
+  function showError(message) {
+    $(document.createElement('div')).attr({'class': 'popup-error'})
+      .append($(document.createElement('div'))
+               .attr({'class': 'error-message'}).text(message))
+      .appendTo('body')
+      .fadeIn("slow")
+      .delay(2000)
+      .fadeOut("slow");
+  }
+
+  /** Add a link the user uses to open the comments popup. */
+  $.fn.comment = function() {
+    return this.each(function() {
+      var id = $(this).attr('id').substring(1);
+      var count = COMMENT_METADATA[id];
+      var title = count + ' comment' + (count == 1 ? '' : 's');
+      var image = count > 0 ? opts.commentBrightImage : opts.commentImage;
+      var addcls = count == 0 ? ' nocomment' : '';
+      $(this)
+        .append(
+          $(document.createElement('a')).attr({
+            href: '#',
+            'class': 'sphinx-comment-open' + addcls,
+            id: 'ao' + id
+          })
+            .append($(document.createElement('img')).attr({
+              src: image,
+              alt: 'comment',
+              title: title
+            }))
+            .click(function(event) {
+              event.preventDefault();
+              show($(this).attr('id').substring(2));
+            })
+        )
+        .append(
+          $(document.createElement('a')).attr({
+            href: '#',
+            'class': 'sphinx-comment-close hidden',
+            id: 'ah' + id
+          })
+            .append($(document.createElement('img')).attr({
+              src: opts.closeCommentImage,
+              alt: 'close',
+              title: 'close'
+            }))
+            .click(function(event) {
+              event.preventDefault();
+              hide($(this).attr('id').substring(2));
+            })
+        );
+    });
+  };
+
+  var opts = {
+    processVoteURL: '/_process_vote',
+    addCommentURL: '/_add_comment',
+    getCommentsURL: '/_get_comments',
+    acceptCommentURL: '/_accept_comment',
+    deleteCommentURL: '/_delete_comment',
+    commentImage: '/static/_static/comment.png',
+    closeCommentImage: '/static/_static/comment-close.png',
+    loadingImage: '/static/_static/ajax-loader.gif',
+    commentBrightImage: '/static/_static/comment-bright.png',
+    upArrow: '/static/_static/up.png',
+    downArrow: '/static/_static/down.png',
+    upArrowPressed: '/static/_static/up-pressed.png',
+    downArrowPressed: '/static/_static/down-pressed.png',
+    voting: false,
+    moderator: false
+  };
+
+  if (typeof COMMENT_OPTIONS != "undefined") {
+    opts = jQuery.extend(opts, COMMENT_OPTIONS);
+  }
+
+  var popupTemplate = '\
+    <div class="sphinx-comments" id="sc<%id%>">\
+      <p class="sort-options">\
+        Sort by:\
+        <a href="#" class="sort-option byrating">best rated</a>\
+        <a href="#" class="sort-option byascage">newest</a>\
+        <a href="#" class="sort-option byage">oldest</a>\
+      </p>\
+      <div class="comment-header">Comments</div>\
+      <div class="comment-loading" id="cn<%id%>">\
+        loading comments... <img src="<%loadingImage%>" alt="" /></div>\
+      <ul id="cl<%id%>" class="comment-ul"></ul>\
+      <div id="ca<%id%>">\
+      <p class="add-a-comment">Add a comment\
+        (<a href="#" class="comment-markup" id="ab<%id%>">markup</a>):</p>\
+      <div class="comment-markup-box" id="mb<%id%>">\
+        reStructured text markup: <i>*emph*</i>, <b>**strong**</b>, \
+        <code>``code``</code>, \
+        code blocks: <code>::</code> and an indented block after blank line</div>\
+      <form method="post" id="cf<%id%>" class="comment-form" action="">\
+        <textarea name="comment" cols="80"></textarea>\
+        <p class="propose-button">\
+          <a href="#" id="pc<%id%>" class="show-propose-change">\
+            Propose a change &#9657;\
+          </a>\
+          <a href="#" id="hc<%id%>" class="hide-propose-change">\
+            Propose a change &#9663;\
+          </a>\
+        </p>\
+        <textarea name="proposal" id="pt<%id%>" cols="80"\
+                  spellcheck="false"></textarea>\
+        <input type="submit" value="Add comment" />\
+        <input type="hidden" name="node" value="<%id%>" />\
+        <input type="hidden" name="parent" value="" />\
+      </form>\
+      </div>\
+    </div>';
+
+  var commentTemplate = '\
+    <div id="cd<%id%>" class="sphinx-comment<%css_class%>">\
+      <div class="vote">\
+        <div class="arrow">\
+          <a href="#" id="uv<%id%>" class="vote" title="vote up">\
+            <img src="<%upArrow%>" />\
+          </a>\
+          <a href="#" id="uu<%id%>" class="un vote" title="vote up">\
+            <img src="<%upArrowPressed%>" />\
+          </a>\
+        </div>\
+        <div class="arrow">\
+          <a href="#" id="dv<%id%>" class="vote" title="vote down">\
+            <img src="<%downArrow%>" id="da<%id%>" />\
+          </a>\
+          <a href="#" id="du<%id%>" class="un vote" title="vote down">\
+            <img src="<%downArrowPressed%>" />\
+          </a>\
+        </div>\
+      </div>\
+      <div class="comment-content">\
+        <p class="tagline comment">\
+          <span class="user-id"><%username%></span>\
+          <span class="rating"><%pretty_rating%></span>\
+          <span class="delta"><%time.delta%></span>\
+        </p>\
+        <div class="comment-text comment"><#text#></div>\
+        <p class="comment-opts comment">\
+          <a href="#" class="reply hidden" id="rl<%id%>">reply &#9657;</a>\
+          <a href="#" class="close-reply" id="cr<%id%>">reply &#9663;</a>\
+          <a href="#" id="sp<%id%>" class="show-proposal">proposal &#9657;</a>\
+          <a href="#" id="hp<%id%>" class="hide-proposal">proposal &#9663;</a>\
+          <a href="#" id="dc<%id%>" class="delete-comment hidden">delete</a>\
+          <span id="cm<%id%>" class="moderation hidden">\
+            <a href="#" id="ac<%id%>" class="accept-comment">accept</a>\
+          </span>\
+        </p>\
+        <pre class="proposal" id="pr<%id%>">\
+<#proposal_diff#>\
+        </pre>\
+          <ul class="comment-children" id="cl<%id%>"></ul>\
+        </div>\
+        <div class="clearleft"></div>\
+      </div>\
+    </div>';
+
+  var replyTemplate = '\
+    <li>\
+      <div class="reply-div" id="rd<%id%>">\
+        <form id="rf<%id%>">\
+          <textarea name="comment" cols="80"></textarea>\
+          <input type="submit" value="Add reply" />\
+          <input type="button" value="Cancel" />\
+          <input type="hidden" name="parent" value="<%id%>" />\
+          <input type="hidden" name="node" value="" />\
+        </form>\
+      </div>\
+    </li>';
+
+  $(document).ready(function() {
+    init();
+  });
+})(jQuery);
+
+$(document).ready(function() {
+  // add comment anchors for all paragraphs that are commentable
+  $('.sphinx-has-comment').comment();
+
+  // highlight search words in search results
+  $("div.context").each(function() {
+    var params = $.getQueryParameters();
+    var terms = (params.q) ? params.q[0].split(/\s+/) : [];
+    var result = $(this);
+    $.each(terms, function() {
+      result.highlightText(this.toLowerCase(), 'highlighted');
+    });
+  });
+
+  // directly open comment window if requested
+  var anchor = document.location.hash;
+  if (anchor.substring(0, 9) == '#comment-') {
+    $('#ao' + anchor.substring(9)).click();
+    document.location.hash = '#s' + anchor.substring(9);
+  }
+});
diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle
deleted file mode 100644
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index 04f55232..76bb5291 100644
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     <link rel="stylesheet" type="text/css" href="_static/alabaster.css" />
     <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+    <script src="_static/jquery.js"></script>
+    <script src="_static/underscore.js"></script>
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-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
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diff --git a/docs/_build/html/_modules/abc.html b/docs/_build/html/_modules/abc.html
deleted file mode 100644
index 40278c42..00000000
--- a/docs/_build/html/_modules/abc.html
+++ /dev/null
@@ -1,248 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>abc &#8212; Tigramite 5.2 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/alabaster.css" />
-    <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-    <script src="../_static/jquery.js"></script>
-    <script src="../_static/underscore.js"></script>
-    <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-    <script src="../_static/doctools.js"></script>
-    <link rel="index" title="Index" href="../genindex.html" />
-    <link rel="search" title="Search" href="../search.html" />
-   
-  <link rel="stylesheet" href="../_static/custom.css" type="text/css" />
-  
-  
-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for abc</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Copyright 2007 Google, Inc. All Rights Reserved.</span>
-<span class="c1"># Licensed to PSF under a Contributor Agreement.</span>
-
-<span class="sd">&quot;&quot;&quot;Abstract Base Classes (ABCs) according to PEP 3119.&quot;&quot;&quot;</span>
-
-
-<span class="k">def</span> <span class="nf">abstractmethod</span><span class="p">(</span><span class="n">funcobj</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A decorator indicating abstract methods.</span>
-
-<span class="sd">    Requires that the metaclass is ABCMeta or derived from it.  A</span>
-<span class="sd">    class that has a metaclass derived from ABCMeta cannot be</span>
-<span class="sd">    instantiated unless all of its abstract methods are overridden.</span>
-<span class="sd">    The abstract methods can be called using any of the normal</span>
-<span class="sd">    &#39;super&#39; call mechanisms.  abstractmethod() may be used to declare</span>
-<span class="sd">    abstract methods for properties and descriptors.</span>
-
-<span class="sd">    Usage:</span>
-
-<span class="sd">        class C(metaclass=ABCMeta):</span>
-<span class="sd">            @abstractmethod</span>
-<span class="sd">            def my_abstract_method(self, ...):</span>
-<span class="sd">                ...</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">funcobj</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">return</span> <span class="n">funcobj</span>
-
-
-<span class="k">class</span> <span class="nc">abstractclassmethod</span><span class="p">(</span><span class="nb">classmethod</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A decorator indicating abstract classmethods.</span>
-
-<span class="sd">    Deprecated, use &#39;classmethod&#39; with &#39;abstractmethod&#39; instead:</span>
-
-<span class="sd">        class C(ABC):</span>
-<span class="sd">            @classmethod</span>
-<span class="sd">            @abstractmethod</span>
-<span class="sd">            def my_abstract_classmethod(cls, ...):</span>
-<span class="sd">                ...</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callable</span><span class="p">):</span>
-        <span class="n">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">callable</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">abstractstaticmethod</span><span class="p">(</span><span class="nb">staticmethod</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A decorator indicating abstract staticmethods.</span>
-
-<span class="sd">    Deprecated, use &#39;staticmethod&#39; with &#39;abstractmethod&#39; instead:</span>
-
-<span class="sd">        class C(ABC):</span>
-<span class="sd">            @staticmethod</span>
-<span class="sd">            @abstractmethod</span>
-<span class="sd">            def my_abstract_staticmethod(...):</span>
-<span class="sd">                ...</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callable</span><span class="p">):</span>
-        <span class="n">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">callable</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">abstractproperty</span><span class="p">(</span><span class="nb">property</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A decorator indicating abstract properties.</span>
-
-<span class="sd">    Deprecated, use &#39;property&#39; with &#39;abstractmethod&#39; instead:</span>
-
-<span class="sd">        class C(ABC):</span>
-<span class="sd">            @property</span>
-<span class="sd">            @abstractmethod</span>
-<span class="sd">            def my_abstract_property(self):</span>
-<span class="sd">                ...</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-
-
-<span class="k">try</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">_abc</span> <span class="kn">import</span> <span class="p">(</span><span class="n">get_cache_token</span><span class="p">,</span> <span class="n">_abc_init</span><span class="p">,</span> <span class="n">_abc_register</span><span class="p">,</span>
-                      <span class="n">_abc_instancecheck</span><span class="p">,</span> <span class="n">_abc_subclasscheck</span><span class="p">,</span> <span class="n">_get_dump</span><span class="p">,</span>
-                      <span class="n">_reset_registry</span><span class="p">,</span> <span class="n">_reset_caches</span><span class="p">)</span>
-<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">_py_abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">get_cache_token</span>
-    <span class="n">ABCMeta</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="s1">&#39;abc&#39;</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">class</span> <span class="nc">ABCMeta</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Metaclass for defining Abstract Base Classes (ABCs).</span>
-
-<span class="sd">        Use this metaclass to create an ABC.  An ABC can be subclassed</span>
-<span class="sd">        directly, and then acts as a mix-in class.  You can also register</span>
-<span class="sd">        unrelated concrete classes (even built-in classes) and unrelated</span>
-<span class="sd">        ABCs as &#39;virtual subclasses&#39; -- these and their descendants will</span>
-<span class="sd">        be considered subclasses of the registering ABC by the built-in</span>
-<span class="sd">        issubclass() function, but the registering ABC won&#39;t show up in</span>
-<span class="sd">        their MRO (Method Resolution Order) nor will method</span>
-<span class="sd">        implementations defined by the registering ABC be callable (not</span>
-<span class="sd">        even via super()).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="n">mcls</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">bases</span><span class="p">,</span> <span class="n">namespace</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-            <span class="bp">cls</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">mcls</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">bases</span><span class="p">,</span> <span class="n">namespace</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-            <span class="n">_abc_init</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
-            <span class="k">return</span> <span class="bp">cls</span>
-
-        <span class="k">def</span> <span class="nf">register</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">subclass</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Register a virtual subclass of an ABC.</span>
-
-<span class="sd">            Returns the subclass, to allow usage as a class decorator.</span>
-<span class="sd">            &quot;&quot;&quot;</span>
-            <span class="k">return</span> <span class="n">_abc_register</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">subclass</span><span class="p">)</span>
-
-        <span class="k">def</span> <span class="fm">__instancecheck__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">instance</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Override for isinstance(instance, cls).&quot;&quot;&quot;</span>
-            <span class="k">return</span> <span class="n">_abc_instancecheck</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">instance</span><span class="p">)</span>
-
-        <span class="k">def</span> <span class="fm">__subclasscheck__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">subclass</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Override for issubclass(subclass, cls).&quot;&quot;&quot;</span>
-            <span class="k">return</span> <span class="n">_abc_subclasscheck</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">subclass</span><span class="p">)</span>
-
-        <span class="k">def</span> <span class="nf">_dump_registry</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Debug helper to print the ABC registry.&quot;&quot;&quot;</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Class: </span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__module__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__qualname__</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">file</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Inv. counter: </span><span class="si">{</span><span class="n">get_cache_token</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">file</span><span class="p">)</span>
-            <span class="p">(</span><span class="n">_abc_registry</span><span class="p">,</span> <span class="n">_abc_cache</span><span class="p">,</span> <span class="n">_abc_negative_cache</span><span class="p">,</span>
-             <span class="n">_abc_negative_cache_version</span><span class="p">)</span> <span class="o">=</span> <span class="n">_get_dump</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;_abc_registry: </span><span class="si">{</span><span class="n">_abc_registry</span><span class="si">!r}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">file</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;_abc_cache: </span><span class="si">{</span><span class="n">_abc_cache</span><span class="si">!r}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">file</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;_abc_negative_cache: </span><span class="si">{</span><span class="n">_abc_negative_cache</span><span class="si">!r}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">file</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;_abc_negative_cache_version: </span><span class="si">{</span><span class="n">_abc_negative_cache_version</span><span class="si">!r}</span><span class="s2">&quot;</span><span class="p">,</span>
-                  <span class="n">file</span><span class="o">=</span><span class="n">file</span><span class="p">)</span>
-
-        <span class="k">def</span> <span class="nf">_abc_registry_clear</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Clear the registry (for debugging or testing).&quot;&quot;&quot;</span>
-            <span class="n">_reset_registry</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
-
-        <span class="k">def</span> <span class="nf">_abc_caches_clear</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Clear the caches (for debugging or testing).&quot;&quot;&quot;</span>
-            <span class="n">_reset_caches</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">ABC</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Helper class that provides a standard way to create an ABC using</span>
-<span class="sd">    inheritance.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="vm">__slots__</span> <span class="o">=</span> <span class="p">()</span>
-</pre></div>
-
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diff --git a/docs/_build/html/_modules/index.html b/docs/_build/html/_modules/index.html
deleted file mode 100644
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-  <h1>All modules for which code is available</h1>
-<ul><li><a href="abc.html">abc</a></li>
-<li><a href="tigramite/causal_effects.html">tigramite.causal_effects</a></li>
-<li><a href="tigramite/data_processing.html">tigramite.data_processing</a></li>
-<li><a href="tigramite/independence_tests/cmiknn.html">tigramite.independence_tests.cmiknn</a></li>
-<li><a href="tigramite/independence_tests/cmisymb.html">tigramite.independence_tests.cmisymb</a></li>
-<li><a href="tigramite/independence_tests/gpdc.html">tigramite.independence_tests.gpdc</a></li>
-<li><a href="tigramite/independence_tests/gpdc_torch.html">tigramite.independence_tests.gpdc_torch</a></li>
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-<li><a href="tigramite/independence_tests/independence_tests_base.html">tigramite.independence_tests.independence_tests_base</a></li>
-<li><a href="tigramite/independence_tests/oracle_conditional_independence.html">tigramite.independence_tests.oracle_conditional_independence</a></li>
-<li><a href="tigramite/independence_tests/parcorr.html">tigramite.independence_tests.parcorr</a></li>
-<li><a href="tigramite/independence_tests/parcorr_mult.html">tigramite.independence_tests.parcorr_mult</a></li>
-<li><a href="tigramite/independence_tests/parcorr_wls.html">tigramite.independence_tests.parcorr_wls</a></li>
-<li><a href="tigramite/independence_tests/regressionCI.html">tigramite.independence_tests.regressionCI</a></li>
-<li><a href="tigramite/independence_tests/robust_parcorr.html">tigramite.independence_tests.robust_parcorr</a></li>
-<li><a href="tigramite/lpcmci.html">tigramite.lpcmci</a></li>
-<li><a href="tigramite/models.html">tigramite.models</a></li>
-<li><a href="tigramite/pcmci.html">tigramite.pcmci</a></li>
-<li><a href="tigramite/plotting.html">tigramite.plotting</a></li>
-<li><a href="tigramite/toymodels/structural_causal_processes.html">tigramite.toymodels.structural_causal_processes</a></li>
-</ul>
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-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
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diff --git a/docs/_build/html/_modules/tigramite/causal_effects.html b/docs/_build/html/_modules/tigramite/causal_effects.html
deleted file mode 100644
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+++ /dev/null
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-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.causal_effects &#8212; Tigramite 5.2 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
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-            
-  <h1>Source code for tigramite.causal_effects</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">itertools</span>
-<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
-<span class="kn">from</span> <span class="nn">tigramite.models</span> <span class="kn">import</span> <span class="n">Models</span>
-<span class="kn">import</span> <span class="nn">struct</span>
-
-<div class="viewcode-block" id="CausalEffects"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects">[docs]</a><span class="k">class</span> <span class="nc">CausalEffects</span><span class="p">():</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Causal effect estimation.</span>
-
-<span class="sd">    Methods for the estimation of linear or non-parametric causal effects </span>
-<span class="sd">    between (potentially multivariate) X and Y (potentially conditional </span>
-<span class="sd">    on S) by (generalized) backdoor adjustment. Various graph types are </span>
-<span class="sd">    supported, also including hidden variables.</span>
-<span class="sd">    </span>
-<span class="sd">    Linear and non-parametric estimators are based on sklearn. For the </span>
-<span class="sd">    linear case without hidden variables also an efficient estimation </span>
-<span class="sd">    based on Wright&#39;s path coefficients is available. This estimator </span>
-<span class="sd">    also allows to estimate mediation effects.</span>
-
-<span class="sd">    See the corresponding paper [6]_ and tigramite tutorial for an </span>
-<span class="sd">    in-depth introduction. </span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    .. [6] J. Runge, Necessary and sufficient graphical conditions for</span>
-<span class="sd">           optimal adjustment sets in causal graphical models with </span>
-<span class="sd">           hidden variables, Advances in Neural Information Processing</span>
-<span class="sd">           Systems, 2021, 34 </span>
-<span class="sd">           https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html</span>
-
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    graph : array of either shape [N, N], [N, N, tau_max+1], or [N, N, tau_max+1, tau_max+1]</span>
-<span class="sd">        Different graph types are supported, see tutorial.</span>
-<span class="sd">    X : list of tuples</span>
-<span class="sd">        List of tuples [(i, -tau), ...] containing cause variables.</span>
-<span class="sd">    Y : list of tuples</span>
-<span class="sd">        List of tuples [(j, 0), ...] containing effect variables.</span>
-<span class="sd">    S : list of tuples</span>
-<span class="sd">        List of tuples [(i, -tau), ...] containing conditioned variables.  </span>
-<span class="sd">    graph_type : str</span>
-<span class="sd">        Type of graph.</span>
-<span class="sd">    hidden_variables : list of tuples</span>
-<span class="sd">        Hidden variables in format [(i, -tau), ...]. The internal graph is </span>
-<span class="sd">        constructed by a latent projection.</span>
-<span class="sd">    check_SM_overlap : bool</span>
-<span class="sd">        Whether to check whether S overlaps with M.</span>
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">graph</span><span class="p">,</span>
-                 <span class="n">graph_type</span><span class="p">,</span>
-                 <span class="n">X</span><span class="p">,</span>
-                 <span class="n">Y</span><span class="p">,</span>
-                 <span class="n">S</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">hidden_variables</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">check_SM_overlap</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">S</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">S</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">listX</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">listY</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">listS</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">X</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">Y</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">S</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>    
-
-        <span class="c1"># </span>
-        <span class="c1"># Checks regarding graph type</span>
-        <span class="c1">#</span>
-        <span class="n">supported_graphs</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;dag&#39;</span><span class="p">,</span> 
-                            <span class="s1">&#39;admg&#39;</span><span class="p">,</span>
-                            <span class="s1">&#39;tsg_dag&#39;</span><span class="p">,</span>
-                            <span class="s1">&#39;tsg_admg&#39;</span><span class="p">,</span>
-                            <span class="s1">&#39;stationary_dag&#39;</span><span class="p">,</span>
-                            <span class="s1">&#39;stationary_admg&#39;</span><span class="p">,</span>
-
-                            <span class="c1"># &#39;mag&#39;,</span>
-                            <span class="c1"># &#39;tsg_mag&#39;,</span>
-                            <span class="c1"># &#39;stationary_mag&#39;,</span>
-                            <span class="c1"># &#39;pag&#39;,</span>
-                            <span class="c1"># &#39;tsg_pag&#39;,</span>
-                            <span class="c1"># &#39;stationary_pag&#39;,</span>
-                            <span class="p">]</span>
-        <span class="k">if</span> <span class="n">graph_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">supported_graphs</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only graph types </span><span class="si">%s</span><span class="s2"> supported!&quot;</span> <span class="o">%</span><span class="n">supported_graphs</span><span class="p">)</span>
-
-        <span class="c1"># TODO?: check that masking aligns with hidden samples in variables</span>
-        <span class="k">if</span> <span class="n">hidden_variables</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">hidden_variables</span> <span class="o">=</span> <span class="p">[]</span>
-        
-        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_variables</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">hidden_variables</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_variables</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">)))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;XYS overlaps with hidden_variables!&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Only needed for later extension to MAG/PAGs</span>
-        <span class="k">if</span> <span class="s1">&#39;pag&#39;</span> <span class="ow">in</span> <span class="n">graph_type</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">possible</span> <span class="o">=</span> <span class="kc">True</span> 
-            <span class="bp">self</span><span class="o">.</span><span class="n">definite_status</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">possible</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">definite_status</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="c1"># Not needed for now...</span>
-        <span class="c1"># self.ignore_time_bounds = False</span>
-
-        <span class="c1"># Construct internal graph from input graph depending on graph type</span>
-        <span class="c1"># and hidden variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_construct_graph</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span> <span class="n">graph_type</span><span class="o">=</span><span class="n">graph_type</span><span class="p">,</span>
-                              <span class="n">hidden_variables</span><span class="o">=</span><span class="n">hidden_variables</span><span class="p">)</span>
-
-        <span class="c1"># print(self.graph.shape)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_graph</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_XYS</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">ancX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_ancestors</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ancY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_ancestors</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ancS</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_ancestors</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
-
-        <span class="c1"># If X is not in anc(Y), then no causal link exists</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ancY</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">X</span><span class="p">))</span> <span class="o">==</span> <span class="nb">set</span><span class="p">():</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">no_causal_path</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No causal path from X to Y exists.&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">no_causal_path</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="c1"># Get mediators</span>
-        <span class="n">mediators</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mediators</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">)</span> 
-
-        <span class="n">M</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">mediators</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">M</span> <span class="o">=</span> <span class="n">M</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">listM</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">):</span>
-            <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">varlag</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;X, Y, S must have time lags inside graph.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Overlap between X and Y.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">)))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Conditions S overlap with X or Y.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># # TODO: need to prove that this is sufficient for non-identifiability!</span>
-        <span class="c1"># if len(self.X.intersection(self._get_descendants(self.M))) &gt; 0:</span>
-        <span class="c1">#     raise ValueError(&quot;Not identifiable: Overlap between X and des(M)&quot;)</span>
-
-        <span class="k">if</span> <span class="n">check_SM_overlap</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Conditions S overlap with mediators M.&quot;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">desX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_descendants</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">desY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_descendants</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">desM</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_descendants</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">descendants</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">desY</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">desM</span><span class="p">)</span>
-
-        <span class="c1"># Define forb as X and descendants of YM</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">forbidden_nodes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">descendants</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>  <span class="c1">#.union(S)</span>
-
-        <span class="c1"># Define valid ancestors</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">vancs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ancX</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ancY</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ancS</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forbidden_nodes</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">desX</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Warning: Potentially outside assumptions: Conditions S overlap with des(X)&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Here only check if S overlaps with des(Y), leave the option that S</span>
-        <span class="c1"># contains variables in des(M) to the user</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">desY</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Not identifiable: Conditions S overlap with des(Y).&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Initializing CausalEffects class</span><span class="se">\n</span><span class="s2">##&quot;</span>
-                  <span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Input:&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">graph_type = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">graph_type</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">X = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">listX</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Y = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">listY</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">S = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">listS</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">M = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">listM</span>
-                  <span class="p">)</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_variables</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">hidden_variables = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_variables</span>
-                      <span class="p">)</span> 
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">no_causal_path</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No causal path from X to Y exists!&quot;</span><span class="p">)</span>
-
-
-    <span class="k">def</span> <span class="nf">_construct_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">graph_type</span><span class="p">,</span> <span class="n">hidden_variables</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Construct internal graph object based on input graph and hidden variables.</span>
-
-<span class="sd">           Uses the latent projection operation.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-
-        <span class="k">if</span> <span class="n">graph_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;dag&#39;</span><span class="p">,</span> <span class="s1">&#39;admg&#39;</span><span class="p">]:</span> 
-            <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;graph_type in [&#39;dag&#39;, &#39;admg&#39;] assumes graph.shape=(N, N).&quot;</span><span class="p">)</span>
-            <span class="c1"># Convert to shape [N, N, 1, 1] with dummy dimension</span>
-            <span class="c1"># to process as tsg_dag or tsg_admg with potential hidden variables</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-            
-            <span class="c1"># tau_max needed in _get_latent_projection_graph</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="mi">0</span>
-
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hidden_variables</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_latent_projection_graph</span><span class="p">()</span> <span class="c1"># stationary=False)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span> <span class="o">=</span> <span class="s2">&quot;tsg_admg&quot;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="c1"># graph = self.graph</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span> <span class="o">=</span> <span class="s1">&#39;tsg_&#39;</span> <span class="o">+</span> <span class="n">graph_type</span>
-
-        <span class="k">elif</span> <span class="n">graph_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;tsg_dag&#39;</span><span class="p">,</span> <span class="s1">&#39;tsg_admg&#39;</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">4</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;tsg-graph_type assumes graph.shape=(N, N, tau_max+1, tau_max+1).&quot;</span><span class="p">)</span>
-
-            <span class="c1"># Then tau_max is implicitely derived from</span>
-            <span class="c1"># the dimensions </span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="n">graph</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
-
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hidden_variables</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_latent_projection_graph</span><span class="p">()</span> <span class="c1">#, stationary=False)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span> <span class="o">=</span> <span class="s2">&quot;tsg_admg&quot;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span> <span class="o">=</span> <span class="n">graph_type</span>   
-
-        <span class="k">elif</span> <span class="n">graph_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;stationary_dag&#39;</span><span class="p">,</span> <span class="s1">&#39;stationary_admg&#39;</span><span class="p">]:</span>
-            <span class="c1"># Currently only stationary_dag without hidden variables is supported</span>
-            <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;stationary graph_type assumes graph.shape=(N, N, tau_max+1).&quot;</span><span class="p">)</span>
-            
-            <span class="c1"># # TODO: remove if theory for stationary ADMGs is clear</span>
-            <span class="c1"># if graph_type == &#39;stationary_dag&#39; and len(hidden_variables) &gt; 0:</span>
-            <span class="c1">#     raise ValueError(&quot;Hidden variables currently not supported for &quot;</span>
-            <span class="c1">#                      &quot;stationary_dag.&quot;)</span>
-
-            <span class="c1"># For a stationary DAG without hidden variables it&#39;s sufficient to consider</span>
-            <span class="c1"># a tau_max that includes the parents of X, Y, M, and S. A conservative</span>
-            <span class="c1"># estimate thereof is simply the lag-dimension of the stationary DAG plus</span>
-            <span class="c1"># the maximum lag of XYS.</span>
-            <span class="n">statgraph_tau_max</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
-            <span class="n">maxlag_XYS</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">):</span>
-                <span class="n">maxlag_XYS</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">maxlag_XYS</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">varlag</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">maxlag_XYS</span> <span class="o">+</span> <span class="n">statgraph_tau_max</span>
-
-            <span class="n">stat_graph</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-
-            <span class="c1">#########################################		</span>
-            <span class="c1"># Use this tau_max and construct ADMG by assuming paths of</span>
-            <span class="c1"># maximal lag 10*tau_max... TO BE REVISED!</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="n">graph</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_latent_projection_graph</span><span class="p">(</span><span class="n">stationary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span> <span class="o">=</span> <span class="s2">&quot;tsg_admg&quot;</span>
-            <span class="c1">#########################################</span>
-
-            <span class="c1"># Also create stationary graph extended to tau_max</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">stationary_graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">stationary_graph</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="n">stat_graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]]</span> <span class="o">=</span> <span class="n">stat_graph</span>
-
-            <span class="c1"># allowed_edges = [&quot;--&gt;&quot;, &quot;&lt;--&quot;]</span>
-
-            <span class="c1"># # Construct tsg_graph</span>
-            <span class="c1"># graph = np.zeros((self.N, self.N, self.tau_max + 1, self.tau_max + 1), dtype=&#39;&lt;U3&#39;)</span>
-            <span class="c1"># graph[:] = &quot;&quot;</span>
-            <span class="c1"># for (i, j) in itertools.product(range(self.N), range(self.N)):</span>
-            <span class="c1">#     for jt, tauj in enumerate(range(0, self.tau_max + 1)):</span>
-            <span class="c1">#         for it, taui in enumerate(range(tauj, self.tau_max + 1)):</span>
-            <span class="c1">#             tau = abs(taui - tauj)</span>
-            <span class="c1">#             if tau == 0 and j == i:</span>
-            <span class="c1">#                 continue</span>
-            <span class="c1">#             if tau &gt; statgraph_tau_max:</span>
-            <span class="c1">#                 continue                        </span>
-
-            <span class="c1">#             # if tau == 0:</span>
-            <span class="c1">#             #     if stat_graph[i, j, tau] == &#39;--&gt;&#39;:</span>
-            <span class="c1">#             #         graph[i, j, taui, tauj] = &quot;--&gt;&quot; </span>
-            <span class="c1">#             #         graph[j, i, tauj, taui] = &quot;&lt;--&quot; </span>
-
-            <span class="c1">#             #     # elif stat_graph[i, j, tau] == &#39;&lt;--&#39;:</span>
-            <span class="c1">#             #     #     graph[i, j, taui, tauj] = &quot;&lt;--&quot;</span>
-            <span class="c1">#             #     #     graph[j, i, tauj, taui] = &quot;--&gt;&quot; </span>
-            <span class="c1">#             # else:</span>
-            <span class="c1">#             if stat_graph[i, j, tau] == &#39;--&gt;&#39;:</span>
-            <span class="c1">#                 graph[i, j, taui, tauj] = &quot;--&gt;&quot; </span>
-            <span class="c1">#                 graph[j, i, tauj, taui] = &quot;&lt;--&quot; </span>
-            <span class="c1">#             elif stat_graph[i, j, tau] == &#39;&lt;--&#39;:</span>
-            <span class="c1">#                 pass</span>
-            <span class="c1">#             elif stat_graph[i, j, tau] == &#39;&#39;:</span>
-            <span class="c1">#                 pass</span>
-            <span class="c1">#             else:</span>
-            <span class="c1">#                 edge = stat_graph[i, j, tau]</span>
-            <span class="c1">#                 raise ValueError(&quot;Invalid graph edge %s. &quot; %(edge) +</span>
-            <span class="c1">#                      &quot;For graph_type = %s only %s are allowed.&quot; %(graph_type, str(allowed_edges)))</span>
-      
-            <span class="c1">#             # elif stat_graph[i, j, tau] == &#39;&lt;--&#39;:</span>
-            <span class="c1">#             #     graph[i, j, taui, tauj] = &quot;&lt;--&quot;</span>
-            <span class="c1">#             #     graph[j, i, tauj, taui] = &quot;--&gt;&quot; </span>
-
-            <span class="c1"># self.graph_type = &#39;tsg_dag&#39;</span>
-            <span class="c1"># self.graph = graph</span>
-
-
-        <span class="c1"># return (graph, graph_type, self.tau_max, hidden_variables)</span>
-
-            <span class="c1"># max_lag = self._get_maximum_possible_lag(XYZ=list(X.union(Y).union(S)), graph=graph)</span>
-
-            <span class="c1"># stat_mediators = self._get_mediators_stationary_graph(start=X, end=Y, max_lag=max_lag)</span>
-            <span class="c1"># self.tau_max = self._get_maximum_possible_lag(XYZ=list(X.union(Y).union(S).union(stat_mediators)), graph=graph)</span>
-            <span class="c1"># self.tau_max = graph_taumax</span>
-            <span class="c1"># for varlag in X.union(Y).union(S):</span>
-            <span class="c1">#     self.tau_max = max(self.tau_max, abs(varlag[1]))</span>
-
-            <span class="c1"># if verbosity &gt; 0:</span>
-            <span class="c1">#     print(&quot;Setting tau_max = &quot;, self.tau_max)</span>
-
-            <span class="c1"># if tau_max is None:</span>
-            <span class="c1">#     self.tau_max = graph_taumax</span>
-            <span class="c1">#     for varlag in X.union(Y).union(S):</span>
-            <span class="c1">#         self.tau_max = max(self.tau_max, abs(varlag[1]))</span>
-
-            <span class="c1">#     if verbosity &gt; 0:</span>
-            <span class="c1">#         print(&quot;Setting tau_max = &quot;, self.tau_max)</span>
-            <span class="c1"># else:</span>
-                <span class="c1"># self.tau_max = graph_taumax</span>
-                <span class="c1"># # Repeat hidden variable pattern </span>
-                <span class="c1"># # if larger tau_max is given</span>
-                <span class="c1"># if self.tau_max &gt; graph_taumax:</span>
-                <span class="c1">#     for lag in range(graph_taumax + 1, self.tau_max + 1):</span>
-                <span class="c1">#         for j in range(self.N):</span>
-                <span class="c1">#             if (j, -(lag % (graph_taumax+1))) in self.hidden_variables:</span>
-                <span class="c1">#                 self.hidden_variables.add((j, -lag))</span>
-            <span class="c1"># print(self.hidden_variables)</span>
-
-        <span class="c1">#     self.graph = self._get_latent_projection_graph(self.graph, stationary=True)</span>
-        <span class="c1">#     self.graph_type = &quot;tsg_admg&quot;</span>
-        <span class="c1"># else:</span>
-
-    <span class="k">def</span> <span class="nf">_check_XYS</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check whether XYS are sober.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">XYS</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">xys</span> <span class="ow">in</span> <span class="n">XYS</span><span class="p">:</span>
-            <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">xys</span> 
-            <span class="k">if</span> <span class="n">var</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">var</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;XYS vars must be in [0...N]&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">lag</span> <span class="o">&lt;</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="ow">or</span> <span class="n">lag</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;XYS lags must be in [-taumax...0]&quot;</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="CausalEffects.check_XYS_paths"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.check_XYS_paths">[docs]</a>    <span class="k">def</span> <span class="nf">check_XYS_paths</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check whether one can remove nodes from X and Y with no proper causal paths.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        X, Y : cleaned lists of X and Y with irrelevant nodes removed.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># TODO: Also check S...</span>
-        <span class="n">oldX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">oldY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-        <span class="c1"># anc_Y = self._get_ancestors(self.Y)</span>
-        <span class="c1"># anc_S = self._get_ancestors(self.S)</span>
-
-        <span class="c1"># Remove first from X those nodes with no causal path to Y or S</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ancY</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ancS</span><span class="p">)])</span>
-        
-        <span class="c1"># Remove from Y those nodes with no causal path from X</span>
-        <span class="c1"># des_X = self._get_descendants(X)</span>
-
-        <span class="n">Y</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y</span> <span class="k">if</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">desX</span><span class="p">])</span>
-
-        <span class="c1"># Also require that all x in X have proper path to Y or S,</span>
-        <span class="c1"># that is, the first link goes out of x </span>
-        <span class="c1"># and into path nodes</span>
-        <span class="n">mediators_S</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mediators</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">)</span>
-        <span class="n">path_nodes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">mediators_S</span><span class="p">))</span> 
-        <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_all_parents</span><span class="p">(</span><span class="n">path_nodes</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">oldX</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Consider pruning X = </span><span class="si">%s</span><span class="s2"> to X = </span><span class="si">%s</span><span class="s2"> &quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">oldX</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="o">+</span>
-                  <span class="s2">&quot;since only these have causal path to Y&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">oldY</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Consider pruning Y = </span><span class="si">%s</span><span class="s2"> to Y = </span><span class="si">%s</span><span class="s2"> &quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">oldY</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span> <span class="o">+</span>
-                  <span class="s2">&quot;since only these have causal path from X&quot;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="nb">list</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_check_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Checks that graph contains no invalid entries/structure.</span>
-
-<span class="sd">        Assumes graph.shape = (N, N, tau_max+1, tau_max+1)</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">allowed_edges</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;--&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">]</span>
-        <span class="k">if</span> <span class="s1">&#39;admg&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span><span class="p">:</span>
-            <span class="n">allowed_edges</span> <span class="o">+=</span> <span class="p">[</span><span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">,</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="s1">&#39;mag&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span><span class="p">:</span>
-            <span class="n">allowed_edges</span> <span class="o">+=</span> <span class="p">[</span><span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="s1">&#39;pag&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span><span class="p">:</span>
-            <span class="n">allowed_edges</span> <span class="o">+=</span> <span class="p">[</span><span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;o-o&quot;</span><span class="p">,</span> <span class="s2">&quot;o-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-o&quot;</span><span class="p">]</span>                         <span class="c1"># &quot;o--&quot;,</span>
-                        <span class="c1"># &quot;--o&quot;,</span>
-                        <span class="c1"># &quot;x-o&quot;,</span>
-                        <span class="c1"># &quot;o-x&quot;,</span>
-                        <span class="c1"># &quot;x--&quot;,</span>
-                        <span class="c1"># &quot;--x&quot;,</span>
-                        <span class="c1"># &quot;x-&gt;&quot;,</span>
-                        <span class="c1"># &quot;&lt;-x&quot;,</span>
-                        <span class="c1"># &quot;x-x&quot;,</span>
-                    <span class="c1"># ]</span>
-
-        <span class="n">graph_dict</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">)):</span>
-            <span class="n">edge</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span>
-            <span class="c1"># print((i, -taui), edge, (j, -tauj), graph[j, i, tauj, taui])</span>
-            <span class="k">if</span> <span class="n">edge</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s2">&quot;graph needs to have consistent edges (eg&quot;</span>
-                    <span class="s2">&quot; graph[i,j,taui,tauj]=&#39;--&gt;&#39; requires graph[j,i,tauj,taui]=&#39;&lt;--&#39;)&quot;</span>
-                <span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">edge</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">allowed_edges</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid graph edge </span><span class="si">%s</span><span class="s2">. &quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">edge</span><span class="p">)</span> <span class="o">+</span>
-                                 <span class="s2">&quot;For graph_type = </span><span class="si">%s</span><span class="s2"> only </span><span class="si">%s</span><span class="s2"> are allowed.&quot;</span> <span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">allowed_edges</span><span class="p">)))</span>
-
-            <span class="k">if</span> <span class="n">edge</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span> <span class="ow">or</span> <span class="n">edge</span> <span class="o">==</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">:</span>
-                <span class="c1"># Map to (i,-taui, j, tauj) graph</span>
-                <span class="n">indexi</span> <span class="o">=</span> <span class="n">i</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">taui</span>
-                <span class="n">indexj</span> <span class="o">=</span> <span class="n">j</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">tauj</span>
-
-                <span class="n">graph_dict</span><span class="p">[</span><span class="n">indexj</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">indexi</span><span class="p">)</span>
-
-        <span class="c1"># Check for cycles</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_cyclic</span><span class="p">(</span><span class="n">graph_dict</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;graph is cyclic.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># if MAG: check for almost cycles</span>
-        <span class="c1"># if PAG???</span>
-
-    <span class="k">def</span> <span class="nf">_check_cyclic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph_dict</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return True if the graph_dict has a cycle.</span>
-
-<span class="sd">        graph_dict must be represented as a dictionary mapping vertices to</span>
-<span class="sd">        iterables of neighbouring vertices. For example:</span>
-
-<span class="sd">        &gt;&gt;&gt; cyclic({1: (2,), 2: (3,), 3: (1,)})</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; cyclic({1: (2,), 2: (3,), 3: (4,)})</span>
-<span class="sd">        False</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">path</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-        <span class="n">visited</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-        <span class="k">def</span> <span class="nf">visit</span><span class="p">(</span><span class="n">vertex</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">vertex</span> <span class="ow">in</span> <span class="n">visited</span><span class="p">:</span>
-                <span class="k">return</span> <span class="kc">False</span>
-            <span class="n">visited</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">vertex</span><span class="p">)</span>
-            <span class="n">path</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">vertex</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">neighbour</span> <span class="ow">in</span> <span class="n">graph_dict</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">vertex</span><span class="p">,</span> <span class="p">()):</span>
-                <span class="k">if</span> <span class="n">neighbour</span> <span class="ow">in</span> <span class="n">path</span> <span class="ow">or</span> <span class="n">visit</span><span class="p">(</span><span class="n">neighbour</span><span class="p">):</span>
-                    <span class="k">return</span> <span class="kc">True</span>
-            <span class="n">path</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">vertex</span><span class="p">)</span>
-            <span class="k">return</span> <span class="kc">False</span>
-
-        <span class="k">return</span> <span class="nb">any</span><span class="p">(</span><span class="n">visit</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">graph_dict</span><span class="p">)</span>
-
-<div class="viewcode-block" id="CausalEffects.get_mediators"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.get_mediators">[docs]</a>    <span class="k">def</span> <span class="nf">get_mediators</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns mediator variables on proper causal paths.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        start : set</span>
-<span class="sd">            Set of start nodes.</span>
-<span class="sd">        end : set</span>
-<span class="sd">            Set of end nodes.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        mediators : set</span>
-<span class="sd">            Mediators on causal paths from start to end.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">des_X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_descendants</span><span class="p">(</span><span class="n">start</span><span class="p">)</span>
-
-        <span class="n">mediators</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-        <span class="c1"># Walk along proper causal paths backwards from Y to X</span>
-        <span class="c1"># potential_mediators = set()</span>
-        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">end</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">y</span> 
-            <span class="n">this_level</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span><span class="p">]</span>
-            <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">next_level</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">varlag</span><span class="p">):</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">parent</span>
-                        <span class="c1"># print(varlag, parent, des_X)</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">parent</span> <span class="ow">in</span> <span class="n">des_X</span>
-                            <span class="ow">and</span> <span class="n">parent</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">mediators</span>
-                            <span class="c1"># and parent not in potential_mediators</span>
-                            <span class="ow">and</span> <span class="n">parent</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">start</span>
-                            <span class="ow">and</span> <span class="n">parent</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">end</span>
-                            <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)):</span> <span class="c1"># or self.ignore_time_bounds)):</span>
-                            <span class="n">mediators</span> <span class="o">=</span> <span class="n">mediators</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">parent</span><span class="p">]))</span>
-                            <span class="n">next_level</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">parent</span><span class="p">)</span>
-                            
-                <span class="n">this_level</span> <span class="o">=</span> <span class="n">next_level</span>  
-
-        <span class="k">return</span> <span class="n">mediators</span></div>
-
-    <span class="k">def</span> <span class="nf">_get_mediators_stationary_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns mediator variables on proper causal paths</span>
-<span class="sd">           from X to Y in a stationary graph.&quot;&quot;&quot;</span>
-
-        <span class="n">des_X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_descendants_stationary_graph</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span>
-
-        <span class="n">mediators</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-        <span class="c1"># Walk along proper causal paths backwards from Y to X</span>
-        <span class="n">potential_mediators</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">end</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">y</span> 
-            <span class="n">this_level</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span><span class="p">]</span>
-            <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">next_level</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adjacents_stationary_graph</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> 
-                                <span class="n">node</span><span class="o">=</span><span class="n">varlag</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;&lt;*-&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;*+&quot;</span><span class="p">],</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">parent</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">parent</span> <span class="ow">in</span> <span class="n">des_X</span>
-                            <span class="ow">and</span> <span class="n">parent</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">mediators</span>
-                            <span class="c1"># and parent not in potential_mediators</span>
-                            <span class="ow">and</span> <span class="n">parent</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">start</span>
-                            <span class="ow">and</span> <span class="n">parent</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">end</span>
-                            <span class="c1"># and (-self.tau_max &lt;= tau &lt;= 0 or self.ignore_time_bounds)</span>
-                            <span class="p">):</span>
-                            <span class="n">mediators</span> <span class="o">=</span> <span class="n">mediators</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">parent</span><span class="p">]))</span>
-                            <span class="n">next_level</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">parent</span><span class="p">)</span>
-                            
-                <span class="n">this_level</span> <span class="o">=</span> <span class="n">next_level</span>  
-
-        <span class="k">return</span> <span class="n">mediators</span>
-
-    <span class="k">def</span> <span class="nf">_reverse_link</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">link</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Reverse a given link, taking care to replace &gt; with &lt; and vice versa.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">link</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="s2">&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-            <span class="n">left_mark</span> <span class="o">=</span> <span class="s2">&quot;&lt;&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">left_mark</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&lt;&quot;</span><span class="p">:</span>
-            <span class="n">right_mark</span> <span class="o">=</span> <span class="s2">&quot;&gt;&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">right_mark</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">left_mark</span> <span class="o">+</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">right_mark</span>
-
-    <span class="k">def</span> <span class="nf">_match_link</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pattern</span><span class="p">,</span> <span class="n">link</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Matches pattern including wildcards with link.</span>
-<span class="sd">           </span>
-<span class="sd">           In an ADMG we have edge types [&quot;--&gt;&quot;, &quot;&lt;--&quot;, &quot;&lt;-&gt;&quot;, &quot;+-&gt;&quot;, &quot;&lt;-+&quot;].</span>
-<span class="sd">           Here +-&gt; corresponds to having both &quot;--&gt;&quot; and &quot;&lt;-&gt;&quot;.</span>
-
-<span class="sd">           In a MAG we have edge types   [&quot;--&gt;&quot;, &quot;&lt;--&quot;, &quot;&lt;-&gt;&quot;, &quot;---&quot;].</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="k">if</span> <span class="n">pattern</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span> <span class="ow">or</span> <span class="n">link</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">True</span> <span class="k">if</span> <span class="n">pattern</span> <span class="o">==</span> <span class="n">link</span> <span class="k">else</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">left_mark</span><span class="p">,</span> <span class="n">middle_mark</span><span class="p">,</span> <span class="n">right_mark</span> <span class="o">=</span> <span class="n">pattern</span>
-            <span class="k">if</span> <span class="n">left_mark</span> <span class="o">!=</span> <span class="s1">&#39;*&#39;</span><span class="p">:</span>
-                <span class="c1"># if link[0] != &#39;+&#39;:</span>
-                    <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">left_mark</span><span class="p">:</span> <span class="k">return</span> <span class="kc">False</span>
-
-            <span class="k">if</span> <span class="n">right_mark</span> <span class="o">!=</span> <span class="s1">&#39;*&#39;</span><span class="p">:</span>
-                <span class="c1"># if link[2] != &#39;+&#39;:</span>
-                    <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="n">right_mark</span><span class="p">:</span> <span class="k">return</span> <span class="kc">False</span> 
-            
-            <span class="k">if</span> <span class="n">middle_mark</span> <span class="o">!=</span> <span class="s1">&#39;*&#39;</span> <span class="ow">and</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">middle_mark</span><span class="p">:</span> <span class="k">return</span> <span class="kc">False</span>    
-                       
-            <span class="k">return</span> <span class="kc">True</span>
-
-    <span class="k">def</span> <span class="nf">_find_adj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node</span><span class="p">,</span> <span class="n">patterns</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">return_link</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find adjacencies of node that match given patterns.&quot;&quot;&quot;</span>
-        
-        <span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph</span>
-
-        <span class="k">if</span> <span class="n">exclude</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">exclude</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="c1">#     exclude = self.hidden_variables</span>
-        <span class="c1"># else:</span>
-        <span class="c1">#     exclude = set(exclude).union(self.hidden_variables)</span>
-
-        <span class="c1"># Setup</span>
-        <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">=</span> <span class="n">node</span>
-        <span class="n">lag_i</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag_i</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">exclude</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">exclude</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">patterns</span><span class="p">)</span> <span class="o">==</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="n">patterns</span> <span class="o">=</span> <span class="p">[</span><span class="n">patterns</span><span class="p">]</span>
-
-        <span class="c1"># Init</span>
-        <span class="n">adj</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="c1"># Find adjacencies going forward/contemp</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_ik</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="n">lag_i</span><span class="p">,:])):</span>
-            <span class="c1"># print((k, lag_ik), graph[i,k,lag_i,lag_ik]) </span>
-            <span class="c1"># matches = [self._match_link(patt, graph[i,k,lag_i,lag_ik]) for patt in patterns]</span>
-            <span class="c1"># if np.any(matches):</span>
-            <span class="k">for</span> <span class="n">patt</span> <span class="ow">in</span> <span class="n">patterns</span><span class="p">:</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">patt</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="n">lag_i</span><span class="p">,</span><span class="n">lag_ik</span><span class="p">]):</span>
-                    <span class="n">match</span> <span class="o">=</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="o">-</span><span class="n">lag_ik</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">match</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclude</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="n">return_link</span><span class="p">:</span>
-                            <span class="n">adj</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="n">lag_i</span><span class="p">,</span><span class="n">lag_ik</span><span class="p">],</span> <span class="n">match</span><span class="p">))</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="n">adj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">match</span><span class="p">)</span>
-                    <span class="k">break</span>
-
-        
-        <span class="c1"># Find adjacencies going backward/contemp</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_ki</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span><span class="n">i</span><span class="p">,:,</span><span class="n">lag_i</span><span class="p">])):</span>  
-            <span class="c1"># print((k, lag_ki), graph[k,i,lag_ki,lag_i]) </span>
-            <span class="c1"># matches = [self._match_link(self._reverse_link(patt), graph[k,i,lag_ki,lag_i]) for patt in patterns]</span>
-            <span class="c1"># if np.any(matches):</span>
-            <span class="k">for</span> <span class="n">patt</span> <span class="ow">in</span> <span class="n">patterns</span><span class="p">:</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">patt</span><span class="p">),</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="n">lag_ki</span><span class="p">,</span><span class="n">lag_i</span><span class="p">]):</span>
-                    <span class="n">match</span> <span class="o">=</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="o">-</span><span class="n">lag_ki</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">match</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclude</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="n">return_link</span><span class="p">:</span>
-                            <span class="n">adj</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="n">lag_ki</span><span class="p">,</span><span class="n">lag_i</span><span class="p">]),</span> <span class="n">match</span><span class="p">))</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="n">adj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">match</span><span class="p">)</span>
-                    <span class="k">break</span>
-     
-        <span class="n">adj</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">adj</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">adj</span>
-
-    <span class="k">def</span> <span class="nf">_is_match</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodei</span><span class="p">,</span> <span class="n">nodej</span><span class="p">,</span> <span class="n">pattern_ij</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check whether the link between X and Y agrees with pattern.&quot;&quot;&quot;</span>
-
-        <span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph</span>
-
-        <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="o">=</span> <span class="n">nodei</span>
-        <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">)</span> <span class="o">=</span> <span class="n">nodej</span>
-        <span class="n">tauij</span> <span class="o">=</span> <span class="n">lag_j</span> <span class="o">-</span> <span class="n">lag_i</span>
-        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tauij</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]:</span>
-            <span class="k">return</span> <span class="kc">False</span>
-        <span class="k">return</span> <span class="p">((</span><span class="n">tauij</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern_ij</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tauij</span><span class="p">]))</span> <span class="ow">or</span>
-               <span class="p">(</span><span class="n">tauij</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">pattern_ij</span><span class="p">),</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tauij</span><span class="p">)])))</span>
-
-    <span class="k">def</span> <span class="nf">_get_children</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">varlag</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns set of children (varlag --&gt; ...) for (lagged) varlag.&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">possible</span><span class="p">:</span>
-            <span class="n">patterns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o*o&#39;</span><span class="p">,</span> <span class="s1">&#39;o*&gt;&#39;</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">patterns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;+*&gt;&#39;</span><span class="p">]</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">node</span><span class="o">=</span><span class="n">varlag</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="n">patterns</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_parents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">varlag</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns set of parents (varlag &lt;-- ...) for (lagged) varlag.&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">possible</span><span class="p">:</span>
-            <span class="n">patterns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;&lt;*-&#39;</span><span class="p">,</span> <span class="s1">&#39;o*o&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;*o&#39;</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">patterns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;&lt;*-&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;*+&#39;</span><span class="p">]</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">node</span><span class="o">=</span><span class="n">varlag</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="n">patterns</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_spouses</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">varlag</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns set of spouses (varlag &lt;-&gt; ...)  for (lagged) varlag.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">node</span><span class="o">=</span><span class="n">varlag</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;&lt;*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;+*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;*+&#39;</span><span class="p">])</span>
-
-    <span class="k">def</span> <span class="nf">_get_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">varlag</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns set of neighbors (varlag --- ...) for (lagged) varlag.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">node</span><span class="o">=</span><span class="n">varlag</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-*-&#39;</span><span class="p">])</span>
-
-    <span class="k">def</span> <span class="nf">_get_ancestors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get ancestors of nodes in W up to time tau_max.</span>
-<span class="sd">        </span>
-<span class="sd">        Includes the nodes themselves.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">ancestors</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">W</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">W</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">w</span> 
-            <span class="n">this_level</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="p">]</span>
-            <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">next_level</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-
-                    <span class="k">for</span> <span class="n">par</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">varlag</span><span class="p">):</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">par</span>
-                        <span class="k">if</span> <span class="n">par</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ancestors</span> <span class="ow">and</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">ancestors</span> <span class="o">=</span> <span class="n">ancestors</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">par</span><span class="p">]))</span>
-                            <span class="n">next_level</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">par</span><span class="p">)</span>
-
-                <span class="n">this_level</span> <span class="o">=</span> <span class="n">next_level</span>       
-
-        <span class="k">return</span> <span class="n">ancestors</span>
-
-    <span class="k">def</span> <span class="nf">_get_all_parents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get parents of nodes in W up to time tau_max.</span>
-<span class="sd">        </span>
-<span class="sd">        Includes the nodes themselves.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">parents</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">W</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">W</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">w</span> 
-            <span class="k">for</span> <span class="n">par</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">par</span>
-                <span class="k">if</span> <span class="n">par</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">parents</span> <span class="ow">and</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">parents</span> <span class="o">=</span> <span class="n">parents</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">par</span><span class="p">]))</span>
-
-        <span class="k">return</span> <span class="n">parents</span>
-
-    <span class="k">def</span> <span class="nf">_get_all_spouses</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get spouses of nodes in W up to time tau_max.</span>
-<span class="sd">        </span>
-<span class="sd">        Includes the nodes themselves.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">spouses</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">W</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">W</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">w</span> 
-            <span class="k">for</span> <span class="n">spouse</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_spouses</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">spouse</span>
-                <span class="k">if</span> <span class="n">spouse</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">spouses</span> <span class="ow">and</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">spouses</span> <span class="o">=</span> <span class="n">spouses</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">spouse</span><span class="p">]))</span>
-
-        <span class="k">return</span> <span class="n">spouses</span>
-
-    <span class="k">def</span> <span class="nf">_get_descendants_stationary_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get descendants of nodes in W up to time t in stationary graph.</span>
-<span class="sd">        </span>
-<span class="sd">        Includes the nodes themselves.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">descendants</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">W</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">W</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">w</span> 
-            <span class="n">this_level</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="p">]</span>
-            <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">next_level</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adjacents_stationary_graph</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> 
-                                <span class="n">node</span><span class="o">=</span><span class="n">varlag</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-*&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;-*+&quot;</span><span class="p">],</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">child</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">child</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">descendants</span> 
-                            <span class="c1"># and (-self.tau_max &lt;= tau &lt;= 0 or self.ignore_time_bounds)</span>
-                            <span class="p">):</span>
-                            <span class="n">descendants</span> <span class="o">=</span> <span class="n">descendants</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">child</span><span class="p">]))</span>
-                            <span class="n">next_level</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">child</span><span class="p">)</span>
-
-                <span class="n">this_level</span> <span class="o">=</span> <span class="n">next_level</span>       
-
-        <span class="k">return</span> <span class="n">descendants</span>
-
-    <span class="k">def</span> <span class="nf">_get_descendants</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get descendants of nodes in W up to time t.</span>
-<span class="sd">        </span>
-<span class="sd">        Includes the nodes themselves.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">descendants</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">W</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">W</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">w</span> 
-            <span class="n">this_level</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="p">]</span>
-            <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">next_level</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_children</span><span class="p">(</span><span class="n">varlag</span><span class="p">):</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">child</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">child</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">descendants</span> 
-                            <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)):</span> <span class="c1"># or self.ignore_time_bounds)):</span>
-                            <span class="n">descendants</span> <span class="o">=</span> <span class="n">descendants</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">child</span><span class="p">]))</span>
-                            <span class="n">next_level</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">child</span><span class="p">)</span>
-
-                <span class="n">this_level</span> <span class="o">=</span> <span class="n">next_level</span>       
-
-        <span class="k">return</span> <span class="n">descendants</span>
-
-    <span class="k">def</span> <span class="nf">_get_collider_path_nodes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">descendants</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get non-descendant collider path nodes and their parents of nodes in W up to time t.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">collider_path_nodes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([])</span>
-        <span class="c1"># print(&quot;descendants &quot;, descendants)</span>
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">W</span><span class="p">:</span>
-            <span class="c1"># print(w)</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">w</span> 
-            <span class="n">this_level</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="p">]</span>
-            <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">next_level</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-                    <span class="c1"># print(&quot;\t&quot;, varlag, self._get_spouses(varlag))</span>
-                    <span class="k">for</span> <span class="n">spouse</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_spouses</span><span class="p">(</span><span class="n">varlag</span><span class="p">):</span>
-                        <span class="c1"># print(&quot;\t\t&quot;, spouse)</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">spouse</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">spouse</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">collider_path_nodes</span>
-                            <span class="ow">and</span> <span class="n">spouse</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">descendants</span> 
-                            <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)):</span> <span class="c1"># or self.ignore_time_bounds)):</span>
-                            <span class="n">collider_path_nodes</span> <span class="o">=</span> <span class="n">collider_path_nodes</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">spouse</span><span class="p">]))</span>
-                            <span class="n">next_level</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">spouse</span><span class="p">)</span>
-
-                <span class="n">this_level</span> <span class="o">=</span> <span class="n">next_level</span>       
-
-        <span class="c1"># Add parents</span>
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">collider_path_nodes</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">par</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">par</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">collider_path_nodes</span>
-                    <span class="ow">and</span> <span class="n">par</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">descendants</span>
-                    <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)):</span> <span class="c1"># or self.ignore_time_bounds)):</span>
-                    <span class="n">collider_path_nodes</span> <span class="o">=</span> <span class="n">collider_path_nodes</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">par</span><span class="p">]))</span>
-
-        <span class="k">return</span> <span class="n">collider_path_nodes</span>
-
-    <span class="k">def</span> <span class="nf">_get_adjacents_stationary_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">node</span><span class="p">,</span> <span class="n">patterns</span><span class="p">,</span> 
-        <span class="n">max_lag</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find adjacencies of node matching patterns in a stationary graph.&quot;&quot;&quot;</span>
-        
-        <span class="c1"># graph = self.graph</span>
-
-        <span class="c1"># Setup</span>
-        <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">=</span> <span class="n">node</span>
-        <span class="k">if</span> <span class="n">exclude</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">exclude</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">patterns</span><span class="p">)</span> <span class="o">==</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="n">patterns</span> <span class="o">=</span> <span class="p">[</span><span class="n">patterns</span><span class="p">]</span>
-
-        <span class="c1"># Init</span>
-        <span class="n">adj</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find adjacencies going forward/contemp</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_ik</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:])):</span>  
-            <span class="n">matches</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">patt</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_ik</span><span class="p">])</span> <span class="k">for</span> <span class="n">patt</span> <span class="ow">in</span> <span class="n">patterns</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">matches</span><span class="p">):</span>
-                <span class="n">match</span> <span class="o">=</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">+</span> <span class="n">lag_ik</span><span class="p">)</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">+</span> <span class="n">lag_ik</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclude</span> <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="n">max_lag</span> <span class="o">&lt;=</span> <span class="n">lag_i</span> <span class="o">+</span> <span class="n">lag_ik</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">):</span> <span class="c1"># or self.ignore_time_bounds):</span>
-                    <span class="n">adj</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_ik</span><span class="p">],</span> <span class="n">match</span><span class="p">))</span>
-        
-        <span class="c1"># Find adjacencies going backward/contemp</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_ki</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span><span class="n">i</span><span class="p">,:])):</span>  
-            <span class="n">matches</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">patt</span><span class="p">),</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_ki</span><span class="p">])</span> <span class="k">for</span> <span class="n">patt</span> <span class="ow">in</span> <span class="n">patterns</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">matches</span><span class="p">):</span>
-                <span class="n">match</span> <span class="o">=</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">lag_ki</span><span class="p">)</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">lag_ki</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclude</span> <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="n">max_lag</span> <span class="o">&lt;=</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">lag_ki</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">):</span> <span class="c1"># or self.ignore_time_bounds):</span>
-                    <span class="n">adj</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_ki</span><span class="p">]),</span> <span class="n">match</span><span class="p">))</span>         
-        
-        <span class="n">adj</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">adj</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">adj</span>
-
-    <span class="k">def</span> <span class="nf">_get_canonical_dag_from_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructs canonical DAG as links_coeffs dictionary from graph.</span>
-
-<span class="sd">        For every &lt;-&gt; link further latent variables are added.</span>
-<span class="sd">        This corresponds to a canonical DAG (Richardson Spirtes 2002).</span>
-
-<span class="sd">        Can be used to evaluate d-separation.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_maxplusone</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_maxplusone</span> <span class="o">-</span> <span class="mi">1</span>
-
-        <span class="n">links</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)}</span>
-
-        <span class="c1"># Add further latent variables to accommodate &lt;-&gt; links</span>
-        <span class="n">latent_index</span> <span class="o">=</span> <span class="n">N</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">)):</span>
-
-            <span class="n">edge_type</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span>
-
-            <span class="c1"># Consider contemporaneous links only once</span>
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">&gt;</span> <span class="n">i</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="k">if</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-            <span class="k">elif</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-            <span class="k">elif</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">latent_index</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">latent_index</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="c1"># elif edge_type == &quot;---&quot;:</span>
-            <span class="c1">#     links[latent_index] = []</span>
-            <span class="c1">#     selection_vars.append(latent_index)</span>
-            <span class="c1">#     links[latent_index].append((i, -tau))</span>
-            <span class="c1">#     links[latent_index].append((j, 0))</span>
-            <span class="c1">#     latent_index += 1</span>
-            <span class="k">elif</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">latent_index</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">latent_index</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="k">elif</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">latent_index</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">latent_index</span> <span class="o">+=</span> <span class="mi">1</span>
-
-        <span class="k">return</span> <span class="n">links</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_maximum_possible_lag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Construct maximum relevant time lag for d-separation in stationary graph.</span>
-
-<span class="sd">        TO BE REVISED!</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">_repeating</span><span class="p">(</span><span class="n">link</span><span class="p">,</span> <span class="n">seen_path</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Returns True if a link or its time-shifted version is already</span>
-<span class="sd">            included in seen_links.&quot;&quot;&quot;</span>
-            <span class="n">i</span><span class="p">,</span> <span class="n">taui</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tauj</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-            <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">seen_link</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">seen_path</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="n">seen_i</span><span class="p">,</span> <span class="n">seen_taui</span> <span class="o">=</span> <span class="n">seen_link</span>
-                <span class="n">seen_j</span><span class="p">,</span> <span class="n">seen_tauj</span> <span class="o">=</span> <span class="n">seen_path</span><span class="p">[</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-
-                <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">seen_i</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="n">seen_j</span>
-                    <span class="ow">and</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tauj</span><span class="o">-</span><span class="n">taui</span><span class="p">)</span> <span class="o">==</span> <span class="nb">abs</span><span class="p">(</span><span class="n">seen_tauj</span><span class="o">-</span><span class="n">seen_taui</span><span class="p">)):</span>
-                    <span class="k">return</span> <span class="kc">True</span>
-
-            <span class="k">return</span> <span class="kc">False</span>
-
-        <span class="c1"># TODO: does this work with PAGs?</span>
-        <span class="c1"># if self.possible:</span>
-        <span class="c1">#     patterns=[&#39;&lt;*-&#39;, &#39;&lt;*o&#39;, &#39;o*o&#39;] </span>
-        <span class="c1"># else:</span>
-        <span class="c1">#     patterns=[&#39;&lt;*-&#39;] </span>
-
-        <span class="n">canonical_dag_links</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_canonical_dag_from_graph</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">XYZ</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">node</span>   <span class="c1"># tau &lt;= 0</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
-
-            <span class="n">causal_path</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">queue</span> <span class="o">=</span> <span class="p">[(</span><span class="n">node</span><span class="p">,</span> <span class="n">causal_path</span><span class="p">)]</span>
-
-            <span class="k">while</span> <span class="n">queue</span><span class="p">:</span>
-                <span class="n">varlag</span><span class="p">,</span> <span class="n">causal_path</span> <span class="o">=</span> <span class="n">queue</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-                <span class="n">causal_path</span> <span class="o">=</span> <span class="p">[</span><span class="n">varlag</span><span class="p">]</span> <span class="o">+</span> <span class="n">causal_path</span>
-
-                <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">varlag</span>
-                <span class="k">for</span> <span class="n">partmp</span> <span class="ow">in</span> <span class="n">canonical_dag_links</span><span class="p">[</span><span class="n">var</span><span class="p">]:</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">tautmp</span> <span class="o">=</span> <span class="n">partmp</span>
-                    <span class="c1"># Get shifted lag since canonical_dag_links is at t=0</span>
-                    <span class="n">tau</span> <span class="o">=</span> <span class="n">tautmp</span> <span class="o">+</span> <span class="n">lag</span>
-                    <span class="n">par</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="p">(</span><span class="n">par</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">causal_path</span><span class="p">):</span>
-                    
-                        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">causal_path</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="n">queue</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">par</span><span class="p">,</span> <span class="n">causal_path</span><span class="p">))</span>
-                            <span class="k">continue</span>
-
-                        <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">causal_path</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">_repeating</span><span class="p">((</span><span class="n">par</span><span class="p">,</span> <span class="n">varlag</span><span class="p">),</span> <span class="n">causal_path</span><span class="p">):</span>
-                            
-                                <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
-                                <span class="n">queue</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">par</span><span class="p">,</span> <span class="n">causal_path</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="n">max_lag</span>
-
-    <span class="k">def</span> <span class="nf">_get_latent_projection_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stationary</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;For DAGs/ADMGs uses the Latent projection operation (Pearl 2009).</span>
-
-<span class="sd">           Assumes a normal or stationary graph with potentially unobserved nodes.</span>
-<span class="sd">           Also allows particular time steps to be unobserved. By stationarity</span>
-<span class="sd">           that pattern of unobserved nodes is repeated into -infinity.</span>
-
-<span class="sd">           Latent projection operation for latents = nodes before t-tau_max or due to &lt;-&gt;:</span>
-<span class="sd">           (i)  auxADMG contains (i, -taui) --&gt; (j, -tauj) iff there is a directed path </span>
-<span class="sd">                (i, -taui) --&gt; ... --&gt; (j, -tauj) on which</span>
-<span class="sd">                every non-endpoint vertex is in hidden variables (= not in observed_vars)</span>
-<span class="sd">                here iff (i, -|taui-tauj|) --&gt; j in graph</span>
-<span class="sd">           (ii) auxADMG contains (i, -taui) &lt;-&gt; (j, -tauj) iff there exists a path of the </span>
-<span class="sd">                form (i, -taui) &lt;-- ... --&gt; (j, -tauj) on</span>
-<span class="sd">                which every non-endpoint vertex is non-collider AND in L (=not in observed_vars)</span>
-<span class="sd">                here iff (i, -|taui-tauj|) &lt;-&gt; j OR there is path </span>
-<span class="sd">                (i, -taui) &lt;-- nodes before t-tau_max --&gt; (j, -tauj)</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="c1"># graph = self.graph</span>
-
-        <span class="c1"># if self.hidden_variables is None:</span>
-        <span class="c1">#     hidden_variables_here = []</span>
-        <span class="c1"># else:</span>
-        <span class="n">hidden_variables_here</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_variables</span>
-
-        <span class="n">aux_graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
-        <span class="n">aux_graph</span><span class="p">[:]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)):</span>
-            <span class="k">for</span> <span class="n">jt</span><span class="p">,</span> <span class="n">tauj</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)):</span>
-                <span class="k">for</span> <span class="n">it</span><span class="p">,</span> <span class="n">taui</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)):</span>
-                    <span class="n">tau</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">taui</span> <span class="o">-</span> <span class="n">tauj</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="n">i</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">taui</span><span class="p">)</span> <span class="ow">in</span> <span class="n">hidden_variables_here</span> <span class="ow">or</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tauj</span><span class="p">)</span> <span class="ow">in</span> <span class="n">hidden_variables_here</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># print(&quot;\n&quot;)</span>
-                    <span class="c1"># print((i, -taui), (j, -tauj))</span>
-
-                    <span class="n">cond_i_xy</span> <span class="o">=</span> <span class="p">(</span>
-                            <span class="c1"># tau &lt;= graph_taumax </span>
-                        <span class="c1"># and (graph[i, j, tau] == &#39;--&gt;&#39; or graph[i, j, tau] == &#39;+-&gt;&#39;) </span>
-                        <span class="c1">#     )</span>
-                          <span class="c1"># and </span>
-                          <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span> <span class="c1">#graph=graph,</span>
-                                                <span class="n">start</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">taui</span><span class="p">)],</span>
-                                                 <span class="n">end</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tauj</span><span class="p">)],</span>
-                                                 <span class="n">conditions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                                 <span class="n">starts_with</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;+*&gt;&#39;</span><span class="p">],</span>
-                                                 <span class="n">ends_with</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;+*&gt;&#39;</span><span class="p">],</span>
-                                                 <span class="n">path_type</span><span class="o">=</span><span class="s1">&#39;causal&#39;</span><span class="p">,</span>
-                                                 <span class="n">hidden_by_taumax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                                 <span class="n">hidden_variables</span><span class="o">=</span><span class="n">hidden_variables_here</span><span class="p">,</span>
-                                                 <span class="n">stationary_graph</span><span class="o">=</span><span class="n">stationary</span><span class="p">,</span>
-                                                 <span class="p">))</span>
-                    <span class="n">cond_i_yx</span> <span class="o">=</span> <span class="p">(</span>
-                        <span class="c1"># tau &lt;= graph_taumax </span>
-                        <span class="c1"># and (graph[i, j, tau] == &#39;&lt;--&#39; or graph[i, j, tau] == &#39;&lt;-+&#39;) </span>
-                        <span class="c1">#     )</span>
-                        <span class="c1"># and </span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span> <span class="c1">#graph=graph,</span>
-                                              <span class="n">start</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tauj</span><span class="p">)],</span>
-                                               <span class="n">end</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">taui</span><span class="p">)],</span>
-                                               <span class="n">conditions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                               <span class="n">starts_with</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;+*&gt;&#39;</span><span class="p">],</span>
-                                               <span class="n">ends_with</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;+*&gt;&#39;</span><span class="p">],</span>
-                                               <span class="n">path_type</span><span class="o">=</span><span class="s1">&#39;causal&#39;</span><span class="p">,</span>
-                                               <span class="n">hidden_by_taumax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                               <span class="n">hidden_variables</span><span class="o">=</span><span class="n">hidden_variables_here</span><span class="p">,</span>
-                                               <span class="n">stationary_graph</span><span class="o">=</span><span class="n">stationary</span><span class="p">,</span>
-                                               <span class="p">))</span>
-                    <span class="k">if</span> <span class="n">stationary</span><span class="p">:</span>
-                        <span class="n">hidden_by_taumax_here</span> <span class="o">=</span> <span class="kc">True</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">hidden_by_taumax_here</span> <span class="o">=</span> <span class="kc">False</span>
-
-                    <span class="n">cond_ii</span> <span class="o">=</span> <span class="p">(</span>
-                        <span class="c1"># tau &lt;= graph_taumax </span>
-                                <span class="c1"># and </span>
-                                <span class="p">(</span>
-                                <span class="c1">#     graph[i, j, tau] == &#39;&lt;-&gt;&#39; </span>
-                                <span class="c1"># or graph[i, j, tau] == &#39;+-&gt;&#39; or graph[i, j, tau] == &#39;&lt;-+&#39;)) </span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span> <span class="c1">#graph=graph,</span>
-                                                <span class="n">start</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">taui</span><span class="p">)],</span>
-                                                 <span class="n">end</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tauj</span><span class="p">)],</span>
-                                                 <span class="n">conditions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                                 <span class="n">starts_with</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;&lt;**&#39;</span><span class="p">,</span> <span class="s1">&#39;+**&#39;</span><span class="p">],</span>
-                                                 <span class="n">ends_with</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;**&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;**+&#39;</span><span class="p">],</span>
-                                                 <span class="n">path_type</span><span class="o">=</span><span class="s1">&#39;any&#39;</span><span class="p">,</span>
-                                                 <span class="n">hidden_by_taumax</span><span class="o">=</span><span class="n">hidden_by_taumax_here</span><span class="p">,</span>
-                                                 <span class="n">hidden_variables</span><span class="o">=</span><span class="n">hidden_variables_here</span><span class="p">,</span>
-                                                 <span class="n">stationary_graph</span><span class="o">=</span><span class="n">stationary</span><span class="p">,</span>
-                                                 <span class="p">)))</span>
-
-                    <span class="k">if</span> <span class="n">cond_i_xy</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">cond_i_yx</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">cond_ii</span><span class="p">:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>  <span class="c1">#graph[i, j, tau]</span>
-                        <span class="c1"># if tau == 0:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>  <span class="c1"># graph[j, i, tau]</span>
-                    <span class="k">elif</span> <span class="ow">not</span> <span class="n">cond_i_xy</span> <span class="ow">and</span> <span class="n">cond_i_yx</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">cond_ii</span><span class="p">:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>  <span class="c1">#graph[i, j, tau]</span>
-                        <span class="c1"># if tau == 0:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>  <span class="c1"># graph[j, i, tau]</span>
-                    <span class="k">elif</span> <span class="ow">not</span> <span class="n">cond_i_xy</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">cond_i_yx</span> <span class="ow">and</span> <span class="n">cond_ii</span><span class="p">:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;-&gt;&#39;</span>
-                        <span class="c1"># if tau == 0:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;-&gt;&#39;</span>
-                    <span class="k">elif</span> <span class="n">cond_i_xy</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">cond_i_yx</span> <span class="ow">and</span> <span class="n">cond_ii</span><span class="p">:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;+-&gt;&#39;</span>
-                        <span class="c1"># if tau == 0:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;-+&#39;</span>                        
-                    <span class="k">elif</span> <span class="ow">not</span> <span class="n">cond_i_xy</span> <span class="ow">and</span> <span class="n">cond_i_yx</span> <span class="ow">and</span> <span class="n">cond_ii</span><span class="p">:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;-+&#39;</span>
-                        <span class="c1"># if tau == 0:</span>
-                        <span class="n">aux_graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;+-&gt;&#39;</span> 
-                    <span class="k">elif</span> <span class="n">cond_i_xy</span> <span class="ow">and</span> <span class="n">cond_i_yx</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cycle between </span><span class="si">%s</span><span class="s2"> and </span><span class="si">%s</span><span class="s2">!&quot;</span> <span class="o">%</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">taui</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tauj</span><span class="p">)))</span>
-                    <span class="c1"># print(aux_graph[i, j, taui, tauj])</span>
-
-                    <span class="c1"># print((i, -taui), (j, -tauj), cond_i_xy, cond_i_yx, cond_ii, aux_graph[i, j, taui, tauj], aux_graph[j, i, tauj, taui])</span>
-
-        <span class="k">return</span> <span class="n">aux_graph</span>
-
-    <span class="k">def</span> <span class="nf">_check_path</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
-        <span class="c1"># graph, </span>
-        <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span>
-        <span class="n">conditions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-        <span class="n">starts_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">ends_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">path_type</span><span class="o">=</span><span class="s1">&#39;any&#39;</span><span class="p">,</span>
-        <span class="c1"># causal_children=None,</span>
-        <span class="n">stationary_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">hidden_by_taumax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">hidden_variables</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check whether an open/active path between start and end given conditions exists.</span>
-<span class="sd">        </span>
-<span class="sd">        Also allows to restrict start and end patterns and to consider causal/non-causal paths</span>
-
-<span class="sd">        hidden_by_taumax and hidden_variables are relevant for the latent projection operation.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-
-        <span class="k">if</span> <span class="n">conditions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">conditions</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([])</span>
-        <span class="c1"># if conditioned_variables is None:</span>
-        <span class="c1">#     S = []</span>
-
-        <span class="n">start</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">start</span><span class="p">)</span>
-        <span class="n">end</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">end</span><span class="p">)</span>
-        <span class="n">conditions</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">conditions</span><span class="p">)</span>
-        
-        <span class="c1"># Get maximal possible time lag of a connecting path</span>
-        <span class="c1"># See Thm. XXXX - TO BE REVISED!</span>
-        <span class="n">XYZ</span> <span class="o">=</span> <span class="n">start</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">end</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">conditions</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">stationary_graph</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">10</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span>  <span class="c1"># TO BE REVISED! self._get_maximum_possible_lag(XYZ, self.graph)</span>
-            <span class="n">causal_children</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_mediators_stationary_graph</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">end</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="n">causal_children</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_mediators</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">end</span><span class="p">))</span>
-       
-        <span class="c1"># if hidden_variables is None:</span>
-        <span class="c1">#     hidden_variables = set([])</span>
-
-        <span class="k">if</span> <span class="n">hidden_by_taumax</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">hidden_variables</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">hidden_variables</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([])</span>
-            <span class="n">hidden_variables</span> <span class="o">=</span> <span class="n">hidden_variables</span><span class="o">.</span><span class="n">union</span><span class="p">([(</span><span class="n">k</span><span class="p">,</span> <span class="o">-</span><span class="n">tauk</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> 
-                                            <span class="k">for</span> <span class="n">tauk</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
-
-        <span class="c1"># print(&quot;causal_children &quot;, causal_children)</span>
-
-        <span class="k">if</span> <span class="n">starts_with</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">starts_with</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;***&#39;</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">starts_with</span><span class="p">)</span> <span class="o">==</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="n">starts_with</span> <span class="o">=</span> <span class="p">[</span><span class="n">starts_with</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">ends_with</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ends_with</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;***&#39;</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">ends_with</span><span class="p">)</span> <span class="o">==</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="n">ends_with</span> <span class="o">=</span> <span class="p">[</span><span class="n">ends_with</span><span class="p">]</span>
-        <span class="c1">#</span>
-        <span class="c1"># Breadth-first search to find connection</span>
-        <span class="c1">#</span>
-        <span class="c1"># print(&quot;\nstart, starts_with, ends_with, end &quot;, start, starts_with, ends_with, end)</span>
-        <span class="c1"># print(&quot;hidden_variables &quot;, hidden_variables)</span>
-        <span class="n">start_from</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">start</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">stationary_graph</span><span class="p">:</span>
-                <span class="n">link_neighbors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adjacents_stationary_graph</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> <span class="n">node</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="n">starts_with</span><span class="p">,</span> 
-                                        <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">start</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">link_neighbors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">node</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="n">starts_with</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="n">return_link</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            
-            <span class="k">for</span> <span class="n">link_neighbor</span> <span class="ow">in</span> <span class="n">link_neighbors</span><span class="p">:</span>
-                <span class="n">link</span><span class="p">,</span> <span class="n">neighbor</span> <span class="o">=</span> <span class="n">link_neighbor</span>
-
-                <span class="c1"># if before_taumax and neighbor[1] &gt;= -self.tau_max:</span>
-                <span class="c1">#     continue</span>
-
-                <span class="k">if</span> <span class="p">(</span><span class="n">hidden_variables</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">neighbor</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">end</span>
-                                    <span class="ow">and</span> <span class="n">neighbor</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">hidden_variables</span><span class="p">):</span>
-                    <span class="k">continue</span>
-
-                <span class="k">if</span> <span class="n">path_type</span> <span class="o">==</span> <span class="s1">&#39;non_causal&#39;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">neighbor</span> <span class="ow">in</span> <span class="n">causal_children</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="n">link</span><span class="p">)</span> 
-                        <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="s1">&#39;+*&gt;&#39;</span><span class="p">,</span> <span class="n">link</span><span class="p">)):</span>
-                        <span class="k">continue</span>
-                <span class="k">elif</span> <span class="n">path_type</span> <span class="o">==</span> <span class="s1">&#39;causal&#39;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">neighbor</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">causal_children</span><span class="p">):</span> <span class="c1"># or self._match_link(&#39;&lt;**&#39;, link)):</span>
-                        <span class="k">continue</span>                    
-                <span class="n">start_from</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">link</span><span class="p">,</span> <span class="n">neighbor</span><span class="p">))</span>
-
-        <span class="c1"># print(&quot;start, end, start_from &quot;, start, end, start_from)</span>
-
-        <span class="n">visited</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">varlag_i</span><span class="p">,</span> <span class="n">link_ik</span><span class="p">,</span> <span class="n">varlag_k</span><span class="p">)</span> <span class="ow">in</span> <span class="n">start_from</span><span class="p">:</span>
-            <span class="n">visited</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">link_ik</span><span class="p">,</span> <span class="n">varlag_k</span><span class="p">))</span>
-
-        <span class="c1"># Traversing through motifs i *-* k *-* j</span>
-        <span class="k">while</span> <span class="n">start_from</span><span class="p">:</span>
-
-            <span class="c1"># print(&quot;Continue &quot;, start_from)</span>
-            <span class="c1"># for (link_ik, varlag_k) in start_from:</span>
-            <span class="n">removables</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">varlag_i</span><span class="p">,</span> <span class="n">link_ik</span><span class="p">,</span> <span class="n">varlag_k</span><span class="p">)</span> <span class="ow">in</span> <span class="n">start_from</span><span class="p">:</span>
-
-                <span class="c1"># print(&quot;varlag_k in end &quot;, varlag_k in end, link_ik)</span>
-                <span class="k">if</span> <span class="n">varlag_k</span> <span class="ow">in</span> <span class="n">end</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">patt</span><span class="p">,</span> <span class="n">link_ik</span><span class="p">)</span> <span class="k">for</span> <span class="n">patt</span> <span class="ow">in</span> <span class="n">ends_with</span><span class="p">]):</span>
-                        <span class="c1"># print(&quot;Connected &quot;, varlag_i, link_ik, varlag_k)</span>
-                        <span class="k">return</span> <span class="kc">True</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">removables</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">varlag_i</span><span class="p">,</span> <span class="n">link_ik</span><span class="p">,</span> <span class="n">varlag_k</span><span class="p">))</span>
-
-            <span class="k">for</span> <span class="n">removable</span> <span class="ow">in</span> <span class="n">removables</span><span class="p">:</span>
-                <span class="n">start_from</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">removable</span><span class="p">)</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">start_from</span><span class="p">)</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span>
-                <span class="k">return</span> <span class="kc">False</span>
-
-            <span class="c1"># Get any neighbor from starting nodes</span>
-            <span class="c1"># link_ik, varlag_k = start_from.pop()</span>
-            <span class="n">varlag_i</span><span class="p">,</span> <span class="n">link_ik</span><span class="p">,</span> <span class="n">varlag_k</span> <span class="o">=</span> <span class="n">start_from</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-
-            <span class="c1"># print(&quot;Get k = &quot;, link_ik, varlag_k)</span>
-            <span class="c1"># print(&quot;start_from &quot;, start_from)</span>
-            <span class="c1"># print(&quot;visited    &quot;, visited)</span>
-
-            <span class="k">if</span> <span class="n">stationary_graph</span><span class="p">:</span>
-                <span class="n">link_neighbors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adjacents_stationary_graph</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> <span class="n">node</span><span class="o">=</span><span class="n">varlag_k</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="s1">&#39;***&#39;</span><span class="p">,</span> 
-                                        <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">start</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">link_neighbors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">node</span><span class="o">=</span><span class="n">varlag_k</span><span class="p">,</span> <span class="n">patterns</span><span class="o">=</span><span class="s1">&#39;***&#39;</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="n">return_link</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            
-            <span class="c1"># print(&quot;link_neighbors &quot;, link_neighbors)</span>
-            <span class="k">for</span> <span class="n">link_neighbor</span> <span class="ow">in</span> <span class="n">link_neighbors</span><span class="p">:</span>
-                <span class="n">link_kj</span><span class="p">,</span> <span class="n">varlag_j</span> <span class="o">=</span> <span class="n">link_neighbor</span>
-                <span class="c1"># print(&quot;Walk &quot;, link_ik, varlag_k, link_kj, varlag_j)</span>
-
-                <span class="c1"># print (&quot;visited &quot;, (link_kj, varlag_j), visited)</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">link_kj</span><span class="p">,</span> <span class="n">varlag_j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">visited</span><span class="p">:</span>
-                <span class="c1"># if (varlag_i, link_kj, varlag_j) in visited:</span>
-                    <span class="c1"># print(&quot;in visited&quot;)</span>
-                    <span class="k">continue</span>
-                <span class="c1"># print(&quot;Not in visited&quot;)</span>
-
-                <span class="k">if</span> <span class="n">path_type</span> <span class="o">==</span> <span class="s1">&#39;causal&#39;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="n">link_kj</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="s1">&#39;+*&gt;&#39;</span><span class="p">,</span> <span class="n">link_kj</span><span class="p">)):</span>
-                        <span class="k">continue</span> 
-
-                <span class="c1"># If motif  i *-* k *-* j is open, </span>
-                <span class="c1"># then add link_kj, varlag_j to visited and start_from</span>
-                <span class="n">left_mark</span> <span class="o">=</span> <span class="n">link_ik</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                <span class="n">right_mark</span> <span class="o">=</span> <span class="n">link_kj</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="c1"># print(left_mark, right_mark)</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">definite_status</span><span class="p">:</span>
-                    <span class="c1"># Exclude paths that are not definite_status implying that any of the following</span>
-                    <span class="c1"># motifs occurs:</span>
-                    <span class="c1"># i *-&gt; k o-* j</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">left_mark</span> <span class="o">==</span> <span class="s1">&#39;&gt;&#39;</span> <span class="ow">and</span> <span class="n">right_mark</span> <span class="o">==</span> <span class="s1">&#39;o&#39;</span><span class="p">):</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># i *-o k &lt;-* j</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">left_mark</span> <span class="o">==</span> <span class="s1">&#39;o&#39;</span> <span class="ow">and</span> <span class="n">right_mark</span> <span class="o">==</span> <span class="s1">&#39;&lt;&#39;</span><span class="p">):</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># i *-o k o-* j and i and j are adjacent</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">left_mark</span> <span class="o">==</span> <span class="s1">&#39;o&#39;</span> <span class="ow">and</span> <span class="n">right_mark</span> <span class="o">==</span> <span class="s1">&#39;o&#39;</span>
-                        <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_match</span><span class="p">(</span><span class="n">varlag_i</span><span class="p">,</span> <span class="n">varlag_j</span><span class="p">,</span> <span class="s2">&quot;***&quot;</span><span class="p">)):</span>
-                        <span class="k">continue</span>
-
-                    <span class="c1"># If k is in conditions and motif is *-o k o-*, then motif is blocked since</span>
-                    <span class="c1"># i and j are non-adjacent due to the check above</span>
-                    <span class="k">if</span> <span class="n">varlag_k</span> <span class="ow">in</span> <span class="n">conditions</span> <span class="ow">and</span> <span class="p">(</span><span class="n">left_mark</span> <span class="o">==</span> <span class="s1">&#39;o&#39;</span> <span class="ow">and</span> <span class="n">right_mark</span> <span class="o">==</span> <span class="s1">&#39;o&#39;</span><span class="p">):</span>
-                        <span class="c1"># print(&quot;Motif closed &quot;, link_ik, varlag_k, link_kj, varlag_j )</span>
-                        <span class="k">continue</span>  <span class="c1"># [(&#39;&gt;&#39;, &#39;&lt;&#39;), (&#39;&gt;&#39;, &#39;+&#39;), (&#39;+&#39;, &#39;&lt;&#39;), (&#39;+&#39;, &#39;+&#39;)]</span>
-
-                <span class="c1"># If k is in conditions and left or right mark is tail &#39;-&#39;, then motif is blocked</span>
-                <span class="k">if</span> <span class="n">varlag_k</span> <span class="ow">in</span> <span class="n">conditions</span> <span class="ow">and</span> <span class="p">(</span><span class="n">left_mark</span> <span class="o">==</span> <span class="s1">&#39;-&#39;</span> <span class="ow">or</span> <span class="n">right_mark</span> <span class="o">==</span> <span class="s1">&#39;-&#39;</span><span class="p">):</span>
-                    <span class="c1"># print(&quot;Motif closed &quot;, link_ik, varlag_k, link_kj, varlag_j )</span>
-                    <span class="k">continue</span>  <span class="c1"># [(&#39;&gt;&#39;, &#39;&lt;&#39;), (&#39;&gt;&#39;, &#39;+&#39;), (&#39;+&#39;, &#39;&lt;&#39;), (&#39;+&#39;, &#39;+&#39;)]</span>
-
-                <span class="c1"># If k is not in conditions and left and right mark are heads &#39;&gt;&lt;&#39;, then motif is blocked</span>
-                <span class="k">if</span> <span class="n">varlag_k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">conditions</span> <span class="ow">and</span> <span class="p">(</span><span class="n">left_mark</span> <span class="o">==</span> <span class="s1">&#39;&gt;&#39;</span> <span class="ow">and</span> <span class="n">right_mark</span> <span class="o">==</span> <span class="s1">&#39;&lt;&#39;</span><span class="p">):</span>
-                    <span class="c1"># print(&quot;Motif closed &quot;, link_ik, varlag_k, link_kj, varlag_j )</span>
-                    <span class="k">continue</span>  <span class="c1"># [(&#39;&gt;&#39;, &#39;&lt;&#39;), (&#39;&gt;&#39;, &#39;+&#39;), (&#39;+&#39;, &#39;&lt;&#39;), (&#39;+&#39;, &#39;+&#39;)]</span>
-
-                <span class="c1"># if (before_taumax and varlag_j not in end </span>
-                <span class="c1">#     and varlag_j[1] &gt;= -self.tau_max):</span>
-                <span class="c1">#     # print(&quot;before_taumax &quot;, varlag_j)</span>
-                <span class="c1">#     continue</span>
-
-                <span class="k">if</span> <span class="p">(</span><span class="n">hidden_variables</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">varlag_j</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">end</span>
-                                    <span class="ow">and</span> <span class="n">varlag_j</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">hidden_variables</span><span class="p">):</span>
-                    <span class="k">continue</span>
-
-                <span class="c1"># Motif is open</span>
-                <span class="c1"># print(&quot;Motif open &quot;, link_ik, varlag_k, link_kj, varlag_j )</span>
-                <span class="c1"># start_from.add((link_kj, varlag_j))</span>
-                <span class="n">visited</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">link_kj</span><span class="p">,</span> <span class="n">varlag_j</span><span class="p">))</span>
-                <span class="n">start_from</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">varlag_k</span><span class="p">,</span> <span class="n">link_kj</span><span class="p">,</span> <span class="n">varlag_j</span><span class="p">))</span>
-                <span class="c1"># visited.add((varlag_k, link_kj, varlag_j))</span>
-
-
-        <span class="c1"># print(&quot;Separated&quot;)</span>
-        <span class="k">return</span> <span class="kc">False</span>
-
-<div class="viewcode-block" id="CausalEffects.get_optimal_set"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.get_optimal_set">[docs]</a>    <span class="k">def</span> <span class="nf">get_optimal_set</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
-        <span class="n">alternative_conditions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">minimize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">return_separate_sets</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns optimal adjustment set.</span>
-<span class="sd">        </span>
-<span class="sd">        See Runge NeurIPS 2021.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        alternative_conditions : set of tuples</span>
-<span class="sd">            Used only internally in optimality theorem. If None, self.S is used.</span>
-<span class="sd">        minimize : {False, True, &#39;colliders_only&#39;} </span>
-<span class="sd">            Minimize optimal set. If True, minimize such that no subset </span>
-<span class="sd">            can be removed without making it invalid. If &#39;colliders_only&#39;,</span>
-<span class="sd">            only colliders are minimized.</span>
-<span class="sd">        return_separate_sets : bool</span>
-<span class="sd">            Whether to return tuple of parents, colliders, collider_parents, and S.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        Oset_S : False or list or tuple of lists</span>
-<span class="sd">            Returns optimal adjustment set if a valid set exists, otherwise False.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-
-        <span class="c1"># Needed for optimality theorem where Osets for alternative S are tested</span>
-        <span class="k">if</span> <span class="n">alternative_conditions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">S</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-            <span class="n">vancs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vancs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">S</span> <span class="o">=</span> <span class="n">alternative_conditions</span>
-            <span class="n">newancS</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_ancestors</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
-            <span class="n">vancs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ancX</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ancY</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">newancS</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forbidden_nodes</span>
-
-            <span class="c1"># vancs = self._get_ancestors(list(self.X.union(self.Y).union(S))) - self.forbidden_nodes</span>
-
-        <span class="c1"># descendants = self._get_descendants(self.Y.union(self.M))</span>
-
-        <span class="c1"># Sufficient condition for non-identifiability</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">descendants</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">False</span>  <span class="c1"># raise ValueError(&quot;Not identifiable: Overlap between X and des(M)&quot;)</span>
-
-        <span class="c1">##</span>
-        <span class="c1">## Construct O-set</span>
-        <span class="c1">##</span>
-
-        <span class="c1"># Start with parents </span>
-        <span class="n">parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_parents</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">))</span> <span class="c1"># set([])</span>
-
-        <span class="c1"># Remove forbidden nodes</span>
-        <span class="n">parents</span> <span class="o">=</span> <span class="n">parents</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forbidden_nodes</span>
-
-        <span class="c1"># Construct valid collider path nodes</span>
-        <span class="n">colliders</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([])</span>
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">):</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">w</span> 
-            <span class="n">this_level</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="p">]</span>
-            <span class="n">non_suitable_nodes</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">next_level</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-                    <span class="n">suitable_spouses</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_spouses</span><span class="p">(</span><span class="n">varlag</span><span class="p">))</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">non_suitable_nodes</span><span class="p">)</span>
-                    <span class="k">for</span> <span class="n">spouse</span> <span class="ow">in</span> <span class="n">suitable_spouses</span><span class="p">:</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">spouse</span>
-                        <span class="k">if</span> <span class="n">spouse</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">:</span>
-                            <span class="k">return</span> <span class="kc">False</span>
-
-                        <span class="k">if</span> <span class="p">(</span><span class="c1"># Node not already in set</span>
-                            <span class="n">spouse</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">colliders</span>  <span class="c1">#.union(parents)</span>
-                            <span class="c1"># not forbidden</span>
-                            <span class="ow">and</span> <span class="n">spouse</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">forbidden_nodes</span> 
-                            <span class="c1"># in time bounds</span>
-                            <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># or self.ignore_time_bounds)</span>
-                            <span class="ow">and</span> <span class="p">(</span><span class="n">spouse</span> <span class="ow">in</span> <span class="n">vancs</span>
-                                <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span><span class="c1">#graph=self.graph, </span>
-                                    <span class="n">start</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="p">[</span><span class="n">spouse</span><span class="p">],</span> 
-                                                    <span class="n">conditions</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">parents</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">vancs</span><span class="p">))</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">),</span>
-                                                    <span class="p">))</span>
-                                <span class="p">):</span>
-                                <span class="n">colliders</span> <span class="o">=</span> <span class="n">colliders</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">spouse</span><span class="p">]))</span>
-                                <span class="n">next_level</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">spouse</span><span class="p">)</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="k">if</span> <span class="n">spouse</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">colliders</span><span class="p">:</span>
-                                <span class="n">non_suitable_nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">spouse</span><span class="p">)</span>
-
-
-                <span class="n">this_level</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">next_level</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">non_suitable_nodes</span><span class="p">)</span>  
-
-        <span class="c1"># Add parents and raise Error if not identifiable</span>
-        <span class="n">collider_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_parents</span><span class="p">(</span><span class="n">colliders</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">collider_parents</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">False</span>
-
-        <span class="n">colliders_and_their_parents</span> <span class="o">=</span> <span class="n">colliders</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">collider_parents</span><span class="p">)</span>
-
-        <span class="c1"># Add valid collider path nodes and their parents</span>
-        <span class="n">Oset</span> <span class="o">=</span> <span class="n">parents</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">colliders_and_their_parents</span><span class="p">)</span>
-
-
-        <span class="k">if</span> <span class="n">minimize</span><span class="p">:</span> 
-            <span class="n">removable</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="c1"># First remove all those that have no path from X</span>
-            <span class="n">sorted_Oset</span> <span class="o">=</span>  <span class="n">Oset</span>
-            <span class="k">if</span> <span class="n">minimize</span> <span class="o">==</span> <span class="s1">&#39;colliders_only&#39;</span><span class="p">:</span>
-                <span class="n">sorted_Oset</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">sorted_Oset</span> <span class="k">if</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">]</span>
-
-            <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">sorted_Oset</span><span class="p">:</span>
-                <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span><span class="c1">#graph=self.graph, </span>
-                    <span class="n">start</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="p">[</span><span class="n">node</span><span class="p">],</span> 
-                                <span class="n">conditions</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">Oset</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">node</span><span class="p">]))</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">))):</span>
-                    <span class="n">removable</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">node</span><span class="p">)</span> 
-
-            <span class="n">Oset</span> <span class="o">=</span> <span class="n">Oset</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">removable</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">minimize</span> <span class="o">==</span> <span class="s1">&#39;colliders_only&#39;</span><span class="p">:</span>
-                <span class="n">sorted_Oset</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Oset</span> <span class="k">if</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">]</span>
-
-            <span class="n">removable</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="c1"># Next remove all those with no direct connection to Y</span>
-            <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">sorted_Oset</span><span class="p">:</span>
-                <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span><span class="c1">#graph=self.graph, </span>
-                    <span class="n">start</span><span class="o">=</span><span class="p">[</span><span class="n">node</span><span class="p">],</span> <span class="n">end</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">,</span> 
-                            <span class="n">conditions</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">Oset</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">node</span><span class="p">]))</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">),</span>
-                            <span class="n">ends_with</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;**&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;**+&#39;</span><span class="p">])):</span> 
-                    <span class="n">removable</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">node</span><span class="p">)</span> 
-
-            <span class="n">Oset</span> <span class="o">=</span> <span class="n">Oset</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">removable</span><span class="p">)</span>
-
-        <span class="n">Oset_S</span> <span class="o">=</span> <span class="n">Oset</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">return_separate_sets</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">parents</span><span class="p">,</span> <span class="n">colliders</span><span class="p">,</span> <span class="n">collider_parents</span><span class="p">,</span> <span class="n">S</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">Oset_S</span><span class="p">)</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_get_collider_paths_optimality</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_nodes</span><span class="p">,</span> <span class="n">target_nodes</span><span class="p">,</span>
-        <span class="n">condition</span><span class="p">,</span> 
-        <span class="n">inside_set</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-        <span class="n">start_with_tail_or_head</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> 
-        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns relevant collider paths to check optimality.</span>
-
-<span class="sd">        Iterates over collider paths within O-set via depth-first search</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">source_nodes</span><span class="p">:</span>
-            <span class="c1"># Only used to return *all* collider paths </span>
-            <span class="c1"># (needed in optimality theorem)</span>
-            
-            <span class="n">coll_path</span> <span class="o">=</span> <span class="p">[]</span>
-
-            <span class="n">queue</span> <span class="o">=</span> <span class="p">[(</span><span class="n">w</span><span class="p">,</span> <span class="n">coll_path</span><span class="p">)]</span>
-
-            <span class="n">non_valid_subsets</span> <span class="o">=</span> <span class="p">[]</span>
-
-            <span class="k">while</span> <span class="n">queue</span><span class="p">:</span>
-
-                <span class="n">varlag</span><span class="p">,</span> <span class="n">coll_path</span> <span class="o">=</span> <span class="n">queue</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-
-                <span class="n">coll_path</span> <span class="o">=</span> <span class="n">coll_path</span> <span class="o">+</span> <span class="p">[</span><span class="n">varlag</span><span class="p">]</span>
-
-                <span class="n">suitable_nodes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_spouses</span><span class="p">(</span><span class="n">varlag</span><span class="p">))</span>
-
-                <span class="k">if</span> <span class="n">start_with_tail_or_head</span> <span class="ow">and</span> <span class="n">coll_path</span> <span class="o">==</span> <span class="p">[</span><span class="n">w</span><span class="p">]:</span>
-                    <span class="n">children</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_children</span><span class="p">(</span><span class="n">varlag</span><span class="p">))</span>
-                    <span class="n">suitable_nodes</span> <span class="o">=</span> <span class="n">suitable_nodes</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">children</span><span class="p">)</span>
- 
-                <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">suitable_nodes</span><span class="p">:</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">node</span>
-                    <span class="k">if</span> <span class="p">((</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># or self.ignore_time_bounds)</span>
-                        <span class="ow">and</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">coll_path</span><span class="p">):</span>
-
-                        <span class="k">if</span> <span class="n">condition</span> <span class="o">==</span> <span class="s1">&#39;II&#39;</span> <span class="ow">and</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">target_nodes</span> <span class="ow">and</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vancs</span><span class="p">:</span>
-                            <span class="k">continue</span>
-
-                        <span class="k">if</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">inside_set</span><span class="p">:</span>
-                            <span class="k">if</span> <span class="n">condition</span> <span class="o">==</span> <span class="s1">&#39;I&#39;</span><span class="p">:</span>
-                                <span class="n">non_valid</span> <span class="o">=</span> <span class="kc">False</span>
-                                <span class="k">for</span> <span class="n">pathset</span> <span class="ow">in</span> <span class="n">non_valid_subsets</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
-                                    <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">pathset</span><span class="p">)</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">coll_path</span> <span class="o">+</span> <span class="p">[</span><span class="n">node</span><span class="p">])):</span>
-                                        <span class="n">non_valid</span> <span class="o">=</span> <span class="kc">True</span>
-                                        <span class="k">break</span>
-                                <span class="k">if</span> <span class="n">non_valid</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                                    <span class="n">queue</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">node</span><span class="p">,</span> <span class="n">coll_path</span><span class="p">))</span> 
-                                <span class="k">else</span><span class="p">:</span>
-                                    <span class="k">continue</span>
-                            <span class="k">elif</span> <span class="n">condition</span> <span class="o">==</span> <span class="s1">&#39;II&#39;</span><span class="p">:</span>
-                                <span class="n">queue</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">node</span><span class="p">,</span> <span class="n">coll_path</span><span class="p">))</span>
-
-                        <span class="k">if</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">target_nodes</span><span class="p">:</span>  
-                            <span class="c1"># yield coll_path</span>
-                            <span class="c1"># collider_paths[node].append(coll_path) </span>
-                            <span class="k">if</span> <span class="n">condition</span> <span class="o">==</span> <span class="s1">&#39;I&#39;</span><span class="p">:</span>         
-                                <span class="c1"># Construct OπiN</span>
-                                <span class="n">Sprime</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">coll_path</span><span class="p">)</span>
-                                <span class="n">OpiN</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_optimal_set</span><span class="p">(</span><span class="n">alternative_conditions</span><span class="o">=</span><span class="n">Sprime</span><span class="p">)</span>
-                                <span class="k">if</span> <span class="n">OpiN</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                                    <span class="n">queue</span> <span class="o">=</span> <span class="p">[(</span><span class="n">q_node</span><span class="p">,</span> <span class="n">q_path</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">q_node</span><span class="p">,</span> <span class="n">q_path</span><span class="p">)</span> <span class="ow">in</span> <span class="n">queue</span> <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">coll_path</span><span class="p">)</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">q_path</span> <span class="o">+</span> <span class="p">[</span><span class="n">q_node</span><span class="p">]))</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">]</span>
-                                    <span class="n">non_valid_subsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">coll_path</span><span class="p">)</span>
-                                <span class="k">else</span><span class="p">:</span>
-                                    <span class="k">return</span> <span class="kc">False</span>
-
-                            <span class="k">elif</span> <span class="n">condition</span> <span class="o">==</span> <span class="s1">&#39;II&#39;</span><span class="p">:</span>
-                                <span class="k">return</span> <span class="kc">True</span>
-                                <span class="c1"># yield coll_path</span>
- 
-        <span class="k">if</span> <span class="n">condition</span> <span class="o">==</span> <span class="s1">&#39;I&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">True</span>
-        <span class="k">elif</span> <span class="n">condition</span> <span class="o">==</span> <span class="s1">&#39;II&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">False</span>
-        <span class="c1"># return collider_paths</span>
-
-
-<div class="viewcode-block" id="CausalEffects.check_optimality"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.check_optimality">[docs]</a>    <span class="k">def</span> <span class="nf">check_optimality</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check whether optimal adjustment set exists according to Thm. 3 in Runge NeurIPS 2021.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        optimality : bool</span>
-<span class="sd">            Returns True if an optimal adjustment set exists, otherwise False.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Cond. 0: Exactly one valid adjustment set exists</span>
-        <span class="n">cond_0</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_all_valid_adjustment_sets</span><span class="p">(</span><span class="n">check_one_set_exists</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
-
-        <span class="c1">#</span>
-        <span class="c1"># Cond. I</span>
-        <span class="c1">#</span>
-        <span class="n">parents</span><span class="p">,</span> <span class="n">colliders</span><span class="p">,</span> <span class="n">collider_parents</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_optimal_set</span><span class="p">(</span><span class="n">return_separate_sets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">Oset</span> <span class="o">=</span> <span class="n">parents</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">colliders</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">collider_parents</span><span class="p">)</span>
-        <span class="n">n_nodes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_spouses</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">colliders</span><span class="p">))</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forbidden_nodes</span> <span class="o">-</span> <span class="n">Oset</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">M</span> <span class="o">-</span> <span class="n">colliders</span>
-
-        <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">n_nodes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="c1"># # (1) There are no spouses N ∈ sp(YMC) \ (forbOS)</span>
-            <span class="n">cond_I</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">else</span><span class="p">:</span>
-            
-            <span class="c1"># (2) For all N ∈ N and all its collider paths i it holds that </span>
-            <span class="c1"># OπiN does not block all non-causal paths from X to Y</span>
-            <span class="c1"># cond_I = True</span>
-            <span class="n">cond_I</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_collider_paths_optimality</span><span class="p">(</span>
-                <span class="n">source_nodes</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">n_nodes</span><span class="p">),</span> <span class="n">target_nodes</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">)),</span>
-                <span class="n">condition</span><span class="o">=</span><span class="s1">&#39;I&#39;</span><span class="p">,</span> 
-                <span class="n">inside_set</span><span class="o">=</span><span class="n">Oset</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">),</span> <span class="n">start_with_tail_or_head</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="p">)</span>
-
-        <span class="c1">#</span>
-        <span class="c1"># Cond. II</span>
-        <span class="c1">#</span>
-        <span class="n">e_nodes</span> <span class="o">=</span> <span class="n">Oset</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">parents</span><span class="p">)</span>
-        <span class="n">cond_II</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">for</span> <span class="n">E</span> <span class="ow">in</span> <span class="n">e_nodes</span><span class="p">:</span>
-            <span class="n">Oset_minusE</span> <span class="o">=</span> <span class="n">Oset</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">E</span><span class="p">]))</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span><span class="c1">#graph=self.graph, </span>
-                <span class="n">start</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="p">[</span><span class="n">E</span><span class="p">],</span> 
-                                <span class="n">conditions</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">)</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">Oset_minusE</span><span class="p">)):</span>
-                   
-                <span class="n">cond_II</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_collider_paths_optimality</span><span class="p">(</span>
-                    <span class="n">target_nodes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">),</span> 
-                    <span class="n">source_nodes</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">E</span><span class="p">])),</span>
-                    <span class="n">condition</span><span class="o">=</span><span class="s1">&#39;II&#39;</span><span class="p">,</span> 
-                    <span class="n">inside_set</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">Oset</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">)),</span>
-                    <span class="n">start_with_tail_or_head</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
-               
-                <span class="k">if</span> <span class="n">cond_II</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Non-optimal due to E = &quot;</span><span class="p">,</span> <span class="n">E</span><span class="p">)</span>
-                    <span class="k">break</span>
-
-        <span class="n">optimality</span> <span class="o">=</span> <span class="p">(</span><span class="n">cond_0</span> <span class="ow">or</span> <span class="p">(</span><span class="n">cond_I</span> <span class="ow">and</span> <span class="n">cond_II</span><span class="p">))</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Optimality = </span><span class="si">%s</span><span class="s2"> with cond_0 = </span><span class="si">%s</span><span class="s2">, cond_I = </span><span class="si">%s</span><span class="s2">, cond_II = </span><span class="si">%s</span><span class="s2">&quot;</span>
-                    <span class="o">%</span>  <span class="p">(</span><span class="n">optimality</span><span class="p">,</span> <span class="n">cond_0</span><span class="p">,</span> <span class="n">cond_I</span><span class="p">,</span> <span class="n">cond_II</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">optimality</span></div>
-
-    <span class="k">def</span> <span class="nf">_check_validity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Checks whether Z is a valid adjustment set.&quot;&quot;&quot;</span>
-
-        <span class="c1"># causal_children = list(self.M.union(self.Y))</span>
-        <span class="n">backdoor_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span><span class="c1">#graph=self.graph, </span>
-            <span class="n">start</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">),</span> 
-                            <span class="n">conditions</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
-                            <span class="c1"># causal_children=causal_children,</span>
-                            <span class="n">path_type</span> <span class="o">=</span> <span class="s1">&#39;non_causal&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">backdoor_path</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">True</span>
-    
-    <span class="k">def</span> <span class="nf">_get_adjust_set</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
-        <span class="n">minimize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns Adjust-set.</span>
-<span class="sd">        </span>
-<span class="sd">        See van der Zander, B.; Liśkiewicz, M. &amp; Textor, J.</span>
-<span class="sd">        Separators and adjustment sets in causal graphs: Complete </span>
-<span class="sd">        criteria and an algorithmic framework </span>
-<span class="sd">        Artificial Intelligence, Elsevier, 2019, 270, 1-40</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">vancs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vancs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="n">minimize</span><span class="p">:</span>
-            <span class="c1"># Get removable nodes by computing minimal valid set from Z</span>
-            <span class="k">if</span> <span class="n">minimize</span> <span class="o">==</span> <span class="s1">&#39;keep_parentsYM&#39;</span><span class="p">:</span>
-                <span class="n">minimize_nodes</span> <span class="o">=</span> <span class="n">vancs</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_parents</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">)))</span>
-
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">minimize_nodes</span> <span class="o">=</span> <span class="n">vancs</span>
-
-            <span class="c1"># Zprime2 = Zprime</span>
-            <span class="c1"># First remove all nodes that have no unique path to X given Oset</span>
-            <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">minimize_nodes</span><span class="p">:</span>
-                <span class="c1"># path = self.oracle.check_shortest_path(X=X, Y=[node], </span>
-                <span class="c1">#     Z=list(vancs - set([node])), </span>
-                <span class="c1">#     max_lag=None, </span>
-                <span class="c1">#     starts_with=None, #&#39;arrowhead&#39;, </span>
-                <span class="c1">#     forbidden_nodes=None, #list(Zprime - set([node])), </span>
-                <span class="c1">#     return_path=False)</span>
-                <span class="n">path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span><span class="c1">#graph=self.graph, </span>
-                    <span class="n">start</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="p">[</span><span class="n">node</span><span class="p">],</span> 
-                    <span class="n">conditions</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">vancs</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">node</span><span class="p">])),</span> 
-                     <span class="p">)</span>
-  
-                <span class="k">if</span> <span class="n">path</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                    <span class="n">vancs</span> <span class="o">=</span> <span class="n">vancs</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">node</span><span class="p">])</span>
-
-            <span class="k">if</span> <span class="n">minimize</span> <span class="o">==</span> <span class="s1">&#39;keep_parentsYM&#39;</span><span class="p">:</span>
-                <span class="n">minimize_nodes</span> <span class="o">=</span> <span class="n">vancs</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_parents</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">)))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">minimize_nodes</span> <span class="o">=</span> <span class="n">vancs</span>
-
-            <span class="c1"># print(Zprime2) </span>
-            <span class="c1"># Next remove all nodes that have no unique path to Y given Oset_min</span>
-            <span class="c1"># Z = Zprime2</span>
-            <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">minimize_nodes</span><span class="p">:</span>
-
-                <span class="n">path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_path</span><span class="p">(</span><span class="c1">#graph=self.graph, </span>
-                    <span class="n">start</span><span class="o">=</span><span class="p">[</span><span class="n">node</span><span class="p">],</span> <span class="n">end</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">,</span> 
-                    <span class="n">conditions</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">vancs</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">node</span><span class="p">]))</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">),</span>
-                    <span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">path</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                   <span class="n">vancs</span> <span class="o">=</span> <span class="n">vancs</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">node</span><span class="p">])</span>  
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_validity</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">vancs</span><span class="p">))</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">vancs</span><span class="p">)</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_all_valid_adjustment_sets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
-        <span class="n">check_one_set_exists</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">yield_index</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructs all valid adjustment sets or just checks whether one exists.</span>
-<span class="sd">        </span>
-<span class="sd">        See van der Zander, B.; Liśkiewicz, M. &amp; Textor, J.</span>
-<span class="sd">        Separators and adjustment sets in causal graphs: Complete </span>
-<span class="sd">        criteria and an algorithmic framework </span>
-<span class="sd">        Artificial Intelligence, Elsevier, 2019, 270, 1-40</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">cond_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">)</span>
-        <span class="n">all_vars</span> <span class="o">=</span> <span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-                    <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-
-        <span class="n">all_vars_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">all_vars</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forbidden_nodes</span>
-
-
-        <span class="k">def</span> <span class="nf">find_sep</span><span class="p">(</span><span class="n">I</span><span class="p">,</span> <span class="n">R</span><span class="p">):</span>
-            <span class="n">Rprime</span> <span class="o">=</span> <span class="n">R</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y</span>
-            <span class="c1"># TODO: anteriors and NOT ancestors where</span>
-            <span class="c1"># anteriors include --- links in causal paths</span>
-            <span class="c1"># print(I)</span>
-            <span class="n">XYI</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">I</span><span class="p">))</span>
-            <span class="c1"># print(XYI)</span>
-            <span class="n">ancs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_ancestors</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">XYI</span><span class="p">))</span>
-            <span class="n">Z</span> <span class="o">=</span> <span class="n">ancs</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">Rprime</span><span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_validity</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                <span class="k">return</span> <span class="kc">False</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">Z</span>
-
-
-        <span class="k">def</span> <span class="nf">list_sep</span><span class="p">(</span><span class="n">I</span><span class="p">,</span> <span class="n">R</span><span class="p">):</span>
-            <span class="c1"># print(find_sep(X, Y, I, R))</span>
-            <span class="k">if</span> <span class="n">find_sep</span><span class="p">(</span><span class="n">I</span><span class="p">,</span> <span class="n">R</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
-                <span class="c1"># print(I,R)</span>
-                <span class="k">if</span> <span class="n">I</span> <span class="o">==</span> <span class="n">R</span><span class="p">:</span> 
-                    <span class="c1"># print(&#39;---&gt;&#39;, I)</span>
-                    <span class="k">yield</span> <span class="n">I</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="c1"># Pick arbitrary node from R-I</span>
-                    <span class="n">RminusI</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">R</span> <span class="o">-</span> <span class="n">I</span><span class="p">)</span>
-                    <span class="c1"># print(R, I, RminusI)</span>
-                    <span class="n">v</span> <span class="o">=</span> <span class="n">RminusI</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                    <span class="c1"># print(&quot;here &quot;, X, Y, I.union(set([v])), R)</span>
-                    <span class="k">yield from</span> <span class="n">list_sep</span><span class="p">(</span><span class="n">I</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">v</span><span class="p">])),</span> <span class="n">R</span><span class="p">)</span>
-                    <span class="k">yield from</span> <span class="n">list_sep</span><span class="p">(</span><span class="n">I</span><span class="p">,</span> <span class="n">R</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">v</span><span class="p">]))</span>
-
-        <span class="c1"># print(&quot;all &quot;, X, Y, cond_set, all_vars_set)</span>
-        <span class="n">all_sets</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">I</span> <span class="o">=</span> <span class="n">cond_set</span>
-        <span class="n">R</span> <span class="o">=</span> <span class="n">all_vars_set</span>
-        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">valid_set</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">list_sep</span><span class="p">(</span><span class="n">I</span><span class="p">,</span> <span class="n">R</span><span class="p">)):</span>
-            <span class="c1"># print(valid_set)</span>
-            <span class="n">all_sets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">valid_set</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">check_one_set_exists</span> <span class="ow">and</span> <span class="n">index</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">break</span>
-
-            <span class="k">if</span> <span class="n">yield_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">index</span> <span class="o">==</span> <span class="n">yield_index</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">valid_set</span>
-
-        <span class="k">if</span> <span class="n">yield_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-        <span class="k">if</span> <span class="n">check_one_set_exists</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">return</span> <span class="kc">True</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">return</span> <span class="kc">False</span>
-
-        <span class="k">return</span> <span class="n">all_sets</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_causal_paths</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_nodes</span><span class="p">,</span> <span class="n">target_nodes</span><span class="p">,</span>
-        <span class="n">mediators</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">mediated_through</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">proper_paths</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns causal paths via depth-first search.</span>
-
-<span class="sd">        Allows to restrict paths through mediated_through.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">source_nodes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">source_nodes</span><span class="p">)</span>
-        <span class="n">target_nodes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">target_nodes</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">mediators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">mediators</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">mediators</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">mediators</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">mediated_through</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">mediated_through</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">mediated_through</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">mediated_through</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">proper_paths</span><span class="p">:</span>
-             <span class="n">inside_set</span> <span class="o">=</span> <span class="n">mediators</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">target_nodes</span><span class="p">)</span> <span class="o">-</span> <span class="n">source_nodes</span>
-        <span class="k">else</span><span class="p">:</span>
-             <span class="n">inside_set</span> <span class="o">=</span> <span class="n">mediators</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">target_nodes</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">source_nodes</span><span class="p">)</span>
-
-        <span class="n">all_causal_paths</span> <span class="o">=</span> <span class="p">{}</span>         
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">source_nodes</span><span class="p">:</span>
-            <span class="n">all_causal_paths</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">target_nodes</span><span class="p">:</span>
-                <span class="n">all_causal_paths</span><span class="p">[</span><span class="n">w</span><span class="p">][</span><span class="n">z</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">source_nodes</span><span class="p">:</span>
-            
-            <span class="n">causal_path</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">queue</span> <span class="o">=</span> <span class="p">[(</span><span class="n">w</span><span class="p">,</span> <span class="n">causal_path</span><span class="p">)]</span>
-
-            <span class="k">while</span> <span class="n">queue</span><span class="p">:</span>
-
-                <span class="n">varlag</span><span class="p">,</span> <span class="n">causal_path</span> <span class="o">=</span> <span class="n">queue</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-                <span class="n">causal_path</span> <span class="o">=</span> <span class="n">causal_path</span> <span class="o">+</span> <span class="p">[</span><span class="n">varlag</span><span class="p">]</span>
-                <span class="n">suitable_nodes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_children</span><span class="p">(</span><span class="n">varlag</span><span class="p">)</span>
-                    <span class="p">)</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">inside_set</span><span class="p">)</span>
-                <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">suitable_nodes</span><span class="p">:</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">node</span>
-                    <span class="k">if</span> <span class="p">((</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># or self.ignore_time_bounds)</span>
-                        <span class="ow">and</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">causal_path</span><span class="p">):</span>
-
-                        <span class="n">queue</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">node</span><span class="p">,</span> <span class="n">causal_path</span><span class="p">))</span> 
- 
-                        <span class="k">if</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">target_nodes</span><span class="p">:</span>  
-                            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">mediated_through</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">causal_path</span><span class="p">)</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">mediated_through</span><span class="p">))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">continue</span>
-                            <span class="k">else</span><span class="p">:</span>
-                                <span class="n">all_causal_paths</span><span class="p">[</span><span class="n">w</span><span class="p">][</span><span class="n">node</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">causal_path</span> <span class="o">+</span> <span class="p">[</span><span class="n">node</span><span class="p">])</span> 
-
-        <span class="k">return</span> <span class="n">all_causal_paths</span>
-
-<div class="viewcode-block" id="CausalEffects.fit_total_effect"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.fit_total_effect">[docs]</a>    <span class="k">def</span> <span class="nf">fit_total_effect</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-        <span class="n">dataframe</span><span class="p">,</span> 
-        <span class="n">estimator</span><span class="p">,</span>
-        <span class="n">adjustment_set</span><span class="o">=</span><span class="s1">&#39;optimal&#39;</span><span class="p">,</span>
-        <span class="n">conditional_estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  
-        <span class="n">data_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">ignore_identifiability</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a fitted model for the total causal effect of X on Y </span>
-<span class="sd">           conditional on S.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        dataframe : data object</span>
-<span class="sd">            Tigramite dataframe object. It must have the attributes dataframe.values</span>
-<span class="sd">            yielding a numpy array of shape (observations T, variables N) and</span>
-<span class="sd">            optionally a mask of the same shape and a missing values flag.</span>
-<span class="sd">        estimator : sklearn model object</span>
-<span class="sd">            For example, sklearn.linear_model.LinearRegression() for a linear</span>
-<span class="sd">            regression model.</span>
-<span class="sd">        adjustment_set : str or list of tuples</span>
-<span class="sd">            If &#39;optimal&#39; the Oset is used, if &#39;minimized_optimal&#39; the minimized Oset,</span>
-<span class="sd">            and if &#39;colliders_minimized_optimal&#39;, the colliders-minimized Oset.</span>
-<span class="sd">            If a list of tuples is passed, this set is used.</span>
-<span class="sd">        conditional_estimator : sklearn model object, optional (default: None)</span>
-<span class="sd">            Used to fit conditional causal effects in nested regression. </span>
-<span class="sd">            If None, the same model as for estimator is used.</span>
-<span class="sd">        data_transform : sklearn preprocessing object, optional (default: None)</span>
-<span class="sd">            Used to transform data prior to fitting. For example,</span>
-<span class="sd">            sklearn.preprocessing.StandardScaler for simple standardization. The</span>
-<span class="sd">            fitted parameters are stored.</span>
-<span class="sd">        mask_type : {None, &#39;y&#39;,&#39;x&#39;,&#39;z&#39;,&#39;xy&#39;,&#39;xz&#39;,&#39;yz&#39;,&#39;xyz&#39;}</span>
-<span class="sd">            Masking mode: Indicators for which variables in the dependence</span>
-<span class="sd">            measure I(X; Y | Z) the samples should be masked. If None, the mask</span>
-<span class="sd">            is not used. Explained in tutorial on masking and missing values.</span>
-<span class="sd">        ignore_identifiability : bool</span>
-<span class="sd">            Only applies to adjustment sets supplied by user. Ignores if that </span>
-<span class="sd">            set leads to a non-identifiable effect.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">no_causal_path</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No causal path from X to Y exists.&quot;</span><span class="p">)</span>
-            <span class="k">return</span> <span class="bp">self</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">dataframe</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conditional_estimator</span> <span class="o">=</span> <span class="n">conditional_estimator</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">N</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Dataset dimensions inconsistent with number of variables in graph.&quot;</span><span class="p">)</span>
-
-
-        <span class="k">if</span> <span class="n">adjustment_set</span> <span class="o">==</span> <span class="s1">&#39;optimal&#39;</span><span class="p">:</span>
-            <span class="c1"># Check optimality and use either optimal or colliders_only set</span>
-            <span class="n">adjustment_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_optimal_set</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="n">adjustment_set</span> <span class="o">==</span> <span class="s1">&#39;colliders_minimized_optimal&#39;</span><span class="p">:</span>
-            <span class="n">adjustment_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_optimal_set</span><span class="p">(</span><span class="n">minimize</span><span class="o">=</span><span class="s1">&#39;colliders_only&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">adjustment_set</span> <span class="o">==</span> <span class="s1">&#39;minimized_optimal&#39;</span><span class="p">:</span>
-            <span class="n">adjustment_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_optimal_set</span><span class="p">(</span><span class="n">minimize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">ignore_identifiability</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_validity</span><span class="p">(</span><span class="n">adjustment_set</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Chosen adjustment_set is not valid.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">adjustment_set</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Causal effect not identifiable via adjustment.&quot;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">adjustment_set</span> <span class="o">=</span> <span class="n">adjustment_set</span>
-
-        <span class="c1"># Fit model of Y on X and Z (and conditions)</span>
-        <span class="c1"># Build the model</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">Models</span><span class="p">(</span>
-                        <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span>
-                        <span class="n">model</span><span class="o">=</span><span class="n">estimator</span><span class="p">,</span>
-                        <span class="n">conditional_model</span><span class="o">=</span><span class="n">conditional_estimator</span><span class="p">,</span>
-                        <span class="n">data_transform</span><span class="o">=</span><span class="n">data_transform</span><span class="p">,</span>
-                        <span class="n">mask_type</span><span class="o">=</span><span class="n">mask_type</span><span class="p">,</span>
-                        <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>      
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">get_general_fitted_model</span><span class="p">(</span>
-                <span class="n">Y</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">listX</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">adjustment_set</span><span class="p">),</span>
-                <span class="n">conditions</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">listS</span><span class="p">,</span>
-                <span class="n">tau_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span>
-                <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;tau_max&#39;</span><span class="p">,</span>
-                <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-<div class="viewcode-block" id="CausalEffects.predict_total_effect"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.predict_total_effect">[docs]</a>    <span class="k">def</span> <span class="nf">predict_total_effect</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
-        <span class="n">intervention_data</span><span class="p">,</span> 
-        <span class="n">conditions_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">pred_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">return_further_pred_results</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">aggregation_func</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span>
-        <span class="n">transform_interventions_and_prediction</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Predict effect of intervention with fitted model.</span>
-
-<span class="sd">        Uses the model.predict() function of the sklearn model.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        intervention_data : numpy array</span>
-<span class="sd">            Numpy array of shape (time, len(X)) that contains the do(X) values.</span>
-<span class="sd">        conditions_data : data object, optional</span>
-<span class="sd">            Numpy array of shape (time, len(S)) that contains the S=s values.</span>
-<span class="sd">        pred_params : dict, optional</span>
-<span class="sd">            Optional parameters passed on to sklearn prediction function.</span>
-<span class="sd">        return_further_pred_results : bool, optional (default: False)</span>
-<span class="sd">            In case the predictor class returns more than just the expected value,</span>
-<span class="sd">            the entire results can be returned.</span>
-<span class="sd">        aggregation_func : callable</span>
-<span class="sd">            Callable applied to output of &#39;predict&#39;. Default is &#39;np.mean&#39;.</span>
-<span class="sd">        transform_interventions_and_prediction : bool (default: False)</span>
-<span class="sd">            Whether to perform the inverse data_transform on prediction results.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        Results from prediction: an array of shape  (time, len(Y)).</span>
-<span class="sd">        If estimate_confidence = True, then a tuple is returned.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">intervention_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listX</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;intervention_data.shape[1] must be len(X).&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">conditions_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">conditions_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listS</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;conditions_data.shape[1] must be len(S).&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">conditions_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">intervention_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;conditions_data.shape[0] must match intervention_data.shape[0].&quot;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">conditions_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listS</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;conditions_data specified, but S=None or empty.&quot;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">conditions_data</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;S specified, but conditions_data is None.&quot;</span><span class="p">)</span>
-
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">no_causal_path</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No causal path from X to Y exists.&quot;</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">intervention_data</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">)))</span>
-
-        <span class="n">effect</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">get_general_prediction</span><span class="p">(</span>
-            <span class="n">intervention_data</span><span class="o">=</span><span class="n">intervention_data</span><span class="p">,</span>
-            <span class="n">conditions_data</span><span class="o">=</span><span class="n">conditions_data</span><span class="p">,</span>
-            <span class="n">pred_params</span><span class="o">=</span><span class="n">pred_params</span><span class="p">,</span>
-            <span class="n">return_further_pred_results</span><span class="o">=</span><span class="n">return_further_pred_results</span><span class="p">,</span>
-            <span class="n">transform_interventions_and_prediction</span><span class="o">=</span><span class="n">transform_interventions_and_prediction</span><span class="p">,</span>
-            <span class="n">aggregation_func</span><span class="o">=</span><span class="n">aggregation_func</span><span class="p">,)</span> 
-
-        <span class="k">return</span> <span class="n">effect</span></div>
-
-<div class="viewcode-block" id="CausalEffects.fit_wright_effect"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.fit_wright_effect">[docs]</a>    <span class="k">def</span> <span class="nf">fit_wright_effect</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-        <span class="n">dataframe</span><span class="p">,</span> 
-        <span class="n">mediation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">method</span><span class="o">=</span><span class="s1">&#39;parents&#39;</span><span class="p">,</span>
-        <span class="n">links_coeffs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  
-        <span class="n">data_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a fitted model for the total or mediated causal effect of X on Y </span>
-<span class="sd">           potentially through mediator variables.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        dataframe : data object</span>
-<span class="sd">            Tigramite dataframe object. It must have the attributes dataframe.values</span>
-<span class="sd">            yielding a numpy array of shape (observations T, variables N) and</span>
-<span class="sd">            optionally a mask of the same shape and a missing values flag.</span>
-<span class="sd">        mediation : None, &#39;direct&#39;, or list of tuples</span>
-<span class="sd">            If None, total effect is estimated, if &#39;direct&#39; then only the direct effect is estimated,</span>
-<span class="sd">            else only those causal paths are considerd that pass at least through one of these mediator nodes.</span>
-<span class="sd">        method : {&#39;parents&#39;, &#39;links_coeffs&#39;, &#39;optimal&#39;}</span>
-<span class="sd">            Method to use for estimating Wright&#39;s path coefficients. If &#39;optimal&#39;, </span>
-<span class="sd">            the Oset is used, if &#39;links_coeffs&#39;, the coefficients in links_coeffs are used,</span>
-<span class="sd">            if &#39;parents&#39;, the parents are used (only valid for DAGs).</span>
-<span class="sd">        links_coeffs : dict</span>
-<span class="sd">            Only used if method = &#39;links_coeffs&#39;.</span>
-<span class="sd">            Dictionary of format: {0:[((i, -tau), coeff),...], 1:[...],</span>
-<span class="sd">            ...} for all variables where i must be in [0..N-1] and tau &gt;= 0 with</span>
-<span class="sd">            number of variables N. coeff must be a float.</span>
-<span class="sd">        data_transform : None</span>
-<span class="sd">            Not implemented for Wright estimator. Complicated for missing samples.</span>
-<span class="sd">        mask_type : {None, &#39;y&#39;,&#39;x&#39;,&#39;z&#39;,&#39;xy&#39;,&#39;xz&#39;,&#39;yz&#39;,&#39;xyz&#39;}</span>
-<span class="sd">            Masking mode: Indicators for which variables in the dependence</span>
-<span class="sd">            measure I(X; Y | Z) the samples should be masked. If None, the mask</span>
-<span class="sd">            is not used. Explained in tutorial on masking and missing values.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">no_causal_path</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No causal path from X to Y exists.&quot;</span><span class="p">)</span>
-            <span class="k">return</span> <span class="bp">self</span>
-
-        <span class="k">if</span> <span class="n">data_transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;data_transform not implemented for Wright estimator.&quot;</span>
-                             <span class="s2">&quot; You can preprocess data yourself beforehand.&quot;</span><span class="p">)</span>
-
-        <span class="kn">import</span> <span class="nn">sklearn.linear_model</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">dataframe</span>
-        <span class="n">estimator</span> <span class="o">=</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">()</span>
-
-        <span class="c1"># Fit model of Y on X and Z (and conditions)</span>
-        <span class="c1"># Build the model</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">Models</span><span class="p">(</span>
-                        <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span>
-                        <span class="n">model</span><span class="o">=</span><span class="n">estimator</span><span class="p">,</span>
-                        <span class="n">data_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="c1">#data_transform,</span>
-                        <span class="n">mask_type</span><span class="o">=</span><span class="n">mask_type</span><span class="p">,</span>
-                        <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">mediators</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">M</span>  <span class="c1"># self.get_mediators(start=self.X, end=self.Y)</span>
-
-        <span class="k">if</span> <span class="n">mediation</span> <span class="o">==</span> <span class="s1">&#39;direct&#39;</span><span class="p">:</span>
-            <span class="n">causal_paths</span> <span class="o">=</span> <span class="p">{}</span>         
-            <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">:</span>
-                <span class="n">causal_paths</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
-                        <span class="n">causal_paths</span><span class="p">[</span><span class="n">w</span><span class="p">][</span><span class="n">z</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[</span><span class="n">w</span><span class="p">,</span> <span class="n">z</span><span class="p">]]</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">causal_paths</span><span class="p">[</span><span class="n">w</span><span class="p">][</span><span class="n">z</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">causal_paths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_causal_paths</span><span class="p">(</span><span class="n">source_nodes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> 
-                <span class="n">target_nodes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">,</span> <span class="n">mediators</span><span class="o">=</span><span class="n">mediators</span><span class="p">,</span> 
-                <span class="n">mediated_through</span><span class="o">=</span><span class="n">mediation</span><span class="p">,</span> <span class="n">proper_paths</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;links_coeffs&#39;</span><span class="p">:</span>
-            <span class="n">coeffs</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">medy</span> <span class="ow">in</span> <span class="p">[</span><span class="n">med</span> <span class="k">for</span> <span class="n">med</span> <span class="ow">in</span> <span class="n">mediators</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">]:</span>
-                <span class="n">coeffs</span><span class="p">[</span><span class="n">medy</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="n">j</span><span class="p">,</span> <span class="n">tauj</span> <span class="o">=</span> <span class="n">medy</span>
-                <span class="k">for</span> <span class="n">ipar</span><span class="p">,</span> <span class="n">par_coeff</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">links_coeffs</span><span class="p">[</span><span class="n">medy</span><span class="p">[</span><span class="mi">0</span><span class="p">]]):</span>
-                    <span class="n">par</span><span class="p">,</span> <span class="n">coeff</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">par_coeff</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">taui</span> <span class="o">=</span> <span class="n">par</span>
-                    <span class="n">taui_shifted</span> <span class="o">=</span> <span class="n">taui</span> <span class="o">+</span> <span class="n">tauj</span>
-                    <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">par</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">max_lag</span><span class="p">)</span>
-                    <span class="n">coeffs</span><span class="p">[</span><span class="n">medy</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">taui_shifted</span><span class="p">)]</span> <span class="o">=</span> <span class="n">coeff</span> <span class="c1">#self.fit_results[j][(j, 0)][&#39;model&#39;].coef_[ipar]</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">max_lag</span>
-            <span class="c1"># print(coeffs)</span>
-
-        <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;optimal&#39;</span><span class="p">:</span>
-            <span class="c1"># all_parents = {}</span>
-            <span class="n">coeffs</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">medy</span> <span class="ow">in</span> <span class="p">[</span><span class="n">med</span> <span class="k">for</span> <span class="n">med</span> <span class="ow">in</span> <span class="n">mediators</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">]:</span>
-                <span class="n">coeffs</span><span class="p">[</span><span class="n">medy</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="n">mediator_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_parents</span><span class="p">([</span><span class="n">medy</span><span class="p">])</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">mediators</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">))</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">medy</span><span class="p">])</span>
-                <span class="n">all_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_parents</span><span class="p">([</span><span class="n">medy</span><span class="p">])</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">medy</span><span class="p">])</span>
-                <span class="k">for</span> <span class="n">par</span> <span class="ow">in</span> <span class="n">mediator_parents</span><span class="p">:</span>
-                    <span class="n">Sprime</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">all_parents</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">par</span><span class="p">,</span> <span class="n">medy</span><span class="p">])</span>
-                    <span class="n">causal_effects</span> <span class="o">=</span> <span class="n">CausalEffects</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> 
-                                        <span class="n">X</span><span class="o">=</span><span class="p">[</span><span class="n">par</span><span class="p">],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[</span><span class="n">medy</span><span class="p">],</span> <span class="n">S</span><span class="o">=</span><span class="n">Sprime</span><span class="p">,</span>
-                                        <span class="n">graph_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span><span class="p">,</span>
-                                        <span class="n">check_SM_overlap</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                        <span class="p">)</span>
-                    <span class="n">oset</span> <span class="o">=</span> <span class="n">causal_effects</span><span class="o">.</span><span class="n">get_optimal_set</span><span class="p">()</span>
-                    <span class="c1"># print(medy, par, list(set(all_parents)), oset)</span>
-                    <span class="k">if</span> <span class="n">oset</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Not identifiable via Wright&#39;s method.&quot;</span><span class="p">)</span>
-                    <span class="n">fit_res</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">get_general_fitted_model</span><span class="p">(</span>
-                        <span class="n">Y</span><span class="o">=</span><span class="p">[</span><span class="n">medy</span><span class="p">],</span> <span class="n">X</span><span class="o">=</span><span class="p">[</span><span class="n">par</span><span class="p">],</span> <span class="n">Z</span><span class="o">=</span><span class="n">oset</span><span class="p">,</span>
-                        <span class="n">tau_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span>
-                        <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;tau_max&#39;</span><span class="p">,</span>
-                        <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                    <span class="n">coeffs</span><span class="p">[</span><span class="n">medy</span><span class="p">][</span><span class="n">par</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_res</span><span class="p">[</span><span class="n">medy</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;parents&#39;</span><span class="p">:</span>
-            <span class="n">coeffs</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">medy</span> <span class="ow">in</span> <span class="p">[</span><span class="n">med</span> <span class="k">for</span> <span class="n">med</span> <span class="ow">in</span> <span class="n">mediators</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">]:</span>
-                <span class="n">coeffs</span><span class="p">[</span><span class="n">medy</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="c1"># mediator_parents = self._get_all_parents([medy]).intersection(mediators.union(self.X)) - set([medy])</span>
-                <span class="n">all_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_parents</span><span class="p">([</span><span class="n">medy</span><span class="p">])</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">medy</span><span class="p">])</span>
-                <span class="k">if</span> <span class="s1">&#39;dag&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_type</span><span class="p">:</span>
-                    <span class="n">spouses</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_spouses</span><span class="p">([</span><span class="n">medy</span><span class="p">])</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="n">medy</span><span class="p">])</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">spouses</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;method == &#39;parents&#39; only possible for &quot;</span>
-                                         <span class="s2">&quot;causal paths without adjacent bi-directed links!&quot;</span><span class="p">)</span>
-
-                <span class="c1"># print(j, all_parents[j])</span>
-                <span class="c1"># if len(all_parents[j]) &gt; 0:</span>
-                <span class="c1"># print(medy, list(all_parents))</span>
-                <span class="n">fit_res</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">get_general_fitted_model</span><span class="p">(</span>
-                    <span class="n">Y</span><span class="o">=</span><span class="p">[</span><span class="n">medy</span><span class="p">],</span> <span class="n">X</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">all_parents</span><span class="p">),</span> <span class="n">Z</span><span class="o">=</span><span class="p">[],</span>
-                    <span class="n">conditions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                    <span class="n">tau_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span>
-                    <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;tau_max&#39;</span><span class="p">,</span>
-                    <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-                <span class="k">for</span> <span class="n">ipar</span><span class="p">,</span> <span class="n">par</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">all_parents</span><span class="p">)):</span>
-                    <span class="c1"># print(par, fit_res[medy][&#39;model&#39;].coef_[ipar])</span>
-                    <span class="n">coeffs</span><span class="p">[</span><span class="n">medy</span><span class="p">][</span><span class="n">par</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_res</span><span class="p">[</span><span class="n">medy</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="n">ipar</span><span class="p">]</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;method must be &#39;optimal&#39;, &#39;links_coeffs&#39;, or &#39;parents&#39;.&quot;</span><span class="p">)</span>
-        
-        <span class="c1"># Effect is sum over products over all path coefficients</span>
-        <span class="c1"># from x in X to y in Y</span>
-        <span class="n">effect</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listX</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">):</span>
-            <span class="n">effect</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)]</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="k">for</span> <span class="n">causal_path</span> <span class="ow">in</span> <span class="n">causal_paths</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">y</span><span class="p">]:</span>
-                <span class="n">effect_here</span> <span class="o">=</span> <span class="mf">1.</span>
-                <span class="c1"># print(x, y, causal_path)</span>
-                <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">causal_path</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">taui</span> <span class="o">=</span> <span class="n">node</span>
-                    <span class="n">j</span><span class="p">,</span> <span class="n">tauj</span> <span class="o">=</span> <span class="n">causal_path</span><span class="p">[</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-                    <span class="c1"># tau_ij = abs(tauj - taui)</span>
-                    <span class="c1"># print((j, tauj), (i, taui))</span>
-                    <span class="n">effect_here</span> <span class="o">*=</span> <span class="n">coeffs</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="n">tauj</span><span class="p">)][(</span><span class="n">i</span><span class="p">,</span> <span class="n">taui</span><span class="p">)]</span>
-
-                <span class="n">effect</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)]</span> <span class="o">+=</span> <span class="n">effect_here</span>
-               
-        <span class="c1"># Make fitted coefficients available as attribute</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">coeffs</span> <span class="o">=</span> <span class="n">coeffs</span>
-
-        <span class="c1"># Modify and overwrite variables in self.model</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">listY</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">listX</span>  
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">conditions</span> <span class="o">=</span> <span class="p">[]</span> 
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">cut_off</span> <span class="o">=</span> <span class="s1">&#39;tau_max&#39;</span> <span class="c1"># &#39;max_lag_or_tau_max&#39;</span>
-
-        <span class="k">class</span> <span class="nc">dummy_fit_class</span><span class="p">():</span>
-            <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y_here</span><span class="p">,</span> <span class="n">listX_here</span><span class="p">,</span> <span class="n">effect_here</span><span class="p">):</span>
-                <span class="n">dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">listX_here</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">coeff_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">effect_here</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_here</span><span class="p">)]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">listX_here</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-                <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">coeff_array</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-        <span class="n">fit_results</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">:</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dummy_fit_class</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">listX</span><span class="p">,</span> <span class="n">effect</span><span class="p">)</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;data_transform&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">data_transform</span><span class="p">)</span>
-
-        <span class="c1"># self.effect = effect</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit_results</span> <span class="o">=</span> <span class="n">fit_results</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
- 
-<div class="viewcode-block" id="CausalEffects.predict_wright_effect"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.predict_wright_effect">[docs]</a>    <span class="k">def</span> <span class="nf">predict_wright_effect</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
-        <span class="n">intervention_data</span><span class="p">,</span> 
-        <span class="n">pred_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Predict linear effect of intervention with fitted Wright-model.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        intervention_data : numpy array</span>
-<span class="sd">            Numpy array of shape (time, len(X)) that contains the do(X) values.</span>
-<span class="sd">        pred_params : dict, optional</span>
-<span class="sd">            Optional parameters passed on to sklearn prediction function.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        Results from prediction: an array of shape  (time, len(Y)).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">intervention_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;intervention_data.shape[1] must be len(X).&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">no_causal_path</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No causal path from X to Y exists.&quot;</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">intervention_data</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">)))</span>
-
-        <span class="n">intervention_T</span><span class="p">,</span> <span class="n">lenX</span> <span class="o">=</span> <span class="n">intervention_data</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">lenY</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">)</span>
-
-        <span class="n">predicted_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">intervention_T</span><span class="p">,</span> <span class="n">lenY</span><span class="p">))</span>
-        <span class="n">pred_dict</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">iy</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">):</span>
-            <span class="c1"># Print message</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">## Predicting target </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-                <span class="k">if</span> <span class="n">pred_params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">pred_params</span><span class="p">):</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">pred_params</span><span class="p">[</span><span class="n">key</span><span class="p">]))</span>
-            <span class="c1"># Default value for pred_params</span>
-            <span class="k">if</span> <span class="n">pred_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">pred_params</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="c1"># Check this is a valid target</span>
-            <span class="k">if</span> <span class="n">y</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit_results</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;y = </span><span class="si">%s</span><span class="s2"> not yet fitted&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-
-            <span class="c1"># data_transform is too complicated for Wright estimator</span>
-            <span class="c1"># Transform the data if needed</span>
-            <span class="c1"># fitted_data_transform = self.model.fit_results[y][&#39;fitted_data_transform&#39;]</span>
-            <span class="c1"># if fitted_data_transform is not None:</span>
-            <span class="c1">#     intervention_data = fitted_data_transform[&#39;X&#39;].transform(X=intervention_data)</span>
-
-            <span class="c1"># Now iterate through interventions (and potentially S)</span>
-            <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">dox_vals</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">intervention_data</span><span class="p">):</span>
-                <span class="c1"># Construct XZS-array</span>
-                <span class="n">intervention_array</span> <span class="o">=</span> <span class="n">dox_vals</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">lenX</span><span class="p">)</span> 
-                <span class="n">predictor_array</span> <span class="o">=</span> <span class="n">intervention_array</span>
-
-                <span class="n">predicted_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-                <span class="n">X</span><span class="o">=</span><span class="n">predictor_array</span><span class="p">,</span> <span class="o">**</span><span class="n">pred_params</span><span class="p">)</span>
-                <span class="n">predicted_array</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="n">iy</span><span class="p">]</span> <span class="o">=</span> <span class="n">predicted_vals</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-                <span class="c1"># data_transform is too complicated for Wright estimator</span>
-                <span class="c1"># if fitted_data_transform is not None:</span>
-                <span class="c1">#     rescaled = fitted_data_transform[&#39;Y&#39;].inverse_transform(X=predicted_array[index, iy].reshape(-1, 1))</span>
-                <span class="c1">#     predicted_array[index, iy] = rescaled.squeeze()</span>
-
-        <span class="k">return</span> <span class="n">predicted_array</span></div>
-
-
-<div class="viewcode-block" id="CausalEffects.fit_bootstrap_of"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.fit_bootstrap_of">[docs]</a>    <span class="k">def</span> <span class="nf">fit_bootstrap_of</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">method_args</span><span class="p">,</span> 
-                        <span class="n">boot_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-                        <span class="n">boot_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                        <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Runs chosen method on bootstrap samples drawn from DataFrame.</span>
-<span class="sd">        </span>
-<span class="sd">        Bootstraps for tau=0 are drawn from [max_lag, ..., T] and all lagged</span>
-<span class="sd">        variables constructed in DataFrame.construct_array are consistently</span>
-<span class="sd">        shifted with respect to this bootsrap sample to ensure that lagged</span>
-<span class="sd">        relations in the bootstrap sample are preserved.</span>
-
-<span class="sd">        This function fits the models, predict_bootstrap_of can then be used</span>
-<span class="sd">        to get confidence intervals for the effect of interventions.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        method : str</span>
-<span class="sd">            Chosen method among valid functions in this class.</span>
-<span class="sd">        method_args : dict</span>
-<span class="sd">            Arguments passed to method.</span>
-<span class="sd">        boot_samples : int</span>
-<span class="sd">            Number of bootstrap samples to draw.</span>
-<span class="sd">        boot_blocklength : int, optional (default: 1)</span>
-<span class="sd">            Block length for block-bootstrap.</span>
-<span class="sd">        seed : int, optional(default = None)</span>
-<span class="sd">            Seed for RandomState (default_rng)</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># if dataframe.analysis_mode != &#39;single&#39;:</span>
-        <span class="c1">#     raise ValueError(&quot;CausalEffects class currently only supports single &quot;</span>
-        <span class="c1">#                      &quot;datasets.&quot;)</span>
-
-        <span class="n">valid_methods</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;fit_total_effect&#39;</span><span class="p">,</span>
-                         <span class="s1">&#39;fit_wright_effect&#39;</span><span class="p">,</span>
-                          <span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">valid_methods</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;method must be one of </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">valid_methods</span><span class="p">))</span>
-
-        <span class="c1"># First call the method on the original dataframe </span>
-        <span class="c1"># to make available adjustment set etc</span>
-        <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">)(</span><span class="o">**</span><span class="n">method_args</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Running Bootstrap of </span><span class="si">%s</span><span class="s2"> &quot;</span> <span class="o">%</span> <span class="n">method</span> <span class="o">+</span>
-                  <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span>
-                  <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">boot_samples = </span><span class="si">%s</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">boot_samples</span> <span class="o">+</span>
-                  <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">boot_blocklength = </span><span class="si">%s</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">boot_blocklength</span>
-                  <span class="p">)</span>
-
-        <span class="n">method_args_bootstrap</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">method_args</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_results</span> <span class="o">=</span> <span class="p">{}</span>
-
-        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">boot_samples</span><span class="p">):</span>
-            <span class="c1"># # Replace dataframe in method args by bootstrapped dataframe</span>
-            <span class="c1"># method_args_bootstrap[&#39;dataframe&#39;].bootstrap = boot_draw</span>
-            <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">+</span><span class="n">b</span><span class="p">)</span>
-
-            <span class="n">method_args_bootstrap</span><span class="p">[</span><span class="s1">&#39;dataframe&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">bootstrap</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;boot_blocklength&#39;</span><span class="p">:</span><span class="n">boot_blocklength</span><span class="p">,</span>
-                                                            <span class="s1">&#39;random_state&#39;</span><span class="p">:</span><span class="n">random_state</span><span class="p">}</span>
-
-            <span class="c1"># Call method and save fitted model</span>
-            <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">)(</span><span class="o">**</span><span class="n">method_args_bootstrap</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_results</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
-
-        <span class="c1"># Reset model</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="CausalEffects.predict_bootstrap_of"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.predict_bootstrap_of">[docs]</a>    <span class="k">def</span> <span class="nf">predict_bootstrap_of</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">method_args</span><span class="p">,</span> 
-                        <span class="n">conf_lev</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span>
-                        <span class="n">return_individual_bootstrap_results</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Predicts with fitted bootstraps.</span>
-
-<span class="sd">        To be used after fitting with fit_bootstrap_of. Only uses the </span>
-<span class="sd">        expected values of the predict function, not potential other output.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        method : str</span>
-<span class="sd">            Chosen method among valid functions in this class.</span>
-<span class="sd">        method_args : dict</span>
-<span class="sd">            Arguments passed to method.</span>
-<span class="sd">        conf_lev : float, optional (default: 0.9)</span>
-<span class="sd">            Two-sided confidence interval.</span>
-<span class="sd">        return_individual_bootstrap_results : bool</span>
-<span class="sd">            Returns the individual bootstrap predictions.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        confidence_intervals : numpy array of </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">valid_methods</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;predict_total_effect&#39;</span><span class="p">,</span>
-                         <span class="s1">&#39;predict_wright_effect&#39;</span><span class="p">,</span>
-                          <span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">valid_methods</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;method must be one of </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">valid_methods</span><span class="p">))</span>
-
-        
-        <span class="n">lenY</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">listY</span><span class="p">)</span>
-        <span class="n">intervention_T</span><span class="p">,</span> <span class="n">lenX</span> <span class="o">=</span> <span class="n">method_args</span><span class="p">[</span><span class="s1">&#39;intervention_data&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">boot_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_results</span><span class="p">)</span>
-        <span class="n">bootstrap_predicted_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">boot_samples</span><span class="p">,</span> <span class="n">intervention_T</span><span class="p">,</span> <span class="n">lenY</span><span class="p">))</span>
-        
-        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_results</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_results</span><span class="p">[</span><span class="n">b</span><span class="p">]</span>
-            <span class="n">boot_effect</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">)(</span><span class="o">**</span><span class="n">method_args</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">boot_effect</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
-                <span class="n">bootstrap_predicted_array</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="n">boot_effect</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">bootstrap_predicted_array</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="n">boot_effect</span>
-
-        <span class="c1"># Reset model</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span>
-
-        <span class="c1"># Confidence intervals for val_matrix; interval is two-sided</span>
-        <span class="n">c_int</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">conf_lev</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">)</span>
-        <span class="n">confidence_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span>
-                <span class="n">bootstrap_predicted_array</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">q</span> <span class="o">=</span> <span class="p">[</span><span class="mi">100</span><span class="o">*</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">c_int</span><span class="p">),</span> <span class="mi">100</span><span class="o">*</span><span class="n">c_int</span><span class="p">])[:,:,</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">return_individual_bootstrap_results</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">bootstrap_predicted_array</span><span class="p">,</span> <span class="n">confidence_interval</span>
-
-        <span class="k">return</span> <span class="n">confidence_interval</span></div>
-
-
-<div class="viewcode-block" id="CausalEffects.get_graph_from_dict"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.get_graph_from_dict">[docs]</a>    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">get_graph_from_dict</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to convert dictionary of links to graph array format.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ---------</span>
-<span class="sd">        links : dict</span>
-<span class="sd">            Dictionary of form {0:[((0, -1), coeff, func), ...], 1:[...], ...}.</span>
-<span class="sd">            Also format {0:[(0, -1), ...], 1:[...], ...} is allowed.</span>
-<span class="sd">        tau_max : int or None</span>
-<span class="sd">            Maximum lag. If None, the maximum lag in links is used.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape (N, N, tau_max+1)</span>
-<span class="sd">            Matrix format of graph with 1 for true links and 0 else.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">_get_minmax_lag</span><span class="p">(</span><span class="n">links</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Helper function to retrieve tau_min and tau_max from links.</span>
-<span class="sd">            &quot;&quot;&quot;</span>
-
-            <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-            <span class="c1"># Get maximum time lag</span>
-            <span class="n">min_lag</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-                <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_props</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                        <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                        <span class="c1"># func = link_props[2]</span>
-                        <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                            <span class="n">min_lag</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">min_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-                            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span>
-                        <span class="n">min_lag</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">min_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-                        <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>   
-
-            <span class="k">return</span> <span class="n">min_lag</span><span class="p">,</span> <span class="n">max_lag</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-        <span class="c1"># Get maximum time lag</span>
-        <span class="n">min_lag</span><span class="p">,</span> <span class="n">max_lag</span> <span class="o">=</span> <span class="n">_get_minmax_lag</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-        <span class="c1"># Set maximum lag</span>
-        <span class="k">if</span> <span class="n">tau_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">tau_max</span> <span class="o">=</span> <span class="n">max_lag</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">max_lag</span> <span class="o">&gt;</span> <span class="n">tau_max</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;tau_max is smaller than maximum lag = </span><span class="si">%d</span><span class="s2"> &quot;</span>
-                                 <span class="s2">&quot;found in links, use tau_max=None or larger &quot;</span>
-                                 <span class="s2">&quot;value&quot;</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">)</span>
-
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">links</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-            <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_props</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-                    <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                    <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                    <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">var</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">)]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-                        <span class="k">if</span> <span class="n">lag</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">var</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">)]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-                    <span class="k">if</span> <span class="n">lag</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>
-
-        <span class="k">return</span> <span class="n">graph</span></div></div>
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="c1"># Consider some toy data</span>
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">import</span> <span class="nn">tigramite.toymodels.structural_causal_processes</span> <span class="k">as</span> <span class="nn">toys</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
-    <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
-    <span class="kn">import</span> <span class="nn">sys</span>
-
-    <span class="kn">import</span> <span class="nn">sklearn</span>
-    <span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span><span class="p">,</span> <span class="n">LogisticRegression</span>
-    <span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
-
-
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-    <span class="n">coeff</span> <span class="o">=</span> <span class="mf">.5</span>
- 
-    <span class="n">links_coeffs</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">1</span><span class="p">:</span> <span class="p">[((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">2</span><span class="p">:</span> <span class="p">[((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)]</span>
-             <span class="p">}</span>
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    <span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">structural_causal_process</span><span class="p">(</span>
-        <span class="n">links_coeffs</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
-    <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">CausalEffects</span><span class="o">.</span><span class="n">get_graph_from_dict</span><span class="p">(</span><span class="n">links_coeffs</span><span class="p">)</span>
-
-    <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)]</span>
-    <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-
-    <span class="c1"># Initialize class as `stationary_dag`</span>
-    <span class="n">causal_effects</span> <span class="o">=</span> <span class="n">CausalEffects</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">graph_type</span><span class="o">=</span><span class="s1">&#39;stationary_dag&#39;</span><span class="p">,</span> 
-                                <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">S</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                                <span class="n">hidden_variables</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                                <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-    <span class="n">causal_effects</span><span class="o">.</span><span class="n">fit_wright_effect</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
-                            <span class="c1"># links_coeffs = links_coeffs,</span>
-                            <span class="c1"># mediation = [(1, 0), (1, -1), (1, -2)]</span>
-                            <span class="p">)</span>
-
-    <span class="n">intervention_data</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="n">y1</span> <span class="o">=</span> <span class="n">causal_effects</span><span class="o">.</span><span class="n">predict_wright_effect</span><span class="p">(</span> 
-            <span class="n">intervention_data</span><span class="o">=</span><span class="n">intervention_data</span><span class="p">,</span>
-            <span class="p">)</span>
-
-    <span class="n">intervention_data</span> <span class="o">=</span> <span class="mf">0.</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="n">y2</span> <span class="o">=</span> <span class="n">causal_effects</span><span class="o">.</span><span class="n">predict_wright_effect</span><span class="p">(</span> 
-            <span class="n">intervention_data</span><span class="o">=</span><span class="n">intervention_data</span><span class="p">,</span>
-            <span class="p">)</span>
-
-    <span class="n">beta</span> <span class="o">=</span> <span class="p">(</span><span class="n">y1</span> <span class="o">-</span> <span class="n">y2</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Causal effect is </span><span class="si">%.5f</span><span class="s2">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">beta</span><span class="p">))</span>
-
-    <span class="c1"># tp.plot_time_series_graph(</span>
-    <span class="c1">#     graph = causal_effects.graph,</span>
-    <span class="c1">#     save_name=&#39;Example_graph.pdf&#39;,</span>
-    <span class="c1">#     # special_nodes=special_nodes,</span>
-    <span class="c1">#     var_names=var_names,</span>
-    <span class="c1">#     figsize=(8, 4),</span>
-    <span class="c1">#     )</span>
-
-    <span class="c1"># T = 100</span>
-    <span class="c1"># def lin_f(x): return x</span>
-    <span class="c1"># auto_coeff = 0.3</span>
-    <span class="c1"># coeff = 2.</span>
-    <span class="c1"># links = {</span>
-    <span class="c1">#         0: [((0, -1), auto_coeff, lin_f)], </span>
-    <span class="c1">#         1: [((1, -1), auto_coeff, lin_f), ((0, -1), coeff, lin_f)], </span>
-    <span class="c1">#         # 2: [((2, -1), auto_coeff, lin_f), ((1, 0), coeff, lin_f)],</span>
-    <span class="c1">#         }</span>
-    <span class="c1"># data, nonstat = toys.structural_causal_process(links, T=T, </span>
-    <span class="c1">#                             noises=None, seed=7)</span>
-
-    <span class="c1"># # Create some missing values</span>
-    <span class="c1"># data[-10:,:] = 999.</span>
-    <span class="c1"># var_names = range(2)</span>
-    <span class="c1"># dataframe = pp.DataFrame(data, var_names=var_names,</span>
-    <span class="c1">#  missing_flag=999.) </span>
-
-
-    <span class="c1"># # Construct expert knowledge graph from links here </span>
-    <span class="c1"># # links = {0: [(0, -1)],</span>
-    <span class="c1"># #          1: [(1, -1), (0, -1)],</span>
-    <span class="c1"># #          2: [(2, -1), (1, 0),],</span>
-    <span class="c1"># #          }</span>
-    <span class="c1"># # Use staticmethod to get graph</span>
-    <span class="c1"># graph = CausalEffects.get_graph_from_dict(links, tau_max=None)</span>
-    
-    <span class="c1"># # We are interested in lagged total effect of X on Y</span>
-    <span class="c1"># X = [(0, -1)]</span>
-    <span class="c1"># Y = [(1, 0)]</span>
-
-    <span class="c1"># # Initialize class as `stationary_dag`</span>
-    <span class="c1"># causal_effects = CausalEffects(graph, graph_type=&#39;stationary_dag&#39;, </span>
-    <span class="c1">#                             X=X, Y=Y, S=None, </span>
-    <span class="c1">#                             hidden_variables=None, </span>
-    <span class="c1">#                             verbosity=0)</span>
-
-    <span class="c1"># # print(data)</span>
-    <span class="c1"># # Optimal adjustment set (is used by default)</span>
-    <span class="c1"># # print(causal_effects.get_optimal_set())</span>
-
-    <span class="c1"># # # Fit causal effect model from observational data</span>
-    <span class="c1"># causal_effects.fit_total_effect(</span>
-    <span class="c1">#     dataframe=dataframe, </span>
-    <span class="c1">#     # mask_type=&#39;y&#39;,</span>
-    <span class="c1">#     estimator=LinearRegression(),</span>
-    <span class="c1">#     )</span>
-
-
-    <span class="c1"># # # Fit causal effect model from observational data</span>
-    <span class="c1"># causal_effects.fit_bootstrap_of(</span>
-    <span class="c1">#     method=&#39;fit_total_effect&#39;,</span>
-    <span class="c1">#     method_args={&#39;dataframe&#39;:dataframe,  </span>
-    <span class="c1">#     # mask_type=&#39;y&#39;,</span>
-    <span class="c1">#     &#39;estimator&#39;:LinearRegression()</span>
-    <span class="c1">#     },</span>
-    <span class="c1">#     seed=4</span>
-    <span class="c1">#     )</span>
-
-
-    <span class="c1"># # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go</span>
-    <span class="c1"># dox_vals = np.array([1.]) #np.linspace(0., 1., 1)</span>
-    <span class="c1"># intervention_data = dox_vals.reshape(len(dox_vals), len(X))</span>
-    <span class="c1"># pred_Y = causal_effects.predict_total_effect( </span>
-    <span class="c1">#         intervention_data=intervention_data)</span>
-    <span class="c1"># print(pred_Y)</span>
-
-
-
-
-    <span class="c1"># # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go</span>
-    <span class="c1"># dox_vals = np.array([1.]) #np.linspace(0., 1., 1)</span>
-    <span class="c1"># intervention_data = dox_vals.reshape(len(dox_vals), len(X))</span>
-    <span class="c1"># conf = causal_effects.predict_bootstrap_of(</span>
-    <span class="c1">#     method=&#39;predict_total_effect&#39;,</span>
-    <span class="c1">#     method_args={&#39;intervention_data&#39;:intervention_data})</span>
-    <span class="c1"># print(conf)</span>
-
-
-
-    <span class="c1"># # # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go</span>
-    <span class="c1"># # dox_vals = np.array([1.]) #np.linspace(0., 1., 1)</span>
-    <span class="c1"># # intervention_data = dox_vals.reshape(len(dox_vals), len(X))</span>
-    <span class="c1"># # pred_Y = causal_effects.predict_total_effect( </span>
-    <span class="c1"># #         intervention_data=intervention_data)</span>
-    <span class="c1"># # print(pred_Y)</span>
-
-
-
-    <span class="c1"># # Fit causal effect model from observational data</span>
-    <span class="c1"># causal_effects.fit_wright_effect(</span>
-    <span class="c1">#     dataframe=dataframe, </span>
-    <span class="c1">#     # mask_type=&#39;y&#39;,</span>
-    <span class="c1">#     # estimator=LinearRegression(),</span>
-    <span class="c1">#     # data_transform=StandardScaler(),</span>
-    <span class="c1">#     )</span>
-
-    <span class="c1"># # # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go</span>
-    <span class="c1"># dox_vals = np.linspace(0., 1., 5)</span>
-    <span class="c1"># intervention_data = dox_vals.reshape(len(dox_vals), len(X))</span>
-    <span class="c1"># pred_Y = causal_effects.predict_wright_effect( </span>
-    <span class="c1">#         intervention_data=intervention_data)</span>
-    <span class="c1"># print(pred_Y)</span>
-</pre></div>
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-  <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite data processing functions.&quot;&quot;&quot;</span>
-
-<span class="c1"># Authors: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#          Andreas Gerhardus &lt;andreas.gerhardus@dlr.de&gt;</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span><span class="p">,</span> <span class="n">OrderedDict</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">scipy.sparse</span>
-<span class="kn">import</span> <span class="nn">scipy.sparse.linalg</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
-<span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
-
-<div class="viewcode-block" id="DataFrame"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.DataFrame">[docs]</a><span class="k">class</span> <span class="nc">DataFrame</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Data object containing single or multiple time series arrays and optional </span>
-<span class="sd">    mask.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data : array-like</span>
-<span class="sd">        if analysis_mode == &#39;single&#39;:</span>
-<span class="sd">            1) Numpy array of shape (observations T, variables N)</span>
-<span class="sd">            OR</span>
-<span class="sd">            2) Dictionary with a single entry whose value is a numpy array of</span>
-<span class="sd">            shape (observations T, variables N)</span>
-<span class="sd">        if analysis_mode == &#39;multiple&#39;:</span>
-<span class="sd">            1) Numpy array of shape (multiple datasets M, observations T,</span>
-<span class="sd">            variables N)</span>
-<span class="sd">            OR</span>
-<span class="sd">            2) Dictionary whose values are numpy arrays of shape</span>
-<span class="sd">            (observations T_i, variables N), where the number of observations</span>
-<span class="sd">            T_i may vary across the multiple datasets but the number of variables</span>
-<span class="sd">            N is fixed. </span>
-<span class="sd">    mask : array-like, optional (default: None)</span>
-<span class="sd">        Optional mask array, must be of same format and shape as data.</span>
-<span class="sd">   type_mask : array-like</span>
-<span class="sd">        Binary data array of same shape as array which describes whether </span>
-<span class="sd">        individual samples in a variable (or all samples) are continuous </span>
-<span class="sd">        or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-<span class="sd">    missing_flag : number, optional (default: None)</span>
-<span class="sd">        Flag for missing values in dataframe. Dismisses all time slices of</span>
-<span class="sd">        samples where missing values occur in any variable. For</span>
-<span class="sd">        remove_missing_upto_maxlag=True also flags samples for all lags up to</span>
-<span class="sd">        2*tau_max (more precisely, this depends on the cut_off argument in</span>
-<span class="sd">        self.construct_array(), see further below). This avoids biases, see</span>
-<span class="sd">        section on masking in Supplement of [1]_.</span>
-<span class="sd">    vector_vars : dict</span>
-<span class="sd">        Dictionary of vector variables of the form,</span>
-<span class="sd">        Eg. {0: [(0, 0), (1, 0)], 1: [(2, 0)], 2: [(3, 0)], 3: [(4, 0)]}</span>
-<span class="sd">        The keys are the new vectorized variables and respective tuple values</span>
-<span class="sd">        are the individual components of the vector variables. In the method of</span>
-<span class="sd">        construct_array(), the individual components are parsed from vector_vars</span>
-<span class="sd">        and added (accounting for lags) to the list that creates X, Y and Z for</span>
-<span class="sd">        conditional independence test.</span>
-<span class="sd">    var_names : list of strings, optional (default: range(N))</span>
-<span class="sd">        Names of variables, must match the number of variables. If None is</span>
-<span class="sd">        passed, variables are enumerated as [0, 1, ...]</span>
-<span class="sd">    datatime : array-like, optional (default: None)</span>
-<span class="sd">        Timelabel array. If None, range(T) is used.</span>
-<span class="sd">    remove_missing_upto_maxlag : bool, optional (default: False)</span>
-<span class="sd">        Whether to remove not only missing samples, but also all neighboring</span>
-<span class="sd">        samples up to max_lag (as given by cut_off in construct_array).</span>
-<span class="sd">    analysis_mode : string, optional (default: &#39;single&#39;)</span>
-<span class="sd">        Must be &#39;single&#39; or &#39;multiple&#39;.</span>
-<span class="sd">        Determines whether data contains a single (potentially multivariate)</span>
-<span class="sd">        time series (--&gt; &#39;single&#39;) or multiple time series (--&gt; &#39;multiple&#39;).</span>
-<span class="sd">    reference_points : None, int, or list (or 1D array) of integers,</span>
-<span class="sd">        optional (default:None)</span>
-<span class="sd">        Determines the time steps --- relative to the shared time axis as</span>
-<span class="sd">        defined by the optional time_offset argument (see below) --- that are</span>
-<span class="sd">        used to create samples for conditional independence testing.</span>
-<span class="sd">        Set to [0, 1, ..., T_max-1] if None is passed, where T_max is</span>
-<span class="sd">        self.largest_time_step, see below.</span>
-<span class="sd">        All values smaller than 0 and bigger than T_max-1 will be ignored.</span>
-<span class="sd">        At least one value must be in [0, 1, ..., T_max-1].</span>
-<span class="sd">    time_offsets : None or dict, optional (default: None)</span>
-<span class="sd">        if analysis_mode == &#39;single&#39;:</span>
-<span class="sd">            Must be None.</span>
-<span class="sd">            Shared time axis defined by the time indices of the single time series</span>
-<span class="sd">        if analysis_mode == &#39;multiple&#39; and data is numpy array:</span>
-<span class="sd">            Must be None.</span>
-<span class="sd">            All datasets are assumed to be already aligned in time with</span>
-<span class="sd">            respect to a shared time axis, which is the time axis of data</span>
-<span class="sd">        if analysis_mode == &#39;multiple&#39; and data is dictionary:</span>
-<span class="sd">            Must be dictionary of the form {key(m): time_offset(m), ...} whose</span>
-<span class="sd">            set of keys agrees with the set of keys of data and whose values are</span>
-<span class="sd">            non-negative integers, at least one of which is 0. The value</span>
-<span class="sd">            time_offset(m) defines the time offset of dataset m with</span>
-<span class="sd">            respect to a shared time axis.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    self._initialized_from : string</span>
-<span class="sd">        Specifies the data format in which data was given at instantiation.</span>
-<span class="sd">        Possible values: &#39;2d numpy array&#39;, &#39;3d numpy array&#39;, &#39;dict&#39;.</span>
-<span class="sd">    self.values : dictionary</span>
-<span class="sd">        Dictionary holding the observations given by data internally mapped to a</span>
-<span class="sd">        dictionary representation as follows:</span>
-<span class="sd">        If analysis_mode == &#39;single&#39;:</span>
-<span class="sd">            If self._initialized_from == &#39;2d numpy array&#39;:</span>
-<span class="sd">                Is {0: data}</span>
-<span class="sd">            If self._initialized_from == &#39;dict&#39;:</span>
-<span class="sd">                Is data</span>
-<span class="sd">        If analysis_mode == &#39;multiple&#39;:</span>
-<span class="sd">            If self._initialized_from == &#39;3d numpy array&#39;:</span>
-<span class="sd">                Is {m: data[m, :, :] for m in range(data.shape[0])}</span>
-<span class="sd">            If self._initialized_from == &#39;dict&#39;:</span>
-<span class="sd">                Is data</span>
-<span class="sd">    self.datasets: list</span>
-<span class="sd">        List of the keys identifiying the multiple datasets, i.e.,</span>
-<span class="sd">        list(self.values.keys())</span>
-<span class="sd">    self.mask : dictionary</span>
-<span class="sd">        Mask internally mapped to a dictionary representation in the same way as</span>
-<span class="sd">        data is mapped to self.values</span>
-<span class="sd">    self.type_mask : array-like</span>
-<span class="sd">        Binary data array of same shape as array which describes whether </span>
-<span class="sd">        individual samples in a variable (or all samples) are continuous </span>
-<span class="sd">        or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-<span class="sd">    self.missing_flag:</span>
-<span class="sd">        Is missing_flag</span>
-<span class="sd">    self.var_names:</span>
-<span class="sd">        If var_names is not None:</span>
-<span class="sd">            Is var_names</span>
-<span class="sd">        If var_names is None:</span>
-<span class="sd">            Is {i: i for i in range(self.N)}</span>
-<span class="sd">    self.datatime : dictionary</span>
-<span class="sd">        Time axis for each of the multiple datasets.</span>
-<span class="sd">    self.analysis_mode : string</span>
-<span class="sd">        Is analysis_mode</span>
-<span class="sd">    self.reference_points: array-like</span>
-<span class="sd">        If reference_points is not None:</span>
-<span class="sd">            1D numpy array holding all specified reference_points, less those</span>
-<span class="sd">            smaller than 0 and larger than self.largest_time_step-1</span>
-<span class="sd">        If reference_points is None:</span>
-<span class="sd">            Is np.array(range(self.largest_time_step))</span>
-<span class="sd">    self.time_offsets : dictionary</span>
-<span class="sd">        If time_offsets is not None:</span>
-<span class="sd">            Is time_offsets</span>
-<span class="sd">        If time_offsets is None:</span>
-<span class="sd">            Is {key: 0 for key in self.values.keys()}</span>
-<span class="sd">    self.M : int</span>
-<span class="sd">        Number of datasets</span>
-<span class="sd">    self.N : int</span>
-<span class="sd">        Number of variables (constant across datasets)</span>
-<span class="sd">    self.T : dictionary</span>
-<span class="sd">        Dictionary {key(m): T(m), ...}, where T(m) is the time length of</span>
-<span class="sd">        datasets m and key(m) its identifier as in self.values</span>
-<span class="sd">    self.largest_time_step : int</span>
-<span class="sd">        max_{0 &lt;= m &lt;= M} [ T(m) + time_offset(m)], i.e., the largest (latest)</span>
-<span class="sd">        time step relative to the shared time axis for which at least one</span>
-<span class="sd">        observation exists in the dataset.</span>
-<span class="sd">    self.bootstrap : dictionary</span>
-<span class="sd">        Whether to use bootstrap. Must be a dictionary with keys random_state,</span>
-<span class="sd">        boot_samples, and boot_blocklength.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">missing_flag</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vector_vars</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">datatime</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">analysis_mode</span> <span class="o">=</span><span class="s1">&#39;single&#39;</span><span class="p">,</span> <span class="n">reference_points</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">time_offsets</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">remove_missing_upto_maxlag</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-
-        <span class="c1"># Check that a valid analysis mode, specified by the argument</span>
-        <span class="c1"># &#39;analysis_mode&#39;, has been chosen</span>
-        <span class="k">if</span> <span class="n">analysis_mode</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;single&#39;</span><span class="p">,</span> <span class="s1">&#39;multiple&#39;</span><span class="p">]:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">=</span> <span class="n">analysis_mode</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;&#39;analysis_mode&#39; is &#39;</span><span class="si">{}</span><span class="s2">&#39;, must be &#39;single&#39; or &quot;</span>\
-                <span class="s2">&quot;&#39;multiple&#39;.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">analysis_mode</span><span class="p">))</span>
-
-        <span class="c1"># Check for correct type and format of &#39;data&#39;, internally cast to the</span>
-        <span class="c1"># analysis mode &#39;multiple&#39; case in dictionary representation</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">==</span> <span class="s1">&#39;single&#39;</span><span class="p">:</span>
-            <span class="c1"># In this case the &#39;time_offset&#39; functionality must not be used</span>
-            <span class="k">if</span> <span class="n">time_offsets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;&#39;time_offsets&#39; must be None in analysis &quot;</span>\
-                    <span class="s2">&quot;mode&#39;single&#39;.&quot;</span><span class="p">)</span>
-
-            <span class="c1"># &#39;data&#39; must be either</span>
-            <span class="c1"># - np.ndarray of shape (T, N)</span>
-            <span class="c1"># - np.ndarray of shape (1, T, N)</span>
-            <span class="c1"># - a dictionary with one element whose value is a np.ndarray of</span>
-            <span class="c1"># shape (T, N)</span>
-            
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-                <span class="n">_data_shape</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_data_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">data</span><span class="p">)}</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_initialized_from</span> <span class="o">=</span> <span class="s2">&quot;2d numpy array&quot;</span>
-                <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">_data_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">_data_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:])}</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_initialized_from</span> <span class="o">=</span> <span class="s2">&quot;3d numpy array&quot;</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;single&#39;, &#39;data&#39; given &quot;</span>\
-                        <span class="s2">&quot;as np.ndarray. &#39;data&#39; is of shape </span><span class="si">{}</span><span class="s2">, must be of &quot;</span>\
-                        <span class="s2">&quot;shape (T, N) or (1, T, N).&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_data_shape</span><span class="p">))</span>
-
-            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="n">_data</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">_data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-                        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">_initialized_from</span> <span class="o">=</span> <span class="s2">&quot;dict&quot;</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;single&#39;, &quot;</span>\
-                                <span class="s2">&quot;&#39;data&#39;given as dictionary. The single value &quot;</span>\
-                                <span class="s2">&quot;is a np.ndarray of shape </span><span class="si">{}</span><span class="s2">, must be of &quot;</span>\
-                                <span class="s2">&quot;shape (T, N).&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;single&#39;, &#39;data&#39; &quot;</span>\
-                            <span class="s2">&quot;given as dictionary. The single value is of type &quot;</span>\
-                            <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">, must be np.ndarray.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">_data</span><span class="p">)))</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;single&#39;, &#39;data&#39; given &quot;</span>\
-                        <span class="s2">&quot;as dictionary. There are </span><span class="si">{}</span><span class="s2"> entries in &#39;data&#39;, there &quot;</span>\
-                        <span class="s2">&quot;must be exactly one entry.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)))</span>
-
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;single&#39;. &#39;data&#39; is of type &quot;</span>\
-                    <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">, must be np.ndarray or dict.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)))</span>
-
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">==</span> <span class="s1">&#39;multiple&#39;</span><span class="p">:</span>
-            <span class="c1"># &#39;data&#39; must either be a</span>
-            <span class="c1"># - np.ndarray of shape (M, T, N)</span>
-            <span class="c1"># - dict whose values of are np.ndarray of shape (T_i, N), where T_i</span>
-            <span class="c1"># may vary across the values</span>
-
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-                <span class="n">_data_shape</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_data_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">_data_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])}</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_initialized_from</span> <span class="o">=</span> <span class="s2">&quot;3d numpy array&quot;</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;multiple&#39;, &#39;data&#39; &quot;</span>\
-                        <span class="s2">&quot;given as np.ndarray. &#39;data&#39; is of shape </span><span class="si">{}</span><span class="s2">, must be &quot;</span>\
-                        <span class="s2">&quot;of shape (M, T, N).&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_data_shape</span><span class="p">))</span>
-
-                <span class="c1"># In this case the &#39;time_offset&#39; functionality must not be used</span>
-                <span class="k">if</span> <span class="n">time_offsets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;multiple&#39;. Since &quot;</span>\
-                        <span class="s2">&quot;&#39;data&#39; is given as np.ndarray, &#39;time_offsets&#39; must &quot;</span>\
-                        <span class="s2">&quot;be None.&quot;</span><span class="p">)</span>
-
-            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
-                <span class="n">_N_list</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-                <span class="k">for</span> <span class="n">dataset_key</span><span class="p">,</span> <span class="n">dataset_data</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset_data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-                        <span class="n">_dataset_data_shape</span> <span class="o">=</span> <span class="n">dataset_data</span><span class="o">.</span><span class="n">shape</span>
-                        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_dataset_data_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-                            <span class="n">_N_list</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_dataset_data_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;multiple&#39;, &quot;</span>\
-                                <span class="s2">&quot;&#39;data&#39; given as dictionary. &#39;data&#39;[</span><span class="si">{}</span><span class="s2">] is of &quot;</span>\
-                                <span class="s2">&quot;shape </span><span class="si">{}</span><span class="s2">, must be of shape (T_i, N).&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                                    <span class="n">dataset_key</span><span class="p">,</span> <span class="n">_dataset_data_shape</span><span class="p">))</span>
-
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;multiple&#39;, &#39;data&#39; &quot;</span>\
-                            <span class="s2">&quot;given as dictionary. &#39;data&#39;[</span><span class="si">{}</span><span class="s2">] is of type </span><span class="si">{}</span><span class="s2">, &quot;</span>\
-                            <span class="s2">&quot;must be np.ndarray.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset_key</span><span class="p">,</span>
-                                <span class="nb">type</span><span class="p">(</span><span class="n">dataset_data</span><span class="p">)))</span>
-
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_N_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_initialized_from</span> <span class="o">=</span> <span class="s2">&quot;dict&quot;</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;multiple&#39;, &#39;data&#39; &quot;</span>\
-                        <span class="s2">&quot;given as dictionary. All entries must be np.ndarrays &quot;</span>\
-                        <span class="s2">&quot;of shape (T_i, N), where T_i may vary across the &quot;</span>\
-                        <span class="s2">&quot;entries while N must not vary. In the given &#39;data&#39; N &quot;</span>\
-                        <span class="s2">&quot;varies.&quot;</span><span class="p">)</span>
-
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;multiple&#39;. &#39;data&#39; is of &quot;</span>\
-                    <span class="s2">&quot;type </span><span class="si">{}</span><span class="s2">, must be np.ndarray or dict.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)))</span>
-
-        <span class="c1"># Store the keys of the datasets in a separated attribute</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
-        <span class="c1"># Save the data format and check for NaNs:</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">M</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="c1"># (Number of datasets)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">T</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span> <span class="c1"># (Time lengths of the individual datasets)</span>
-        <span class="k">for</span> <span class="n">dataset_key</span><span class="p">,</span> <span class="n">dataset_data</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">dataset_data</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;NaNs in the data.&quot;</span><span class="p">)</span>
-
-            <span class="n">_dataset_data_shape</span> <span class="o">=</span> <span class="n">dataset_data</span><span class="o">.</span><span class="n">shape</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">_dataset_data_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">Ndata</span> <span class="o">=</span> <span class="n">_dataset_data_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># (Number of variables) </span>
-            <span class="c1"># N does not vary across the datasets</span>
-
-        <span class="c1"># Setup dictionary of variables for vector mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span> <span class="o">=</span> <span class="n">vector_vars</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Ndata</span><span class="p">),</span> <span class="p">[[(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> 
-                                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Ndata</span><span class="p">)]))</span>
-        <span class="c1"># TODO: check vector_vars!</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span><span class="p">)</span>
-
-        <span class="c1"># Warnings</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">==</span> <span class="s1">&#39;single&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">&gt;</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">values</span><span class="p">())):</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;single&#39;, &#39;data&#39;.shape = (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">);&quot;</span>\
-                <span class="s2">&quot; is it of shape (observations, variables)?&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">==</span> <span class="s1">&#39;multiple&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">M</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;In analysis mode &#39;multiple&#39;. There is just a &quot;</span>\
-                <span class="s2">&quot;single dataset, is this as intended?&#39;&quot;</span><span class="p">)</span>
-
-
-        <span class="c1"># Save the variable names. If unspecified, use the default</span>
-        <span class="k">if</span> <span class="n">var_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span> <span class="o">=</span> <span class="n">var_names</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="p">)</span>
-            
-        <span class="bp">self</span><span class="o">.</span><span class="n">type_mask</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="n">type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">,</span> <span class="n">check_type_mask</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="c1"># Check and prepare the time offsets</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_set_time_offsets</span><span class="p">(</span><span class="n">time_offsets</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">time_offsets_is_none</span> <span class="o">=</span> <span class="n">time_offsets</span> <span class="ow">is</span> <span class="kc">None</span>
-
-        <span class="c1"># Set the default datatime if unspecified</span>
-        <span class="k">if</span> <span class="n">datatime</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">datatime</span> <span class="o">=</span> <span class="p">{</span><span class="n">m</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time_offsets</span><span class="p">[</span><span class="n">m</span><span class="p">],</span> 
-                <span class="bp">self</span><span class="o">.</span><span class="n">time_offsets</span><span class="p">[</span><span class="n">m</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="n">m</span><span class="p">])</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">datatime</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">datatime</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">datatime</span><span class="p">}</span>   
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">datatime</span> <span class="o">=</span> <span class="n">datatime</span>
-
-        <span class="c1"># Save the largest/smallest relevant time step</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">largest_time_step</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">values</span><span class="p">())),</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time_offsets</span><span class="o">.</span><span class="n">values</span><span class="p">())))</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">smallest_time_step</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">values</span><span class="p">())),</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time_offsets</span><span class="o">.</span><span class="n">values</span><span class="p">())))</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
-
-        <span class="c1"># Check and prepare the reference points</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_set_reference_points</span><span class="p">(</span><span class="n">reference_points</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">reference_points_is_none</span> <span class="o">=</span> <span class="n">reference_points</span> <span class="ow">is</span> <span class="kc">None</span>
-
-        <span class="c1"># Save the &#39;missing_flag&#39; value</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span> <span class="o">=</span> <span class="n">missing_flag</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">dataset_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">remove_missing_upto_maxlag</span> <span class="o">=</span> <span class="n">remove_missing_upto_maxlag</span>
-
-        <span class="c1"># If PCMCI.run_bootstrap_of is called, then the</span>
-        <span class="c1"># bootstrap random draw can be set here</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap</span> <span class="o">=</span> <span class="kc">None</span>
-
-
-    <span class="k">def</span> <span class="nf">_check_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">check_type_mask</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Checks that the mask is:</span>
-<span class="sd">            * The same shape as the data</span>
-<span class="sd">            * Is an numpy ndarray (or subtype)</span>
-<span class="sd">            * Does not contain any NaN entries</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Check that there is a mask if required</span>
-        <span class="n">_use_mask</span> <span class="o">=</span> <span class="n">mask</span>
-
-        <span class="c1"># If we have a mask, check it</span>
-        <span class="k">if</span> <span class="n">_use_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># Check data type and generic format of &#39;mask&#39;, map to multiple datasets mode</span>
-            <span class="c1"># dictionary representation</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">_use_mask</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_use_mask</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-                    <span class="n">_use_mask_dict</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">_use_mask</span><span class="p">}</span>
-                <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">_use_mask</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">_use_mask</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">:</span>
-                        <span class="n">_use_mask_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">_use_mask</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">)}</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Shape mismatch: </span><span class="si">{}</span><span class="s2"> datasets &quot;</span>\
-                            <span class="s2">&quot; in &#39;data&#39; but </span><span class="si">{}</span><span class="s2"> in &#39;mask&#39;, must be &quot;</span>\
-                            <span class="s2">&quot;identical.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">,</span> <span class="n">_use_mask</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;&#39;data&#39; given as 3d np.ndarray. &quot;</span>\
-                        <span class="s2">&quot;&#39;mask&#39; is np.ndarray of shape </span><span class="si">{}</span><span class="s2">, must be of &quot;</span>\
-                        <span class="s2">&quot;shape (M, T, N).&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_use_mask</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
-
-            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">_use_mask</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_use_mask</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">dataset_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-                        <span class="k">if</span> <span class="n">_use_mask</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">dataset_key</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;&#39;data&#39; has key </span><span class="si">{}</span><span class="s2"> (type </span><span class="si">{}</span><span class="s2">) &quot;</span>\
-                                <span class="s2">&quot;but &#39;mask&#39; does not, keys must be &quot;</span>\
-                                <span class="s2">&quot;identical.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset_key</span><span class="p">,</span>
-                                    <span class="nb">type</span><span class="p">(</span><span class="n">dataset_key</span><span class="p">)))</span>
-
-                    <span class="n">_use_mask_dict</span> <span class="o">=</span> <span class="n">_use_mask</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Shape mismatch: </span><span class="si">{}</span><span class="s2"> datasets &quot;</span>\
-                        <span class="s2">&quot;in &#39;data&#39; but </span><span class="si">{}</span><span class="s2"> in &#39;mask&#39;, must be &quot;</span>\
-                        <span class="s2">&quot;identical.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">_use_mask</span><span class="p">)))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;&#39;mask&#39; is of type &quot;</span>\
-                    <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">, must be dict or array.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">_use_mask</span><span class="p">)))</span>
-
-            <span class="c1"># Check for consistency with shape of &#39;self.values&#39; and for NaNs</span>
-            <span class="k">for</span> <span class="n">dataset_key</span><span class="p">,</span> <span class="n">dataset_data</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="n">_use_mask_dict_data</span> <span class="o">=</span> <span class="n">_use_mask_dict</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> 
-                <span class="k">if</span> <span class="n">_use_mask_dict_data</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">dataset_data</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">_use_mask_dict_data</span><span class="p">))</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;NaNs in the data mask&quot;</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">check_type_mask</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="ow">not</span> <span class="nb">set</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">_use_mask_dict_data</span><span class="p">))</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])):</span>
-                            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type mask contains other values than 0 and 1&quot;</span><span class="p">)</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">==</span> <span class="s1">&#39;single&#39;</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Shape mismatch: &#39;data&#39; is of shape &quot;</span>\
-                            <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">, &#39;mask&#39; is of shape </span><span class="si">{}</span><span class="s2">. Must be &quot;</span>\
-                            <span class="s2">&quot;identical.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset_data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
-                                <span class="n">_use_mask_dict_data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
-                    <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">==</span> <span class="s1">&#39;multiple&#39;</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Shape mismatch: dataset </span><span class="si">{}</span><span class="s2"> &quot;</span>\
-                            <span class="s2">&quot;is of shape </span><span class="si">{}</span><span class="s2"> in &#39;data&#39; and of shape </span><span class="si">{}</span><span class="s2"> in &quot;</span>\
-                            <span class="s2">&quot;&#39;mask&#39;. Must be identical.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset_key</span><span class="p">,</span>
-                                <span class="n">dataset_data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
-                                <span class="n">_use_mask_dict_data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
-
-            <span class="c1"># Return the mask in dictionary format</span>
-            <span class="k">return</span> <span class="n">_use_mask_dict</span>
-
-    <span class="k">def</span> <span class="nf">_check_and_set_time_offsets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">time_offsets</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check the argument &#39;time_offsets&#39; for consistency and bring into</span>
-<span class="sd">        canonical format&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">time_offsets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-
-            <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">==</span> <span class="s1">&#39;multiple&#39;</span>
-            <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_initialized_from</span> <span class="o">==</span> <span class="s1">&#39;dict&#39;</span>
-
-            <span class="c1"># Check data type and generic format of &#39;time_offsets&#39;, map to</span>
-            <span class="c1"># dictionary representation</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">time_offsets</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">time_offsets</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">dataset_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-                        <span class="k">if</span> <span class="n">time_offsets</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">dataset_key</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;&#39;data&#39; has key </span><span class="si">{}</span><span class="s2"> (type </span><span class="si">{}</span><span class="s2">) but &quot;</span>\
-                                <span class="s2">&quot;&#39;time_offsets&#39; does not, keys must be &quot;</span>\
-                                <span class="s2">&quot;identical.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset_key</span><span class="p">,</span>
-                                    <span class="nb">type</span><span class="p">(</span><span class="n">dataset_key</span><span class="p">)))</span>
-
-                    <span class="bp">self</span><span class="o">.</span><span class="n">time_offsets</span> <span class="o">=</span> <span class="n">time_offsets</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Shape mismatch: </span><span class="si">{}</span><span class="s2"> datasets in &quot;</span>\
-                        <span class="s2">&quot;&#39;data&#39; but </span><span class="si">{}</span><span class="s2"> in &#39;time_offsets&#39;, must be &quot;</span>\
-                        <span class="s2">&quot;identical.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">time_offsets</span><span class="p">)))</span>
-
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;&#39;time_offsets&#39; is of type </span><span class="si">{}</span><span class="s2">, must be &quot;</span>\
-                    <span class="s2">&quot;dict.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">time_offsets</span><span class="p">)))</span>
-
-            <span class="c1"># All time offsets must be non-negative integers, at least one of</span>
-            <span class="c1"># which is zero</span>
-            <span class="n">found_zero_time_offset</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="k">for</span> <span class="n">time_offset</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">time_offsets</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-                <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">issubdtype</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">time_offset</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="n">time_offset</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="n">time_offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">found_zero_time_offset</span> <span class="o">=</span> <span class="kc">True</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;A dataset has time offset &quot;</span>\
-                            <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">, must be non-negative.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time_offset</span><span class="p">))</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;There is a time offset of type </span><span class="si">{}</span><span class="s2">, must &quot;</span>\
-                        <span class="s2">&quot;be int.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">time_offset</span><span class="p">)))</span>
-
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">found_zero_time_offset</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;At least one time offset must be 0.&quot;</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># If no time offsets are specified, all of them are zero</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">time_offsets</span> <span class="o">=</span> <span class="p">{</span><span class="n">dataset_key</span><span class="p">:</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">dataset_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span>
-
-    <span class="k">def</span> <span class="nf">_check_and_set_reference_points</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reference_points</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check the argument &#39;reference_point&#39; for consistency and bring into</span>
-<span class="sd">        canonical format&quot;&quot;&quot;</span>
-
-        <span class="c1"># Check type of &#39;reference_points&#39; and its elements</span>
-        <span class="k">if</span> <span class="n">reference_points</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># If no reference point is specified, use as many reference points</span>
-            <span class="c1"># as possible</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">largest_time_step</span><span class="p">))</span>
-
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reference_points</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="c1"># If a single reference point is specified as an int, convert it to</span>
-            <span class="c1"># a single element numpy array</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">reference_points</span><span class="p">])</span>
-
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reference_points</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-            <span class="c1"># Check that all reference points are ints</span>
-            <span class="k">for</span> <span class="n">ref_point</span> <span class="ow">in</span> <span class="n">reference_points</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">issubdtype</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">ref_point</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">):</span>
-                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;All reference points must be integers.&quot;</span><span class="p">)</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="n">reference_points</span>
-
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reference_points</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="c1"># Check that all reference points are ints</span>
-            <span class="k">for</span> <span class="n">ref_point</span> <span class="ow">in</span> <span class="n">reference_points</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ref_point</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;All reference points must be integers.&quot;</span><span class="p">)</span>
-
-            <span class="c1"># If given as a list, cast to numpy array</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">reference_points</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Unsupported data type of &#39;reference_points&#39;: Is &quot;</span>\
-                <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">, must be None or int or a list or np.ndarray of &quot;</span>\
-                <span class="s2">&quot;ints.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">reference_points</span><span class="p">)))</span>
-
-        <span class="c1"># Remove negative reference points</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Some reference points were negative. These are &quot;</span>\
-                <span class="s2">&quot;removed.&quot;</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1"># Remove reference points that are larger than the largest time step</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">largest_time_step</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Some reference points were larger than the largest &quot;</span>\
-                <span class="s2">&quot;relevant time step, which here is </span><span class="si">{}</span><span class="s2">. These are &quot;</span>\
-                <span class="s2">&quot;removed.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">largest_time_step</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">largest_time_step</span><span class="p">]</span>
-
-        <span class="c1"># Raise an error if no valid reference points was specified</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No valid reference point.&quot;</span><span class="p">)</span> 
-
-
-<div class="viewcode-block" id="DataFrame.construct_array"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.DataFrame.construct_array">[docs]</a>    <span class="k">def</span> <span class="nf">construct_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span>
-                        <span class="n">extraZ</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                        <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                        <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                        <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span>
-                        <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructs array from variables X, Y, Z from data.</span>
-<span class="sd">        Data is of shape (T, N) if analysis_mode == &#39;single&#39;, where T is the</span>
-<span class="sd">        time series length and N the number of variables, and of (n_ens, T, N)</span>
-<span class="sd">        if analysis_mode == &#39;multiple&#39;.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z, extraZ : list of tuples</span>
-<span class="sd">            For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of</span>
-<span class="sd">            the form [(var1, -lag), (var2, -lag), ...]. At least one varlag in Y </span>
-<span class="sd">            has to be at lag zero. extraZ is only used in CausalEffects class.</span>
-<span class="sd">        tau_max : int</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X and Z all have the same sample size.</span>
-<span class="sd">        mask : array-like, optional (default: None)</span>
-<span class="sd">            Optional mask array, must be of same shape as data.  If it is set,</span>
-<span class="sd">            then it overrides the self.mask assigned to the dataframe. If it is</span>
-<span class="sd">            None, then the self.mask is used, if it exists.</span>
-<span class="sd">        mask_type : {None, &#39;y&#39;,&#39;x&#39;,&#39;z&#39;,&#39;xy&#39;,&#39;xz&#39;,&#39;yz&#39;,&#39;xyz&#39;}</span>
-<span class="sd">            Masking mode: Indicators for which variables in the dependence</span>
-<span class="sd">            measure I(X; Y | Z) the samples should be masked. If None, the mask</span>
-<span class="sd">            is not used. Explained in tutorial on masking and missing values.</span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            Binary data array of same shape as array which describes whether </span>
-<span class="sd">            individual samples in a variable (or all samples) are continuous </span>
-<span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-<span class="sd">            If it is set, then it overrides the self.type_mask assigned to the dataframe.</span>
-<span class="sd">        return_cleaned_xyz : bool, optional (default: False)</span>
-<span class="sd">            Whether to return cleaned X,Y,Z, where possible duplicates are</span>
-<span class="sd">            removed.</span>
-<span class="sd">        do_checks : bool, optional (default: True)</span>
-<span class="sd">            Whether to perform sanity checks on input X,Y,Z</span>
-<span class="sd">        remove_overlaps : bool, optional (default: True)</span>
-<span class="sd">            Whether to remove variables from Z/extraZ if they overlap with X or Y.</span>
-<span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;tau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;,</span>
-<span class="sd">                    2xtau_max_future}</span>
-<span class="sd">            If cut_off == &#39;2xtau_max&#39;:</span>
-<span class="sd">                - 2*tau_max samples are cut off at the beginning of the time</span>
-<span class="sd">                series (&#39;beginning&#39; here refers to the temporally first time</span>
-<span class="sd">                steps). This guarantees that (as long as no mask is used) all</span>
-<span class="sd">                MCI tests are conducted on the same samples, independent of X,</span>
-<span class="sd">                Y, and Z.</span>
-<span class="sd">                - If at time step t_missing a data value is missing, then the</span>
-<span class="sd">                time steps t_missing, ..., t_missing + 2*tau_max are cut out.</span>
-<span class="sd">                The latter part only holds if remove_missing_upto_maxlag=True.</span>
-<span class="sd">            If cut_off ==  &#39;max_lag&#39;:</span>
-<span class="sd">                - max_lag(X, Y, Z) samples are cut off at the beginning of the</span>
-<span class="sd">                time series, where max_lag(X, Y, Z) is the maximum lag of all</span>
-<span class="sd">                nodes in X, Y, and Z. These are all samples that can in</span>
-<span class="sd">                principle be used.</span>
-<span class="sd">                - If at time step t_missing a data value is missing, then the</span>
-<span class="sd">                time steps t_missing, ..., t_missing + max_lag(X, Y, Z) are cut</span>
-<span class="sd">                out.</span>
-<span class="sd">                The latter part only holds if remove_missing_upto_maxlag=True.</span>
-<span class="sd">            If cut_off == &#39;max_lag_or_tau_max&#39;:</span>
-<span class="sd">                - max(max_lag(X, Y, Z), tau_max) are cut off at the beginning.</span>
-<span class="sd">                This may be useful for modeling by comparing multiple models on</span>
-<span class="sd">                the same samples. </span>
-<span class="sd">                - If at time step t_missing a data value is missing, then the</span>
-<span class="sd">                time steps</span>
-<span class="sd">                t_missing, ..., t_missing + max(max_lag(X, Y, Z), tau_max)</span>
-<span class="sd">                are cut out.</span>
-<span class="sd">                The latter part only holds if remove_missing_upto_maxlag=True.</span>
-<span class="sd">            If cut_off == &#39;tau_max&#39;:</span>
-<span class="sd">                - tau_max samples are cut off at the beginning.</span>
-<span class="sd">                This may be useful for modeling by comparing multiple models on</span>
-<span class="sd">                the same samples. </span>
-<span class="sd">                - If at time step t_missing a data value is missing, then the</span>
-<span class="sd">                time steps</span>
-<span class="sd">                t_missing, ..., t_missing + max(max_lag(X, Y, Z), tau_max)</span>
-<span class="sd">                are cut out.</span>
-<span class="sd">                The latter part only holds if remove_missing_upto_maxlag=True.</span>
-<span class="sd">            If cut_off == &#39;2xtau_max_future&#39;:</span>
-<span class="sd">                First, the relevant time steps are determined as for cut_off ==</span>
-<span class="sd">                &#39;max_lag&#39;. Then, the temporally latest time steps are removed</span>
-<span class="sd">                such that the same number of time steps remains as there would</span>
-<span class="sd">                be for cut_off == &#39;2xtau_max&#39;. This may be useful when one is</span>
-<span class="sd">                mostly interested in the temporally first time steps and would</span>
-<span class="sd">                like all MCI tests to be performed on the same *number* of</span>
-<span class="sd">                samples. Note, however, that while the *number* of samples is</span>
-<span class="sd">                the same for all MCI tests, the samples themselves may be</span>
-<span class="sd">                different.</span>
-<span class="sd">        verbosity : int, optional (default: 0)</span>
-<span class="sd">            Level of verbosity.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        array, xyz [,XYZ], type_mask : Tuple of data array of shape (dim, n_samples),</span>
-<span class="sd">            xyz identifier array of shape (dim,) identifying which row in array</span>
-<span class="sd">            corresponds to X, Y, and Z, and the type mask that indicates which samples</span>
-<span class="sd">            are continuous or discrete. For example:: X = [(0, -1)],</span>
-<span class="sd">            Y = [(1, 0)], Z = [(1, -1), (0, -2)] yields an array of shape</span>
-<span class="sd">            (4, n_samples) and xyz is xyz = numpy.array([0,1,2,2]). If</span>
-<span class="sd">            return_cleaned_xyz is True, also outputs the cleaned XYZ lists.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># # This version does not yet work with bootstrap</span>
-        <span class="c1"># try:</span>
-        <span class="c1">#     assert self.bootstrap is None</span>
-        <span class="c1"># except AssertionError:</span>
-        <span class="c1">#     print(&quot;This version does not yet work with bootstrap.&quot;)</span>
-        <span class="c1">#     raise</span>
-
-        <span class="k">if</span> <span class="n">extraZ</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">extraZ</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># If vector-valued variables exist, add them</span>
-        <span class="k">def</span> <span class="nf">vectorize</span><span class="p">(</span><span class="n">varlag</span><span class="p">):</span>     
-            <span class="n">vectorized_var</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">varlag</span><span class="p">:</span>
-                <span class="k">for</span> <span class="p">(</span><span class="n">vector_var</span><span class="p">,</span> <span class="n">vector_lag</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span><span class="p">[</span><span class="n">var</span><span class="p">]:</span>
-                    <span class="n">vectorized_var</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">vector_var</span><span class="p">,</span> <span class="n">vector_lag</span> <span class="o">+</span> <span class="n">lag</span><span class="p">))</span>
-            <span class="k">return</span> <span class="n">vectorized_var</span>
-
-        <span class="n">X</span> <span class="o">=</span> <span class="n">vectorize</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> 
-        <span class="n">Y</span> <span class="o">=</span> <span class="n">vectorize</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> 
-        <span class="n">Z</span> <span class="o">=</span> <span class="n">vectorize</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span> 
-        <span class="n">extraZ</span> <span class="o">=</span> <span class="n">vectorize</span><span class="p">(</span><span class="n">extraZ</span><span class="p">)</span> 
-
-
-        <span class="c1"># Remove duplicates in X, Y, Z, extraZ</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">OrderedDict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
-        <span class="n">Y</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">OrderedDict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">OrderedDict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-        <span class="n">extraZ</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">OrderedDict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">extraZ</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="n">remove_overlaps</span><span class="p">:</span>
-            <span class="c1"># If a node in Z occurs already in X or Y, remove it from Z</span>
-            <span class="n">Z</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z</span> <span class="k">if</span> <span class="p">(</span><span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">X</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">)]</span>
-            <span class="n">extraZ</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">extraZ</span> <span class="k">if</span> <span class="p">(</span><span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">X</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">)]</span>
-
-        <span class="n">XYZ</span> <span class="o">=</span> <span class="n">X</span> <span class="o">+</span> <span class="n">Y</span> <span class="o">+</span> <span class="n">Z</span> <span class="o">+</span> <span class="n">extraZ</span>
-        <span class="n">dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)</span>
-
-        <span class="c1"># Check that all lags are non-positive and indices are in [0,N-1]</span>
-        <span class="k">if</span> <span class="n">do_checks</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_check_nodes</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">Ndata</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
-
-        <span class="c1"># Use the mask, override if needed</span>
-        <span class="n">_mask</span> <span class="o">=</span> <span class="n">mask</span>
-        <span class="k">if</span> <span class="n">_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">_mask</span><span class="p">)</span>
-            
-        <span class="n">_type_mask</span> <span class="o">=</span> <span class="n">type_mask</span>
-        <span class="k">if</span> <span class="n">_type_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">_type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">type_mask</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">_type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">_type_mask</span><span class="p">,</span> <span class="n">check_type_mask</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="c1"># Figure out what cut off we will be using</span>
-        <span class="k">if</span> <span class="n">cut_off</span> <span class="o">==</span> <span class="s1">&#39;2xtau_max&#39;</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">tau_max</span>
-        <span class="k">elif</span> <span class="n">cut_off</span> <span class="o">==</span> <span class="s1">&#39;max_lag&#39;</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">())</span>
-        <span class="k">elif</span> <span class="n">cut_off</span> <span class="o">==</span> <span class="s1">&#39;tau_max&#39;</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="n">tau_max</span>
-        <span class="k">elif</span> <span class="n">cut_off</span> <span class="o">==</span> <span class="s1">&#39;max_lag_or_tau_max&#39;</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()),</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cut_off</span> <span class="o">==</span> <span class="s1">&#39;2xtau_max_future&#39;</span><span class="p">:</span>
-            <span class="c1">## TODO: CHECK THIS</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">())</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;max_lag must be in {&#39;2xtau_max&#39;, &#39;tau_max&#39;, &#39;max_lag&#39;, &quot;</span>\
-                <span class="s2">&quot;&#39;max_lag_or_tau_max&#39;, &#39;2xtau_max_future&#39;}&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Setup XYZ identifier</span>
-        <span class="n">index_code</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span> <span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
-                      <span class="s1">&#39;y&#39;</span> <span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
-                      <span class="s1">&#39;z&#39;</span> <span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
-                      <span class="s1">&#39;e&#39;</span> <span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">index_code</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
-                        <span class="k">for</span> <span class="n">var</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">extraZ</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;z&#39;</span><span class="p">,</span> <span class="s1">&#39;e&#39;</span><span class="p">])</span>
-                        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">var</span><span class="p">])</span>
-
-        <span class="c1"># Run through all datasets and fill a dictionary holding the</span>
-        <span class="c1"># samples taken from the individual datasets</span>
-        <span class="n">samples_datasets</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="n">type_masks</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">use_indices_dataset_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-
-        <span class="k">for</span> <span class="n">dataset_key</span><span class="p">,</span> <span class="n">dataset_data</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-
-            <span class="c1"># Apply time offset to the reference points</span>
-            <span class="n">ref_points_here</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">time_offsets</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span>
-
-            <span class="c1"># Remove reference points that are out of bounds or are to be</span>
-            <span class="c1"># excluded given the choice of &#39;cut_off&#39;</span>
-            <span class="n">ref_points_here</span> <span class="o">=</span> <span class="n">ref_points_here</span><span class="p">[</span><span class="n">ref_points_here</span> <span class="o">&gt;=</span> <span class="n">max_lag</span><span class="p">]</span>
-            <span class="n">ref_points_here</span> <span class="o">=</span> <span class="n">ref_points_here</span><span class="p">[</span><span class="n">ref_points_here</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]]</span>
-
-            <span class="c1"># Keep track of which reference points would have remained for</span>
-            <span class="c1"># max_lag == 2*tau_max</span>
-            <span class="k">if</span> <span class="n">cut_off</span> <span class="o">==</span> <span class="s1">&#39;2xtau_max_future&#39;</span><span class="p">:</span>
-                <span class="n">ref_points_here_2_tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">time_offsets</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span>
-                <span class="n">ref_points_here_2_tau_max</span> <span class="o">=</span> <span class="n">ref_points_here_2_tau_max</span><span class="p">[</span><span class="n">ref_points_here_2_tau_max</span>  <span class="o">&gt;=</span> <span class="mi">2</span><span class="o">*</span><span class="n">tau_max</span><span class="p">]</span>
-                <span class="n">ref_points_here_2_tau_max</span> <span class="o">=</span> <span class="n">ref_points_here_2_tau_max</span><span class="p">[</span><span class="n">ref_points_here_2_tau_max</span>  <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]]</span>
-
-            <span class="c1"># Sort the valid reference points (not needed, but might be useful</span>
-            <span class="c1"># for detailed debugging)</span>
-            <span class="n">ref_points_here</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)</span>
-
-            <span class="c1"># For cut_off == &#39;2xtau_max_future&#39; reduce the samples size the</span>
-            <span class="c1"># number of samples that would have been obtained for cut_off ==</span>
-            <span class="c1"># &#39;2xtau_max&#39;, removing the temporally latest ones</span>
-            <span class="k">if</span> <span class="n">cut_off</span> <span class="o">==</span> <span class="s1">&#39;2xtau_max_future&#39;</span><span class="p">:</span>
-                <span class="n">n_to_cut_off</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here_2_tau_max</span><span class="p">)</span>
-                <span class="k">assert</span> <span class="n">n_to_cut_off</span> <span class="o">&gt;=</span> <span class="mi">0</span>
-                <span class="k">if</span> <span class="n">n_to_cut_off</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">ref_points_here</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)</span>
-                    <span class="n">ref_points_here</span> <span class="o">=</span> <span class="n">ref_points_here</span><span class="p">[:</span><span class="o">-</span><span class="n">n_to_cut_off</span><span class="p">]</span>
-
-            <span class="c1"># If no valid reference points are left, continue with the next dataset</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-
-                <span class="n">boot_blocklength</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap</span><span class="p">[</span><span class="s1">&#39;boot_blocklength&#39;</span><span class="p">]</span>
-
-                <span class="k">if</span> <span class="n">boot_blocklength</span> <span class="o">==</span> <span class="s1">&#39;cube_root&#39;</span><span class="p">:</span>
-                    <span class="n">boot_blocklength</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">3</span><span class="p">)))</span>
-                <span class="c1"># elif boot_blocklength == &#39;from_autocorrelation&#39;:</span>
-                <span class="c1">#     boot_blocklength = \</span>
-                <span class="c1">#         get_block_length(overlapping_residuals.T, xyz=np.zeros(N), mode=&#39;confidence&#39;)</span>
-                <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">boot_blocklength</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">int</span> <span class="ow">and</span> <span class="n">boot_blocklength</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="k">pass</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;boot_blocklength must be integer &gt; 0, &#39;cube_root&#39;, or &#39;from_autocorrelation&#39;&quot;</span><span class="p">)</span>
-
-                <span class="c1"># Chooses THE SAME random seed for every dataset, maybe that&#39;s what we want...</span>
-                <span class="c1"># If the reference points are all the same, this will give the same bootstrap</span>
-                <span class="c1"># draw. However, if they are NOT the same, they will differ. </span>
-                <span class="c1"># TODO: Decide whether bootstrap draws should be the same for each dataset and</span>
-                <span class="c1"># how to achieve that if the reference points differ...</span>
-                <span class="c1"># random_state = self.bootstrap[&#39;random_state&#39;]</span>
-                <span class="n">random_state</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bootstrap</span><span class="p">[</span><span class="s1">&#39;random_state&#39;</span><span class="p">])</span>
-
-                <span class="c1"># Determine the number of blocks total, rounding up for non-integer</span>
-                <span class="c1"># amounts</span>
-                <span class="n">n_blks</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">))</span><span class="o">/</span><span class="n">boot_blocklength</span><span class="p">))</span>
-
-                <span class="k">if</span> <span class="n">n_blks</span> <span class="o">&lt;</span> <span class="mi">10</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only </span><span class="si">%d</span><span class="s2"> block(s) for block-sampling,&quot;</span>  <span class="o">%</span><span class="n">n_blks</span> <span class="o">+</span>
-                                     <span class="s2">&quot;choose smaller boot_blocklength!&quot;</span><span class="p">)</span>
-
-                <span class="c1"># Get the starting indices for the blocks</span>
-                <span class="n">blk_strt</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)</span> <span class="o">-</span> <span class="n">boot_blocklength</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">n_blks</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-                <span class="c1"># Get the empty array of block resampled values</span>
-                <span class="n">boot_draw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_blks</span><span class="o">*</span><span class="n">boot_blocklength</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int&#39;</span><span class="p">)</span>
-                <span class="c1"># Fill the array of block resamples</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">boot_blocklength</span><span class="p">):</span>
-                    <span class="n">boot_draw</span><span class="p">[</span><span class="n">i</span><span class="p">::</span><span class="n">boot_blocklength</span><span class="p">]</span> <span class="o">=</span> <span class="n">ref_points_here</span><span class="p">[</span><span class="n">blk_strt</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span>
-                <span class="c1"># Cut to proper length</span>
-                <span class="n">ref_points_here</span> <span class="o">=</span> <span class="n">boot_draw</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)]</span>
-
-            <span class="c1"># Construct the data array holding the samples taken from the</span>
-            <span class="c1"># current dataset</span>
-            <span class="n">samples_datasets</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">dataset_data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">XYZ</span><span class="p">):</span>
-                <span class="n">samples_datasets</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">dataset_data</span><span class="p">[</span><span class="n">ref_points_here</span> <span class="o">+</span> <span class="n">lag</span><span class="p">,</span> <span class="n">var</span><span class="p">]</span>
-
-            <span class="c1"># Build the mask array corresponding to this dataset</span>
-            <span class="k">if</span> <span class="n">_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">mask_dataset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;bool&#39;</span><span class="p">)</span>
-                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">XYZ</span><span class="p">):</span>
-                    <span class="n">mask_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">_mask</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">][</span><span class="n">ref_points_here</span> <span class="o">+</span> <span class="n">lag</span><span class="p">,</span> <span class="n">var</span><span class="p">]</span>
-
-            <span class="c1"># Take care of masking</span>
-            <span class="n">use_indices_dataset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;int&#39;</span><span class="p">)</span>
-
-            <span class="c1"># Build the type mask array corresponding to this dataset</span>
-            <span class="k">if</span> <span class="n">_type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">type_mask_dataset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;bool&#39;</span><span class="p">)</span>
-                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">XYZ</span><span class="p">):</span>
-                    <span class="n">type_mask_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">_type_mask</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">][</span><span class="n">ref_points_here</span> <span class="o">+</span> <span class="n">lag</span><span class="p">,</span> <span class="n">var</span><span class="p">]</span>
-                <span class="n">type_masks</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">type_mask_dataset</span>
-            
-            <span class="c1"># Remove all values that have missing value flag, and optionally as well the time</span>
-            <span class="c1"># slices that occur up to max_lag after</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">missing_anywhere</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">samples_datasets</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))[</span><span class="mi">0</span><span class="p">])</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">remove_missing_upto_maxlag</span><span class="p">:</span>
-                    <span class="n">idx_to_remove</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">idx</span> <span class="o">+</span> <span class="n">tau</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">missing_anywhere</span> <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">idx_to_remove</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">idx</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">missing_anywhere</span><span class="p">)</span>
-                
-                <span class="n">use_indices_dataset</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">idx_to_remove</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int&#39;</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
-            
-            <span class="k">if</span> <span class="n">_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="c1"># Remove samples with mask == 1 conditional on which mask_type</span>
-                <span class="c1"># is used</span>
-
-                <span class="c1"># Iterate over defined mapping from letter index to number index,</span>
-                <span class="c1"># i.e. &#39;x&#39; -&gt; 0, &#39;y&#39; -&gt; 1, &#39;z&#39;-&gt; 2, &#39;e&#39;-&gt; 3</span>
-                <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">cde</span> <span class="ow">in</span> <span class="n">index_code</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                    <span class="c1"># Check if the letter index is in the mask type</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">mask_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">idx</span> <span class="ow">in</span> <span class="n">mask_type</span><span class="p">):</span>
-                        <span class="c1"># If so, check if any of the data that correspond to the</span>
-                        <span class="c1"># letter index is masked by taking the product along the</span>
-                        <span class="c1"># node-data to return a time slice selection, where 0</span>
-                        <span class="c1"># means the time slice will not be used</span>
-                        <span class="n">slice_select</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">mask_dataset</span><span class="p">[</span><span class="n">xyz</span> <span class="o">==</span> <span class="n">cde</span><span class="p">,</span> <span class="p">:]</span> <span class="o">==</span> <span class="kc">False</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-                        <span class="n">use_indices_dataset</span> <span class="o">*=</span> <span class="n">slice_select</span>
-
-            <span class="c1"># Accordingly update the data array</span>
-            <span class="n">samples_datasets</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">samples_datasets</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">][:,</span> <span class="n">use_indices_dataset</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span>
-
-        <span class="c1">## end for dataset_key, dataset_data in self.values.items()</span>
-
-        <span class="c1"># Save used indices as attribute</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">use_indices_dataset_dict</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ref_points_here</span><span class="p">[</span><span class="n">use_indices_dataset</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">use_indices_dataset_dict</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Concatenate the arrays of all datasets</span>
-        <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">samples_datasets</span><span class="o">.</span><span class="n">values</span><span class="p">()),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">_type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">type_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">type_masks</span><span class="o">.</span><span class="n">values</span><span class="p">()),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">type_array</span> <span class="o">=</span> <span class="kc">None</span>
-        
-        <span class="c1"># print(np.where(np.isnan(array)))</span>
-        <span class="c1"># print(array.shape)</span>
-
-        <span class="c1"># Check whether there is any valid sample</span>
-        <span class="k">if</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No valid samples&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Print information about the constructed array</span>
-        <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">print_array_info</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span><span class="p">,</span> <span class="n">mask_type</span><span class="p">,</span> <span class="n">type_array</span><span class="p">,</span> <span class="n">extraZ</span><span class="p">)</span>
-
-        <span class="c1"># Return the array and xyz and optionally (X, Y, Z)</span>
-        <span class="k">if</span> <span class="n">return_cleaned_xyz</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">),</span> <span class="n">type_array</span>
-
-        <span class="k">return</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_array</span></div>
-
-    <span class="k">def</span> <span class="nf">_check_nodes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">dim</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Checks that:</span>
-<span class="sd">        * The requests XYZ nodes have the correct shape</span>
-<span class="sd">        * All lags are non-positive</span>
-<span class="sd">        * All indices are less than N</span>
-<span class="sd">        * One of the Y nodes has zero lag</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        Y : list of tuples</span>
-<span class="sd">            Of the form [(var, -tau)], where var specifies the variable</span>
-<span class="sd">            index and tau the time lag.</span>
-<span class="sd">        XYZ : list of tuples</span>
-<span class="sd">            List of nodes chosen for current independence test</span>
-<span class="sd">        N : int</span>
-<span class="sd">            Total number of listed nodes</span>
-<span class="sd">        dim : int</span>
-<span class="sd">            Number of nodes excluding repeated nodes</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;X, Y, Z must be lists of tuples in format&quot;</span>
-                             <span class="s2">&quot; [(var, -lag),...], eg., [(2, -2), (1, 0), ...]&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nodes are </span><span class="si">%s</span><span class="s2">, &quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)</span> <span class="o">+</span>
-                             <span class="s2">&quot;but all lags must be non-positive&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">N</span><span class="p">)</span>
-                <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;var indices </span><span class="si">%s</span><span class="s2">,&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span>
-                             <span class="s2">&quot; but must be in [0, </span><span class="si">%d</span><span class="s2">]&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="c1"># if np.all(np.array(Y)[:, 1] != 0):</span>
-        <span class="c1">#     raise ValueError(&quot;Y-nodes are %s, &quot; % str(Y) +</span>
-        <span class="c1">#                      &quot;but one of the Y-nodes must have zero lag&quot;)</span>
-
-<div class="viewcode-block" id="DataFrame.print_array_info"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.DataFrame.print_array_info">[docs]</a>    <span class="k">def</span> <span class="nf">print_array_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">missing_flag</span><span class="p">,</span> <span class="n">mask_type</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">extraZ</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Print info about the constructed array</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : Data array of shape (dim, T)</span>
-<span class="sd">            Data array.</span>
-<span class="sd">        X, Y, Z, extraZ : list of tuples</span>
-<span class="sd">            For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],</span>
-<span class="sd">            where var specifies the variable index. X typically is of the form</span>
-<span class="sd">            [(varX, -tau)] with tau denoting the time lag and Z can be</span>
-<span class="sd">            multivariate [(var1, -lag), (var2, -lag), ...] .</span>
-<span class="sd">        missing_flag : number, optional (default: None)</span>
-<span class="sd">            Flag for missing values. Dismisses all time slices of samples where</span>
-<span class="sd">            missing values occur in any variable and also flags samples for all</span>
-<span class="sd">            lags up to 2*tau_max. This avoids biases, see section on masking in</span>
-<span class="sd">            Supplement of [1]_.</span>
-<span class="sd">        mask_type : {&#39;y&#39;,&#39;x&#39;,&#39;z&#39;,&#39;xy&#39;,&#39;xz&#39;,&#39;yz&#39;,&#39;xyz&#39;}</span>
-<span class="sd">            Masking mode: Indicators for which variables in the dependence</span>
-<span class="sd">            measure I(X; Y | Z) the samples should be masked. If None, the mask</span>
-<span class="sd">            is not used. Explained in tutorial on masking and missing values.</span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            Binary data array of same shape as array which describes whether </span>
-<span class="sd">            individual samples in a variable (or all samples) are continuous </span>
-<span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">extraZ</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">extraZ</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">indt</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span> <span class="o">*</span> <span class="mi">12</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">indt</span> <span class="o">+</span> <span class="s2">&quot;Constructed array of shape </span><span class="si">%s</span><span class="s2"> from&quot;</span><span class="o">%</span><span class="nb">str</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span>
-              <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="n">indt</span> <span class="o">+</span> <span class="s2">&quot;X = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">+</span>
-              <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="n">indt</span> <span class="o">+</span> <span class="s2">&quot;Y = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">+</span>
-              <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="n">indt</span> <span class="o">+</span> <span class="s2">&quot;Z = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-        <span class="k">if</span> <span class="n">extraZ</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>  
-            <span class="nb">print</span><span class="p">(</span><span class="n">indt</span> <span class="o">+</span> <span class="s2">&quot;extraZ = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">extraZ</span><span class="p">))</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">mask_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="n">indt</span><span class="o">+</span><span class="s2">&quot;with masked samples in </span><span class="si">%s</span><span class="s2"> removed&quot;</span> <span class="o">%</span> <span class="n">mask_type</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="n">indt</span><span class="o">+</span><span class="s2">&quot;with </span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">iscrete values&quot;</span> <span class="o">%</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span><span class="o">/</span><span class="n">type_mask</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="n">indt</span><span class="o">+</span><span class="s2">&quot;with missing values = </span><span class="si">%s</span><span class="s2"> removed&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="get_acf"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.get_acf">[docs]</a><span class="k">def</span> <span class="nf">get_acf</span><span class="p">(</span><span class="n">series</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns autocorrelation function.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    series : 1D-array</span>
-<span class="sd">        data series to compute autocorrelation from</span>
-
-<span class="sd">    max_lag : int, optional (default: None)</span>
-<span class="sd">        maximum lag for autocorrelation function. If None is passed, 10% of</span>
-<span class="sd">        the data series length are used.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    autocorr : array of shape (max_lag + 1,)</span>
-<span class="sd">        Autocorrelation function.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Set the default max lag</span>
-    <span class="k">if</span> <span class="n">max_lag</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mf">0.1</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">series</span><span class="p">)))</span>
-    <span class="c1"># Initialize the result</span>
-    <span class="n">autocorr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="c1"># Iterate over possible lags</span>
-    <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="c1"># Set the values</span>
-        <span class="n">y1_vals</span> <span class="o">=</span> <span class="n">series</span><span class="p">[</span><span class="n">lag</span><span class="p">:]</span>
-        <span class="n">y2_vals</span> <span class="o">=</span> <span class="n">series</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">series</span><span class="p">)</span> <span class="o">-</span> <span class="n">lag</span><span class="p">]</span>
-        <span class="c1"># Calculate the autocorrelation</span>
-        <span class="n">autocorr</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">y1_vals</span><span class="p">,</span> <span class="n">y2_vals</span><span class="p">,</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">autocorr</span></div>
-
-<div class="viewcode-block" id="get_block_length"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.get_block_length">[docs]</a><span class="k">def</span> <span class="nf">get_block_length</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">mode</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns optimal block length for significance and confidence tests.</span>
-
-<span class="sd">    Determine block length using approach in Mader (2013) [Eq. (6)] which</span>
-<span class="sd">    improves the method of Pfeifer (2005) with non-overlapping blocks In</span>
-<span class="sd">    case of multidimensional X, the max is used. Further details in [1]_.</span>
-<span class="sd">    Two modes are available. For mode=&#39;significance&#39;, only the indices</span>
-<span class="sd">    corresponding to X are shuffled in array. For mode=&#39;confidence&#39; all</span>
-<span class="sd">    variables are jointly shuffled. If the autocorrelation curve fit fails,</span>
-<span class="sd">    a block length of 5% of T is used. The block length is limited to a</span>
-<span class="sd">    maximum of 10% of T.</span>
-
-<span class="sd">    Mader et al., Journal of Neuroscience Methods,</span>
-<span class="sd">    Volume 219, Issue 2, 15 October 2013, Pages 285-291</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    array : array-like</span>
-<span class="sd">        data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">    xyz : array of ints</span>
-<span class="sd">        XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">    mode : str</span>
-<span class="sd">        Which mode to use.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    block_len : int</span>
-<span class="sd">        Optimal block length.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Inject a dependency on siganal, optimize</span>
-    <span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">signal</span><span class="p">,</span> <span class="n">optimize</span>
-    <span class="c1"># Get the shape of the array</span>
-    <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-    <span class="c1"># Initiailize the indices</span>
-    <span class="n">indices</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;significance&#39;</span><span class="p">:</span>
-        <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="c1"># Maximum lag for autocov estimation</span>
-    <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span><span class="o">*</span><span class="n">T</span><span class="p">)</span>
-    <span class="c1"># Define the function to optimize against</span>
-    <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">a_const</span><span class="p">,</span> <span class="n">decay</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">a_const</span> <span class="o">*</span> <span class="n">decay</span><span class="o">**</span><span class="n">x_vals</span>
-
-    <span class="c1"># Calculate the block length</span>
-    <span class="n">block_len</span> <span class="o">=</span> <span class="mi">1</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">:</span>
-        <span class="c1"># Get decay rate of envelope of autocorrelation functions</span>
-        <span class="c1"># via hilbert trafo</span>
-        <span class="n">autocov</span> <span class="o">=</span> <span class="n">get_acf</span><span class="p">(</span><span class="n">series</span><span class="o">=</span><span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">)</span>
-        <span class="n">autocov</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
-        <span class="n">hilbert</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">hilbert</span><span class="p">(</span><span class="n">autocov</span><span class="p">))</span>
-        <span class="c1"># Try to fit the curve</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">popt</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">optimize</span><span class="o">.</span><span class="n">curve_fit</span><span class="p">(</span>
-                <span class="n">f</span><span class="o">=</span><span class="n">func</span><span class="p">,</span>
-                <span class="n">xdata</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span>
-                <span class="n">ydata</span><span class="o">=</span><span class="n">hilbert</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="n">phi</span> <span class="o">=</span> <span class="n">popt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="c1"># Formula of Pfeifer (2005) assuming non-overlapping blocks</span>
-            <span class="n">l_opt</span> <span class="o">=</span> <span class="p">(</span><span class="mf">4.</span> <span class="o">*</span> <span class="n">T</span> <span class="o">*</span> <span class="p">(</span><span class="n">phi</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">)</span> <span class="o">+</span> <span class="n">phi</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
-                     <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">phi</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span>
-            <span class="n">block_len</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">block_len</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">l_opt</span><span class="p">))</span>
-        <span class="k">except</span> <span class="ne">RuntimeError</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Error - curve_fit failed for estimating block_shuffle length, using&quot;</span>
-                  <span class="s2">&quot; block_len = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="mf">.05</span> <span class="o">*</span> <span class="n">T</span><span class="p">)))</span>
-            <span class="c1"># block_len = max(int(.05 * T), block_len)</span>
-    <span class="c1"># Limit block length to a maximum of 10% of T</span>
-    <span class="n">block_len</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">block_len</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span> <span class="o">*</span> <span class="n">T</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">block_len</span></div>
-
-
-<div class="viewcode-block" id="lowhighpass_filter"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.lowhighpass_filter">[docs]</a><span class="k">def</span> <span class="nf">lowhighpass_filter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">cutperiod</span><span class="p">,</span> <span class="n">pass_periods</span><span class="o">=</span><span class="s1">&#39;low&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Butterworth low- or high pass filter.</span>
-
-<span class="sd">    This function applies a linear filter twice, once forward and once</span>
-<span class="sd">    backwards. The combined filter has linear phase.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data : array</span>
-<span class="sd">        Data array of shape (time, variables).</span>
-<span class="sd">    cutperiod : int</span>
-<span class="sd">        Period of cutoff.</span>
-<span class="sd">    pass_periods : str, optional (default: &#39;low&#39;)</span>
-<span class="sd">        Either &#39;low&#39; or &#39;high&#39; to act as a low- or high-pass filter</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    data : array</span>
-<span class="sd">        Filtered data array.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">butter</span><span class="p">,</span> <span class="n">filtfilt</span>
-    <span class="k">except</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Could not import scipy.signal for butterworth filtering!&#39;</span><span class="p">)</span>
-
-    <span class="n">fs</span> <span class="o">=</span> <span class="mf">1.</span>
-    <span class="n">order</span> <span class="o">=</span> <span class="mi">3</span>
-    <span class="n">ws</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">/</span> <span class="n">cutperiod</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.5</span> <span class="o">*</span> <span class="n">fs</span><span class="p">)</span>
-    <span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">butter</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="n">ws</span><span class="p">,</span> <span class="n">pass_periods</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">filtfilt</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-            <span class="n">data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">filtfilt</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
-
-    <span class="k">return</span> <span class="n">data</span></div>
-
-
-<div class="viewcode-block" id="smooth"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.smooth">[docs]</a><span class="k">def</span> <span class="nf">smooth</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">smooth_width</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span>
-           <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">residuals</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns either smoothed time series or its residuals.</span>
-
-<span class="sd">    the difference between the original and the smoothed time series</span>
-<span class="sd">    (=residuals) of a kernel smoothing with gaussian (smoothing kernel width =</span>
-<span class="sd">    twice the sigma!) or heaviside window, equivalent to a running mean.</span>
-
-<span class="sd">    Assumes data of shape (T, N) or (T,)</span>
-<span class="sd">    :rtype: array</span>
-<span class="sd">    :returns: smoothed/residual data</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data : array</span>
-<span class="sd">        Data array of shape (time, variables).</span>
-<span class="sd">    smooth_width : float</span>
-<span class="sd">        Window width of smoothing, 2*sigma for a gaussian.</span>
-<span class="sd">    kernel : str, optional (default: &#39;gaussian&#39;)</span>
-<span class="sd">        Smoothing kernel, &#39;gaussian&#39; or &#39;heaviside&#39; for a running mean.</span>
-<span class="sd">    mask : bool array, optional (default: None)</span>
-<span class="sd">        Data mask where True labels masked samples.</span>
-<span class="sd">    residuals : bool, optional (default: False)</span>
-<span class="sd">        True if residuals should be returned instead of smoothed data.</span>
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    data : array-like</span>
-<span class="sd">        Smoothed/residual data.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> </span><span class="si">%s</span><span class="s2"> smoothing with &quot;</span> <span class="o">%</span> <span class="p">({</span><span class="kc">True</span><span class="p">:</span> <span class="s2">&quot;Take residuals of a &quot;</span><span class="p">,</span>
-                                      <span class="kc">False</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">}[</span><span class="n">residuals</span><span class="p">],</span> <span class="n">kernel</span><span class="p">)</span> <span class="o">+</span>
-          <span class="s2">&quot;window width </span><span class="si">%.2f</span><span class="s2"> (=2*sigma for a gaussian!)&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">smooth_width</span><span class="p">))</span>
-
-    <span class="n">totaltime</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-        <span class="n">window</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">totaltime</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">totaltime</span><span class="p">))</span> <span class="o">-</span>
-                             <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">totaltime</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">totaltime</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-                             <span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">((</span><span class="mf">2.</span> <span class="o">*</span> <span class="n">smooth_width</span> <span class="o">/</span> <span class="mf">2.</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="k">elif</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;heaviside&#39;</span><span class="p">:</span>
-        <span class="kn">import</span> <span class="nn">scipy.linalg</span>
-        <span class="n">wtmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">totaltime</span><span class="p">)</span>
-        <span class="n">wtmp</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">smooth_width</span> <span class="o">/</span> <span class="mf">2.</span><span class="p">))]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">window</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">toeplitz</span><span class="p">(</span><span class="n">wtmp</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">smoothed_data</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span> <span class="o">*</span> <span class="n">window</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">window</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">smoothed_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="n">smoothed_data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="n">data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">window</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">window</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">smoothed_data</span> <span class="o">=</span> <span class="p">((</span><span class="n">data</span> <span class="o">*</span> <span class="n">window</span> <span class="o">*</span> <span class="p">(</span><span class="n">mask</span><span class="o">==</span><span class="kc">False</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span>
-                             <span class="p">(</span><span class="n">window</span> <span class="o">*</span> <span class="p">(</span><span class="n">mask</span><span class="o">==</span><span class="kc">False</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">smoothed_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="n">smoothed_data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">((</span>
-                    <span class="n">data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">window</span> <span class="o">*</span> <span class="p">(</span><span class="n">mask</span><span class="o">==</span><span class="kc">False</span><span class="p">)[:,</span> <span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span>
-                    <span class="p">(</span><span class="n">window</span> <span class="o">*</span> <span class="p">(</span><span class="n">mask</span><span class="o">==</span><span class="kc">False</span><span class="p">)[:,</span> <span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-
-    <span class="k">if</span> <span class="n">residuals</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">data</span> <span class="o">-</span> <span class="n">smoothed_data</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">smoothed_data</span></div>
-
-
-<div class="viewcode-block" id="weighted_avg_and_std"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.weighted_avg_and_std">[docs]</a><span class="k">def</span> <span class="nf">weighted_avg_and_std</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">weights</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the weighted average and standard deviation.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    values : array</span>
-<span class="sd">        Data array of shape (time, variables).</span>
-<span class="sd">    axis : int</span>
-<span class="sd">        Axis to average/std about</span>
-<span class="sd">    weights : array</span>
-<span class="sd">        Weight array of shape (time, variables).</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    (average, std) : tuple of arrays</span>
-<span class="sd">        Tuple of weighted average and standard deviation along axis.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">values</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">values</span><span class="p">)]</span> <span class="o">=</span> <span class="mf">0.</span>
-    <span class="n">average</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span>
-
-    <span class="n">variance</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">weights</span> <span class="o">*</span> <span class="p">(</span><span class="n">values</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">average</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span>
-                                    <span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span> <span class="o">/</span> <span class="n">weights</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="p">(</span><span class="n">average</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">variance</span><span class="p">))</span></div>
-
-
-<div class="viewcode-block" id="time_bin_with_mask"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.time_bin_with_mask">[docs]</a><span class="k">def</span> <span class="nf">time_bin_with_mask</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time_bin_length</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns time binned data where only about non-masked values is averaged.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data : array</span>
-<span class="sd">        Data array of shape (time, variables).</span>
-<span class="sd">    time_bin_length : int</span>
-<span class="sd">        Length of time bin.</span>
-<span class="sd">    mask : bool array, optional (default: None)</span>
-<span class="sd">        Data mask where True labels masked samples.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    (bindata, T) : tuple of array and int</span>
-<span class="sd">        Tuple of time-binned data array and new length of array.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">T</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-    <span class="n">time_bin_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">time_bin_length</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">sample_selector</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="c1"># Invert mask</span>
-        <span class="n">sample_selector</span> <span class="o">=</span> <span class="p">(</span><span class="n">mask</span> <span class="o">==</span> <span class="kc">False</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="mf">1.</span><span class="p">:</span>
-        <span class="n">data</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">mask</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">sample_selector</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-
-    <span class="n">bindata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
-        <span class="p">(</span><span class="n">T</span> <span class="o">//</span> <span class="n">time_bin_length</span><span class="p">,)</span> <span class="o">+</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T</span> <span class="o">-</span> <span class="n">time_bin_length</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
-                                    <span class="n">time_bin_length</span><span class="p">)):</span>
-        <span class="c1"># print weighted_avg_and_std(fulldata[i:i+time_bin_length], axis=0,</span>
-        <span class="c1"># weights=sample_selector[i:i+time_bin_length])[0]</span>
-        <span class="n">bindata</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">weighted_avg_and_std</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span> <span class="o">+</span> <span class="n">time_bin_length</span><span class="p">],</span>
-                                              <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                                              <span class="n">weights</span><span class="o">=</span><span class="n">sample_selector</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span> <span class="o">+</span>
-                                              <span class="n">time_bin_length</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="n">T</span><span class="p">,</span> <span class="n">grid_size</span> <span class="o">=</span> <span class="n">bindata</span><span class="o">.</span><span class="n">shape</span>
-
-    <span class="k">return</span> <span class="p">(</span><span class="n">bindata</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">T</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="trafo2normal"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.trafo2normal">[docs]</a><span class="k">def</span> <span class="nf">trafo2normal</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">thres</span><span class="o">=</span><span class="mf">0.001</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Transforms input data to standard normal marginals.</span>
-
-<span class="sd">    Assumes data.shape = (T, dim)</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data : array</span>
-<span class="sd">        Data array of shape (time, variables).</span>
-<span class="sd">    thres : float</span>
-<span class="sd">        Set outer points in CDF to this value.</span>
-<span class="sd">    mask : bool array, optional (default: None)</span>
-<span class="sd">        Data mask where True labels masked samples.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    normal_data : array-like</span>
-<span class="sd">        data with standard normal marginals.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">trafo</span><span class="p">(</span><span class="n">xi</span><span class="p">):</span>
-        <span class="n">xisorted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
-        <span class="n">yi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">xi</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">xi</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">xi</span><span class="p">,</span> <span class="n">xisorted</span><span class="p">,</span> <span class="n">yi</span><span class="p">)</span>
-
-    <span class="n">normal_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">nonmasked</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">nonmasked</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">mask</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="kc">False</span><span class="p">))</span>
-
-        <span class="n">u</span> <span class="o">=</span> <span class="n">trafo</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">nonmasked</span><span class="p">])</span>
-        <span class="n">u</span><span class="p">[</span><span class="n">u</span><span class="o">==</span><span class="mf">0.</span><span class="p">]</span> <span class="o">=</span> <span class="n">thres</span>
-        <span class="n">u</span><span class="p">[</span><span class="n">u</span><span class="o">==</span><span class="mf">1.</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="n">thres</span>
-        <span class="n">normal_data</span><span class="p">[</span><span class="n">nonmasked</span><span class="p">]</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">u</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-            <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">nonmasked</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">nonmasked</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">mask</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="kc">False</span><span class="p">))</span>
-                <span class="c1"># nonmasked = np.where(mask[:, i]==0)</span>
-            <span class="c1"># print(data[:, i].shape, nonmasked.shape)</span>
-            <span class="n">uniform</span> <span class="o">=</span> <span class="n">trafo</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">][</span><span class="n">nonmasked</span><span class="p">])</span>
-            
-            <span class="c1"># print(data[-3:, i][nonmasked])</span>
-
-            <span class="n">uniform</span><span class="p">[</span><span class="n">uniform</span><span class="o">==</span><span class="mf">0.</span><span class="p">]</span> <span class="o">=</span> <span class="n">thres</span>
-            <span class="n">uniform</span><span class="p">[</span><span class="n">uniform</span><span class="o">==</span><span class="mf">1.</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="n">thres</span>
-            <span class="n">normal_data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">][</span><span class="n">nonmasked</span><span class="p">]</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">uniform</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">normal_data</span></div>
-
-<span class="nd">@jit</span>
-<span class="k">def</span> <span class="nf">_get_patterns</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">array_mask</span><span class="p">,</span> <span class="n">patt</span><span class="p">,</span> <span class="n">patt_mask</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="n">fac</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span>
-    <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float&#39;</span><span class="p">)</span>
-
-    <span class="n">start</span> <span class="o">=</span> <span class="n">step</span> <span class="o">*</span> <span class="p">(</span><span class="n">dim</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">N</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="mi">1</span>
-            <span class="n">ave</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim</span><span class="p">):</span>
-                <span class="n">tau</span> <span class="o">=</span> <span class="n">k</span> <span class="o">*</span> <span class="n">step</span>
-                <span class="n">v</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span>
-                <span class="n">ave</span> <span class="o">+=</span> <span class="n">v</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
-                <span class="n">mask</span> <span class="o">*=</span> <span class="n">array_mask</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span>
-            <span class="n">ave</span> <span class="o">/=</span> <span class="n">dim</span>
-            <span class="n">var</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim</span><span class="p">):</span>
-                <span class="n">var</span> <span class="o">+=</span> <span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">-</span> <span class="n">ave</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
-            <span class="n">var</span> <span class="o">/=</span> <span class="n">dim</span>
-            <span class="n">weights</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">start</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="n">p</span> <span class="o">=</span> <span class="mi">1</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">p</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="p">):</span>
-                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">v</span><span class="p">[</span><span class="n">i</span><span class="p">]):</span>
-                        <span class="n">p</span> <span class="o">+=</span> <span class="n">fac</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">patt</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">start</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">p</span>
-            <span class="n">patt_mask</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">start</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">mask</span>
-
-    <span class="k">return</span> <span class="n">patt</span><span class="p">,</span> <span class="n">patt_mask</span><span class="p">,</span> <span class="n">weights</span>
-
-<div class="viewcode-block" id="ordinal_patt_array"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.ordinal_patt_array">[docs]</a><span class="k">def</span> <span class="nf">ordinal_patt_array</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">array_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                        <span class="n">weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns symbolified array of ordinal patterns.</span>
-
-<span class="sd">    Each data vector (X_t, ..., X_t+(dim-1)*step) is converted to its rank</span>
-<span class="sd">    vector. E.g., (0.2, -.6, 1.2) --&gt; (1,0,2) which is then assigned to a</span>
-<span class="sd">    unique integer (see Article). There are faculty(dim) possible rank vectors.</span>
-
-<span class="sd">    Note that the symb_array is step*(dim-1) shorter than the original array!</span>
-
-<span class="sd">    Reference: B. Pompe and J. Runge (2011). Momentary information transfer as</span>
-<span class="sd">    a coupling measure of time series. Phys. Rev. E, 83(5), 1-12.</span>
-<span class="sd">    doi:10.1103/PhysRevE.83.051122</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    array : array-like</span>
-<span class="sd">        Data array of shape (time, variables).</span>
-<span class="sd">    array_mask : bool array</span>
-<span class="sd">        Data mask where True labels masked samples.</span>
-<span class="sd">    dim : int, optional (default: 2)</span>
-<span class="sd">        Pattern dimension</span>
-<span class="sd">    step : int, optional (default: 1)</span>
-<span class="sd">        Delay of pattern embedding vector.</span>
-<span class="sd">    weights : bool, optional (default: False)</span>
-<span class="sd">        Whether to return array of variances of embedding vectors as weights.</span>
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    patt, patt_mask [, patt_time] : tuple of arrays</span>
-<span class="sd">        Tuple of converted pattern array and new length</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">from</span> <span class="nn">scipy.misc</span> <span class="kn">import</span> <span class="n">factorial</span>
-
-    <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float64&#39;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">array_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">assert</span> <span class="n">array_mask</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="s1">&#39;int32&#39;</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">array_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
-
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">T</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">array_mask</span> <span class="o">=</span> <span class="n">array_mask</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-    <span class="c1"># Add noise to destroy ties...</span>
-    <span class="n">array</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1E-6</span> <span class="o">*</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-              <span class="o">*</span> <span class="n">random_state</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float64&#39;</span><span class="p">))</span>
-
-    <span class="n">patt_time</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">step</span> <span class="o">*</span> <span class="p">(</span><span class="n">dim</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="n">T</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-    <span class="k">if</span> <span class="n">dim</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">patt_time</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Dim mist be &gt; 1 and length of delay vector smaller &quot;</span>
-                         <span class="s2">&quot;array length.&quot;</span><span class="p">)</span>
-
-    <span class="n">patt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">patt_time</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
-    <span class="n">weights_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">patt_time</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float64&#39;</span><span class="p">)</span>
-
-    <span class="n">patt_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">patt_time</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
-
-    <span class="c1"># Precompute factorial for c-code... patterns of dimension</span>
-    <span class="c1"># larger than 10 are not supported</span>
-    <span class="n">fac</span> <span class="o">=</span> <span class="n">factorial</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
-
-    <span class="c1"># _get_patterns assumes mask=0 to be a masked value</span>
-    <span class="n">array_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">array_mask</span> <span class="o">==</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
-
-    <span class="p">(</span><span class="n">patt</span><span class="p">,</span> <span class="n">patt_mask</span><span class="p">,</span> <span class="n">weights_array</span><span class="p">)</span> <span class="o">=</span> <span class="n">_get_patterns</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">array_mask</span><span class="p">,</span> <span class="n">patt</span><span class="p">,</span> <span class="n">patt_mask</span><span class="p">,</span> <span class="n">weights_array</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="n">fac</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span>
-
-    <span class="n">weights_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">weights_array</span><span class="p">)</span>
-    <span class="n">patt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">patt</span><span class="p">)</span>
-    <span class="c1"># Transform back to mask=1 implying a masked value</span>
-    <span class="n">patt_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">patt_mask</span><span class="p">)</span> <span class="o">==</span> <span class="kc">False</span>
-
-    <span class="k">if</span> <span class="n">weights</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">patt</span><span class="p">,</span> <span class="n">patt_mask</span><span class="p">,</span> <span class="n">patt_time</span><span class="p">,</span> <span class="n">weights_array</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">patt</span><span class="p">,</span> <span class="n">patt_mask</span><span class="p">,</span> <span class="n">patt_time</span></div>
-
-
-<div class="viewcode-block" id="quantile_bin_array"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.quantile_bin_array">[docs]</a><span class="k">def</span> <span class="nf">quantile_bin_array</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">6</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns symbolified array with equal-quantile binning.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data : array</span>
-<span class="sd">        Data array of shape (time, variables).</span>
-<span class="sd">    bins : int, optional (default: 6)</span>
-<span class="sd">        Number of bins.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    symb_array : array</span>
-<span class="sd">        Converted data of integer type.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">T</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
-
-    <span class="c1"># get the bin quantile steps</span>
-    <span class="n">bin_edge</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">T</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">bins</span><span class="p">)))</span>
-
-    <span class="n">symb_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
-
-    <span class="c1"># get the lower edges of the bins for every time series</span>
-    <span class="n">edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[::</span><span class="n">bin_edge</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span>
-    <span class="n">bins</span> <span class="o">=</span> <span class="n">edges</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="c1"># This gives the symbolic time series</span>
-    <span class="n">symb_array</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">edges</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">bins</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
-        <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
-
-    <span class="k">return</span> <span class="n">symb_array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="var_process"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.var_process">[docs]</a><span class="k">def</span> <span class="nf">var_process</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">use</span><span class="o">=</span><span class="s1">&#39;inv_inno_cov&#39;</span><span class="p">,</span>
-                <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">initial_values</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a vector-autoregressive process with correlated innovations.</span>
-
-<span class="sd">    Wrapper around var_network with possibly more user-friendly input options.</span>
-
-<span class="sd">    DEPRECATED. Will be removed in future.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;data generating models are now in toymodels folder: &quot;</span>
-         <span class="s2">&quot;from tigramite.toymodels import structural_causal_processes as toys.&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="kc">None</span></div>
-
-<div class="viewcode-block" id="structural_causal_process"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.structural_causal_process">[docs]</a><span class="k">def</span> <span class="nf">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                        <span class="n">intervention</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">intervention_type</span><span class="o">=</span><span class="s1">&#39;hard&#39;</span><span class="p">,</span>
-                        <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a structural causal process with contemporaneous and lagged</span>
-<span class="sd">    dependencies.</span>
-
-<span class="sd">    DEPRECATED. Will be removed in future.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;data generating models are now in toymodels folder: &quot;</span>
-         <span class="s2">&quot;from tigramite.toymodels import structural_causal_processes as toys.&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="kc">None</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="kn">from</span> <span class="nn">tigramite.toymodels.structural_causal_processes</span> <span class="kn">import</span> <span class="n">structural_causal_process</span>
-    <span class="c1">## Generate some time series from a structural causal process</span>
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-    <span class="k">def</span> <span class="nf">nonlin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mf">5.</span> <span class="o">*</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="mf">20.</span><span class="p">))</span>
-
-    <span class="n">links</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">1</span><span class="p">:</span> <span class="p">[((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">nonlin_f</span><span class="p">)],</span>
-             <span class="mi">2</span><span class="p">:</span> <span class="p">[((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.7</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="p">}</span>
-
-    <span class="n">random_state_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">random_state_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">random_state_3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-
-    <span class="n">noises</span> <span class="o">=</span> <span class="p">[</span><span class="n">random_state_1</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">,</span> <span class="n">random_state_2</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">,</span> <span class="n">random_state_3</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">]</span>
-
-    <span class="n">ens</span> <span class="o">=</span> <span class="mi">3</span>
-    <span class="n">data_ens</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ens</span><span class="p">):</span>
-        <span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span>
-                <span class="n">T</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="n">noises</span><span class="p">)</span>
-        <span class="n">data</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mf">999.</span>
-        <span class="n">data_ens</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span>
-    <span class="c1"># print(data.shape)</span>
-
-    <span class="n">frame</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">data_ens</span><span class="p">,</span> <span class="n">missing_flag</span><span class="o">=</span><span class="mf">999.</span><span class="p">,</span>
-        <span class="n">analysis_mode</span> <span class="o">=</span> <span class="s1">&#39;multiple&#39;</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="n">frame</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-
-    <span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-    <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-    <span class="n">Z</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">)]</span>
-    <span class="n">tau_max</span><span class="o">=</span><span class="mi">5</span>
-    <span class="n">frame</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span>
-                        <span class="n">extraZ</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                        <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                        <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span>
-                        <span class="n">verbosity</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-</pre></div>
-
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="../../index.html">Tigramite</a></h1>
-
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-
-<h3>Navigation</h3>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../index.html">Documentation overview</a><ul>
-  <li><a href="../index.html">Module code</a><ul>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../search.html" method="get">
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diff --git a/docs/_build/html/_modules/tigramite/independence_tests/cmiknn.html b/docs/_build/html/_modules/tigramite/independence_tests/cmiknn.html
deleted file mode 100644
index 68c29513..00000000
--- a/docs/_build/html/_modules/tigramite/independence_tests/cmiknn.html
+++ /dev/null
@@ -1,580 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.independence_tests.cmiknn &#8212; Tigramite 5.2 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
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-        <div class="bodywrapper">
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-          <div class="body" role="main">
-            
-  <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">special</span><span class="p">,</span> <span class="n">spatial</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-<span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<div class="viewcode-block" id="CMIknn"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknn.CMIknn">[docs]</a><span class="k">class</span> <span class="nc">CMIknn</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Conditional mutual information test based on nearest-neighbor estimator.</span>
-
-<span class="sd">    Conditional mutual information is the most general dependency measure coming</span>
-<span class="sd">    from an information-theoretic framework. It makes no assumptions about the</span>
-<span class="sd">    parametric form of the dependencies by directly estimating the underlying</span>
-<span class="sd">    joint density. The test here is based on the estimator in  S. Frenzel and B.</span>
-<span class="sd">    Pompe, Phys. Rev. Lett. 99, 204101 (2007), combined with a shuffle test to</span>
-<span class="sd">    generate  the distribution under the null hypothesis of independence first</span>
-<span class="sd">    used in [3]_. The knn-estimator is suitable only for variables taking a</span>
-<span class="sd">    continuous range of values. For discrete variables use the CMIsymb class.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    CMI is given by</span>
-
-<span class="sd">    .. math:: I(X;Y|Z) &amp;= \int p(z)  \iint  p(x,y|z) \log</span>
-<span class="sd">                \frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)} \,dx dy dz</span>
-
-<span class="sd">    Its knn-estimator is given by</span>
-
-<span class="sd">    .. math:: \widehat{I}(X;Y|Z)  &amp;=   \psi (k) + \frac{1}{T} \sum_{t=1}^T</span>
-<span class="sd">            \left[ \psi(k_{Z,t}) - \psi(k_{XZ,t}) - \psi(k_{YZ,t}) \right]</span>
-
-<span class="sd">    where :math:`\psi` is the Digamma function.  This estimator has as a</span>
-<span class="sd">    parameter the number of nearest-neighbors :math:`k` which determines the</span>
-<span class="sd">    size of hyper-cubes around each (high-dimensional) sample point. Then</span>
-<span class="sd">    :math:`k_{Z,},k_{XZ},k_{YZ}` are the numbers of neighbors in the respective</span>
-<span class="sd">    subspaces.</span>
-
-<span class="sd">    :math:`k` can be viewed as a density smoothing parameter (although it is</span>
-<span class="sd">    data-adaptive unlike fixed-bandwidth estimators). For large :math:`k`, the</span>
-<span class="sd">    underlying dependencies are more smoothed and CMI has a larger bias,</span>
-<span class="sd">    but lower variance, which is more important for significance testing. Note</span>
-<span class="sd">    that the estimated CMI values can be slightly negative while CMI is a non-</span>
-<span class="sd">    negative quantity.</span>
-
-<span class="sd">    This method requires the scipy.spatial.cKDTree package.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    .. [3] J. Runge (2018): Conditional Independence Testing Based on a</span>
-<span class="sd">           Nearest-Neighbor Estimator of Conditional Mutual Information.</span>
-<span class="sd">           In Proceedings of the 21st International Conference on Artificial</span>
-<span class="sd">           Intelligence and Statistics.</span>
-<span class="sd">           http://proceedings.mlr.press/v84/runge18a.html</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    knn : int or float, optional (default: 0.2)</span>
-<span class="sd">        Number of nearest-neighbors which determines the size of hyper-cubes</span>
-<span class="sd">        around each (high-dimensional) sample point. If smaller than 1, this is</span>
-<span class="sd">        computed as a fraction of T, hence knn=knn*T. For knn larger or equal to</span>
-<span class="sd">        1, this is the absolute number.</span>
-
-<span class="sd">    shuffle_neighbors : int, optional (default: 5)</span>
-<span class="sd">        Number of nearest-neighbors within Z for the shuffle surrogates which</span>
-<span class="sd">        determines the size of hyper-cubes around each (high-dimensional) sample</span>
-<span class="sd">        point.</span>
-
-<span class="sd">    transform : {&#39;ranks&#39;, &#39;standardize&#39;,  &#39;uniform&#39;, False}, optional</span>
-<span class="sd">        (default: &#39;ranks&#39;)</span>
-<span class="sd">        Whether to transform the array beforehand by standardizing</span>
-<span class="sd">        or transforming to uniform marginals.</span>
-
-<span class="sd">    workers : int (optional, default = -1)</span>
-<span class="sd">        Number of workers to use for parallel processing. If -1 is given</span>
-<span class="sd">        all processors are used. Default: -1.</span>
-
-<span class="sd">    significance : str, optional (default: &#39;shuffle_test&#39;)</span>
-<span class="sd">        Type of significance test to use. For CMIknn only &#39;fixed_thres&#39; and</span>
-<span class="sd">        &#39;shuffle_test&#39; are available.</span>
-
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to parent class CondIndTest.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">knn</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-                 <span class="n">shuffle_neighbors</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-                 <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;ranks&#39;</span><span class="p">,</span>
-                 <span class="n">workers</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># Set the member variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">=</span> <span class="n">knn</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span> <span class="o">=</span> <span class="n">shuffle_neighbors</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;cmi_knn&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">workers</span> <span class="o">=</span> <span class="n">workers</span>
-        <span class="c1"># Call the parent constructor</span>
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">significance</span><span class="o">=</span><span class="n">significance</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="c1"># Print some information about construction</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;knn/T = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;knn = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;shuffle_neighbors = </span><span class="si">%d</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">)</span>
-
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">_get_nearest_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">knn</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns nearest neighbors according to Frenzel and Pompe (2007).</span>
-
-<span class="sd">        Retrieves the distances eps to the k-th nearest neighbors for every</span>
-<span class="sd">        sample in joint space XYZ and returns the numbers of nearest neighbors</span>
-<span class="sd">        within eps in subspaces Z, XZ, YZ.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        knn : int or float</span>
-<span class="sd">            Number of nearest-neighbors which determines the size of hyper-cubes</span>
-<span class="sd">            around each (high-dimensional) sample point. If smaller than 1, this</span>
-<span class="sd">            is computed as a fraction of T, hence knn=knn*T. For knn larger or</span>
-<span class="sd">            equal to 1, this is the absolute number.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        k_xz, k_yz, k_z : tuple of arrays of shape (T,)</span>
-<span class="sd">            Nearest neighbors in subspaces.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">xyz</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># Add noise to destroy ties...</span>
-        <span class="n">array</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1E-6</span> <span class="o">*</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-                  <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;standardize&#39;</span><span class="p">:</span>
-            <span class="c1"># Standardize</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-            <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-            <span class="c1"># FIXME: If the time series is constant, return nan rather than</span>
-            <span class="c1"># raising Exception</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-                <span class="c1"># raise ValueError(&quot;nans after standardizing, &quot;</span>
-                <span class="c1">#                  &quot;possibly constant array!&quot;)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trafo2uniform</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;ranks&#39;</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">tree_xyz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="n">knn</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                  <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">)[</span><span class="mi">0</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-
-        <span class="c1"># To search neighbors &lt; eps</span>
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">epsarray</span><span class="p">,</span> <span class="mf">0.99999</span><span class="p">)</span>
-
-        <span class="c1"># Subsample indices</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1"># Find nearest neighbors in subspaces</span>
-        <span class="n">xz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">))]</span>
-        <span class="n">tree_xz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">xz</span><span class="p">)</span>
-        <span class="n">k_xz</span> <span class="o">=</span> <span class="n">tree_xz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">xz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="n">yz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">y_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">))]</span>
-        <span class="n">tree_yz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">yz</span><span class="p">)</span>
-        <span class="n">k_yz</span> <span class="o">=</span> <span class="n">tree_yz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">yz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">]</span>
-            <span class="n">tree_z</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">tree_z</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Number of neighbors is T when z is empty.</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span>
-
-<div class="viewcode-block" id="CMIknn.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknn.CMIknn.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns CMI estimate as described in Frenzel and Pompe PRL (2007).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">knn_here</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">knn_here</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">))</span>
-
-
-        <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_nearest_neighbors</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span>
-                                                      <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                      <span class="n">knn</span><span class="o">=</span><span class="n">knn_here</span><span class="p">)</span>
-
-        <span class="n">val</span> <span class="o">=</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">knn_here</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_xz</span><span class="p">)</span> <span class="o">+</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_yz</span><span class="p">)</span> <span class="o">-</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_z</span><span class="p">))</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-        <span class="k">return</span> <span class="n">val</span></div>
-
-
-<div class="viewcode-block" id="CMIknn.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknn.CMIknn.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for nearest-neighbor shuffle significance test.</span>
-
-<span class="sd">        For non-empty Z, overwrites get_shuffle_significance from the parent</span>
-<span class="sd">        class  which is a block shuffle test, which does not preserve</span>
-<span class="sd">        dependencies of X and Y with Z. Here the parameter shuffle_neighbors is</span>
-<span class="sd">        used to permute only those values :math:`x_i` and :math:`x_j` for which</span>
-<span class="sd">        :math:`z_j` is among the nearest niehgbors of :math:`z_i`. If Z is</span>
-<span class="sd">        empty, the block-shuffle test is used.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># Skip shuffle test if value is above threshold</span>
-        <span class="c1"># if value &gt; self.minimum threshold:</span>
-        <span class="c1">#     if return_null_dist:</span>
-        <span class="c1">#         return 0., None</span>
-        <span class="c1">#     else:</span>
-        <span class="c1">#         return 0.</span>
-
-        <span class="c1"># max_neighbors = max(1, int(max_neighbor_ratio*T))</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span> <span class="o">&lt;</span> <span class="n">T</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;            nearest-neighbor shuffle significance &quot;</span>
-                      <span class="s2">&quot;test with n = </span><span class="si">%d</span><span class="s2"> and </span><span class="si">%d</span><span class="s2"> surrogates&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                      <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">))</span>
-
-            <span class="c1"># Get nearest neighbors around each sample point in Z</span>
-            <span class="n">z_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">z_indices</span><span class="p">,</span> <span class="p">:])</span>
-            <span class="n">tree_xyz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">z_array</span><span class="p">)</span>
-            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">z_array</span><span class="p">,</span>
-                                       <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">,</span>
-                                       <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                       <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-            <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">sam</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">):</span>
-
-                <span class="c1"># Generate random order in which to go through indices loop in</span>
-                <span class="c1"># next step</span>
-                <span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-                <span class="c1"># Shuffle neighbor indices for each sample index</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)):</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-                <span class="c1"># neighbors = self.random_state.permuted(neighbors, axis=1)</span>
-                
-                <span class="c1"># Select a series of neighbor indices that contains as few as</span>
-                <span class="c1"># possible duplicates</span>
-                <span class="n">restricted_permutation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_restricted_permutation</span><span class="p">(</span>
-                        <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span>
-                        <span class="n">shuffle_neighbors</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">,</span>
-                        <span class="n">neighbors</span><span class="o">=</span><span class="n">neighbors</span><span class="p">,</span>
-                        <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
-
-                <span class="n">array_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x_indices</span><span class="p">:</span>
-                    <span class="n">array_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">restricted_permutation</span><span class="p">]</span>
-
-                <span class="n">null_dist</span><span class="p">[</span><span class="n">sam</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array_shuffled</span><span class="p">,</span>
-                                                             <span class="n">xyz</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">null_dist</span> <span class="o">=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_get_shuffle_dist</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                           <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">,</span>
-                                           <span class="n">sig_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                           <span class="n">sig_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                                           <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="p">(</span><span class="n">null_dist</span> <span class="o">&gt;=</span> <span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="n">return_null_dist</span><span class="p">:</span>
-            <span class="c1"># Sort</span>
-            <span class="n">null_dist</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-            <span class="k">return</span> <span class="n">pval</span><span class="p">,</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-
-<div class="viewcode-block" id="CMIknn.get_conditional_entropy"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknn.CMIknn.get_conditional_entropy">[docs]</a>    <span class="k">def</span> <span class="nf">get_conditional_entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the nearest-neighbor conditional entropy estimate of H(X|Y).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ---------- </span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,). Here only uses 0 for X and </span>
-<span class="sd">            1 for Y.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Entropy estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">knn_here</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">knn_here</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">))</span>
-
-
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-
-        <span class="c1"># Add noise to destroy ties...</span>
-        <span class="n">array</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1E-6</span> <span class="o">*</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-                  <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;standardize&#39;</span><span class="p">:</span>
-            <span class="c1"># Standardize</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-            <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-            <span class="c1"># FIXME: If the time series is constant, return nan rather than</span>
-            <span class="c1"># raising Exception</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="c1"># if np.isnan(array).sum() != 0:</span>
-            <span class="c1">#     raise ValueError(&quot;nans after standardizing, &quot;</span>
-            <span class="c1">#                      &quot;possibly constant array!&quot;)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trafo2uniform</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;ranks&#39;</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-
-        <span class="c1"># Compute conditional entropy as H(X|Y) = H(X) - I(X;Y)</span>
-
-        <span class="c1"># First compute H(X)</span>
-        <span class="c1"># Use cKDTree to get distances eps to the k-th nearest neighbors for</span>
-        <span class="c1"># every sample in joint space X with maximum norm</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">dim_x</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="mi">1</span> <span class="ow">in</span> <span class="n">xyz</span><span class="p">:</span>
-            <span class="n">dim_y</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">dim_x</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">dim_y</span> <span class="o">=</span> <span class="mi">0</span>
-
-
-        <span class="n">x_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">x_indices</span><span class="p">,</span> <span class="p">:])</span>
-        <span class="n">tree_xyz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">x_array</span><span class="p">)</span>
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">x_array</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="n">knn_here</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                  <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">)[</span><span class="mi">0</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-
-        <span class="n">h_x</span> <span class="o">=</span> <span class="o">-</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">knn_here</span><span class="p">)</span> <span class="o">+</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> <span class="o">+</span> <span class="n">dim_x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">epsarray</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-        <span class="c1"># Then compute MI(X;Y)</span>
-        <span class="k">if</span> <span class="n">dim_y</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">xyz_here</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">index</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">xyz</span> <span class="k">if</span> <span class="n">index</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">index</span> <span class="o">==</span> <span class="mi">1</span><span class="p">])</span>
-            <span class="n">array_xy</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">x_indices</span><span class="p">)</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">y_indices</span><span class="p">),</span> <span class="p">:]</span>
-            <span class="n">i_xy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array_xy</span><span class="p">,</span> <span class="n">xyz_here</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">i_xy</span> <span class="o">=</span> <span class="mf">0.</span>
-
-        <span class="n">h_x_y</span> <span class="o">=</span> <span class="n">h_x</span> <span class="o">-</span> <span class="n">i_xy</span>
-
-        <span class="k">return</span> <span class="n">h_x_y</span></div>
-
-
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">get_restricted_permutation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">shuffle_neighbors</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
-
-        <span class="n">restricted_permutation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-        <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">sample_index</span> <span class="ow">in</span> <span class="n">order</span><span class="p">:</span>
-            <span class="n">m</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">use</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="n">sample_index</span><span class="p">,</span> <span class="n">m</span><span class="p">]</span>
-
-            <span class="k">while</span> <span class="p">((</span><span class="n">use</span> <span class="ow">in</span> <span class="n">used</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">m</span> <span class="o">&lt;</span> <span class="n">shuffle_neighbors</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)):</span>
-                <span class="n">m</span> <span class="o">+=</span> <span class="mi">1</span>
-                <span class="n">use</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="n">sample_index</span><span class="p">,</span> <span class="n">m</span><span class="p">]</span>
-
-            <span class="n">restricted_permutation</span><span class="p">[</span><span class="n">sample_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">use</span>
-            <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">used</span><span class="p">,</span> <span class="n">use</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">restricted_permutation</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-    <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-    <span class="n">cmi</span> <span class="o">=</span> <span class="n">CMIknn</span><span class="p">(</span><span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                   <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
-                   <span class="n">fixed_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                   <span class="n">sig_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-                   <span class="n">sig_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                   <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">,</span>
-                   <span class="n">knn</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                   <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    <span class="n">dimz</span> <span class="o">=</span> <span class="mi">1</span>
-
-    <span class="c1"># Continuous data</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.8</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.8</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;X _|_ Y&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;X _|_ Y | Z&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">))</span>
-</pre></div>
-
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diff --git a/docs/_build/html/_modules/tigramite/independence_tests/cmiknnmixed.html b/docs/_build/html/_modules/tigramite/independence_tests/cmiknnmixed.html
deleted file mode 100644
index ab67cc88..00000000
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+++ /dev/null
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-<!DOCTYPE html>
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-  <h1>Source code for tigramite.independence_tests.cmiknnmixed</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Oana Popescu, Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">special</span><span class="p">,</span> <span class="n">spatial</span>
-<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">BallTree</span><span class="p">,</span> <span class="n">NearestNeighbors</span>
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">OneHotEncoder</span><span class="p">,</span> <span class="n">MinMaxScaler</span>
-<span class="kn">from</span> <span class="nn">sklearn.utils.extmath</span> <span class="kn">import</span> <span class="n">cartesian</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-<span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="c1"># profiling</span>
-<span class="kn">import</span> <span class="nn">cProfile</span><span class="o">,</span> <span class="nn">pstats</span><span class="o">,</span> <span class="nn">io</span>
-<span class="kn">from</span> <span class="nn">pstats</span> <span class="kn">import</span> <span class="n">SortKey</span>
-
-
-<div class="viewcode-block" id="CMIknnMixed"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed">[docs]</a><span class="k">class</span> <span class="nc">CMIknnMixed</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Conditional mutual information test based on nearest-neighbor estimator.</span>
-
-<span class="sd">    Conditional mutual information is the most general dependency measure coming</span>
-<span class="sd">    from an information-theoretic framework. It makes no assumptions about the</span>
-<span class="sd">    parametric form of the dependencies by directly estimating the underlying</span>
-<span class="sd">    joint density. The test here is based on the estimator in  S. Frenzel and B.</span>
-<span class="sd">    Pompe, Phys. Rev. Lett. 99, 204101 (2007), combined with a shuffle test to</span>
-<span class="sd">    generate  the distribution under the null hypothesis of independence first</span>
-<span class="sd">    used in the reference below. The knn-estimator is suitable only for variables taking a</span>
-<span class="sd">    continuous range of values. For discrete variables use the CMIsymb class.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    CMI is given by</span>
-
-<span class="sd">    .. math:: I(X;Y|Z) &amp;= \int p(z)  \iint  p(x,y|z) \log</span>
-<span class="sd">                \frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)} \,dx dy dz</span>
-
-<span class="sd">    Its knn-estimator is given by</span>
-
-<span class="sd">    .. math:: \widehat{I}(X;Y|Z)  &amp;=   \psi (k) + \frac{1}{T} \sum_{t=1}^T</span>
-<span class="sd">            \left[ \psi(k_{Z,t}) - \psi(k_{XZ,t}) - \psi(k_{YZ,t}) \right]</span>
-
-<span class="sd">    where :math:`\psi` is the Digamma function.  This estimator has as a</span>
-<span class="sd">    parameter the number of nearest-neighbors :math:`k` which determines the</span>
-<span class="sd">    size of hyper-cubes around each (high-dimensional) sample point. Then</span>
-<span class="sd">    :math:`k_{Z,},k_{XZ},k_{YZ}` are the numbers of neighbors in the respective</span>
-<span class="sd">    subspaces.</span>
-
-<span class="sd">    :math:`k` can be viewed as a density smoothing parameter (although it is</span>
-<span class="sd">    data-adaptive unlike fixed-bandwidth estimators). For large :math:`k`, the</span>
-<span class="sd">    underlying dependencies are more smoothed and CMI has a larger bias,</span>
-<span class="sd">    but lower variance, which is more important for significance testing. Note</span>
-<span class="sd">    that the estimated CMI values can be slightly negative while CMI is a non-</span>
-<span class="sd">    negative quantity.</span>
-<span class="sd">    </span>
-<span class="sd">    For the case of mixed variables, the distance metric changes from the L-inf </span>
-<span class="sd">    norm to ...</span>
-
-<span class="sd">    This method requires the scikit-learn package.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    J. Runge (2018): Conditional Independence Testing Based on a</span>
-<span class="sd">           Nearest-Neighbor Estimator of Conditional Mutual Information.</span>
-<span class="sd">           In Proceedings of the 21st International Conference on Artificial</span>
-<span class="sd">           Intelligence and Statistics.</span>
-<span class="sd">           http://proceedings.mlr.press/v84/runge18a.html</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    knn : int or float, optional (default: 0.2)</span>
-<span class="sd">        Number of nearest-neighbors which determines the size of hyper-cubes</span>
-<span class="sd">        around each (high-dimensional) sample point. If smaller than 1, this is</span>
-<span class="sd">        computed as a fraction of T, hence knn=knn*T. For knn larger or equal to</span>
-<span class="sd">        1, this is the absolute number.</span>
-<span class="sd">        </span>
-<span class="sd">    estimator : string, optional (default: &#39;MS&#39;)</span>
-<span class="sd">        The type of estimator to be used. Three options are available:</span>
-<span class="sd">        Mesner and Shalizi (2021): &#39;MS&#39;, Frenzel and Pompe (2007) with </span>
-<span class="sd">        infinite distance for points from different categories: &#39;FPinf&#39;,</span>
-<span class="sd">        and Zao et.al. (2022) where entropies are computed conditional on </span>
-<span class="sd">        the discrete dimensions of X,Y and Z. </span>
-
-<span class="sd">    shuffle_neighbors : int, optional (default: 5)</span>
-<span class="sd">        Number of nearest-neighbors within Z for the shuffle surrogates which</span>
-<span class="sd">        determines the size of hyper-cubes around each (high-dimensional) sample</span>
-<span class="sd">        point.</span>
-
-<span class="sd">    transform : {&#39;ranks&#39;, &#39;standardize&#39;,  &#39;uniform&#39;, False}, optional</span>
-<span class="sd">        (default: &#39;ranks&#39;)</span>
-<span class="sd">        Whether to transform the array beforehand by standardizing</span>
-<span class="sd">        or transforming to uniform marginals.</span>
-
-<span class="sd">    workers : int (optional, default = -1)</span>
-<span class="sd">        Number of workers to use for parallel processing. If -1 is given</span>
-<span class="sd">        all processors are used. Default: -1.</span>
-<span class="sd">        </span>
-<span class="sd">    rho: list of float, optional (default: [np.inf])</span>
-<span class="sd">        Hyperparameters used for weighting the discrete variable distances. </span>
-<span class="sd">        If not initialized, the distance will be set to np.inf, such that discrete</span>
-<span class="sd">        variables with different values will never be considered neighbors. </span>
-<span class="sd">        Otherwise the rho</span>
-<span class="sd">        ...</span>
-
-<span class="sd">    significance : str, optional (default: &#39;shuffle_test&#39;)</span>
-<span class="sd">        Type of significance test to use. For CMIknn only &#39;fixed_thres&#39; and</span>
-<span class="sd">        &#39;shuffle_test&#39; are available.</span>
-
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to parent class CondIndTest.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">knn</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                 <span class="n">estimator</span><span class="o">=</span><span class="s1">&#39;MS&#39;</span><span class="p">,</span>
-                 <span class="n">use_local_knn</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">shuffle_neighbors</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-                 <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;standardize&#39;</span><span class="p">,</span>
-                 <span class="n">scale_range</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                 <span class="n">perc</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">workers</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># Set the member variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">=</span> <span class="n">knn</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">=</span> <span class="n">estimator</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span> <span class="o">=</span> <span class="n">use_local_knn</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span> <span class="o">=</span> <span class="n">shuffle_neighbors</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
-        <span class="k">if</span> <span class="n">perc</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">perc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">perc</span> <span class="o">=</span> <span class="n">perc</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">scale_range</span> <span class="o">=</span> <span class="n">scale_range</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;cmi_knn_mixed&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">workers</span> <span class="o">=</span> <span class="n">workers</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="mf">1e-5</span>
-            
-        <span class="c1"># Call the parent constructor</span>
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">significance</span><span class="o">=</span><span class="n">significance</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="c1"># Print some information about construction</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;knn/T = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;knn = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;shuffle_neighbors = </span><span class="si">%d</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">)</span>
-            
-    <span class="k">def</span> <span class="nf">_standardize_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">dim</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Standardizes a given array with dimensions dim.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        dim: int</span>
-<span class="sd">            number of dimensions of the data.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            The standardized array.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-        <span class="c1"># FIXME: If the time series is constant, return nan rather than</span>
-        <span class="c1"># raising Exception</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="c1"># raise ValueError(&quot;nans after standardizing, &quot;</span>
-            <span class="c1">#                  &quot;possibly constant array!&quot;)</span>
-        <span class="k">return</span> <span class="n">array</span>
-    
-    <span class="k">def</span> <span class="nf">_scale_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">minmax</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)):</span>
-        <span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">(</span><span class="n">minmax</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-            
-    <span class="k">def</span> <span class="nf">_transform_mixed_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">add_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Applies data transformations to the continuous dimensions of the given data.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        add_noise : bool (default False)</span>
-<span class="sd">            Defines whether to add small normal noise to the continuous data.</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            The array with the continuous data transformed. </span>
-<span class="sd">            </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">continuous_idxs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>  
-        <span class="n">cont_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">continuous_idxs</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">add_noise</span><span class="p">:</span>
-            <span class="c1"># Add noise to destroy ties</span>
-            <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1E-6</span> <span class="o">*</span> <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">cont_dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-                  <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;standardize&#39;</span><span class="p">:</span>
-            <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_standardize_array</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">],</span> <span class="n">cont_dim</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;scale&#39;</span><span class="p">:</span>
-            <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scale_array</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">],</span> <span class="n">minmax</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">scale_range</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Unknown transform&#39;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">array</span>
-         
-    
-    <span class="k">def</span> <span class="nf">_transform_to_one_hot_mixed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                                    <span class="n">type_mask</span><span class="p">,</span>
-                                    <span class="n">zero_inf</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        
-        <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="n">mixed_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-        <span class="n">narray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">nxyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">xyz</span><span class="p">)</span>
-        <span class="n">ntype_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span>
-
-        <span class="n">appended_columns</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">)):</span>
-            <span class="c1"># print(i)</span>
-            <span class="k">if</span> <span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">encoder</span> <span class="o">=</span> <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
-                <span class="n">i</span> <span class="o">+=</span> <span class="n">appended_columns</span>
-                <span class="n">data</span> <span class="o">=</span> <span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span>
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">encoder_df</span> <span class="o">=</span> <span class="n">encoder</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
-                <span class="k">if</span> <span class="n">zero_inf</span><span class="p">:</span>
-                    <span class="n">encoder_df</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">encoder_df</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">9999999</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="p">[</span><span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">*</span> <span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-                <span class="n">narray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">narray</span><span class="p">[:,</span> <span class="p">:</span><span class="n">i</span><span class="p">],</span> <span class="n">encoder_df</span><span class="p">,</span> <span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-                <span class="n">nxyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">nxyz</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span> <span class="n">xyz_val</span><span class="p">,</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]])</span>
-                <span class="n">ntype_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">ntype_mask</span><span class="p">[:,</span> <span class="p">:</span><span class="n">i</span><span class="p">],</span>
-                                             <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> 
-                                             <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]],</span> 
-                                            <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-                <span class="n">appended_columns</span> <span class="o">+=</span> <span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
-
-            <span class="k">elif</span> <span class="n">mixed_idx_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">i</span> <span class="o">+=</span> <span class="n">appended_columns</span>
-                <span class="n">data</span> <span class="o">=</span> <span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span>
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-
-                <span class="c1"># print(i, narray[:, i], ntype_mask[:, i])</span>
-                <span class="c1"># find categories </span>
-                <span class="n">categories</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
-                <span class="n">cont_vars</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]))</span>
-
-                <span class="n">encoder</span> <span class="o">=</span> <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="n">categories</span><span class="p">],</span> <span class="n">handle_unknown</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">encoder_df</span> <span class="o">=</span> <span class="n">encoder</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
-                <span class="k">if</span> <span class="n">zero_inf</span><span class="p">:</span>
-                    <span class="n">encoder_df</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">encoder_df</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">9999999</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">cont_vars</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
-
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="p">[</span><span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">*</span> <span class="p">(</span><span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-                <span class="n">cont_column</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-                <span class="n">narray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">narray</span><span class="p">[:,</span> <span class="p">:</span><span class="n">i</span><span class="p">],</span> <span class="n">cont_column</span><span class="p">,</span> <span class="n">encoder_df</span><span class="p">,</span> <span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-                <span class="n">nxyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">nxyz</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span> <span class="n">xyz_val</span><span class="p">,</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]])</span>
-                <span class="n">ntype_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">ntype_mask</span><span class="p">[:,</span> <span class="p">:</span><span class="n">i</span><span class="p">],</span> 
-                                             <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">cont_column</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> 
-                                             <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> 
-                                             <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]],</span> 
-                                            <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-                <span class="n">appended_columns</span> <span class="o">+=</span> <span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="n">ndiscrete_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">ntype_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">narray</span><span class="p">,</span> <span class="n">nxyz</span><span class="p">,</span> <span class="n">ntype_mask</span><span class="p">,</span> <span class="n">ndiscrete_idx_list</span>         
-
-        
-
-<div class="viewcode-block" id="CMIknnMixed.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test.</span>
-
-<span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
-<span class="sd">        classes must specify a function get_dependence_measure and either or</span>
-<span class="sd">        both functions get_analytic_significance and  get_shuffle_significance.</span>
-<span class="sd">        If recycle_residuals is True, also _get_single_residuals must be</span>
-<span class="sd">        available.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : list of tuples</span>
-<span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
-<span class="sd">            variable index and tau the time lag.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;}</span>
-<span class="sd">            How many samples to cutoff at the beginning. The default is</span>
-<span class="sd">            &#39;2xtau_max&#39;, which guarantees that MCI tests are all conducted on</span>
-<span class="sd">            the same samples. For modeling, &#39;max_lag_or_tau_max&#39; can be used,</span>
-<span class="sd">            which uses the maximum of tau_max and the conditions, which is</span>
-<span class="sd">            useful to compare multiple models on the same sample.  Last,</span>
-<span class="sd">            &#39;max_lag&#39; uses as much samples as possible.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-<span class="sd">            The test statistic value and the p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Get the array to test on</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">cut_off</span><span class="p">)</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">XYZ</span>
-
-        <span class="c1"># Record the dimensions</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Ensure it is a valid array</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
-
-        <span class="n">combined_hash</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array_hash</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">combined_hash</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-            <span class="n">cached</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">cached</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="c1"># Get the dependence measure, reycling residuals if need be</span>
-            <span class="n">val</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                              <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span> 
-            <span class="c1"># Get the p-value</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span>
-                                        <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-            
-            <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="n">cached</span><span class="p">,</span>
-                                         <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-        <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="CMIknnMixed.run_test_raw"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.run_test_raw">[docs]</a>    <span class="k">def</span> <span class="nf">run_test_raw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">y_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">val_only</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test directly on input arrays x, y, z.</span>
-
-<span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
-<span class="sd">        classes must specify a function get_dependence_measure and either or</span>
-<span class="sd">        both functions get_analytic_significance and  get_shuffle_significance.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x, y, z : arrays</span>
-<span class="sd">            x,y,z are of the form (samples, dimension).</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as [x,y,z] which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-
-<span class="sd">            The test statistic value and the p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;x,y must be arrays of shape (samples, dimension)&quot;</span>
-                             <span class="s2">&quot; where dimension can be 1.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;z must be array of shape (samples, dimension)&quot;</span>
-                             <span class="s2">&quot; where dimension can be 1.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">x_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">y_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;x_type and y_type must be set.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># Get the array to test on</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-            <span class="n">type_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-
-            <span class="c1"># xyz is the dimension indicator</span>
-            <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
-                           <span class="p">[</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Get the array to test on</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">z</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-            <span class="n">type_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">z_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-
-            <span class="c1"># xyz is the dimension indicator</span>
-            <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
-                           <span class="p">[</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
-                           <span class="p">[</span><span class="mi">2</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">z</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
-
-        <span class="c1"># Record the dimensions</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Ensure it is a valid array</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
-        <span class="c1"># Get the dependence measure</span>
-        <span class="n">val</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">val_only</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">val</span>
-        <span class="c1"># Get the p-value</span>
-        <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
-    
-<div class="viewcode-block" id="CMIknnMixed.get_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span>
-                         <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                         <span class="n">sig_override</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the p-value from whichever significance function is specified</span>
-<span class="sd">        for this test.  If an override is used, then it will call a different</span>
-<span class="sd">        function then specified by self.significance</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        T : int</span>
-<span class="sd">            Sample length</span>
-
-<span class="sd">        dim : int</span>
-<span class="sd">            Dimensionality, ie, number of features.</span>
-
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        sig_override : string</span>
-<span class="sd">            Must be in &#39;analytic&#39;, &#39;shuffle_test&#39;, &#39;fixed_thres&#39;</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float or numpy.nan</span>
-<span class="sd">            P-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Defaults to the self.significance member value</span>
-        <span class="n">use_sig</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span>
-        <span class="k">if</span> <span class="n">sig_override</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">use_sig</span> <span class="o">=</span> <span class="n">sig_override</span>
-        <span class="c1"># Check if we are using the analytic significance</span>
-        <span class="k">if</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;analytic&#39;</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Analytic significance not defined for CMIknnMixed!&quot;</span><span class="p">)</span>
-        <span class="c1"># Check if we are using the shuffle significance</span>
-        <span class="k">elif</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;shuffle_test&#39;</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_shuffle_significance</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span>
-                                                 <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                 <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">,</span>
-                                                 <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="c1"># Check if we are using the fixed_thres significance</span>
-        <span class="k">elif</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_fixed_thres_significance</span><span class="p">(</span>
-                    <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">,</span>
-                    <span class="n">fixed_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> not known.&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span><span class="p">)</span>
-        <span class="c1"># Return the calculated value</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-    
-    
-    <span class="k">def</span> <span class="nf">_compute_discrete_entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">disc_values</span><span class="p">,</span> <span class="n">discrete_idxs</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">):</span>
-        <span class="n">current_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">discrete_idxs</span><span class="p">]</span> <span class="o">==</span> <span class="n">disc_values</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">discrete_idxs</span><span class="p">)]</span>
-
-        <span class="n">count</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">current_array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="n">count</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mf">0.</span>
-
-        <span class="n">prob</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">count</span><span class="p">)</span> <span class="o">/</span> <span class="n">num_samples</span>
-        <span class="c1"># print(prob)</span>
-        <span class="n">disc_entropy</span> <span class="o">=</span> <span class="n">prob</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span>
-        <span class="c1"># print(&#39;d&#39;, disc_entropy)</span>
-        <span class="k">return</span> <span class="n">disc_entropy</span>
-    
-    
-    <span class="k">def</span> <span class="nf">compute_discrete_entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">disc_values</span><span class="p">,</span> <span class="n">discrete_idxs</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">):</span>
-        <span class="n">current_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">discrete_idxs</span><span class="p">]</span> <span class="o">==</span> <span class="n">disc_values</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">discrete_idxs</span><span class="p">)]</span>
-
-        <span class="n">count</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">current_array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="n">count</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mf">0.</span>
-
-        <span class="n">prob</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">count</span><span class="p">)</span> <span class="o">/</span> <span class="n">num_samples</span>
-        <span class="n">disc_entropy</span> <span class="o">=</span> <span class="n">prob</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">disc_entropy</span>
-    
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">_get_nearest_neighbors_zeroinf_onehot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span>
-                                   <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns nearest neighbors according to Frenzel and Pompe (2007).</span>
-
-<span class="sd">        Retrieves the distances eps to the k-th nearest neighbors for every</span>
-<span class="sd">        sample in joint space XYZ and returns the numbers of nearest neighbors</span>
-<span class="sd">        within eps in subspaces Z, XZ, YZ. Accepts points as neighbors only </span>
-<span class="sd">        if the points are not at infinite distance. </span>
-<span class="sd">        Two points have infinite distance when the values for the discrete </span>
-<span class="sd">        dimensions of the points do not match.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        knn : int or float</span>
-<span class="sd">            Number of nearest-neighbors which determines the size of hyper-cubes</span>
-<span class="sd">            around each (high-dimensional) sample point. If smaller than 1, this</span>
-<span class="sd">            is computed as a fraction of T, hence knn=knn*T. For knn larger or</span>
-<span class="sd">            equal to 1, this is the absolute number.</span>
-<span class="sd">        </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        k_xz, k_yz, k_z : tuple of arrays of shape (T,)</span>
-<span class="sd">            Nearest neighbors in subspaces.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">xyz</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_mixed_data</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">)</span>
-            
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">type_mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">T</span>
-        
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">,</span> <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_to_one_hot_mixed</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> 
-                                                                                    <span class="n">xyz</span><span class="p">,</span> 
-                                                                                    <span class="n">type_mask</span><span class="p">,</span>
-                                                                                    <span class="n">zero_inf</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            
-        <span class="c1"># Subsample indices</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">xz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-        <span class="n">yz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-     
-        <span class="c1"># Fit trees</span>
-        <span class="n">tree_xyz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">knn</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                   <span class="n">distance_upper_bound</span><span class="o">=</span><span class="mi">9999999</span><span class="p">)</span>
-        
-        <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
-        
-        
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">knn</span><span class="p">]</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">:</span>
-                <span class="n">replacement_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-                <span class="n">r</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">replacement_idx</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">perc</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-                <span class="n">epsarray</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">r</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">epsarray</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">knn</span><span class="p">]</span>
-                
-        
-        <span class="n">neighbors_radius_xyz</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>
-        
-        <span class="n">k_tilde</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">))]</span>
-            
-        <span class="c1"># compute entropies</span>
-        <span class="n">xz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">]</span>
-        <span class="n">tree_xz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">xz</span><span class="p">)</span>
-        <span class="n">k_xz</span> <span class="o">=</span> <span class="n">tree_xz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">xz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        
-        <span class="n">yz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">]</span>
-        <span class="n">tree_yz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">yz</span><span class="p">)</span>
-        <span class="n">k_yz</span> <span class="o">=</span> <span class="n">tree_yz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">yz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">]</span>
-            <span class="n">tree_z</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">tree_z</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Number of neighbors is T when z is empty.</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float&#39;</span><span class="p">)</span>
-            
-        <span class="n">k_xz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_xz</span><span class="p">])</span>
-        <span class="n">k_yz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_yz</span><span class="p">])</span>
-        <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_z</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">k_tilde</span><span class="p">,</span> <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span>
-    
-<div class="viewcode-block" id="CMIknnMixed.get_dependence_measure_zeroinf"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_dependence_measure_zeroinf">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure_zeroinf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                                   <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns CMI estimate according to Frenzel and Pompe with an</span>
-<span class="sd">        altered distance metric: the 0-inf metric, which attributes </span>
-<span class="sd">        infinite distance to points where the values for the discrete dimensions</span>
-<span class="sd">        do not coincide.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-<span class="sd">        </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># compute knn</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-        
-
-        <span class="n">knn_tilde</span><span class="p">,</span> <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_nearest_neighbors_zeroinf_onehot</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span>
-                                                                                 <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                                                 <span class="n">knn</span><span class="o">=</span><span class="n">knn</span><span class="p">,</span>
-                                                                                 <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="n">non_zero</span> <span class="o">=</span> <span class="n">knn_tilde</span> <span class="o">-</span> <span class="n">k_xz</span> <span class="o">-</span> <span class="n">k_yz</span> <span class="o">+</span> <span class="n">k_z</span>
-        
-        <span class="n">non_zero_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">non_zero</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">non_zero</span><span class="p">)</span>
-        
-        <span class="n">val</span> <span class="o">=</span> <span class="p">(</span><span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">knn_tilde</span><span class="p">)</span> <span class="o">-</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_xz</span><span class="p">)</span> <span class="o">-</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_yz</span><span class="p">)</span> <span class="o">+</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_z</span><span class="p">))</span>
-        
-        <span class="n">val</span> <span class="o">=</span> <span class="n">val</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">val</span><span class="p">)]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> 
-
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">non_zero_count</span></div>
-    
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">_get_nearest_neighbors_MS_one_hot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                                          <span class="n">knn</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns nearest neighbors according to Messner and Shalizi (2021).</span>
-
-<span class="sd">        Retrieves the distances eps to the k-th nearest neighbors for every</span>
-<span class="sd">        sample in joint space XYZ and returns the numbers of nearest neighbors</span>
-<span class="sd">        within eps in subspaces Z, XZ, YZ. Uses a custom-defined metric for </span>
-<span class="sd">        discrete variables.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        knn : int or float</span>
-<span class="sd">            Number of nearest-neighbors which determines the size of hyper-cubes</span>
-<span class="sd">            around each (high-dimensional) sample point. If smaller than 1, this</span>
-<span class="sd">            is computed as a fraction of T, hence knn=knn*T. For knn larger or</span>
-<span class="sd">            equal to 1, this is the absolute number.</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        k_tilde, k_xz, k_yz, k_z : tuple of arrays of shape (T,)</span>
-<span class="sd">            Nearest neighbors in XYZ, XZ, YZ, and Z subspaces.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">xyz</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_mixed_data</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">)</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">type_mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">T</span>
-        
-        <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">,</span> <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_to_one_hot_mixed</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> 
-                                                                                    <span class="n">xyz</span><span class="p">,</span> 
-                                                                                    <span class="n">type_mask</span><span class="p">)</span>
-        
-        <span class="c1"># Subsample indices</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">xz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-        <span class="n">yz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-            
-        <span class="c1"># Fit trees</span>
-        <span class="n">tree_xyz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">knn</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">)</span>
-        
-        
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        
-        <span class="n">neighbors_radius_xyz</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> 
-                                                         <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">)</span>
-        
-        <span class="c1"># search again for neighbors in the radius to find all of them</span>
-        <span class="c1"># in the discrete case k_tilde can be larger than the given knn</span>
-        <span class="n">k_tilde</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">))])</span>
-            
-        <span class="c1"># compute entropies</span>
-        <span class="n">xz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">]</span>
-        <span class="n">tree_xz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">xz</span><span class="p">)</span>
-        <span class="n">k_xz</span> <span class="o">=</span> <span class="n">tree_xz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">xz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                        <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        
-        <span class="n">yz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">]</span>
-        <span class="n">tree_yz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">yz</span><span class="p">)</span>
-        <span class="n">k_yz</span> <span class="o">=</span> <span class="n">tree_yz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">yz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> 
-                                        <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">]</span>
-            <span class="n">tree_z</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">tree_z</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                          <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Number of neighbors is T when z is empty.</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float&#39;</span><span class="p">)</span>
-        
-        <span class="n">k_xz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_xz</span><span class="p">])</span>
-        <span class="n">k_yz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_yz</span><span class="p">])</span>
-        <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_z</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">k_tilde</span><span class="p">,</span> <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span>
-    
-
-<div class="viewcode-block" id="CMIknnMixed.get_dependence_measure_MS"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_dependence_measure_MS">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure_MS</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                  <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns CMI estimate as described in Messner and Shalizi (2021).</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-<span class="sd">        </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># compute knn</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-        
-        
-        <span class="n">knn_tilde</span><span class="p">,</span> <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_nearest_neighbors_MS_one_hot</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span>
-                                                                            <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                                            <span class="n">knn</span><span class="o">=</span><span class="n">knn</span><span class="p">,</span>
-                                                                            <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        
-        <span class="n">non_zero</span> <span class="o">=</span> <span class="n">knn_tilde</span> <span class="o">-</span> <span class="n">k_xz</span> <span class="o">-</span> <span class="n">k_yz</span> <span class="o">+</span> <span class="n">k_z</span>
-        
-        <span class="n">non_zero_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">non_zero</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">non_zero</span><span class="p">)</span>
-        
-        <span class="n">val</span> <span class="o">=</span> <span class="p">(</span><span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">knn_tilde</span><span class="p">)</span> <span class="o">-</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_xz</span><span class="p">)</span> <span class="o">-</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_yz</span><span class="p">)</span> <span class="o">+</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_z</span><span class="p">))</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">val</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">val</span><span class="p">)]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> 
-
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">non_zero_count</span></div>
-
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">_compute_continuous_entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">knn</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns entropy estimate as described by Kozachenko and Leonenko (1987).</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        knn : int</span>
-<span class="sd">            number of nearest-neighbors to use.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">T</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="k">if</span> <span class="n">T</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mf">0.</span>
-
-        <span class="k">if</span> <span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">rint</span><span class="p">(</span><span class="n">knn</span> <span class="o">*</span> <span class="n">T</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">tree</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="n">knn</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> 
-                              <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span>
-                              <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">)[</span><span class="mi">0</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">epsarray</span><span class="p">[</span><span class="n">epsarray</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
-        <span class="n">num_non_zero</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">epsarray</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">num_non_zero</span> <span class="o">==</span>  <span class="mi">0</span><span class="p">:</span> 
-            <span class="n">cmi_hat</span> <span class="o">=</span> <span class="mf">0.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">avg_dist</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">num_non_zero</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">epsarray</span><span class="p">))</span>
-            <span class="n">cmi_hat</span> <span class="o">=</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">num_non_zero</span><span class="p">)</span> <span class="o">-</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">knn</span><span class="p">)</span> <span class="o">+</span> <span class="n">avg_dist</span>
-
-        <span class="k">return</span> <span class="n">cmi_hat</span>
-    
-    <span class="k">def</span> <span class="nf">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> 
-                                              <span class="n">discrete_values</span><span class="p">,</span> 
-                                              <span class="n">discrete_idxs</span><span class="p">,</span> 
-                                              <span class="n">continuous_idxs</span><span class="p">,</span> 
-                                              <span class="n">total_num_samples</span><span class="p">,</span> 
-                                              <span class="n">knn</span><span class="p">,</span> 
-                                              <span class="n">use_local_knn</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns entropy estimates for a given array as described in ... add citation.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        discrete_values : tuple of dimension (len(discrete_idxs))</span>
-<span class="sd">            values of discrete variables for which the entropy is computed</span>
-<span class="sd">        </span>
-<span class="sd">        discrete_idxs : array of ints</span>
-<span class="sd">            indices of the dimensions with discrete data</span>
-<span class="sd">        </span>
-<span class="sd">        continuous_idxs : array of ints</span>
-<span class="sd">            indices of the dimensions with continuous data</span>
-<span class="sd">        </span>
-<span class="sd">        total_num_samples : int</span>
-<span class="sd">            total number of samples</span>
-<span class="sd">        </span>
-<span class="sd">        knn : int or float</span>
-<span class="sd">            if int, number of nearest-neighbors to use</span>
-<span class="sd">            if float, percentage of the number of samples </span>
-<span class="sd">            </span>
-<span class="sd">        use_local_knn : bool (default False)</span>
-<span class="sd">            if True, the knn is computed as a percentage of the number of samples</span>
-<span class="sd">            for one realization of the discrete values in each subspace, </span>
-<span class="sd">            otherwise the same knn is used for all subspaces.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val_continuous entropy, val_discrete_entropy : float, float</span>
-<span class="sd">            Tuple consisting of estimate for the entropy term for the continuous variables,</span>
-<span class="sd">            and the estimate for the entropy term for the discrete variables.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="c1"># select data for which the discrete values are the given ones</span>
-        <span class="n">current_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">discrete_idxs</span><span class="p">]</span> <span class="o">==</span> <span class="n">discrete_values</span><span class="p">,</span> 
-                                     <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">discrete_idxs</span><span class="p">)]</span>
-        <span class="c1"># if we do not have samples, we cannot estimate CMI</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">current_array</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span>
-
-        <span class="n">T</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">current_array</span><span class="o">.</span><span class="n">shape</span>
-        
-        <span class="c1"># if we have more samples than knns and samples are not purely discrete, we can</span>
-        <span class="c1"># compute CMI</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">continuous_idxs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">T</span> <span class="o">&gt;</span> <span class="n">knn</span><span class="p">:</span>
-            <span class="n">val_continuous_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">current_array</span><span class="p">[:,</span> <span class="n">continuous_idxs</span><span class="p">],</span> <span class="n">knn</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">val_continuous_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-        <span class="n">prob</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">total_num_samples</span>
-        
-        <span class="c1"># multiply by probabilities of occurence</span>
-        <span class="n">val_continuous_entropy</span> <span class="o">*=</span> <span class="n">prob</span>
-        <span class="c1"># compute entropy for that occurence</span>
-        <span class="n">val_discrete_entropy</span> <span class="o">=</span> <span class="n">prob</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">val_continuous_entropy</span><span class="p">,</span> <span class="n">val_discrete_entropy</span>
-
-<div class="viewcode-block" id="CMIknnMixed.get_dependence_measure_conditional"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_dependence_measure_conditional">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure_conditional</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                           <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns CMI estimate as described in ....</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># compute knn</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span>
-            
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">xyz</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-        
-        <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_mixed_data</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">)</span>
-        
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">type_mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">T</span>
-        
-        <span class="c1">#TODO</span>
-        
-        <span class="c1"># continue working with discrete idx list</span>
-        <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Variables are continuous, cannot use CMIknnMixed conditional!&quot;</span><span class="p">)</span>
-            
-<span class="c1">#         if np.sum(discrete_idx_list) != np.sum(any_discrete_idx_list):</span>
-<span class="c1">#             raise ValueError(&quot;Variables contain mixtures, cannot use CMIknnMixed conditional!&quot;)</span>
-            
-        <span class="c1"># Subsample indices</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">xz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-        <span class="n">yz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-        
-        <span class="n">discrete_xz_indices</span> <span class="o">=</span> <span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">xz_indices</span><span class="p">]</span>
-        <span class="n">discrete_yz_indices</span> <span class="o">=</span> <span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">yz_indices</span><span class="p">]</span>
-        <span class="n">discrete_z_indices</span> <span class="o">=</span> <span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">z_indices</span><span class="p">]</span>
-
-        <span class="n">discrete_xyz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">discrete_xz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_xz_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">discrete_yz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_yz_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>    
-        <span class="n">discrete_z_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_z_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>  
-
-        <span class="n">continuous_xyz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">continuous_xz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_xz_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">continuous_yz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_yz_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>    
-        <span class="n">continuous_z_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_z_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> 
-        
-        <span class="c1"># get the number of unique values for each category of the discrete variable</span>
-        <span class="c1"># add empty set for code not to break when accessing [0]</span>
-        <span class="n">num_xz_classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">][:,</span> <span class="n">index</span><span class="p">])</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_xz_indices</span><span class="p">))</span> <span class="k">if</span> <span class="p">(</span><span class="n">discrete_xz_indices</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)]</span>
-        <span class="n">num_yz_classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">][:,</span> <span class="n">index</span><span class="p">])</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_yz_indices</span><span class="p">))</span> <span class="k">if</span> <span class="p">(</span><span class="n">discrete_yz_indices</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)]</span>
-        <span class="n">num_z_classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">][:,</span> <span class="n">index</span><span class="p">])</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_z_indices</span><span class="p">))</span> <span class="k">if</span> <span class="p">(</span><span class="n">discrete_z_indices</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)]</span>
-        <span class="n">num_xyz_classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">index</span><span class="p">])</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">))</span> <span class="k">if</span> <span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)]</span>
-        
-        <span class="c1"># print(&#39;num classes&#39;, num_xyz_classes, num_xz_classes, num_yz_classes, num_z_classes)siz</span>
-
-        <span class="n">xyz_cartesian_product</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">xz_cartesian_product</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">yz_cartesian_product</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">z_cartesian_product</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_xyz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">xyz_cartesian_product</span> <span class="o">=</span> <span class="n">cartesian</span><span class="p">(</span><span class="n">num_xyz_classes</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_xyz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">xyz_cartesian_product</span> <span class="o">=</span> <span class="n">num_xyz_classes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_xz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">xz_cartesian_product</span> <span class="o">=</span> <span class="n">cartesian</span><span class="p">(</span><span class="n">num_xz_classes</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_xz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">xz_cartesian_product</span> <span class="o">=</span> <span class="n">num_xz_classes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_yz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">yz_cartesian_product</span> <span class="o">=</span> <span class="n">cartesian</span><span class="p">(</span><span class="n">num_yz_classes</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_yz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">yz_cartesian_product</span> <span class="o">=</span> <span class="n">num_yz_classes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_z_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">z_cartesian_product</span> <span class="o">=</span> <span class="n">cartesian</span><span class="p">(</span><span class="n">num_z_classes</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_z_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z_cartesian_product</span> <span class="o">=</span> <span class="n">num_z_classes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    
-        <span class="c1"># print(&#39;cartesian&#39;, xyz_cartesian_product)</span>
-        <span class="c1"># , xz_cartesian_product, yz_cartesian_product, z_cartesian_product)</span>
-                
-        <span class="c1"># compute entropies in XYZ subspace </span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">xyz_cartesian_product</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">xyz_cmi</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="n">xyz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">entry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">xyz_cartesian_product</span><span class="p">):</span>
-                <span class="n">xyz_cont_entropy</span><span class="p">,</span> <span class="n">xyz_disc_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">entry</span><span class="p">,</span>
-                                                                       <span class="n">discrete_xyz_idx</span><span class="p">,</span> 
-                                                                       <span class="n">continuous_xyz_idx</span><span class="p">,</span> 
-                                                                       <span class="n">T</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span>
-                                                                       <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span><span class="p">)</span>
-                <span class="n">xyz_cmi</span> <span class="o">+=</span> <span class="n">xyz_cont_entropy</span>
-                <span class="n">xyz_entropy</span> <span class="o">-=</span> <span class="n">xyz_disc_entropy</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">xyz_cmi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">knn</span><span class="p">)</span>
-            <span class="n">xyz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-        <span class="c1"># print(xyz_cmi, xyz_entropy)</span>
-        
-        <span class="c1"># compute entropies in XZ subspace</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">xz_cartesian_product</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">xz_cmi</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="n">xz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">entry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">xz_cartesian_product</span><span class="p">):</span>
-                <span class="n">xz_cont_entropy</span><span class="p">,</span> <span class="n">xz_disc_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">],</span> <span class="n">entry</span><span class="p">,</span> 
-                                                                     <span class="n">discrete_xz_idx</span><span class="p">,</span> 
-                                                                     <span class="n">continuous_xz_idx</span><span class="p">,</span> 
-                                                                     <span class="n">T</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span> 
-                                                                     <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span><span class="p">)</span>
-                <span class="n">xz_cmi</span> <span class="o">+=</span> <span class="n">xz_cont_entropy</span>
-                <span class="n">xz_entropy</span> <span class="o">-=</span> <span class="n">xz_disc_entropy</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">xz_cmi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">],</span> <span class="n">knn</span><span class="p">)</span>
-            <span class="n">xz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-        <span class="c1"># compute entropies in Xy subspace</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">yz_cartesian_product</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">yz_cmi</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="n">yz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">entry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">yz_cartesian_product</span><span class="p">):</span>
-                <span class="n">yz_cont_entropy</span><span class="p">,</span> <span class="n">yz_disc_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">],</span> <span class="n">entry</span><span class="p">,</span> 
-                                                                     <span class="n">discrete_yz_idx</span><span class="p">,</span> 
-                                                                     <span class="n">continuous_yz_idx</span><span class="p">,</span> 
-                                                                     <span class="n">T</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span> 
-                                                                     <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span><span class="p">)</span>
-                <span class="n">yz_cmi</span> <span class="o">+=</span> <span class="n">yz_cont_entropy</span>
-                <span class="n">yz_entropy</span> <span class="o">-=</span> <span class="n">yz_disc_entropy</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">yz_cmi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">],</span> <span class="n">knn</span><span class="p">)</span>
-            <span class="n">yz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-
-        <span class="c1"># compute entropies in Z subspace</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_cartesian_product</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z_cmi</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="n">z_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">entry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">z_cartesian_product</span><span class="p">):</span>
-                <span class="n">z_cont_entropy</span><span class="p">,</span> <span class="n">z_disc_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">],</span> 
-                                                                   <span class="n">entry</span><span class="p">,</span> 
-                                                                   <span class="n">discrete_z_idx</span><span class="p">,</span> 
-                                                                   <span class="n">continuous_z_idx</span><span class="p">,</span> 
-                                                                   <span class="n">T</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span> 
-                                                                   <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span><span class="p">)</span>
-                <span class="n">z_cmi</span> <span class="o">+=</span> <span class="n">z_cont_entropy</span>
-                <span class="n">z_entropy</span> <span class="o">-=</span> <span class="n">z_disc_entropy</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">z_cmi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">],</span> <span class="n">knn</span><span class="p">)</span>
-            <span class="n">z_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-        <span class="c1"># put it all together for the CMI estimation</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">xz_cmi</span> <span class="o">+</span> <span class="n">yz_cmi</span> <span class="o">-</span> <span class="n">xyz_cmi</span> <span class="o">-</span> <span class="n">z_cmi</span> <span class="o">+</span> <span class="n">xz_entropy</span> <span class="o">+</span> <span class="n">yz_entropy</span> <span class="o">-</span> <span class="n">xyz_entropy</span> <span class="o">-</span> <span class="n">z_entropy</span>
-        
-        <span class="n">entropies</span> <span class="o">=</span> <span class="p">(</span><span class="n">xz_cmi</span><span class="p">,</span> <span class="n">yz_cmi</span><span class="p">,</span> <span class="n">xyz_cmi</span><span class="p">,</span> <span class="n">z_cmi</span><span class="p">,</span> <span class="n">xz_entropy</span><span class="p">,</span> <span class="n">yz_entropy</span><span class="p">,</span> <span class="n">xyz_entropy</span><span class="p">,</span> <span class="n">z_entropy</span><span class="p">)</span>
-            
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">entropies</span></div>
-    
-<div class="viewcode-block" id="CMIknnMixed.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                               <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calls the appropriate function to estimate CMI.</span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,)</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># check that data is really mixed</span>
-        <span class="k">if</span> <span class="n">type_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type mask cannot be none for CMIknnMixed!&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type mask contains other values than 0 and 1!&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">==</span> <span class="s1">&#39;MS&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure_MS</span><span class="p">(</span><span class="n">array</span><span class="p">,</span>
-                                                  <span class="n">xyz</span><span class="p">,</span>
-                                                  <span class="n">type_mask</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">==</span> <span class="s1">&#39;cond&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure_conditional</span><span class="p">(</span><span class="n">array</span><span class="p">,</span>
-                                                           <span class="n">xyz</span><span class="p">,</span>
-                                                           <span class="n">type_mask</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">==</span> <span class="s1">&#39;FPinf&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure_zeroinf</span><span class="p">(</span><span class="n">array</span><span class="p">,</span>
-                                                   <span class="n">xyz</span><span class="p">,</span>
-                                                   <span class="n">type_mask</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No such estimator available!&#39;</span><span class="p">)</span></div>
-            
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">get_restricted_permutation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">shuffle_neighbors</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
-
-        <span class="n">restricted_permutation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-        <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">sample_index</span> <span class="ow">in</span> <span class="n">order</span><span class="p">:</span>
-            <span class="n">neighbors_to_use</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="n">sample_index</span><span class="p">]</span>
-            <span class="n">m</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">use</span> <span class="o">=</span> <span class="n">neighbors_to_use</span><span class="p">[</span><span class="n">m</span><span class="p">]</span>
-            <span class="k">while</span> <span class="p">((</span><span class="n">use</span> <span class="ow">in</span> <span class="n">used</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">m</span> <span class="o">&lt;</span> <span class="n">shuffle_neighbors</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)):</span>
-                <span class="n">m</span> <span class="o">+=</span> <span class="mi">1</span>
-                <span class="n">use</span> <span class="o">=</span> <span class="n">neighbors_to_use</span><span class="p">[</span><span class="n">m</span><span class="p">]</span>
-            <span class="n">restricted_permutation</span><span class="p">[</span><span class="n">sample_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">use</span>
-            <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">used</span><span class="p">,</span> <span class="n">use</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">restricted_permutation</span>
-
-
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">_generate_random_permutation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">x_indices</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">):</span>
-
-        <span class="n">T</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Generate random order in which to go through indices loop in</span>
-        <span class="c1"># next step</span>
-        <span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-                <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
-                <span class="n">n</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
-
-        <span class="c1"># Select a series of neighbor indices that contains as few as</span>
-        <span class="c1"># possible duplicates</span>
-        <span class="n">restricted_permutation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_restricted_permutation</span><span class="p">(</span>
-                <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span>
-                <span class="n">shuffle_neighbors</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">,</span>
-                <span class="n">neighbors</span><span class="o">=</span><span class="n">n</span><span class="p">,</span>
-                <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
-
-        <span class="n">array_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">type_mask_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x_indices</span><span class="p">:</span>
-            <span class="n">array_shuffled</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">restricted_permutation</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-            <span class="n">type_mask_shuffled</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">[</span><span class="n">restricted_permutation</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">array_shuffled</span><span class="p">,</span> <span class="n">type_mask_shuffled</span>
-
-<div class="viewcode-block" id="CMIknnMixed.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_shuffle_significance">[docs]</a>    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                 <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for nearest-neighbor shuffle significance test.</span>
-
-<span class="sd">        For non-empty Z, overwrites get_shuffle_significance from the parent</span>
-<span class="sd">        class  which is a block shuffle test, which does not preserve</span>
-<span class="sd">        dependencies of X and Y with Z. Here the parameter shuffle_neighbors is</span>
-<span class="sd">        used to permute only those values :math:`x_i` and :math:`x_j` for which</span>
-<span class="sd">        :math:`z_j` is among the nearest neighbors of :math:`z_i`. If Z is</span>
-<span class="sd">        empty, the block-shuffle test is used.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-<span class="sd">        </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-            
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span> <span class="o">&lt;</span> <span class="n">T</span><span class="p">:</span>
-            
-            <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">T</span>
-            <span class="n">type_mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">T</span>
-
-            <span class="c1"># discrete_idx_list = np.where(np.all(type_mask == 1, axis=0), 1, 0)</span>
-
-            <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">,</span> <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_to_one_hot_mixed</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">,</span>
-                                                                                        <span class="n">zero_inf</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-            <span class="c1"># max_neighbors = max(1, int(max_neighbor_ratio*T))</span>
-            <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;            nearest-neighbor shuffle significance &quot;</span>
-                      <span class="s2">&quot;test with n = </span><span class="si">%d</span><span class="s2"> and </span><span class="si">%d</span><span class="s2"> surrogates&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                      <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">))</span>
-            <span class="c1"># Get nearest neighbors around each sample point in Z</span>
-            <span class="n">z_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">]</span>
-            <span class="n">tree_xyz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">z_array</span><span class="p">)</span>
-            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">z_array</span><span class="p">,</span>
-                                       <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
-                                       <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                       <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span>
-                                       <span class="n">distance_upper_bound</span><span class="o">=</span><span class="mi">9999999</span><span class="p">,</span>
-                                       <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
-            
-            <span class="c1"># remove all neighbors with distance infinite -&gt; from another class </span>
-            <span class="c1"># for those that are discrete </span>
-            <span class="n">valid_neighbors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-            <span class="c1"># fill valid neighbors with point -&gt; if infinite, the neighbor will </span>
-            <span class="c1"># be the point itself</span>
-            <span class="n">valid_neighbors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">valid_neighbors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">valid_neighbors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-            
-            <span class="n">valid_neighbors</span><span class="p">[</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">]</span>
-
-            <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">)</span>
-            
-            <span class="k">for</span> <span class="n">sam</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">):</span>
-                <span class="n">array_shuffled</span><span class="p">,</span> <span class="n">type_mask_shuffled</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generate_random_permutation</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> 
-                                                                                       <span class="n">valid_neighbors</span><span class="p">,</span> 
-                                                                                       <span class="n">x_indices</span><span class="p">,</span>
-                                                                                       <span class="n">type_mask</span><span class="p">)</span>
-                <span class="n">null_dist</span><span class="p">[</span><span class="n">sam</span><span class="p">],</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array_shuffled</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
-                                                                <span class="n">xyz</span><span class="p">,</span>
-                                                                <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask_shuffled</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">null_dist</span> <span class="o">=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_get_shuffle_dist</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                           <span class="n">sig_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                           <span class="n">sig_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                                           <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">,</span>
-                                           <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="p">(</span><span class="n">null_dist</span> <span class="o">&gt;=</span> <span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        
-        <span class="k">if</span> <span class="n">return_null_dist</span><span class="p">:</span>
-            <span class="c1"># Sort</span>
-            <span class="n">null_dist</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-            <span class="k">return</span> <span class="n">pval</span><span class="p">,</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-
-
-
-
-    
-    <span class="k">def</span> <span class="nf">_get_shuffle_dist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                          <span class="n">sig_samples</span><span class="p">,</span> <span class="n">sig_blocklength</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                          <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                          <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns shuffle distribution of test statistic.</span>
-
-<span class="sd">        The rows in array corresponding to the X-variable are shuffled using</span>
-<span class="sd">        a block-shuffle approach.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        dependence_measure : object</span>
-<span class="sd">           Dependence measure function must be of form</span>
-<span class="sd">           dependence_measure(array, xyz) and return a numeric value</span>
-
-<span class="sd">        sig_samples : int, optional (default: 100)</span>
-<span class="sd">            Number of samples for shuffle significance test.</span>
-
-<span class="sd">        sig_blocklength : int, optional (default: None)</span>
-<span class="sd">            Block length for block-shuffle significance test. If None, the</span>
-<span class="sd">            block length is determined from the decay of the autocovariance as</span>
-<span class="sd">            explained in [1]_.</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        verbosity : int, optional (default: 0)</span>
-<span class="sd">            Level of verbosity.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dist : array of shape (sig_samples,)</span>
-<span class="sd">            Contains the sorted test statistic values estimated from the</span>
-<span class="sd">            shuffled arrays.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">dim_x</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_indices</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">sig_blocklength</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">sig_blocklength</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_block_length</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                                     <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;significance&#39;</span><span class="p">)</span>
-            
-        <span class="n">n_blks</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">/</span><span class="n">sig_blocklength</span><span class="p">))</span>
-        
-        <span class="c1"># print &#39;n_blks &#39;, n_blks</span>
-        <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;            Significance test with block-length = </span><span class="si">%d</span><span class="s2"> &quot;</span>
-                  <span class="s2">&quot;...&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">sig_blocklength</span><span class="p">))</span>
-
-        <span class="n">array_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">type_mask_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="c1"># block_starts = np.arange(0, T - sig_blocklength, sig_blocklength)</span>
-        <span class="n">block_starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_blks</span> <span class="o">*</span> <span class="n">sig_blocklength</span><span class="p">,</span> <span class="n">sig_blocklength</span><span class="p">)</span>
-        
-    
-        <span class="c1"># Dividing the array up into n_blks of length sig_blocklength may</span>
-        <span class="c1"># leave a tail. This tail is later randomly inserted</span>
-        <span class="n">tail</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">n_blks</span><span class="o">*</span><span class="n">sig_blocklength</span><span class="p">:]</span>
-        
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">sig_samples</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">sam</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sig_samples</span><span class="p">):</span>
-
-            <span class="n">blk_starts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">block_starts</span><span class="p">)[:</span><span class="n">n_blks</span><span class="p">]</span>
-
-            <span class="n">x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim_x</span><span class="p">,</span> <span class="n">n_blks</span><span class="o">*</span><span class="n">sig_blocklength</span><span class="p">),</span>
-                                  <span class="n">dtype</span><span class="o">=</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-            <span class="n">type_x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim_x</span><span class="p">,</span> <span class="n">n_blks</span><span class="o">*</span><span class="n">sig_blocklength</span><span class="p">),</span>
-                                  <span class="n">dtype</span><span class="o">=</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">x_indices</span><span class="p">):</span>
-                <span class="k">for</span> <span class="n">blk</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sig_blocklength</span><span class="p">):</span>
-                    <span class="n">x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">blk</span><span class="p">::</span><span class="n">sig_blocklength</span><span class="p">]</span> <span class="o">=</span> \
-                            <span class="n">array</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="n">blk_starts</span> <span class="o">+</span> <span class="n">blk</span><span class="p">]</span>
-
-                    <span class="n">type_x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">blk</span><span class="p">::</span><span class="n">sig_blocklength</span><span class="p">]</span> <span class="o">=</span> \
-                            <span class="n">type_mask</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="n">blk_starts</span> <span class="o">+</span> <span class="n">blk</span><span class="p">]</span>
-
-            <span class="c1"># Insert tail randomly somewhere</span>
-            <span class="k">if</span> <span class="n">tail</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">insert_tail_at</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">block_starts</span><span class="p">)</span>
-                <span class="n">x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">x_shuffled</span><span class="p">,</span> <span class="n">insert_tail_at</span><span class="p">,</span>
-                                       <span class="n">tail</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-                <span class="n">type_x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">type_x_shuffled</span><span class="p">,</span> <span class="n">insert_tail_at</span><span class="p">,</span>
-                                       <span class="n">tail</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-                
-    
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">x_indices</span><span class="p">):</span>
-                <span class="n">array_shuffled</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">type_mask_shuffled</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">type_x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                
-            <span class="n">null_dist</span><span class="p">[</span><span class="n">sam</span><span class="p">],</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array_shuffled</span><span class="p">,</span>
-                                                         <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                         <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask_shuffled</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">null_dist</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">CMIknn</span>
-    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-    <span class="n">random_state_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">cmi</span> <span class="o">=</span> <span class="n">CMIknnMixed</span><span class="p">(</span><span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                       <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
-                       <span class="c1"># estimator=&#39;cond&#39;,</span>
-                       <span class="n">use_local_knn</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                       <span class="n">fixed_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                       <span class="n">sig_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-                       <span class="n">sig_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                       <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;scale&#39;</span><span class="p">,</span>
-                       <span class="n">knn</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                       <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-    <span class="c1"># cmiknn = CMIknn(mask_type=None,</span>
-    <span class="c1">#                    significance=&#39;shuffle_test&#39;,</span>
-    <span class="c1">#                    # estimator=&#39;FPinf&#39;,</span>
-    <span class="c1">#                    # use_local_knn=True,</span>
-    <span class="c1">#                    fixed_thres=None,</span>
-    <span class="c1">#                    sig_samples=500,</span>
-    <span class="c1">#                    sig_blocklength=1,</span>
-    <span class="c1">#                    transform=&#39;none&#39;,</span>
-    <span class="c1">#                    knn=0.1,</span>
-    <span class="c1">#                    verbosity=0)</span>
-
-
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    <span class="n">dimz</span> <span class="o">=</span> <span class="mi">1</span>
-
-    <span class="c1"># Discrete data</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">)</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-        <span class="n">prob</span> <span class="o">=</span> <span class="mf">0.2</span> <span class="o">+</span> <span class="n">val</span><span class="o">*</span><span class="mf">0.6</span>
-        <span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">])</span>
-        <span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">])</span>
-
-    <span class="c1"># Continuous data</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-    <span class="n">z2</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">)</span>
-    <span class="n">zfull</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">z</span><span class="p">,</span> <span class="n">z2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;X _|_ Y&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">zfull</span><span class="p">,</span> 
-                    <span class="n">x_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">),</span>
-                    <span class="n">y_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">),</span>
-                    <span class="n">z_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> 
-                    <span class="c1"># val_only=True)</span>
-                    <span class="p">))</span>
-
-    <span class="c1"># print(cmiknn.run_test_raw(x, y, z=None))</span>
-        <span class="c1">#   </span>
-    <span class="c1"># print(&#39;X _|_ Y | Z&#39;)</span>
-    <span class="c1"># print(cmi.run_test_raw(x, y, z=z, </span>
-    <span class="c1">#                 x_type=np.zeros(T, dtype=&#39;bool&#39;), </span>
-    <span class="c1">#                 y_type=np.zeros(T, dtype=&#39;bool&#39;),</span>
-    <span class="c1">#                 z_type=np.zeros(T, dtype=&#39;bool&#39;)))</span>
-
-</pre></div>
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-  <h1>Source code for tigramite.independence_tests.cmisymb</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Sagar Nagaraj Simha, Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy.stats.contingency</span> <span class="kn">import</span> <span class="n">crosstab</span>
-<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
-<span class="kn">import</span> <span class="nn">multiprocessing</span>
-<span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
-
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-
-<div class="viewcode-block" id="CMIsymb"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmisymb.CMIsymb">[docs]</a><span class="k">class</span> <span class="nc">CMIsymb</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Conditional mutual information test for discrete/categorical data.</span>
-
-<span class="sd">    Conditional mutual information is the most general dependency measure</span>
-<span class="sd">    coming from an information-theoretic framework. It makes no assumptions</span>
-<span class="sd">    about the parametric form of the dependencies by directly estimating the</span>
-<span class="sd">    underlying joint density. The test here is based on directly estimating</span>
-<span class="sd">    the joint distribution assuming symbolic input, combined with a</span>
-<span class="sd">    local shuffle test to generate  the distribution under the null hypothesis of</span>
-<span class="sd">    independence. This estimator is suitable only for discrete variables.</span>
-<span class="sd">    For continuous variables use the CMIknn class and for mixed-variable</span>
-<span class="sd">    datasets the CMIknnMixed class (including mixed-type variables).</span>
-
-<span class="sd">    Allows for multi-dimensional X, Y.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    CMI and its estimator are given by</span>
-
-<span class="sd">    .. math:: I(X;Y|Z) &amp;= \sum p(z)  \sum \sum  p(x,y|z) \log</span>
-<span class="sd">                \frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)} \,dx dy dz</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_symbs : int, optional (default: None)</span>
-<span class="sd">        Number of symbols in input data. Should be at least as large as the</span>
-<span class="sd">        maximum array entry + 1. If None, n_symbs is inferred by scipy&#39;s crosstab.</span>
-
-<span class="sd">    significance : str, optional (default: &#39;shuffle_test&#39;)</span>
-<span class="sd">        Type of significance test to use. For CMIsymb only &#39;fixed_thres&#39; and</span>
-<span class="sd">        &#39;shuffle_test&#39; are available.</span>
-
-<span class="sd">    sig_blocklength : int, optional (default: 1)</span>
-<span class="sd">        Block length for block-shuffle significance test.</span>
-
-<span class="sd">    conf_blocklength : int, optional (default: 1)</span>
-<span class="sd">        Block length for block-bootstrap.</span>
-
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to parent class CondIndTest.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">n_symbs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
-                 <span class="n">sig_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="n">conf_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># Setup the member variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;cmi_symb&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_symbs</span> <span class="o">=</span> <span class="n">n_symbs</span>
-        <span class="c1"># Call the parent constructor</span>
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                             <span class="n">significance</span><span class="o">=</span><span class="n">significance</span><span class="p">,</span>
-                             <span class="n">sig_blocklength</span><span class="o">=</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                             <span class="n">conf_blocklength</span><span class="o">=</span><span class="n">conf_blocklength</span><span class="p">,</span>
-                             <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;n_symbs = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_symbs</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">conf_blocklength</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Automatic block-length estimations from decay of &quot;</span>
-                          <span class="s2">&quot;autocorrelation may not be correct for discrete &quot;</span>
-                          <span class="s2">&quot;data&quot;</span><span class="p">)</span>
-
-<div class="viewcode-block" id="CMIsymb.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmisymb.CMIsymb.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns CMI estimate based on contingency table from scipy&#39;s crosstab</span>
-<span class="sd">        to approximate probability mass.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">_</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_symbs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">levels</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Assuming same list of levels for (z, y, x).</span>
-            <span class="n">levels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_symbs</span><span class="p">),</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">xyz</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
-
-        <span class="c1"># High-dimensional contingency table</span>
-        <span class="n">_</span><span class="p">,</span> <span class="n">hist</span> <span class="o">=</span> <span class="n">crosstab</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">xyz</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">T</span><span class="p">))),</span> <span class="n">levels</span><span class="o">=</span><span class="n">levels</span><span class="p">,</span>
-                           <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="k">def</span> <span class="nf">_plogp_vector</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Precalculation of p*log(p) needed for entropies.&quot;&quot;&quot;</span>
-            <span class="n">gfunc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">T</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">gfunc</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">=</span> <span class="n">data</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-            <span class="k">def</span> <span class="nf">plogp_func</span><span class="p">(</span><span class="n">time</span><span class="p">):</span>
-                <span class="k">return</span> <span class="n">gfunc</span><span class="p">[</span><span class="n">time</span><span class="p">]</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">plogp_func</span><span class="p">)</span>
-
-        <span class="c1"># Dimensions are hist are (X, Y, Z^1, .... Z^dz)</span>
-        <span class="c1"># plogp = _plogp_vector(T)</span>
-        <span class="c1"># hxyz = (-(plogp(hist)).sum() + plogp(T)) / float(T)</span>
-        <span class="c1"># hxz = (-(plogp(hist.sum(axis=1))).sum() + plogp(T)) / float(T)</span>
-        <span class="c1"># hyz = (-(plogp(hist.sum(axis=0))).sum() + plogp(T)) / float(T)</span>
-        <span class="c1"># hz = (-(plogp(hist.sum(axis=0).sum(axis=0))).sum() + plogp(T)) / float(T)</span>
-
-        <span class="c1"># Multivariate X, Y version</span>
-        <span class="n">plogp</span> <span class="o">=</span> <span class="n">_plogp_vector</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="n">hxyz</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">plogp</span><span class="p">(</span><span class="n">hist</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span> <span class="n">plogp</span><span class="p">(</span><span class="n">T</span><span class="p">))</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="n">hxz</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">plogp</span><span class="p">(</span><span class="n">hist</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span> <span class="n">plogp</span><span class="p">(</span><span class="n">T</span><span class="p">))</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="n">hyz</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">plogp</span><span class="p">(</span><span class="n">hist</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span> <span class="n">plogp</span><span class="p">(</span><span class="n">T</span><span class="p">))</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="n">hz</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">plogp</span><span class="p">(</span><span class="n">hist</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">))[</span><span class="mi">0</span><span class="p">]))))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span> <span class="n">plogp</span><span class="p">(</span><span class="n">T</span><span class="p">))</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">hxz</span> <span class="o">+</span> <span class="n">hyz</span> <span class="o">-</span> <span class="n">hz</span> <span class="o">-</span> <span class="n">hxyz</span>
-
-        <span class="k">return</span> <span class="n">val</span></div>
-
-<div class="viewcode-block" id="CMIsymb.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmisymb.CMIsymb.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for shuffle significance test.</span>
-
-<span class="sd">        Performes a local permutation test: x_i values are only permuted with</span>
-<span class="sd">        those x_j for which z_i = z_j. Samples are drawn without replacement</span>
-<span class="sd">        as much as possible.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns.</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for original (unshuffled) estimate.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="c1"># Get neighbors around each sample point in z</span>
-            <span class="n">z_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">z_indices</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span>
-            <span class="c1"># Unique combinations of z in the data (z1, z2, z3 ...)</span>
-            <span class="n">z_comb</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">z_array</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-            <span class="c1"># Create neighbor indices of length z_comb with default as -1.</span>
-            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">z_comb</span><span class="p">),</span> <span class="n">T</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-            <span class="c1"># Neighborhood indices for each unique combination in z_comb.</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">z_comb</span><span class="p">)):</span>
-                <span class="n">neighbor_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">z_array</span> <span class="o">==</span> <span class="n">z_comb</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbor_indices</span><span class="p">)]</span> <span class="o">=</span> <span class="n">neighbor_indices</span>
-
-            <span class="n">num_cores</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
-            <span class="n">random_seeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">integers</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">iinfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">)</span>
-            <span class="n">null_dist</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">num_cores</span><span class="p">)(</span>
-                <span class="n">delayed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parallelize_shuffles</span><span class="p">)(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">,</span> <span class="n">x_indices</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">z_comb</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span> <span class="k">for</span> <span class="n">seed</span> <span class="ow">in</span> <span class="n">random_seeds</span><span class="p">)</span>
-            <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">null_dist</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">null_dist</span> <span class="o">=</span> \
-                <span class="bp">self</span><span class="o">.</span><span class="n">_get_shuffle_dist</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                       <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">,</span>
-                                       <span class="n">sig_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                       <span class="n">sig_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                                       <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="p">(</span><span class="n">null_dist</span> <span class="o">&gt;=</span> <span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="n">return_null_dist</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pval</span><span class="p">,</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">parallelize_shuffles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">,</span> <span class="n">x_indices</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">z_comb</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="c1"># Generate random order in which to go through samples.</span>
-        <span class="c1"># order = self.random_state.permutation(T).astype(&#39;int32&#39;)</span>
-        <span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-        <span class="n">order</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
-
-        <span class="n">restricted_permutation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
-        <span class="c1"># A global list of used indices across time samples and combinations.</span>
-        <span class="c1"># Since there are no repetitive (z) indices across combinations, a global list can be used.</span>
-        <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">sample_index</span> <span class="ow">in</span> <span class="n">order</span><span class="p">:</span>
-            <span class="c1"># Get the index of the z combination for sample_index in z_comb</span>
-            <span class="n">z_choice_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">z_comb</span> <span class="o">==</span> <span class="n">array</span><span class="p">[</span><span class="n">z_indices</span><span class="p">,</span> <span class="n">sample_index</span><span class="p">])</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">neighbors_choices</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="n">z_choice_index</span><span class="p">][</span><span class="n">neighbors</span><span class="p">[</span><span class="n">z_choice_index</span><span class="p">]</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-            <span class="c1"># Shuffle neighbors in-place to randomize the choice of indices</span>
-            <span class="c1"># self.random_state.shuffle(neighbors_choices)</span>
-            <span class="n">rng</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">neighbors_choices</span><span class="p">)</span>
-
-            <span class="c1"># Permuting indices</span>
-            <span class="n">m</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">use</span> <span class="o">=</span> <span class="n">neighbors_choices</span><span class="p">[</span><span class="n">m</span><span class="p">]</span>
-            <span class="k">while</span> <span class="p">((</span><span class="n">use</span> <span class="ow">in</span> <span class="n">used</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">m</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors_choices</span><span class="p">))):</span>
-                <span class="n">m</span> <span class="o">+=</span> <span class="mi">1</span>
-                <span class="n">use</span> <span class="o">=</span> <span class="n">neighbors_choices</span><span class="p">[</span><span class="n">m</span><span class="p">]</span>
-
-            <span class="n">restricted_permutation</span><span class="p">[</span><span class="n">sample_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">use</span>
-            <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">used</span><span class="p">,</span> <span class="n">use</span><span class="p">)</span>
-
-        <span class="n">array_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x_indices</span><span class="p">:</span>
-            <span class="n">array_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">restricted_permutation</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array_shuffled</span><span class="p">,</span>
-                                                     <span class="n">xyz</span><span class="p">)</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-    <span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
-    <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">cmi</span> <span class="o">=</span> <span class="n">CMIsymb</span><span class="p">(</span><span class="n">sig_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
-
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    <span class="n">dimz</span> <span class="o">=</span> <span class="mi">10</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">)</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-        <span class="n">prob</span> <span class="o">=</span> <span class="mf">0.2</span><span class="o">+</span><span class="n">val</span><span class="o">*</span><span class="mf">0.6</span>
-        <span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">])</span>
-        <span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">])</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">))</span>
-</pre></div>
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-
-<!DOCTYPE html>
-
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-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.independence_tests.gpdc &#8212; Tigramite 5.2 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
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-            
-  <h1>Source code for tigramite.independence_tests.gpdc</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">import</span> <span class="nn">json</span><span class="o">,</span> <span class="nn">warnings</span><span class="o">,</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">pathlib</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">dcor</span>
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">gaussian_process</span>
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-
-<span class="k">class</span> <span class="nc">GaussProcReg</span><span class="p">():</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Gaussian processes abstract base class.</span>
-
-<span class="sd">    GP is estimated with scikit-learn and allows to flexibly specify kernels and</span>
-<span class="sd">    hyperparameters or let them be optimized automatically. The kernel specifies</span>
-<span class="sd">    the covariance function of the GP. Parameters can be passed on to</span>
-<span class="sd">    ``GaussianProcessRegressor`` using the gp_params dictionary. If None is</span>
-<span class="sd">    passed, the kernel &#39;1.0 * RBF(1.0) + WhiteKernel()&#39; is used with alpha=0 as</span>
-<span class="sd">    default. Note that the kernel&#39;s hyperparameters are optimized during</span>
-<span class="sd">    fitting.</span>
-
-<span class="sd">    When the null distribution is not analytically available, but can be</span>
-<span class="sd">    precomputed with the function generate_and_save_nulldists(...) which saves</span>
-<span class="sd">    a \*.npz file containing the null distribution for different sample sizes.</span>
-<span class="sd">    This file can then be supplied as null_dist_filename.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    null_samples : int</span>
-<span class="sd">        Number of null samples to use</span>
-
-<span class="sd">    cond_ind_test : CondIndTest</span>
-<span class="sd">        Conditional independence test that this Gaussian Process Regressor will</span>
-<span class="sd">        calculate the null distribution for.  This is used to grab the</span>
-<span class="sd">        get_dependence_measure function.</span>
-
-<span class="sd">    gp_params : dictionary, optional (default: None)</span>
-<span class="sd">        Dictionary with parameters for ``GaussianProcessRegressor``.</span>
-
-<span class="sd">    null_dist_filename : str, otional (default: None)</span>
-<span class="sd">        Path to file containing null distribution.</span>
-
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">null_samples</span><span class="p">,</span>
-                 <span class="n">cond_ind_test</span><span class="p">,</span>
-                 <span class="n">gp_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">null_dist_filename</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="c1"># Set the dependence measure function</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">cond_ind_test</span>
-        <span class="c1"># Set member variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">gp_params</span> <span class="o">=</span> <span class="n">gp_params</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
-        <span class="c1"># Set the null distribution defaults</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span> <span class="o">=</span> <span class="n">null_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">null_dists</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">null_dist_filename</span> <span class="o">=</span> <span class="n">null_dist_filename</span>
-        <span class="c1"># Check if we are loading a null distrubtion from a cached file</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">null_dist_filename</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">null_dists</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span> <span class="o">=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_load_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">null_dist_filename</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_load_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Load a precomputed null distribution from a \*.npz file.  This</span>
-<span class="sd">        distribution can be calculated using generate_and_save_nulldists(...).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        filename : strng</span>
-<span class="sd">            Path to the \*.npz file</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dists, null_samples : dict, int</span>
-<span class="sd">            The null distirbution as a dictionary of distributions keyed by</span>
-<span class="sd">            sample size, the number of null samples in total.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">null_dist_file</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
-        <span class="n">null_dists</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">null_dist_file</span><span class="p">[</span><span class="s1">&#39;T&#39;</span><span class="p">],</span>
-                              <span class="n">null_dist_file</span><span class="p">[</span><span class="s1">&#39;exact_dist&#39;</span><span class="p">]))</span>
-        <span class="n">null_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">null_dist_file</span><span class="p">[</span><span class="s1">&#39;exact_dist&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-        <span class="k">return</span> <span class="n">null_dists</span><span class="p">,</span> <span class="n">null_samples</span>
-
-    <span class="k">def</span> <span class="nf">_generate_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span>
-                           <span class="n">add_to_null_dists</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates null distribution for pairwise independence tests.</span>
-
-<span class="sd">        Generates the null distribution for sample size df. Assumes pairwise</span>
-<span class="sd">        samples transformed to uniform marginals. Uses get_dependence_measure</span>
-<span class="sd">        available in class and generates self.sig_samples random samples. Adds</span>
-<span class="sd">        the null distributions to self.null_dists.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        df : int</span>
-<span class="sd">            Degrees of freedom / sample size to generate null distribution for.</span>
-<span class="sd">        add_to_null_dists : bool, optional (default: True)</span>
-<span class="sd">            Whether to add the null dist to the dictionary of null dists or</span>
-<span class="sd">            just return it.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dist : array of shape [df,]</span>
-<span class="sd">            Only returned,if add_to_null_dists is False.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Generating null distribution for df = </span><span class="si">%d</span><span class="s2">. &quot;</span> <span class="o">%</span> <span class="n">df</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">add_to_null_dists</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;For faster computations, run function &quot;</span>
-                      <span class="s2">&quot;generate_and_save_nulldists(...) to &quot;</span>
-                      <span class="s2">&quot;precompute null distribution and load *.npz file with &quot;</span>
-                      <span class="s2">&quot;argument null_dist_filename&quot;</span><span class="p">)</span>
-
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
-
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span><span class="p">):</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="n">df</span><span class="p">))</span>
-            <span class="n">null_dist</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">)</span>
-
-        <span class="n">null_dist</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">add_to_null_dists</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">null_dists</span><span class="p">[</span><span class="n">df</span><span class="p">]</span> <span class="o">=</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">null_dist</span>
-
-    <span class="k">def</span> <span class="nf">_generate_and_save_nulldists</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_sizes</span><span class="p">,</span> <span class="n">null_dist_filename</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates and saves null distribution for pairwise independence</span>
-<span class="sd">        tests.</span>
-
-<span class="sd">        Generates the null distribution for different sample sizes. Calls</span>
-<span class="sd">        generate_nulldist. Null dists are saved to disk as</span>
-<span class="sd">        self.null_dist_filename.npz. Also adds the null distributions to</span>
-<span class="sd">        self.null_dists.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        sample_sizes : list</span>
-<span class="sd">            List of sample sizes.</span>
-
-<span class="sd">        null_dist_filename : str</span>
-<span class="sd">            Name to save file containing null distributions.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">null_dist_filename</span> <span class="o">=</span> <span class="n">null_dist_filename</span>
-
-        <span class="n">null_dists</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">sample_sizes</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">iT</span><span class="p">,</span> <span class="n">T</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample_sizes</span><span class="p">):</span>
-            <span class="n">null_dists</span><span class="p">[</span><span class="n">iT</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generate_nulldist</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">add_to_null_dists</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">null_dists</span><span class="p">[</span><span class="n">T</span><span class="p">]</span> <span class="o">=</span> <span class="n">null_dists</span><span class="p">[</span><span class="n">iT</span><span class="p">]</span>
-
-        <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">null_dist_filename</span><span class="p">,</span>
-                 <span class="n">exact_dist</span><span class="o">=</span><span class="n">null_dists</span><span class="p">,</span>
-                 <span class="n">T</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sample_sizes</span><span class="p">))</span>
-
-    <span class="k">def</span> <span class="nf">_get_single_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-                              <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                              <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                              <span class="n">return_likelihood</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns residuals of Gaussian process regression.</span>
-
-<span class="sd">        Performs a GP regression of the variable indexed by target_var on the</span>
-<span class="sd">        conditions Z. Here array is assumed to contain X and Y as the first two</span>
-<span class="sd">        rows with the remaining rows (if present) containing the conditions Z.</span>
-<span class="sd">        Optionally returns the estimated mean and the likelihood.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        target_var : {0, 1}</span>
-<span class="sd">            Variable to regress out conditions from.</span>
-
-<span class="sd">        standardize : bool, optional (default: True)</span>
-<span class="sd">            Whether to standardize the array beforehand.</span>
-
-<span class="sd">        return_means : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the estimated regression line.</span>
-
-<span class="sd">        return_likelihood : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the log_marginal_likelihood of the fitted GP</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        resid [, mean, likelihood] : array-like</span>
-<span class="sd">            The residual of the regression and optionally the estimated mean</span>
-<span class="sd">            and/or the likelihood.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gp_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">gp_params</span> <span class="o">=</span> <span class="p">{}</span>
-
-        <span class="k">if</span> <span class="n">dim</span> <span class="o">&lt;=</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">return_likelihood</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">array</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:],</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-            <span class="k">return</span> <span class="n">array</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:]</span>
-
-        <span class="c1"># Standardize</span>
-        <span class="k">if</span> <span class="n">standardize</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-            <span class="c1"># if np.isnan(array).sum() != 0:</span>
-            <span class="c1">#     raise ValueError(&quot;nans after standardizing, &quot;</span>
-            <span class="c1">#                      &quot;possibly constant array!&quot;)</span>
-
-        <span class="n">target_series</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-
-        <span class="c1"># Overwrite default kernel and alpha values</span>
-        <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gp_params</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">if</span> <span class="s1">&#39;kernel&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gp_params</span><span class="p">):</span>
-            <span class="n">kernel</span> <span class="o">=</span> <span class="n">gaussian_process</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">RBF</span><span class="p">()</span> <span class="o">+</span>\
-             <span class="n">gaussian_process</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">WhiteKernel</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">kernel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gp_params</span><span class="p">[</span><span class="s1">&#39;kernel&#39;</span><span class="p">]</span>
-            <span class="k">del</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;kernel&#39;</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="s1">&#39;alpha&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gp_params</span><span class="p">):</span>
-            <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gp_params</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span>
-            <span class="k">del</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span>
-
-        <span class="n">gp</span> <span class="o">=</span> <span class="n">gaussian_process</span><span class="o">.</span><span class="n">GaussianProcessRegressor</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
-                                               <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                                               <span class="o">**</span><span class="n">params</span><span class="p">)</span>
-
-        <span class="n">gp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">target_series</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">gp</span><span class="o">.</span><span class="n">kernel_</span><span class="p">,</span> <span class="n">gp</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">return_likelihood</span><span class="p">:</span>
-            <span class="n">likelihood</span> <span class="o">=</span> <span class="n">gp</span><span class="o">.</span><span class="n">log_marginal_likelihood</span><span class="p">()</span>
-
-        <span class="n">mean</span> <span class="o">=</span> <span class="n">gp</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">z</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-        <span class="n">resid</span> <span class="o">=</span> <span class="n">target_series</span> <span class="o">-</span> <span class="n">mean</span>
-
-        <span class="k">if</span> <span class="n">return_means</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">return_likelihood</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">(</span><span class="n">resid</span><span class="p">,</span> <span class="n">mean</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">return_likelihood</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">return_means</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">(</span><span class="n">resid</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">return_means</span> <span class="ow">and</span> <span class="n">return_likelihood</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">resid</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">likelihood</span>
-        <span class="k">return</span> <span class="n">resid</span>
-
-    <span class="k">def</span> <span class="nf">_get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns log marginal likelihood for GP regression.</span>
-
-<span class="sd">        Fits a GP model of the parents to variable j and returns the negative</span>
-<span class="sd">        log marginal likelihood as a model selection score. Is used to determine</span>
-<span class="sd">        optimal hyperparameters in PCMCI, in particular the pc_alpha value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of target variable in data array.</span>
-
-<span class="sd">        parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...] containing parents.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">        score : float</span>
-<span class="sd">            Model score.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>   <span class="c1"># dummy variable here</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="n">parents</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> \
-                <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span>
-                    <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
-                    <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                    <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                    <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                    <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                    <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">_</span><span class="p">,</span> <span class="n">logli</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span>
-                                              <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                                              <span class="n">return_likelihood</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="n">score</span> <span class="o">=</span> <span class="o">-</span><span class="n">logli</span>
-        <span class="k">return</span> <span class="n">score</span>
-
-<div class="viewcode-block" id="GPDC"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc.GPDC">[docs]</a><span class="k">class</span> <span class="nc">GPDC</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;GPDC conditional independence test based on Gaussian processes and distance correlation.</span>
-
-<span class="sd">    GPDC is based on a Gaussian process (GP) regression and a distance</span>
-<span class="sd">    correlation test on the residuals [2]_. GP is estimated with scikit-learn</span>
-<span class="sd">    and allows to flexibly specify kernels and hyperparameters or let them be</span>
-<span class="sd">    optimized automatically. The distance correlation test is implemented with</span>
-<span class="sd">    cython. Here the null distribution is not analytically available, but can be</span>
-<span class="sd">    precomputed with the function generate_and_save_nulldists(...) which saves a</span>
-<span class="sd">    \*.npz file containing the null distribution for different sample sizes.</span>
-<span class="sd">    This file can then be supplied as null_dist_filename.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    GPDC is based on a Gaussian process (GP) regression and a distance</span>
-<span class="sd">    correlation test on the residuals. Distance correlation is described in</span>
-<span class="sd">    [2]_. To test :math:`X \perp Y | Z`, first :math:`Z` is regressed out from</span>
-<span class="sd">    :math:`X` and :math:`Y` assuming the  model</span>
-
-<span class="sd">    .. math::  X &amp; =  f_X(Z) + \epsilon_{X} \\</span>
-<span class="sd">        Y &amp; =  f_Y(Z) + \epsilon_{Y}  \\</span>
-<span class="sd">        \epsilon_{X,Y} &amp;\sim \mathcal{N}(0, \sigma^2)</span>
-
-<span class="sd">    using GP regression. Here :math:`\sigma^2` and the kernel bandwidth are</span>
-<span class="sd">    optimzed using ``sklearn``. Then the residuals  are transformed to uniform</span>
-<span class="sd">    marginals yielding :math:`r_X,r_Y` and their dependency is tested with</span>
-
-<span class="sd">    .. math::  \mathcal{R}\left(r_X, r_Y\right)</span>
-
-<span class="sd">    The null distribution of the distance correlation should be pre-computed.</span>
-<span class="sd">    Otherwise it is computed during runtime.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [2] Gabor J. Szekely, Maria L. Rizzo, and Nail K. Bakirov: Measuring and</span>
-<span class="sd">           testing dependence by correlation of distances,</span>
-<span class="sd">           https://arxiv.org/abs/0803.4101</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    null_dist_filename : str, otional (default: None)</span>
-<span class="sd">        Path to file containing null distribution.</span>
-
-<span class="sd">    gp_params : dictionary, optional (default: None)</span>
-<span class="sd">        Dictionary with parameters for ``GaussianProcessRegressor``.</span>
-
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to parent class GaussProcReg.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">null_dist_filename</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">gp_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;gp_dc&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="c1"># Call the parent constructor</span>
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="c1"># Build the regressor</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span> <span class="o">=</span> <span class="n">GaussProcReg</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                     <span class="bp">self</span><span class="p">,</span>
-                                     <span class="n">gp_params</span><span class="o">=</span><span class="n">gp_params</span><span class="p">,</span>
-                                     <span class="n">null_dist_filename</span><span class="o">=</span><span class="n">null_dist_filename</span><span class="p">,</span>
-                                     <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;null_dist_filename = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">null_dist_filename</span><span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">gp_params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span>  <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">gp_params</span><span class="p">):</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">gp_params</span><span class="p">[</span><span class="n">key</span><span class="p">]))</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_load_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Load a precomputed null distribution from a \*.npz file.  This</span>
-<span class="sd">        distribution can be calculated using generate_and_save_nulldists(...).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        filename : strng</span>
-<span class="sd">            Path to the \*.npz file</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dists, null_samples : dict, int</span>
-<span class="sd">            The null distirbution as a dictionary of distributions keyed by</span>
-<span class="sd">            sample size, the number of null samples in total.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_load_nulldist</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
-
-<div class="viewcode-block" id="GPDC.generate_nulldist"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc.GPDC.generate_nulldist">[docs]</a>    <span class="k">def</span> <span class="nf">generate_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">add_to_null_dists</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates null distribution for pairwise independence tests.</span>
-
-<span class="sd">        Generates the null distribution for sample size df. Assumes pairwise</span>
-<span class="sd">        samples transformed to uniform marginals. Uses get_dependence_measure</span>
-<span class="sd">        available in class and generates self.sig_samples random samples. Adds</span>
-<span class="sd">        the null distributions to self.gauss_pr.null_dists.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        df : int</span>
-<span class="sd">            Degrees of freedom / sample size to generate null distribution for.</span>
-
-<span class="sd">        add_to_null_dists : bool, optional (default: True)</span>
-<span class="sd">            Whether to add the null dist to the dictionary of null dists or</span>
-<span class="sd">            just return it.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dist : array of shape [df,]</span>
-<span class="sd">            Only returned,if add_to_null_dists is False.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_generate_nulldist</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">add_to_null_dists</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="GPDC.generate_and_save_nulldists"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc.GPDC.generate_and_save_nulldists">[docs]</a>    <span class="k">def</span> <span class="nf">generate_and_save_nulldists</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_sizes</span><span class="p">,</span> <span class="n">null_dist_filename</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates and saves null distribution for pairwise independence</span>
-<span class="sd">        tests.</span>
-
-<span class="sd">        Generates the null distribution for different sample sizes. Calls</span>
-<span class="sd">        generate_nulldist. Null dists are saved to disk as</span>
-<span class="sd">        self.null_dist_filename.npz. Also adds the null distributions to</span>
-<span class="sd">        self.gauss_pr.null_dists.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        sample_sizes : list</span>
-<span class="sd">            List of sample sizes.</span>
-
-<span class="sd">        null_dist_filename : str</span>
-<span class="sd">            Name to save file containing null distributions.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_generate_and_save_nulldists</span><span class="p">(</span><span class="n">sample_sizes</span><span class="p">,</span>
-                                                   <span class="n">null_dist_filename</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_get_single_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-                              <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                              <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                              <span class="n">return_likelihood</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns residuals of Gaussian process regression.</span>
-
-<span class="sd">        Performs a GP regression of the variable indexed by target_var on the</span>
-<span class="sd">        conditions Z. Here array is assumed to contain X and Y as the first two</span>
-<span class="sd">        rows with the remaining rows (if present) containing the conditions Z.</span>
-<span class="sd">        Optionally returns the estimated mean and the likelihood.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        target_var : {0, 1}</span>
-<span class="sd">            Variable to regress out conditions from.</span>
-
-<span class="sd">        standardize : bool, optional (default: True)</span>
-<span class="sd">            Whether to standardize the array beforehand.</span>
-
-<span class="sd">        return_means : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the estimated regression line.</span>
-
-<span class="sd">        return_likelihood : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the log_marginal_likelihood of the fitted GP</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        resid [, mean, likelihood] : array-like</span>
-<span class="sd">            The residual of the regression and optionally the estimated mean</span>
-<span class="sd">            and/or the likelihood.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span>
-            <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-            <span class="n">return_means</span><span class="p">,</span>
-            <span class="n">standardize</span><span class="p">,</span>
-            <span class="n">return_likelihood</span><span class="p">)</span>
-
-<div class="viewcode-block" id="GPDC.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc.GPDC.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns log marginal likelihood for GP regression.</span>
-
-<span class="sd">        Fits a GP model of the parents to variable j and returns the negative</span>
-<span class="sd">        log marginal likelihood as a model selection score. Is used to determine</span>
-<span class="sd">        optimal hyperparameters in PCMCI, in particular the pc_alpha value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of target variable in data array.</span>
-
-<span class="sd">        parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...] containing parents.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">        score : float</span>
-<span class="sd">            Model score.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_get_model_selection_criterion</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="GPDC.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc.GPDC.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return GPDC measure.</span>
-
-<span class="sd">        Estimated as the distance correlation of the residuals of a GP</span>
-<span class="sd">        regression.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            GPDC test statistic.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dcorr</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">]))</span>
-        <span class="k">return</span> <span class="n">val</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_get_dcorr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array_resid</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return distance correlation coefficient.</span>
-
-<span class="sd">        The variables are transformed to uniform marginals using the empirical</span>
-<span class="sd">        cumulative distribution function beforehand. Here the null distribution</span>
-<span class="sd">        is not analytically available, but can be precomputed with the function</span>
-<span class="sd">        generate_and_save_nulldists(...) which saves a \*.npz file containing</span>
-<span class="sd">        the null distribution for different sample sizes. This file can then be</span>
-<span class="sd">        supplied as null_dist_filename.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array_resid : array-like</span>
-<span class="sd">            data array must be of shape (2, T)</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Distance correlation coefficient.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Remove ties before applying transformation to uniform marginals</span>
-        <span class="c1"># array_resid = self._remove_ties(array_resid, verbosity=4)</span>
-        <span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trafo2uniform</span><span class="p">(</span><span class="n">array_resid</span><span class="p">)</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">dcor</span><span class="o">.</span><span class="n">distance_correlation</span><span class="p">(</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;AVL&#39;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">val</span>
-
-<div class="viewcode-block" id="GPDC.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc.GPDC.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for shuffle significance test.</span>
-
-<span class="sd">        For residual-based test statistics only the residuals are shuffled.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">array_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">])</span>
-        <span class="n">xyz_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_shuffle_dist</span><span class="p">(</span><span class="n">array_resid</span><span class="p">,</span> <span class="n">xyz_resid</span><span class="p">,</span>
-                                           <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">,</span>
-                                           <span class="n">sig_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                           <span class="n">sig_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                                           <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="p">(</span><span class="n">null_dist</span> <span class="o">&gt;=</span> <span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="n">return_null_dist</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pval</span><span class="p">,</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="GPDC.get_analytic_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc.GPDC.get_analytic_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for the distance correlation coefficient.</span>
-
-<span class="sd">        The null distribution for necessary degrees of freedom (df) is loaded.</span>
-<span class="sd">        If not available, the null distribution is generated with the function</span>
-<span class="sd">        generate_nulldist(). It is recommended to generate the nulldists for a</span>
-<span class="sd">        wide range of sample sizes beforehand with the function</span>
-<span class="sd">        generate_and_save_nulldists(...). The distance correlation coefficient</span>
-<span class="sd">        is one-sided. If the degrees of freedom are less than 1, numpy.nan is</span>
-<span class="sd">        returned.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        T : int</span>
-<span class="sd">            Sample length</span>
-
-<span class="sd">        dim : int</span>
-<span class="sd">            Dimensionality, ie, number of features.</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float or numpy.nan</span>
-<span class="sd">            p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># GP regression approximately doesn&#39;t cost degrees of freedom</span>
-        <span class="n">df</span> <span class="o">=</span> <span class="n">T</span>
-
-        <span class="k">if</span> <span class="n">df</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># idx_near = (np.abs(self.sample_sizes - df)).argmin()</span>
-            <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">null_dists</span><span class="p">):</span>
-            <span class="c1"># if np.abs(self.sample_sizes[idx_near] - df) / float(df) &gt; 0.01:</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Null distribution for GPDC not available &quot;</span>
-                          <span class="s2">&quot;for deg. of freed. = </span><span class="si">%d</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="n">df</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">generate_nulldist</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-            <span class="n">null_dist_here</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">null_dists</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">df</span><span class="p">)]</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">null_dist_here</span> <span class="o">&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">pval</span></div></div>
-
-</pre></div>
-
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-          
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-<h3>Related Topics</h3>
-<ul>
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-  <h3 id="searchlabel">Quick search</h3>
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-    <meta charset="utf-8" />
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-    <title>tigramite.independence_tests.gpdc_torch &#8212; Tigramite 5.2 documentation</title>
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-            
-  <h1>Source code for tigramite.independence_tests.gpdc_torch</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">import</span> <span class="nn">json</span><span class="o">,</span> <span class="nn">warnings</span><span class="o">,</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">pathlib</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">gc</span>
-<span class="kn">import</span> <span class="nn">dcor</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">gpytorch</span>
-<span class="kn">from</span> <span class="nn">.LBFGS</span> <span class="kn">import</span> <span class="n">FullBatchLBFGS</span>
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-
-<span class="k">class</span> <span class="nc">GaussProcRegTorch</span><span class="p">():</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Gaussian processes abstract base class.</span>
-
-<span class="sd">    GP is estimated with gpytorch. Note that the kernel&#39;s hyperparameters are</span>
-<span class="sd">    optimized during fitting.</span>
-
-<span class="sd">    When the null distribution is not analytically available, but can be</span>
-<span class="sd">    precomputed with the function generate_and_save_nulldists(...) which saves</span>
-<span class="sd">    a \*.npz file containing the null distribution for different sample sizes.</span>
-<span class="sd">    This file can then be supplied as null_dist_filename.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    null_samples : int</span>
-<span class="sd">        Number of null samples to use</span>
-
-<span class="sd">    cond_ind_test : CondIndTest</span>
-<span class="sd">        Conditional independence test that this Gaussian Proccess Regressor will</span>
-<span class="sd">        calculate the null distribution for.  This is used to grab the</span>
-<span class="sd">        get_dependence_measure function.</span>
-
-<span class="sd">    null_dist_filename : str, otional (default: None)</span>
-<span class="sd">        Path to file containing null distribution.</span>
-
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">null_samples</span><span class="p">,</span>
-                 <span class="n">cond_ind_test</span><span class="p">,</span>
-                 <span class="n">null_dist_filename</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">checkpoint_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="c1"># Set the dependence measure function</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">cond_ind_test</span>
-        <span class="c1"># Set member variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
-        <span class="c1"># Set the null distribution defaults</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span> <span class="o">=</span> <span class="n">null_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">null_dists</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">null_dist_filename</span> <span class="o">=</span> <span class="n">null_dist_filename</span>
-        <span class="c1"># Check if we are loading a null distrubtion from a cached file</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">null_dist_filename</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">null_dists</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span> <span class="o">=</span> \
-                <span class="bp">self</span><span class="o">.</span><span class="n">_load_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">null_dist_filename</span><span class="p">)</span>
-        <span class="c1"># Size for batching</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">checkpoint_size</span> <span class="o">=</span> <span class="n">checkpoint_size</span>
-
-    <span class="k">def</span> <span class="nf">_load_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Load a precomputed null distribution from a \*.npz file.  This</span>
-<span class="sd">        distribution can be calculated using generate_and_save_nulldists(...).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        filename : strng</span>
-<span class="sd">            Path to the \*.npz file</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dists, null_samples : dict, int</span>
-<span class="sd">            The null distirbution as a dictionary of distributions keyed by</span>
-<span class="sd">            sample size, the number of null samples in total.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">null_dist_file</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
-        <span class="n">null_dists</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">null_dist_file</span><span class="p">[</span><span class="s1">&#39;T&#39;</span><span class="p">],</span>
-                              <span class="n">null_dist_file</span><span class="p">[</span><span class="s1">&#39;exact_dist&#39;</span><span class="p">]))</span>
-        <span class="n">null_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">null_dist_file</span><span class="p">[</span><span class="s1">&#39;exact_dist&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-        <span class="k">return</span> <span class="n">null_dists</span><span class="p">,</span> <span class="n">null_samples</span>
-
-    <span class="k">def</span> <span class="nf">_generate_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span>
-                           <span class="n">add_to_null_dists</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates null distribution for pairwise independence tests.</span>
-
-<span class="sd">        Generates the null distribution for sample size df. Assumes pairwise</span>
-<span class="sd">        samples transformed to uniform marginals. Uses get_dependence_measure</span>
-<span class="sd">        available in class and generates self.sig_samples random samples. Adds</span>
-<span class="sd">        the null distributions to self.null_dists.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        df : int</span>
-<span class="sd">            Degrees of freedom / sample size to generate null distribution for.</span>
-<span class="sd">        add_to_null_dists : bool, optional (default: True)</span>
-<span class="sd">            Whether to add the null dist to the dictionary of null dists or</span>
-<span class="sd">            just return it.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dist : array of shape [df,]</span>
-<span class="sd">            Only returned,if add_to_null_dists is False.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Generating null distribution for df = </span><span class="si">%d</span><span class="s2">. &quot;</span> <span class="o">%</span> <span class="n">df</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">add_to_null_dists</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;For faster computations, run function &quot;</span>
-                      <span class="s2">&quot;generate_and_save_nulldists(...) to &quot;</span>
-                      <span class="s2">&quot;precompute null distribution and load *.npz file with &quot;</span>
-                      <span class="s2">&quot;argument null_dist_filename&quot;</span><span class="p">)</span>
-
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span><span class="p">):</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="n">df</span><span class="p">))</span>
-            <span class="n">null_dist</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span>
-                <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">)</span>
-
-        <span class="n">null_dist</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">add_to_null_dists</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">null_dists</span><span class="p">[</span><span class="n">df</span><span class="p">]</span> <span class="o">=</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">null_dist</span>
-
-    <span class="k">def</span> <span class="nf">_generate_and_save_nulldists</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_sizes</span><span class="p">,</span> <span class="n">null_dist_filename</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates and saves null distribution for pairwise independence</span>
-<span class="sd">        tests.</span>
-
-<span class="sd">        Generates the null distribution for different sample sizes. Calls</span>
-<span class="sd">        generate_nulldist. Null dists are saved to disk as</span>
-<span class="sd">        self.null_dist_filename.npz. Also adds the null distributions to</span>
-<span class="sd">        self.null_dists.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        sample_sizes : list</span>
-<span class="sd">            List of sample sizes.</span>
-
-<span class="sd">        null_dist_filename : str</span>
-<span class="sd">            Name to save file containing null distributions.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">null_dist_filename</span> <span class="o">=</span> <span class="n">null_dist_filename</span>
-
-        <span class="n">null_dists</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">sample_sizes</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">null_samples</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">iT</span><span class="p">,</span> <span class="n">T</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample_sizes</span><span class="p">):</span>
-            <span class="n">null_dists</span><span class="p">[</span><span class="n">iT</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generate_nulldist</span><span class="p">(</span>
-                <span class="n">T</span><span class="p">,</span> <span class="n">add_to_null_dists</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">null_dists</span><span class="p">[</span><span class="n">T</span><span class="p">]</span> <span class="o">=</span> <span class="n">null_dists</span><span class="p">[</span><span class="n">iT</span><span class="p">]</span>
-
-        <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">null_dist_filename</span><span class="p">,</span>
-                 <span class="n">exact_dist</span><span class="o">=</span><span class="n">null_dists</span><span class="p">,</span>
-                 <span class="n">T</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sample_sizes</span><span class="p">))</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_single_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-                                    <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                    <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                    <span class="n">return_likelihood</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                    <span class="n">training_iter</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-                                    <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns residuals of Gaussian process regression.</span>
-
-<span class="sd">        Performs a GP regression of the variable indexed by target_var on the</span>
-<span class="sd">        conditions Z. Here array is assumed to contain X and Y as the first two</span>
-<span class="sd">        rows with the remaining rows (if present) containing the conditions Z.</span>
-<span class="sd">        Optionally returns the estimated mean and the likelihood.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        target_var : {0, 1}</span>
-<span class="sd">            Variable to regress out conditions from.</span>
-
-<span class="sd">        standardize : bool, optional (default: True)</span>
-<span class="sd">            Whether to standardize the array beforehand.</span>
-
-<span class="sd">        return_means : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the estimated regression line.</span>
-
-<span class="sd">        return_likelihood : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the log_marginal_likelihood of the fitted GP.</span>
-
-<span class="sd">        training_iter : int, optional (default: 50)</span>
-<span class="sd">            Number of training iterations.</span>
-
-<span class="sd">        lr : float, optional (default: 0.1)</span>
-<span class="sd">            Learning rate (default: 0.1).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        resid [, mean, likelihood] : array-like</span>
-<span class="sd">            The residual of the regression and optionally the estimated mean</span>
-<span class="sd">            and/or the likelihood.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="n">dim</span> <span class="o">&lt;=</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">return_likelihood</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">array</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:],</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-            <span class="k">return</span> <span class="n">array</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:]</span>
-
-        <span class="c1"># Implement using PyTorch</span>
-        <span class="c1"># Standardize</span>
-        <span class="k">if</span> <span class="n">standardize</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-            <span class="c1"># if np.isnan(array).any():</span>
-            <span class="c1">#     raise ValueError(&quot;Nans after standardizing, &quot;</span>
-            <span class="c1">#                      &quot;possibly constant array!&quot;)</span>
-
-        <span class="n">target_series</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">train_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">z</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
-        <span class="n">train_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">target_series</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
-
-        <span class="n">device_type</span> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-        <span class="n">output_device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device_type</span><span class="p">)</span>
-        <span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span> <span class="o">=</span> <span class="n">train_x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">output_device</span><span class="p">),</span> <span class="n">train_y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">output_device</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">device_type</span> <span class="o">==</span> <span class="s1">&#39;cuda&#39;</span><span class="p">:</span>
-            <span class="c1"># If GPU is available, use MultiGPU with Kernel Partitioning</span>
-            <span class="n">n_devices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device_count</span><span class="p">()</span>
-            <span class="k">class</span> <span class="nc">mExactGPModel</span><span class="p">(</span><span class="n">gpytorch</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">ExactGP</span><span class="p">):</span>
-                <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">,</span> <span class="n">n_devices</span><span class="p">):</span>
-                    <span class="nb">super</span><span class="p">(</span><span class="n">mExactGPModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">mean_module</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">()</span>
-                    <span class="n">base_covar_module</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">ScaleKernel</span><span class="p">(</span><span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">RBFKernel</span><span class="p">())</span>
-
-                    <span class="bp">self</span><span class="o">.</span><span class="n">covar_module</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">MultiDeviceKernel</span><span class="p">(</span>
-                        <span class="n">base_covar_module</span><span class="p">,</span> <span class="n">device_ids</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">n_devices</span><span class="p">),</span>
-                        <span class="n">output_device</span><span class="o">=</span><span class="n">output_device</span>
-                    <span class="p">)</span>
-
-                <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-                    <span class="n">mean_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean_module</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-                    <span class="n">covar_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">covar_module</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-                    <span class="k">return</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">distributions</span><span class="o">.</span><span class="n">MultivariateNormal</span><span class="p">(</span><span class="n">mean_x</span><span class="p">,</span> <span class="n">covar_x</span><span class="p">)</span>
-
-            <span class="k">def</span> <span class="nf">mtrain</span><span class="p">(</span><span class="n">train_x</span><span class="p">,</span>
-                      <span class="n">train_y</span><span class="p">,</span>
-                      <span class="n">n_devices</span><span class="p">,</span>
-                      <span class="n">output_device</span><span class="p">,</span>
-                      <span class="n">checkpoint_size</span><span class="p">,</span>
-                      <span class="n">preconditioner_size</span><span class="p">,</span>
-                      <span class="n">n_training_iter</span><span class="p">,</span>
-                      <span class="p">):</span>
-                <span class="n">likelihood</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">likelihoods</span><span class="o">.</span><span class="n">GaussianLikelihood</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">output_device</span><span class="p">)</span>
-                <span class="n">model</span> <span class="o">=</span> <span class="n">mExactGPModel</span><span class="p">(</span><span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">,</span> <span class="n">n_devices</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">output_device</span><span class="p">)</span>
-                <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-                <span class="n">likelihood</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-
-                <span class="n">optimizer</span> <span class="o">=</span> <span class="n">FullBatchLBFGS</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
-                <span class="c1"># &quot;Loss&quot; for GPs - the marginal log likelihood</span>
-                <span class="n">mll</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">mlls</span><span class="o">.</span><span class="n">ExactMarginalLogLikelihood</span><span class="p">(</span><span class="n">likelihood</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
-
-                <span class="k">with</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">beta_features</span><span class="o">.</span><span class="n">checkpoint_kernel</span><span class="p">(</span><span class="n">checkpoint_size</span><span class="p">),</span> \
-                        <span class="n">gpytorch</span><span class="o">.</span><span class="n">settings</span><span class="o">.</span><span class="n">max_preconditioner_size</span><span class="p">(</span><span class="n">preconditioner_size</span><span class="p">):</span>
-
-                    <span class="k">def</span> <span class="nf">closure</span><span class="p">():</span>
-                        <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
-                        <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">train_x</span><span class="p">)</span>
-                        <span class="n">loss</span> <span class="o">=</span> <span class="o">-</span><span class="n">mll</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">train_y</span><span class="p">)</span>
-                        <span class="k">return</span> <span class="n">loss</span>
-
-                    <span class="n">loss</span> <span class="o">=</span> <span class="n">closure</span><span class="p">()</span>
-                    <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-
-                    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_training_iter</span><span class="p">):</span>
-                        <span class="n">options</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;closure&#39;</span><span class="p">:</span> <span class="n">closure</span><span class="p">,</span> <span class="s1">&#39;current_loss&#39;</span><span class="p">:</span> <span class="n">loss</span><span class="p">,</span> <span class="s1">&#39;max_ls&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">}</span>
-                        <span class="n">loss</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">fail</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">options</span><span class="p">)</span>
-
-<span class="w">                        </span><span class="sd">&#39;&#39;&#39;print(&#39;Iter %d/%d - Loss: %.3f   lengthscale: %.3f   noise: %.3f&#39; % (</span>
-<span class="sd">                            i + 1, n_training_iter, loss.item(),</span>
-<span class="sd">                            model.covar_module.module.base_kernel.lengthscale.item(),</span>
-<span class="sd">                            model.likelihood.noise.item()</span>
-<span class="sd">                        ))&#39;&#39;&#39;</span>
-
-                        <span class="k">if</span> <span class="n">fail</span><span class="p">:</span>
-                            <span class="c1"># print(&#39;Convergence reached!&#39;)</span>
-                            <span class="k">break</span>
-
-                <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">,</span> <span class="n">mll</span>
-
-            <span class="k">def</span> <span class="nf">find_best_gpu_setting</span><span class="p">(</span><span class="n">train_x</span><span class="p">,</span>
-                                      <span class="n">train_y</span><span class="p">,</span>
-                                      <span class="n">n_devices</span><span class="p">,</span>
-                                      <span class="n">output_device</span><span class="p">,</span>
-                                      <span class="n">preconditioner_size</span>
-                                      <span class="p">):</span>
-                <span class="n">N</span> <span class="o">=</span> <span class="n">train_x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-                <span class="c1"># Find the optimum partition/checkpoint size by decreasing in powers of 2</span>
-                <span class="c1"># Start with no partitioning (size = 0)</span>
-                <span class="n">settings</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">N</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">**</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">N</span><span class="p">))))]</span>
-
-                <span class="k">for</span> <span class="n">checkpoint_size</span> <span class="ow">in</span> <span class="n">settings</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Number of devices: </span><span class="si">{}</span><span class="s1"> -- Kernel partition size: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_devices</span><span class="p">,</span> <span class="n">checkpoint_size</span><span class="p">))</span>
-                    <span class="k">try</span><span class="p">:</span>
-                        <span class="c1"># Try a full forward and backward pass with this setting to check memory usage</span>
-                        <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">mtrain</span><span class="p">(</span><span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span>
-                                     <span class="n">n_devices</span><span class="o">=</span><span class="n">n_devices</span><span class="p">,</span> <span class="n">output_device</span><span class="o">=</span><span class="n">output_device</span><span class="p">,</span>
-                                     <span class="n">checkpoint_size</span><span class="o">=</span><span class="n">checkpoint_size</span><span class="p">,</span>
-                                     <span class="n">preconditioner_size</span><span class="o">=</span><span class="n">preconditioner_size</span><span class="p">,</span> <span class="n">n_training_iter</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-                        <span class="c1"># when successful, break out of for-loop and jump to finally block</span>
-                        <span class="k">break</span>
-                    <span class="k">except</span> <span class="ne">RuntimeError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-                        <span class="k">pass</span>
-                    <span class="k">except</span> <span class="ne">AttributeError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-                        <span class="k">pass</span>
-                    <span class="k">finally</span><span class="p">:</span>
-                        <span class="c1"># handle CUDA OOM error</span>
-                        <span class="n">gc</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
-                        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">empty_cache</span><span class="p">()</span>
-                <span class="k">return</span> <span class="n">checkpoint_size</span>
-
-            <span class="c1"># Set a large enough preconditioner size to reduce the number of CG iterations run</span>
-            <span class="n">preconditioner_size</span> <span class="o">=</span> <span class="mi">100</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">checkpoint_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">checkpoint_size</span> <span class="o">=</span> <span class="n">find_best_gpu_setting</span><span class="p">(</span><span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span>
-                                                        <span class="n">n_devices</span><span class="o">=</span><span class="n">n_devices</span><span class="p">,</span>
-                                                        <span class="n">output_device</span><span class="o">=</span><span class="n">output_device</span><span class="p">,</span>
-                                                        <span class="n">preconditioner_size</span><span class="o">=</span><span class="n">preconditioner_size</span><span class="p">)</span>
-
-            <span class="n">model</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">,</span> <span class="n">mll</span> <span class="o">=</span> <span class="n">mtrain</span><span class="p">(</span><span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span>
-                                      <span class="n">n_devices</span><span class="o">=</span><span class="n">n_devices</span><span class="p">,</span> <span class="n">output_device</span><span class="o">=</span><span class="n">output_device</span><span class="p">,</span>
-                                      <span class="n">checkpoint_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpoint_size</span><span class="p">,</span>
-                                      <span class="n">preconditioner_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-                                      <span class="n">n_training_iter</span><span class="o">=</span><span class="n">training_iter</span><span class="p">)</span>
-
-            <span class="c1"># Get into evaluation (predictive posterior) mode</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-            <span class="n">likelihood</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-
-            <span class="c1"># Make predictions by feeding model through likelihood</span>
-            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">(),</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">settings</span><span class="o">.</span><span class="n">fast_pred_var</span><span class="p">(),</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">beta_features</span><span class="o">.</span><span class="n">checkpoint_kernel</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
-                <span class="n">mean</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">train_x</span><span class="p">)</span><span class="o">.</span><span class="n">loc</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
-                <span class="n">loglik</span> <span class="o">=</span> <span class="n">mll</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">train_x</span><span class="p">),</span> <span class="n">train_y</span><span class="p">)</span><span class="o">*</span><span class="n">T</span>
-
-            <span class="n">resid</span> <span class="o">=</span> <span class="p">(</span><span class="n">train_y</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># If only CPU is available, we will use the simplest form of GP model, exact inference</span>
-            <span class="k">class</span> <span class="nc">ExactGPModel</span><span class="p">(</span><span class="n">gpytorch</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">ExactGP</span><span class="p">):</span>
-                <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">):</span>
-                    <span class="nb">super</span><span class="p">(</span><span class="n">ExactGPModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-                        <span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">mean_module</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">()</span>
-
-                    <span class="c1"># We only use the RBF kernel here, the WhiteNoiseKernel is deprecated</span>
-                    <span class="c1"># and its featured integrated into the Likelihood-Module.</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">covar_module</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">ScaleKernel</span><span class="p">(</span><span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">RBFKernel</span><span class="p">())</span>
-
-                <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-                    <span class="n">mean_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean_module</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-                    <span class="n">covar_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">covar_module</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-                    <span class="k">return</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">distributions</span><span class="o">.</span><span class="n">MultivariateNormal</span><span class="p">(</span><span class="n">mean_x</span><span class="p">,</span> <span class="n">covar_x</span><span class="p">)</span>
-
-            <span class="c1"># initialize likelihood and model</span>
-            <span class="n">likelihood</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">likelihoods</span><span class="o">.</span><span class="n">GaussianLikelihood</span><span class="p">()</span>
-            <span class="n">model</span> <span class="o">=</span> <span class="n">ExactGPModel</span><span class="p">(</span><span class="n">train_x</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">)</span>
-
-            <span class="c1"># Find optimal model hyperparameters</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-            <span class="n">likelihood</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-
-            <span class="c1"># Use the adam optimizer</span>
-            <span class="c1"># Includes GaussianLikelihood parameters</span>
-            <span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
-
-            <span class="c1"># &quot;Loss&quot; for GPs - the marginal log likelihood</span>
-            <span class="n">mll</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">mlls</span><span class="o">.</span><span class="n">ExactMarginalLogLikelihood</span><span class="p">(</span><span class="n">likelihood</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
-
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">training_iter</span><span class="p">):</span>
-                <span class="c1"># Zero gradients from previous iteration</span>
-                <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
-                <span class="c1"># Output from model</span>
-                <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">train_x</span><span class="p">)</span>
-
-                <span class="c1"># Calc loss and backprop gradients</span>
-                <span class="n">loss</span> <span class="o">=</span> <span class="o">-</span><span class="n">mll</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">train_y</span><span class="p">)</span>
-                <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-                <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
-
-            <span class="c1"># Get into evaluation (predictive posterior) mode</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-            <span class="n">likelihood</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-
-            <span class="c1"># Make predictions by feeding model through likelihood</span>
-            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">(),</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">settings</span><span class="o">.</span><span class="n">fast_pred_var</span><span class="p">():</span>
-                <span class="n">mean</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">train_x</span><span class="p">)</span><span class="o">.</span><span class="n">loc</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
-                <span class="n">loglik</span> <span class="o">=</span> <span class="n">mll</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">train_x</span><span class="p">),</span> <span class="n">train_y</span><span class="p">)</span> <span class="o">*</span> <span class="n">T</span>
-
-            <span class="n">resid</span> <span class="o">=</span> <span class="p">(</span><span class="n">train_y</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="n">return_means</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">return_likelihood</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">resid</span><span class="p">,</span> <span class="n">mean</span>
-        <span class="k">elif</span> <span class="n">return_likelihood</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">return_means</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">resid</span><span class="p">,</span> <span class="n">loglik</span>
-        <span class="k">elif</span> <span class="n">return_means</span> <span class="ow">and</span> <span class="n">return_likelihood</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">resid</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">loglik</span>
-        <span class="k">return</span> <span class="n">resid</span>
-
-    <span class="k">def</span> <span class="nf">_get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns log marginal likelihood for GP regression.</span>
-
-<span class="sd">        Fits a GP model of the parents to variable j and returns the negative</span>
-<span class="sd">        log marginal likelihood as a model selection score. Is used to determine</span>
-<span class="sd">        optimal hyperparameters in PCMCI, in particular the pc_alpha value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of target variable in data array.</span>
-
-<span class="sd">        parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...] containing parents.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">        score : float</span>
-<span class="sd">            Model score.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>   <span class="c1"># dummy variable here</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="n">parents</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> \
-            <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span>
-                <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
-                <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">_</span><span class="p">,</span> <span class="n">logli</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span>
-                                              <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                                              <span class="n">return_likelihood</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="n">score</span> <span class="o">=</span> <span class="o">-</span><span class="n">logli</span>
-        <span class="k">return</span> <span class="n">score</span>
-
-
-<div class="viewcode-block" id="GPDCtorch"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch">[docs]</a><span class="k">class</span> <span class="nc">GPDCtorch</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;GPDC conditional independence test based on Gaussian processes and distance correlation. Here with gpytorch implementation.</span>
-
-<span class="sd">    GPDC is based on a Gaussian process (GP) regression and a distance</span>
-<span class="sd">    correlation test on the residuals [2]_. GP is estimated with gpytorch.</span>
-<span class="sd">    The distance correlation test is implemented with the dcor package available</span>
-<span class="sd">    from pip. Here the null distribution is not analytically available, but can be</span>
-<span class="sd">    precomputed with the function generate_and_save_nulldists(...) which saves a</span>
-<span class="sd">    \*.npz file containing the null distribution for different sample sizes.</span>
-<span class="sd">    This file can then be supplied as null_dist_filename.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    GPDC is based on a Gaussian process (GP) regression and a distance</span>
-<span class="sd">    correlation test on the residuals. Distance correlation is described in</span>
-<span class="sd">    [2]_. To test :math:`X \perp Y | Z`, first :math:`Z` is regressed out from</span>
-<span class="sd">    :math:`X` and :math:`Y` assuming the  model</span>
-
-<span class="sd">    .. math::  X &amp; =  f_X(Z) + \epsilon_{X} \\</span>
-<span class="sd">        Y &amp; =  f_Y(Z) + \epsilon_{Y}  \\</span>
-<span class="sd">        \epsilon_{X,Y} &amp;\sim \mathcal{N}(0, \sigma^2)</span>
-
-<span class="sd">    using GP regression. Here :math:`\sigma^2` and the kernel bandwidth are</span>
-<span class="sd">    optimzed using ``gpytorch``. Then the residuals  are transformed to uniform</span>
-<span class="sd">    marginals yielding :math:`r_X,r_Y` and their dependency is tested with</span>
-
-<span class="sd">    .. math::  \mathcal{R}\left(r_X, r_Y\right)</span>
-
-<span class="sd">    The null distribution of the distance correlation should be pre-computed.</span>
-<span class="sd">    Otherwise it is computed during runtime.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    null_dist_filename : str, otional (default: None)</span>
-<span class="sd">        Path to file containing null distribution.</span>
-
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to parent class GaussProcRegTorch.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">null_dist_filename</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;gp_dc&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="c1"># Call the parent constructor</span>
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="c1"># Build the regressor</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span> <span class="o">=</span> <span class="n">GaussProcRegTorch</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                     <span class="bp">self</span><span class="p">,</span>
-                                     <span class="n">null_dist_filename</span><span class="o">=</span><span class="n">null_dist_filename</span><span class="p">,</span>
-                                     <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;null_dist_filename = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">null_dist_filename</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_load_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Load a precomputed null distribution from a \*.npz file.  This</span>
-<span class="sd">        distribution can be calculated using generate_and_save_nulldists(...).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        filename : strng</span>
-<span class="sd">            Path to the \*.npz file</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dists, null_samples : dict, int</span>
-<span class="sd">            The null distirbution as a dictionary of distributions keyed by</span>
-<span class="sd">            sample size, the number of null samples in total.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_load_nulldist</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
-
-<div class="viewcode-block" id="GPDCtorch.generate_nulldist"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.generate_nulldist">[docs]</a>    <span class="k">def</span> <span class="nf">generate_nulldist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">add_to_null_dists</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates null distribution for pairwise independence tests.</span>
-
-<span class="sd">        Generates the null distribution for sample size df. Assumes pairwise</span>
-<span class="sd">        samples transformed to uniform marginals. Uses get_dependence_measure</span>
-<span class="sd">        available in class and generates self.sig_samples random samples. Adds</span>
-<span class="sd">        the null distributions to self.gauss_pr.null_dists.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        df : int</span>
-<span class="sd">            Degrees of freedom / sample size to generate null distribution for.</span>
-
-<span class="sd">        add_to_null_dists : bool, optional (default: True)</span>
-<span class="sd">            Whether to add the null dist to the dictionary of null dists or</span>
-<span class="sd">            just return it.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dist : array of shape [df,]</span>
-<span class="sd">            Only returned,if add_to_null_dists is False.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_generate_nulldist</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">add_to_null_dists</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="GPDCtorch.generate_and_save_nulldists"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.generate_and_save_nulldists">[docs]</a>    <span class="k">def</span> <span class="nf">generate_and_save_nulldists</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_sizes</span><span class="p">,</span> <span class="n">null_dist_filename</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates and saves null distribution for pairwise independence</span>
-<span class="sd">        tests.</span>
-
-<span class="sd">        Generates the null distribution for different sample sizes. Calls</span>
-<span class="sd">        generate_nulldist. Null dists are saved to disk as</span>
-<span class="sd">        self.null_dist_filename.npz. Also adds the null distributions to</span>
-<span class="sd">        self.gauss_pr.null_dists.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        sample_sizes : list</span>
-<span class="sd">            List of sample sizes.</span>
-
-<span class="sd">        null_dist_filename : str</span>
-<span class="sd">            Name to save file containing null distributions.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_generate_and_save_nulldists</span><span class="p">(</span><span class="n">sample_sizes</span><span class="p">,</span>
-                                                   <span class="n">null_dist_filename</span><span class="p">)</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_get_single_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-                                    <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                    <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                    <span class="n">return_likelihood</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                    <span class="n">training_iter</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-                                    <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns residuals of Gaussian process regression.</span>
-
-<span class="sd">        Performs a GP regression of the variable indexed by target_var on the</span>
-<span class="sd">        conditions Z. Here array is assumed to contain X and Y as the first two</span>
-<span class="sd">        rows with the remaining rows (if present) containing the conditions Z.</span>
-<span class="sd">        Optionally returns the estimated mean and the likelihood.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        target_var : {0, 1}</span>
-<span class="sd">            Variable to regress out conditions from.</span>
-
-<span class="sd">        standardize : bool, optional (default: True)</span>
-<span class="sd">            Whether to standardize the array beforehand.</span>
-
-<span class="sd">        return_means : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the estimated regression line.</span>
-
-<span class="sd">        return_likelihood : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the log_marginal_likelihood of the fitted GP</span>
-
-<span class="sd">        training_iter : int, optional (default: 50)</span>
-<span class="sd">            Number of training iterations.</span>
-
-<span class="sd">        lr : float, optional (default: 0.1)</span>
-<span class="sd">            Learning rate (default: 0.1).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        resid [, mean, likelihood] : array-like</span>
-<span class="sd">            The residual of the regression and optionally the estimated mean</span>
-<span class="sd">            and/or the likelihood.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span>
-            <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-            <span class="n">return_means</span><span class="p">,</span>
-            <span class="n">standardize</span><span class="p">,</span>
-            <span class="n">return_likelihood</span><span class="p">,</span>
-            <span class="n">training_iter</span><span class="p">,</span>
-            <span class="n">lr</span><span class="p">)</span>
-
-<div class="viewcode-block" id="GPDCtorch.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns log marginal likelihood for GP regression.</span>
-
-<span class="sd">        Fits a GP model of the parents to variable j and returns the negative</span>
-<span class="sd">        log marginal likelihood as a model selection score. Is used to determine</span>
-<span class="sd">        optimal hyperparameters in PCMCI, in particular the pc_alpha value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of target variable in data array.</span>
-
-<span class="sd">        parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...] containing parents.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">        score : float</span>
-<span class="sd">            Model score.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">_get_model_selection_criterion</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="GPDCtorch.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return GPDC measure.</span>
-
-<span class="sd">        Estimated as the distance correlation of the residuals of a GP</span>
-<span class="sd">        regression.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            GPDC test statistic.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dcorr</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">]))</span>
-        <span class="k">return</span> <span class="n">val</span></div>
-
-    <span class="k">def</span> <span class="nf">_get_dcorr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array_resid</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return distance correlation coefficient.</span>
-
-<span class="sd">        The variables are transformed to uniform marginals using the empirical</span>
-<span class="sd">        cumulative distribution function beforehand. Here the null distribution</span>
-<span class="sd">        is not analytically available, but can be precomputed with the function</span>
-<span class="sd">        generate_and_save_nulldists(...) which saves a \*.npz file containing</span>
-<span class="sd">        the null distribution for different sample sizes. This file can then be</span>
-<span class="sd">        supplied as null_dist_filename.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array_resid : array-like</span>
-<span class="sd">            data array must be of shape (2, T)</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Distance correlation coefficient.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Remove ties before applying transformation to uniform marginals</span>
-        <span class="c1"># array_resid = self._remove_ties(array_resid, verbosity=4)</span>
-        <span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trafo2uniform</span><span class="p">(</span><span class="n">array_resid</span><span class="p">)</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">dcor</span><span class="o">.</span><span class="n">distance_correlation</span><span class="p">(</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;AVL&#39;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">val</span>
-
-<div class="viewcode-block" id="GPDCtorch.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for shuffle significance test.</span>
-
-<span class="sd">        For residual-based test statistics only the residuals are shuffled.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">array_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">])</span>
-        <span class="n">xyz_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_shuffle_dist</span><span class="p">(</span><span class="n">array_resid</span><span class="p">,</span> <span class="n">xyz_resid</span><span class="p">,</span>
-                                           <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">,</span>
-                                           <span class="n">sig_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                           <span class="n">sig_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                                           <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="p">(</span><span class="n">null_dist</span> <span class="o">&gt;=</span> <span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="n">return_null_dist</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pval</span><span class="p">,</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="GPDCtorch.get_analytic_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_analytic_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for the distance correlation coefficient.</span>
-
-<span class="sd">        The null distribution for necessary degrees of freedom (df) is loaded.</span>
-<span class="sd">        If not available, the null distribution is generated with the function</span>
-<span class="sd">        generate_nulldist(). It is recommended to generate the nulldists for a</span>
-<span class="sd">        wide range of sample sizes beforehand with the function</span>
-<span class="sd">        generate_and_save_nulldists(...). The distance correlation coefficient</span>
-<span class="sd">        is one-sided. If the degrees of freedom are less than 1, numpy.nan is</span>
-<span class="sd">        returned.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        T : int</span>
-<span class="sd">            Sample length</span>
-
-<span class="sd">        dim : int</span>
-<span class="sd">            Dimensionality, ie, number of features.</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float or numpy.nan</span>
-<span class="sd">            p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># GP regression approximately doesn&#39;t cost degrees of freedom</span>
-        <span class="n">df</span> <span class="o">=</span> <span class="n">T</span>
-
-        <span class="k">if</span> <span class="n">df</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># idx_near = (np.abs(self.sample_sizes - df)).argmin()</span>
-            <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">null_dists</span><span class="p">):</span>
-                <span class="c1"># if np.abs(self.sample_sizes[idx_near] - df) / float(df) &gt; 0.01:</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Null distribution for GPDC not available &quot;</span>
-                          <span class="s2">&quot;for deg. of freed. = </span><span class="si">%d</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="n">df</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">generate_nulldist</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-            <span class="n">null_dist_here</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gauss_pr</span><span class="o">.</span><span class="n">null_dists</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">df</span><span class="p">)]</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">null_dist_here</span> <span class="o">&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">pval</span></div></div>
-
-</pre></div>
-
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-<h3>Related Topics</h3>
-<ul>
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-
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-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.independence_tests.gsquared &#8212; Tigramite 5.2 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
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-            
-  <h1>Source code for tigramite.independence_tests.gsquared</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Sagar Nagaraj Simha, Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">special</span><span class="p">,</span> <span class="n">spatial</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">chi2</span>
-<span class="kn">from</span> <span class="nn">scipy.special</span> <span class="kn">import</span> <span class="n">xlogy</span>
-<span class="kn">from</span> <span class="nn">scipy.stats.contingency</span> <span class="kn">import</span> <span class="n">crosstab</span>
-<span class="kn">from</span> <span class="nn">scipy.stats.contingency</span> <span class="kn">import</span> <span class="n">expected_freq</span>
-<span class="kn">from</span> <span class="nn">scipy.stats.contingency</span> <span class="kn">import</span> <span class="n">margins</span>
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-
-<div class="viewcode-block" id="Gsquared"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gsquared.Gsquared">[docs]</a><span class="k">class</span> <span class="nc">Gsquared</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;G-squared conditional independence test for categorical data.</span>
-
-<span class="sd">    Uses Chi2 as the null distribution and the method from [7]_ to</span>
-<span class="sd">    adjust the degrees of freedom. Valid only asymptotically, recommended are</span>
-<span class="sd">    above 1000-2000 samples (depends on data). For smaller sample sizes use the</span>
-<span class="sd">    CMIsymb class which includes a local permutation test.</span>
-
-<span class="sd">    Assumes one-dimensional X, Y.</span>
-
-<span class="sd">    This method requires the scipy.stats package.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    The general formula is</span>
-
-<span class="sd">    .. math:: G(X;Y|Z) &amp;= 2 n \sum p(z)  \sum \sum  p(x,y|z) \log</span>
-<span class="sd">                \frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)}</span>
-
-<span class="sd">    where :math:`n` is the sample size. This is simply :math:`2 n CMI(X;Y|Z)`.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    .. [7] Bishop, Y.M.M., Fienberg, S.E. and Holland, P.W. (1975) Discrete</span>
-<span class="sd">           Multivariate Analysis: Theory and Practice. MIT Press, Cambridge.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_symbs : int, optional (default: None)</span>
-<span class="sd">        Number of symbols in input data. Should be at least as large as the</span>
-<span class="sd">        maximum array entry + 1. If None, n_symbs is inferred by scipy&#39;s crosstab</span>
-
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to parent class CondIndTest.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-    
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">n_symbs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        
-        <span class="c1"># Setup the member variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;gsquared&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_symbs</span> <span class="o">=</span> <span class="n">n_symbs</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;n_symbs = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_symbs</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
-
-<div class="viewcode-block" id="Gsquared.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gsquared.Gsquared.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns Gsquared/G-test test statistic.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns.</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            G-squared estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">_</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1"># Flip 2D-array so that order is ([zn...z0, ym...y0, xk...x0], T). The</span>
-        <span class="c1"># contingency table is built in this order to ease creating subspaces</span>
-        <span class="c1"># of Z=z.</span>
-        <span class="n">array_flip</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flipud</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-
-        <span class="c1"># When n_symbs is given, levels=range(0, n_symbs). If data does not</span>
-        <span class="c1"># have a symbol in levels, then count=0 in the corresponding N-D</span>
-        <span class="c1"># position of contingency table. If levels does not contain a certain</span>
-        <span class="c1"># symbol that is present in the data, then the symbol from data is</span>
-        <span class="c1"># ignored. If None, then levels are inferred from data (default).</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_symbs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">levels</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">levels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_symbs</span><span class="p">),</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">xyz</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>  
-            <span class="c1"># Assuming same list of levels for (z, y, x).</span>
-
-        <span class="n">_</span><span class="p">,</span> <span class="n">observed</span> <span class="o">=</span> <span class="n">crosstab</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">array_flip</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">xyz</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">T</span><span class="p">))),</span> <span class="n">levels</span><span class="o">=</span><span class="n">levels</span><span class="p">,</span>
-                           <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="n">observed_shape</span> <span class="o">=</span> <span class="n">observed</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">gsquare</span> <span class="o">=</span> <span class="mf">0.0</span>
-        <span class="n">dof</span> <span class="o">=</span> <span class="mi">0</span>
-
-        <span class="c1"># The following loop is over the z-subspace to sum over the G-squared</span>
-        <span class="c1"># statistic and count empty entries to adjust the degrees of freedom.</span>
-
-        <span class="c1"># TODO: Can be further optimized to operate entirely on observed array</span>
-        <span class="c1"># without &#39;for&#39;, to operate only within slice of z. sparse=True can</span>
-        <span class="c1"># also optimize further.</span>
-
-        <span class="c1"># For each permutation of z = (zn ... z1, z0). Example - (0...1,0,1)</span>
-        <span class="k">for</span> <span class="n">zs</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">ndindex</span><span class="p">(</span><span class="n">observed_shape</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)]):</span>
-            <span class="n">observedYX</span> <span class="o">=</span> <span class="n">observed</span><span class="p">[</span><span class="n">zs</span><span class="p">]</span>
-            <span class="n">mY</span><span class="p">,</span> <span class="n">mX</span> <span class="o">=</span> <span class="n">margins</span><span class="p">(</span><span class="n">observedYX</span><span class="p">)</span>
-
-            <span class="k">if</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">mY</span><span class="p">)</span><span class="o">!=</span><span class="mi">0</span><span class="p">):</span>
-                <span class="n">expectedYX</span> <span class="o">=</span> <span class="n">expected_freq</span><span class="p">(</span><span class="n">observedYX</span><span class="p">)</span>
-                <span class="n">gsquare</span> <span class="o">+=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">xlogy</span><span class="p">(</span><span class="n">observedYX</span><span class="p">,</span> <span class="n">observedYX</span><span class="p">)</span> 
-                                      <span class="o">-</span> <span class="n">xlogy</span><span class="p">(</span><span class="n">observedYX</span><span class="p">,</span> <span class="n">expectedYX</span><span class="p">))</span>
-
-                <span class="c1"># Check how many rows and columns are all-zeros. i.e. how may</span>
-                <span class="c1"># marginals are zero in expected-frq</span>
-                <span class="n">nzero_rows</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="o">~</span><span class="n">expectedYX</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span> 
-                <span class="n">nzero_cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="o">~</span><span class="n">expectedYX</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
-
-                <span class="c1"># Compute dof. Reduce by 1 dof for every marginal row &amp; column=</span>
-                <span class="c1"># 0 and add to global degrees of freedom [adapted from</span>
-                <span class="c1"># Bishop, 1975].</span>
-                <span class="n">cardYX</span> <span class="o">=</span> <span class="n">observedYX</span><span class="o">.</span><span class="n">shape</span>
-                <span class="n">dof</span> <span class="o">+=</span> <span class="p">((</span><span class="n">cardYX</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">nzero_rows</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">cardYX</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">nzero_cols</span><span class="p">))</span>
-
-        <span class="c1"># dof cannot be lesser than 1</span>
-        <span class="n">dof</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">dof</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_temp_dof</span> <span class="o">=</span> <span class="n">dof</span>
-        <span class="k">return</span> <span class="n">gsquare</span></div>
-
-<div class="viewcode-block" id="Gsquared.get_analytic_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.gsquared.Gsquared.get_analytic_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the p_value of test statistic value &#39;value&#39;, according to a</span>
-<span class="sd">           chi-square distribution with &#39;dof&#39; degrees of freedom.&quot;&quot;&quot;</span>
-                      
-        <span class="c1"># Calculate the p_value</span>
-        <span class="n">p_value</span> <span class="o">=</span> <span class="n">chi2</span><span class="o">.</span><span class="n">sf</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_temp_dof</span><span class="p">)</span>
-        <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_temp_dof</span>
-
-        <span class="k">return</span> <span class="n">p_value</span></div></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-    <span class="n">seed</span><span class="o">=</span><span class="mi">42</span>
-    <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">cmi</span> <span class="o">=</span> <span class="n">Gsquared</span><span class="p">()</span>
-
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    <span class="n">dimz</span> <span class="o">=</span> <span class="mi">3</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">)</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-        <span class="n">prob</span> <span class="o">=</span> <span class="mf">0.2</span><span class="o">+</span><span class="n">val</span><span class="o">*</span><span class="mf">0.6</span>
-        <span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">])</span>
-        <span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">])</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">))</span>
-</pre></div>
-
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-          
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-      </div>
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-        <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="../../../index.html">Tigramite</a></h1>
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-<h3>Related Topics</h3>
-<ul>
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-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../search.html" method="get">
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diff --git a/docs/_build/html/_modules/tigramite/independence_tests/independence_tests_base.html b/docs/_build/html/_modules/tigramite/independence_tests/independence_tests_base.html
deleted file mode 100644
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--- a/docs/_build/html/_modules/tigramite/independence_tests/independence_tests_base.html
+++ /dev/null
@@ -1,1187 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.independence_tests.independence_tests_base &#8212; Tigramite 5.2 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
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-  
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-        <div class="bodywrapper">
-          
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-          <div class="body" role="main">
-            
-  <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">abc</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">six</span>
-<span class="kn">from</span> <span class="nn">hashlib</span> <span class="kn">import</span> <span class="n">sha1</span>
-
-
-<div class="viewcode-block" id="CondIndTest"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest">[docs]</a><span class="nd">@six</span><span class="o">.</span><span class="n">add_metaclass</span><span class="p">(</span><span class="n">abc</span><span class="o">.</span><span class="n">ABCMeta</span><span class="p">)</span>
-<span class="k">class</span> <span class="nc">CondIndTest</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Base class of conditional independence tests.</span>
-
-<span class="sd">    Provides useful general functions for different independence tests such as</span>
-<span class="sd">    shuffle significance testing and bootstrap confidence estimation. Also</span>
-<span class="sd">    handles masked samples. Other test classes can inherit from this class.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    seed : int, optional(default = 42)</span>
-<span class="sd">        Seed for RandomState (default_rng)</span>
-
-<span class="sd">    mask_type : str, optional (default = None)</span>
-<span class="sd">        Must be in {None, &#39;y&#39;,&#39;x&#39;,&#39;z&#39;,&#39;xy&#39;,&#39;xz&#39;,&#39;yz&#39;,&#39;xyz&#39;}</span>
-<span class="sd">        Masking mode: Indicators for which variables in the dependence measure</span>
-<span class="sd">        I(X; Y | Z) the samples should be masked. If None, the mask is not used. </span>
-<span class="sd">        Explained in tutorial on masking and missing values.</span>
-
-<span class="sd">    significance : str, optional (default: &#39;analytic&#39;)</span>
-<span class="sd">        Type of significance test to use. In this package &#39;analytic&#39;,</span>
-<span class="sd">        &#39;fixed_thres&#39; and &#39;shuffle_test&#39; are available.</span>
-
-<span class="sd">    fixed_thres : float, optional (default: 0.1)</span>
-<span class="sd">        If significance is &#39;fixed_thres&#39;, this specifies the threshold for the</span>
-<span class="sd">        absolute value of the dependence measure.</span>
-
-<span class="sd">    sig_samples : int, optional (default: 500)</span>
-<span class="sd">        Number of samples for shuffle significance test.</span>
-
-<span class="sd">    sig_blocklength : int, optional (default: None)</span>
-<span class="sd">        Block length for block-shuffle significance test. If None, the</span>
-<span class="sd">        block length is determined from the decay of the autocovariance as</span>
-<span class="sd">        explained in [1]_.</span>
-
-<span class="sd">    confidence : str, optional (default: None)</span>
-<span class="sd">        Specify type of confidence estimation. If False, numpy.nan is returned.</span>
-<span class="sd">        &#39;bootstrap&#39; can be used with any test, for ParCorr also &#39;analytic&#39; is</span>
-<span class="sd">        implemented.</span>
-
-<span class="sd">    conf_lev : float, optional (default: 0.9)</span>
-<span class="sd">        Two-sided confidence interval.</span>
-
-<span class="sd">    conf_samples : int, optional (default: 100)</span>
-<span class="sd">        Number of samples for bootstrap.</span>
-
-<span class="sd">    conf_blocklength : int, optional (default: None)</span>
-<span class="sd">        Block length for block-bootstrap. If None, the block length is</span>
-<span class="sd">        determined from the decay of the autocovariance as explained in [1]_.</span>
-
-<span class="sd">    recycle_residuals : bool, optional (default: False)</span>
-<span class="sd">        Specifies whether residuals should be stored. This may be faster, but</span>
-<span class="sd">        can cost considerable memory.</span>
-
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-<div class="viewcode-block" id="CondIndTest.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_dependence_measure">[docs]</a>    <span class="nd">@abc</span><span class="o">.</span><span class="n">abstractmethod</span>
-    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Abstract function that all concrete classes must instantiate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">pass</span></div>
-
-    <span class="nd">@abc</span><span class="o">.</span><span class="n">abstractproperty</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Abstract property to store the type of independence test.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">pass</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
-                 <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;analytic&#39;</span><span class="p">,</span>
-                 <span class="n">fixed_thres</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                 <span class="n">sig_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-                 <span class="n">sig_blocklength</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">confidence</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">conf_lev</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span>
-                 <span class="n">conf_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-                 <span class="n">conf_blocklength</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">recycle_residuals</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="c1"># Set the dataframe to None for now, will be reset during pcmci call</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="c1"># Set the options</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">=</span> <span class="n">significance</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span> <span class="o">=</span> <span class="n">sig_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span> <span class="o">=</span> <span class="n">sig_blocklength</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span> <span class="o">=</span> <span class="n">fixed_thres</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="c1"># If we recycle residuals, then set up a residual cache</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="n">recycle_residuals</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="c1"># If we use a mask, we cannot recycle residuals</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">set_mask_type</span><span class="p">(</span><span class="n">mask_type</span><span class="p">)</span>
-
-        <span class="c1"># Set the confidence type and details</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span> <span class="o">=</span> <span class="n">confidence</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conf_lev</span> <span class="o">=</span> <span class="n">conf_lev</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conf_samples</span> <span class="o">=</span> <span class="n">conf_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conf_blocklength</span> <span class="o">=</span> <span class="n">conf_blocklength</span>
-
-        <span class="c1"># Print information about the</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">print_info</span><span class="p">()</span>
-
-<div class="viewcode-block" id="CondIndTest.set_mask_type"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.set_mask_type">[docs]</a>    <span class="k">def</span> <span class="nf">set_mask_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mask_type</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Setter for mask type to ensure that this option does not clash with</span>
-<span class="sd">        recycle_residuals.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        mask_type : str</span>
-<span class="sd">            Must be in {None, &#39;y&#39;,&#39;x&#39;,&#39;z&#39;,&#39;xy&#39;,&#39;xz&#39;,&#39;yz&#39;,&#39;xyz&#39;}</span>
-<span class="sd">            Masking mode: Indicators for which variables in the dependence measure</span>
-<span class="sd">            I(X; Y | Z) the samples should be masked. If None, the mask is not used. </span>
-<span class="sd">            Explained in tutorial on masking and missing values.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Set the mask type</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span> <span class="o">=</span> <span class="n">mask_type</span>
-        <span class="c1"># Check if this clashes with residual recycling</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Using a mask disables recycling residuals.&quot;</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="c1"># Check the mask type is keyed correctly</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask_type</span><span class="p">()</span></div>
-
-<div class="viewcode-block" id="CondIndTest.print_info"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.print_info">[docs]</a>    <span class="k">def</span> <span class="nf">print_info</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Print information about the conditional independence test parameters</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">info_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"># Initialize conditional independence test</span><span class="se">\n\n</span><span class="s2">Parameters:&quot;</span>
-        <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">independence test = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span>
-        <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">significance = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span>
-        <span class="c1"># Check if we are using a shuffle test</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;shuffle_test&#39;</span><span class="p">:</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sig_samples = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sig_blocklength = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span>
-        <span class="c1"># Check if we are using a fixed threshold</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">fixed_thres = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span>
-        <span class="c1"># Check if we have a confidence type</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span><span class="p">:</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">confidence = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">conf_lev = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">conf_lev</span>
-        <span class="c1"># Check if this confidence type is boostrapping</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span> <span class="o">==</span> <span class="s1">&#39;bootstrap&#39;</span><span class="p">:</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">conf_samples = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">conf_samples</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">conf_blocklength = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">conf_blocklength</span>
-        <span class="c1"># Check if we use a non-trivial mask type</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">mask_type = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span>
-        <span class="c1"># Check if we are recycling residuals or not</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span><span class="p">:</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">recycle_residuals = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span>
-        <span class="c1"># Print the information string</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">info_str</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_check_mask_type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        mask_type : str, optional (default = None)</span>
-<span class="sd">            Must be in {None, &#39;y&#39;,&#39;x&#39;,&#39;z&#39;,&#39;xy&#39;,&#39;xz&#39;,&#39;yz&#39;,&#39;xyz&#39;}</span>
-<span class="sd">            Masking mode: Indicators for which variables in the dependence measure</span>
-<span class="sd">            I(X; Y | Z) the samples should be masked. If None, the mask is not used. </span>
-<span class="sd">            Explained in tutorial on masking and missing values.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">mask_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;z&#39;</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">mask_set</span><span class="p">:</span>
-                <span class="n">err_msg</span> <span class="o">=</span> <span class="s2">&quot;mask_type = </span><span class="si">%s</span><span class="s2">,&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span> <span class="o">+</span> <span class="s2">&quot; but must be&quot;</span> <span class="o">+</span>\
-                          <span class="s2">&quot; list containing &#39;x&#39;,&#39;y&#39;,&#39;z&#39;, or any combination&quot;</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">err_msg</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="CondIndTest.get_analytic_confidence"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_analytic_confidence">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_confidence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">conf_lev</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Base class assumption that this is not implemented.  Concrete classes</span>
-<span class="sd">        should override when possible.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Analytic confidence not&quot;</span><span class="o">+</span>\
-                                  <span class="s2">&quot; implemented for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="CondIndTest.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Base class assumption that this is not implemented.  Concrete classes</span>
-<span class="sd">        should override when possible.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Model selection not&quot;</span><span class="o">+</span>\
-                                  <span class="s2">&quot; implemented for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="CondIndTest.get_analytic_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_analytic_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Base class assumption that this is not implemented.  Concrete classes</span>
-<span class="sd">        should override when possible.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Analytic significance not&quot;</span><span class="o">+</span>\
-                                  <span class="s2">&quot; implemented for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="CondIndTest.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Base class assumption that this is not implemented.  Concrete classes</span>
-<span class="sd">        should override when possible.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Shuffle significance not&quot;</span><span class="o">+</span>\
-                                  <span class="s2">&quot; implemented for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_get_single_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-                              <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Base class assumption that this is not implemented.  Concrete classes</span>
-<span class="sd">        should override when possible.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Residual calculation not&quot;</span><span class="o">+</span>\
-                                  <span class="s2">&quot; implemented for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span>
-
-<div class="viewcode-block" id="CondIndTest.set_dataframe"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.set_dataframe">[docs]</a>    <span class="k">def</span> <span class="nf">set_dataframe</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Initialize and check the dataframe.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        dataframe : data object</span>
-<span class="sd">            Set tigramite dataframe object. It must have the attributes</span>
-<span class="sd">            dataframe.values yielding a numpy array of shape (observations T,</span>
-<span class="sd">            variables N) and optionally a mask of the same shape and a missing</span>
-<span class="sd">            values flag.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">dataframe</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;mask_type is not None, but no mask in dataframe.&quot;</span><span class="p">)</span>
-            <span class="n">dataframe</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_keyfy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to make lists unique.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">z</span><span class="p">)))</span>
-
-    <span class="k">def</span> <span class="nf">_get_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span>
-                   <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convencience wrapper around construct_array.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;par_corr&#39;</span><span class="p">,</span> <span class="s1">&#39;par_corr_wls&#39;</span><span class="p">,</span> <span class="s1">&#39;robust_par_corr&#39;</span><span class="p">,</span> <span class="s1">&#39;regressionCI&#39;</span><span class="p">,</span> <span class="s1">&#39;gsquared&#39;</span><span class="p">,</span> <span class="s1">&#39;gp_dc&#39;</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;X and Y for </span><span class="si">%s</span><span class="s2"> must be univariate.&quot;</span> <span class="o">%</span>
-                                        <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span>
-        <span class="c1"># Call the wrapped function</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
-                                              <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                              <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                              <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                              <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                              <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                              <span class="n">cut_off</span><span class="o">=</span><span class="n">cut_off</span><span class="p">,</span>
-                                              <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
-    
-    <span class="k">def</span> <span class="nf">_get_array_hash</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to get hash of array.</span>
-
-<span class="sd">        For a CI test X _|_ Y | Z the order of variables within X or Y or Z </span>
-<span class="sd">        does not matter and also the order X and Y can be swapped.</span>
-<span class="sd">        Hence, to compare hashes of the whole array, we order accordingly</span>
-<span class="sd">        to create a unique, order-independent hash. </span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : Data array of shape (dim, T)</span>
-<span class="sd">            Data array.</span>
-<span class="sd">        xyz : array</span>
-<span class="sd">            Identifier array of shape (dim,) identifying which row in array</span>
-<span class="sd">            corresponds to X, Y, and Z</span>
-<span class="sd">        XYZ : list of tuples</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        combined_hash : str</span>
-<span class="sd">            Hash that identifies uniquely an array of XYZ      </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">XYZ</span>
-
-        <span class="c1"># First check whether CI result was already computed</span>
-        <span class="c1"># by checking whether hash of (xyz, array) already exists</span>
-        <span class="c1"># Individually sort X, Y, Z since for a CI test it does not matter</span>
-        <span class="c1"># how they are aranged</span>
-        <span class="n">x_orderd</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)),</span> <span class="n">key</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">)</span>
-        <span class="n">arr_x</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">][</span><span class="n">x_orderd</span><span class="p">]</span>
-        <span class="n">x_hash</span> <span class="o">=</span> <span class="n">sha1</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ascontiguousarray</span><span class="p">(</span><span class="n">arr_x</span><span class="p">))</span><span class="o">.</span><span class="n">hexdigest</span><span class="p">()</span>
-
-        <span class="n">y_orderd</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">)),</span> <span class="n">key</span><span class="o">=</span><span class="n">Y</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">)</span>
-        <span class="n">arr_y</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">][</span><span class="n">y_orderd</span><span class="p">]</span>
-        <span class="n">y_hash</span> <span class="o">=</span> <span class="n">sha1</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ascontiguousarray</span><span class="p">(</span><span class="n">arr_y</span><span class="p">))</span><span class="o">.</span><span class="n">hexdigest</span><span class="p">()</span>
-
-        <span class="n">z_orderd</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">)),</span> <span class="n">key</span><span class="o">=</span><span class="n">Z</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">)</span>
-        <span class="n">arr_z</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">xyz</span><span class="o">==</span><span class="mi">2</span><span class="p">][</span><span class="n">z_orderd</span><span class="p">]</span>
-        <span class="n">z_hash</span> <span class="o">=</span> <span class="n">sha1</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ascontiguousarray</span><span class="p">(</span><span class="n">arr_z</span><span class="p">))</span><span class="o">.</span><span class="n">hexdigest</span><span class="p">()</span>
-
-        <span class="n">sorted_xy</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span><span class="n">x_hash</span><span class="p">,</span> <span class="n">y_hash</span><span class="p">])</span>
-        <span class="n">combined_hash</span> <span class="o">=</span> <span class="p">(</span><span class="n">sorted_xy</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sorted_xy</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">z_hash</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">combined_hash</span>
-
-
-<div class="viewcode-block" id="CondIndTest.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test.</span>
-
-<span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
-<span class="sd">        classes must specify a function get_dependence_measure and either or</span>
-<span class="sd">        both functions get_analytic_significance and  get_shuffle_significance.</span>
-<span class="sd">        If recycle_residuals is True, also _get_single_residuals must be</span>
-<span class="sd">        available.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : list of tuples</span>
-<span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
-<span class="sd">            variable index and tau the time lag.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;}</span>
-<span class="sd">            How many samples to cutoff at the beginning. The default is</span>
-<span class="sd">            &#39;2xtau_max&#39;, which guarantees that MCI tests are all conducted on</span>
-<span class="sd">            the same samples. For modeling, &#39;max_lag_or_tau_max&#39; can be used,</span>
-<span class="sd">            which uses the maximum of tau_max and the conditions, which is</span>
-<span class="sd">            useful to compare multiple models on the same sample.  Last,</span>
-<span class="sd">            &#39;max_lag&#39; uses as much samples as possible.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-<span class="sd">            The test statistic value and the p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Get the array to test on</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">cut_off</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">XYZ</span>
-        
-        <span class="c1"># Record the dimensions</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Ensure it is a valid array</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
-
-        <span class="n">combined_hash</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array_hash</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">combined_hash</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-            <span class="n">cached</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">cached</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="c1"># Get the dependence measure, reycling residuals if need be</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dependence_measure_recycle</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">)</span>
-            <span class="c1"># Get the p-value</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="n">cached</span><span class="p">,</span>
-                                         <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-        <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="CondIndTest.run_test_raw"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw">[docs]</a>    <span class="k">def</span> <span class="nf">run_test_raw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">y_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test directly on input arrays x, y, z.</span>
-
-<span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
-<span class="sd">        classes must specify a function get_dependence_measure and either or</span>
-<span class="sd">        both functions get_analytic_significance and  get_shuffle_significance.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x, y, z : arrays</span>
-<span class="sd">            x,y,z are of the form (samples, dimension).</span>
-
-<span class="sd">        x_type, y_type, z_type : array-like</span>
-<span class="sd">            data arrays of same shape as x, y and z respectively, which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and</span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-
-<span class="sd">            The test statistic value and the p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;x,y must be arrays of shape (samples, dimension)&quot;</span>
-                             <span class="s2">&quot; where dimension can be 1.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;z must be array of shape (samples, dimension)&quot;</span>
-                             <span class="s2">&quot; where dimension can be 1.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">x_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">y_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">z_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">has_type_mask</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">has_type_mask</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="k">if</span> <span class="n">x_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_type_mask</span><span class="p">:</span>
-            <span class="n">x_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">y_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_type_mask</span><span class="p">:</span>
-            <span class="n">y_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># Get the array to test on</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">has_type_mask</span><span class="p">:</span>
-                <span class="n">type_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-
-            <span class="c1"># xyz is the dimension indicator</span>
-            <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
-                           <span class="p">[</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Get the array to test on</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">z</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">z_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_type_mask</span><span class="p">:</span>
-                <span class="n">z_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">z</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int&#39;</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">has_type_mask</span><span class="p">:</span>
-                <span class="n">type_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">z_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-            <span class="c1"># xyz is the dimension indicator</span>
-            <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
-                           <span class="p">[</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
-                           <span class="p">[</span><span class="mi">2</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">z</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
-
-        <span class="c1"># Record the dimensions</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Ensure it is a valid array</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
-        <span class="c1"># Get the dependence measure</span>
-        <span class="k">if</span> <span class="n">has_type_mask</span><span class="p">:</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">)</span>
-
-        <span class="c1"># Get the p-value</span>
-        <span class="k">if</span> <span class="n">has_type_mask</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
-                    <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
-                    <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span>            
-        <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
-
-    <span class="k">def</span> <span class="nf">_get_dependence_measure_recycle</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the dependence_measure, optionally recycling residuals</span>
-
-<span class="sd">        If self.recycle_residuals is True, also _get_single_residuals must be</span>
-<span class="sd">        available.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : list of tuples</span>
-<span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
-<span class="sd">            variable index and tau the time lag.</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        array : array</span>
-<span class="sd">            Data array of shape (dim, T)</span>
-
-<span class="sd">       type_mask : array-like</span>
-<span class="sd">            Binary data array of same shape as array which describes whether </span>
-<span class="sd">            individual samples in a variable (or all samples) are continuous </span>
-<span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-
-<span class="sd">        Return</span>
-<span class="sd">        ------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Test statistic</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Check if we are recycling residuals</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span><span class="p">:</span>
-            <span class="c1"># Get or calculate the cached residuals</span>
-            <span class="n">x_resid</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_cached_residuals</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-            <span class="n">y_resid</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_cached_residuals</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="c1"># Make a new residual array</span>
-            <span class="n">array_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_resid</span><span class="p">,</span> <span class="n">y_resid</span><span class="p">])</span>
-            <span class="n">xyz_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-            <span class="c1"># Return the dependence measure</span>
-            <span class="c1"># data type can only be continuous in this case</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array_resid</span><span class="p">,</span> <span class="n">xyz_resid</span><span class="p">)</span>
-
-        <span class="c1"># If not, return the dependence measure on the array and xyz</span>
-        <span class="k">if</span> <span class="n">type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                                        <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_cached_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x_nodes</span><span class="p">,</span> <span class="n">z_nodes</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Retrieve or calculate the cached residuals for the given node sets.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">            x_nodes : list of tuples</span>
-<span class="sd">                List of nodes, X or Y normally. Used to key the residual cache</span>
-<span class="sd">                during lookup</span>
-
-<span class="sd">            z_nodes : list of tuples</span>
-<span class="sd">                List of nodes, Z normally</span>
-
-<span class="sd">            target_var : int</span>
-<span class="sd">                Key to differentiate X from Y.</span>
-<span class="sd">                x_nodes == X =&gt; 0, x_nodes == Y =&gt; 1</span>
-
-<span class="sd">            array : array</span>
-<span class="sd">                Data array of shape (dim, T)</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">            x_resid : array</span>
-<span class="sd">                Residuals calculated by _get_single_residual</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Check if we have calculated these residuals</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_keyfy</span><span class="p">(</span><span class="n">x_nodes</span><span class="p">,</span> <span class="n">z_nodes</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">):</span>
-            <span class="n">x_resid</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_keyfy</span><span class="p">(</span><span class="n">x_nodes</span><span class="p">,</span> <span class="n">z_nodes</span><span class="p">)]</span>
-        <span class="c1"># If not, calculate the residuals</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">x_resid</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="n">target_var</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">z_nodes</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_keyfy</span><span class="p">(</span><span class="n">x_nodes</span><span class="p">,</span> <span class="n">z_nodes</span><span class="p">)]</span> <span class="o">=</span> <span class="n">x_resid</span>
-        <span class="c1"># Return these residuals</span>
-        <span class="k">return</span> <span class="n">x_resid</span>
-
-<div class="viewcode-block" id="CondIndTest.get_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span>
-                         <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                         <span class="n">sig_override</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the p-value from whichever significance function is specified</span>
-<span class="sd">        for this test.  If an override is used, then it will call a different</span>
-<span class="sd">        function then specified by self.significance</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        T : int</span>
-<span class="sd">            Sample length</span>
-
-<span class="sd">        dim : int</span>
-<span class="sd">            Dimensionality, ie, number of features.</span>
-<span class="sd">            </span>
-<span class="sd">       type_mask : array-like</span>
-<span class="sd">            Binary data array of same shape as array which describes whether </span>
-<span class="sd">            individual samples in a variable (or all samples) are continuous </span>
-<span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-
-<span class="sd">        sig_override : string</span>
-<span class="sd">            Must be in &#39;analytic&#39;, &#39;shuffle_test&#39;, &#39;fixed_thres&#39;</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float or numpy.nan</span>
-<span class="sd">            P-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Defaults to the self.significance member value</span>
-        <span class="n">use_sig</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span>
-        <span class="k">if</span> <span class="n">sig_override</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">use_sig</span> <span class="o">=</span> <span class="n">sig_override</span>
-        <span class="c1"># Check if we are using the analytic significance</span>
-        <span class="k">if</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;analytic&#39;</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_analytic_significance</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">)</span>
-        <span class="c1"># Check if we are using the shuffle significance</span>
-        <span class="k">elif</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;shuffle_test&#39;</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_shuffle_significance</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span>
-                                                 <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                 <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
-        <span class="c1"># Check if we are using the fixed_thres significance</span>
-        <span class="k">elif</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_fixed_thres_significance</span><span class="p">(</span>
-                    <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">,</span>
-                    <span class="n">fixed_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> not known.&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span><span class="p">)</span>
-        <span class="c1"># Return the calculated value</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="CondIndTest.get_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
-                    <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Estimate dependence measure.</span>
-
-<span class="sd">        Calls the dependence measure function. The child classes must specify</span>
-<span class="sd">        a function get_dependence_measure.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y [, Z] : list of tuples</span>
-<span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
-<span class="sd">            variable index and tau the time lag.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-<span class="sd">        </span>
-<span class="sd">       type_mask : array-like</span>
-<span class="sd">            Binary data array of same shape as array which describes whether </span>
-<span class="sd">            individual samples in a variable (or all samples) are continuous </span>
-<span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            The test statistic value.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Make the array</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">),</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="n">D</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Check it is valid</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
-        <span class="c1"># Return the dependence measure</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dependence_measure_recycle</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="CondIndTest.get_confidence"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_confidence">[docs]</a>    <span class="k">def</span> <span class="nf">get_confidence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                       <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform confidence interval estimation.</span>
-
-<span class="sd">        Calls the dependence measure and confidence test functions. The child</span>
-<span class="sd">        classes can specify a function get_dependence_measure and</span>
-<span class="sd">        get_analytic_confidence or get_bootstrap_confidence. If confidence is</span>
-<span class="sd">        False, (numpy.nan, numpy.nan) is returned.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : list of tuples</span>
-<span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
-<span class="sd">            variable index and tau the time lag.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-<span class="sd">            </span>
-<span class="sd">       type_mask : array-like</span>
-<span class="sd">            Binary data array of same shape as array which describes whether </span>
-<span class="sd">            individual samples in a variable (or all samples) are continuous </span>
-<span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        (conf_lower, conf_upper) : Tuple of floats</span>
-<span class="sd">            Upper and lower confidence bound of confidence interval.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Check if a confidence type has been defined</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span><span class="p">:</span>
-            <span class="c1"># Ensure the confidence level given makes sense</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">conf_lev</span> <span class="o">&lt;</span> <span class="mf">.5</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">conf_lev</span> <span class="o">&gt;=</span> <span class="mf">1.</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;conf_lev = </span><span class="si">%.2f</span><span class="s2">, &quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">conf_lev</span> <span class="o">+</span>
-                                 <span class="s2">&quot;but must be between 0.5 and 1&quot;</span><span class="p">)</span>
-            <span class="n">half_conf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conf_samples</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">conf_lev</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span> <span class="o">==</span> <span class="s1">&#39;bootstrap&#39;</span> <span class="ow">and</span>  <span class="n">half_conf</span> <span class="o">&lt;</span> <span class="mf">1.</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;conf_samples*(1.-conf_lev)/2 is </span><span class="si">%.2f</span><span class="s2">&quot;</span>
-                                 <span class="o">%</span> <span class="n">half_conf</span> <span class="o">+</span> <span class="s2">&quot;, must be &gt;&gt; 1&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span><span class="p">:</span>
-            <span class="c1"># Make and check the array</span>
-            <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-            <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
-
-            <span class="c1"># Check if we are using analytic confidence or bootstrapping it</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span> <span class="o">==</span> <span class="s1">&#39;analytic&#39;</span><span class="p">:</span>
-                <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">)</span>
-                <span class="p">(</span><span class="n">conf_lower</span><span class="p">,</span> <span class="n">conf_upper</span><span class="p">)</span> <span class="o">=</span> \
-                        <span class="bp">self</span><span class="o">.</span><span class="n">get_analytic_confidence</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">T</span><span class="o">-</span><span class="n">dim</span><span class="p">,</span>
-                                                     <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">,</span>
-                                                     <span class="n">conf_lev</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">conf_lev</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span> <span class="o">==</span> <span class="s1">&#39;bootstrap&#39;</span><span class="p">:</span>
-                <span class="c1"># Overwrite analytic values</span>
-                <span class="p">(</span><span class="n">conf_lower</span><span class="p">,</span> <span class="n">conf_upper</span><span class="p">)</span> <span class="o">=</span> \
-                        <span class="bp">self</span><span class="o">.</span><span class="n">get_bootstrap_confidence</span><span class="p">(</span>
-                            <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                            <span class="n">conf_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">conf_samples</span><span class="p">,</span>
-                            <span class="n">conf_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">conf_blocklength</span><span class="p">,</span>
-                            <span class="n">conf_lev</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">conf_lev</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> confidence estimation not implemented&quot;</span>
-                                 <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-        <span class="c1"># Cache the confidence interval</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conf</span> <span class="o">=</span> <span class="p">(</span><span class="n">conf_lower</span><span class="p">,</span> <span class="n">conf_upper</span><span class="p">)</span>
-        <span class="c1"># Return the confidence interval</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">conf_lower</span><span class="p">,</span> <span class="n">conf_upper</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_print_cond_ind_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print results from conditional independence test.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Test stastistic value.</span>
-
-<span class="sd">        pval : float, optional (default: None)</span>
-<span class="sd">            p-value</span>
-
-<span class="sd">        conf : tuple of floats, optional (default: None)</span>
-<span class="sd">            Confidence bounds.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">printstr</span> <span class="o">=</span> <span class="s2">&quot;        val = </span><span class="si">% .3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">val</span><span class="p">)</span>      
-        <span class="k">if</span> <span class="n">pval</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | pval = </span><span class="si">%.5f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">pval</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">conf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | conf bounds = (</span><span class="si">%.3f</span><span class="s2">, </span><span class="si">%.3f</span><span class="s2">)&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                <span class="n">conf</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conf</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">cached</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">({</span><span class="mi">0</span><span class="p">:</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span><span class="s2">&quot;[cached]&quot;</span><span class="p">}[</span><span class="n">cached</span><span class="p">])</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="n">printstr</span><span class="p">)</span>
-
-<div class="viewcode-block" id="CondIndTest.get_bootstrap_confidence"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_bootstrap_confidence">[docs]</a>    <span class="k">def</span> <span class="nf">get_bootstrap_confidence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">dependence_measure</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                 <span class="n">conf_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">conf_blocklength</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                 <span class="n">conf_lev</span><span class="o">=</span><span class="mf">.95</span><span class="p">,</span> 
-                                 <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform bootstrap confidence interval estimation.</span>
-
-<span class="sd">        With conf_blocklength &gt; 1 or None a block-bootstrap is performed.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        dependence_measure : function (default = self.get_dependence_measure)</span>
-<span class="sd">            Dependence measure function must be of form</span>
-<span class="sd">            dependence_measure(array, xyz) and return a numeric value</span>
-
-<span class="sd">        conf_lev : float, optional (default: 0.9)</span>
-<span class="sd">            Two-sided confidence interval.</span>
-
-<span class="sd">        conf_samples : int, optional (default: 100)</span>
-<span class="sd">            Number of samples for bootstrap.</span>
-
-<span class="sd">        conf_blocklength : int, optional (default: None)</span>
-<span class="sd">            Block length for block-bootstrap. If None, the block length is</span>
-<span class="sd">            determined from the decay of the autocovariance as explained in</span>
-<span class="sd">            [1]_.</span>
-
-<span class="sd">       type_mask : array-like</span>
-<span class="sd">            Binary data array of same shape as array which describes whether </span>
-<span class="sd">            individual samples in a variable (or all samples) are continuous </span>
-<span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-
-<span class="sd">        verbosity : int, optional (default: 0)</span>
-<span class="sd">            Level of verbosity.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        (conf_lower, conf_upper) : Tuple of floats</span>
-<span class="sd">            Upper and lower confidence bound of confidence interval.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Check if a dependence measure if provided or if to use default</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">dependence_measure</span><span class="p">:</span>
-            <span class="n">dependence_measure</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span>
-
-        <span class="c1"># confidence interval is two-sided</span>
-        <span class="n">c_int</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">conf_lev</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># If not block length is given, determine the optimal block length.</span>
-        <span class="c1"># This has a maximum of 10% of the time sample length</span>
-        <span class="k">if</span> <span class="n">conf_blocklength</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">conf_blocklength</span> <span class="o">=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_get_block_length</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;confidence&#39;</span><span class="p">)</span>
-        <span class="c1"># Determine the number of blocks total, rounding up for non-integer</span>
-        <span class="c1"># amounts</span>
-        <span class="n">n_blks</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">/</span><span class="n">conf_blocklength</span><span class="p">))</span>
-
-        <span class="c1"># Print some information</span>
-        <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;            block_bootstrap confidence intervals&quot;</span>
-                  <span class="s2">&quot; with block-length = </span><span class="si">%d</span><span class="s2"> ...&quot;</span> <span class="o">%</span> <span class="n">conf_blocklength</span><span class="p">)</span>
-
-        <span class="c1"># Generate the block bootstrapped distribution</span>
-        <span class="n">bootdist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">conf_samples</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">smpl</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">conf_samples</span><span class="p">):</span>
-            <span class="c1"># Get the starting indices for the blocks</span>
-            <span class="n">blk_strt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">integers</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T</span> <span class="o">-</span> <span class="n">conf_blocklength</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_blks</span><span class="p">)</span>
-            <span class="c1"># Get the empty array of block resampled values</span>
-            <span class="n">array_bootstrap</span> <span class="o">=</span> \
-                    <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="n">n_blks</span><span class="o">*</span><span class="n">conf_blocklength</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-            <span class="c1"># Fill the array of block resamples</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">conf_blocklength</span><span class="p">):</span>
-                <span class="n">array_bootstrap</span><span class="p">[:,</span> <span class="n">i</span><span class="p">::</span><span class="n">conf_blocklength</span><span class="p">]</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">blk_strt</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span>
-            <span class="c1"># Cut to proper length</span>
-            <span class="n">array_bootstrap</span> <span class="o">=</span> <span class="n">array_bootstrap</span><span class="p">[:,</span> <span class="p">:</span><span class="n">T</span><span class="p">]</span>
-
-            <span class="n">bootdist</span><span class="p">[</span><span class="n">smpl</span><span class="p">]</span> <span class="o">=</span> <span class="n">dependence_measure</span><span class="p">(</span><span class="n">array_bootstrap</span><span class="p">,</span> <span class="n">xyz</span><span class="p">)</span>
-
-        <span class="c1"># Sort and get quantile</span>
-        <span class="n">bootdist</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-        <span class="n">conf_lower</span> <span class="o">=</span> <span class="n">bootdist</span><span class="p">[</span><span class="nb">int</span><span class="p">((</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">c_int</span><span class="p">)</span> <span class="o">*</span> <span class="n">conf_samples</span><span class="p">)]</span>
-        <span class="n">conf_upper</span> <span class="o">=</span> <span class="n">bootdist</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">c_int</span> <span class="o">*</span> <span class="n">conf_samples</span><span class="p">)]</span>
-        <span class="c1"># Return the confidance limits as a tuple</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">conf_lower</span><span class="p">,</span> <span class="n">conf_upper</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_get_acf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">series</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns autocorrelation function.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        series : 1D-array</span>
-<span class="sd">            data series to compute autocorrelation from</span>
-
-<span class="sd">        max_lag : int, optional (default: None)</span>
-<span class="sd">            maximum lag for autocorrelation function. If None is passed, 10% of</span>
-<span class="sd">            the data series length are used.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        autocorr : array of shape (max_lag + 1,)</span>
-<span class="sd">            Autocorrelation function.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Set the default max lag</span>
-        <span class="k">if</span> <span class="n">max_lag</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mf">0.1</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">series</span><span class="p">)))</span>
-        <span class="c1"># Initialize the result</span>
-        <span class="n">autocorr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="c1"># Iterate over possible lags</span>
-        <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="c1"># Set the values</span>
-            <span class="n">y1_vals</span> <span class="o">=</span> <span class="n">series</span><span class="p">[</span><span class="n">lag</span><span class="p">:]</span>
-            <span class="n">y2_vals</span> <span class="o">=</span> <span class="n">series</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">series</span><span class="p">)</span> <span class="o">-</span> <span class="n">lag</span><span class="p">]</span>
-            <span class="c1"># Calculate the autocorrelation</span>
-            <span class="n">autocorr</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">y1_vals</span><span class="p">,</span> <span class="n">y2_vals</span><span class="p">,</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">autocorr</span>
-
-    <span class="k">def</span> <span class="nf">_get_block_length</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">mode</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns optimal block length for significance and confidence tests.</span>
-
-<span class="sd">        Determine block length using approach in Mader (2013) [Eq. (6)] which</span>
-<span class="sd">        improves the method of Peifer (2005) with non-overlapping blocks In</span>
-<span class="sd">        case of multidimensional X, the max is used. Further details in [1]_.</span>
-<span class="sd">        Two modes are available. For mode=&#39;significance&#39;, only the indices</span>
-<span class="sd">        corresponding to X are shuffled in array. For mode=&#39;confidence&#39; all</span>
-<span class="sd">        variables are jointly shuffled. If the autocorrelation curve fit fails,</span>
-<span class="sd">        a block length of 5% of T is used. The block length is limited to a</span>
-<span class="sd">        maximum of 10% of T.</span>
-
-<span class="sd">        Mader et al., Journal of Neuroscience Methods,</span>
-<span class="sd">        Volume 219, Issue 2, 15 October 2013, Pages 285-291</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        mode : str</span>
-<span class="sd">            Which mode to use.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        block_len : int</span>
-<span class="sd">            Optimal block length.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Inject a dependency on siganal, optimize</span>
-        <span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">signal</span><span class="p">,</span> <span class="n">optimize</span>
-        <span class="c1"># Get the shape of the array</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Initiailize the indices</span>
-        <span class="n">indices</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;significance&#39;</span><span class="p">:</span>
-            <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1"># Maximum lag for autocov estimation</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span><span class="o">*</span><span class="n">T</span><span class="p">)</span>
-        <span class="c1"># Define the function to optimize against</span>
-        <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">a_const</span><span class="p">,</span> <span class="n">decay</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">a_const</span> <span class="o">*</span> <span class="n">decay</span><span class="o">**</span><span class="n">x_vals</span>
-
-        <span class="c1"># Calculate the block length</span>
-        <span class="n">block_len</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">:</span>
-            <span class="c1"># Get decay rate of envelope of autocorrelation functions</span>
-            <span class="c1"># via hilbert trafo</span>
-            <span class="n">autocov</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_acf</span><span class="p">(</span><span class="n">series</span><span class="o">=</span><span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">)</span>
-            <span class="n">autocov</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
-            <span class="n">hilbert</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">hilbert</span><span class="p">(</span><span class="n">autocov</span><span class="p">))</span>
-            <span class="c1"># Try to fit the curve</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">popt</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">optimize</span><span class="o">.</span><span class="n">curve_fit</span><span class="p">(</span>
-                    <span class="n">f</span><span class="o">=</span><span class="n">func</span><span class="p">,</span>
-                    <span class="n">xdata</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span>
-                    <span class="n">ydata</span><span class="o">=</span><span class="n">hilbert</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">phi</span> <span class="o">=</span> <span class="n">popt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="c1"># Formula of Peifer (2005) assuming non-overlapping blocks</span>
-                <span class="n">l_opt</span> <span class="o">=</span> <span class="p">(</span><span class="mf">4.</span> <span class="o">*</span> <span class="n">T</span> <span class="o">*</span> <span class="p">(</span><span class="n">phi</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">)</span> <span class="o">+</span> <span class="n">phi</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
-                         <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">phi</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span>
-                <span class="n">block_len</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">block_len</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">l_opt</span><span class="p">))</span>
-            <span class="k">except</span> <span class="ne">RuntimeError</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error - curve_fit failed in block_shuffle, using&quot;</span>
-                      <span class="s2">&quot; block_len = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="mf">.05</span> <span class="o">*</span> <span class="n">T</span><span class="p">)))</span>
-                <span class="c1"># block_len = max(int(.05 * T), block_len)</span>
-        <span class="c1"># Limit block length to a maximum of 10% of T</span>
-        <span class="n">block_len</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">block_len</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span> <span class="o">*</span> <span class="n">T</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">block_len</span>
-
-    <span class="k">def</span> <span class="nf">_get_shuffle_dist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">dependence_measure</span><span class="p">,</span>
-                          <span class="n">sig_samples</span><span class="p">,</span> <span class="n">sig_blocklength</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                          <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns shuffle distribution of test statistic.</span>
-
-<span class="sd">        The rows in array corresponding to the X-variable are shuffled using</span>
-<span class="sd">        a block-shuffle approach.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">       dependence_measure : object</span>
-<span class="sd">           Dependence measure function must be of form</span>
-<span class="sd">           dependence_measure(array, xyz) and return a numeric value</span>
-
-<span class="sd">        sig_samples : int, optional (default: 100)</span>
-<span class="sd">            Number of samples for shuffle significance test.</span>
-
-<span class="sd">        sig_blocklength : int, optional (default: None)</span>
-<span class="sd">            Block length for block-shuffle significance test. If None, the</span>
-<span class="sd">            block length is determined from the decay of the autocovariance as</span>
-<span class="sd">            explained in [1]_.</span>
-
-<span class="sd">        verbosity : int, optional (default: 0)</span>
-<span class="sd">            Level of verbosity.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dist : array of shape (sig_samples,)</span>
-<span class="sd">            Contains the sorted test statistic values estimated from the</span>
-<span class="sd">            shuffled arrays.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">dim_x</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_indices</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">sig_blocklength</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">sig_blocklength</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_block_length</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                                     <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;significance&#39;</span><span class="p">)</span>
-
-        <span class="n">n_blks</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">/</span><span class="n">sig_blocklength</span><span class="p">))</span>
-        <span class="c1"># print &#39;n_blks &#39;, n_blks</span>
-        <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;            Significance test with block-length = </span><span class="si">%d</span><span class="s2"> &quot;</span>
-                  <span class="s2">&quot;...&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">sig_blocklength</span><span class="p">))</span>
-
-        <span class="n">array_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">block_starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T</span> <span class="o">-</span> <span class="n">sig_blocklength</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sig_blocklength</span><span class="p">)</span>
-
-        <span class="c1"># Dividing the array up into n_blks of length sig_blocklength may</span>
-        <span class="c1"># leave a tail. This tail is later randomly inserted</span>
-        <span class="n">tail</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">n_blks</span><span class="o">*</span><span class="n">sig_blocklength</span><span class="p">:]</span>
-
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">sig_samples</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">sam</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sig_samples</span><span class="p">):</span>
-
-            <span class="n">blk_starts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">block_starts</span><span class="p">)[:</span><span class="n">n_blks</span><span class="p">]</span>
-
-            <span class="n">x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim_x</span><span class="p">,</span> <span class="n">n_blks</span><span class="o">*</span><span class="n">sig_blocklength</span><span class="p">),</span>
-                                  <span class="n">dtype</span><span class="o">=</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">x_indices</span><span class="p">):</span>
-                <span class="k">for</span> <span class="n">blk</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sig_blocklength</span><span class="p">):</span>
-                    <span class="n">x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">blk</span><span class="p">::</span><span class="n">sig_blocklength</span><span class="p">]</span> <span class="o">=</span> \
-                            <span class="n">array</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="n">blk_starts</span> <span class="o">+</span> <span class="n">blk</span><span class="p">]</span>
-
-            <span class="c1"># Insert tail randomly somewhere</span>
-            <span class="k">if</span> <span class="n">tail</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">insert_tail_at</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">block_starts</span><span class="p">)</span>
-                <span class="n">x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">x_shuffled</span><span class="p">,</span> <span class="n">insert_tail_at</span><span class="p">,</span>
-                                       <span class="n">tail</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">x_indices</span><span class="p">):</span>
-                <span class="n">array_shuffled</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-
-            <span class="n">null_dist</span><span class="p">[</span><span class="n">sam</span><span class="p">]</span> <span class="o">=</span> <span class="n">dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array_shuffled</span><span class="p">,</span>
-                                                <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">null_dist</span>
-
-<div class="viewcode-block" id="CondIndTest.get_fixed_thres_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_fixed_thres_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">fixed_thres</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns signficance for thresholding test.</span>
-
-<span class="sd">        Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1 else.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-
-<span class="sd">        fixed_thres : number</span>
-<span class="sd">            Fixed threshold, is made positive.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : bool</span>
-<span class="sd">            Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1</span>
-<span class="sd">            else.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">fixed_thres</span><span class="p">):</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">1.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
-
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-    <span class="k">def</span> <span class="nf">_trafo2uniform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Transforms input array to uniform marginals.</span>
-
-<span class="sd">        Assumes x.shape = (dim, T)</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x : array-like</span>
-<span class="sd">            Input array.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        u : array-like</span>
-<span class="sd">            array with uniform marginals.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">trafo</span><span class="p">(</span><span class="n">xi</span><span class="p">):</span>
-            <span class="n">xisorted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
-            <span class="n">yi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">xi</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">xi</span><span class="p">))</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">xi</span><span class="p">,</span> <span class="n">xisorted</span><span class="p">,</span> <span class="n">yi</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">u</span> <span class="o">=</span> <span class="n">trafo</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-                <span class="n">u</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">trafo</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">return</span> <span class="n">u</span></div>
-</pre></div>
-
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="../../../index.html">Tigramite</a></h1>
-
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-<h3>Navigation</h3>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../index.html">Documentation overview</a><ul>
-  <li><a href="../../index.html">Module code</a><ul>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
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-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.independence_tests.oracle_conditional_independence &#8212; Tigramite 5.2 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
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-        <div class="bodywrapper">
-          
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-          <div class="body" role="main">
-            
-  <h1>Source code for tigramite.independence_tests.oracle_conditional_independence</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span><span class="p">,</span> <span class="n">OrderedDict</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">combinations</span><span class="p">,</span> <span class="n">permutations</span>
-
-
-<div class="viewcode-block" id="OracleCI"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI">[docs]</a><span class="k">class</span> <span class="nc">OracleCI</span><span class="p">:</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Oracle of conditional independence test X _|_ Y | Z given a graph.</span>
-
-<span class="sd">    Class around link_coeff causal ground truth. X _|_ Y | Z is based on</span>
-<span class="sd">    assessing whether X and Y are d-separated given Z in the graph.</span>
-
-<span class="sd">    Class can be used just like a Tigramite conditional independence class</span>
-<span class="sd">    (e.g., ParCorr). The main use is for unit testing of PCMCI methods.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">        Causal graph.</span>
-<span class="sd">    links : dict</span>
-<span class="sd">        Dictionary of form {0:[(0, -1), ...], 1:[...], ...}.</span>
-<span class="sd">        Alternatively can also digest {0: [((0, -1), coeff, func)], ...}.</span>
-<span class="sd">    observed_vars : None or list, optional (default: None)</span>
-<span class="sd">        Subset of keys in links definining which variables are </span>
-<span class="sd">        observed. If None, then all variables are observed.</span>
-<span class="sd">    selection_vars : None or list, optional (default: None)</span>
-<span class="sd">        Subset of keys in links definining which variables are </span>
-<span class="sd">        selected (= always conditioned on at every time lag).</span>
-<span class="sd">        If None, then no variables are selected.</span>
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># documentation</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">observed_vars</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">selection_vars</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">graph</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">graph_is_mag</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_max</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">graph_is_mag</span> <span class="o">=</span> <span class="n">graph_is_mag</span>
-
-        <span class="k">if</span> <span class="n">links</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">graph</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Either links or graph must be specified!&quot;</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="c1"># Get canonical DAG from graph, potentially interpreted as MAG</span>
-                <span class="c1"># self.tau_max = graph.shape[2]</span>
-                <span class="p">(</span><span class="n">links</span><span class="p">,</span> 
-                 <span class="n">observed_vars</span><span class="p">,</span> 
-                 <span class="n">selection_vars</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_links_from_graph</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-                <span class="c1"># # TODO make checks and tau_max?</span>
-                <span class="c1"># self.graph = graph</span>
-
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;oracle_ci&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">links</span> <span class="o">=</span> <span class="n">links</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-        <span class="c1"># self.tau_max = self._get_minmax_lag(self.links)</span>
-
-        <span class="c1"># Initialize already computed dsepsets of X, Y, Z</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dsepsets</span> <span class="o">=</span> <span class="p">{}</span>
-
-        <span class="c1"># Initialize observed vars</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span> <span class="o">=</span> <span class="n">observed_vars</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">)</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;observed_vars must be subset of range(N).&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span> <span class="o">!=</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;observed_vars must ordered.&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">)):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;observed_vars must not contain duplicates.&quot;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span> <span class="o">=</span> <span class="n">selection_vars</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span><span class="p">)</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selection_vars must be subset of range(N).&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span> <span class="o">!=</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selection_vars must ordered.&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span><span class="p">)):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selection_vars must not contain duplicates.&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># ToDO: maybe allow to use user-tau_max, otherwise deduced from links</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_graph_from_links</span><span class="p">(</span><span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
-
-<div class="viewcode-block" id="OracleCI.set_dataframe"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.set_dataframe">[docs]</a>    <span class="k">def</span> <span class="nf">set_dataframe</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Dummy function.&quot;&quot;&quot;</span>
-        <span class="k">pass</span></div>
-
-    <span class="k">def</span> <span class="nf">_check_XYZ</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Checks variables X, Y, Z.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : list of tuples</span>
-<span class="sd">            For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],</span>
-<span class="sd">            where var specifies the variable index. X typically is of the form</span>
-<span class="sd">            [(varX, -tau)] with tau denoting the time lag and Z can be</span>
-<span class="sd">            multivariate [(var1, -lag), (var2, -lag), ...] .</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        X, Y, Z : tuple</span>
-<span class="sd">            Cleaned X, Y, Z.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Get the length in time and the number of nodes</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span>
-
-        <span class="c1"># Remove duplicates in X, Y, Z</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">OrderedDict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
-        <span class="n">Y</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">OrderedDict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">OrderedDict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-        <span class="c1"># If a node in Z occurs already in X or Y, remove it from Z</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z</span> <span class="k">if</span> <span class="p">(</span><span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">X</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">)]</span>
-
-        <span class="c1"># Check that all lags are non-positive and indices are in [0,N-1]</span>
-        <span class="n">XYZ</span> <span class="o">=</span> <span class="n">X</span> <span class="o">+</span> <span class="n">Y</span> <span class="o">+</span> <span class="n">Z</span>
-        <span class="n">dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)</span>
-        <span class="c1"># Ensure that XYZ makes sense</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;X, Y, Z must be lists of tuples in format&quot;</span>
-                             <span class="s2">&quot; [(var, -lag),...], eg., [(2, -2), (1, 0), ...]&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nodes are </span><span class="si">%s</span><span class="s2">, &quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)</span> <span class="o">+</span>
-                             <span class="s2">&quot;but all lags must be non-positive&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">N</span><span class="p">)</span>
-                <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;var indices </span><span class="si">%s</span><span class="s2">,&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">XYZ</span><span class="p">)[:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span>
-                             <span class="s2">&quot; but must be in [0, </span><span class="si">%d</span><span class="s2">]&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Y</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Y-nodes are </span><span class="si">%s</span><span class="s2">, &quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">+</span>
-                             <span class="s2">&quot;but one of the Y-nodes must have zero lag&quot;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_lagged_parents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">var_lag</span><span class="p">,</span> <span class="n">exclude_contemp</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">only_non_causal_paths</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal_children</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to yield lagged parents for var_lag from</span>
-<span class="sd">        self.links_coeffs.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        var_lag : tuple</span>
-<span class="sd">            Tuple of variable and lag which is assumed &lt;= 0.</span>
-<span class="sd">        exclude_contemp : bool</span>
-<span class="sd">            Whether contemporaneous links should be exluded.</span>
-
-<span class="sd">        Yields</span>
-<span class="sd">        ------</span>
-<span class="sd">        Next lagged parent.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">var_lag</span>
-
-        <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">[</span><span class="n">var</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_props</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link_props</span>
-                <span class="n">coeff</span> <span class="o">=</span> <span class="mf">1.</span>
-            <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">exclude_contemp</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="n">only_non_causal_paths</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">var_lag</span> <span class="ow">in</span> <span class="n">causal_children</span><span class="p">):</span>
-                            <span class="k">yield</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">)</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="k">yield</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_children</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to get children from links.</span>
-
-<span class="sd">        Note that for children the lag is positive.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        children : dict</span>
-<span class="sd">            Dictionary of form {0:[(0, 1), (3, 0), ...], 1:[], ...}.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">)</span>
-        <span class="n">children</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">j</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)])</span>
-
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_props</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                    <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link_props</span>
-                    <span class="n">coeff</span> <span class="o">=</span> <span class="mf">1.</span>
-                <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">children</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)))</span>
-
-        <span class="k">return</span> <span class="n">children</span>
-
-    <span class="k">def</span> <span class="nf">_get_lagged_children</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">var_lag</span><span class="p">,</span> <span class="n">children</span><span class="p">,</span> <span class="n">exclude_contemp</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-           <span class="n">only_non_causal_paths</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal_children</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to yield lagged children for var_lag from children.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        var_lag : tuple</span>
-<span class="sd">            Tuple of variable and lag which is assumed &lt;= 0.</span>
-<span class="sd">        children : dict</span>
-<span class="sd">            Dictionary of form {0:[(0, 1), (3, 0), ...], 1:[], ...}.</span>
-<span class="sd">        exclude_contemp : bool</span>
-<span class="sd">            Whether contemporaneous links should be exluded.</span>
-
-<span class="sd">        Yields</span>
-<span class="sd">        ------</span>
-<span class="sd">        Next lagged child.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">var_lag</span>
-        <span class="c1"># lagged_parents = []</span>
-
-        <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">children</span><span class="p">[</span><span class="n">var</span><span class="p">]:</span>
-            <span class="n">k</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">child</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">exclude_contemp</span> <span class="ow">and</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
-                <span class="c1"># lagged_parents.append((i, lag + tau))</span>
-                <span class="k">if</span> <span class="n">only_non_causal_paths</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">var_lag</span> <span class="ow">in</span> <span class="n">X</span> <span class="ow">and</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="n">causal_children</span><span class="p">):</span>
-                        <span class="k">yield</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">)</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">yield</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_non_blocked_ancestors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">conds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;non_repeating&#39;</span><span class="p">,</span>
-                                    <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to return the non-blocked ancestors of variables Y.</span>
-
-<span class="sd">        Returns a dictionary of ancestors for every y in Y. y is a tuple (</span>
-<span class="sd">        var, lag) where lag &lt;= 0. All ancestors with directed paths towards y</span>
-<span class="sd">        that are not blocked by conditions in conds are included. In mode</span>
-<span class="sd">        &#39;non_repeating&#39; an ancestor X^i_{t-\tau_i} with link X^i_{t-\tau_i}</span>
-<span class="sd">        --&gt; X^j_{ t-\tau_j} is only included if X^i_{t&#39;-\tau_i} --&gt; X^j_{</span>
-<span class="sd">        t&#39;-\tau_j} is not already part of the ancestors. The most lagged</span>
-<span class="sd">        ancestor for every variable X^i defines the maximum ancestral time</span>
-<span class="sd">        lag, which is also returned. In mode &#39;max_lag&#39; ancestors are included</span>
-<span class="sd">        up to the maximum time lag max_lag.</span>
-
-<span class="sd">        It&#39;s main use is to return the maximum ancestral time lag max_lag of</span>
-<span class="sd">        y in Y for every variable in self.links_coeffs.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        Y : list of tuples</span>
-<span class="sd">            Of the form [(var, -tau)], where var specifies the variable</span>
-<span class="sd">            index and tau the time lag.</span>
-<span class="sd">        conds : list of tuples</span>
-<span class="sd">            Of the form [(var, -tau)], where var specifies the variable</span>
-<span class="sd">            index and tau the time lag.</span>
-<span class="sd">        mode : {&#39;non_repeating&#39;, &#39;max_lag&#39;}</span>
-<span class="sd">            Whether repeating links should be excluded or ancestors should be</span>
-<span class="sd">            followed up to max_lag.</span>
-<span class="sd">        max_lag : int</span>
-<span class="sd">            Maximum time lag to include ancestors.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        ancestors : dict</span>
-<span class="sd">            Includes ancestors for every y in Y.</span>
-<span class="sd">        max_lag : int</span>
-<span class="sd">            Maximum time lag to include ancestors.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">_repeating</span><span class="p">(</span><span class="n">link</span><span class="p">,</span> <span class="n">seen_links</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Returns True if a link or its time-shifted version is already</span>
-<span class="sd">            included in seen_links.&quot;&quot;&quot;</span>
-            <span class="n">i</span><span class="p">,</span> <span class="n">taui</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tauj</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-            <span class="k">for</span> <span class="n">seen_link</span> <span class="ow">in</span> <span class="n">seen_links</span><span class="p">:</span>
-                <span class="n">seen_i</span><span class="p">,</span> <span class="n">seen_taui</span> <span class="o">=</span> <span class="n">seen_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">seen_j</span><span class="p">,</span> <span class="n">seen_tauj</span> <span class="o">=</span> <span class="n">seen_link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-                <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">seen_i</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="n">seen_j</span>
-                    <span class="ow">and</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tauj</span><span class="o">-</span><span class="n">taui</span><span class="p">)</span> <span class="o">==</span> <span class="nb">abs</span><span class="p">(</span><span class="n">seen_tauj</span><span class="o">-</span><span class="n">seen_taui</span><span class="p">)):</span>
-                    <span class="k">return</span> <span class="kc">True</span>
-
-            <span class="k">return</span> <span class="kc">False</span>
-
-        <span class="k">if</span> <span class="n">conds</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">conds</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="n">conds</span> <span class="o">=</span> <span class="p">[</span><span class="n">z</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">conds</span> <span class="k">if</span> <span class="n">z</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">]</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">)</span>
-
-        <span class="c1"># Initialize max. ancestral time lag for every N</span>
-        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;non_repeating&#39;</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">max_lag</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;max_lag must be set in mode = &#39;max_lag&#39;&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">selection_var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span><span class="p">:</span>
-                <span class="c1"># print (selection_var, conds)</span>
-                <span class="c1"># print([(selection_var, -tau_sel) for tau_sel in range(0, max_lag + 1)])</span>
-                <span class="n">conds</span> <span class="o">+=</span> <span class="p">[(</span><span class="n">selection_var</span><span class="p">,</span> <span class="o">-</span><span class="n">tau_sel</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau_sel</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-
-        <span class="n">ancestors</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">y</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">])</span>
-
-        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">y</span>   <span class="c1"># tau &lt;= 0</span>
-            <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;non_repeating&#39;</span><span class="p">:</span>
-                <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
-            <span class="n">seen_links</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">this_level</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span><span class="p">]</span>
-            <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">next_level</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">par</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lagged_parents</span><span class="p">(</span><span class="n">varlag</span><span class="p">):</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">par</span>
-                        <span class="k">if</span> <span class="n">par</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">conds</span> <span class="ow">and</span> <span class="n">par</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ancestors</span><span class="p">[</span><span class="n">y</span><span class="p">]:</span>
-                            <span class="k">if</span> <span class="p">((</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;non_repeating&#39;</span> <span class="ow">and</span>
-                                <span class="ow">not</span> <span class="n">_repeating</span><span class="p">((</span><span class="n">par</span><span class="p">,</span> <span class="n">varlag</span><span class="p">),</span> <span class="n">seen_links</span><span class="p">))</span> <span class="ow">or</span>
-                                <span class="p">(</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;max_lag&#39;</span> <span class="ow">and</span>
-                                 <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">max_lag</span><span class="p">))):</span>
-                                    <span class="n">ancestors</span><span class="p">[</span><span class="n">y</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">par</span><span class="p">)</span>
-                                    <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;non_repeating&#39;</span><span class="p">:</span>
-                                        <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span>
-                                                         <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
-                                    <span class="n">next_level</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">par</span><span class="p">)</span>
-                                    <span class="n">seen_links</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">par</span><span class="p">,</span> <span class="n">varlag</span><span class="p">))</span>
-
-                <span class="n">this_level</span> <span class="o">=</span> <span class="n">next_level</span>
-
-        <span class="k">return</span> <span class="n">ancestors</span><span class="p">,</span> <span class="n">max_lag</span>
-
-    <span class="k">def</span> <span class="nf">_get_maximum_possible_lag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to return the maximum time lag of any confounding path.</span>
-
-<span class="sd">        This is still based on a conjecture!</span>
-
-<span class="sd">        The conjecture states that if and only if X and Y are d-connected given Z</span>
-<span class="sd">        in a stationary DAG, then there exists a confounding path with a maximal</span>
-<span class="sd">        time lag (i.e., the node on that path with maximal lag) given as follows:</span>
-<span class="sd">        For any node in XYZ consider all non-repeating causal paths from the past</span>
-<span class="sd">        to that node, where non-repeating means that a link X^i_{t-\tau_i}</span>
-<span class="sd">        --&gt; X^j_{ t-\tau_j} is only traversed if X^i_{t&#39;-\tau_i} --&gt; X^j_{</span>
-<span class="sd">        t&#39;-\tau_j} is not already part of that path. The most lagged</span>
-<span class="sd">        ancestor for every variable node in XYZ defines the maximum ancestral time</span>
-<span class="sd">        lag, which is returned.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        XYZ : list of tuples</span>
-<span class="sd">            Of the form [(var, -tau)], where var specifies the variable</span>
-<span class="sd">            index and tau the time lag.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        max_lag : int</span>
-<span class="sd">            Maximum time lag of non-repeating causal path ancestors.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">_repeating</span><span class="p">(</span><span class="n">link</span><span class="p">,</span> <span class="n">seen_path</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Returns True if a link or its time-shifted version is already</span>
-<span class="sd">            included in seen_links.&quot;&quot;&quot;</span>
-            <span class="n">i</span><span class="p">,</span> <span class="n">taui</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tauj</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-            <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">seen_link</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">seen_path</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="n">seen_i</span><span class="p">,</span> <span class="n">seen_taui</span> <span class="o">=</span> <span class="n">seen_link</span>
-                <span class="n">seen_j</span><span class="p">,</span> <span class="n">seen_tauj</span> <span class="o">=</span> <span class="n">seen_path</span><span class="p">[</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-
-                <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">seen_i</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="n">seen_j</span>
-                    <span class="ow">and</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tauj</span><span class="o">-</span><span class="n">taui</span><span class="p">)</span> <span class="o">==</span> <span class="nb">abs</span><span class="p">(</span><span class="n">seen_tauj</span><span class="o">-</span><span class="n">seen_taui</span><span class="p">)):</span>
-                    <span class="k">return</span> <span class="kc">True</span>
-
-            <span class="k">return</span> <span class="kc">False</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">)</span>
-
-        <span class="c1"># Initialize max. ancestral time lag for every N</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-    
-        <span class="c1"># Not sure whether this is relevant!</span>
-        <span class="c1"># if self.selection_vars is not None:</span>
-        <span class="c1">#     for selection_var in self.selection_vars:</span>
-        <span class="c1">#         # print (selection_var, conds)</span>
-        <span class="c1">#         # print([(selection_var, -tau_sel) for tau_sel in range(0, max_lag + 1)])</span>
-        <span class="c1">#         conds += [(selection_var, -tau_sel) for tau_sel in range(0, max_lag + 1)]</span>
-
-        <span class="c1"># ancestors = dict([(y, []) for y in Y])</span>
-
-        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">XYZ</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">y</span>   <span class="c1"># tau &lt;= 0</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
-                
-            <span class="n">causal_path</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">queue</span> <span class="o">=</span> <span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="n">causal_path</span><span class="p">)]</span>
-
-            <span class="k">while</span> <span class="n">queue</span><span class="p">:</span>
-                <span class="n">varlag</span><span class="p">,</span> <span class="n">causal_path</span> <span class="o">=</span> <span class="n">queue</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-                <span class="n">causal_path</span> <span class="o">=</span> <span class="p">[</span><span class="n">varlag</span><span class="p">]</span> <span class="o">+</span> <span class="n">causal_path</span>
-
-                <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lagged_parents</span><span class="p">(</span><span class="n">varlag</span><span class="p">):</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">node</span>
-
-                    <span class="k">if</span> <span class="p">(</span><span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">causal_path</span><span class="p">):</span>
-                    
-                        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">causal_path</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="n">queue</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">node</span><span class="p">,</span> <span class="n">causal_path</span><span class="p">))</span>
-                            <span class="k">continue</span>
-
-                        <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">causal_path</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">_repeating</span><span class="p">((</span><span class="n">node</span><span class="p">,</span> <span class="n">varlag</span><span class="p">),</span> <span class="n">causal_path</span><span class="p">):</span>
-                            
-                                <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
-                                <span class="n">queue</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">node</span><span class="p">,</span> <span class="n">causal_path</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Max. non-repeated ancestral time lag: &quot;</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">max_lag</span>
-
-    <span class="k">def</span> <span class="nf">_get_descendants</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">children</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">ignore_time_bounds</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get descendants of nodes in W up to time t.</span>
-<span class="sd">        </span>
-<span class="sd">        Includes the nodes themselves.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">descendants</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">W</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">W</span><span class="p">:</span>
-            <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">w</span> 
-            <span class="n">this_level</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="p">]</span>
-            <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">next_level</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">varlag</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lagged_children</span><span class="p">(</span><span class="n">varlag</span><span class="p">,</span> <span class="n">children</span><span class="p">):</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">child</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">child</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">descendants</span> 
-                            <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="n">max_lag</span> <span class="o">&lt;=</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">ignore_time_bounds</span><span class="p">)):</span>
-                            <span class="n">descendants</span> <span class="o">=</span> <span class="n">descendants</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">child</span><span class="p">]))</span>
-                            <span class="n">next_level</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">child</span><span class="p">)</span>
-
-                <span class="n">this_level</span> <span class="o">=</span> <span class="n">next_level</span>       
-
-        <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">descendants</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_has_any_path</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">conds</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-        <span class="n">starts_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ends_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">forbidden_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">only_non_causal_paths</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">check_optimality_cond</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">optimality_cond_des_YM</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">optimality_cond_Y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">only_collider_paths_with_vancs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">XYS</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">return_path</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns True if X and Y are d-connected by any open path.</span>
-
-<span class="sd">        Does breadth-first search from both X and Y and meets in the middle.</span>
-<span class="sd">        Paths are walked according to the d-separation rules where paths can</span>
-<span class="sd">        only traverse motifs &lt;-- v &lt;-- or &lt;-- v --&gt; or --&gt; v --&gt; or</span>
-<span class="sd">        --&gt; [v] &lt;-- where [.] indicates that v is conditioned on.</span>
-<span class="sd">        Furthermore, paths nodes (v, t) need to fulfill max_lag &lt;= t &lt;= 0</span>
-<span class="sd">        and links cannot be traversed backwards.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y : lists of tuples</span>
-<span class="sd">            Of the form [(var, -tau)], where var specifies the variable</span>
-<span class="sd">            index and tau the time lag.</span>
-<span class="sd">        conds : list of tuples</span>
-<span class="sd">            Of the form [(var, -tau)], where var specifies the variable</span>
-<span class="sd">            index and tau the time lag.</span>
-<span class="sd">        max_lag : int</span>
-<span class="sd">            Maximum time lag.</span>
-<span class="sd">        starts_with : {None, &#39;tail&#39;, &#39;arrohead&#39;}</span>
-<span class="sd">            Whether to only consider paths starting with particular mark at X.</span>
-<span class="sd">        ends_with : {None, &#39;tail&#39;, &#39;arrohead&#39;}</span>
-<span class="sd">            Whether to only consider paths ending with particular mark at Y.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">max_lag</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">conds</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">conds</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_maximum_possible_lag</span><span class="p">(</span><span class="n">X</span><span class="o">+</span><span class="n">Y</span><span class="o">+</span><span class="n">conds</span><span class="p">)</span>
-
-        <span class="k">def</span> <span class="nf">_walk_to_parents</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Helper function to update paths when walking to parents.&quot;&quot;&quot;</span>
-            <span class="n">found_connection</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lagged_parents</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> 
-                <span class="n">only_non_causal_paths</span><span class="o">=</span><span class="n">only_non_causal_paths</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> 
-                <span class="n">causal_children</span><span class="o">=</span><span class="n">causal_children</span><span class="p">):</span>
-                <span class="c1"># Cannot walk into conditioned parents and</span>
-                <span class="c1"># cannot walk beyond t or max_lag</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="n">w</span>
-
-                <span class="k">if</span> <span class="n">w</span> <span class="o">==</span> <span class="n">x</span> <span class="ow">and</span> <span class="n">starts_with</span> <span class="o">==</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">:</span>
-                    <span class="k">continue</span>
-
-                <span class="k">if</span> <span class="n">w</span> <span class="o">==</span> <span class="n">y</span> <span class="ow">and</span> <span class="n">ends_with</span> <span class="o">==</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">:</span>
-                    <span class="k">continue</span>
-
-                <span class="k">if</span> <span class="p">(</span><span class="n">w</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">conds</span> <span class="ow">and</span> <span class="n">w</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">forbidden_nodes</span> <span class="ow">and</span>
-                    <span class="c1"># (w, v) not in seen_links and</span>
-                    <span class="n">t</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">abs</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">max_lag</span><span class="p">):</span>
-                    <span class="c1"># if ((w, &#39;tail&#39;) not in this_path and </span>
-                    <span class="c1">#     (w, None) not in this_path):</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">w</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">this_path</span> <span class="ow">or</span> 
-                        <span class="p">(</span><span class="s1">&#39;tail&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">this_path</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="ow">and</span> <span class="kc">None</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">this_path</span><span class="p">[</span><span class="n">w</span><span class="p">])):</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Walk parent: </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2">  &quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
-                        <span class="n">fringe</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">w</span><span class="p">,</span> <span class="s1">&#39;tail&#39;</span><span class="p">))</span>
-                        <span class="k">if</span> <span class="n">w</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">this_path</span><span class="p">:</span>
-                            <span class="n">this_path</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;tail&#39;</span> <span class="p">:</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">)}</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="n">this_path</span><span class="p">[</span><span class="n">w</span><span class="p">][</span><span class="s1">&#39;tail&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">)</span>
-                        <span class="c1"># seen_links.append((v, w))</span>
-                    <span class="c1"># Determine whether X and Y are connected</span>
-                    <span class="c1"># (w, None) indicates the start or end node X/Y</span>
-                    <span class="c1"># if ((w, &#39;tail&#39;) in other_path </span>
-                    <span class="c1">#    or (w, &#39;arrowhead&#39;) in other_path</span>
-                    <span class="c1">#    or (w, None) in other_path):</span>
-                    <span class="k">if</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">other_path</span><span class="p">:</span>
-                        <span class="n">found_connection</span> <span class="o">=</span> <span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="s1">&#39;tail&#39;</span><span class="p">)</span> 
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Found connection: &quot;</span><span class="p">,</span> <span class="n">found_connection</span><span class="p">)</span>  
-                        <span class="k">break</span>
-            <span class="k">return</span> <span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span>
-
-        <span class="k">def</span> <span class="nf">_walk_to_children</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Helper function to update paths when walking to children.&quot;&quot;&quot;</span>
-            <span class="n">found_connection</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lagged_children</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">children</span><span class="p">,</span> 
-                <span class="n">only_non_causal_paths</span><span class="o">=</span><span class="n">only_non_causal_paths</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> 
-                <span class="n">causal_children</span><span class="o">=</span><span class="n">causal_children</span><span class="p">):</span>
-                <span class="c1"># You can also walk into conditioned children,</span>
-                <span class="c1"># but cannot walk beyond t or max_lag</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="n">w</span>
-
-                <span class="k">if</span> <span class="n">w</span> <span class="o">==</span> <span class="n">x</span> <span class="ow">and</span> <span class="n">starts_with</span> <span class="o">==</span> <span class="s1">&#39;tail&#39;</span><span class="p">:</span>
-                    <span class="k">continue</span>
-
-                <span class="k">if</span> <span class="n">w</span> <span class="o">==</span> <span class="n">y</span> <span class="ow">and</span> <span class="n">ends_with</span> <span class="o">==</span> <span class="s1">&#39;tail&#39;</span><span class="p">:</span>
-                    <span class="k">continue</span>
-
-                <span class="k">if</span> <span class="p">(</span><span class="n">w</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">forbidden_nodes</span> <span class="ow">and</span>
-                    <span class="c1"># (w, v) not in seen_links and</span>
-                    <span class="n">t</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">abs</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">max_lag</span><span class="p">):</span>
-                    <span class="c1"># if ((w, &#39;arrowhead&#39;) not in this_path and </span>
-                    <span class="c1">#     (w, None) not in this_path):</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">w</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">this_path</span> <span class="ow">or</span> 
-                        <span class="p">(</span><span class="s1">&#39;arrowhead&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">this_path</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="ow">and</span> <span class="kc">None</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">this_path</span><span class="p">[</span><span class="n">w</span><span class="p">])):</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Walk child:  </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2">  &quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
-                        <span class="n">fringe</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">w</span><span class="p">,</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">))</span>
-                        <span class="c1"># this_path[(w, &#39;arrowhead&#39;)] = (v, &#39;tail&#39;)</span>
-                        <span class="k">if</span> <span class="n">w</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">this_path</span><span class="p">:</span>
-                            <span class="n">this_path</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;arrowhead&#39;</span> <span class="p">:</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;tail&#39;</span><span class="p">)}</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="n">this_path</span><span class="p">[</span><span class="n">w</span><span class="p">][</span><span class="s1">&#39;arrowhead&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;tail&#39;</span><span class="p">)</span>
-                        <span class="c1"># seen_links.append((v, w))</span>
-                    <span class="c1"># Determine whether X and Y are connected</span>
-                    <span class="c1"># If the other_path contains w with a tail, then w must</span>
-                    <span class="c1"># NOT be conditioned on. Alternatively, if the other_path</span>
-                    <span class="c1"># contains w with an arrowhead, then w must be</span>
-                    <span class="c1"># conditioned on.</span>
-                    <span class="c1"># if (((w, &#39;tail&#39;) in other_path and w not in conds)</span>
-                    <span class="c1">#    or ((w, &#39;arrowhead&#39;) in other_path and w in conds)</span>
-                    <span class="c1">#    or (w, None) in other_path):</span>
-                    <span class="k">if</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">other_path</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="p">((</span><span class="s1">&#39;tail&#39;</span> <span class="ow">in</span> <span class="n">other_path</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="ow">and</span> <span class="n">w</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">conds</span><span class="p">)</span> <span class="ow">or</span>
-                            <span class="p">(</span><span class="s1">&#39;arrowhead&#39;</span> <span class="ow">in</span> <span class="n">other_path</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="ow">and</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">conds</span><span class="p">)</span> <span class="ow">or</span>
-                            <span class="p">(</span><span class="kc">None</span> <span class="ow">in</span> <span class="n">other_path</span><span class="p">[</span><span class="n">w</span><span class="p">])):</span>
-                            <span class="n">found_connection</span> <span class="o">=</span> <span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">)</span> 
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Found connection: &quot;</span><span class="p">,</span> <span class="n">found_connection</span><span class="p">)</span> 
-                            <span class="k">break</span>
-            <span class="k">return</span> <span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span>
-
-        <span class="k">def</span> <span class="nf">_walk_fringe</span><span class="p">(</span><span class="n">this_level</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Helper function to walk each fringe, i.e., the path from X and Y,</span>
-<span class="sd">            respectively.&quot;&quot;&quot;</span>
-            <span class="n">found_connection</span> <span class="o">=</span> <span class="kc">False</span>
-
-            <span class="k">if</span> <span class="n">starts_with</span> <span class="o">==</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">this_level</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
-                    <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span>
-                         <span class="n">this_path</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_to_parents</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> 
-                                                       <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">)</span>
-                    <span class="k">return</span> <span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span>
-
-            <span class="k">elif</span> <span class="n">starts_with</span> <span class="o">==</span> <span class="s1">&#39;tail&#39;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">this_level</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
-                    <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span>
-                         <span class="n">this_path</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_to_children</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> 
-                                                       <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">)</span>
-                    <span class="k">return</span> <span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span> 
-
-            <span class="k">if</span> <span class="n">ends_with</span> <span class="o">==</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">this_level</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
-                    <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span>
-                         <span class="n">this_path</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_to_parents</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> 
-                                                       <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">)</span>
-                    <span class="k">return</span> <span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span>
-
-            <span class="k">elif</span> <span class="n">ends_with</span> <span class="o">==</span> <span class="s1">&#39;tail&#39;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">this_level</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">this_level</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
-                    <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span>
-                         <span class="n">this_path</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_to_children</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> 
-                                                       <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">)</span>
-                    <span class="k">return</span> <span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span> 
-
-            <span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">mark</span> <span class="ow">in</span> <span class="n">this_level</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">conds</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">mark</span> <span class="o">==</span> <span class="s1">&#39;arrowhead&#39;</span> <span class="ow">or</span> <span class="n">mark</span> <span class="o">==</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="n">directed</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                        <span class="c1"># Motif: --&gt; [v] &lt;--</span>
-                        <span class="c1"># If standing on a condition and coming from an</span>
-                        <span class="c1"># arrowhead, you can only walk into parents</span>
-                        <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span>
-                         <span class="n">this_path</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_to_parents</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> 
-                                                       <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">found_connection</span><span class="p">:</span> <span class="k">break</span>            
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">only_collider_paths_with_vancs</span><span class="p">:</span>
-                        <span class="k">continue</span>
-
-                    <span class="k">if</span> <span class="p">(</span><span class="n">mark</span> <span class="o">==</span> <span class="s1">&#39;tail&#39;</span> <span class="ow">or</span> <span class="n">mark</span> <span class="o">==</span> <span class="kc">None</span><span class="p">):</span>
-                        <span class="c1"># Motif: &lt;-- v &lt;-- or &lt;-- v --&gt;</span>
-                        <span class="c1"># If NOT standing on a condition and coming from</span>
-                        <span class="c1"># a tail mark, you can walk into parents or </span>
-                        <span class="c1"># children</span>
-                        <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span>
-                         <span class="n">this_path</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_to_parents</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> 
-                                                       <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">found_connection</span><span class="p">:</span> <span class="k">break</span> 
-                        
-                        <span class="k">if</span> <span class="ow">not</span> <span class="n">directed</span><span class="p">:</span>
-                            <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span>
-                             <span class="n">this_path</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_to_children</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> 
-                                                           <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">)</span>
-                            <span class="k">if</span> <span class="n">found_connection</span><span class="p">:</span> <span class="k">break</span> 
-                      
-                    <span class="k">elif</span> <span class="n">mark</span> <span class="o">==</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">:</span>
-                        <span class="c1"># Motif: --&gt; v --&gt;</span>
-                        <span class="c1"># If NOT standing on a condition and coming from</span>
-                        <span class="c1"># an arrowhead mark, you can only walk into</span>
-                        <span class="c1"># children</span>
-                        <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span>
-                         <span class="n">this_path</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_to_children</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> 
-                                                       <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">found_connection</span><span class="p">:</span> <span class="k">break</span>
-
-                        <span class="k">if</span> <span class="n">check_optimality_cond</span> <span class="ow">and</span> <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">:</span>
-                            <span class="c1"># if v is not descendant of YM</span>
-                            <span class="c1"># and v is not connected to Y given X OS\Cu</span>
-                            <span class="c1"># print(&quot;v = &quot;, v)</span>
-                            <span class="n">cond4a</span> <span class="o">=</span> <span class="n">v</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">optimality_cond_des_YM</span>
-                            <span class="n">cond4b</span> <span class="o">=</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_has_any_path</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[</span><span class="n">v</span><span class="p">],</span> <span class="n">Y</span><span class="o">=</span><span class="n">optimality_cond_Y</span><span class="p">,</span> 
-                                <span class="n">conds</span><span class="o">=</span><span class="n">conds</span> <span class="o">+</span> <span class="n">X</span><span class="p">,</span> 
-                                <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                                <span class="n">starts_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                <span class="n">ends_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  
-                                <span class="n">forbidden_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="c1">#list(prelim_Oset), </span>
-                                <span class="n">return_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                            <span class="c1"># print(cond4a, cond4b)</span>
-                            <span class="k">if</span> <span class="n">cond4a</span> <span class="ow">and</span> <span class="n">cond4b</span><span class="p">:</span>
-                                <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span>
-                                 <span class="n">this_path</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_to_parents</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> 
-                                                               <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span><span class="p">)</span>
-                                <span class="c1"># print(found_connection)</span>
-                                <span class="k">if</span> <span class="n">found_connection</span><span class="p">:</span> <span class="k">break</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Updated fringe: &quot;</span><span class="p">,</span> <span class="n">fringe</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">found_connection</span><span class="p">,</span> <span class="n">fringe</span><span class="p">,</span> <span class="n">this_path</span><span class="p">,</span> <span class="n">other_path</span>
-
-        <span class="k">def</span> <span class="nf">backtrace_path</span><span class="p">():</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Helper function to get path from start point, end point, </span>
-<span class="sd">            and connection found.&quot;&quot;&quot;</span>
-
-            <span class="n">path</span> <span class="o">=</span> <span class="p">[</span><span class="n">found_connection</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-            <span class="n">node</span><span class="p">,</span> <span class="n">mark</span> <span class="o">=</span> <span class="n">found_connection</span>
-
-            <span class="k">if</span> <span class="s1">&#39;tail&#39;</span> <span class="ow">in</span> <span class="n">pred</span><span class="p">[</span><span class="n">node</span><span class="p">]:</span>
-                <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;tail&#39;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;arrowhead&#39;</span>
-            <span class="c1"># print(found_connection)</span>
-            <span class="k">while</span> <span class="n">path</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">x</span><span class="p">:</span>
-                <span class="c1"># print(path, node, mark, pred[node])</span>
-                <span class="n">prev_node</span><span class="p">,</span> <span class="n">prev_mark</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="n">node</span><span class="p">][</span><span class="n">mark</span><span class="p">]</span>
-                <span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prev_node</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">prev_mark</span> <span class="o">==</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">prev_node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">conds</span><span class="p">:</span>
-                        <span class="c1"># if pass_through_colliders:</span>
-                        <span class="c1">#     if &#39;tail&#39; in pred[prev_node] and pred[prev_node][&#39;tail&#39;] != (node, mark):</span>
-                        <span class="c1">#         mark = &#39;tail&#39;</span>
-                        <span class="c1">#     else:</span>
-                        <span class="c1">#         mark = &#39;arrowhead&#39;</span>
-                        <span class="c1"># else:</span>
-                            <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;tail&#39;</span>
-                    <span class="k">elif</span> <span class="n">prev_node</span> <span class="ow">in</span> <span class="n">conds</span><span class="p">:</span>
-                        <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;arrowhead&#39;</span>
-                <span class="k">elif</span> <span class="n">prev_mark</span> <span class="o">==</span> <span class="s1">&#39;tail&#39;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="s1">&#39;tail&#39;</span> <span class="ow">in</span> <span class="n">pred</span><span class="p">[</span><span class="n">prev_node</span><span class="p">]</span> <span class="ow">and</span> <span class="n">pred</span><span class="p">[</span><span class="n">prev_node</span><span class="p">][</span><span class="s1">&#39;tail&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">mark</span><span class="p">):</span>
-                        <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;tail&#39;</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;arrowhead&#39;</span> 
-                <span class="n">node</span> <span class="o">=</span> <span class="n">prev_node</span>
-
-            <span class="n">path</span><span class="o">.</span><span class="n">reverse</span><span class="p">()</span>
-
-            <span class="n">node</span><span class="p">,</span> <span class="n">mark</span> <span class="o">=</span> <span class="n">found_connection</span>
-            <span class="k">if</span> <span class="s1">&#39;tail&#39;</span> <span class="ow">in</span> <span class="n">succ</span><span class="p">[</span><span class="n">node</span><span class="p">]:</span>
-                <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;tail&#39;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;arrowhead&#39;</span>
-
-            <span class="k">while</span> <span class="n">path</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">y</span><span class="p">:</span>
-                <span class="n">next_node</span><span class="p">,</span> <span class="n">next_mark</span> <span class="o">=</span> <span class="n">succ</span><span class="p">[</span><span class="n">node</span><span class="p">][</span><span class="n">mark</span><span class="p">]</span>
-                <span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">next_node</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">next_mark</span> <span class="o">==</span> <span class="s1">&#39;arrowhead&#39;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">next_node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">conds</span><span class="p">:</span>
-                        <span class="c1"># if pass_through_colliders:</span>
-                        <span class="c1">#     if &#39;tail&#39; in succ[next_node] and succ[next_node][&#39;tail&#39;] != (node, mark):</span>
-                        <span class="c1">#         mark = &#39;tail&#39;</span>
-                        <span class="c1">#     else:</span>
-                        <span class="c1">#         mark = &#39;arrowhead&#39;</span>
-                        <span class="c1"># else:</span>
-                            <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;tail&#39;</span>
-                    <span class="k">elif</span> <span class="n">next_node</span> <span class="ow">in</span> <span class="n">conds</span><span class="p">:</span>
-                        <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;arrowhead&#39;</span>
-                <span class="k">elif</span> <span class="n">next_mark</span> <span class="o">==</span> <span class="s1">&#39;tail&#39;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="s1">&#39;tail&#39;</span> <span class="ow">in</span> <span class="n">succ</span><span class="p">[</span><span class="n">next_node</span><span class="p">]</span> <span class="ow">and</span> <span class="n">succ</span><span class="p">[</span><span class="n">next_node</span><span class="p">][</span><span class="s1">&#39;tail&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">mark</span><span class="p">):</span>
-                        <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;tail&#39;</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">mark</span> <span class="o">=</span> <span class="s1">&#39;arrowhead&#39;</span> 
-                <span class="n">node</span> <span class="o">=</span> <span class="n">next_node</span>
-
-            <span class="k">return</span> <span class="n">path</span>
-
-
-        <span class="k">if</span> <span class="n">conds</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">conds</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="k">if</span> <span class="n">forbidden_nodes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">forbidden_nodes</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="n">conds</span> <span class="o">=</span> <span class="p">[</span><span class="n">z</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">conds</span> <span class="k">if</span> <span class="n">z</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Y</span> <span class="ow">and</span> <span class="n">z</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
-        <span class="c1"># print(X, Y, conds)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">selection_var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span><span class="p">:</span>
-                <span class="n">conds</span> <span class="o">+=</span> <span class="p">[(</span><span class="n">selection_var</span><span class="p">,</span> <span class="o">-</span><span class="n">tau_sel</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau_sel</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">)</span>
-        <span class="n">children</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_children</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="n">only_non_causal_paths</span><span class="p">:</span>
-            <span class="n">anc_Y_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_non_blocked_ancestors</span><span class="p">(</span><span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">conds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;max_lag&#39;</span><span class="p">,</span>
-                                    <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="c1"># print(anc_Y_dict)</span>
-            <span class="n">anc_Y</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">:</span>
-                <span class="n">anc_Y</span> <span class="o">+=</span> <span class="n">anc_Y_dict</span><span class="p">[</span><span class="n">y</span><span class="p">]</span>
-            <span class="n">des_X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_descendants</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">children</span><span class="o">=</span><span class="n">children</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">)</span>
-            <span class="n">mediators</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">anc_Y</span><span class="p">)</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">des_X</span><span class="p">))</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-
-            <span class="n">causal_children</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">mediators</span><span class="p">)</span> <span class="o">+</span> <span class="n">Y</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">causal_children</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">if</span> <span class="n">only_collider_paths_with_vancs</span><span class="p">:</span>
-            <span class="n">vancs_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_non_blocked_ancestors</span><span class="p">(</span><span class="n">Y</span><span class="o">=</span><span class="n">XYS</span><span class="p">,</span> <span class="n">conds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;max_lag&#39;</span><span class="p">,</span>
-                                    <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">vancs</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-            <span class="k">for</span> <span class="n">xys</span> <span class="ow">in</span> <span class="n">XYS</span><span class="p">:</span>
-                <span class="n">vancs</span> <span class="o">=</span> <span class="n">vancs</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">vancs_dict</span><span class="p">[</span><span class="n">xys</span><span class="p">]))</span>
-            <span class="n">vancs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">vancs</span><span class="p">)</span> <span class="o">+</span> <span class="n">XYS</span>
-            <span class="n">conds</span> <span class="o">=</span> <span class="n">vancs</span>
-        <span class="c1"># else:</span>
-        <span class="c1">#     vancs = None</span>
-
-        <span class="c1"># Iterate through nodes in X and Y</span>
-        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">:</span>
-          <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">:</span>
-
-            <span class="c1"># seen_links = []</span>
-            <span class="c1"># predecessor and successors in search</span>
-            <span class="c1"># (x, None) where None indicates start/end nodes, later (v,</span>
-            <span class="c1"># &#39;tail&#39;) or (w, &#39;arrowhead&#39;) indicate how a link ends at a node</span>
-            <span class="n">pred</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span> <span class="p">:</span> <span class="p">{</span><span class="kc">None</span><span class="p">:</span> <span class="kc">None</span><span class="p">}}</span>
-            <span class="n">succ</span> <span class="o">=</span> <span class="p">{</span><span class="n">y</span> <span class="p">:</span> <span class="p">{</span><span class="kc">None</span><span class="p">:</span> <span class="kc">None</span><span class="p">}}</span>
-
-            <span class="c1"># initialize fringes, start with forward from X</span>
-            <span class="n">forward_fringe</span> <span class="o">=</span> <span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="kc">None</span><span class="p">)]</span>
-            <span class="n">reverse_fringe</span> <span class="o">=</span> <span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="kc">None</span><span class="p">)]</span>
-
-            <span class="k">while</span> <span class="n">forward_fringe</span> <span class="ow">and</span> <span class="n">reverse_fringe</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">forward_fringe</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">reverse_fringe</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Walk from X since len(X_fringe)=</span><span class="si">%d</span><span class="s2"> &quot;</span>
-                              <span class="s2">&quot;&lt;= len(Y_fringe)=</span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">forward_fringe</span><span class="p">),</span> 
-                                <span class="nb">len</span><span class="p">(</span><span class="n">reverse_fringe</span><span class="p">)))</span>
-                    <span class="n">this_level</span> <span class="o">=</span> <span class="n">forward_fringe</span>
-                    <span class="n">forward_fringe</span> <span class="o">=</span> <span class="p">[]</span>    
-                    <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">forward_fringe</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> 
-                     <span class="n">succ</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_fringe</span><span class="p">(</span><span class="n">this_level</span><span class="p">,</span> <span class="n">forward_fringe</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> 
-                                                <span class="n">succ</span><span class="p">)</span>
-
-                    <span class="c1"># print(pred)</span>
-                    <span class="k">if</span> <span class="n">found_connection</span><span class="p">:</span> 
-                        <span class="k">if</span> <span class="n">return_path</span><span class="p">:</span>
-                            <span class="n">backtraced_path</span> <span class="o">=</span> <span class="n">backtrace_path</span><span class="p">()</span>
-                            <span class="k">return</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">node</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> 
-                                    <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">backtraced_path</span> 
-                                    <span class="k">if</span> <span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">]</span>
-                        <span class="k">else</span><span class="p">:</span> 
-                            <span class="k">return</span> <span class="kc">True</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Walk from Y since len(X_fringe)=</span><span class="si">%d</span><span class="s2"> &quot;</span>
-                              <span class="s2">&quot;&gt; len(Y_fringe)=</span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">forward_fringe</span><span class="p">),</span> 
-                                <span class="nb">len</span><span class="p">(</span><span class="n">reverse_fringe</span><span class="p">)))</span>
-                    <span class="n">this_level</span> <span class="o">=</span> <span class="n">reverse_fringe</span>
-                    <span class="n">reverse_fringe</span> <span class="o">=</span> <span class="p">[]</span>
-                    <span class="p">(</span><span class="n">found_connection</span><span class="p">,</span> <span class="n">reverse_fringe</span><span class="p">,</span> <span class="n">succ</span><span class="p">,</span> 
-                     <span class="n">pred</span><span class="p">)</span> <span class="o">=</span> <span class="n">_walk_fringe</span><span class="p">(</span><span class="n">this_level</span><span class="p">,</span> <span class="n">reverse_fringe</span><span class="p">,</span> <span class="n">succ</span><span class="p">,</span> 
-                                                <span class="n">pred</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="n">found_connection</span><span class="p">:</span> 
-                        <span class="k">if</span> <span class="n">return_path</span><span class="p">:</span>
-                            <span class="n">backtraced_path</span> <span class="o">=</span> <span class="n">backtrace_path</span><span class="p">()</span>
-                            <span class="k">return</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">node</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> 
-                                    <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">backtraced_path</span> 
-                                    <span class="k">if</span> <span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">]</span>
-                        <span class="k">else</span><span class="p">:</span> 
-                            <span class="k">return</span> <span class="kc">True</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X_fringe = </span><span class="si">%s</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">forward_fringe</span><span class="p">)</span> <span class="o">+</span>
-                          <span class="s2">&quot;Y_fringe = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">reverse_fringe</span><span class="p">))</span>           
-
-        <span class="k">return</span> <span class="kc">False</span>
-
-    <span class="k">def</span> <span class="nf">_is_dsep</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns whether X and Y are d-separated given Z in the graph.</span>
-
-<span class="sd">        X, Y, Z are of the form (var, lag) for lag &lt;= 0. D-separation is</span>
-<span class="sd">        based on:</span>
-
-<span class="sd">        1. Assessing the maximum time lag max_lag possible for any confounding</span>
-<span class="sd">        path (see _get_maximum_possible_lag(...)).</span>
-
-<span class="sd">        2. Using the time series graph truncated at max_lag we then test</span>
-<span class="sd">        d-separation between X and Y conditional on Z using breadth-first</span>
-<span class="sd">        search of non-blocked paths according to d-separation rules.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : list of tuples</span>
-<span class="sd">            List of variables chosen for current independence test.</span>
-<span class="sd">        max_lag : int, optional (default: None)</span>
-<span class="sd">            Used here to constrain the _is_dsep function to the graph</span>
-<span class="sd">            truncated at max_lag instead of identifying the max_lag from</span>
-<span class="sd">            ancestral search.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        dseparated : bool, or path</span>
-<span class="sd">            True if X and Y are d-separated given Z in the graph.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Testing X=</span><span class="si">%s</span><span class="s2"> d-sep Y=</span><span class="si">%s</span><span class="s2"> given Z=</span><span class="si">%s</span><span class="s2"> in TSG&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="n">Z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">Z</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="k">if</span> <span class="n">max_lag</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># max_lags = dict([(j, max_lag) for j in range(N)])</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Set max. time lag to: &quot;</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_maximum_possible_lag</span><span class="p">(</span><span class="n">X</span><span class="o">+</span><span class="n">Y</span><span class="o">+</span><span class="n">Z</span><span class="p">)</span>
-
-        <span class="c1"># Store overall max. lag</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="n">max_lag</span>
-
-        <span class="c1"># _has_any_path is the main function that searches open paths</span>
-        <span class="n">any_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_has_any_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">conds</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">any_path</span><span class="p">:</span>
-            <span class="n">dseparated</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">dseparated</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="k">return</span> <span class="n">dseparated</span>
-
-<div class="viewcode-block" id="OracleCI.check_shortest_path"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.check_shortest_path">[docs]</a>    <span class="k">def</span> <span class="nf">check_shortest_path</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span>
-                 <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># compute_ancestors=False, </span>
-                 <span class="n">starts_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ends_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                 <span class="n">forbidden_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">only_non_causal_paths</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">check_optimality_cond</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">optimality_cond_des_YM</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">optimality_cond_Y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">return_path</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns path between X and Y given Z in the graph.</span>
-
-<span class="sd">        X, Y, Z are of the form (var, lag) for lag &lt;= 0. D-separation is</span>
-<span class="sd">        based on:</span>
-
-<span class="sd">        1. Assessing maximum time lag max_lag of last ancestor of any X, Y, Z</span>
-<span class="sd">        with non-blocked (by Z), non-repeating directed path towards X, Y, Z</span>
-<span class="sd">        in the graph. &#39;non_repeating&#39; means that an ancestor X^i_{ t-\tau_i}</span>
-<span class="sd">        with link X^i_{t-\tau_i} --&gt; X^j_{ t-\tau_j} is only included if</span>
-<span class="sd">        X^i_{t&#39;-\tau_i} --&gt; X^j_{ t&#39;-\tau_j} for t&#39;!=t is not already part of</span>
-<span class="sd">        the ancestors.</span>
-
-<span class="sd">        2. Using the time series graph truncated at max_lag we then test</span>
-<span class="sd">        d-separation between X and Y conditional on Z using breadth-first</span>
-<span class="sd">        search of non-blocked paths according to d-separation rules including</span>
-<span class="sd">        selection variables.</span>
-
-<span class="sd">        Optionally only considers paths starting/ending with specific marks)</span>
-<span class="sd">        and makes available the ancestors up to max_lag of X, Y, Z. This may take </span>
-<span class="sd">        a very long time, however.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : list of tuples</span>
-<span class="sd">            List of variables chosen for testing paths.</span>
-<span class="sd">        max_lag : int, optional (default: None)</span>
-<span class="sd">            Used here to constrain the has_path function to the graph</span>
-<span class="sd">            truncated at max_lag instead of identifying the max_lag from</span>
-<span class="sd">            ancestral search.</span>
-<span class="sd">        compute_ancestors : bool</span>
-<span class="sd">            Whether to also make available the ancestors for X, Y, Z as</span>
-<span class="sd">            self.anc_all_x, self.anc_all_y, and self.anc_all_z, respectively.</span>
-<span class="sd">        starts_with : {None, &#39;tail&#39;, &#39;arrohead&#39;}</span>
-<span class="sd">            Whether to only consider paths starting with particular mark at X.</span>
-<span class="sd">        ends_with : {None, &#39;tail&#39;, &#39;arrohead&#39;}</span>
-<span class="sd">            Whether to only consider paths ending with particular mark at Y.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        path : list or False</span>
-<span class="sd">            Returns path or False if no path exists.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">)</span>
-
-        <span class="c1"># Translate from observed_vars index to full variable set index</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
-        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">]</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">]</span>
-
-        <span class="c1"># print(X)</span>
-        <span class="c1"># print(Y)</span>
-        <span class="c1"># print(Z)</span>
-
-        <span class="k">if</span> <span class="n">check_optimality_cond</span><span class="p">:</span>
-            <span class="n">optimality_cond_des_YM</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> 
-                                        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">optimality_cond_des_YM</span><span class="p">]</span>
-            <span class="n">optimality_cond_Y</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> 
-                                    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">optimality_cond_Y</span><span class="p">]</span>
-
-        <span class="c1"># Get the array to test on</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_XYZ</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Testing X=</span><span class="si">%s</span><span class="s2"> d-sep Y=</span><span class="si">%s</span><span class="s2"> given Z=</span><span class="si">%s</span><span class="s2"> in TSG&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="n">max_lag</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># max_lags = dict([(j, max_lag) for j in range(N)])</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Set max. time lag to: &quot;</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_maximum_possible_lag</span><span class="p">(</span><span class="n">X</span><span class="o">+</span><span class="n">Y</span><span class="o">+</span><span class="n">Z</span><span class="p">)</span>
-
-        <span class="c1"># Store overall max. lag</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="n">max_lag</span>
-
-        <span class="c1"># _has_any_path is the main function that searches open paths</span>
-        <span class="n">any_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_has_any_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">conds</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">,</span> 
-                                      <span class="n">starts_with</span><span class="o">=</span><span class="n">starts_with</span><span class="p">,</span> <span class="n">ends_with</span><span class="o">=</span><span class="n">ends_with</span><span class="p">,</span>
-                                      <span class="n">return_path</span><span class="o">=</span><span class="n">return_path</span><span class="p">,</span>
-                                      <span class="n">directed</span><span class="o">=</span><span class="n">directed</span><span class="p">,</span>
-                                      <span class="n">only_non_causal_paths</span><span class="o">=</span><span class="n">only_non_causal_paths</span><span class="p">,</span>
-                                      <span class="n">check_optimality_cond</span><span class="o">=</span><span class="n">check_optimality_cond</span><span class="p">,</span>
-                                      <span class="n">optimality_cond_des_YM</span><span class="o">=</span><span class="n">optimality_cond_des_YM</span><span class="p">,</span>
-                                      <span class="n">optimality_cond_Y</span><span class="o">=</span><span class="n">optimality_cond_Y</span><span class="p">,</span>
-                                      <span class="n">forbidden_nodes</span><span class="o">=</span><span class="n">forbidden_nodes</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">any_path</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">return_path</span><span class="p">:</span>
-                <span class="n">any_path_observed</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">node</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">any_path</span> 
-                             <span class="k">if</span> <span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">any_path_observed</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">else</span><span class="p">:</span> 
-            <span class="n">any_path_observed</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;_has_any_path     = &quot;</span><span class="p">,</span> <span class="n">any_path</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;_has_any_path_obs = &quot;</span><span class="p">,</span> <span class="n">any_path_observed</span><span class="p">)</span>
-
-
-        <span class="c1"># if compute_ancestors:</span>
-        <span class="c1">#     if self.verbosity &gt; 0:</span>
-        <span class="c1">#         print(&quot;Compute ancestors.&quot;)</span>
-
-        <span class="c1">#     # Get ancestors up to maximum ancestral time lag incl. repeated</span>
-        <span class="c1">#     # links</span>
-        <span class="c1">#     self.anc_all_x, _ = self._get_non_blocked_ancestors(X, conds=Z,</span>
-        <span class="c1">#                                     mode=&#39;max_lag&#39;, max_lag=max_lag)</span>
-        <span class="c1">#     self.anc_all_y, _ = self._get_non_blocked_ancestors(Y, conds=Z,</span>
-        <span class="c1">#                                     mode=&#39;max_lag&#39;, max_lag=max_lag)</span>
-        <span class="c1">#     self.anc_all_z, _ = self._get_non_blocked_ancestors(Z, conds=Z,</span>
-        <span class="c1">#                                     mode=&#39;max_lag&#39;, max_lag=max_lag)</span>
-
-        <span class="k">return</span> <span class="n">any_path_observed</span></div>
-
-<div class="viewcode-block" id="OracleCI.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform oracle conditional independence test.</span>
-
-<span class="sd">        Calls the d-separation function.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : list of tuples</span>
-<span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
-<span class="sd">            variable index in the observed_vars and tau the time lag.</span>
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Not used here.</span>
-<span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;}</span>
-<span class="sd">            Not used here.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-<span class="sd">            The test statistic value and the p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">Z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">Z</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Translate from observed_vars index to full variable set index</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
-        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">]</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">]</span>
-
-        <span class="c1"># Get the array to test on</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_XYZ</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">str</span><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dsepsets</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">dsepsets</span><span class="p">[</span><span class="nb">str</span><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_dsep</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dsepsets</span><span class="p">[</span><span class="nb">str</span><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))]:</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">1.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
-
-        <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                         <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-        <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="OracleCI.get_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns dependence measure.</span>
-
-<span class="sd">        Returns 0 if X and Y are d-separated given Z in the graph and 1 else.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y [, Z] : list of tuples</span>
-<span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
-<span class="sd">            variable index in the observed_vars and tau the time lag.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            The test statistic value.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Translate from observed_vars index to full variable set index</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
-        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">]</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">[</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">]</span>
-
-        <span class="c1"># Check XYZ</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">_check_XYZ</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">str</span><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dsepsets</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">dsepsets</span><span class="p">[</span><span class="nb">str</span><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_dsep</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dsepsets</span><span class="p">[</span><span class="nb">str</span><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))]:</span>
-            <span class="k">return</span> <span class="mf">0.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mf">1.</span></div>
-
-    <span class="k">def</span> <span class="nf">_print_cond_ind_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print results from conditional independence test.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Test stastistic value.</span>
-<span class="sd">        pval : float, optional (default: None)</span>
-<span class="sd">            p-value</span>
-<span class="sd">        conf : tuple of floats, optional (default: None)</span>
-<span class="sd">            Confidence bounds.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">printstr</span> <span class="o">=</span> <span class="s2">&quot;        val = </span><span class="si">%.3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">val</span><span class="p">)</span>      
-        <span class="k">if</span> <span class="n">pval</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | pval = </span><span class="si">%.5f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">pval</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">conf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | conf bounds = (</span><span class="si">%.3f</span><span class="s2">, </span><span class="si">%.3f</span><span class="s2">)&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                <span class="n">conf</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conf</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">cached</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">({</span><span class="mi">0</span><span class="p">:</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span><span class="s2">&quot;[cached]&quot;</span><span class="p">}[</span><span class="n">cached</span><span class="p">])</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="n">printstr</span><span class="p">)</span>
-
-<div class="viewcode-block" id="OracleCI.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Base class assumption that this is not implemented.  Concrete classes</span>
-<span class="sd">        should override when possible.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Model selection not&quot;</span><span class="o">+</span>\
-                                  <span class="s2">&quot; implemented for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_reverse_patt</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">patt</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Inverts a link pattern&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">patt</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="s2">&quot;&quot;</span>
-
-        <span class="n">left_mark</span><span class="p">,</span> <span class="n">middle_mark</span><span class="p">,</span> <span class="n">right_mark</span> <span class="o">=</span> <span class="n">patt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">patt</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">patt</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">left_mark</span> <span class="o">==</span> <span class="s2">&quot;&lt;&quot;</span><span class="p">:</span>
-            <span class="n">new_right_mark</span> <span class="o">=</span> <span class="s2">&quot;&gt;&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">new_right_mark</span> <span class="o">=</span> <span class="n">left_mark</span>
-        <span class="k">if</span> <span class="n">right_mark</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-            <span class="n">new_left_mark</span> <span class="o">=</span> <span class="s2">&quot;&lt;&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">new_left_mark</span> <span class="o">=</span> <span class="n">right_mark</span>
-
-        <span class="k">return</span> <span class="n">new_left_mark</span> <span class="o">+</span> <span class="n">middle_mark</span> <span class="o">+</span> <span class="n">new_right_mark</span>
-
-
-<div class="viewcode-block" id="OracleCI.get_links_from_graph"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_links_from_graph">[docs]</a>    <span class="k">def</span> <span class="nf">get_links_from_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Constructs links_coeffs dictionary, observed_vars, </span>
-<span class="sd">        and selection_vars from graph array (MAG or DAG).</span>
-
-<span class="sd">        In the case of MAGs, for every &lt;-&gt; or --- link further</span>
-<span class="sd">        latent and selection variables, respectively, are added.</span>
-<span class="sd">        This corresponds to a canonical DAG (Richardson Spirtes 2002).</span>
-
-<span class="sd">        For ADMGs &quot;---&quot; are not supported, but also links of type &quot;+-&gt;&quot;</span>
-<span class="sd">        exist, which corresponds to having both &quot;--&gt;&quot; and &quot;&lt;-&gt;&quot;.</span>
-
-<span class="sd">        Can be used to evaluate d-separation in MAG/DAGs.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="s2">&quot;U3&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;graph must be of type &#39;&lt;U3&#39;!&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_is_mag</span><span class="p">:</span>
-            <span class="n">edge_types</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;--&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;---&quot;</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">edge_types</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;--&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">]</span> <span class="c1">#, &quot;--+&quot;, &quot;+--&quot;]</span>
-
-
-        <span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_maxplusone</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_maxplusone</span> <span class="o">-</span> <span class="mi">1</span>
-
-        <span class="n">observed_vars</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">))</span>
-
-        <span class="n">selection_vars</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="n">links</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">observed_vars</span> <span class="p">}</span>
-
-        <span class="c1"># Add further latent variables to accommodate &lt;-&gt; and --- links</span>
-        <span class="n">latent_index</span> <span class="o">=</span> <span class="n">N</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">)):</span>
-
-            <span class="n">edge_type</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span>
-
-            <span class="k">if</span> <span class="n">edge_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">edge_types</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s2">&quot;Links can only be in </span><span class="si">%s</span><span class="s2"> &quot;</span> <span class="o">%</span><span class="nb">str</span><span class="p">(</span><span class="n">edge_types</span><span class="p">)</span>
-                <span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">edge_type</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_patt</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]):</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                        <span class="s2">&quot;graph needs to have consistent lag-zero patterns (eg&quot;</span>
-                        <span class="s2">&quot; graph[i,j,0]=&#39;--&gt;&#39; requires graph[j,i,0]=&#39;&lt;--&#39;)&quot;</span>
-                    <span class="p">)</span>
-
-                <span class="c1"># Consider contemporaneous links only once</span>
-                <span class="k">if</span> <span class="n">j</span> <span class="o">&gt;</span> <span class="n">i</span><span class="p">:</span>
-                    <span class="k">continue</span>
-
-            <span class="c1"># Restrict lagged links</span>
-            <span class="k">else</span><span class="p">:</span> 
-                <span class="k">if</span> <span class="n">edge_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;--&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;---&quot;</span><span class="p">,</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">]:</span> <span class="c1">#, &quot;--+&quot;]:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                        <span class="s2">&quot;Lagged links can only be in [&#39;--&gt;&#39;, &#39;&lt;-&gt;&#39;, &#39;---&#39;, &#39;+-&gt;&#39;]&quot;</span>
-                    <span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-            <span class="k">elif</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-            <span class="k">elif</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">latent_index</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">latent_index</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="k">elif</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;---&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">latent_index</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="n">selection_vars</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">latent_index</span><span class="p">)</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">latent_index</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">latent_index</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="n">latent_index</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="k">elif</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">latent_index</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">latent_index</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="k">elif</span> <span class="n">edge_type</span> <span class="o">==</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">latent_index</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">latent_index</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">latent_index</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="c1"># elif edge_type == &quot;+--&quot;:</span>
-            <span class="c1">#     links[i].append((j, -tau))</span>
-            <span class="c1">#     links[latent_index] = []</span>
-            <span class="c1">#     selection_vars.append(latent_index)</span>
-            <span class="c1">#     links[latent_index].append((i, -tau))</span>
-            <span class="c1">#     links[latent_index].append((j, 0))</span>
-            <span class="c1">#     latent_index += 1</span>
-            <span class="c1"># elif edge_type == &quot;--+&quot;:</span>
-            <span class="c1">#     links[j].append((i, -tau))</span>
-            <span class="c1">#     links[latent_index] = []</span>
-            <span class="c1">#     selection_vars.append(latent_index)</span>
-            <span class="c1">#     links[latent_index].append((i, -tau))</span>
-            <span class="c1">#     links[latent_index].append((j, 0))</span>
-            <span class="c1">#     latent_index += 1</span>
-
-        <span class="k">return</span> <span class="n">links</span><span class="p">,</span> <span class="n">observed_vars</span><span class="p">,</span> <span class="n">selection_vars</span></div>
-
-    <span class="k">def</span> <span class="nf">_get_minmax_lag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">links</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to retrieve tau_min and tau_max from links</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-        <span class="c1"># Get maximum time lag</span>
-        <span class="n">min_lag</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_props</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                    <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span>
-                    <span class="n">coeff</span> <span class="o">=</span> <span class="mf">1.</span>                
-                <span class="c1"># func = link_props[2]</span>
-                <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">min_lag</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">min_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-                    <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">min_lag</span><span class="p">,</span> <span class="n">max_lag</span>
-
-<div class="viewcode-block" id="OracleCI.get_graph_from_links"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_graph_from_links">[docs]</a>    <span class="k">def</span> <span class="nf">get_graph_from_links</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Constructs graph (DAG or MAG or ADMG) from links, observed_vars, </span>
-<span class="sd">        and selection_vars.</span>
-
-<span class="sd">        For ADMGs uses the Latent projection operation (Pearl 2009).</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># TODO: use MAG from DAG construction procedure (lecture notes)</span>
-        <span class="c1"># issues with tau_max?</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_is_mag</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;ADMGs do not support selection_vars.&quot;</span><span class="p">)</span>
-
-        <span class="n">N_all</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">)</span>
-
-        <span class="c1"># If tau_max is None, compute from links_coeffs</span>
-        <span class="n">_</span><span class="p">,</span> <span class="n">max_lag_links</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_minmax_lag</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">links</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">tau_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">tau_max</span> <span class="o">=</span> <span class="n">max_lag_links</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">max_lag_links</span> <span class="o">&gt;</span> <span class="n">tau_max</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;tau_max must be &gt;= maximum lag in links_coeffs; choose tau_max=None&quot;</span><span class="p">)</span>
-
-        <span class="c1"># print(&quot;max_lag_links &quot;, max_lag_links)</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">)</span>
-
-        <span class="c1"># Init graph</span>
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
-        <span class="n">graph</span><span class="p">[:]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-        <span class="c1"># We will enumerate the observed variables with (i,j) which refers to the index in MAG graph</span>
-        <span class="c1"># while x, y iterates through the variables in the underlying DAG</span>
-
-        <span class="c1"># Loop over the observed variables</span>
-        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">):</span>
-          <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-              <span class="k">if</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span> <span class="o">!=</span> <span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_is_mag</span><span class="p">:</span>
-                    <span class="n">dag_anc_y</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_non_blocked_ancestors</span><span class="p">(</span><span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">conds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                                            <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;max_lag&#39;</span><span class="p">,</span>
-                                            <span class="n">max_lag</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
-                    <span class="c1"># Only consider observed ancestors</span>
-                    <span class="n">mag_anc_y</span> <span class="o">=</span> <span class="p">[</span><span class="n">anc</span> <span class="k">for</span> <span class="n">anc</span> <span class="ow">in</span> <span class="n">dag_anc_y</span><span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> 
-                                <span class="k">if</span> <span class="n">anc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">]</span>
-
-                    <span class="n">dag_anc_x</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_non_blocked_ancestors</span><span class="p">(</span><span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)],</span> 
-                                    <span class="n">conds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;max_lag&#39;</span><span class="p">,</span>
-                                    <span class="n">max_lag</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
-                    
-                    <span class="c1"># Only consider observed ancestors</span>
-                    <span class="n">mag_anc_x</span> <span class="o">=</span> <span class="p">[</span><span class="n">anc</span> <span class="k">for</span> <span class="n">anc</span> <span class="ow">in</span> <span class="n">dag_anc_x</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> 
-                                <span class="k">if</span> <span class="n">anc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">]</span>   
-
-                    <span class="c1"># Add selection variable ancestors</span>
-                    <span class="n">dag_anc_s</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-                    <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selection_vars</span><span class="p">:</span>
-                        <span class="n">dag_anc_s_here</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_non_blocked_ancestors</span><span class="p">(</span><span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">s</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> 
-                                    <span class="n">conds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;max_lag&#39;</span><span class="p">,</span>
-                                    <span class="n">max_lag</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
-                        <span class="n">dag_anc_s</span> <span class="o">=</span> <span class="n">dag_anc_s</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">dag_anc_s_here</span><span class="p">[(</span><span class="n">s</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]))</span>
-                    
-                    <span class="n">dag_anc_s</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dag_anc_s</span><span class="p">)</span>
-                    <span class="c1"># Only consider observed ancestors</span>
-                    <span class="n">mag_anc_s</span> <span class="o">=</span> <span class="p">[</span><span class="n">anc</span> <span class="k">for</span> <span class="n">anc</span> <span class="ow">in</span> <span class="n">dag_anc_s</span> 
-                                <span class="k">if</span> <span class="n">anc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span><span class="p">]</span>
-
-                    <span class="n">Z</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="n">z</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">mag_anc_y</span> <span class="o">+</span> <span class="n">mag_anc_x</span> <span class="o">+</span> <span class="n">mag_anc_s</span> <span class="k">if</span> <span class="n">z</span> <span class="o">!=</span> <span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">z</span> <span class="o">!=</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)])</span>
-                    <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span>
-                    
-                    <span class="n">separated</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_dsep</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-                    
-                    <span class="c1"># If X and Y are connected given Z, mark a link</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="n">separated</span><span class="p">:</span>
-                        <span class="c1"># (i, -tau) --&gt; j</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="n">dag_anc_y</span><span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">+</span> <span class="n">dag_anc_s</span> <span class="ow">and</span> <span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dag_anc_x</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="o">+</span> <span class="n">dag_anc_s</span><span class="p">:</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-                            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>
-
-                        <span class="k">elif</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dag_anc_y</span><span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">+</span> <span class="n">dag_anc_s</span> <span class="ow">and</span> <span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dag_anc_x</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="o">+</span> <span class="n">dag_anc_s</span><span class="p">:</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-&gt;&quot;</span>
-                            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-&gt;&quot;</span>
-
-                        <span class="k">elif</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="n">dag_anc_y</span><span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">+</span> <span class="n">dag_anc_s</span> <span class="ow">and</span> <span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">dag_anc_x</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="o">+</span> <span class="n">dag_anc_s</span><span class="p">:</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;---&quot;</span>
-                            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;---&quot;</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">&gt;=</span> <span class="n">i</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># edge_types = [&quot;--&gt;&quot;, &quot;&lt;-&gt;&quot;, &quot;+-&gt;&quot;]</span>
-                    <span class="c1"># Latent projection operation:</span>
-                    <span class="c1"># (i)  ADMG contains i --&gt; j iff there is a directed path x --&gt; ... --&gt; y on which</span>
-                    <span class="c1">#      every non-endpoint vertex is in hidden variables (= not in observed_vars)</span>
-                    <span class="c1"># (ii) ADMG contains i &lt;-&gt; j iff there exists a path of the form x &lt;-- ... --&gt; y on</span>
-                    <span class="c1">#      which every non-endpoint vertex is non-collider AND in L (=not in observed_vars)</span>
-                    <span class="n">observed_varslags</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([(</span><span class="n">v</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_vars</span>
-                                                   <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span> <span class="o">-</span> <span class="nb">set</span><span class="p">([(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">),</span> <span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)])</span>
-                    <span class="n">cond_one_xy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_has_any_path</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> 
-                        <span class="n">conds</span><span class="o">=</span><span class="p">[],</span> 
-                        <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                        <span class="n">starts_with</span><span class="o">=</span><span class="s1">&#39;tail&#39;</span><span class="p">,</span>
-                        <span class="n">ends_with</span><span class="o">=</span><span class="s1">&#39;arrowhead&#39;</span><span class="p">,</span>
-                        <span class="n">directed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>  
-                        <span class="n">forbidden_nodes</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">observed_varslags</span><span class="p">),</span> 
-                        <span class="n">return_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="n">cond_one_yx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_has_any_path</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> 
-                            <span class="n">conds</span><span class="o">=</span><span class="p">[],</span> 
-                            <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                            <span class="n">starts_with</span><span class="o">=</span><span class="s1">&#39;tail&#39;</span><span class="p">,</span>
-                            <span class="n">ends_with</span><span class="o">=</span><span class="s1">&#39;arrowhead&#39;</span><span class="p">,</span>
-                            <span class="n">directed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>  
-                            <span class="n">forbidden_nodes</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">observed_varslags</span><span class="p">),</span> 
-                            <span class="n">return_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">cond_one_yx</span> <span class="o">=</span> <span class="kc">False</span>
-                    <span class="n">cond_two</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_has_any_path</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> 
-                        <span class="n">conds</span><span class="o">=</span><span class="p">[],</span> 
-                        <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                        <span class="n">starts_with</span><span class="o">=</span><span class="s1">&#39;arrowhead&#39;</span><span class="p">,</span>
-                        <span class="n">ends_with</span><span class="o">=</span><span class="s1">&#39;arrowhead&#39;</span><span class="p">,</span>
-                        <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>  
-                        <span class="n">forbidden_nodes</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">observed_varslags</span><span class="p">),</span> 
-                        <span class="n">return_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">cond_one_xy</span> <span class="ow">and</span> <span class="n">cond_one_yx</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cyclic graph!&quot;</span><span class="p">)</span>
-                    <span class="c1"># print((x, -tau), y, cond_one_xy, cond_one_yx, cond_two)</span>
-
-                    <span class="c1"># Only (i) holds: i --&gt; j</span>
-                    <span class="k">if</span> <span class="n">cond_one_xy</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">cond_two</span><span class="p">:</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-                        <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>
-                    <span class="k">elif</span> <span class="n">cond_one_yx</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">cond_two</span><span class="p">:</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>
-                        <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-
-                    <span class="c1"># Only (ii) holds: i &lt;-&gt; j</span>
-                    <span class="k">elif</span> <span class="ow">not</span> <span class="n">cond_one_xy</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">cond_one_yx</span> <span class="ow">and</span> <span class="n">cond_two</span><span class="p">:</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-&gt;&quot;</span>
-                        <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-&gt;&quot;</span>
-
-                    <span class="c1"># Both (i) and (ii) hold: i +-&gt; j</span>
-                    <span class="k">elif</span> <span class="n">cond_one_xy</span> <span class="ow">and</span> <span class="n">cond_two</span><span class="p">:</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;+-&gt;&quot;</span>
-                        <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-+&quot;</span>
-                    <span class="k">elif</span> <span class="n">cond_one_yx</span> <span class="ow">and</span> <span class="n">cond_two</span><span class="p">:</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-+&quot;</span>
-                        <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;+-&gt;&quot;</span>
-                    <span class="c1"># print((i, -tau), j, cond_one_xy, cond_one_yx, cond_two)</span>
-
-        <span class="k">return</span> <span class="n">graph</span></div>
-
-<div class="viewcode-block" id="OracleCI.get_confidence"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_confidence">[docs]</a>    <span class="k">def</span> <span class="nf">get_confidence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;For compatibility with PCMCI.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        None</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="kc">None</span></div></div>
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-
-    <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
-    <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-
-    <span class="c1"># Define the stationary DAG</span>
-    <span class="n">links</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span> <span class="p">:</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)]}</span>
-    <span class="n">observed_vars</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-
-    <span class="n">oracle</span> <span class="o">=</span> <span class="n">OracleCI</span><span class="p">(</span><span class="n">links</span><span class="o">=</span><span class="n">links</span><span class="p">,</span> 
-        <span class="n">observed_vars</span><span class="o">=</span><span class="n">observed_vars</span><span class="p">,</span> 
-        <span class="n">graph_is_mag</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="c1">#         selection_vars=selection_vars,</span>
-        <span class="c1">#     verbosity=2</span>
-        <span class="p">)</span>
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">oracle</span><span class="o">.</span><span class="n">graph</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,:,</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="n">tp</span><span class="o">.</span><span class="n">plot_time_series_graph</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span> <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span>
-                <span class="n">save_name</span><span class="o">=</span><span class="s2">&quot;tsg.pdf&quot;</span><span class="p">)</span>
-
-    <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-    <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-    <span class="n">Z</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="c1"># node = (3, 0)</span>
-    <span class="c1"># prelim_Oset = set([(3, 0)])</span>
-    <span class="c1"># S = set([])</span>
-    <span class="c1"># collider_path_nodes = set([])</span>
-    <span class="n">path</span> <span class="o">=</span> <span class="n">oracle</span><span class="o">.</span><span class="n">_has_any_path</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> 
-                            <span class="n">conds</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span> 
-                            <span class="n">max_lag</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> 
-                            <span class="n">starts_with</span><span class="o">=</span><span class="s1">&#39;arrowhead&#39;</span><span class="p">,</span>
-                            <span class="n">ends_with</span><span class="o">=</span><span class="s1">&#39;arrowhead&#39;</span><span class="p">,</span>  
-                            <span class="n">forbidden_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                            <span class="n">return_path</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-------------------------------&quot;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">oracle</span><span class="o">.</span><span class="n">_get_maximum_possible_lag</span><span class="p">(</span><span class="n">X</span><span class="o">+</span><span class="n">Z</span><span class="p">))</span> <span class="c1">#(X = X, Y = Y, Z = Z))</span>
-
-    <span class="c1"># cond_ind_test = OracleCI(graph=graph)</span>
-    <span class="c1"># links, observed_vars, selection_vars = cond_ind_test.get_links_from_graph(graph)</span>
-    <span class="c1"># print(&quot;{&quot;)</span>
-    <span class="c1"># for j in links.keys():</span>
-    <span class="c1">#     parents = repr([(p, &#39;coeff&#39;, &#39;lin_f&#39;) for p in links[j]])</span>
-    <span class="c1">#     print(f&quot;{j: 1d}&quot; &quot;:&quot;  f&quot;{parents:s},&quot;)</span>
-    <span class="c1"># print(repr(observed_vars))</span>
-    <span class="c1"># cond_ind_test = OracleCI(graph=graph, verbosity=2)</span>
-
-    <span class="c1"># X = [(0, 0)]</span>
-    <span class="c1"># Y = [(2, 0)]</span>
-    <span class="c1"># Z = [(7, 0), (3, 0), (6, 0), (5, 0), (4, 0)] #(1, -3), (1, -2), (0, -2), (0, -1), (0, -3)]</span>
-    <span class="c1"># #(j, -2) for j in range(N)] + [(j, 0) for j in range(N)]</span>
-
-    <span class="c1"># # print(oracle._get_non_blocked_ancestors(Z, Z=None, mode=&#39;max_lag&#39;,</span>
-    <span class="c1"># #                                     max_lag=2))</span>
-    <span class="c1"># # cond_ind_test = OracleCI(links, observed_vars=observed_vars, verbosity=2)</span>
-
-    <span class="c1"># print(cond_ind_test.get_shortest_path(X=X, Y=Y, Z=Z,</span>
-    <span class="c1">#              max_lag=None, compute_ancestors=False, </span>
-    <span class="c1">#              backdoor=True))</span>
-   
-    <span class="c1"># anc_x=None  #oracle.anc_all_x[X[0]]</span>
-    <span class="c1"># anc_y=None #oracle.anc_all_y[Y[0]]</span>
-    <span class="c1"># anc_xy=None # []</span>
-    <span class="c1"># # # for z in Z:</span>
-    <span class="c1"># # #     anc_xy += oracle.anc_all_z[z]</span>
-    
-    <span class="c1"># fig, ax = tp.plot_tsg(links, </span>
-    <span class="c1">#             X=[(observed_vars[x[0]], x[1]) for x in X], </span>
-    <span class="c1">#             Y=[(observed_vars[y[0]], y[1]) for y in Y], </span>
-    <span class="c1">#             Z=[(observed_vars[z[0]], z[1]) for z in Z],</span>
-    <span class="c1">#     anc_x=anc_x, anc_y=anc_y, </span>
-    <span class="c1">#     anc_xy=anc_xy)</span>
-
-    <span class="c1"># fig.savefig(&quot;/home/rung_ja/Downloads/tsg.pdf&quot;)</span>
-</pre></div>
-
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-    <title>tigramite.independence_tests.parcorr &#8212; Tigramite 5.2 documentation</title>
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-  <h1>Source code for tigramite.independence_tests.parcorr</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-
-<div class="viewcode-block" id="ParCorr"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr.ParCorr">[docs]</a><span class="k">class</span> <span class="nc">ParCorr</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Partial correlation test.</span>
-
-<span class="sd">    Partial correlation is estimated through linear ordinary least squares (OLS)</span>
-<span class="sd">    regression and a test for non-zero linear Pearson correlation on the</span>
-<span class="sd">    residuals.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    To test :math:`X \perp Y | Z`, first :math:`Z` is regressed out from</span>
-<span class="sd">    :math:`X` and :math:`Y` assuming the  model</span>
-
-<span class="sd">    .. math::  X &amp; =  Z \beta_X + \epsilon_{X} \\</span>
-<span class="sd">        Y &amp; =  Z \beta_Y + \epsilon_{Y}</span>
-
-<span class="sd">    using OLS regression. Then the dependency of the residuals is tested with</span>
-<span class="sd">    the Pearson correlation test.</span>
-
-<span class="sd">    .. math::  \rho\left(r_X, r_Y\right)</span>
-
-<span class="sd">    For the ``significance=&#39;analytic&#39;`` Student&#39;s-*t* distribution with</span>
-<span class="sd">    :math:`T-D_Z-2` degrees of freedom is implemented.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to Parent class CondIndTest.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># documentation</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;par_corr&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_single_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-                              <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                              <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns residuals of linear multiple regression.</span>
-
-<span class="sd">        Performs a OLS regression of the variable indexed by target_var on the</span>
-<span class="sd">        conditions Z. Here array is assumed to contain X and Y as the first two</span>
-<span class="sd">        rows with the remaining rows (if present) containing the conditions Z.</span>
-<span class="sd">        Optionally returns the estimated regression line.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        target_var : {0, 1}</span>
-<span class="sd">            Variable to regress out conditions from.</span>
-
-<span class="sd">        standardize : bool, optional (default: True)</span>
-<span class="sd">            Whether to standardize the array beforehand. Must be used for</span>
-<span class="sd">            partial correlation.</span>
-
-<span class="sd">        return_means : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the estimated regression line.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        resid [, mean] : array-like</span>
-<span class="sd">            The residual of the regression and optionally the estimated line.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">dim_z</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">-</span> <span class="mi">2</span>
-
-        <span class="c1"># Standardize</span>
-        <span class="k">if</span> <span class="n">standardize</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-            <span class="c1"># if np.isnan(array).sum() != 0:</span>
-            <span class="c1">#     raise ValueError(&quot;nans after standardizing, &quot;</span>
-            <span class="c1">#                      &quot;possibly constant array!&quot;)</span>
-
-        <span class="n">y</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:]</span>
-
-        <span class="k">if</span> <span class="n">dim_z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="mi">2</span><span class="p">:,</span> <span class="p">:])</span>
-            <span class="n">beta_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">beta_hat</span><span class="p">)</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">mean</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="n">y</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">if</span> <span class="n">return_means</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">(</span><span class="n">resid</span><span class="p">,</span> <span class="n">mean</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">resid</span>
-
-<div class="viewcode-block" id="ParCorr.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr.ParCorr.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return partial correlation.</span>
-
-<span class="sd">        Estimated as the Pearson correlation of the residuals of a linear</span>
-<span class="sd">        OLS regression.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Partial correlation coefficient.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">val</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">pearsonr</span><span class="p">(</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">val</span></div>
-
-<div class="viewcode-block" id="ParCorr.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr.ParCorr.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for shuffle significance test.</span>
-
-<span class="sd">        For residual-based test statistics only the residuals are shuffled.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">array_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">])</span>
-        <span class="n">xyz_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_shuffle_dist</span><span class="p">(</span><span class="n">array_resid</span><span class="p">,</span> <span class="n">xyz_resid</span><span class="p">,</span>
-                                           <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">,</span>
-                                           <span class="n">sig_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                           <span class="n">sig_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                                           <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="p">(</span><span class="n">null_dist</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span><span class="p">))</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-        <span class="c1"># Adjust p-value for two-sided measures</span>
-        <span class="k">if</span> <span class="n">pval</span> <span class="o">&lt;</span> <span class="mf">1.</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">*=</span> <span class="mf">2.</span>
-
-        <span class="k">if</span> <span class="n">return_null_dist</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pval</span><span class="p">,</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="ParCorr.get_analytic_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr.ParCorr.get_analytic_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns analytic p-value from Student&#39;s t-test for the Pearson</span>
-<span class="sd">        correlation coefficient.</span>
-
-<span class="sd">        Assumes two-sided correlation. If the degrees of freedom are less than</span>
-<span class="sd">        1, numpy.nan is returned.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        T : int</span>
-<span class="sd">            Sample length</span>
-
-<span class="sd">        dim : int</span>
-<span class="sd">            Dimensionality, ie, number of features.</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float or numpy.nan</span>
-<span class="sd">            P-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Get the number of degrees of freedom</span>
-        <span class="n">deg_f</span> <span class="o">=</span> <span class="n">T</span> <span class="o">-</span> <span class="n">dim</span>
-
-        <span class="k">if</span> <span class="n">deg_f</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-        <span class="k">elif</span> <span class="nb">abs</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">sys</span><span class="o">.</span><span class="n">float_info</span><span class="o">.</span><span class="n">min</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.0</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">trafo_val</span> <span class="o">=</span> <span class="n">value</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">deg_f</span><span class="o">/</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">value</span><span class="o">*</span><span class="n">value</span><span class="p">))</span>
-            <span class="c1"># Two sided significance level</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">sf</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">trafo_val</span><span class="p">),</span> <span class="n">deg_f</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span>
-
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="ParCorr.get_analytic_confidence"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr.ParCorr.get_analytic_confidence">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_confidence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">conf_lev</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns analytic confidence interval for correlation coefficient.</span>
-
-<span class="sd">        Based on Student&#39;s t-distribution.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        df : int</span>
-<span class="sd">            degrees of freedom of the test</span>
-
-<span class="sd">        conf_lev : float</span>
-<span class="sd">            Confidence interval, eg, 0.9</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        (conf_lower, conf_upper) : Tuple of floats</span>
-<span class="sd">            Upper and lower confidence bound of confidence interval.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Confidence interval is two-sided</span>
-        <span class="n">c_int</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">conf_lev</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.</span><span class="p">)</span>
-
-        <span class="n">value_tdist</span> <span class="o">=</span> <span class="n">value</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">value</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
-        <span class="n">conf_lower</span> <span class="o">=</span> <span class="p">(</span><span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">q</span><span class="o">=</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">c_int</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="n">value_tdist</span><span class="p">)</span>
-                      <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">df</span> <span class="o">+</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">q</span><span class="o">=</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">c_int</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">df</span><span class="p">,</span>
-                                                 <span class="n">loc</span><span class="o">=</span><span class="n">value_tdist</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
-        <span class="n">conf_upper</span> <span class="o">=</span> <span class="p">(</span><span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">q</span><span class="o">=</span><span class="n">c_int</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="n">value_tdist</span><span class="p">)</span>
-                      <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">df</span> <span class="o">+</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">q</span><span class="o">=</span><span class="n">c_int</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">df</span><span class="p">,</span>
-                                                 <span class="n">loc</span><span class="o">=</span><span class="n">value_tdist</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">conf_lower</span><span class="p">,</span> <span class="n">conf_upper</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ParCorr.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr.ParCorr.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">corrected_aic</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns Akaike&#39;s Information criterion modulo constants.</span>
-
-<span class="sd">        Fits a linear model of the parents to variable j and returns the</span>
-<span class="sd">        score. Leave-one-out cross-validation is asymptotically equivalent to</span>
-<span class="sd">        AIC for ordinary linear regression models. Here used to determine</span>
-<span class="sd">        optimal hyperparameters in PCMCI, in particular the pc_alpha value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of target variable in data array.</span>
-
-<span class="sd">        parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...] containing parents.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">        score : float</span>
-<span class="sd">            Model score.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>   <span class="c1"># dummy variable here</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="n">parents</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
-                                                    <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                    <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                                    <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                                    <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                    <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="c1"># Get RSS</span>
-        <span class="n">rss</span> <span class="o">=</span> <span class="p">(</span><span class="n">y</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="c1"># Number of parameters</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">-</span> <span class="mi">1</span>
-        <span class="c1"># Get AIC</span>
-        <span class="k">if</span> <span class="n">corrected_aic</span><span class="p">:</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span> <span class="o">+</span> <span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">T</span> <span class="o">-</span> <span class="n">p</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span>
-        <span class="k">return</span> <span class="n">score</span></div></div>
-</pre></div>
-
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-
-<!DOCTYPE html>
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-  <head>
-    <meta charset="utf-8" />
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-  <h1>Source code for tigramite.independence_tests.parcorr_mult</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-
-<div class="viewcode-block" id="ParCorrMult"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult">[docs]</a><span class="k">class</span> <span class="nc">ParCorrMult</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Partial correlation test for multivariate X and Y.</span>
-
-<span class="sd">    Multivariate partial correlation is estimated through ordinary least squares (OLS)</span>
-<span class="sd">    regression and some test for multivariate dependency among the residuals.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    To test :math:`X \perp Y | Z`, first :math:`Z` is regressed out from</span>
-<span class="sd">    :math:`X` and :math:`Y` assuming the  model</span>
-
-<span class="sd">    .. math::  X &amp; =  Z \beta_X + \epsilon_{X} \\</span>
-<span class="sd">        Y &amp; =  Z \beta_Y + \epsilon_{Y}</span>
-
-<span class="sd">    using OLS regression. Then different measures for the dependency among the residuals </span>
-<span class="sd">    can be used. Currently only a test for zero correlation on the maximum of the residuals&#39; </span>
-<span class="sd">    correlation is performed.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    correlation_type : {&#39;max_corr&#39;}</span>
-<span class="sd">        Which dependency measure to use on residuals.</span>
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to Parent class CondIndTest.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># documentation</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">correlation_type</span><span class="o">=</span><span class="s1">&#39;max_corr&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;par_corr_mult&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">correlation_type</span> <span class="o">=</span> <span class="n">correlation_type</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;max_corr&#39;</span><span class="p">]:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;correlation_type must be in [&#39;max_corr&#39;].&quot;</span><span class="p">)</span>
-
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_single_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-                              <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                              <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns residuals of linear multiple regression.</span>
-
-<span class="sd">        Performs a OLS regression of the variable indexed by target_var on the</span>
-<span class="sd">        conditions Z. Here array is assumed to contain X and Y as the first two</span>
-<span class="sd">        rows with the remaining rows (if present) containing the conditions Z.</span>
-<span class="sd">        Optionally returns the estimated regression line.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        target_var : {0, 1}</span>
-<span class="sd">            Variable to regress out conditions from.</span>
-
-<span class="sd">        standardize : bool, optional (default: True)</span>
-<span class="sd">            Whether to standardize the array beforehand. Must be used for</span>
-<span class="sd">            partial correlation.</span>
-
-<span class="sd">        return_means : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the estimated regression line.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        resid [, mean] : array-like</span>
-<span class="sd">            The residual of the regression and optionally the estimated line.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">dim_z</span> <span class="o">=</span> <span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-
-        <span class="c1"># Standardize</span>
-        <span class="k">if</span> <span class="n">standardize</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-            <span class="c1"># if np.isnan(array).sum() != 0:</span>
-            <span class="c1">#     raise ValueError(&quot;nans after standardizing, &quot;</span>
-            <span class="c1">#                      &quot;possibly constant array!&quot;)</span>
-
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="n">target_var</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="p">:])</span>
-
-        <span class="k">if</span> <span class="n">dim_z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="p">:])</span>
-            <span class="n">beta_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">beta_hat</span><span class="p">)</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">mean</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="n">y</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">if</span> <span class="n">return_means</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">resid</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">mean</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">resid</span><span class="p">)</span>
-
-<div class="viewcode-block" id="ParCorrMult.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return multivariate kernel correlation coefficient.</span>
-
-<span class="sd">        Estimated as some dependency measure on the</span>
-<span class="sd">        residuals of a linear OLS regression.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Partial correlation coefficient.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">dim_x</span> <span class="o">=</span> <span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="n">dim_y</span> <span class="o">=</span> <span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">array_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_vals</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim_x</span><span class="p">,</span> <span class="n">T</span><span class="p">),</span> <span class="n">y_vals</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim_y</span><span class="p">,</span> <span class="n">T</span><span class="p">)))</span>
-        <span class="n">xyz_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">index_code</span> <span class="k">for</span> <span class="n">index_code</span> <span class="ow">in</span> <span class="n">xyz</span> <span class="k">if</span> <span class="n">index_code</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">])</span>
-
-        <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mult_corr</span><span class="p">(</span><span class="n">array_resid</span><span class="p">,</span> <span class="n">xyz_resid</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">val</span></div>
-
-<div class="viewcode-block" id="ParCorrMult.mult_corr"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.mult_corr">[docs]</a>    <span class="k">def</span> <span class="nf">mult_corr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return multivariate dependency measure.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        standardize : bool, optional (default: True)</span>
-<span class="sd">            Whether to standardize the array beforehand. Must be used for</span>
-<span class="sd">            partial correlation.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Multivariate dependency measure.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">dim_x</span> <span class="o">=</span> <span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="n">dim_y</span> <span class="o">=</span> <span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-
-        <span class="c1"># Standardize</span>
-        <span class="k">if</span> <span class="n">standardize</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-            <span class="c1"># if np.isnan(array).sum() != 0:</span>
-            <span class="c1">#     raise ValueError(&quot;nans after standardizing, &quot;</span>
-            <span class="c1">#                      &quot;possibly constant array!&quot;)</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]]</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]]</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation_type</span> <span class="o">==</span> <span class="s1">&#39;max_corr&#39;</span><span class="p">:</span>
-            <span class="c1"># Get (positive or negative) absolute maximum correlation value</span>
-            <span class="n">corr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)[:</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">):]</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="n">corr</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">corr</span><span class="p">))]</span>
-
-            <span class="c1"># val = 0.</span>
-            <span class="c1"># for x_vals in x:</span>
-            <span class="c1">#     for y_vals in y:</span>
-            <span class="c1">#         val_here, _ = stats.pearsonr(x_vals, y_vals)</span>
-            <span class="c1">#         val = max(val, np.abs(val_here))</span>
-        
-        <span class="c1"># elif self.correlation_type == &#39;linear_hsci&#39;:</span>
-        <span class="c1">#     # For linear kernel and standardized data (centered and divided by std)</span>
-        <span class="c1">#     # biased V -statistic of HSIC reduces to sum of squared inner products</span>
-        <span class="c1">#     # over all dimensions</span>
-        <span class="c1">#     val = ((x.dot(y.T)/float(n))**2).sum()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Currently only&quot;</span>
-                                      <span class="s2">&quot;correlation_type == &#39;max_corr&#39; implemented.&quot;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">val</span></div>
-
-<div class="viewcode-block" id="ParCorrMult.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for shuffle significance test.</span>
-
-<span class="sd">        For residual-based test statistics only the residuals are shuffled.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">dim_x</span> <span class="o">=</span> <span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="n">dim_y</span> <span class="o">=</span> <span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">array_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_vals</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim_x</span><span class="p">,</span> <span class="n">T</span><span class="p">),</span> <span class="n">y_vals</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim_y</span><span class="p">,</span> <span class="n">T</span><span class="p">)))</span>
-        <span class="n">xyz_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">index_code</span> <span class="k">for</span> <span class="n">index_code</span> <span class="ow">in</span> <span class="n">xyz</span> <span class="k">if</span> <span class="n">index_code</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">])</span>
-
-
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_shuffle_dist</span><span class="p">(</span><span class="n">array_resid</span><span class="p">,</span> <span class="n">xyz_resid</span><span class="p">,</span>
-                                           <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">,</span>
-                                           <span class="n">sig_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                           <span class="n">sig_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                                           <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="p">(</span><span class="n">null_dist</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span><span class="p">))</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-        <span class="c1"># Adjust p-value for two-sided measures</span>
-        <span class="k">if</span> <span class="n">pval</span> <span class="o">&lt;</span> <span class="mf">1.</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">*=</span> <span class="mf">2.</span>
-
-        <span class="c1"># Adjust p-value for dimensions of x and y (conservative Bonferroni-correction)</span>
-        <span class="c1"># pval *= dim_x*dim_y</span>
-
-        <span class="k">if</span> <span class="n">return_null_dist</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pval</span><span class="p">,</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="ParCorrMult.get_analytic_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_analytic_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns analytic p-value depending on correlation_type.</span>
-
-<span class="sd">        Assumes two-sided correlation. If the degrees of freedom are less than</span>
-<span class="sd">        1, numpy.nan is returned.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        T : int</span>
-<span class="sd">            Sample length</span>
-
-<span class="sd">        dim : int</span>
-<span class="sd">            Dimensionality, ie, number of features.</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float or numpy.nan</span>
-<span class="sd">            P-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Get the number of degrees of freedom</span>
-        <span class="n">deg_f</span> <span class="o">=</span> <span class="n">T</span> <span class="o">-</span> <span class="n">dim</span>
-
-        <span class="n">dim_x</span> <span class="o">=</span> <span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="n">dim_y</span> <span class="o">=</span> <span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation_type</span> <span class="o">==</span> <span class="s1">&#39;max_corr&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">deg_f</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">pval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-            <span class="k">elif</span> <span class="nb">abs</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">sys</span><span class="o">.</span><span class="n">float_info</span><span class="o">.</span><span class="n">min</span><span class="p">:</span>
-                <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.0</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">trafo_val</span> <span class="o">=</span> <span class="n">value</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">deg_f</span><span class="o">/</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">value</span><span class="o">*</span><span class="n">value</span><span class="p">))</span>
-                <span class="c1"># Two sided significance level</span>
-                <span class="n">pval</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">sf</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">trafo_val</span><span class="p">),</span> <span class="n">deg_f</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Currently only&quot;</span>
-                                      <span class="s2">&quot;correlation_type == &#39;max_corr&#39; implemented.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Adjust p-value for dimensions of x and y (conservative Bonferroni-correction)</span>
-        <span class="n">pval</span> <span class="o">*=</span> <span class="n">dim_x</span><span class="o">*</span><span class="n">dim_y</span>
-
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="ParCorrMult.get_analytic_confidence"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_analytic_confidence">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_confidence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">conf_lev</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Base class assumption that this is not implemented.  Concrete classes</span>
-<span class="sd">        should override when possible.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Analytic confidence not&quot;</span><span class="o">+</span>\
-                                  <span class="s2">&quot; implemented for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="ParCorrMult.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Base class assumption that this is not implemented.  Concrete classes</span>
-<span class="sd">        should override when possible.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Model selection not&quot;</span><span class="o">+</span>\
-                                  <span class="s2">&quot; implemented for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span></div></div>
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
-    <span class="c1"># import numpy as np</span>
-    <span class="kn">import</span> <span class="nn">timeit</span>
-
-    <span class="n">seed</span><span class="o">=</span><span class="mi">3</span>
-    <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">cmi</span> <span class="o">=</span> <span class="n">ParCorrMult</span><span class="p">(</span>
-            <span class="c1"># significance = &#39;shuffle_test&#39;,</span>
-            <span class="c1"># sig_samples=1000,</span>
-        <span class="p">)</span>
-
-    <span class="n">rate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
-        <span class="n">data</span><span class="p">[:,</span><span class="mi">2</span><span class="p">]</span> <span class="o">+=</span> <span class="o">-</span><span class="mf">0.5</span><span class="o">*</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
-        <span class="c1"># data[:,1] += data[:,2]</span>
-        <span class="n">dataframe</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-        <span class="n">cmi</span><span class="o">.</span><span class="n">set_dataframe</span><span class="p">(</span><span class="n">dataframe</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="n">cmi</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span>
-                <span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)],</span> <span class="c1">#, (1,0)], </span>
-                <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)],</span> <span class="c1">#, (3, 0)], </span>
-                <span class="c1"># Z=[(5,0)]</span>
-                <span class="n">Z</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
-        
-        <span class="n">rate</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span> <span class="o">&lt;=</span> <span class="mf">0.1</span>
-        <span class="c1"># print(cmi.run_test(X=[(0,0),(1,0)], Y=[(2,0), (3, 0)], Z=[(5,0)]))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">rate</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
-</pre></div>
-
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-  <h1>Source code for tigramite.independence_tests.parcorr_wls</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">from</span> <span class="nn">tigramite.independence_tests.parcorr</span> <span class="kn">import</span> <span class="n">ParCorr</span>
-<span class="kn">from</span> <span class="nn">tigramite.independence_tests.robust_parcorr</span> <span class="kn">import</span> <span class="n">RobustParCorr</span>
-<span class="kn">from</span> <span class="nn">tigramite</span> <span class="kn">import</span> <span class="n">data_processing</span> <span class="k">as</span> <span class="n">pp</span>
-
-
-<div class="viewcode-block" id="ParCorrWLS"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_wls.ParCorrWLS">[docs]</a><span class="k">class</span> <span class="nc">ParCorrWLS</span><span class="p">(</span><span class="n">ParCorr</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Weighted partial correlation test.</span>
-
-<span class="sd">    Partial correlation is estimated through linear weighted least squares (WLS)</span>
-<span class="sd">    regression and a test for non-zero linear Pearson correlation on the</span>
-<span class="sd">    residuals.</span>
-<span class="sd">    Either the variances, i.e. weights, are known, or they can be estimated using non-parametric regression</span>
-<span class="sd">    (using k nearest neighbour).</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    To test :math:`X \perp Y | Z`, first :math:`Z` is regressed out from</span>
-<span class="sd">    :math:`X` and :math:`Y` assuming the  model</span>
-
-<span class="sd">    .. math::  X &amp; =  Z \beta_X + \epsilon_{X} \\</span>
-<span class="sd">        Y &amp; =  Z \beta_Y + \epsilon_{Y}</span>
-
-<span class="sd">    using WLS regression. Here, we do not assume homoskedasticity of the error terms.</span>
-<span class="sd">    Then the dependency of the residuals is tested with</span>
-<span class="sd">    the Pearson correlation test.</span>
-
-<span class="sd">    .. math::  \rho\left(r_X, r_Y\right)</span>
-
-<span class="sd">    For the ``significance=&#39;analytic&#39;`` Student&#39;s-*t* distribution with</span>
-<span class="sd">    :math:`T-D_Z-2` degrees of freedom is implemented.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    gt_std_matrix: array-like, optional (default: None)</span>
-<span class="sd">        Standard deviations of the noise of shape (T, nb_nodes)</span>
-<span class="sd">    expert_knowledge: string or dict (default: time-dependent heteroskedasticity)</span>
-<span class="sd">        Either string &quot;time-dependent heteroskedasticity&quot; meaning that every variable only has time-dependent</span>
-<span class="sd">        heteroskedasticity, or string &quot;homoskedasticity&quot; where we assume homoskedasticity for all variables, or</span>
-<span class="sd">        dictionary containing expert knowledge about heteroskedastic relationships as list of tuples or strings.</span>
-<span class="sd">    window_size: int (default: 10)</span>
-<span class="sd">        Number of nearest neighbours that we are using for estimating the variance function.</span>
-<span class="sd">    robustify: bool (default: False)</span>
-<span class="sd">        Indicates whether the robust partial correlation test should be used, i.e. whether the data should be</span>
-<span class="sd">        transformed to normal marginals before testing</span>
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to Parent class ParCorr.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># documentation</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gt_std_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">expert_knowledge</span><span class="o">=</span><span class="s2">&quot;time-dependent heteroskedasticity&quot;</span><span class="p">,</span>
-                 <span class="n">window_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">robustify</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">gt_std_matrix</span> <span class="o">=</span> <span class="n">gt_std_matrix</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">expert_knowledge</span> <span class="o">=</span> <span class="n">expert_knowledge</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">window_size</span> <span class="o">=</span> <span class="n">window_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">robustify</span> <span class="o">=</span> <span class="n">robustify</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">stds</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="n">ParCorr</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-        <span class="n">recycle_residuals</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>   <span class="c1"># Doesn&#39;t work with ParCorrWLS</span>
-         <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;par_corr_wls&#39;</span>
-
-    <span class="k">def</span> <span class="nf">_get_stds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span>
-                  <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Estimate standard deviations of X and Y, or use ground truth standard deviations depending on</span>
-<span class="sd">        user-supplied background knowledge.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gt_std_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">stds_dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gt_std_matrix</span><span class="p">,</span>
-                                          <span class="n">datatime</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gt_std_matrix</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]))})</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">stds</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">stds_dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
-                                                                <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                                <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                                                <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                                <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                                <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                                <span class="n">cut_off</span><span class="o">=</span><span class="n">cut_off</span><span class="p">,</span>
-                                                                <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">expert_knowledge</span> <span class="o">==</span> <span class="s2">&quot;time-dependent heteroskedasticity&quot;</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">expert_knowledge</span> <span class="o">=</span> <span class="p">{</span><span class="n">variable</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;time-dependent heteroskedasticity&quot;</span><span class="p">]</span>
-                                         <span class="k">for</span> <span class="n">variable</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">expert_knowledge</span> <span class="o">==</span> <span class="s2">&quot;homoskedasticity&quot;</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">expert_knowledge</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">if</span> <span class="nb">any</span><span class="p">([</span><span class="nb">type</span><span class="p">(</span><span class="n">item</span><span class="p">)</span> <span class="o">==</span> <span class="nb">tuple</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">expert_knowledge</span><span class="o">.</span><span class="n">items</span><span class="p">()]):</span>
-                <span class="c1"># if there is parent-dependent heteroskedasticity specified in the expert knowledge,</span>
-                <span class="c1"># prepare data for all nodes in the same way as X, Y and Z such that we can later obtain data</span>
-                <span class="c1"># for the heteroskedasticity-inducing parent</span>
-
-                <span class="n">nodes</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">N</span><span class="p">)],</span>
-                                                                  <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                                  <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                                                  <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                                  <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                                  <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                                  <span class="n">cut_off</span><span class="o">=</span><span class="n">cut_off</span><span class="p">,</span>
-                                                                  <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">nodes</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-                <span class="n">node_indices</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="c1"># print(XYZ)</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">XYZ</span><span class="p">:</span>
-                    <span class="n">node_indices</span> <span class="o">+=</span> <span class="n">i</span>
-                <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">node_indices</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="n">j</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="c1"># collect prepped array-data of all nodes at lag zero</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">j</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="n">nodes</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
-            <span class="c1"># estimate the weights based on expert knowledge on heteroskedastic relationships</span>
-            <span class="n">stds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_std_estimation</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">stds</span> <span class="o">=</span> <span class="n">stds</span>
-            <span class="k">return</span> <span class="n">stds</span>
-
-    <span class="k">def</span> <span class="nf">_get_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convenience wrapper around construct_array. Simultaneously, construct self.stds which needs to correspond</span>
-<span class="sd">        to the variables in the array.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;par_corr_wls&#39;</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;X and Y for </span><span class="si">%s</span><span class="s2"> must be univariate.&quot;</span> <span class="o">%</span>
-                                 <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span>
-
-        <span class="c1"># Call the wrapped function</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
-                                                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                            <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                                            <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="n">return_cleaned_xyz</span><span class="p">,</span>
-                                                            <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                            <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                            <span class="n">cut_off</span><span class="o">=</span><span class="n">cut_off</span><span class="p">,</span>
-                                                            <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
-        <span class="n">array_copy</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_get_stds</span><span class="p">(</span><span class="n">array_copy</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">cut_off</span><span class="p">,</span> <span class="n">verbosity</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">type_mask</span>
-
-    <span class="k">def</span> <span class="nf">_estimate_std_time</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">target_var</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Estimate the standard deviations of the error terms using the squared-residuals approach. First calculate</span>
-<span class="sd">        the absolute value of the residuals using OLS, then smooth them using a sliding window while keeping the time</span>
-<span class="sd">        order of the residuals.</span>
-<span class="sd">        In this way we can approximate variances that are time-dependent.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        arr: array</span>
-<span class="sd">            Data array of shape (dim, T)</span>
-<span class="sd">        target_var: {0, 1}</span>
-<span class="sd">            Variable to regress out conditions from.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        std_est: array</span>
-<span class="sd">            Standard deviation array of shape (T,)</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">dim_z</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">-</span> <span class="mi">2</span>
-        <span class="c1"># Standardization not necessary for variance estimation</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:])</span>
-
-        <span class="k">if</span> <span class="n">dim_z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="mi">2</span><span class="p">:,</span> <span class="p">:])</span>
-            <span class="n">beta_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">beta_hat</span><span class="p">)</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-
-        <span class="c1"># average variance within window</span>
-        <span class="n">std_est</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-            <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">convolve</span><span class="p">(</span><span class="n">resid</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">),</span> <span class="s1">&#39;valid&#39;</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">std_est</span>
-
-    <span class="k">def</span> <span class="nf">_estimate_std_parent</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span> <span class="n">target_lag</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Estimate the standard deviations of the error terms using a residual-based approach.</span>
-<span class="sd">        First calculate the absolute value of the residuals using OLS, then smooth them by averaging over the k ones</span>
-<span class="sd">        that are closest in H-value. In this way we are able to deal with parent-dependent heteroskedasticity.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        arr: array</span>
-<span class="sd">            Data array of shape (dim, T)</span>
-<span class="sd">        target_var: {0, 1}</span>
-<span class="sd">            Variable to obtain noise variance approximation for.</span>
-<span class="sd">        target_lag: -int</span>
-<span class="sd">            Lag of the variable to obtain noise variance approximation for.</span>
-<span class="sd">        H: of the form [(var, -tau)], where var specifies the variable index and tau the time lag</span>
-<span class="sd">            Variable to use for the sorting of the residuals, i.e. variable that the heteroskedasticity depends on.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        std_est: array</span>
-<span class="sd">            Standard deviation array of shape (T,)</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># print(dim, T)</span>
-        <span class="n">dim_z</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">-</span> <span class="mi">2</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:])</span>
-
-        <span class="k">if</span> <span class="n">dim_z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="mi">2</span><span class="p">:,</span> <span class="p">:])</span>
-            <span class="n">beta_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">beta_hat</span><span class="p">)</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span>
-            <span class="n">lag</span> <span class="o">=</span> <span class="n">H</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">target_lag</span>
-
-            <span class="c1"># order the residuals w.r.t. the heteroskedasticity-inducing parent corresponding to sample h</span>
-            <span class="n">h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">H</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">T</span><span class="p">):</span> <span class="n">lag</span><span class="p">])</span>
-            <span class="c1"># print(h.shape,H[0], np.abs(self.data.shape[1] - T))</span>
-            <span class="n">ordered_z_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>
-            <span class="n">ordered_z_ind</span> <span class="o">=</span> <span class="n">ordered_z_ind</span> <span class="o">*</span> <span class="p">(</span><span class="n">ordered_z_ind</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
-            <span class="n">revert_argsort</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">ordered_z_ind</span><span class="p">)</span>
-
-            <span class="n">truncate_resid</span> <span class="o">=</span> <span class="n">resid</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">):]</span>
-            <span class="n">sorted_resid</span> <span class="o">=</span> <span class="n">truncate_resid</span><span class="p">[</span><span class="n">ordered_z_ind</span><span class="p">]</span>
-
-            <span class="c1"># smooth the nearest neighbour residuals</span>
-            <span class="n">variance_est_sorted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
-                 <span class="n">np</span><span class="o">.</span><span class="n">convolve</span><span class="p">(</span><span class="n">sorted_resid</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">),</span> <span class="s1">&#39;valid&#39;</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">,))</span>
-            <span class="n">std_est</span> <span class="o">=</span> <span class="n">variance_est_sorted</span><span class="p">[</span><span class="n">revert_argsort</span><span class="p">]</span>
-            <span class="n">std_est</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">std_est</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))))</span>
-            <span class="n">std_est</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">std_est</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-            <span class="n">std_est</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
-                 <span class="n">np</span><span class="o">.</span><span class="n">convolve</span><span class="p">(</span><span class="n">resid</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">),</span> <span class="s1">&#39;valid&#39;</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="n">std_est</span>
-
-    <span class="k">def</span> <span class="nf">_get_std_estimation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Use expert knowledge on the heteroskedastic relationships contained in self.expert_knowledge to estimate the</span>
-<span class="sd">        standard deviations of the error terms.</span>
-<span class="sd">        The expert knowledge can specify whether there is sampling index / time dependent heteroskedasticity,</span>
-<span class="sd">        heteroskedasticity with respect to a specified parent, or homoskedasticity.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array</span>
-<span class="sd">            Data array of shape (dim, T)</span>
-<span class="sd">            </span>
-<span class="sd">        X, Y : list of tuples</span>
-<span class="sd">            X,Y are of the form [(var, -tau)], where var specifies the</span>
-<span class="sd">            variable index and tau the time lag.</span>
-
-<span class="sd">        Return</span>
-<span class="sd">        ------</span>
-<span class="sd">        stds: array-like</span>
-<span class="sd">            Array of standard deviations of error terms for X and Y of shape (2, T).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">stds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="n">T</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">variable</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]]):</span>
-            <span class="c1"># Here we assume that it is known what the heteroskedasticity function depends on for every variable</span>
-            <span class="k">if</span> <span class="n">variable</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">expert_knowledge</span><span class="p">:</span>
-                <span class="n">hs_source</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">expert_knowledge</span><span class="p">[</span><span class="n">variable</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="mi">0</span><span class="p">]</span>
-                <span class="k">if</span> <span class="n">hs_source</span> <span class="o">==</span> <span class="s2">&quot;time-dependent heteroskedasticity&quot;</span><span class="p">:</span>
-                    <span class="n">stds</span><span class="p">[</span><span class="n">count</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_std_time</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">count</span><span class="p">)</span>
-                <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">hs_source</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
-                    <span class="n">stds</span><span class="p">[</span><span class="n">count</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_std_parent</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">count</span><span class="p">,</span> <span class="n">variable</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                                                            <span class="n">hs_source</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">stds</span>
-
-    <span class="k">def</span> <span class="nf">_get_single_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-                              <span class="n">standardize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                              <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns residuals of weighted linear multiple regression.</span>
-
-<span class="sd">        Performs a WLS regression of the variable indexed by target_var on the</span>
-<span class="sd">        conditions Z. Here array is assumed to contain X and Y as the first two</span>
-<span class="sd">        rows with the remaining rows (if present) containing the conditions Z.</span>
-<span class="sd">        Optionally returns the estimated regression line.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns.</span>
-
-<span class="sd">        target_var : {0, 1}</span>
-<span class="sd">            Variable to regress out conditions from.</span>
-
-<span class="sd">        standardize : bool, optional (default: True)</span>
-<span class="sd">            Whether to standardize the array beforehand. Must be used for</span>
-<span class="sd">            partial correlation.</span>
-
-<span class="sd">        return_means : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the estimated regression line.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        resid [, mean] : array-like</span>
-<span class="sd">            The residual of the regression and optionally the estimated line.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">dim_z</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">-</span> <span class="mi">2</span>
-
-        <span class="n">x_vals_sum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">x_vals_has_nan</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">x_vals_sum</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">x_vals_has_nan</span><span class="p">:</span> 
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;array has nans&quot;</span><span class="p">)</span>
-
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">stds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stds</span><span class="p">[</span><span class="n">target_var</span><span class="p">]</span>
-
-        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;No estimated or ground truth standard deviations supplied for weights. &quot;</span>
-                          <span class="s2">&quot;Assume homoskedasticity, i.e. all weights are 1.&quot;</span><span class="p">)</span>
-            <span class="n">stds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-
-        <span class="c1"># Standardize</span>
-        <span class="k">if</span> <span class="n">standardize</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="n">x_vals_sum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-            <span class="n">x_vals_has_nan</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">x_vals_sum</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">x_vals_has_nan</span><span class="p">:</span> 
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;array has nans&quot;</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:])</span>
-        <span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reciprocal</span><span class="p">(</span><span class="n">stds</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="n">dim_z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="mi">2</span><span class="p">:,</span> <span class="p">:])</span>
-            <span class="c1"># include weights in z and y</span>
-            <span class="n">zw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span>
-            <span class="n">yw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
-            <span class="n">beta_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">zw</span><span class="p">,</span> <span class="n">yw</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">beta_hat</span><span class="p">)</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">mean</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
-            <span class="n">resid_vals_sum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">resid</span><span class="p">)</span>
-            <span class="n">resid_vals_has_nan</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">resid_vals_sum</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">resid_vals_has_nan</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;resid has nans&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># resid = y</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">if</span> <span class="n">return_means</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">resid</span><span class="p">,</span> <span class="n">mean</span>
-        <span class="k">return</span> <span class="n">resid</span>
-
-<div class="viewcode-block" id="ParCorrWLS.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">robustify</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">RobustParCorr</span><span class="o">.</span><span class="n">trafo2normal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">ParCorr</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="ParCorrWLS.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">robustify</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">RobustParCorr</span><span class="o">.</span><span class="n">trafo2normal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">ParCorr</span><span class="o">.</span><span class="n">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="ParCorrWLS.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">corrected_aic</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns Akaike&#39;s Information criterion modulo constants.</span>
-
-<span class="sd">        Fits a linear model of the parents to variable j and returns the</span>
-<span class="sd">        score. Leave-one-out cross-validation is asymptotically equivalent to</span>
-<span class="sd">        AIC for ordinary linear regression models. Here used to determine</span>
-<span class="sd">        optimal hyperparameters in PCMCI, in particular the pc_alpha value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of target variable in data array.</span>
-
-<span class="sd">        parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...] containing parents.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">        score : float</span>
-<span class="sd">            Model score.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>   <span class="c1"># dummy variable here</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="n">parents</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">,</span>
-                                           <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># Transform to normal marginals</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">robustify</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">RobustParCorr</span><span class="o">.</span><span class="n">trafo2normal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span>
-
-        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="c1"># Get RSS</span>
-        <span class="n">rss</span> <span class="o">=</span> <span class="p">(</span><span class="n">y</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="c1"># Number of parameters</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">-</span> <span class="mi">1</span>
-        <span class="c1"># Get AIC</span>
-        <span class="k">if</span> <span class="n">corrected_aic</span><span class="p">:</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span> <span class="o">+</span> <span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">T</span> <span class="o">-</span> <span class="n">p</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span>
-        <span class="k">return</span> <span class="n">score</span></div></div>
-</pre></div>
-
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="../../../index.html">Tigramite</a></h1>
-
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-<h3>Navigation</h3>
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-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../index.html">Documentation overview</a><ul>
-  <li><a href="../../index.html">Module code</a><ul>
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-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
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-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.independence_tests.regressionCI &#8212; Tigramite 5.2 documentation</title>
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-            
-  <h1>Source code for tigramite.independence_tests.regressionCI</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Tom Hochsprung &lt;tom.hochsprung@dlr.de&gt;, Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">chi2</span><span class="p">,</span> <span class="n">normaltest</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span><span class="p">,</span> <span class="n">LogisticRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
-
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-<span class="c1"># from numba import jit   # could make it even faster, also acticate @jit(forceobj=True)</span>
-
-
-<div class="viewcode-block" id="RegressionCI"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.regressionCI.RegressionCI">[docs]</a><span class="k">class</span> <span class="nc">RegressionCI</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Flexible parametric conditional independence tests for continuous, categorical, or mixed data.</span>
-
-<span class="sd">    Assumes one-dimensional X, Y.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    To test :math:`X \perp Y | Z`, the regressions Y|XZ vs Y|Z, or, depending</span>
-<span class="sd">    on certain criteria, X|YZ vs X|Z are compared. For that, the notion of</span>
-<span class="sd">    the deviance is employed. If the fits of the respective regressions do</span>
-<span class="sd">    not differ significantly (measured using the deviance), the null</span>
-<span class="sd">    hypotheses of conditional independence is &quot;accepted&quot;. This approach</span>
-<span class="sd">    assumes that X and Y are univariate, and Z can be either empty,</span>
-<span class="sd">    univariate or multivariate. Moreover, this approach works for all</span>
-<span class="sd">    combinations of &quot;discrete&quot; and &quot;continuous&quot; X, Y and respective columns</span>
-<span class="sd">    of Z; depending on the case, linear regression or multinomial regression</span>
-<span class="sd">    is employed.</span>
-
-<span class="sd">    Assumes one-dimensional X, Y.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to parent class CondIndTest.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-    
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        
-        <span class="c1"># Setup the member variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;regression_ci&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<div class="viewcode-block" id="RegressionCI.set_dataframe"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.regressionCI.RegressionCI.set_dataframe">[docs]</a>    <span class="k">def</span> <span class="nf">set_dataframe</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Initialize and check the dataframe.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        dataframe : data object</span>
-<span class="sd">            Set tigramite dataframe object. It must have the attributes</span>
-<span class="sd">            dataframe.values yielding a numpy array of shape (observations T,</span>
-<span class="sd">            variables N) and optionally a mask of the same shape and a missing</span>
-<span class="sd">            values flag.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">dataframe</span>
-        
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;mask_type is not None, but no mask in dataframe.&quot;</span><span class="p">)</span>
-            <span class="n">dataframe</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span>
-        
-        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">type_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;type_mask cannot be None for RegressionCI.&quot;</span><span class="p">)</span>
-        <span class="n">dataframe</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">type_mask</span><span class="p">,</span> <span class="n">check_type_mask</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
-
-    <span class="c1"># @jit(forceobj=True)</span>
-<div class="viewcode-block" id="RegressionCI.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.regressionCI.RegressionCI.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns test statistic.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns.</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            array of same shape as array which describes whether samples</span>
-<span class="sd">            are continuous or discrete: 0s for continuous and</span>
-<span class="sd">            1s for discrete</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            test estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">convert_to_one_hot</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">nb_classes</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Convert an iterable of indices to one-hot encoded labels.&quot;&quot;&quot;</span>
-            
-            <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-            <span class="c1"># categories need to be mapped to 0, 1, ... in this function</span>
-            <span class="n">targets</span> <span class="o">=</span> <span class="n">targets</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nb_classes</span><span class="p">)[</span><span class="n">targets</span><span class="p">]</span>
-
-        <span class="k">def</span> <span class="nf">do_componentwise_one_hot_encoding</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">var_type</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;A function that one-hot encodes all categorical components of X&quot;&quot;&quot;</span>
-           
-            <span class="n">T</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
-            <span class="n">X_new</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="n">T</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
-            <span class="c1"># componentwise dummy-encoding (if necessary, otherwise, keep component as usual):</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">var_type</span><span class="p">)):</span>
-                <span class="k">if</span> <span class="n">var_type</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="n">nb_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]))</span>
-                    <span class="n">X_new</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">X_new</span><span class="p">,</span> <span class="n">convert_to_one_hot</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span> <span class="n">nb_classes</span><span class="o">=</span><span class="n">nb_classes</span><span class="p">)))</span>
-                <span class="k">elif</span> <span class="n">var_type</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">X_new</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">X_new</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">))))</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;type_mask only allows entries in {0, 1}&quot;</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">X_new</span>
-
-        <span class="k">def</span> <span class="nf">calc_deviance_logistic</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">var_type</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Calculates the deviance (i.e., 2 * log-likelihood) for a multinomial logistic regression</span>
-<span class="sd">            (with standard regression assumptions)</span>
-<span class="sd">            &quot;&quot;&quot;</span>
-
-            <span class="c1"># 1-hot-encode all categorical columns</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="n">do_componentwise_one_hot_encoding</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">var_type</span><span class="o">=</span><span class="n">var_type</span><span class="p">)</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-            <span class="c1"># do logistic regression</span>
-            <span class="n">model</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">multi_class</span><span class="o">=</span><span class="s1">&#39;multinomial&#39;</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s1">&#39;lbfgs&#39;</span><span class="p">)</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-            <span class="n">deviance</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">metrics</span><span class="o">.</span><span class="n">log_loss</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">normalize</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-            <span class="c1"># dofs: +2 for intercept (+1) (not too important, cancels out later anyway)</span>
-            <span class="n">dof</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="k">return</span> <span class="n">deviance</span><span class="p">,</span> <span class="n">dof</span>
-
-        <span class="k">def</span> <span class="nf">calc_deviance_linear</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">var_type</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Calculates the deviance (i.e., 2 * log-likelihood) for a linear regression</span>
-<span class="sd">            (with standard regression assumptions</span>
-<span class="sd">            &quot;&quot;&quot;</span>
-
-            <span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># p is not important for later</span>
-            <span class="c1"># 1-hot-encode all categorical columns</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="n">do_componentwise_one_hot_encoding</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">var_type</span> <span class="o">=</span> <span class="n">var_type</span><span class="p">)</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-            <span class="c1"># do linear regression</span>
-            <span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-            <span class="c1"># predictions based on fitted model</span>
-            <span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-            <span class="c1"># residual sum of squares</span>
-            <span class="n">rss</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">((</span><span class="n">preds</span> <span class="o">-</span> <span class="n">y</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
-            <span class="c1"># deviance (only the term with the rss-term is important, the rest cancels out later anyway)</span>
-            <span class="c1"># deviance is calculated as -2*log-likelihood</span>
-            <span class="n">deviance</span> <span class="o">=</span> <span class="n">n</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span> <span class="o">/</span> <span class="n">n</span><span class="p">)</span> <span class="o">+</span> <span class="n">n</span>
-            <span class="c1"># dofs: +2 for intercept (+1)  (not too important, cancels out later anyway)</span>
-            <span class="n">dof</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_features_in_</span>  <span class="o">+</span> <span class="mi">1</span>
-            <span class="k">return</span> <span class="n">deviance</span><span class="p">,</span> <span class="n">dof</span>
-
-        <span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span><span class="n">series</span><span class="p">):</span>
-            <span class="n">value</span><span class="p">,</span> <span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">series</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="n">norm_counts</span> <span class="o">=</span> <span class="n">counts</span> <span class="o">/</span> <span class="n">counts</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-            <span class="k">return</span> <span class="o">-</span><span class="p">(</span><span class="n">norm_counts</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">norm_counts</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">x_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">y_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
-
-        <span class="n">x_type</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">[</span><span class="n">x_indices</span><span class="p">]</span>
-        <span class="n">y_type</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">[</span><span class="n">y_indices</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="n">z_type</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">z_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
-            <span class="n">z_type</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">[</span><span class="n">z_indices</span><span class="p">]</span>
-            <span class="n">z_type</span> <span class="o">=</span> <span class="n">z_type</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="c1"># check, whether within X and within Y all datapoints have the same datatype</span>
-        <span class="k">if</span> <span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">!=</span> <span class="n">x_type</span><span class="o">.</span><span class="n">min</span><span class="p">())</span> <span class="ow">or</span> <span class="p">(</span><span class="n">y_type</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">!=</span> <span class="n">y_type</span><span class="o">.</span><span class="n">min</span><span class="p">())):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;All samples regarding X or respectively Y must have the same datatype&quot;</span><span class="p">)</span>
-        
-        <span class="n">x_type</span> <span class="o">=</span> <span class="n">x_type</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-        <span class="n">y_type</span> <span class="o">=</span> <span class="n">y_type</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-
-        <span class="c1"># if z was (originally) None, then just an intercept is fitted ...</span>
-        <span class="c1"># Now, different cases for X discrete/continuous and Y discrete/continuous</span>
-        
-        <span class="c1"># Case 1: X continuous, Y continuous</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">x_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">y_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="c1"># Use the more normal variable as dependent variable TODO: makes sense?</span>
-            <span class="k">if</span> <span class="n">normaltest</span><span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">normaltest</span><span class="p">(</span><span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="n">dep_var</span> <span class="o">=</span> <span class="n">y</span>
-                <span class="n">rest</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>
-                <span class="n">rest_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x_type</span><span class="p">,</span> <span class="n">z_type</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dep_var</span> <span class="o">=</span> <span class="n">x</span>
-                <span class="n">rest</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>  
-                <span class="n">rest_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">y_type</span><span class="p">,</span> <span class="n">z_type</span><span class="p">))</span>
-              
-            <span class="c1"># Fit Y | Z</span>
-            <span class="n">dev1</span><span class="p">,</span> <span class="n">dof1</span> <span class="o">=</span> <span class="n">calc_deviance_linear</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">dep_var</span><span class="p">,</span> <span class="n">var_type</span> <span class="o">=</span> <span class="n">z_type</span><span class="p">)</span>
-            <span class="c1"># Fit Y | ZX</span>
-            <span class="n">dev2</span><span class="p">,</span> <span class="n">dof2</span> <span class="o">=</span> <span class="n">calc_deviance_linear</span><span class="p">(</span><span class="n">rest</span><span class="p">,</span> <span class="n">dep_var</span><span class="p">,</span> <span class="n">var_type</span><span class="o">=</span><span class="n">rest_type</span><span class="p">)</span>
-            <span class="c1"># print(dev1, dev2, np.abs(dev1 - dev2))</span>
-
-        <span class="c1"># Case 2: X discrete, Y continuous</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">x_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">y_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="n">xz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>
-            <span class="c1"># Fit Y | Z</span>
-            <span class="n">dev1</span><span class="p">,</span> <span class="n">dof1</span> <span class="o">=</span> <span class="n">calc_deviance_linear</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">var_type</span> <span class="o">=</span> <span class="n">z_type</span><span class="p">)</span>
-            <span class="c1"># Fit Y | XZ</span>
-            <span class="n">dev2</span><span class="p">,</span> <span class="n">dof2</span> <span class="o">=</span> <span class="n">calc_deviance_linear</span><span class="p">(</span><span class="n">xz</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">var_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x_type</span><span class="p">,</span> <span class="n">z_type</span><span class="p">)))</span>
-        
-        <span class="c1"># Case 3: X continuous, Y discrete</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">x_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">y_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="n">yz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>
-            <span class="c1"># Fit X | Z</span>
-            <span class="n">dev1</span><span class="p">,</span> <span class="n">dof1</span> <span class="o">=</span> <span class="n">calc_deviance_linear</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">var_type</span> <span class="o">=</span> <span class="n">z_type</span><span class="p">)</span>
-            <span class="c1"># Fit X | YZ</span>
-            <span class="n">dev2</span><span class="p">,</span> <span class="n">dof2</span> <span class="o">=</span> <span class="n">calc_deviance_linear</span><span class="p">(</span><span class="n">yz</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">var_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">y_type</span><span class="p">,</span> <span class="n">z_type</span><span class="p">)))</span>
-        
-        <span class="c1"># Case 4: X discrete, Y discrete</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">x_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">y_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="c1"># Use the variable with smaller entropy as dependent variable TODO: makes sense?</span>
-            <span class="k">if</span> <span class="n">entropy</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">entropy</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
-                <span class="n">dep_var</span> <span class="o">=</span> <span class="n">y</span>
-                <span class="n">rest</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>
-                <span class="n">rest_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x_type</span><span class="p">,</span> <span class="n">z_type</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dep_var</span> <span class="o">=</span> <span class="n">x</span>
-                <span class="n">rest</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>  
-                <span class="n">rest_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">y_type</span><span class="p">,</span> <span class="n">z_type</span><span class="p">))</span>
-            <span class="c1"># xz = np.hstack((x, z))</span>
-            <span class="c1"># Fit Y | Z</span>
-            <span class="n">dev1</span><span class="p">,</span> <span class="n">dof1</span> <span class="o">=</span> <span class="n">calc_deviance_logistic</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">dep_var</span><span class="p">,</span> <span class="n">var_type</span> <span class="o">=</span> <span class="n">z_type</span><span class="p">)</span>
-            <span class="c1"># Fit Y | XZ</span>
-            <span class="n">dev2</span><span class="p">,</span> <span class="n">dof2</span> <span class="o">=</span> <span class="n">calc_deviance_logistic</span><span class="p">(</span><span class="n">rest</span><span class="p">,</span> <span class="n">dep_var</span><span class="p">,</span> <span class="n">var_type</span><span class="o">=</span><span class="n">rest_type</span><span class="p">)</span>
-
-        <span class="c1"># calculate the difference between the deviance for the smaller and for the larger model</span>
-        <span class="c1"># (i.e., the actual deviance)</span>
-        <span class="n">stat</span> <span class="o">=</span> <span class="n">dev1</span> <span class="o">-</span> <span class="n">dev2</span>
-        <span class="n">dof</span> <span class="o">=</span> <span class="n">dof2</span> <span class="o">-</span> <span class="n">dof1</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_temp_dof</span> <span class="o">=</span> <span class="n">dof</span>
-        <span class="k">return</span> <span class="n">stat</span></div>
-
-<div class="viewcode-block" id="RegressionCI.get_analytic_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.regressionCI.RegressionCI.get_analytic_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the p_value of test statistic.</span>
-
-<span class="sd">        According to a chi-square distribution with &#39;dof&#39; degrees of freedom.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-                      
-        <span class="c1"># Calculate the p_value</span>
-        <span class="n">p_value</span> <span class="o">=</span> <span class="n">chi2</span><span class="o">.</span><span class="n">sf</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_temp_dof</span><span class="p">)</span>
-        <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_temp_dof</span>
-
-        <span class="k">return</span> <span class="n">p_value</span></div></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-    <span class="n">seed</span><span class="o">=</span><span class="mi">43</span>
-    <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">ci</span> <span class="o">=</span> <span class="n">RegressionCI</span><span class="p">()</span>
-
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">100</span>
-
-    <span class="n">reals</span> <span class="o">=</span> <span class="mi">100</span>
-    <span class="n">rate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">reals</span><span class="p">)</span>
-
-    <span class="n">x_example</span> <span class="o">=</span> <span class="s2">&quot;continuous&quot;</span>
-    <span class="n">y_example</span> <span class="o">=</span> <span class="s2">&quot;continuous&quot;</span>
-    <span class="n">dimz</span> <span class="o">=</span> <span class="mi">1</span>
-    <span class="c1"># z_example = [&quot;discrete&quot;, &quot;continuous&quot;]</span>
-    <span class="n">z_example</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;continuous&quot;</span><span class="p">]</span> <span class="c1">#, &quot;discrete&quot;]</span>
-    <span class="c1"># z_example = None</span>
-    <span class="n">rate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">reals</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">reals</span><span class="p">):</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">dimz</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span>
-            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_example</span><span class="p">)):</span>
-                <span class="k">if</span> <span class="n">z_example</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;discrete&quot;</span><span class="p">:</span>
-                    <span class="n">z</span><span class="p">[:,</span> <span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">T</span><span class="p">)</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">z</span><span class="p">[:,</span> <span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">high</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">T</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">dimz</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">z_example</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;discrete&quot;</span><span class="p">:</span>
-                    <span class="n">val</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-                    <span class="n">prob</span> <span class="o">=</span> <span class="mf">0.2</span> <span class="o">+</span> <span class="n">val</span> <span class="o">*</span> <span class="mf">0.6</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">prob</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">prob</span> <span class="o">=</span> <span class="mf">0.2</span>
-            <span class="k">if</span> <span class="n">x_example</span> <span class="o">==</span> <span class="s2">&quot;discrete&quot;</span><span class="p">:</span>
-                <span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="mf">1.</span> <span class="o">-</span> <span class="n">prob</span><span class="p">])</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.1</span><span class="o">*</span><span class="n">random_state</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="c1"># np.random.uniform(prob, 1)  #np.random.normal(prob, 1)</span>
-            <span class="k">if</span> <span class="n">y_example</span> <span class="o">==</span> <span class="s2">&quot;discrete&quot;</span><span class="p">:</span>
-                <span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">prob</span><span class="p">)])</span> <span class="c1"># + x[t]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">prob</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
-
-        <span class="c1"># # Continuous data</span>
-        <span class="c1"># z = np.random.randn(T, dimz)</span>
-        <span class="c1"># x = (0.5*z[:,0] + np.random.randn(T)).reshape(T, 1)</span>
-        <span class="c1"># y = (0.5*z[:,0] + np.random.randn(T)).reshape(T, 1) #+ 2*x</span>
-
-        <span class="k">if</span> <span class="n">x_example</span> <span class="o">==</span> <span class="s2">&quot;discrete&quot;</span><span class="p">:</span>
-            <span class="n">x_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">x_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">y_example</span> <span class="o">==</span> <span class="s2">&quot;discrete&quot;</span><span class="p">:</span>
-            <span class="n">y_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">y_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">dimz</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_example</span><span class="p">)):</span>
-                <span class="k">if</span> <span class="n">z_example</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;discrete&quot;</span><span class="p">:</span>
-                    <span class="n">z_type</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">z_type</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">z_type</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="n">ci</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">,</span> <span class="n">x_type</span><span class="o">=</span><span class="n">x_type</span><span class="p">,</span> <span class="n">y_type</span><span class="o">=</span><span class="n">y_type</span><span class="p">,</span> <span class="n">z_type</span><span class="o">=</span><span class="n">z_type</span><span class="p">)</span>
-        <span class="n">rate</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
-
-        <span class="c1"># data = np.hstack((x, y, z))</span>
-        <span class="c1"># type_mask = np.zeros(data.shape)</span>
-        <span class="c1"># type_mask[:, 0] = x_example == &quot;discrete&quot;</span>
-        <span class="c1"># type_mask[:, 1] = y_example == &quot;discrete&quot;</span>
-        <span class="c1"># type_mask[:, 2] = z_example == &quot;discrete&quot;</span>
-        <span class="c1"># type_mask = type_mask.astype(&#39;int&#39;)</span>
-        <span class="c1"># # print(type_mask)</span>
-        <span class="c1"># dataframe = pp.DataFrame(data=data, type_mask=type_mask)</span>
-        <span class="c1"># ci.set_dataframe(dataframe)</span>
-        
-        <span class="c1"># val, pval = ci.run_test(X=[(0, 0)], Y=[(1, 0)], Z=[(2, 0)])</span>
-        <span class="c1"># rate[i] = pval</span>
-
-    <span class="nb">print</span><span class="p">((</span><span class="n">rate</span> <span class="o">&lt;=</span> <span class="mf">0.05</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
-
-
-</pre></div>
-
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-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.independence_tests.robust_parcorr &#8212; Tigramite 5.2 documentation</title>
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-            
-  <h1>Source code for tigramite.independence_tests.robust_parcorr</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-
-<div class="viewcode-block" id="RobustParCorr"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr">[docs]</a><span class="k">class</span> <span class="nc">RobustParCorr</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Robust partial correlation test based on non-paranormal models.</span>
-
-<span class="sd">    Partial correlation is estimated through transformation to standard</span>
-<span class="sd">    normal marginals, ordinary least squares (OLS) regression, and a test for</span>
-<span class="sd">    non-zero linear Pearson correlation on the residuals.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    To test :math:`X \perp Y | Z`, firstly, each marginal is transformed to be</span>
-<span class="sd">    standard normally distributed. For that, the transform</span>
-<span class="sd">    :math:`\Phi^{-1}\circ\hat{F}` is used. Here, :math:`\Phi^{-1}` is the</span>
-<span class="sd">     quantile function of a standard normal distribution and </span>
-<span class="sd">     :math:`\hat{F}` is the empirical distribution function for the respective</span>
-<span class="sd">     marginal.</span>
-
-
-<span class="sd">    This idea stems from the literature on nonparanormal models, see:</span>
-
-<span class="sd">    - Han Liu, John Lafferty, and Larry Wasserman. The nonparanormal:</span>
-<span class="sd">      semiparametric estimation of high dimensional undirected graphs. J.</span>
-<span class="sd">      Mach. Learn. Res., 10:2295–2328, 2009.</span>
-
-<span class="sd">    - Han Liu, Fang Han, Ming Yuan, John Lafferty, and Larry Wasserman.</span>
-<span class="sd">      High-dimensional semiparametric Gaussian copula graphical models. Ann.</span>
-<span class="sd">      Statist., 40(4):2293–2326, 2012a.</span>
-
-<span class="sd">    - Naftali Harris, Mathias Drton. PC Algorithm for Nonparanormal Graphical</span>
-<span class="sd">      Models. Journal of Machine Learning Research, 14: 3365-3383, 2013.</span>
-
-<span class="sd">    Afterwards (where Z, X, and Y are now assumed to be transformed to the</span>
-<span class="sd">    standard normal scale):</span>
-
-<span class="sd">    :math:`Z` is regressed out from</span>
-<span class="sd">    :math:`X` and :math:`Y` assuming the  model</span>
-
-<span class="sd">    .. math::  X &amp; =  Z \beta_X + \epsilon_{X} \\</span>
-<span class="sd">        Y &amp; =  Z \beta_Y + \epsilon_{Y}</span>
-
-<span class="sd">    using OLS regression. Then the dependency of the residuals is tested with</span>
-<span class="sd">    the Pearson correlation test.</span>
-
-<span class="sd">    .. math::  \rho\left(r_X, r_Y\right)</span>
-
-<span class="sd">    For the ``significance=&#39;analytic&#39;`` Student&#39;s-*t* distribution with</span>
-<span class="sd">    :math:`T-D_Z-2` degrees of freedom is implemented.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to Parent class CondIndTest.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># documentation</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;robust_par_corr&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<div class="viewcode-block" id="RobustParCorr.trafo2normal"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.trafo2normal">[docs]</a>    <span class="k">def</span> <span class="nf">trafo2normal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">thres</span><span class="o">=</span><span class="mf">0.00001</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Transforms input array to standard normal marginals.</span>
-<span class="sd">        </span>
-<span class="sd">        For that, the code first transforms to uniform :math:`[0,1]` marginals</span>
-<span class="sd">        using the empirical distribution function, and then transforms to</span>
-<span class="sd">        normal marginals by applying the quantile function of a standard</span>
-<span class="sd">        normal. Assumes x.shape = (dim, T)</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x : array-like</span>
-<span class="sd">            Input array.</span>
-
-<span class="sd">        thres : float</span>
-<span class="sd">            Small number between 0 and 1; after transformation to the uniform</span>
-<span class="sd">            scale, all values that are too close to zero are replaced by thres,</span>
-<span class="sd">            similarly, all values that are too close to one, are replaced by</span>
-<span class="sd">            1-thres. This avoids NaNs.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        normal : array-like</span>
-<span class="sd">            array with normal marginals.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">trafo</span><span class="p">(</span><span class="n">xi</span><span class="p">):</span>
-            <span class="n">xisorted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
-            <span class="n">yi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">xi</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">xi</span><span class="p">))</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">xi</span><span class="p">,</span> <span class="n">xisorted</span><span class="p">,</span> <span class="n">yi</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">u</span> <span class="o">=</span> <span class="n">trafo</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-            <span class="n">u</span><span class="p">[</span><span class="n">u</span><span class="o">==</span><span class="mf">0.</span><span class="p">]</span> <span class="o">=</span> <span class="n">thres</span>
-            <span class="n">u</span><span class="p">[</span><span class="n">u</span><span class="o">==</span><span class="mf">1.</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="n">thres</span>
-            <span class="n">normal</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">u</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">normal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-                <span class="n">uniform</span> <span class="o">=</span> <span class="n">trafo</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        
-                <span class="n">uniform</span><span class="p">[</span><span class="n">uniform</span><span class="o">==</span><span class="mf">0.</span><span class="p">]</span> <span class="o">=</span> <span class="n">thres</span>
-                <span class="n">uniform</span><span class="p">[</span><span class="n">uniform</span><span class="o">==</span><span class="mf">1.</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="n">thres</span>
-                <span class="n">normal</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">uniform</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">normal</span></div>
-
-    <span class="k">def</span> <span class="nf">_get_single_residuals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="p">,</span>
-                              <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                              <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns residuals of linear multiple regression.</span>
-
-<span class="sd">        Performs a OLS regression of the variable indexed by target_var on the</span>
-<span class="sd">        conditions Z. Here array is assumed to contain X and Y as the first two</span>
-<span class="sd">        rows with the remaining rows (if present) containing the conditions Z.</span>
-<span class="sd">        Optionally returns the estimated regression line.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        target_var : {0, 1}</span>
-<span class="sd">            Variable to regress out conditions from.</span>
-
-<span class="sd">        standardize : bool, optional (default: True)</span>
-<span class="sd">            Whether to standardize the array beforehand. Must be used for</span>
-<span class="sd">            partial correlation.</span>
-
-<span class="sd">        return_means : bool, optional (default: False)</span>
-<span class="sd">            Whether to return the estimated regression line.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        resid [, mean] : array-like</span>
-<span class="sd">            The residual of the regression and optionally the estimated line.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">dim_z</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">-</span> <span class="mi">2</span>
-
-        <span class="c1"># Standardize</span>
-        <span class="k">if</span> <span class="n">standardize</span><span class="p">:</span>
-            <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-            <span class="c1"># if np.isnan(array).sum() != 0:</span>
-            <span class="c1">#     raise ValueError(&quot;nans after standardizing, &quot;</span>
-            <span class="c1">#                      &quot;possibly constant array!&quot;)</span>
-
-        <span class="n">y</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">target_var</span><span class="p">,</span> <span class="p">:]</span>
-
-        <span class="k">if</span> <span class="n">dim_z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fastCopyAndTranspose</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="mi">2</span><span class="p">:,</span> <span class="p">:])</span>
-            <span class="n">beta_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">beta_hat</span><span class="p">)</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">mean</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">resid</span> <span class="o">=</span> <span class="n">y</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">if</span> <span class="n">return_means</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">(</span><span class="n">resid</span><span class="p">,</span> <span class="n">mean</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">resid</span>
-
-<div class="viewcode-block" id="RobustParCorr.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return partial correlation.</span>
-
-<span class="sd">        Marginals are firstly transformed to standard normal scale. Dependence</span>
-<span class="sd">        Measure is then estimated as the Pearson correlation of the residuals</span>
-<span class="sd">        of a linear OLS regression.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Partial correlation coefficient.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Transform to normal marginals</span>
-        <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">trafo2normal</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">val</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">pearsonr</span><span class="p">(</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">val</span></div>
-
-<div class="viewcode-block" id="RobustParCorr.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for shuffle significance test.</span>
-
-<span class="sd">        Firstly, each marginal is transformed to the standard normal scale.</span>
-<span class="sd">        For residual-based test statistics only the residuals are shuffled.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Transform to normal marginals</span>
-        <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">trafo2normal</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-
-        <span class="n">x_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">array_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_vals</span><span class="p">,</span> <span class="n">y_vals</span><span class="p">])</span>
-        <span class="n">xyz_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_shuffle_dist</span><span class="p">(</span><span class="n">array_resid</span><span class="p">,</span> <span class="n">xyz_resid</span><span class="p">,</span>
-                                           <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">,</span>
-                                           <span class="n">sig_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                           <span class="n">sig_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                                           <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="p">(</span><span class="n">null_dist</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span><span class="p">))</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-        <span class="c1"># Adjust p-value for two-sided measures</span>
-        <span class="k">if</span> <span class="n">pval</span> <span class="o">&lt;</span> <span class="mf">1.</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">*=</span> <span class="mf">2.</span>
-
-        <span class="k">if</span> <span class="n">return_null_dist</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pval</span><span class="p">,</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="RobustParCorr.get_analytic_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_analytic_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">xyz</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns analytic p-value from Student&#39;s t-test for the Pearson</span>
-<span class="sd">        correlation coefficient.</span>
-
-<span class="sd">        Assumes two-sided correlation. If the degrees of freedom are less than</span>
-<span class="sd">        1, numpy.nan is returned.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        T : int</span>
-<span class="sd">            Sample length</span>
-
-<span class="sd">        dim : int</span>
-<span class="sd">            Dimensionality, ie, number of features.</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float or numpy.nan</span>
-<span class="sd">            P-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Get the number of degrees of freedom</span>
-        <span class="n">deg_f</span> <span class="o">=</span> <span class="n">T</span> <span class="o">-</span> <span class="n">dim</span>
-
-        <span class="k">if</span> <span class="n">deg_f</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-        <span class="k">elif</span> <span class="nb">abs</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">sys</span><span class="o">.</span><span class="n">float_info</span><span class="o">.</span><span class="n">min</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.0</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">trafo_val</span> <span class="o">=</span> <span class="n">value</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">deg_f</span><span class="o">/</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">value</span><span class="o">*</span><span class="n">value</span><span class="p">))</span>
-            <span class="c1"># Two sided significance level</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">sf</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">trafo_val</span><span class="p">),</span> <span class="n">deg_f</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span>
-
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="RobustParCorr.get_analytic_confidence"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_analytic_confidence">[docs]</a>    <span class="k">def</span> <span class="nf">get_analytic_confidence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">conf_lev</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns analytic confidence interval for correlation coefficient.</span>
-
-<span class="sd">        Based on Student&#39;s t-distribution.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        df : int</span>
-<span class="sd">            degrees of freedom of the test</span>
-
-<span class="sd">        conf_lev : float</span>
-<span class="sd">            Confidence interval, eg, 0.9</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        (conf_lower, conf_upper) : Tuple of floats</span>
-<span class="sd">            Upper and lower confidence bound of confidence interval.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Confidence interval is two-sided</span>
-        <span class="n">c_int</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">conf_lev</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.</span><span class="p">)</span>
-
-        <span class="n">value_tdist</span> <span class="o">=</span> <span class="n">value</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">value</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
-        <span class="n">conf_lower</span> <span class="o">=</span> <span class="p">(</span><span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">q</span><span class="o">=</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">c_int</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="n">value_tdist</span><span class="p">)</span>
-                      <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">df</span> <span class="o">+</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">q</span><span class="o">=</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">c_int</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">df</span><span class="p">,</span>
-                                                 <span class="n">loc</span><span class="o">=</span><span class="n">value_tdist</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
-        <span class="n">conf_upper</span> <span class="o">=</span> <span class="p">(</span><span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">q</span><span class="o">=</span><span class="n">c_int</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="n">value_tdist</span><span class="p">)</span>
-                      <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">df</span> <span class="o">+</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">q</span><span class="o">=</span><span class="n">c_int</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">df</span><span class="p">,</span>
-                                                 <span class="n">loc</span><span class="o">=</span><span class="n">value_tdist</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">conf_lower</span><span class="p">,</span> <span class="n">conf_upper</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="RobustParCorr.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">corrected_aic</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns Akaike&#39;s Information criterion modulo constants.</span>
-
-<span class="sd">        First of all, each marginal is transformed to the standard normal</span>
-<span class="sd">        scale. For this, each marginal is transformed to the uniform scale</span>
-<span class="sd">        using the empirical distribution function and then, transformed to</span>
-<span class="sd">        the standard normal scale by applying the quantile function of a</span>
-<span class="sd">        standard normal. Afterwards, fits a linear model of the parents to</span>
-<span class="sd">        variable j and returns the score. Leave-one-out cross-validation is</span>
-<span class="sd">        asymptotically equivalent to AIC for ordinary linear regression</span>
-<span class="sd">        models. Here used to determine optimal hyperparameters in </span>
-<span class="sd">        PCMCI(plus), in particular the pc_alpha value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of target variable in data array.</span>
-
-<span class="sd">        parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...] containing parents.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">        score : float</span>
-<span class="sd">            Model score.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>   <span class="c1"># dummy variable here</span>
-        <span class="n">Z</span> <span class="o">=</span> <span class="n">parents</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
-                                                    <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                    <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                                    <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                                    <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                    <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># Transform to normal marginals</span>
-        <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">trafo2normal</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-
-        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_single_residuals</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target_var</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">return_means</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="c1"># Get RSS</span>
-        <span class="n">rss</span> <span class="o">=</span> <span class="p">(</span><span class="n">y</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="c1"># Number of parameters</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">-</span> <span class="mi">1</span>
-        <span class="c1"># Get AIC</span>
-        <span class="k">if</span> <span class="n">corrected_aic</span><span class="p">:</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span> <span class="o">+</span> <span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">T</span> <span class="o">-</span> <span class="n">p</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span>
-        <span class="k">return</span> <span class="n">score</span></div></div>
-</pre></div>
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-  <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span><span class="p">,</span> <span class="n">combinations</span>
-<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
-
-<span class="kn">from</span> <span class="nn">.pcmci_base</span> <span class="kn">import</span> <span class="n">PCMCIbase</span>
-
-<div class="viewcode-block" id="LPCMCI"><a class="viewcode-back" href="../../index.html#tigramite.lpcmci.LPCMCI">[docs]</a><span class="k">class</span> <span class="nc">LPCMCI</span><span class="p">(</span><span class="n">PCMCIbase</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot; LPCMCI is an algorithm for causal discovery in large-scale times series that allows for latent confounders and</span>
-<span class="sd">    learns lag-specific causal relationships. The algorithm is introduced and explained in:</span>
-<span class="sd">    [1] Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders.</span>
-<span class="sd">    Advances in Neural Information Processing Systems, 2020, 33.</span>
-<span class="sd">    https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</span>
-<span class="sd">    NOTE: This method is still EXPERIMENTAL since the default settings of hyperparameters are still being fine-tuned.</span>
-<span class="sd">    We actually invite feedback on which work best in applications and numerical experiments.</span>
-<span class="sd">    The main function, which applies the algorithm, is &#39;run_lpcmci&#39;.</span>
-
-<span class="sd">    Parameters passed to the constructor:</span>
-<span class="sd">    - dataframe:</span>
-<span class="sd">        Tigramite dataframe object that contains the the time series dataset \bold{X}</span>
-<span class="sd">    - cond_ind_test:</span>
-<span class="sd">        A conditional independence test object that specifies which conditional independence test CI is to be used</span>
-<span class="sd">    - verbosity:</span>
-<span class="sd">        Controls the verbose output self.run_lpcmci() and the function it calls.</span>
-
-<span class="sd">    Parameters passed to self.run_lpcmci():</span>
-<span class="sd">    Note: The default values are still being tuned and some parameters might be removed in the future.</span>
-<span class="sd">    - link_assumptions: dict or None</span>
-<span class="sd">        Two-level nested dictionary such that link_assumptions[j][(i, lag_i)], where 0 &lt;= j, i &lt;= N-1 (with N the number of component</span>
-<span class="sd">        time series) and -tau_max &lt;= lag_i &lt;= -tau_min, is a string which specifies background knowledge about the link from X^i_{t+lag_i} to</span>
-<span class="sd">        X^j_t. These are the possibilities for this string and the corresponding claim:</span>
-<span class="sd">            &#39;-?&gt;&#39;   : X^i_{t+lag_i} is an ancestor of X^j_t.</span>
-<span class="sd">            &#39;--&gt;&#39;   : X^i_{t+lag_i} is an ancestor of X^j_t, and there is a link between X^i_{t+lag_i} and X^j_t</span>
-<span class="sd">            &#39;&lt;?-&#39;   : Only allowed for lag_i = 0. X^j_t is an ancestor of X^i_t.</span>
-<span class="sd">            &#39;&lt;--&#39;   : Only allowed for lag_i = 0. X^j_t is an ancestor of X^i_t, and there is a link between X^i_t and X^j_t</span>
-<span class="sd">            &#39;&lt;?&gt;&#39;   : Neither X^i_{t+lag_i} is an ancestor of X^j_t nor the other way around</span>
-<span class="sd">            &#39;&lt;-&gt;&#39;   : Neither X^i_{t+lag_i} is an ancestor of X^j_t nor the other way around, and there is a link between X^i_{t+lag_i}</span>
-<span class="sd">                      and X^j_t</span>
-<span class="sd">            &#39;o?&gt;&#39;   : X^j_t is not an ancestor of X^i_{t+lag_i} (for lag_i &lt; 0 this background knowledge is (for the default settings of</span>
-<span class="sd">                      self.run_lpcmci()) imposed automatically)</span>
-<span class="sd">            &#39;o-&gt;&#39;   : X^j_t is not an ancestor of X^i_{t+lag_i}, and there is a link between X^i_{t+lag_i} and X^j_t</span>
-<span class="sd">            &#39;&lt;?o&#39;   : Only allowed for lag_i = 0. X^i_t is not an ancestor of X^j_t</span>
-<span class="sd">            &#39;&lt;-o&#39;   : Only allowed for lag_i = 0. X^i_t is not an ancestor of X^j_t, and there is a link between X^i_t and X^j_t</span>
-<span class="sd">            &#39;o-o&#39;   : Only allowed for lag_i = 0. There is a link between X^i_t and X^j_t</span>
-<span class="sd">            &#39;o?o&#39;   : Only allowed for lag_i = 0. No claim is made</span>
-<span class="sd">            &#39;&#39;      : There is no link between X^i_{t+lag_i} and X^j_t.</span>
-
-<span class="sd">        Another way to specify the absent link is if the form of the link between (i, lag_i) and (j, 0) is not specified by the dictionary, that is, if either</span>
-<span class="sd">        link_assumptions[j] does not exist or link_assumptions[j] does exist but link_assumptions[j][(i, lag_i)] does</span>
-<span class="sd">        not exist, then the link between (i, lag_i) and (j, 0) is assumed to be absent.</span>
-<span class="sd">    - tau_min:</span>
-<span class="sd">        The assumed minimum time lag, i.e., links with a lag smaller than tau_min are assumed to be absent.</span>
-<span class="sd">    - tau_max:</span>
-<span class="sd">        The maximum considered time lag, i.e., the algorithm learns a DPAG on a time window [t-\taumax, t] with \tau_max + 1 time steps.</span>
-<span class="sd">        It is *not* assumed that in the underlying time series DAG there are no links with a lag larger than \tau_max.</span>
-<span class="sd">    - pc_alpha:</span>
-<span class="sd">        The significance level of conditional independence tests</span>
-<span class="sd">    - n_preliminary_iterations:</span>
-<span class="sd">        Determines the number of iterations in the preliminary phase of LPCMCI, corresponding to the &#39;k&#39; in LPCMCI(k) in [1].</span>
-<span class="sd">    - max_cond_px:</span>
-<span class="sd">        Consider a pair of variables (X^i_{t-\tau}, X^j_t) with \tau &gt; 0. In Algorithm S2 in [1] (here this is</span>
-<span class="sd">        self._run_ancestral_removal_phase()), the algorithm does not test for conditional independence given subsets of</span>
-<span class="sd">        apds_t(X^i_{t-\tau}, X^j_t, C(G)) of cardinality higher than max_cond_px. In Algorithm S3 in [1] (here this is</span>
-<span class="sd">        self._run_non_ancestral_removal_phase()), the algorithm does not test for conditional independence given subsets of</span>
-<span class="sd">        napds_t(X^i_{t-\tau}, X^j_t, C(G)) of cardinality higher than max_cond_px.</span>
-<span class="sd">    - max_p_global:</span>
-<span class="sd">        Restricts all conditional independence tests to conditioning sets with cardinality smaller or equal to max_p_global</span>
-<span class="sd">    - max_p_non_ancestral:</span>
-<span class="sd">        Restricts all conditional independence tests in the second removal phase (here this is self._run_dsep_removal_phase()) to</span>
-<span class="sd">        conditioning sets with cardinality smaller or equal to max_p_global</span>
-<span class="sd">    - max_q_global:</span>
-<span class="sd">        For each ordered pair (X^i_{t-\tau}, X^j_t) of adjacent variables and for each cardinality of the conditioning sets test at most</span>
-<span class="sd">        max_q_global many conditioning sets (when summing over all tested cardinalities more than max_q_global tests may be made)</span>
-<span class="sd">    - max_pds_set:</span>
-<span class="sd">        In Algorithm S3 (here this is self._run_non_ancestral_removal_phase()), the algorithm tests for conditional independence given</span>
-<span class="sd">        subsets of the relevant napds_t sets. If for a given link the set napds_t(X^j_t, X^i_{t-\tau}, C(G)) has more than max_pds_set many</span>
-<span class="sd">        elements (or, if the link is also tested in the opposite directed, if napds_t(X^i_{t-\tau}, X^j_t, C(G)) has more than max_pds_set</span>
-<span class="sd">        elements), this link is not tested.</span>
-<span class="sd">    - prelim_with_collider_rules:</span>
-<span class="sd">        If True: As in pseudocode</span>
-<span class="sd">        If False: Line 22 of Algorithm S2 in [1] is replaced by line 18 of Algorithm S2 when Algorithm S2 is called from the preliminary</span>
-<span class="sd">        phase (not in the last application of Algorithm S2 directly before Algorithm S3 is applied)</span>
-<span class="sd">    - parents_of_lagged:</span>
-<span class="sd">        If True: As in pseudocode</span>
-<span class="sd">        If False: The default conditioning set is pa(X^j_t, C(G)) rather than pa({X^j_t, X^i_{t-\tau}, C(G)) for tau &gt; 0</span>
-<span class="sd">    - prelim_only:</span>
-<span class="sd">        If True, stop after the preliminary phase. Can be used for detailed performance analysis</span>
-<span class="sd">    - break_once_separated:</span>
-<span class="sd">        If True: As in pseudocode</span>
-<span class="sd">        If False: The break commands are removed from Algorithms S2 and S3 in in [1]</span>
-<span class="sd">    - no_non_ancestral_phase:</span>
-<span class="sd">        If True, do not execute Algorithm S3. Can be used for detailed performance analysis</span>
-<span class="sd">    - use_a_pds_t_for_majority:</span>
-<span class="sd">        If True: As in pseudocode</span>
-<span class="sd">        If False: The search for separating sets instructed by the majority rule is made given subsets adj(X^j_t, C(G)) rather than</span>
-<span class="sd">        subsets of apds_t(X^j_t, X^i_{t-\tau}, C(G))</span>
-<span class="sd">    - orient_contemp:</span>
-<span class="sd">        If orient_contemp == 1: As in pseudocode of Algorithm S2 in [1]</span>
-<span class="sd">        If orient_contemp == 2: Also orient contemporaneous links in line 18 of Algorithm S2</span>
-<span class="sd">        If orient_comtemp == 0: Also not orient contemporaneous links in line 22 of Algorithm S2</span>
-<span class="sd">    - update_middle_marks:</span>
-<span class="sd">        If True: As in pseudoce of Algorithms S2 and S3 in [1]</span>
-<span class="sd">        If False: The MMR rule is not applied</span>
-<span class="sd">    - prelim_rules:</span>
-<span class="sd">        If prelim_rules == 1: As in pseudocode of Algorithm S2 in [1]</span>
-<span class="sd">        If prelim_rules == 0: Exclude rules R9^prime and R10^\prime from line 18 in Algorithm S2</span>
-<span class="sd">    - fix_all_edges_before_final_orientation:</span>
-<span class="sd">        When one of max_p_global, max_p_non_ancestral, max_q_global or max_pds_set is not np.inf, the algorithm may terminate although not</span>
-<span class="sd">        all middle marks are empty. All orientation rules are nevertheless sound, since the rules always check for the appropriate middle</span>
-<span class="sd">        marks. If fix_all_edges_before_final_orientation is True, all middle marks are set to the empty middle mark by force, followed by</span>
-<span class="sd">        another application of the rules.</span>
-<span class="sd">    - auto_first:</span>
-<span class="sd">        If True: As in pseudcode of Algorithms S2 and S3 in [1]</span>
-<span class="sd">        If False: Autodependency links are not prioritized even before contemporaneous links</span>
-<span class="sd">    - remember_only_parents:</span>
-<span class="sd">        If True: As in pseudocode of Algorithm 1</span>
-<span class="sd">        If False: If X^i_{t-\tau} has been marked as ancestor of X^j_t at any point of a preliminary iteration but the link between</span>
-<span class="sd">        X^i_{t-\tau} and X^j_t was removed later, the link is nevertheless initialized with a tail at X^i_{t-\tau} in the re-initialization</span>
-<span class="sd">    - no_apr:</span>
-<span class="sd">        If no_apr == 0: As in pseudcode of Algorithms S2 and S3 in [1]</span>
-<span class="sd">        If no_apr == 1: The APR is not applied by Algorithm S2, except in line 22 of its last call directly before the call of Algorithm S3</span>
-<span class="sd">        If no_apr == 2: The APR is never applied</span>
-
-<span class="sd">    Return value of self.run_lpcmci():</span>
-<span class="sd">        graph : array of shape (N, N, tau_max+1)</span>
-<span class="sd">            Resulting DPAG, representing the learned causal relationships.</span>
-<span class="sd">        val_matrix : array of shape (N, N, tau_max+1)</span>
-<span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-
-<span class="sd">    A note on middle marks:</span>
-<span class="sd">        For convenience (to have strings of the same lengths) we here internally denote the empty middle mark by &#39;-&#39;.  For post-processing</span>
-<span class="sd">        purposes all middle marks are set to the empty middle mark (here &#39;-&#39;).</span>
-<span class="sd">    </span>
-<span class="sd">    A note on wildcards:</span>
-<span class="sd">        The middle mark wildcard \ast and the edge mark wildcard are here represented as *, the edge mark wildcard \star as +</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">,</span> <span class="n">cond_ind_test</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Class constructor. Store:</span>
-<span class="sd">                i)      data</span>
-<span class="sd">                ii)     conditional independence test object</span>
-<span class="sd">                iii)    some instance attributes&quot;&quot;&quot;</span>
-
-        <span class="c1"># Init base class</span>
-        <span class="n">PCMCIbase</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
-                        <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">,</span>
-                        <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
-
-<div class="viewcode-block" id="LPCMCI.run_lpcmci"><a class="viewcode-back" href="../../index.html#tigramite.lpcmci.LPCMCI.run_lpcmci">[docs]</a>    <span class="k">def</span> <span class="nf">run_lpcmci</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                    <span class="n">link_assumptions</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                    <span class="n">tau_min</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
-                    <span class="n">tau_max</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> 
-                    <span class="n">pc_alpha</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span>
-                    <span class="n">n_preliminary_iterations</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
-                    <span class="n">max_cond_px</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
-                    <span class="n">max_p_global</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                    <span class="n">max_p_non_ancestral</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                    <span class="n">max_q_global</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                    <span class="n">max_pds_set</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                    <span class="n">prelim_with_collider_rules</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                    <span class="n">parents_of_lagged</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                    <span class="n">prelim_only</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-                    <span class="n">break_once_separated</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                    <span class="n">no_non_ancestral_phase</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>     
-                    <span class="n">use_a_pds_t_for_majority</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                    <span class="n">orient_contemp</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
-                    <span class="n">update_middle_marks</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                    <span class="n">prelim_rules</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
-                    <span class="n">fix_all_edges_before_final_orientation</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                    <span class="n">auto_first</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                    <span class="n">remember_only_parents</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                    <span class="n">no_apr</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Run LPCMCI on the dataset and with the conditional independence test passed to the class constructor and with the</span>
-<span class="sd">        options passed to this function.&quot;&quot;&quot;</span>
-
-        <span class="c1">#######################################################################################################################</span>
-        <span class="c1">#######################################################################################################################</span>
-        <span class="c1"># Step 0: Initializations</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_initialize</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="p">,</span> <span class="n">n_preliminary_iterations</span><span class="p">,</span> <span class="n">max_cond_px</span><span class="p">,</span> <span class="n">max_p_global</span><span class="p">,</span>
-            <span class="n">max_p_non_ancestral</span><span class="p">,</span> <span class="n">max_q_global</span><span class="p">,</span> <span class="n">max_pds_set</span><span class="p">,</span> <span class="n">prelim_with_collider_rules</span><span class="p">,</span> <span class="n">parents_of_lagged</span><span class="p">,</span> <span class="n">prelim_only</span><span class="p">,</span>
-            <span class="n">break_once_separated</span><span class="p">,</span> <span class="n">no_non_ancestral_phase</span><span class="p">,</span> <span class="n">use_a_pds_t_for_majority</span><span class="p">,</span> <span class="n">orient_contemp</span><span class="p">,</span> <span class="n">update_middle_marks</span><span class="p">,</span>
-            <span class="n">prelim_rules</span><span class="p">,</span> <span class="n">fix_all_edges_before_final_orientation</span><span class="p">,</span> <span class="n">auto_first</span><span class="p">,</span> <span class="n">remember_only_parents</span><span class="p">,</span> <span class="n">no_apr</span><span class="p">)</span>
-
-        <span class="c1">#######################################################################################################################</span>
-        <span class="c1">#######################################################################################################################</span>
-        <span class="c1"># Step 1: Preliminary phases</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_preliminary_iterations</span><span class="p">):</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">=======================================================&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=======================================================&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Starting preliminary phase </span><span class="si">{:2}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-
-            <span class="c1"># In the preliminary phases, auto-lag links are tested with first priority. Among the auto-lag links, different lags are</span>
-            <span class="c1"># not distinguished. All other links have lower priority, among which those which shorter lags have higher priority</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_run_ancestral_removal_phase</span><span class="p">(</span><span class="n">prelim</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Preliminary phase </span><span class="si">{:2}</span><span class="s2"> complete&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Graph:</span><span class="se">\n</span><span class="s2">--------------------------------&quot;</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_print_graph_dict</span><span class="p">()</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;--------------------------------&quot;</span><span class="p">)</span>
-
-            <span class="c1"># When the option self.prelim_only is chosen, do not re-initialize in the last iteration</span>
-            <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_preliminary_iterations</span> <span class="o">-</span> <span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">prelim_only</span><span class="p">:</span>
-                <span class="k">break</span>
-
-            <span class="c1"># Remember ancestorships, re-initialize and re-apply the remembered ancestorships</span>
-            <span class="n">def_ancs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">remember_only_parents</span><span class="p">:</span>
-                <span class="n">smaller_def_ancs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-                    <span class="n">smaller_def_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">def_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">}</span>
-                <span class="n">def_ancs</span> <span class="o">=</span> <span class="n">smaller_def_ancs</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">_initialize_run_memory</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_apply_new_ancestral_information</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">def_ancs</span><span class="p">)</span>
-
-        <span class="c1">#######################################################################################################################</span>
-        <span class="c1">#######################################################################################################################</span>
-        <span class="c1"># Step 2: Full ancestral phase</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">prelim_only</span><span class="p">:</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">=======================================================&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=======================================================&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Starting final ancestral phase&quot;</span><span class="p">)</span>
-
-            <span class="c1"># In the standard ancestral phase, links are prioritized in the same as in the preliminary phases</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_run_ancestral_removal_phase</span><span class="p">()</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Final ancestral phase complete&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Graph:</span><span class="se">\n</span><span class="s2">--------------------------------&quot;</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_print_graph_dict</span><span class="p">()</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;--------------------------------&quot;</span><span class="p">)</span>
-
-        <span class="c1">#######################################################################################################################</span>
-        <span class="c1">#######################################################################################################################</span>
-        <span class="c1"># Step 3: Non-ancestral phase</span>
-        <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">prelim_only</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">no_non_ancestral_phase</span><span class="p">):</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">=======================================================&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=======================================================&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Starting non-ancestral phase&quot;</span><span class="p">)</span>
-
-            <span class="c1"># In the non-ancestral phase, large lags are prioritized</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_run_non_ancestral_removal_phase</span><span class="p">()</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Non-ancestral phase complete&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Graph:</span><span class="se">\n</span><span class="s2">--------------------------------&quot;</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_print_graph_dict</span><span class="p">()</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;--------------------------------&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fix_all_edges_before_final_orientation</span><span class="p">:</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">=======================================================&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=======================================================&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final rule application phase&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Setting all middle marks to &#39;-&#39;&quot;</span><span class="p">)</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">_fix_all_edges</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_run_orientation_phase</span><span class="p">(</span><span class="n">rule_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rules_all</span><span class="p">,</span> <span class="n">only_lagged</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
-
-        <span class="c1">#######################################################################################################################</span>
-        <span class="c1">#######################################################################################################################</span>
-
-        <span class="c1"># Verbose output</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">=======================================================&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=======================================================&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">LPCMCI has converged&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Final graph:</span><span class="se">\n</span><span class="s2">--------------------------------&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;--------------------------------&quot;</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_graph_dict</span><span class="p">()</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;--------------------------------&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;--------------------------------</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Max search set: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_na_search_set_found</span><span class="p">))</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Max na-pds set: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_na_pds_set_found</span><span class="p">))</span>
-
-        <span class="c1"># Post processing</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_fix_all_edges</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict2graph</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict_to_matrix</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pval_max</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">default</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">val_min_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict_to_matrix</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pval_max_val</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">default</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cardinality_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict_to_matrix</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pval_max_card</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">default</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
-
-        <span class="c1"># Build and return the return dictionariy</span>
-        <span class="n">return_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;graph&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span>
-                       <span class="s2">&quot;p_matrix&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_matrix</span><span class="p">,</span>
-                       <span class="s2">&quot;val_matrix&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_min_matrix</span><span class="p">}</span>
-        <span class="k">return</span> <span class="n">return_dict</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="p">,</span> <span class="n">n_preliminary_iterations</span><span class="p">,</span> <span class="n">max_cond_px</span><span class="p">,</span> <span class="n">max_p_global</span><span class="p">,</span>
-        <span class="n">max_p_non_ancestral</span><span class="p">,</span> <span class="n">max_q_global</span><span class="p">,</span> <span class="n">max_pds_set</span><span class="p">,</span> <span class="n">prelim_with_collider_rules</span><span class="p">,</span> <span class="n">parents_of_lagged</span><span class="p">,</span> <span class="n">prelim_only</span><span class="p">,</span>
-        <span class="n">break_once_separated</span><span class="p">,</span> <span class="n">no_non_ancestral_phase</span><span class="p">,</span> <span class="n">use_a_pds_t_for_majority</span><span class="p">,</span> <span class="n">orient_contemp</span><span class="p">,</span> <span class="n">update_middle_marks</span><span class="p">,</span> <span class="n">prelim_rules</span><span class="p">,</span>
-        <span class="n">fix_all_edges_before_final_orientation</span><span class="p">,</span> <span class="n">auto_first</span><span class="p">,</span> <span class="n">remember_only_parents</span><span class="p">,</span> <span class="n">no_apr</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Function for</span>
-<span class="sd">            i)      saving the arguments passed to self.run_lpcmci() as instance attributes</span>
-<span class="sd">            ii)     initializing various memory variables for storing the current graph, sepsets etc.</span>
-<span class="sd">            &quot;&quot;&quot;</span>
-
-        <span class="c1"># Save the arguments passed to self.run_lpcmci()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span> <span class="o">=</span> <span class="n">link_assumptions</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_min</span> <span class="o">=</span> <span class="n">tau_min</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_max</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span> <span class="o">=</span> <span class="n">pc_alpha</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_preliminary_iterations</span> <span class="o">=</span> <span class="n">n_preliminary_iterations</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">=</span> <span class="n">max_cond_px</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_p_global</span> <span class="o">=</span> <span class="n">max_p_global</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_p_non_ancestral</span> <span class="o">=</span> <span class="n">max_p_non_ancestral</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span> <span class="o">=</span> <span class="n">max_q_global</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_pds_set</span> <span class="o">=</span> <span class="n">max_pds_set</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">prelim_with_collider_rules</span> <span class="o">=</span> <span class="n">prelim_with_collider_rules</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">parents_of_lagged</span> <span class="o">=</span> <span class="n">parents_of_lagged</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">prelim_only</span> <span class="o">=</span> <span class="n">prelim_only</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">break_once_separated</span> <span class="o">=</span> <span class="n">break_once_separated</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">no_non_ancestral_phase</span> <span class="o">=</span> <span class="n">no_non_ancestral_phase</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">use_a_pds_t_for_majority</span> <span class="o">=</span> <span class="n">use_a_pds_t_for_majority</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">orient_contemp</span> <span class="o">=</span> <span class="n">orient_contemp</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">update_middle_marks</span> <span class="o">=</span> <span class="n">update_middle_marks</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">prelim_rules</span> <span class="o">=</span> <span class="n">prelim_rules</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fix_all_edges_before_final_orientation</span> <span class="o">=</span> <span class="n">fix_all_edges_before_final_orientation</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">auto_first</span> <span class="o">=</span> <span class="n">auto_first</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">remember_only_parents</span> <span class="o">=</span> <span class="n">remember_only_parents</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">no_apr</span> <span class="o">=</span> <span class="n">no_apr</span>
-
-        <span class="k">if</span> <span class="n">pc_alpha</span> <span class="o">&lt;</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="n">pc_alpha</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Choose 0 &lt;= pc_alpha &lt;= 1&quot;</span><span class="p">)</span>
-            
-        <span class="c1"># Check that validity of tau_min and tau_max</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_tau_min_tau_max</span><span class="p">()</span>
-
-        <span class="c1"># Check the validity of &#39;link_assumptions&#39;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_check_link_assumptions</span><span class="p">()</span>
-
-        <span class="c1"># Rules to be executed at the end of a preliminary phase</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_rules_prelim_final</span><span class="o">=</span> <span class="p">[[</span><span class="s2">&quot;APR&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-08&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-02&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-01&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-09&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-10&quot;</span><span class="p">]]</span>
-
-        <span class="c1"># Rules to be executed within the while loop of a preliminary phase</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_rules_prelim</span> <span class="o">=</span> <span class="p">[[</span><span class="s2">&quot;APR&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-08&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-02&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-01&quot;</span><span class="p">]]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prelim_rules</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rules_prelim_final</span>
-
-        <span class="c1"># Full list of all rules</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_rules_all</span> <span class="o">=</span> <span class="p">[[</span><span class="s2">&quot;APR&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-08&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-02&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-01&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-00-d&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-00-c&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-03&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;R-04&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-09&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-10&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-00-b&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;ER-00-a&quot;</span><span class="p">]]</span>
-
-        <span class="c1"># Initialize various memory variables for storing the current graph, sepsets etc.</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_initialize_run_memory</span><span class="p">()</span>
-
-        <span class="c1"># Return</span>
-        <span class="k">return</span> <span class="kc">True</span>
-
-    <span class="k">def</span> <span class="nf">_check_tau_min_tau_max</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check whether the choice of tau_min and tau_max is valid.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_min</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;tau_min = </span><span class="si">{}</span><span class="s2">, &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_min</span><span class="p">)</span> <span class="o">+</span> \
-                             <span class="s2">&quot;tau_max = </span><span class="si">{}</span><span class="s2">, &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span> <span class="o">+</span> \
-                             <span class="s2">&quot;but 0 &lt;= tau_min &lt;= tau_max required.&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_check_link_assumptions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check the validity of user-specified &#39;link_assumptions&#39;.</span>
-
-<span class="sd">        The checks assert:</span>
-<span class="sd">        - Valid dictionary keys</span>
-<span class="sd">        - Valid edge types</span>
-<span class="sd">        - That no causal cycle is specified</span>
-<span class="sd">        - That no almost causal cycle is specified</span>
-
-<span class="sd">        The checks do not assert that maximality is not violated.&quot;&quot;&quot;</span>
-
-        <span class="c1"># Ancestorship matrices</span>
-        <span class="n">ancs_mat_contemp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s2">&quot;int32&quot;</span><span class="p">)</span>
-        <span class="n">ancs_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="o">*</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="o">*</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s2">&quot;int32&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Run through the outer dictionary</span>
-        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">links_j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-
-            <span class="c1"># Check validity of keys of outer dictionary</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">j</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The argument &#39;link_assumption&#39; must be a &quot;</span>\
-                    <span class="s2">&quot;dictionary whose keys are in {0, 1, ..., N-1}, where N &quot;</span>\
-                    <span class="s2">&quot;is the number of component time series. Here, &quot;</span>\
-                    <span class="sa">f</span><span class="s2">&quot;N = </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
-
-            <span class="c1"># Run through the inner dictionary</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="n">link_ij</span> <span class="ow">in</span> <span class="n">links_j</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-
-                <span class="c1"># Check validity of keys of inner dictionary</span>
-                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span> <span class="ow">and</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The dictionary &#39;link_assumptions[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">] &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;must not have the key (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0), because this refers &quot;</span>\
-                        <span class="s2">&quot;to a self-link.&quot;</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="p">(</span><span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">i</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-                    <span class="ow">or</span> <span class="ow">not</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">lag_i</span> <span class="o">&lt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_min</span><span class="p">)):</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;All values of &#39;link_assumptions&#39; must &quot;</span>\
-                        <span class="s2">&quot;be dictionaries whose keys are of the form (i, &quot;</span>\
-                        <span class="s2">&quot;lag_i), where i in {0, 1, ..., N-1} with N the &quot;</span>\
-                        <span class="s2">&quot;number of component time series and lag_i in &quot;</span>\
-                        <span class="s2">&quot;{-tau_max, ..., -tau_min} with tau_max the maximum &quot;</span>\
-                        <span class="s2">&quot;considered time lag and tau_min the minimum assumed &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;time lag. Here, N = </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="si">}</span><span class="s2"> and tau_max = &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="si">}</span><span class="s2"> and tau_min = </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_min</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
-
-                <span class="c1"># Check for validity of entries. At the same time mark the</span>
-                <span class="c1"># ancestorships in ancs_mat_contemp and ancs_mat</span>
-
-                <span class="k">if</span> <span class="n">link_ij</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-
-                    <span class="c1"># Check for symmetry of lag zero links</span>
-                    <span class="k">if</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-
-                        <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span>
-                            <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="ow">is</span> <span class="kc">None</span>
-                            <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="p">[</span><span class="n">i</span><span class="p">][(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">):</span>
-                            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The lag zero links specified by &quot;</span>\
-                                <span class="s2">&quot;&#39;link_assumptions&#39; must be symmetric: Because&quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">)] = &#39;&#39;, &quot;</span>\
-                                <span class="s2">&quot; there must also be &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">)] = &#39;&#39;.&quot;</span><span class="p">)</span>
-                    <span class="k">continue</span>
-
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_ij</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">lag_i</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid link: &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">. Allowed are: &#39;-?&gt;&#39;, &#39;--&gt;&#39;, &#39;&lt;?&gt;&#39;, &quot;</span>\
-                            <span class="s2">&quot;&#39;&lt;-&gt;&#39;, &#39;o?&gt;&#39;, &#39;o-&gt;&#39;.&quot;</span><span class="p">)</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid link: &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">. Allowed are: &#39;-?&gt;&#39;, &#39;--&gt;&#39;, &#39;&lt;?&gt;&#39;, &quot;</span>\
-                            <span class="s2">&quot;&#39;&lt;-&gt;&#39;, &#39;o?&gt;&#39;, &#39;o-&gt;&#39;, &#39;&lt;?-&#39;, &#39;&lt;--&#39;, &#39;&lt;?o&#39;, &#39;&lt;--&#39;, &quot;</span>\
-                            <span class="s2">&quot;&#39;o-o&#39;, &#39;o?o&#39;.&quot;</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">link_ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-
-                    <span class="k">if</span> <span class="n">link_ij</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid link: &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">. The first character &#39;-&#39;, which says &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;that (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">) is an ancestor (cause) of &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0). Hence, (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0) is a non-ancestor &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;(non-cause) of (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">) and the third &quot;</span>\
-                            <span class="s2">&quot;character must be &#39;&gt;&#39;.&quot;</span><span class="p">)</span>
-
-                    <span class="c1"># Mark the ancestorship</span>
-                    <span class="k">if</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="n">ancs_mat_contemp</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-                    <span class="k">for</span> <span class="n">Delta_t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag_i</span><span class="p">)):</span>
-                        <span class="n">ancs_mat</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="o">*</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">lag_i</span><span class="p">)</span> <span class="o">+</span> <span class="n">Delta_t</span><span class="p">)</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="o">*</span><span class="n">Delta_t</span> <span class="o">+</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-
-                <span class="k">elif</span> <span class="n">link_ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;&lt;&quot;</span><span class="p">,</span> <span class="s2">&quot;o&quot;</span><span class="p">]:</span>
-
-                    <span class="k">if</span> <span class="n">lag_i</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-
-                        <span class="k">if</span> <span class="n">link_ij</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-                            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid link: &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">. Since </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2"> &lt; 0, (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0) &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;cannot be an ancestor (cause) of &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">). Hence, the third character &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;must be &#39;&gt;&#39;.&quot;</span><span class="p">)</span>
-
-                    <span class="k">else</span><span class="p">:</span>
-
-                        <span class="k">if</span> <span class="n">link_ij</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;o&quot;</span><span class="p">]:</span>
-                            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid link: &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">. The third character must be one &quot;</span>\
-                                <span class="s2">&quot;of the following: 1) &#39;-&#39;, which says that &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0) is an ancestor (cause) of &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">). 2) &#39;&gt;&#39;, which says that &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0) is a non-ancestor (non-cause) of &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">). 3) &#39;o&#39;, which says that it is &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;unknown whether or not (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">) is an &quot;</span>\
-                                <span class="sa">f</span><span class="s2">&quot;ancestor (cause) of (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">).&quot;</span><span class="p">)</span>
-
-                        <span class="k">if</span> <span class="n">link_ij</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-
-                            <span class="k">if</span> <span class="n">link_ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&lt;&quot;</span><span class="p">:</span>
-                                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid link: &quot;</span>\
-                                    <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                                    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">. The third character is &#39;-&#39;, &quot;</span>\
-                                    <span class="sa">f</span><span class="s2">&quot;which says that (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">) is an &quot;</span>\
-                                    <span class="sa">f</span><span class="s2">&quot;ancestor (cause) of (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, 0). Hence, &quot;</span>\
-                                    <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, 0) is a non-ancestor (non-cause) &quot;</span>\
-                                    <span class="sa">f</span><span class="s2">&quot;of (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">) and the first character &quot;</span>\
-                                    <span class="s2">&quot;must be &#39;&lt;&#39;.&quot;</span><span class="p">)</span>
-
-                            <span class="c1"># Mark the ancestorship</span>
-                            <span class="n">ancs_mat_contemp</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-                            <span class="k">for</span> <span class="n">Delta_t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                                <span class="n">ancs_mat</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="o">*</span><span class="n">Delta_t</span> <span class="o">+</span> <span class="n">j</span><span class="p">,</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="o">*</span><span class="n">Delta_t</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid link: &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">. The first character must be one of the &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;following: 1) &#39;-&#39;, which says that (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">) &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;is an ancestor (cause) of (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0). 2) &#39;&lt;&#39;, which &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;says that (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">) is a non-ancestor &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;(non-cause) of (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0). 3) &#39;o&#39;, which says that it&quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;is unknown whether or not (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">) is an &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;ancestor (cause) of (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">).&quot;</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">link_ij</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="s2">&quot;?&quot;</span><span class="p">]:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid link: &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">. The second character must be one of the &quot;</span>\
-                        <span class="s2">&quot;following: 1) &#39;-&#39;, which says that the link &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">) </span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0) is definitely &quot;</span>\
-                        <span class="s2">&quot;part of the graph. 2) &#39;?&#39;, which says that link &quot;</span>\
-                        <span class="s2">&quot;might be but does not need to be part of the graph.&quot;</span><span class="p">)</span>
-
-                <span class="c1"># Check for symmetry of lag zero links</span>
-                <span class="k">if</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-
-                    <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span>
-                        <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="ow">is</span> <span class="kc">None</span>
-                        <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="p">[</span><span class="n">i</span><span class="p">][(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">link_ij</span><span class="p">)):</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The lag zero links specified by &quot;</span>\
-                            <span class="s2">&quot;&#39;link_assumptions&#39; must be symmetric: Because &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">&#39; there must also be &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;&#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">)] = &quot;</span>\
-                            <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">link_ij</span><span class="p">)</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Check for contemporaneous cycles</span>
-        <span class="n">ancs_mat_contemp_to_N</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">matrix_power</span><span class="p">(</span><span class="n">ancs_mat_contemp</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">ancs_mat_contemp_to_N</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;According to &#39;link_assumptions&#39;, there is a &quot;</span>\
-                <span class="s2">&quot;contemporaneous causal cycle. Causal cycles are not allowed.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Check for almost directed cycles</span>
-        <span class="n">ancs_mat_summed</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">ancs_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s2">&quot;int32&quot;</span><span class="p">)</span> <span class="o">-</span> <span class="n">ancs_mat</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">links_j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="n">link_ij</span> <span class="ow">in</span> <span class="n">links_j</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">link_ij</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span>
-                    <span class="ow">and</span> <span class="n">link_ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&lt;&quot;</span>
-                    <span class="ow">and</span> <span class="n">ancs_mat_summed</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="o">*</span><span class="nb">abs</span><span class="p">(</span><span class="n">lag_i</span><span class="p">)</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">):</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Inconsistency in &#39;link_assumptions&#39;: &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;Since &#39;link_assumptions&#39;[</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">][(</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">)] &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;= </span><span class="si">{</span><span class="n">link_ij</span><span class="si">}</span><span class="s2">, variable (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">) is a &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;non-ancestor (non-cause) of (</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0). At the same &quot;</span>\
-                        <span class="s2">&quot;time, however, &#39;link_assumptions&#39; specifies a &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;directed path (causal path) from (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lag_i</span><span class="si">}</span><span class="s2">) to &quot;</span>\
-                        <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">, 0).&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Replace absent entries by &#39;&#39;</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">j</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau_i</span><span class="p">):</span> <span class="s2">&quot;&quot;</span>
-                    <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">tau_i</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">)}</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="o">+</span><span class="mi">1</span><span class="p">)):</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">tau_i</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">):</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau_i</span><span class="p">))</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau_i</span><span class="p">)]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">_initialize_run_memory</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Function for initializing various memory variables for storing the current graph, sepsets etc.&quot;&quot;&quot;</span>
-        
-        <span class="c1"># Initialize the nested dictionary for storing the current graph.</span>
-        <span class="c1"># Syntax: self.graph_dict[j][(i, -tau)] gives the string representing the link from X^i_{t-tau} to X^j_t</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span> <span class="s2">&quot;o?o&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">if</span> <span class="n">j</span> <span class="o">!=</span> <span class="n">i</span><span class="p">}</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_middle_marks</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">):</span> <span class="s2">&quot;oL&gt;&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">):</span> <span class="s2">&quot;o?&gt;&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)})</span>
-
-        <span class="c1"># Initialize the nested dictionary for storing separating sets</span>
-        <span class="c1"># Syntax: self.sepsets[j][(i, -tau)] stores separating sets of X^i_{t-tau} to X^j_t. For tau = 0, i &lt; j.</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sepsets</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">):</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="n">tau</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">)}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-
-        <span class="c1"># Initialize dictionaries for storing known ancestorships, non-ancestorships, and ambiguous ancestorships</span>
-        <span class="c1"># Syntax: self.def_ancs[j] contains the set of all known ancestors of X^j_t. Equivalently for the others</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ambiguous_ancestorships</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-
-        <span class="c1"># Initialize nested dictionaries for saving the maximal p-value among all conditional independence tests of a given</span>
-        <span class="c1"># pair of variables as well as the corresponding test statistic values and conditioning set cardinalities</span>
-        <span class="c1"># Syntax: As for self.sepsets</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pval_max</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">):</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="n">tau</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">)}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_val</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">):</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="n">tau</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">)}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_card</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">):</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="n">tau</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">)}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>                                        
-        <span class="c1"># Initialize a nested dictionary for caching na-pds-sets</span>
-        <span class="c1"># Syntax: self._na_pds_t[(i, t_i)][(j, t_j)] stores na_pds_t((i, t_i), (j, t_j))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_na_pds_t</span> <span class="o">=</span> <span class="p">{(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tau_j</span><span class="p">):</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau_j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)}</span>
-
-        <span class="c1"># Initialize a variable for remembering the maximal cardinality among all calculated na-pds-sets, as well as the</span>
-        <span class="c1"># maximial cardinality of any search set in the non-ancestral phase</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_na_search_set_found</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_na_pds_set_found</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-
-        <span class="c1"># Apply the restriction imposed by tau_min</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_apply_tau_min_restriction</span><span class="p">()</span>
-
-        <span class="c1"># Apply the background knowledge given by background_knowledge</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_apply_link_assumptions</span><span class="p">()</span>
-
-        <span class="c1"># Return</span>
-        <span class="k">return</span> <span class="kc">True</span>
-
-    <span class="k">def</span> <span class="nf">_apply_tau_min_restriction</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Apply the restrictions imposed by a non-zero tau_min:</span>
-<span class="sd">        - Remove all links of lag smaller than tau_min from self.graph_dict</span>
-<span class="sd">        - Set the corresponding entries in self.pval_max, self.pval_max_val, and self.pval_max_card to np.inf, -np.inf, np.inf</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_min</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">j</span> <span class="o">!=</span> <span class="n">i</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">pval_max</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_val</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_card</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-
-    <span class="k">def</span> <span class="nf">_apply_link_assumptions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Apply the background knowledge specified by &#39;link_assumptions&#39;:</span>
-<span class="sd">        - Write the specified edge types to self.graph_dict</span>
-<span class="sd">        - Set the corresponding entries in self.pval_max to np.inf, in self.pval_max_val to -np.inf, and in</span>
-<span class="sd">        - to self.pval_max_card to np.inf</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">links_j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="n">link</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-
-                <span class="c1"># Apply background knowledge</span>
-                <span class="k">if</span> <span class="n">link</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span> <span class="ow">and</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;?&quot;</span> <span class="ow">and</span> <span class="n">lag_i</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_middle_marks</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)]</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;L&quot;</span> <span class="o">+</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)]</span> <span class="o">=</span> <span class="n">link</span>
-
-                <span class="c1"># If background knowledge amounts to absence of link, set the corresponding entries in</span>
-                <span class="c1"># self.pval_max to 2, in self.pval_max_val to -np.inf, and in self.pval_max_card to None to np.inf</span>
-                <span class="k">if</span> <span class="n">link</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span> <span class="ow">and</span> <span class="p">(</span><span class="n">lag_i</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">):</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">pval_max</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_val</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_card</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-
-    <span class="k">def</span> <span class="nf">_run_ancestral_removal_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prelim</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Run an ancestral edge removal phase, this is Algorithm S2&quot;&quot;&quot;</span>
-
-        <span class="c1"># Iterate until convergence</span>
-        <span class="c1"># p_pc is the cardinality of the non-default part of the conditioning sets. The full conditioning sets may have</span>
-        <span class="c1"># higher cardinality due to default conditioning on known parents</span>
-        <span class="n">p_pc</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">while_broken</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
-
-            <span class="c1">##########################################################################################################</span>
-            <span class="c1">### Run the next removal iteration #######################################################################</span>
-
-            <span class="c1"># Force-quit while loop when p_pc exceeds the limit put by self.max_p_global</span>
-            <span class="k">if</span> <span class="n">p_pc</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_p_global</span><span class="p">:</span>
-                <span class="n">while_broken</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="k">break</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">p_pc</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Starting test phase</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;p = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">p_pc</span><span class="p">))</span>
-
-            <span class="c1"># Variables to memorize the occurence and absence of certain events in the below edge removal phase</span>
-            <span class="n">has_converged</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="n">any_removal</span> <span class="o">=</span> <span class="kc">False</span>
-
-            <span class="c1"># Generate the prioritized link list</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_first</span><span class="p">:</span>
-
-                <span class="n">link_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="mi">0</span><span class="p">))]</span>
-                <span class="n">link_list</span> <span class="o">=</span> <span class="n">link_list</span> <span class="o">+</span> <span class="p">[</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">lag</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-
-            <span class="k">else</span><span class="p">:</span>
-
-                <span class="n">link_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">lag</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-
-
-            <span class="c1"># Run through all elements of link_list. Each element of link_list specifies ordered pairs of variables whose</span>
-            <span class="c1"># connecting edges are then subjected to conditional independence tests</span>
-            <span class="k">for</span> <span class="n">links</span> <span class="ow">in</span> <span class="n">link_list</span><span class="p">:</span>
-
-                <span class="c1"># Memory variables for storing edges that are marked for removal</span>
-                <span class="n">to_remove</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-
-                <span class="c1"># Iterate through all edges specified by links. Note that since the variables paris are ordered, (A, B) and (B, A)</span>
-                <span class="c1"># are seen as different pairs.</span>
-                <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">links</span><span class="p">:</span>
-
-                    <span class="c1"># Decode the elements of links into pairs of variables (X, Y)</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pair</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">pair</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pair</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-                        <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">pair</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">pair</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pair</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-                        <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">pair</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
-
-                        <span class="c1"># Do not test auto-links twice</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_first</span> <span class="ow">and</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                            <span class="k">continue</span>
-
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Exclusion of links ###############################################################################</span>
-
-                    <span class="c1"># Exclude the current link if ...</span>
-                    <span class="c1"># ... X = Y</span>
-                    <span class="k">if</span> <span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># ... X &gt; Y</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-                        <span class="k">continue</span>
-
-                    <span class="c1"># Get the current link</span>
-                    <span class="n">link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-                    <span class="c1"># Moreover exclude the current link if ...</span>
-                    <span class="c1"># ... X and Y are not adjacent anymore</span>
-                    <span class="k">if</span> <span class="n">link</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># ... the link is definitely part of G</span>
-                    <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-                        <span class="k">continue</span>
-
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Determine  which tests the link will be  subjected to  ###########################################</span>
-
-                    <span class="c1"># Depending on the middle mark on the link between X and Y as well as on some global options, we may not need</span>
-                    <span class="c1"># to search for separating set among the potential parents of Y and/or X.</span>
-                    <span class="n">test_Y</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;R&quot;</span><span class="p">,</span> <span class="s2">&quot;!&quot;</span><span class="p">]</span> <span class="k">else</span> <span class="kc">False</span>
-                    <span class="n">test_X</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="p">(</span><span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;L&quot;</span><span class="p">,</span> <span class="s2">&quot;!&quot;</span><span class="p">]</span> <span class="ow">and</span> <span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">&gt;=</span> <span class="n">p_pc</span><span class="p">)))</span> <span class="k">else</span> <span class="kc">False</span>
-                    
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Preparation PC search set and default conditioning set ###########################################</span>
-
-                    <span class="k">if</span> <span class="n">test_Y</span><span class="p">:</span>
-                        <span class="n">S_default_YX</span><span class="p">,</span> <span class="n">S_search_YX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_default_and_search_sets</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="s2">&quot;ancestral&quot;</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="n">test_X</span><span class="p">:</span>
-                        <span class="n">S_default_XY</span><span class="p">,</span> <span class="n">S_search_XY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_default_and_search_sets</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s2">&quot;ancestral&quot;</span><span class="p">)</span>
-
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Middle mark updates ##############################################################################</span>
-
-                    <span class="n">any_middle_mark_update</span> <span class="o">=</span> <span class="kc">False</span>
-
-                    <span class="c1"># Note: Updating the middle marks here, within the for-loop, does not spoil order independence. In fact, this</span>
-                    <span class="c1"># update does not influence the flow of the for-loop at all</span>
-                    <span class="k">if</span> <span class="n">test_Y</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">S_search_YX</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">p_pc</span><span class="p">:</span>
-                            <span class="c1"># Note that X is smaller than Y. If S_search_YX exists and has fewer than p elements, X and Y are not</span>
-                            <span class="c1"># d-separated by S \subset Par(Y). Therefore, the middle mark on the edge between X and Y can be updated</span>
-                            <span class="c1"># with &#39;R&#39;</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">_apply_middle_mark</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s2">&quot;R&quot;</span><span class="p">)</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="c1"># Since S_search_YX exists and has hat least p_pc elements, the link between X and Y will be subjected to</span>
-                            <span class="c1"># conditional independenc tests. Therefore, the algorithm has not converged yet.</span>
-                            <span class="n">has_converged</span> <span class="o">=</span> <span class="kc">False</span>
-
-                    <span class="k">if</span> <span class="n">test_X</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">S_search_XY</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">p_pc</span><span class="p">:</span>
-                            <span class="c1"># Note that X is smaller than Y. If S_search_XY exists and has fewer than p elements, X and Y are not</span>
-                            <span class="c1"># d-separated by S \subset Par(X). Therefore, the middle mark on the edge between X and Y can be updated</span>
-                            <span class="c1"># with &#39;L&#39;</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">_apply_middle_mark</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s2">&quot;L&quot;</span><span class="p">)</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="c1"># Since S_search_YX exists and has hat least p_pc elements, the link between X and Y will be subjected to</span>
-                            <span class="c1"># conditional independenc tests. Therefore, the algorithm has not converged yet.</span>
-                            <span class="n">has_converged</span> <span class="o">=</span> <span class="kc">False</span>
-
-                    <span class="c1">######################################################################################################</span>
-
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Tests for conditional independence ###############################################################</span>
-
-                    <span class="c1"># If option self.break_once_separated is True, the below for-loops will be broken immediately once a separating set</span>
-                    <span class="c1"># has been found. In conjunction with the modified majority rule employed for orienting links, order independence</span>
-                    <span class="c1"># (with respect to the index &#39;i&#39; on X^i_t) then requires that the tested conditioning sets are ordered in an order</span>
-                    <span class="c1"># independent way. Here, the minimal effect size of previous conditional independence tests serve as an order</span>
-                    <span class="c1"># independent order criterion.</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">break_once_separated</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">):</span>
-                        <span class="k">if</span> <span class="n">test_Y</span><span class="p">:</span>
-                            <span class="n">S_search_YX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sort_search_set</span><span class="p">(</span><span class="n">S_search_YX</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">test_X</span><span class="p">:</span>
-                            <span class="n">S_search_XY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sort_search_set</span><span class="p">(</span><span class="n">S_search_XY</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
-
-                    <span class="c1"># Run through all cardinality p_pc subsets of S_search_YX</span>
-                    <span class="k">if</span> <span class="n">test_Y</span><span class="p">:</span>
-
-                        <span class="n">q_count</span> <span class="o">=</span> <span class="mi">0</span>
-                        <span class="k">for</span> <span class="n">S_pc</span> <span class="ow">in</span> <span class="n">combinations</span><span class="p">(</span><span class="n">S_search_YX</span><span class="p">,</span> <span class="n">p_pc</span><span class="p">):</span>
-
-                            <span class="n">q_count</span> <span class="o">=</span> <span class="n">q_count</span> <span class="o">+</span> <span class="mi">1</span>
-                            <span class="k">if</span> <span class="n">q_count</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">:</span>
-                                <span class="k">break</span>
-
-                            <span class="c1"># Build the full conditioning set</span>
-                            <span class="n">Z</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">S_pc</span><span class="p">)</span>
-                            <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_YX</span><span class="p">)</span>
-
-                            <span class="c1"># Test conditional independence of X and Y given Z</span>
-                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ANC(Y):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                                    <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">S_default_YX</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">S_pc</span><span class="p">]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                            <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic</span>
-                            <span class="c1"># values and conditioning set cardinalities</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-                            <span class="c1"># Check whether test result was significant</span>
-                            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-
-                                <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
-                                <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-                                <span class="bp">self</span><span class="o">.</span><span class="n">_save_sepset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="s2">&quot;wm&quot;</span><span class="p">))</span>
-            
-                                <span class="c1"># Verbose output</span>
-                                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:11}</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> given </span><span class="si">{}</span><span class="s2"> union </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;independent&quot;</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">S_pc</span><span class="p">,</span> <span class="n">S_default_YX</span><span class="p">))</span>
-
-                                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">break_once_separated</span><span class="p">:</span>
-                                    <span class="k">break</span>
-
-                    <span class="c1"># Run through all cardinality p_pc subsets of S_search_XY</span>
-                    <span class="k">if</span> <span class="n">test_X</span><span class="p">:</span>
-
-                        <span class="n">q_count</span> <span class="o">=</span> <span class="mi">0</span>
-                        <span class="k">for</span> <span class="n">S_pc</span> <span class="ow">in</span> <span class="n">combinations</span><span class="p">(</span><span class="n">S_search_XY</span><span class="p">,</span> <span class="n">p_pc</span><span class="p">):</span>
-
-                            <span class="n">q_count</span> <span class="o">=</span> <span class="n">q_count</span> <span class="o">+</span> <span class="mi">1</span>
-                            <span class="k">if</span> <span class="n">q_count</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">:</span>
-                                <span class="k">break</span>
-
-                            <span class="c1"># Build the full conditioning set</span>
-                            <span class="n">Z</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">S_pc</span><span class="p">)</span>
-                            <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_XY</span><span class="p">)</span>
-
-                            <span class="c1"># Test conditional independence of X and Y given Z</span>
-                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ANC(X):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                                    <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">S_default_XY</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">S_pc</span><span class="p">]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                            <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic</span>
-                            <span class="c1"># values and conditioning set cardinalities</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-                            <span class="c1"># Check whether test result was significant</span>
-                            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-
-                                <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
-                                <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-                                <span class="bp">self</span><span class="o">.</span><span class="n">_save_sepset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="s2">&quot;wm&quot;</span><span class="p">))</span>
-            
-                                <span class="c1"># Verbose output</span>
-                                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:11}</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> given </span><span class="si">{}</span><span class="s2"> union </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;independent&quot;</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">S_pc</span><span class="p">,</span> <span class="n">S_default_XY</span><span class="p">))</span>
-
-                                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">break_once_separated</span><span class="p">:</span>
-                                    <span class="k">break</span>
-
-                <span class="c1"># for pair in links</span>
-
-                <span class="c1">##########################################################################################################</span>
-                <span class="c1">### Remove edges marked for removal in to_remove #########################################################</span>
-
-                <span class="c1"># Run through all of the nested dictionary</span>
-                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-                    <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">to_remove</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-
-                        <span class="c1"># Remember that at least one edge has been removed, remove the edge</span>
-                        <span class="n">any_removal</span> <span class="o">=</span> <span class="kc">True</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-            <span class="c1"># end for links in link_list</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Test phase complete&quot;</span><span class="p">)</span>
-
-            <span class="c1">##############################################################################################################</span>
-            <span class="c1">### Orientations and next step ###############################################################################</span>
-
-            <span class="k">if</span> <span class="n">any_removal</span><span class="p">:</span>
-                <span class="c1"># At least one edge was removed or at least one middle mark has been updated. Therefore: i) apply the restricted set of</span>
-                <span class="c1"># orientation rules, ii) restart the while loop at p_pc = 0, unless all edges have converged, then break the while loop</span>
-
-                <span class="n">only_lagged</span> <span class="o">=</span> <span class="kc">False</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">orient_contemp</span> <span class="o">==</span> <span class="mi">2</span> <span class="k">else</span> <span class="kc">True</span>
-                <span class="n">any_update</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_orientation_phase</span><span class="p">(</span><span class="n">rule_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rules_prelim</span><span class="p">,</span> <span class="n">only_lagged</span> <span class="o">=</span> <span class="n">only_lagged</span><span class="p">)</span>
-
-                <span class="c1"># If the orientation phase made a non-trivial update, then restart the while loop. Else increase p_pc by one</span>
-                <span class="k">if</span> <span class="n">any_update</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_middle_marks</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_update_middle_marks</span><span class="p">()</span>
-                    <span class="n">p_pc</span> <span class="o">=</span> <span class="mi">0</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">p_pc</span> <span class="o">=</span> <span class="n">p_pc</span> <span class="o">+</span> <span class="mi">1</span>
-
-            <span class="k">else</span><span class="p">:</span>
-                <span class="c1"># The graph has not changed at all in this iteration of the while loop. Therefore, if all edges have converged, break the</span>
-                <span class="c1"># while loop. If at least one edge has not yet converged, increase p_pc by one.</span>
-
-                <span class="k">if</span> <span class="n">has_converged</span><span class="p">:</span>
-                    <span class="k">break</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">p_pc</span> <span class="o">=</span> <span class="n">p_pc</span> <span class="o">+</span> <span class="mi">1</span>
-
-        <span class="c1"># end while True</span>
-
-        <span class="c1">##################################################################################################################</span>
-        <span class="c1">### Consistency test and middle mark update ######################################################################</span>
-
-        <span class="c1"># Run through the entire graph</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-
-                <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span>
-                <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-                    <span class="k">continue</span>
-
-                <span class="c1"># Consider only those links that are still part G</span>
-                <span class="n">link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-
-                    <span class="c1"># Consistency check</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="n">while_broken</span><span class="p">:</span>
-                        <span class="k">assert</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;?&quot;</span>
-                        <span class="k">assert</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;L&quot;</span>
-                        <span class="k">assert</span> <span class="p">((</span><span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;R&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">lag_i</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_middle_marks</span><span class="p">))</span>
-                            <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">no_apr</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">))</span>
-
-
-                    <span class="c1"># Update all middle marks to &#39;!&#39;</span>
-                    <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="s2">&quot;!&quot;</span><span class="p">]:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;!&quot;</span> <span class="o">+</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-                    
-
-        <span class="c1">##################################################################################################################</span>
-        <span class="c1">### Final rule applications ######################################################################################</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">prelim</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">prelim_with_collider_rules</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">prelim</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">no_apr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">no_apr</span> <span class="o">-</span> <span class="mi">1</span>
-
-            <span class="n">any_update</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_orientation_phase</span><span class="p">(</span><span class="n">rule_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rules_all</span><span class="p">,</span> <span class="n">only_lagged</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_middle_marks</span> <span class="ow">and</span> <span class="n">any_update</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_update_middle_marks</span><span class="p">()</span>
-
-        <span class="k">else</span><span class="p">:</span>
-
-            <span class="n">only_lagged</span> <span class="o">=</span> <span class="kc">False</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">orient_contemp</span> <span class="o">&gt;=</span> <span class="mi">1</span> <span class="k">else</span> <span class="kc">True</span>
-            <span class="n">any_update</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_orientation_phase</span><span class="p">(</span><span class="n">rule_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rules_prelim_final</span><span class="p">,</span> <span class="n">only_lagged</span> <span class="o">=</span> <span class="n">only_lagged</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_middle_marks</span> <span class="ow">and</span> <span class="n">any_update</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_update_middle_marks</span><span class="p">()</span>
-
-        <span class="c1"># Return</span>
-        <span class="k">return</span> <span class="kc">True</span>
-
-
-    <span class="k">def</span> <span class="nf">_run_non_ancestral_removal_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Run the non-ancestral edge removal phase, this is Algorithm S3&quot;&quot;&quot;</span>
-
-        <span class="c1"># Update of middle marks</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_update_middle_marks</span><span class="p">()</span>
-
-        <span class="c1"># This function initializeds self._graph_full_dict, a nested dictionary representing the graph including links that are</span>
-        <span class="c1"># forward in time. This will make the calculcation of na-pds-t sets easier.</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_initialize_full_graph</span><span class="p">()</span>
-
-        <span class="c1"># Iterate until convergence. Here, p_pc is the cardinality of the non-default part of the conditioning sets. The full</span>
-        <span class="c1"># conditioning sets may have higher cardinality due to default conditioning on known parents</span>
-        <span class="n">p_pc</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
-
-            <span class="c1">##########################################################################################################</span>
-            <span class="c1">### Run the next removal iteration #######################################################################</span>
-
-            <span class="c1"># Force-quit while loop when p_pc exceeds the limit put by self.max_p_global or self.max_p_non_ancestral</span>
-            <span class="k">if</span> <span class="n">p_pc</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_p_global</span> <span class="ow">or</span> <span class="n">p_pc</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_p_non_ancestral</span><span class="p">:</span>
-                <span class="k">break</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">p_pc</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Starting test phase</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;p = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">p_pc</span><span class="p">))</span>
-
-            <span class="c1"># Variables to memorize the occurence and absence of certain events in the below edge removal phase</span>
-            <span class="n">has_converged</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="n">any_removal</span> <span class="o">=</span> <span class="kc">False</span>
-
-            <span class="c1"># Generate the prioritized link list</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_first</span><span class="p">:</span>
-
-                <span class="n">link_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="mi">0</span><span class="p">))]</span>
-                <span class="n">link_list</span> <span class="o">=</span> <span class="n">link_list</span> <span class="o">+</span> <span class="p">[</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">lag</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-
-            <span class="k">else</span><span class="p">:</span>
-
-                <span class="n">link_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">lag</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-
-
-            <span class="c1"># Run through all elements of link_list. Each element of link_list specifies ordered pairs of variables whose connecting</span>
-            <span class="c1"># edges are then subjected to conditional independence tests</span>
-            <span class="k">for</span> <span class="n">links</span> <span class="ow">in</span> <span class="n">link_list</span><span class="p">:</span>
-
-                <span class="c1"># Memory variables for storing edges that are marked for removal</span>
-                <span class="n">to_remove</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-
-                <span class="c1"># Iterate through all edges specified by links. Note that since the variables paris are ordered, (A, B) and (B, A) are</span>
-                <span class="c1"># seen as different pairs.</span>
-                <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">links</span><span class="p">:</span>
-
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pair</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">pair</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pair</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-                        <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">pair</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">pair</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pair</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-                        <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">pair</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
-
-                        <span class="c1"># Do not test auto-links twice</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_first</span> <span class="ow">and</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                            <span class="k">continue</span>
-
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Exclusion of links ###############################################################################</span>
-
-                    <span class="c1"># Exclude the current link if ...</span>
-                    <span class="c1"># ... X = Y</span>
-                    <span class="k">if</span> <span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># ... X &gt; Y</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-                        <span class="k">continue</span>
-
-                    <span class="c1"># Get the current link</span>
-                    <span class="n">link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-                    <span class="c1"># Exclude the current link if ...</span>
-                    <span class="k">if</span> <span class="n">link</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># ... the link is definitely part of G</span>
-                    <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-                        <span class="k">continue</span>
-
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Determine which tests the link will be subjected to  #############################################</span>
-
-                    <span class="c1"># The algorithm always searches for separating sets in na-pds-t(Y, X). Depending on whether the X and Y are</span>
-                    <span class="c1"># contemporaneous on some global options, the algorithm may also search for separating sets in na-pds-t(X, Y)</span>
-                    <span class="n">test_X</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span> <span class="o">&gt;=</span> <span class="n">p_pc</span><span class="p">))</span> <span class="k">else</span> <span class="kc">False</span>
-                    
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Preparation of default conditioning sets and PC search sets ######################################</span>
-
-                    <span class="c1"># Verbose output</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;_get_na_pds_t &quot;</span><span class="p">)</span>
-
-                    <span class="n">S_default_YX</span><span class="p">,</span> <span class="n">S_search_YX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_default_and_search_sets</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="s2">&quot;non-ancestral&quot;</span><span class="p">)</span>
-
-                    <span class="bp">self</span><span class="o">.</span><span class="n">max_na_search_set_found</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_na_search_set_found</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">S_search_YX</span><span class="p">))</span>
-
-                    <span class="k">if</span> <span class="n">test_X</span><span class="p">:</span>
-                        <span class="n">S_default_XY</span><span class="p">,</span> <span class="n">S_search_XY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_default_and_search_sets</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s2">&quot;non-ancestral&quot;</span><span class="p">)</span>
-
-                        <span class="bp">self</span><span class="o">.</span><span class="n">max_na_search_set_found</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_na_search_set_found</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">S_search_XY</span><span class="p">))</span>
-
-                    <span class="c1"># If the search set exceeds the specified bounds, do not test this link</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">S_search_YX</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_pds_set</span> <span class="ow">or</span> <span class="p">(</span><span class="n">test_X</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">S_search_XY</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_pds_set</span><span class="p">):</span>
-                        <span class="k">continue</span>
-
-                    <span class="c1">######################################################################################################</span>
-
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Middle mark updates ##############################################################################</span>
-
-                    <span class="c1"># Note: Updating the middle marks here, within the for-loop, does not spoil order independence. In fact, this</span>
-                    <span class="c1"># update does not influence the flow of the for-loop at all</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">S_search_YX</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">p_pc</span> <span class="ow">or</span> <span class="p">(</span><span class="n">test_X</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">S_search_XY</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">p_pc</span><span class="p">):</span>
-                        <span class="c1"># Mark the link from X to Y as converged, remember the fixation, then continue</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;-&quot;</span> <span class="o">+</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-                        <span class="k">continue</span>
-
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">has_converged</span> <span class="o">=</span> <span class="kc">False</span>
-
-
-                    <span class="c1">######################################################################################################</span>
-                    <span class="c1">### Tests for conditional independence ###############################################################</span>
-
-                    <span class="c1"># If option self.break_once_separated is True, the below for-loops will be broken immediately once a separating set</span>
-                    <span class="c1"># has been found. In conjunction with the modified majority rule employed for orienting links, order independence</span>
-                    <span class="c1"># (with respect to the index &#39;i&#39; on X^i_t) then requires that the tested conditioning sets are ordered in an order</span>
-                    <span class="c1"># independent way. Here, the minimal effect size of previous conditional independence tests serve as an order</span>
-                    <span class="c1"># independent order criterion.</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">break_once_separated</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">):</span>
-                        <span class="n">S_search_YX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sort_search_set</span><span class="p">(</span><span class="n">S_search_YX</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">test_X</span><span class="p">:</span>
-                            <span class="n">S_search_XY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sort_search_set</span><span class="p">(</span><span class="n">S_search_XY</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
-
-                    <span class="c1"># Verbose output</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;for S_pc in combinations(S_search_YX, p_pc)&quot;</span><span class="p">)</span>
-
-                    <span class="c1"># Run through all cardinality p_pc subsets of S_search_YX</span>
-                    <span class="n">q_count</span> <span class="o">=</span> <span class="mi">0</span>
-                    <span class="k">for</span> <span class="n">S_pc</span> <span class="ow">in</span> <span class="n">combinations</span><span class="p">(</span><span class="n">S_search_YX</span><span class="p">,</span> <span class="n">p_pc</span><span class="p">):</span>
-
-                        <span class="n">q_count</span> <span class="o">=</span> <span class="n">q_count</span> <span class="o">+</span> <span class="mi">1</span>
-                        <span class="k">if</span> <span class="n">q_count</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">:</span>
-                            <span class="k">break</span>
-
-                        <span class="c1"># Build the full conditioning set</span>
-                        <span class="n">Z</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">S_pc</span><span class="p">)</span>
-                        <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_YX</span><span class="p">)</span>
-
-                        <span class="c1"># Test conditional independence of X and Y given Z</span>
-                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Non-ANC(Y):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                                <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">S_default_YX</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">S_pc</span><span class="p">]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                        <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic</span>
-                        <span class="c1"># values and conditioning set cardinalities</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-                        <span class="c1"># Check whether test result was significant</span>
-                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-
-                            <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
-                            <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">_save_sepset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="s2">&quot;wm&quot;</span><span class="p">))</span>
-        
-                            <span class="c1"># Verbose output</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:11}</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> given </span><span class="si">{}</span><span class="s2"> union </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;independent&quot;</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">S_pc</span><span class="p">,</span> <span class="n">S_default_YX</span><span class="p">))</span>
-
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">break_once_separated</span><span class="p">:</span>
-                                <span class="k">break</span>
-
-                    <span class="k">if</span> <span class="n">test_X</span><span class="p">:</span>
-
-                        <span class="c1"># Verbose output</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;for S_pc in combinations(S_search_XY, p_pc)&quot;</span><span class="p">)</span>
-
-                        <span class="c1"># Run through all cardinality p_pc subsets of S_search_XY</span>
-                        <span class="n">q_count</span> <span class="o">=</span> <span class="mi">0</span>
-                        <span class="k">for</span> <span class="n">S_pc</span> <span class="ow">in</span> <span class="n">combinations</span><span class="p">(</span><span class="n">S_search_XY</span><span class="p">,</span> <span class="n">p_pc</span><span class="p">):</span>
-
-                            <span class="n">q_count</span> <span class="o">=</span> <span class="n">q_count</span> <span class="o">+</span> <span class="mi">1</span>
-                            <span class="k">if</span> <span class="n">q_count</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">:</span>
-                                <span class="k">break</span>
-
-                            <span class="c1"># Build the full conditioning set</span>
-                            <span class="n">Z</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">S_pc</span><span class="p">)</span>
-                            <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_XY</span><span class="p">)</span>
-
-                            <span class="c1"># Test conditional independence of X and Y given Z</span>
-                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Non-ANC(X):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                                    <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">S_default_XY</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">S_pc</span><span class="p">]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                            <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic</span>
-                            <span class="c1"># values and conditioning set cardinalities</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-                            <span class="c1"># Check whether test result was significant</span>
-                            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-
-                                <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
-                                <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-                                <span class="bp">self</span><span class="o">.</span><span class="n">_save_sepset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="s2">&quot;wm&quot;</span><span class="p">))</span>
-            
-                                <span class="c1"># Verbose output</span>
-                                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:11}</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> given </span><span class="si">{}</span><span class="s2"> union </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;independent&quot;</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">S_pc</span><span class="p">,</span> <span class="n">S_default_YX</span><span class="p">))</span>
-
-                                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">break_once_separated</span><span class="p">:</span>
-                                    <span class="k">break</span>
-
-                <span class="c1"># end for links in link_list</span>
-
-                <span class="c1">##########################################################################################################</span>
-                <span class="c1">### Remove edges marked for removal in to_remove #########################################################</span>
-
-                <span class="c1"># Check whether there is any removal at all</span>
-                <span class="n">any_removal_this</span> <span class="o">=</span> <span class="kc">False</span>
-
-                <span class="c1"># Run through all of the nested dictionary</span>
-                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-                    <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">to_remove</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-
-                        <span class="c1"># Remember that at least one edge has been removed, remove the edge</span>
-                        <span class="n">any_removal</span> <span class="o">=</span> <span class="kc">True</span>
-                        <span class="n">any_removal_this</span> <span class="o">=</span> <span class="kc">True</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-                <span class="c1"># If any_removal_this = True, we need to recalculate full graph dict</span>
-                <span class="k">if</span> <span class="n">any_removal_this</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_initialize_full_graph</span><span class="p">()</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_na_pds_t</span> <span class="o">=</span> <span class="p">{(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tau_j</span><span class="p">):</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau_j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)}</span>
-
-
-            <span class="c1"># end for links in link_list</span>
-
-            <span class="c1"># Verbose output</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Test phase complete&quot;</span><span class="p">)</span>
-
-            <span class="c1">##############################################################################################################</span>
-            <span class="c1">### Orientations and next step ###############################################################################</span>
-
-            <span class="k">if</span> <span class="n">any_removal</span><span class="p">:</span>
-                <span class="c1"># At least one edge was removed or at least one middle mark has been updated. Therefore: i) apply the full set of</span>
-                <span class="c1"># orientation rules, ii) restart the while loop at p_pc = 0, unless all edges have converged, then break the while loop</span>
-
-                <span class="n">any_update</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_orientation_phase</span><span class="p">(</span><span class="n">rule_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rules_all</span><span class="p">,</span> <span class="n">only_lagged</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">any_update</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_initialize_full_graph</span><span class="p">()</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_na_pds_t</span> <span class="o">=</span> <span class="p">{(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tau_j</span><span class="p">):</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau_j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)}</span>
-                    <span class="n">p_pc</span> <span class="o">=</span> <span class="mi">0</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">p_pc</span> <span class="o">=</span> <span class="n">p_pc</span> <span class="o">+</span> <span class="mi">1</span>
-
-            <span class="k">else</span><span class="p">:</span>
-                <span class="c1"># The graph has not changed at all in this iteration of the while loop. Therefore, if all edges have converged, break</span>
-                <span class="c1"># the while loop. If at least one edge has not yet converged, increase p_pc by one.</span>
-
-                <span class="k">if</span> <span class="n">has_converged</span><span class="p">:</span>
-                    <span class="k">break</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">p_pc</span> <span class="o">=</span> <span class="n">p_pc</span> <span class="o">+</span> <span class="mi">1</span>
-
-        <span class="c1"># end while True</span>
-
-        <span class="c1">##################################################################################################################</span>
-        <span class="c1">### Final rule applications ######################################################################################</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_run_orientation_phase</span><span class="p">(</span><span class="n">rule_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rules_all</span><span class="p">,</span> <span class="n">only_lagged</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
-
-        <span class="c1"># Return</span>
-        <span class="k">return</span> <span class="kc">True</span>
-
-
-    <span class="k">def</span> <span class="nf">_run_orientation_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rule_list</span><span class="p">,</span> <span class="n">only_lagged</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Exhaustively apply the rules specified by rule_list, this is Algorithm S4&quot;&quot;&quot;</span>
-
-        <span class="c1"># Verbose output</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Starting orientation phase&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;with rule list: &quot;</span><span class="p">,</span> <span class="n">rule_list</span><span class="p">)</span>
-
-        <span class="c1"># Remember whether this call to _run_orientation_phase has made any update to G</span>
-        <span class="n">restarted_once</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="c1"># Run through all priority levels of rule_list</span>
-        <span class="n">idx</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">while</span> <span class="n">idx</span> <span class="o">&lt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">rule_list</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
-
-            <span class="c1"># Some rule require self._graph_full_dict. Therefore, it is initialized once the while loop (re)-starts at the first</span>
-            <span class="c1"># prioprity level</span>
-            <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_initialize_full_graph</span><span class="p">()</span>
-
-            <span class="c1"># Remember whether G will be updated with new useful information (&#39;x&#39; marks are considered not useful)</span>
-            <span class="n">restart</span> <span class="o">=</span> <span class="kc">False</span>
-
-            <span class="c1">###########################################################################################################</span>
-            <span class="c1">### Rule application ######################################################################################</span>
-
-            <span class="c1"># Get the current rules</span>
-            <span class="n">current_rules</span> <span class="o">=</span> <span class="n">rule_list</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
-
-            <span class="c1"># Prepare a list to remember marked orientations</span>
-            <span class="n">to_orient</span> <span class="o">=</span> <span class="p">[]</span>
-
-            <span class="c1"># Run through all current rules</span>
-            <span class="k">for</span> <span class="n">rule</span> <span class="ow">in</span> <span class="n">current_rules</span><span class="p">:</span>
-
-                <span class="c1"># Verbose output</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">{}</span><span class="s2">:&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rule</span><span class="p">))</span>
-
-                <span class="c1"># Exhaustively apply the rule to the graph...</span>
-                <span class="n">orientations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_rule</span><span class="p">(</span><span class="n">rule</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">)</span>
-
-                <span class="c1"># Verbose output</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="n">new_link</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">set</span><span class="p">(</span><span class="n">orientations</span><span class="p">):</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) ==&gt; (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Marked:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">orientations</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Found nothing&quot;</span><span class="p">)</span>
-
-                <span class="c1"># ... and stage the results for orientation and removal</span>
-                <span class="n">to_orient</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">orientations</span><span class="p">)</span>
-
-            <span class="c1">###########################################################################################################</span>
-            <span class="c1">### Aggregation of marked orientations ####################################################################</span>
-
-            <span class="n">links_to_remove</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-            <span class="n">links_to_fix</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-            <span class="n">new_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-            <span class="n">new_non_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-
-            <span class="c1"># Run through all of the nested dictionary</span>
-            <span class="k">for</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="n">new_link</span><span class="p">)</span> <span class="ow">in</span> <span class="n">to_orient</span><span class="p">:</span>
-
-                <span class="c1"># The old link</span>
-                <span class="n">old_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-
-                <span class="c1"># Is the link marked for removal?</span>
-                <span class="k">if</span> <span class="n">new_link</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">old_link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">links_to_remove</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-                    <span class="k">continue</span>
-
-                <span class="c1"># Assert that no preceeding variable is marked as an ancestor of later variable</span>
-                <span class="k">assert</span> <span class="ow">not</span> <span class="p">(</span><span class="n">lag_i</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">new_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span><span class="p">)</span>
-
-                <span class="c1"># Is the link marked for fixation?</span>
-                <span class="k">if</span> <span class="n">new_link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span> <span class="ow">and</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-                    <span class="n">links_to_fix</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-                <span class="c1"># New ancestral relation of (i, lag_i) to (j, 0)</span>
-                <span class="k">if</span> <span class="n">new_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span> <span class="ow">and</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-                    <span class="n">new_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-                <span class="k">elif</span> <span class="n">new_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&lt;&quot;</span> <span class="ow">and</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&lt;&quot;</span><span class="p">:</span>
-                    <span class="n">new_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-                
-                <span class="c1"># New ancestral relation of (j, 0) to (i, lag_i == 0)</span>
-                <span class="k">if</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">new_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span> <span class="ow">and</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-                        <span class="n">new_ancs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                    <span class="k">elif</span> <span class="n">new_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span> <span class="ow">and</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-                        <span class="n">new_non_ancs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-
-            <span class="c1"># Resolve conflicts about removal and fixation</span>
-            <span class="n">ambiguous_links</span> <span class="o">=</span> <span class="n">links_to_fix</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">links_to_remove</span><span class="p">)</span>
-            <span class="n">links_to_fix</span> <span class="o">=</span> <span class="n">links_to_fix</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">ambiguous_links</span><span class="p">)</span>
-            <span class="n">links_to_remove</span> <span class="o">=</span> <span class="n">links_to_remove</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">ambiguous_links</span><span class="p">)</span>
-
-            <span class="c1">###########################################################################################################</span>
-            <span class="c1">### Removals, update middle marks, update ancestral information ###########################################</span>
-
-            <span class="c1"># Remove links</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">links_to_remove</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-                <span class="n">restart</span> <span class="o">=</span> <span class="kc">True</span>
-
-            <span class="c1"># Fix links</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">links_to_fix</span><span class="p">:</span>
-                <span class="n">old_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="n">new_link</span> <span class="o">=</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;-&quot;</span> <span class="o">+</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-                <span class="n">restart</span> <span class="o">=</span> <span class="kc">True</span>
-
-            <span class="c1"># Mark links as ambiguous</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">ambiguous_links</span><span class="p">:</span>
-                <span class="n">old_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="n">new_link</span> <span class="o">=</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;x&quot;</span> <span class="o">+</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-            <span class="c1"># Update ancestral information. The function called includes conflict resolution</span>
-            <span class="n">restart</span> <span class="o">=</span> <span class="n">restart</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_new_ancestral_information</span><span class="p">(</span><span class="n">new_non_ancs</span><span class="p">,</span> <span class="n">new_ancs</span><span class="p">)</span>
-
-            <span class="c1">###########################################################################################################</span>
-            <span class="c1">### Make separating sets of removed links weakly minimal ##################################################</span>
-
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">links_to_remove</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-
-                <span class="c1"># Verbose output</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Links were removed by rules</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-
-                <span class="n">new_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-                <span class="n">new_non_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-
-                <span class="c1"># Run through all links that have been removed</span>
-                <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">links_to_remove</span><span class="p">:</span>
-
-                    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span>
-                    <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-                    <span class="c1"># Get ancestors of X and Y</span>
-                    <span class="n">ancs_XY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_ancs</span><span class="p">([</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">])</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">})</span>
-
-                    <span class="c1"># Read out all separating sets that were found in the rule phase, then consider only those of minimal</span>
-                    <span class="c1"># cardinality</span>
-                    <span class="n">old_sepsets_all</span> <span class="o">=</span> <span class="p">{</span><span class="n">Z</span> <span class="k">for</span> <span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepsets</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)}</span>
-                    <span class="n">min_size</span> <span class="o">=</span> <span class="nb">min</span><span class="p">({</span><span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span> <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">old_sepsets_all</span><span class="p">})</span>
-                    <span class="n">old_sepsets_smallest</span> <span class="o">=</span> <span class="p">{</span><span class="n">Z</span> <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">old_sepsets_all</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span> <span class="o">==</span> <span class="n">min_size</span><span class="p">}</span>
-
-                    <span class="c1"># For all separating sets of minimal cardinality, find weakly minimal separating subsets</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_delete_sepsets</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_make_sepset_weakly_minimal</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">old_sepsets_smallest</span><span class="p">,</span> <span class="n">ancs_XY</span><span class="p">)</span>
-                    <span class="n">new_sepsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepsets</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-                <span class="c1"># end for (i, j, lag_i) in links_to_remove</span>
-            <span class="c1"># end  if len(links_to_remove) &gt; 0</span>
-
-            <span class="c1"># If any useful new information was found, go back to idx = 0, else increase idx by 1</span>
-            <span class="k">if</span> <span class="n">restart</span><span class="p">:</span>
-                <span class="n">idx</span> <span class="o">=</span> <span class="mi">0</span>
-                <span class="n">restarted_once</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">idx</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span>
-
-        <span class="c1"># end while idx &lt;= len(rule_list) - 1</span>
-
-        <span class="c1"># Verbose output</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Orientation phase complete&quot;</span><span class="p">)</span>
-
-        <span class="c1"># No return value</span>
-        <span class="k">return</span> <span class="n">restarted_once</span>
-
-    <span class="c1">########################################################################################################################</span>
-    <span class="c1">########################################################################################################################</span>
-    <span class="c1">########################################################################################################################</span>
-
-    <span class="k">def</span> <span class="nf">_get_default_and_search_sets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">phase</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the default conditioning set and PC search set&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">phase</span> <span class="o">==</span> <span class="s2">&quot;ancestral&quot;</span><span class="p">:</span>
-
-            <span class="c1"># This is a-pds-t(A, B)</span>
-            <span class="n">S_raw</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_a_pds_t</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-
-            <span class="c1"># Determine the default conditioning set</span>
-            <span class="n">S_default</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span>
-
-            <span class="c1"># Determine the PC search set</span>
-            <span class="n">S_search</span> <span class="o">=</span> <span class="n">S_raw</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">S_default</span><span class="p">)</span>
-
-
-        <span class="k">elif</span> <span class="n">phase</span> <span class="o">==</span> <span class="s2">&quot;non-ancestral&quot;</span><span class="p">:</span>
-
-            <span class="c1"># This is na-pds-t(A, B)</span>
-            <span class="n">S_raw</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_na_pds_t</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">max_na_pds_set_found</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_na_pds_set_found</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">S_raw</span><span class="p">))</span>
-
-            <span class="c1"># Determine the default conditioning set</span>
-            <span class="n">S_default</span> <span class="o">=</span> <span class="n">S_raw</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_ancs</span><span class="p">([</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">]))</span>
-            <span class="n">S_default</span> <span class="o">=</span> <span class="n">S_default</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">))</span>
-            <span class="n">S_default</span> <span class="o">=</span> <span class="n">S_default</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span>
-
-            <span class="c1"># Determine the PC search set</span>
-            <span class="n">S_search</span> <span class="o">=</span> <span class="n">S_raw</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">S_default</span><span class="p">)</span>
-
-        <span class="c1"># Return</span>
-        <span class="k">return</span> <span class="n">S_default</span><span class="p">,</span> <span class="n">S_search</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_new_ancestral_information</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_non_ancs</span><span class="p">,</span> <span class="n">new_ancs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Apply the new ancestorships and non-ancestorships specified by new_non_ancs and new_ancs to the current graph. Conflicts</span>
-<span class="sd">        are resolved by marking. Returns True if any circle mark was turned into a head or tail, else False.&quot;&quot;&quot;</span>
-
-        <span class="c1">#######################################################################################################</span>
-        <span class="c1">### Preprocessing #####################################################################################</span>
-
-        <span class="c1"># Memory variables</span>
-        <span class="n">add_to_def_non_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-        <span class="n">add_to_def_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-        <span class="n">add_to_ambiguous_ancestorships</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-        <span class="n">put_head_or_tail</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="c1"># Default values</span>
-        <span class="k">if</span> <span class="n">new_non_ancs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">new_non_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-
-        <span class="k">if</span> <span class="n">new_ancs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">new_ancs</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-
-        <span class="c1"># Marking A as ancestor of B implies that B is marked as a non-ancestor of A. This is only non-trivial for A before B</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">new_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="k">if</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">new_non_ancs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-
-        <span class="c1">#######################################################################################################</span>
-        <span class="c1">### Conflict resolution ###############################################################################</span>
-
-        <span class="c1"># Iterate through new_non_ancs</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">new_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="c1"># X = (i, lag_i), Y = (j, 0)</span>
-                <span class="c1"># X is marked as non-ancestor for Y</span>
-
-                <span class="c1"># Conflict resolution</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ambiguous_ancestorships</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="c1"># There is a conflict, since it is already marked as ambiguous whether X is an ancestor of Y</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) marked as non-anc of </span><span class="si">{}</span><span class="s2"> but saved as ambiguous&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Conflict:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="k">elif</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="c1"># There is a conflict, since X is already marked as ancestor of Y</span>
-                    <span class="n">add_to_ambiguous_ancestorships</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) marked as non-anc of </span><span class="si">{}</span><span class="s2"> but saved as anc&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Conflict:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="k">elif</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">new_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="c1"># There is a conflict, since X is also marked as a new ancestor of Y</span>
-                    <span class="n">add_to_ambiguous_ancestorships</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) marked as both anc- and non-anc of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Conflict:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="c1"># There is no conflict</span>
-                    <span class="n">add_to_def_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-                    
-        <span class="c1"># Iterate through new_ancs</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">new_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="c1"># X = (i, lag_i), Y = (j, 0)</span>
-                <span class="c1"># X is marked as ancestor for Y</span>
-
-                <span class="c1"># Conflict resolution</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ambiguous_ancestorships</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="c1"># There is a conflict, since it is already marked as ambiguous whether X is an ancestor of Y</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) marked as anc of </span><span class="si">{}</span><span class="s2"> but saved as ambiguous&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Conflict:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="k">elif</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ambiguous_ancestorships</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
-                    <span class="c1"># There is a conflict, since X and Y are contemporaneous and it is already marked ambiguous as whether Y is an</span>
-                    <span class="c1"># ancestor of X</span>
-                    <span class="c1"># Note: This is required here, because X being an ancestor of Y implies that Y is not an ancestor of X. This</span>
-                    <span class="c1"># ambiguity cannot exist when X is before Y</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) marked as anc of </span><span class="si">{}</span><span class="s2"> but saved as ambiguous&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Conflict:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="k">elif</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="c1"># There is a conflict, since X is already marked as non-ancestor of Y</span>
-                    <span class="n">add_to_ambiguous_ancestorships</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) marked as anc of </span><span class="si">{}</span><span class="s2"> but saved as non-anc&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Conflict:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="k">elif</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">new_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="c1"># There is a conflict, since X is also marked as a new non-ancestor of Y</span>
-                    <span class="n">add_to_ambiguous_ancestorships</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) marked as both anc- and non-anc of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Conflict:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="c1"># There is no conflict</span>
-                    <span class="n">add_to_def_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-        <span class="c1">#######################################################################################################</span>
-
-        <span class="c1">#######################################################################################################</span>
-        <span class="c1">### Apply the ambiguous information ###################################################################</span>
-
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">add_to_ambiguous_ancestorships</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-
-                <span class="n">old_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">old_link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;x&quot;</span><span class="p">:</span>
-
-                    <span class="n">new_link</span> <span class="o">=</span> <span class="s2">&quot;x&quot;</span> <span class="o">+</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> Removing (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) as anc of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Update:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> Removing (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) as non-anc of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Update:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">discard</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">discard</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-                <span class="k">if</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> Removing </span><span class="si">{}</span><span class="s2"> as anc of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Update:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                    <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">discard</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                    <span class="c1"># Do we also need the following?</span>
-                    <span class="c1"># self.def_non_ancs[i].discard((j, 0))</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ambiguous_ancestorships</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> Marking ancestorship of (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) to </span><span class="si">{}</span><span class="s2"> as ambiguous&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Update:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="bp">self</span><span class="o">.</span><span class="n">ambiguous_ancestorships</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-        <span class="c1">#######################################################################################################</span>
-        <span class="c1">### Apply the unambiguous information #################################################################</span>
-
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">add_to_def_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-
-                <span class="n">old_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">old_link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&lt;&quot;</span><span class="p">:</span>
-                    <span class="n">new_link</span> <span class="o">=</span> <span class="s2">&quot;&lt;&quot;</span> <span class="o">+</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-                    <span class="n">put_head_or_tail</span> <span class="o">=</span> <span class="kc">True</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> Marking (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) as non-anc of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Update:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>  
-
-                <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">add_to_def_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-
-                <span class="n">old_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">old_link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="n">old_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;-&quot;</span> <span class="ow">or</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">):</span>
-                    <span class="n">new_link</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span> <span class="o">+</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-                    <span class="n">put_head_or_tail</span> <span class="o">=</span> <span class="kc">True</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> Marking (</span><span class="si">{}</span><span class="s2">, </span><span class="si">{:2}</span><span class="s2">) as anc of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Update:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">))</span>
-
-                <span class="k">if</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> Marking </span><span class="si">{}</span><span class="s2"> as non-anc of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Update:&quot;</span><span class="p">,(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-                    <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-
-        <span class="c1">#######################################################################################################</span>
-
-        <span class="k">return</span> <span class="n">put_head_or_tail</span>
-
-    <span class="k">def</span> <span class="nf">_apply_rule</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rule</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Call the orientation-removal-rule specified by the string argument rule.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;APR&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_APR</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-00-a&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER00a</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-00-b&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER00b</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-00-c&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER00c</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-00-d&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER00d</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-01&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER01</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-02&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER02</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-03&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER03</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;R-04&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_R04</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-08&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER08</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-09&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER09</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">rule</span> <span class="o">==</span> <span class="s2">&quot;ER-10&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_ER10</span><span class="p">(</span><span class="n">only_lagged</span><span class="p">)</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_na_pds_t</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the set na_pds_t(A, B), with at least one of them at lag 0&quot;&quot;&quot;</span>
-
-        <span class="c1"># Unpack A and B, then assert that at least one of them is at lag 0</span>
-        <span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">=</span> <span class="n">A</span>
-        <span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">=</span> <span class="n">B</span>
-        <span class="k">assert</span> <span class="n">lag_A</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">lag_B</span> <span class="o">==</span> <span class="mi">0</span>
-
-        <span class="c1"># If na_pds_t(A, B) is in memory, return immediately</span>
-        <span class="n">memo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_na_pds_t</span><span class="p">[</span><span class="n">A</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">memo</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">memo</span>
-
-        <span class="c1"># Else, re-compute na_pds_t(A, B) it according to the current graph and cache it.</span>
-
-        <span class="c1"># Re-compute na_pds_t_1(A, B) according to the current graph</span>
-        <span class="n">na_pds_t_1</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span>
-                    <span class="c1"># W = (var, lag + lag_A) is in na_pds_t_1(A, B) if ...</span>
-                    <span class="k">for</span> <span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_A</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-                    <span class="c1"># ... it is a non-future adjacency of A</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
-                    <span class="c1"># ... and is not B</span>
-                    <span class="ow">and</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">!=</span> <span class="n">B</span>
-                    <span class="c1"># ... and is not before t - tau_max</span>
-                    <span class="ow">and</span> <span class="p">(</span><span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span>
-                    <span class="c1"># ... and is not after both A and B</span>
-                    <span class="c1"># ... (i.e. is not after time t)</span>
-                    <span class="ow">and</span> <span class="p">(</span><span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">0</span>
-                    <span class="c1"># ... and is not a definite non-ancestor of A,</span>
-                    <span class="c1">#     which implies that it is not a definite descendant of A,</span>
-                    <span class="ow">and</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&lt;&quot;</span>
-                    <span class="c1"># ... and is not a definite descendant of B</span>
-                    <span class="c1">#     (i.e., B is not a definite ancestor of W)</span>
-                    <span class="ow">and</span> <span class="p">(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">-</span> <span class="p">(</span><span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">))</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">var</span><span class="p">]</span>
-                    <span class="p">}</span>
-
-        <span class="c1"># Compute na_pds_t_2(A, B)</span>
-
-        <span class="c1"># Find all potential C_1 nodes</span>
-        <span class="n">C1_list</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-        <span class="k">for</span> <span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_full_dict</span><span class="p">[</span><span class="n">var_A</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-
-            <span class="n">node</span> <span class="o">=</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span>
-
-            <span class="c1"># node is added to C1_list if, in addition to being adjacent to A, ...</span>
-            <span class="c1"># ... it is not B</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">==</span> <span class="n">B</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># ... it is not before t - tau_max</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">&lt;</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># ... it is not after B</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">lag_B</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># ... it is not a definite ancestor of A</span>
-            <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># ... it is not a definite descendant of A</span>
-            <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># ... it is not a definite non-ancestor of B,</span>
-            <span class="c1">#     which implies that it is not a definite descendant of B</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="p">(</span><span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">var_B</span><span class="p">]:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># If all tests are passed, node is added to C1_list</span>
-            <span class="n">C1_list</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
-
-        <span class="c1"># end for ((var, lag), link) in self.graph_full_dict[var_A].items()</span>
-
-        <span class="c1"># Breath first search to find (a superset of) na_pds_t_2(A, B)</span>
-
-        <span class="n">visited</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-        <span class="n">start_from</span> <span class="o">=</span> <span class="p">{(</span><span class="n">C1</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span> <span class="k">for</span> <span class="n">C1</span> <span class="ow">in</span> <span class="n">C1_list</span><span class="p">}</span>
-
-        <span class="k">while</span> <span class="n">start_from</span><span class="p">:</span>
-
-            <span class="n">new_start_from</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-            <span class="n">new_do_not_visit</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-            <span class="k">for</span> <span class="p">(</span><span class="n">current_node</span><span class="p">,</span> <span class="n">previous_node</span><span class="p">)</span> <span class="ow">in</span> <span class="n">start_from</span><span class="p">:</span>
-
-                <span class="n">visited</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">current_node</span><span class="p">,</span> <span class="n">previous_node</span><span class="p">))</span>
-
-                <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_full_dict</span><span class="p">[</span><span class="n">current_node</span><span class="p">[</span><span class="mi">0</span><span class="p">]]:</span>
-
-                    <span class="n">next_node</span> <span class="o">=</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">current_node</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-                    <span class="k">if</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="k">if</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">next_node</span><span class="p">,</span> <span class="n">current_node</span><span class="p">)</span> <span class="ow">in</span> <span class="n">visited</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="k">if</span> <span class="n">next_node</span> <span class="o">==</span> <span class="n">previous_node</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="k">if</span> <span class="n">next_node</span> <span class="o">==</span> <span class="n">B</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="k">if</span> <span class="n">next_node</span> <span class="o">==</span> <span class="n">A</span><span class="p">:</span>
-                        <span class="k">continue</span>
-
-                    <span class="n">link_l</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">next_node</span><span class="p">,</span> <span class="n">current_node</span><span class="p">)</span>
-                    <span class="n">link_r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">previous_node</span><span class="p">,</span> <span class="n">current_node</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="n">link_l</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span> <span class="ow">or</span> <span class="n">link_r</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">next_node</span><span class="p">,</span> <span class="n">previous_node</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span> <span class="ow">and</span> <span class="p">(</span><span class="n">link_l</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;o&quot;</span> <span class="ow">or</span> <span class="n">link_r</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;o&quot;</span><span class="p">):</span>
-                        <span class="k">continue</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">-</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">next_node</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="ow">or</span> <span class="p">(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">-</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">next_node</span><span class="p">[</span><span class="mi">0</span><span class="p">]]:</span>
-                        <span class="k">continue</span>
-                    <span class="k">if</span> <span class="p">((</span><span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">lag_A</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">next_node</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">lag_A</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">var_A</span><span class="p">])</span> <span class="ow">and</span> <span class="p">((</span><span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">lag_B</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">next_node</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">var_B</span><span class="p">]):</span>
-                        <span class="k">continue</span>
-
-                    <span class="n">new_start_from</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">next_node</span><span class="p">,</span> <span class="n">current_node</span><span class="p">))</span>
-
-            <span class="n">start_from</span> <span class="o">=</span> <span class="n">new_start_from</span>
-
-        <span class="c1"># end  while start_from</span>
-
-        <span class="n">na_pds_t_2</span> <span class="o">=</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">visited</span><span class="p">}</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_na_pds_t</span><span class="p">[</span><span class="n">A</span><span class="p">][</span><span class="n">B</span><span class="p">]</span> <span class="o">=</span> <span class="n">na_pds_t_1</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">na_pds_t_2</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_na_pds_t</span><span class="p">[</span><span class="n">A</span><span class="p">][</span><span class="n">B</span><span class="p">]</span>
-
-
-    <span class="k">def</span> <span class="nf">_make_sepset_weakly_minimal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z_list</span><span class="p">,</span> <span class="n">ancs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        X and Y are conditionally independent given Z in Z_list However, it is not yet clear whether any of these Z are minimal</span>
-<span class="sd">        separating set.</span>
-
-<span class="sd">        This function finds weakly minimal separating subsets in an order independent way and writes them to the self.sepsets</span>
-<span class="sd">        dictionary. Only certainly weakly minimal separating subsets are retained.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Assert that all Z in Z_list have the same cardinality</span>
-        <span class="k">assert</span> <span class="nb">len</span><span class="p">({</span><span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span> <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">Z_list</span><span class="p">})</span> <span class="o">==</span> <span class="mi">1</span>
-
-        <span class="c1"># Base Case 1:</span>
-        <span class="c1"># Z in Z_list is weakly minimal if len(Z) &lt;= 1 or Z \subset ancs</span>
-        <span class="n">any_weakly_minimal</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">Z_list</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span> <span class="o">&lt;=</span><span class="mi">1</span> <span class="ow">or</span> <span class="n">Z</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="n">ancs</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_save_sepset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="s2">&quot;wm&quot;</span><span class="p">))</span>
-                <span class="n">any_weakly_minimal</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="k">if</span> <span class="n">any_weakly_minimal</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-        <span class="c1"># If not Base Case 1, we need to search for separating subsets. We do this for all Z in Z_list, and build a set sepsets_next_call</span>
-        <span class="c1"># that contains all separating sets for the next recursive call</span>
-        <span class="n">sepsets_next_call</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-        <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">Z_list</span><span class="p">:</span>
-
-            <span class="c1"># Find all nodes A in Z that are not in ancs</span>
-            <span class="n">removable</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">ancs</span><span class="p">)</span>
-
-            <span class="c1"># Test for removal of all nodes in removable</span>
-            <span class="n">new_sepsets</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">val_values</span> <span class="o">=</span> <span class="p">[]</span>
-
-            <span class="k">for</span> <span class="n">A</span> <span class="ow">in</span> <span class="n">removable</span><span class="p">:</span>
-
-                <span class="n">Z_A</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">A</span><span class="p">]</span>
-
-                <span class="c1"># Run the conditional independence test</span>
-                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">Z_A</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MakeMin:    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_A = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                        <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_A</span><span class="p">)]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic</span>
-                <span class="c1"># values and conditioning set cardinalities</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_A</span><span class="p">))</span>
-
-                <span class="c1"># Check whether the test result was significant</span>
-                <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-                    <span class="n">new_sepsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z_A</span><span class="p">))</span>
-                    <span class="n">val_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
-
-            <span class="c1"># If new_sepsets is empty, then Z is already weakly minimal</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_sepsets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_save_sepset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="s2">&quot;wm&quot;</span><span class="p">))</span>
-                <span class="n">any_weakly_minimal</span> <span class="o">=</span> <span class="kc">True</span>
-
-            <span class="c1"># If we did not yet find a weakly minimal separating set</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">any_weakly_minimal</span><span class="p">:</span>
-
-                <span class="c1"># Sort all separating sets in new_sepets by their test statistic, then append those separating sets with maximal statistic</span>
-                <span class="c1"># to sepsets_next_call. This i) guarantees order independence while ii) continuing to test as few as possible separating sets</span>
-                <span class="n">new_sepsets</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">val_values</span><span class="p">,</span> <span class="n">new_sepsets</span><span class="p">),</span> <span class="n">reverse</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)]</span>
-
-                <span class="n">i</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-                <span class="k">while</span> <span class="n">i</span> <span class="o">&lt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_values</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">val_values</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">val_values</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                    <span class="n">sepsets_next_call</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">new_sepsets</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-                    <span class="n">i</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span>
-
-                <span class="k">assert</span> <span class="n">i</span> <span class="o">&gt;=</span> <span class="mi">0</span>
-
-        <span class="c1"># If we did not yet find a weakly minimal separating set, make a recursive call</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">any_weakly_minimal</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_make_sepset_weakly_minimal</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">sepsets_next_call</span><span class="p">,</span> <span class="n">ancs</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-
-    <span class="k">def</span> <span class="nf">_B_not_in_SepSet_AC</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Is B in less than half of the sets in SepSets(A, C)?&quot;&quot;&quot;</span>
-
-        <span class="c1"># Treat A - B - C as the same triple as C - B - A</span>
-        <span class="c1"># Convention: A is before C or, if they are contemporaneous, the index of A is smaller than that of C</span>
-        <span class="k">if</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">or</span> <span class="p">(</span><span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">and</span> <span class="n">C</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_not_in_SepSet_AC</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span>
-
-        <span class="c1"># Remember all separating sets that we will find</span>
-        <span class="n">all_sepsets</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-        <span class="c1"># Get the non-future adjacencies of A and C</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_a_pds_t_for_majority</span><span class="p">:</span>
-            <span class="n">adj_A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_non_future_adj</span><span class="p">([</span><span class="n">A</span><span class="p">])</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-            <span class="n">adj_C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_non_future_adj</span><span class="p">([</span><span class="n">C</span><span class="p">])</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">adj_A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_a_pds_t</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-            <span class="n">adj_C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_a_pds_t</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-
-        <span class="n">Z_add</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-
-        <span class="n">search_A</span> <span class="o">=</span> <span class="n">adj_A</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
-        <span class="n">search_C</span> <span class="o">=</span> <span class="n">adj_C</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">):</span>
-            <span class="n">search_A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sort_search_set</span><span class="p">(</span><span class="n">search_A</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span>
-            <span class="n">search_C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sort_search_set</span><span class="p">(</span><span class="n">search_C</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
-
-        <span class="c1"># Test for independence given all subsets of non-future adjacencies of A</span>
-        <span class="k">if</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-            <span class="n">max_p_A</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">search_A</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_p_global</span><span class="p">])</span> <span class="o">+</span> <span class="mi">1</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">max_p_A</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">search_A</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_p_global</span><span class="p">])</span> <span class="o">+</span> <span class="mi">1</span>
-
-        <span class="c1"># Shift lags</span>
-        <span class="n">search_A</span> <span class="o">=</span> <span class="p">[(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">search_A</span><span class="p">]</span>
-        <span class="n">search_C</span> <span class="o">=</span> <span class="p">[(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">search_C</span><span class="p">]</span>
-        <span class="n">Z_add</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="p">}</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">C</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_p_A</span><span class="p">):</span>
-
-            <span class="n">q_count</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">Z_raw</span> <span class="ow">in</span> <span class="n">combinations</span><span class="p">(</span><span class="n">search_A</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
-
-                <span class="n">q_count</span> <span class="o">=</span> <span class="n">q_count</span> <span class="o">+</span> <span class="mi">1</span>
-                <span class="k">if</span> <span class="n">q_count</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">:</span>
-                    <span class="k">break</span>
-
-                <span class="c1"># Prepare the conditioning set</span>
-                <span class="n">Z</span> <span class="o">=</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z_raw</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">Y</span><span class="p">}</span>
-                <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
-
-                <span class="c1"># Test conditional independence of X and Y given Z</span>
-                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;BnotinSepSetAC(A):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_add = </span><span class="si">%s</span><span class="s2">, Z = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                        <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z_raw</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">Y</span><span class="p">}]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic</span>
-                <span class="c1"># values and conditioning set cardinalities</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-                <span class="c1"># Check whether test result was significant</span>
-                <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-                    <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-        <span class="c1"># Test for independence given all subsets of non-future adjacencies of C</span>
-        <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">search_C</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_p_global</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-
-            <span class="n">q_count</span> <span class="o">=</span> <span class="mi">0</span> 
-            <span class="k">for</span> <span class="n">Z_raw</span> <span class="ow">in</span> <span class="n">combinations</span><span class="p">(</span><span class="n">search_C</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
-
-                <span class="n">q_count</span> <span class="o">=</span> <span class="n">q_count</span> <span class="o">+</span> <span class="mi">1</span>
-                <span class="k">if</span> <span class="n">q_count</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">:</span>
-                    <span class="k">break</span>
-
-                <span class="c1"># Prepare the conditioning set</span>
-                <span class="n">Z</span> <span class="o">=</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z_raw</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">Y</span><span class="p">}</span>
-                <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
-
-                <span class="c1"># Test conditional independence of X and Y given Z</span>
-                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                    <span class="c1"># print(&quot;BnotinSepSetAC(C):    %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f&quot; %</span>
-                    <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z)]), val, pval))</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;BnotinSepSetAC(C):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_add = </span><span class="si">%s</span><span class="s2">, Z = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                        <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z_raw</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">Y</span><span class="p">}]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic</span>
-                <span class="c1"># values and conditioning set cardinalities</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-                <span class="c1"># Check whether test result was significant</span>
-                <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-                    <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-        <span class="c1"># Append the already known sepset</span>
-        <span class="n">all_sepsets</span> <span class="o">=</span> <span class="n">all_sepsets</span><span class="o">.</span><span class="n">union</span><span class="p">({</span><span class="n">Z</span> <span class="k">for</span> <span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepsets</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)})</span>
-
-        <span class="c1"># Count number of sepsets and number of sepsets that contain B</span>
-        <span class="n">n_sepsets</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_sepsets</span><span class="p">)</span>
-        <span class="n">n_sepsets_with_B</span> <span class="o">=</span> <span class="nb">len</span><span class="p">([</span><span class="mi">1</span> <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">all_sepsets</span> <span class="k">if</span> <span class="p">(</span><span class="n">B</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="kc">True</span> <span class="k">if</span> <span class="mi">2</span><span class="o">*</span><span class="n">n_sepsets_with_B</span> <span class="o">&lt;</span> <span class="n">n_sepsets</span> <span class="k">else</span> <span class="kc">False</span>
-
-
-    <span class="k">def</span> <span class="nf">_B_in_SepSet_AC</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Is B in more than half of the sets in SepSets(A, C)?&quot;&quot;&quot;</span>
-
-        <span class="c1"># Treat A - B - C as the same triple as C - B - A</span>
-        <span class="c1"># Convention: A is before C or, if they are contemporaneous, the index of A is smaller than that of C</span>
-        <span class="k">if</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">or</span> <span class="p">(</span><span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">and</span> <span class="n">C</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_in_SepSet_AC</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span>
-
-        <span class="n">link_AB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-        <span class="n">link_CB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">link_AB</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span> <span class="ow">or</span> <span class="n">link_CB</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span> <span class="ow">or</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;-&quot;</span> <span class="ow">or</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;-&quot;</span><span class="p">:</span>
-
-            <span class="c1"># Vote is based on those sets that where found already</span>
-            <span class="n">all_sepsets</span> <span class="o">=</span> <span class="p">{</span><span class="n">Z</span> <span class="k">for</span> <span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepsets</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)}</span>
-
-            <span class="c1"># Count number of sepsets and number of sepsets that contain B</span>
-            <span class="n">n_sepsets</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_sepsets</span><span class="p">)</span>
-            <span class="n">n_sepsets_with_B</span> <span class="o">=</span> <span class="nb">len</span><span class="p">([</span><span class="mi">1</span> <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">all_sepsets</span> <span class="k">if</span> <span class="n">B</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">])</span>
-
-            <span class="k">return</span> <span class="kc">True</span> <span class="k">if</span> <span class="mi">2</span><span class="o">*</span><span class="n">n_sepsets_with_B</span> <span class="o">&gt;</span> <span class="n">n_sepsets</span> <span class="k">else</span> <span class="kc">False</span>
-
-        <span class="k">else</span><span class="p">:</span>
-
-            <span class="c1"># Remember all separating sets that we will find</span>
-            <span class="n">all_sepsets</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-            <span class="c1"># Get the non-future adjacencies of A and C</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_a_pds_t_for_majority</span><span class="p">:</span>
-                <span class="n">adj_A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_non_future_adj</span><span class="p">([</span><span class="n">A</span><span class="p">])</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-                <span class="n">adj_C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_non_future_adj</span><span class="p">([</span><span class="n">C</span><span class="p">])</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">adj_A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_a_pds_t</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-                <span class="n">adj_C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_a_pds_t</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-
-            <span class="n">Z_add</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">})</span>
-
-            <span class="n">search_A</span> <span class="o">=</span> <span class="n">adj_A</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
-            <span class="n">search_C</span> <span class="o">=</span> <span class="n">adj_C</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">):</span>
-                <span class="n">search_A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sort_search_set</span><span class="p">(</span><span class="n">search_A</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span>
-                <span class="n">search_C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sort_search_set</span><span class="p">(</span><span class="n">search_C</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
-
-            <span class="c1"># Test for independence given all subsets of non-future adjacencies of A</span>
-            <span class="k">if</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="n">max_p_A</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">search_A</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_cond_px</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_p_global</span><span class="p">])</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">max_p_A</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">search_A</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_p_global</span><span class="p">])</span> <span class="o">+</span> <span class="mi">1</span>
-
-            <span class="c1"># Shift lags</span>
-            <span class="n">search_A</span> <span class="o">=</span> <span class="p">[(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">search_A</span><span class="p">]</span>
-            <span class="n">search_C</span> <span class="o">=</span> <span class="p">[(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">search_C</span><span class="p">]</span>
-            <span class="n">Z_add</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="p">}</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-            <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">C</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
-
-            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_p_A</span><span class="p">):</span>
-
-                <span class="n">q_count</span> <span class="o">=</span> <span class="mi">0</span>
-                <span class="k">for</span> <span class="n">Z_raw</span> <span class="ow">in</span> <span class="n">combinations</span><span class="p">(</span><span class="n">search_A</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
-
-                    <span class="n">q_count</span> <span class="o">=</span> <span class="n">q_count</span> <span class="o">+</span> <span class="mi">1</span>
-                    <span class="k">if</span> <span class="n">q_count</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">:</span>
-                        <span class="k">break</span>
-
-                    <span class="c1"># Prepare the conditioning set</span>
-                    <span class="n">Z</span> <span class="o">=</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z_raw</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">Y</span><span class="p">}</span>
-                    <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
-
-                    <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="c1"># print(&quot;BinSepSetAC(A):    %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f&quot; %</span>
-                        <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z)]), val, pval))</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;BinSepSetAC(A):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_add = </span><span class="si">%s</span><span class="s2">, Z = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                            <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z_raw</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">Y</span><span class="p">}]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                    <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic</span>
-                    <span class="c1"># values and conditioning set cardinalities</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-                    <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-                        <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-            <span class="c1"># Test for independence given all subsets of non-future adjacencies of C</span>
-            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">search_C</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_p_global</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-
-                <span class="n">q_count</span> <span class="o">=</span> <span class="mi">0</span> 
-                <span class="k">for</span> <span class="n">Z_raw</span> <span class="ow">in</span> <span class="n">combinations</span><span class="p">(</span><span class="n">search_C</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
-
-                    <span class="n">q_count</span> <span class="o">=</span> <span class="n">q_count</span> <span class="o">+</span> <span class="mi">1</span>
-                    <span class="k">if</span> <span class="n">q_count</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_q_global</span><span class="p">:</span>
-                        <span class="k">break</span>
-
-                    <span class="c1"># Prepare the conditioning set</span>
-                    <span class="n">Z</span> <span class="o">=</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z_raw</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">Y</span><span class="p">}</span>
-                    <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
-
-                    <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="c1"># print(&quot;BinSepSetAC(C):     %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f&quot; %</span>
-                        <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z)]), val, pval))</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;BinSepSetAC(C):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_add = </span><span class="si">%s</span><span class="s2">, Z = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                            <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z_raw</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">Y</span><span class="p">}]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                    <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic</span>
-                    <span class="c1"># values and conditioning set cardinalities</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-                    <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-                        <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
-
-            <span class="c1"># Append the already known sepset</span>
-            <span class="n">all_sepsets</span> <span class="o">=</span> <span class="n">all_sepsets</span><span class="o">.</span><span class="n">union</span><span class="p">({</span><span class="n">Z</span> <span class="k">for</span> <span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepsets</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)})</span>
-
-            <span class="c1"># Count number of sepsets and number of sepsets that contain B</span>
-            <span class="n">n_sepsets</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_sepsets</span><span class="p">)</span>
-            <span class="n">n_sepsets_with_B</span> <span class="o">=</span> <span class="nb">len</span><span class="p">([</span><span class="mi">1</span> <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">all_sepsets</span> <span class="k">if</span> <span class="p">(</span><span class="n">B</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">])</span>
-
-            <span class="k">return</span> <span class="kc">True</span> <span class="k">if</span> <span class="mi">2</span><span class="o">*</span><span class="n">n_sepsets_with_B</span> <span class="o">&gt;</span> <span class="n">n_sepsets</span> <span class="k">else</span> <span class="kc">False</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_parents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all known parents of all nodes in node_list&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents_of_lagged</span> <span class="ow">or</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-
-            <span class="n">out</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
-            <span class="k">return</span> <span class="n">out</span><span class="o">.</span><span class="n">union</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">B</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">})</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="k">return</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">B</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">return</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;-&quot;</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_middle_mark</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">char</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Update the middle mark on the link between X and Y with the character char&quot;&quot;&quot;</span>
-
-        <span class="c1"># Get the old link</span>
-        <span class="n">old_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-        <span class="c1"># Determine the new link</span>
-        <span class="k">if</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;?&quot;</span><span class="p">:</span>
-            <span class="n">new_link</span> <span class="o">=</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">char</span> <span class="o">+</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">old_link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;L&quot;</span> <span class="ow">and</span> <span class="n">char</span> <span class="o">==</span> <span class="s2">&quot;R&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">old_link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;R&quot;</span> <span class="ow">and</span> <span class="n">char</span> <span class="o">==</span> <span class="s2">&quot;L&quot;</span><span class="p">):</span>
-            <span class="n">new_link</span> <span class="o">=</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;!&quot;</span> <span class="o">+</span> <span class="n">old_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="kc">False</span>
-
-        <span class="c1"># Write the new link</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="c1"># Return</span>
-        <span class="k">return</span> <span class="kc">True</span>
-
-
-    <span class="k">def</span> <span class="nf">_update_middle_marks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Apply rule MMR&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Middle mark updates</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Run through all links</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-
-                <span class="k">if</span> <span class="n">link</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-                    <span class="k">continue</span>
-
-                <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span>
-                <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-                <span class="c1"># Apply above rule for A = X and B = Y</span>
-                <span class="n">link_XY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-                <span class="n">smaller_XY</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">link_XY</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-
-                    <span class="k">if</span> <span class="n">link_XY</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;?&quot;</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="n">smaller_XY</span><span class="p">:</span>
-                            <span class="n">new_link</span> <span class="o">=</span> <span class="n">link_XY</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;L&gt;&quot;</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="n">new_link</span> <span class="o">=</span> <span class="n">link_XY</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;R&gt;&quot;</span>
-
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-                    <span class="k">elif</span> <span class="p">(</span><span class="n">link_XY</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;R&quot;</span> <span class="ow">and</span> <span class="n">smaller_XY</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">link_XY</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;L&quot;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">smaller_XY</span><span class="p">):</span>
-
-                        <span class="n">new_link</span> <span class="o">=</span> <span class="n">link_XY</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;!&gt;&quot;</span>
-
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-
-                <span class="c1"># Apply above rule for A = Y and B = X</span>
-                <span class="n">link_YX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
-                <span class="n">smaller_YX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">link_YX</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-
-                    <span class="k">if</span> <span class="n">link_YX</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;?&quot;</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="n">smaller_YX</span><span class="p">:</span>
-                            <span class="n">new_link</span> <span class="o">=</span> <span class="n">link_YX</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;L&gt;&quot;</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="n">new_link</span> <span class="o">=</span> <span class="n">link_YX</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;R&gt;&quot;</span>
-
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-   
-
-                    <span class="k">elif</span> <span class="p">(</span><span class="n">link_YX</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;R&quot;</span> <span class="ow">and</span> <span class="n">smaller_YX</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">link_YX</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;L&quot;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">smaller_YX</span><span class="p">):</span>
-
-                        <span class="n">new_link</span> <span class="o">=</span> <span class="n">link_YX</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;!&gt;&quot;</span>
-
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_write_link</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-    
-    <span class="k">def</span> <span class="nf">_is_smaller</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        A node X is said to be smaller than node Y if</span>
-<span class="sd">        i)  X is before Y or</span>
-<span class="sd">        ii) X and Y are contemporaneous and the variable index of X is smaller than that of Y.</span>
-
-<span class="sd">        Return True if X is smaller than Y, else return False</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">Y</span> <span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">or</span> <span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">Y</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">and</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>           
-
-
-    <span class="k">def</span> <span class="nf">_get_a_pds_t</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the set a_pds_t(A, B)&quot;&quot;&quot;</span>
-
-        <span class="c1"># Unpack A and assert that A is at lag 0</span>
-        <span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">=</span> <span class="n">A</span>
-
-        <span class="c1"># Compute a_pds_t(A, B) according to the current graph</span>
-        <span class="k">return</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span>
-                    <span class="c1"># W = (var, lag) is in a_pds_t(A, B) if ...</span>
-                    <span class="k">for</span> <span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_A</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-                    <span class="c1"># ... it is a non-future adjacency of A</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
-                    <span class="c1"># ... and it is not B</span>
-                    <span class="ow">and</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">!=</span> <span class="n">B</span>
-                    <span class="c1"># ... it is not before t - self.tau_max</span>
-                    <span class="ow">and</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span>
-                    <span class="c1"># ... and it is not a definite non-ancestor of A</span>
-                    <span class="ow">and</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&lt;&quot;</span>
-                    <span class="p">}</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_ancs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_list</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the currently known set of ancestors of all nodes in the list node_list. The nodes are not required to be at</span>
-<span class="sd">        lag 0&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output set</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-        <span class="c1"># Run through all nodes</span>
-        <span class="k">for</span> <span class="n">A</span> <span class="ow">in</span> <span class="n">node_list</span><span class="p">:</span>
-            <span class="c1"># Unpack the node</span>
-            <span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">=</span> <span class="n">A</span>
-            <span class="c1"># Add the ancestors of node to out</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">union</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_ancs</span><span class="p">[</span><span class="n">var_A</span><span class="p">]</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span> <span class="o">&gt;=</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">})</span>
-
-        <span class="c1"># Return</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_non_ancs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_list</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the currently known set of non-ancestors of all nodes in the list node_list. The nodes are not required to be</span>
-<span class="sd">        at lag 0&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output set</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-        <span class="c1"># Run through all nodes</span>
-        <span class="k">for</span> <span class="n">A</span> <span class="ow">in</span> <span class="n">node_list</span><span class="p">:</span>
-            <span class="c1"># Unpack the node</span>
-            <span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">=</span> <span class="n">A</span>
-            <span class="c1"># Add the ancestors of node to out</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">union</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">def_non_ancs</span><span class="p">[</span><span class="n">var_A</span><span class="p">]</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span> <span class="o">&gt;=</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">})</span>
-
-        <span class="c1"># Return</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_fix_all_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Remove all non-trivial orientations&quot;&quot;&quot;</span>
-
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-
-                <span class="n">link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">new_link</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;-&quot;</span> <span class="o">+</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)]</span> <span class="o">=</span> <span class="n">new_link</span>
-
-    <span class="c1">########################################################################################################################</span>
-    <span class="c1">########################################################################################################################</span>
-    <span class="c1">########################################################################################################################</span>
-
-    <span class="k">def</span> <span class="nf">_apply_APR</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule APR&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">no_apr</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">out</span>
-
-        <span class="c1"># Get and run through all relevant graphical structures</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-
-                <span class="n">A</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span>
-                <span class="n">B</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="n">lag_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="k">continue</span>
-
-                <span class="c1"># Get the link from A to B</span>
-                <span class="n">link_AB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;-!&gt;&#39;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="n">link_AB</span><span class="p">)</span> \
-                   <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;-R&gt;&#39;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="n">link_AB</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">))</span> \
-                   <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;-L&gt;&#39;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="n">link_AB</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">A</span><span class="p">)):</span>
-
-                    <span class="c1"># Write the new link from A to B to the output list</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">))</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-    <span class="k">def</span> <span class="nf">_apply_ER01</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R1^prime&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all graphical structures that the rule applies to</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;**&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;o*+&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-
-        <span class="c1"># Run through all appropriate graphical structures</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                    <span class="k">continue</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ER01: &quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">))</span>
-
-            <span class="c1"># Check whether the rule applies</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_in_SepSet_AC</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">):</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot; --&gt; in sepset &quot;</span><span class="p">)</span>
-
-                <span class="c1"># Prepare the new link from B to C and append it to the output list</span>
-                <span class="n">link_BC</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
-                <span class="n">new_link_BC</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span> <span class="o">+</span> <span class="n">link_BC</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">new_link_BC</span><span class="p">))</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-    <span class="k">def</span> <span class="nf">_apply_ER02</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R2^prime&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all graphical structures that the rule applies to</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;**&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;+*o&#39;</span><span class="p">))</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="n">all_appropriate_triples</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;**&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;+*o&#39;</span><span class="p">)))</span>
-
-        <span class="c1"># Run through all appropriate graphical structures</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                    <span class="k">continue</span>
-
-            <span class="c1"># The rule applies to all relevant graphical structures. Therefore, prepare the new link and append it to the output list</span>
-            <span class="n">link_AC</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
-            <span class="n">new_link_AC</span> <span class="o">=</span> <span class="n">link_AC</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">link_AC</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-            <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">new_link_AC</span><span class="p">))</span>
-
-            <span class="c1"># print(&quot;Rule 2&quot;, A, self._get_link(A, B), B, self._get_link(B, C), C, self._get_link(A, C), new_link_AC)</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_ER03</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R3^prime&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all graphical structures that the rule applies to</span>
-        <span class="n">all_appropriate_quadruples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_quadruples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;**&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;&lt;**&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> 
-                                                           <span class="n">pattern_il</span><span class="o">=</span><span class="s1">&#39;+*o&#39;</span><span class="p">,</span> <span class="n">pattern_jl</span><span class="o">=</span><span class="s1">&#39;o*+&#39;</span><span class="p">,</span> <span class="n">pattern_kl</span><span class="o">=</span><span class="s1">&#39;+*o&#39;</span><span class="p">)</span>
-
-        <span class="c1"># Run through all appropriate graphical structures</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">D</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_quadruples</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">D</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># Check whether the rule applies</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_in_SepSet_AC</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">D</span><span class="p">,</span> <span class="n">C</span><span class="p">):</span>
-
-                <span class="c1"># Prepare the new link from D to B and append it to the output list</span>
-                <span class="n">link_DB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">D</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-                <span class="n">new_link_DB</span> <span class="o">=</span> <span class="n">link_DB</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">link_DB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">D</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">new_link_DB</span><span class="p">))</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_R04</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R4 (standard FCI rule)&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all relevant triangles W-V-Y</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;&lt;-*&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;o-+&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">)</span>
-
-        <span class="c1"># Run through all of these triangles</span>
-        <span class="k">for</span> <span class="n">triple</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-
-            <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="n">triple</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="p">(</span><span class="n">V</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">Y</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">and</span> <span class="n">W</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">V</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># Get the current link from W to V, which we will need below</span>
-            <span class="n">link_WV</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">V</span><span class="p">)</span>
-
-            <span class="c1"># Find all discriminating paths for this triangle</span>
-            <span class="c1"># Note: To guarantee order independence, we check all discriminating paths. Alternatively, we could check the rule for all</span>
-            <span class="c1"># shortest such paths</span>
-            <span class="n">discriminating_paths</span> <span class="o">=</span>  <span class="bp">self</span><span class="o">.</span><span class="n">_get_R4_discriminating_paths</span><span class="p">(</span><span class="n">triple</span><span class="p">,</span> <span class="n">max_length</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>
-
-            <span class="c1"># Run through all discriminating paths</span>
-            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">discriminating_paths</span><span class="p">:</span>
-
-                <span class="c1"># Get the end point node</span>
-                <span class="n">X_1</span> <span class="o">=</span> <span class="n">path</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-                <span class="c1"># Check which of the two cases of the rule we are in, then append the appropriate new links to the output list</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_in_SepSet_AC</span><span class="p">(</span><span class="n">X_1</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span>
-                    <span class="c1"># New link from V to Y</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">V</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">))</span>
-
-                <span class="k">elif</span> <span class="n">link_WV</span> <span class="o">!=</span> <span class="s2">&quot;&lt;-x&quot;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_not_in_SepSet_AC</span><span class="p">(</span><span class="n">X_1</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span>
-                    <span class="c1"># New link from V to Y</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">V</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">))</span>
-
-                    <span class="c1"># If needed, also the new link from W to V</span>
-                    <span class="k">if</span> <span class="n">link_WV</span> <span class="o">!=</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">:</span>
-                        <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">))</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_ER08</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R8^prime&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all graphical structures that the rule applies to</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;o*+&#39;</span><span class="p">)</span>
-
-        <span class="c1"># Run through all appropriate graphical structures</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># The rule applies to all relevant graphical structures. Therefore, prepare the new link and append it to the output list</span>
-            <span class="n">link_AC</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
-            <span class="n">new_link_AC</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span> <span class="o">+</span> <span class="n">link_AC</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-            <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">new_link_AC</span><span class="p">))</span>
-
-            <span class="c1">#print(&quot;Rule 8:&quot;, A, self._get_link(A, B), B, self._get_link(B, C), C, link_AC, new_link_AC)</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_ER09</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R9^prime&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find unshielded triples B_1 o--*--o A o--*--&gt; C or B_1 &lt;--*--o A o--*--&gt; C or B_1 &lt;--*-- A o--*--&gt; C </span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;o*o&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;o*&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="n">all_appropriate_triples</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;&lt;*o&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;o*&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)))</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="n">all_appropriate_triples</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;&lt;*-&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;o*&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)))</span>
-
-        <span class="c1"># Run through all these triples</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">B_1</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># Check whether A is in SepSet(B_1, C), else the rule does not apply</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_in_SepSet_AC</span><span class="p">(</span><span class="n">B_1</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">):</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># Although we do not yet know whether the rule applies, we here determine the new form of the link from A to C if the rule</span>
-            <span class="c1"># does apply</span>
-            <span class="n">link_AC</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
-            <span class="n">new_link_AC</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span> <span class="o">+</span> <span class="n">link_AC</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-            <span class="n">pair_key</span><span class="p">,</span> <span class="n">new_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">new_link_AC</span><span class="p">)</span>
-
-            <span class="c1"># For the search of uncovered potentially directed paths from B_1 to C, determine the initial pattern as dictated by the link</span>
-            <span class="c1"># from A to B_1</span>
-            <span class="n">first_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B_1</span><span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;o*o&#39;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="n">first_link</span><span class="p">):</span>
-                <span class="n">initial_allowed_patterns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o*o&#39;</span><span class="p">]</span>
-            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;o*&gt;&#39;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="n">first_link</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="n">first_link</span><span class="p">):</span>
-                <span class="n">initial_allowed_patterns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">]</span>
-            
-            <span class="c1"># Return all uncovered potentially directed paths from B_1 to C</span>
-            <span class="c1">#uncovered_pd_paths =  self._find_potentially_directed_paths(B_1, C, initial_allowed_patterns, return_if_any_path_found = False,</span>
-            <span class="c1"># uncovered=True, reduce_allowed_patterns=True, max_length = np.inf)</span>
-
-            <span class="c1"># Find all uncovered potentially directed paths from B_1 to C</span>
-            <span class="n">uncovered_pd_paths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_potentially_directed_uncovered_paths</span><span class="p">(</span><span class="n">B_1</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">initial_allowed_patterns</span><span class="p">)</span>
-
-            <span class="c1"># Run through all of these paths and check i) whether the node adjacent to B_1 is non-adjacent to A, ii) whether condition iv) of</span>
-            <span class="c1"># the rule antecedent is true. If there is any such path, then the link can be oriented</span>
-            <span class="k">for</span> <span class="n">upd_path</span> <span class="ow">in</span> <span class="n">uncovered_pd_paths</span><span class="p">:</span>
-
-                <span class="c1"># Is the node adjacent to B_1 non-adjacent to A (this implies that there are at least three nodes on the path, because else the</span>
-                <span class="c1"># node adjacent to B_1 is C) and is A not part of the path?</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">upd_path</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">3</span> <span class="ow">or</span> <span class="n">A</span> <span class="ow">in</span> <span class="n">upd_path</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">upd_path</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-                    <span class="k">continue</span>
-
-                <span class="c1"># If the link from A to B_1 is into B_1, condition iv) is true</span>
-                <span class="k">if</span> <span class="n">first_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-                    <span class="c1"># Mark the link from A to C for orientation, break the for loop to continue with the next triple</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">pair_key</span><span class="p">,</span> <span class="n">new_link</span><span class="p">))</span>
-                    <span class="k">break</span>
-
-                <span class="c1"># If the link from A to B_1 is not in B_1, we need to check whether B_1 is in SepSet(A, X) where X is the node on upd_path next</span>
-                <span class="c1"># to B_1</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_in_SepSet_AC</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B_1</span><span class="p">,</span> <span class="n">upd_path</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-                    <span class="c1"># Continue with the next upd_path</span>
-                    <span class="k">continue</span>
-
-                <span class="c1"># Now check whether rule iv) for all triples on upd_path</span>
-                <span class="n">path_qualifies</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">upd_path</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="p">):</span>
-                    <span class="c1"># We consider the unshielded triples upd_path[i] - upd_path[i+1] - upd_path[i+2]</span>
-
-                    <span class="c1"># If the link between upd_path[i] and upd_path[i+1] is into the latter, condition iv) is true</span>
-                    <span class="n">left_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
-                    <span class="k">if</span> <span class="n">left_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-                        <span class="c1"># The path qualifies, break the inner for loop</span>
-                        <span class="k">break</span>
-
-                    <span class="c1"># If not, then we need to continue with checking whether upd_path[i+1] in SepSet(upd_path[i+1], upd_path[i+2])</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_in_SepSet_AC</span><span class="p">(</span><span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]):</span>
-                        <span class="c1"># The path does not qualifying, break the inner for loop</span>
-                        <span class="n">path_qualifies</span> <span class="o">=</span> <span class="kc">False</span>
-                        <span class="k">break</span>
-
-                <span class="c1"># The path qualifies, mark the edge from A to C for orientation and break the outer for loop to continue with the next triple</span>
-                <span class="k">if</span> <span class="n">path_qualifies</span><span class="p">:</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">pair_key</span><span class="p">,</span> <span class="n">new_link</span><span class="p">))</span>
-                    <span class="k">break</span>
-
-                <span class="c1"># The path does not qualify, continue with the next upd_path</span>
-
-            <span class="c1"># end for upd_path in uncovered_pd_paths</span>
-        <span class="c1"># end for (B_1, A, C) in all_appropriate_triples</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_ER10</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R10^prime&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all triples A o--&gt; C &lt;-- P_C</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;o*&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;&lt;*-&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="n">all_appropriate_triples</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;o*&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;&lt;*-&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;***&#39;</span><span class="p">)))</span>
-
-        <span class="c1"># Collect all triples for the given pair (A, C)</span>
-        <span class="n">triple_sorting_dict</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">P_C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">triple_sorting_dict</span><span class="o">.</span><span class="n">get</span><span class="p">((</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">))</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">triple_sorting_dict</span><span class="p">[(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)]</span> <span class="o">=</span> <span class="p">[</span><span class="n">P_C</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">triple_sorting_dict</span><span class="p">[(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">P_C</span><span class="p">)</span>
-
-
-        <span class="c1"># Run through all (A, C) pairs</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">triple_sorting_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># Find all uncovered potentially directed paths from A to C through any of the P_C nodes</span>
-            <span class="n">relevant_paths</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">P_C</span> <span class="ow">in</span> <span class="n">triple_sorting_dict</span><span class="p">[(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)]:</span>
-                <span class="k">for</span> <span class="n">upd_path</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_potentially_directed_uncovered_paths</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">P_C</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o*&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o*o&#39;</span><span class="p">]):</span>
-
-                    <span class="c1"># Run through all of these paths and check i) whether the second to last element is not adjacent to C (this requires it to</span>
-                    <span class="c1"># have a least three nodes, because else the second to last element would be A) and ii) whether the left edge of any 3-node</span>
-                    <span class="c1"># sub-path is into the middle nor or, if not, whether the middle node is in the separating set of the two end-point nodes</span>
-                    <span class="c1"># (of the 3-node) sub-path and iii) whether C is not element of the path. If path meets these conditions, add its second node</span>
-                    <span class="c1"># (the adjacent to A) to the set second_nodes</span>
-
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">upd_path</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">3</span> <span class="ow">or</span> <span class="n">C</span> <span class="ow">in</span> <span class="n">upd_path</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">upd_path</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="n">C</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-                        <span class="k">continue</span>
-
-                    <span class="n">upd_path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">C</span><span class="p">)</span>
-
-                    <span class="n">path_qualifies</span> <span class="o">=</span> <span class="kc">True</span>
-                    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">upd_path</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="p">):</span>
-                        <span class="c1"># We consider the unshielded triples upd_path[i] - upd_path[i+1] - upd_path[i+2]</span>
-
-                        <span class="c1"># If the link between upd_path[i] and upd_path[i+1] is into the latter, the path qualifies</span>
-                        <span class="n">left_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
-                        <span class="k">if</span> <span class="n">left_link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-                            <span class="c1"># The path qualifies, break the inner for loop</span>
-                            <span class="k">break</span>
-
-                        <span class="c1"># If not, then we need to continue with checking whether upd_path[i+1] in SepSet(upd_path[i+1], upd_path[i+2])</span>
-                        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_in_SepSet_AC</span><span class="p">(</span><span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">upd_path</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]):</span>
-                            <span class="c1"># The path does not qualify, break the inner for loop</span>
-                            <span class="n">path_qualifies</span> <span class="o">=</span> <span class="kc">False</span>
-                            <span class="k">break</span>
-
-                    <span class="c1"># The path qualifies, add upd_path[i] to second_nodes and continue with the next upd_path</span>
-                    <span class="k">if</span> <span class="n">path_qualifies</span><span class="p">:</span>
-                        <span class="n">relevant_paths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">upd_path</span><span class="p">)</span>
-
-                <span class="c1"># The path does not qualify, continue with the next upd_path</span>
-
-                <span class="c1"># end for path in self._get_potentially_directed_uncovered_paths(A, P_C, [&#39;-*&gt;&#39;, &#39;o*&gt;&#39;, &#39;o*o&#39;])</span>
-            <span class="c1"># end for P_C in triple_sorting_dict[(A, C)]</span>
-
-            <span class="c1"># Find all second nodes on the relevant paths</span>
-            <span class="n">second_nodes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">({</span><span class="n">path</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">relevant_paths</span><span class="p">})</span>
-
-            <span class="c1"># Check whether there is any pair of non-adjacent nodes in second_nodes, such that A is in their separating set. If yes, mark the link</span>
-            <span class="c1"># from A to C for orientation</span>
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">second_nodes</span><span class="p">)),</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">second_nodes</span><span class="p">))):</span>
-
-                <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">second_nodes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">second_nodes</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_in_SepSet_AC</span><span class="p">(</span><span class="n">second_nodes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">A</span><span class="p">,</span> <span class="n">second_nodes</span><span class="p">[</span><span class="n">j</span><span class="p">]):</span>
-                    <span class="c1"># Append new link and break the for loop</span>
-                    <span class="n">link_AC</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
-                    <span class="n">new_link_AC</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span> <span class="o">+</span> <span class="n">link_AC</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">new_link_AC</span><span class="p">))</span>
-                    <span class="k">break</span>
-
-        <span class="c1"># end for (A, C) in triple_sorting_dict.keys()</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_ER00a</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R0^prime a&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all graphical structures that the rule applies to</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;***&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;***&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-
-        <span class="c1"># Run through all appropriate graphical structures</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-
-            <span class="c1"># Unpack A, B, C</span>
-            <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="o">=</span> <span class="n">A</span>
-            <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">)</span> <span class="o">=</span> <span class="n">B</span>
-            <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">)</span> <span class="o">=</span> <span class="n">C</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">or</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># Get all weakly minimal separating sets in SepSet(A, C)</span>
-            <span class="c1"># Remark: The non weakly minimal separating sets may be larger, that&#39;s why we disfavor them</span>
-            <span class="n">sepsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepsets</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
-            <span class="n">sepsets</span> <span class="o">=</span> <span class="p">{</span><span class="n">Z</span> <span class="k">for</span> <span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">status</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span> <span class="k">if</span> <span class="n">status</span> <span class="o">==</span> <span class="s2">&quot;wm&quot;</span><span class="p">}</span>
-
-            <span class="c1">###################################################################################</span>
-            <span class="c1">### Part 1) of the rule ###########################################################</span>
-
-            <span class="n">remove_AB</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="n">link_AB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-
-            <span class="c1"># i) Middle mark must not be &quot;x&quot; or &quot;-&quot;</span>
-            <span class="k">if</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">]:</span>
-                <span class="c1"># Test A indep B given union(SepSet(A, C), intersection(def-anc(B), adj(B))) setminus{A, B} setminus{future of both A and B}</span>
-
-                <span class="c1"># Conditioning on parents</span>
-                <span class="n">Z_add</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span>
-
-                <span class="c1"># Shift the lags appropriately</span>
-                <span class="k">if</span> <span class="n">lag_i</span> <span class="o">&lt;=</span> <span class="n">lag_j</span><span class="p">:</span>
-                    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">lag_j</span><span class="p">)</span> <span class="c1"># A shifted</span>
-                    <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># B shifted</span>
-                    <span class="n">delta_lag</span> <span class="o">=</span> <span class="n">lag_j</span>
-
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span> <span class="o">-</span> <span class="n">lag_i</span><span class="p">)</span> <span class="c1"># B shifted</span>
-                    <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># A shifted</span>
-                    <span class="n">delta_lag</span> <span class="o">=</span> <span class="n">lag_i</span>
-
-                <span class="c1"># Run through all weakly minimal separating sets of A and C</span>
-                <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">:</span>      
-
-                    <span class="c1"># Construct the conditioning set to test</span>
-                    <span class="n">Z_test</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span>
-                    <span class="n">Z_test</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_test</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
-                    <span class="n">Z_add2</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
-
-                    <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="c1"># print(&quot;ER00a(part1):    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f&quot; %</span>
-                        <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z_test)]), val, pval))</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ER00a(part1):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_add = </span><span class="si">%s</span><span class="s2">, Z = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                            <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_add2</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_test</span><span class="p">]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                    <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic values and</span>
-                    <span class="c1"># conditioning set cardinalities</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_test</span><span class="p">))</span>
-
-                    <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-
-                        <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
-                        <span class="n">remove_AB</span> <span class="o">=</span> <span class="kc">True</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_save_sepset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="s2">&quot;nwm&quot;</span><span class="p">))</span>
-
-                <span class="k">if</span> <span class="n">remove_AB</span><span class="p">:</span>
-
-                    <span class="c1"># Remember the edge for removal</span>
-                    <span class="n">pair_key</span><span class="p">,</span> <span class="n">new_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">pair_key</span><span class="p">,</span> <span class="n">new_link</span><span class="p">))</span>
-
-            <span class="c1">###################################################################################</span>
-            <span class="c1">### Part 2) of the rule ###########################################################</span>
-
-            <span class="n">remove_CB</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="n">link_CB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-
-            <span class="c1"># i) Middle mark must not be &quot;x&quot; or &quot;-&quot;</span>
-            <span class="k">if</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">]:</span>
-                <span class="c1"># Test C indep B given union(SepSet(A, C), intersection(def-anc(B), adj(B))) setminus{A, B} setminus{future of both C and B}</span>
-
-                <span class="c1"># Conditioning on parents</span>
-                <span class="n">Z_add</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span>
-
-                <span class="c1"># Shift the lags appropriately</span>
-                <span class="k">if</span> <span class="n">lag_k</span> <span class="o">&lt;=</span> <span class="n">lag_j</span><span class="p">:</span>
-                    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span> <span class="o">-</span> <span class="n">lag_j</span><span class="p">)</span>
-                    <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-                    <span class="n">delta_lag</span> <span class="o">=</span> <span class="n">lag_j</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span> <span class="o">-</span> <span class="n">lag_k</span><span class="p">)</span>
-                    <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-                    <span class="n">delta_lag</span> <span class="o">=</span> <span class="n">lag_k</span>
-
-                <span class="c1"># Run through all weakly minimal separating sets of A and C</span>
-                <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">:</span>
-
-                    <span class="c1"># Construct the conditioning set to test</span>
-                    <span class="n">Z_test</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span>
-                    <span class="n">Z_test</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_test</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
-                    <span class="n">Z_add2</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
-
-                    <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="c1"># print(&quot;ER00a(part2):    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f&quot; %</span>
-                        <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z_test)]), val, pval))</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ER00a(part2):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_add = </span><span class="si">%s</span><span class="s2">, Z = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                            <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_add2</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_test</span><span class="p">]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                    <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic values and</span>
-                    <span class="c1"># conditioning set cardinalities</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_test</span><span class="p">))</span>
-
-                    <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-                        
-                        <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
-                        <span class="n">remove_CB</span> <span class="o">=</span> <span class="kc">True</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_save_sepset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="s2">&quot;nwm&quot;</span><span class="p">))</span>
-
-                <span class="k">if</span> <span class="n">remove_CB</span><span class="p">:</span>
-
-                    <span class="c1"># Remember the edge for removal</span>
-                    <span class="n">pair_key</span><span class="p">,</span> <span class="n">new_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">pair_key</span><span class="p">,</span> <span class="n">new_link</span><span class="p">))</span>
-
-            <span class="c1">###################################################################################</span>
-            <span class="c1">### Part 3) of the rule ###########################################################</span>
-
-            <span class="k">if</span> <span class="n">remove_AB</span> <span class="ow">or</span> <span class="n">remove_CB</span> <span class="ow">or</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">]</span> <span class="ow">or</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">]</span> <span class="ow">or</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;x&quot;</span> <span class="ow">or</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;x&quot;</span> <span class="ow">or</span> <span class="p">(</span><span class="n">link_AB</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span> <span class="ow">and</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">):</span>
-                <span class="k">continue</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_not_in_SepSet_AC</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">):</span>
-
-                <span class="c1"># Prepare the new links and save them to the output</span>
-                <span class="k">if</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-                    <span class="n">new_link_AB</span> <span class="o">=</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">new_link_AB</span><span class="p">))</span>
-
-                <span class="n">new_link_CB</span> <span class="o">=</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                <span class="k">if</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">new_link_CB</span><span class="p">))</span>
-
-        <span class="c1"># end for (A, B, C) in all_appropriate_triples</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_ER00b</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R0^prime b&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all graphical structures that the rule applies to</span>
-        <span class="n">triples_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;**&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;o!+&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-        <span class="n">triples_2</span> <span class="o">=</span> <span class="p">[</span><span class="n">trip</span> <span class="k">for</span> <span class="n">trip</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;**&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;oR+&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">trip</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">trip</span><span class="p">[</span><span class="mi">2</span><span class="p">])]</span>
-        <span class="n">triples_3</span> <span class="o">=</span> <span class="p">[</span><span class="n">trip</span> <span class="k">for</span> <span class="n">trip</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;**&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;oL+&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">trip</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">trip</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">triples_1</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">triples_2</span><span class="p">),</span> <span class="nb">set</span><span class="p">(</span><span class="n">triples_3</span><span class="p">))</span>
-
-        <span class="c1"># Run through all appropriate graphical structures</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-
-            <span class="c1"># Unpack A, B, C</span>
-            <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="o">=</span> <span class="n">A</span>
-            <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">)</span> <span class="o">=</span> <span class="n">B</span>
-            <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">)</span> <span class="o">=</span> <span class="n">C</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># Get all weakly minimal separating sets in SepSet(A, C)</span>
-            <span class="c1"># Remark: The non weakly minimal separating sets may be larger, that&#39;s why we disfavor them</span>
-            <span class="n">sepsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepsets</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
-            <span class="n">sepsets</span> <span class="o">=</span> <span class="p">{</span><span class="n">Z</span> <span class="k">for</span> <span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">status</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span> <span class="k">if</span> <span class="n">status</span> <span class="o">==</span> <span class="s2">&quot;wm&quot;</span><span class="p">}</span>
-
-            <span class="c1">###################################################################################</span>
-            <span class="c1">### Part 1) of the rule ###########################################################</span>
-
-            <span class="n">remove_AB</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="n">link_AB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-
-            <span class="c1"># i) Middle mark must not be &quot;x&quot; or &quot;-&quot;</span>
-            <span class="k">if</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">]:</span>
-                <span class="c1"># Test A indep B given union(SepSet(A, C), intersection(def-anc(B), adj(B))) setminus{A, B} setminus{future of both A and B}</span>
-
-                <span class="c1"># Conditioning on parents</span>
-                <span class="n">Z_add</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_parents</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span>
-
-                <span class="c1"># Shift the lags appropriately</span>
-                <span class="k">if</span> <span class="n">lag_i</span> <span class="o">&lt;=</span> <span class="n">lag_j</span><span class="p">:</span>
-                    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">lag_j</span><span class="p">)</span>
-                    <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-                    <span class="n">delta_lag</span> <span class="o">=</span> <span class="n">lag_j</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span> <span class="o">-</span> <span class="n">lag_i</span><span class="p">)</span>
-                    <span class="n">Y</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-                    <span class="n">delta_lag</span> <span class="o">=</span> <span class="n">lag_i</span>
-
-                <span class="c1"># Run through all weakly minimal separating sets of A and C</span>
-                <span class="k">for</span> <span class="n">Z</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">:</span>
-
-                    <span class="c1"># Construct the conditioning set to test</span>
-                    <span class="n">Z_test</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span>
-                    <span class="n">Z_test</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_test</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
-                    <span class="n">Z_add2</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
-
-                    <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
-                        <span class="c1"># print(&quot;ER00b:    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f&quot; %</span>
-                        <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z_test)]), val, pval))</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ER00b:    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_add = </span><span class="si">%s</span><span class="s2">, Z = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                            <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_add2</span><span class="p">]),</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z_test</span><span class="p">]),</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">))</span>
-
-                    <span class="c1"># Accordingly update dictionaries that keep track of the maximal p-value and the corresponding test statistic values and</span>
-                    <span class="c1"># conditioning set cardinalities</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_test</span><span class="p">))</span>
-
-                    <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
-
-                        <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
-                        <span class="n">remove_AB</span> <span class="o">=</span> <span class="kc">True</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_save_sepset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="s2">&quot;nwm&quot;</span><span class="p">))</span>
-
-                <span class="k">if</span> <span class="n">remove_AB</span><span class="p">:</span>
-                    <span class="c1"># Remember the edge for removal</span>
-                    <span class="n">pair_key</span><span class="p">,</span> <span class="n">new_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">pair_key</span><span class="p">,</span> <span class="n">new_link</span><span class="p">))</span>
-
-            <span class="c1">###################################################################################</span>
-            <span class="c1">### Part 2) of the rule ###########################################################</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="k">continue</span>
-
-            <span class="k">if</span> <span class="n">remove_AB</span> <span class="ow">or</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;x&quot;</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_not_in_SepSet_AC</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">):</span>
-
-                <span class="c1"># Prepare the new link and save it to the output</span>
-                <span class="n">link_CB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-                <span class="n">new_link_CB</span> <span class="o">=</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">new_link_CB</span><span class="p">))</span>
-
-        <span class="c1"># end for (A, B, C) in all_appropriate_triples</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_ER00c</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R0^prime c&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all graphical structures that the rule applies to</span>
-        <span class="n">triples_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;*-*&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;o!+&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-        <span class="n">triples_2</span> <span class="o">=</span> <span class="p">[</span><span class="n">trip</span> <span class="k">for</span> <span class="n">trip</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;*-*&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;oR+&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">trip</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">trip</span><span class="p">[</span><span class="mi">2</span><span class="p">])]</span>
-        <span class="n">triples_3</span> <span class="o">=</span> <span class="p">[</span><span class="n">trip</span> <span class="k">for</span> <span class="n">trip</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;*-*&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;oL+&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_smaller</span><span class="p">(</span><span class="n">trip</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">trip</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">triples_1</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">triples_2</span><span class="p">),</span> <span class="nb">set</span><span class="p">(</span><span class="n">triples_3</span><span class="p">))</span>
-
-        <span class="c1"># Run through all appropriate graphical structures</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span>  <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># Check whether the rule applies</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_not_in_SepSet_AC</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">):</span>
-
-                <span class="c1"># Prepare the new link and append it to the output</span>
-                <span class="n">link_CB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-                <span class="n">new_link_CB</span> <span class="o">=</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">new_link_CB</span><span class="p">))</span>
-
-        <span class="c1"># end for (A, B, C) in all_appropriate_triples</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_apply_ER00d</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">only_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all orientations implied by orientation rule R0^prime d&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output list</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Find all graphical structures that the rule applies to</span>
-        <span class="n">triples_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;*-o&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;o-*&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-        <span class="n">triples_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="o">=</span><span class="s1">&#39;*-&gt;&#39;</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="o">=</span><span class="s1">&#39;o-*&#39;</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-        <span class="n">all_appropriate_triples</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">triples_1</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">triples_2</span><span class="p">))</span>
-
-        <span class="c1"># Run through all appropriate graphical structures</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span> <span class="ow">in</span> <span class="n">all_appropriate_triples</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">only_lagged</span> <span class="ow">and</span> <span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">and</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="k">continue</span>
-
-            <span class="c1"># Check whether the rule applies</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_B_not_in_SepSet_AC</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">):</span>
-                <span class="c1"># Prepare the new links and append them to the output</span>
-
-                <span class="c1"># From C to B</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">only_lagged</span> <span class="ow">or</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                    <span class="n">link_CB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-                    <span class="n">new_link_CB</span> <span class="o">=</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">link_CB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">new_link_CB</span><span class="p">))</span>
-
-                <span class="c1"># If needed, also fromA to B</span>
-                <span class="n">link_AB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">)</span>
-                <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="n">only_lagged</span> <span class="ow">or</span> <span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">B</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">and</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;o&quot;</span><span class="p">:</span>
-                    <span class="n">new_link_AB</span> <span class="o">=</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">link_AB</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;&gt;&quot;</span>
-                    <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pair_key_and_new_link</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">new_link_AB</span><span class="p">))</span>
-
-        <span class="c1"># end for (A, B, C) in all_appropriate_triples</span>
-
-        <span class="c1"># Return the output list</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-    <span class="c1">########################################################################################################################</span>
-    <span class="c1">########################################################################################################################</span>
-    <span class="c1">########################################################################################################################</span>
-
-    <span class="k">def</span> <span class="nf">_print_graph_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print all links in graph_dict&quot;&quot;&quot;</span>
-
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="n">lag_i</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">):</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{}</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">,</span> <span class="n">link</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_link</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the current link from node A to B&quot;&quot;&quot;</span>
-
-        <span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">=</span> <span class="n">A</span>
-        <span class="p">(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span><span class="p">)</span> <span class="o">=</span> <span class="n">B</span>
-
-        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag_A</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">:</span>
-            <span class="k">return</span> <span class="s2">&quot;&quot;</span>
-        <span class="k">elif</span> <span class="n">lag_A</span> <span class="o">&lt;=</span> <span class="n">lag_B</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_B</span><span class="p">][(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">)]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_A</span><span class="p">][(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">-</span> <span class="n">lag_A</span><span class="p">)])</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_non_future_adj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_list</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return all non-future adjacencies of all nodes in node_list&quot;&quot;&quot;</span>
-
-        <span class="c1"># Build the output starting from an empty set</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-        <span class="c1"># For each node W in node_list ...</span>
-        <span class="k">for</span> <span class="n">A</span> <span class="ow">in</span> <span class="n">node_list</span><span class="p">:</span>
-            <span class="c1"># Unpack A</span>
-            <span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">=</span> <span class="n">A</span>
-            <span class="c1"># Add all (current) non-future adjacencies of A to the set out</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">union</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span><span class="p">)</span> <span class="k">for</span> <span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_A</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_A</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">})</span>
-
-        <span class="c1"># Return the desired set</span>
-        <span class="k">return</span> <span class="n">out</span>
-
-    <span class="k">def</span> <span class="nf">_update_pval_val_card_dicts</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">card</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;If &#39;pval&#39; is larger than the current maximal p-value across all previous independence tests for X and Y (stored in self.pval_max)</span>
-<span class="sd">        then: Replace the current values stored in self.pval_max, self.pval_max_val, self.pval_max_card respectively by &#39;pval&#39;, &#39;val&#39;, and &#39;card&#39;.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pval_max</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">pval_max</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_val</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_card</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="n">card</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pval_max</span><span class="p">[</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">Y</span><span class="p">]:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">pval_max</span><span class="p">[</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">Y</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_val</span><span class="p">[</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">Y</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_card</span><span class="p">[</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">Y</span><span class="p">]</span> <span class="o">=</span> <span class="n">card</span>
-
-    <span class="k">def</span> <span class="nf">_save_sepset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Save Z as separating sets of X and Y. Y is assumed to be at lag 0&quot;&quot;&quot;</span>
-
-        <span class="c1"># Unpack X and Y</span>
-        <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="o">=</span> <span class="n">X</span>
-        <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">)</span> <span class="o">=</span> <span class="n">Y</span>
-
-        <span class="k">assert</span> <span class="n">lag_j</span> <span class="o">==</span> <span class="mi">0</span>
-
-        <span class="c1"># Save the sepset</span>
-        <span class="k">if</span> <span class="n">lag_i</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">sepsets</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">X</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">sepsets</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">Y</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_reverse_link</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">link</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Reverse a given link, taking care to replace &gt; with &lt; and vice versa&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">link</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="s2">&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-            <span class="n">left_mark</span> <span class="o">=</span> <span class="s2">&quot;&lt;&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">left_mark</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&lt;&quot;</span><span class="p">:</span>
-            <span class="n">right_mark</span> <span class="o">=</span> <span class="s2">&quot;&gt;&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">right_mark</span> <span class="o">=</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">left_mark</span> <span class="o">+</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">right_mark</span>
-
-
-    <span class="k">def</span> <span class="nf">_write_link</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">verbosity</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Write the information that the link from node A to node B takes the form of new_link into self.graph_dict. Neither is it assumed</span>
-<span class="sd">        that at least of the nodes is at lag 0, nor must A be before B. If A and B are contemporaneous, also the link from B to A is written</span>
-<span class="sd">        as the reverse of new_link&quot;&quot;&quot;</span>
-
-        <span class="c1"># Unpack A and B</span>
-        <span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">=</span> <span class="n">A</span>
-        <span class="p">(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span><span class="p">)</span> <span class="o">=</span> <span class="n">B</span>
-
-        <span class="c1"># Write the link from A to B</span>
-        <span class="k">if</span> <span class="n">lag_A</span> <span class="o">&lt;</span> <span class="n">lag_B</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) ==&gt; (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Writing:&quot;</span><span class="p">,</span> <span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_B</span><span class="p">][(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">)],</span> <span class="n">var_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">,</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">var_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="c1">#print(&quot;Replacing {:3} from ({},{:2}) to {} with {:3}&quot;.format(self.graph_dict[var_B][(var_A, lag_A - lag_B)], var_A, lag_A - lag_B, (var_B, 0), new_link))</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_B</span><span class="p">][(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">)]</span> <span class="o">=</span> <span class="n">new_link</span>
-
-
-        <span class="k">elif</span> <span class="n">lag_A</span> <span class="o">==</span> <span class="n">lag_B</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) ==&gt; (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Writing:&quot;</span><span class="p">,</span> <span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_B</span><span class="p">][(</span><span class="n">var_A</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">var_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">var_A</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">new_link</span><span class="p">,</span> <span class="n">var_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="c1">#print(&quot;Replacing {:3} from ({},{:2}) to {} with {:3}&quot;.format(self.graph_dict[var_B][(var_A, 0)], var_A, 0, (var_B, 0), new_link))</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) ==&gt; (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Writing:&quot;</span><span class="p">,</span> <span class="n">var_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_A</span><span class="p">][(</span><span class="n">var_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">var_A</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">var_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">new_link</span><span class="p">),</span> <span class="n">var_A</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="c1">#print(&quot;Replacing {:3} from ({},{:2}) to {} with {:3}&quot;.format(self.graph_dict[var_A][(var_B, 0)], var_B, 0, (var_A, 0), self._reverse_link(new_link)))</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_B</span><span class="p">][(</span><span class="n">var_A</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">=</span> <span class="n">new_link</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_A</span><span class="p">][(</span><span class="n">var_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">new_link</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:10}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) ==&gt; (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) </span><span class="si">{:3}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">,</span><span class="si">{:2}</span><span class="s2">) &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;Writing:&quot;</span><span class="p">,</span> <span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">-</span> <span class="n">lag_A</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_A</span><span class="p">][(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">-</span> <span class="n">lag_A</span><span class="p">)],</span> <span class="n">var_A</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">-</span> <span class="n">lag_A</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">new_link</span><span class="p">),</span> <span class="n">var_A</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                <span class="c1">#print(&quot;Replacing {:3} from ({},{:2}) to {} with {:3}&quot;.format(self.graph_dict[var_A][(var_B, lag_B - lag_A)], var_B, lag_B - lag_A, (var_A, 0), self._reverse_link(new_link)))</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">var_A</span><span class="p">][(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">-</span> <span class="n">lag_A</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">new_link</span><span class="p">)</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_sepsets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;For two non-adjacent nodes, get the their separating stored in self.sepsets.&quot;&quot;&quot;</span>
-
-        <span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">=</span> <span class="n">A</span>
-        <span class="p">(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span><span class="p">)</span> <span class="o">=</span> <span class="n">B</span>
-
-        <span class="k">def</span> <span class="nf">_shift</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">lag_B</span><span class="p">):</span>
-            <span class="k">return</span> <span class="nb">frozenset</span><span class="p">([(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">lag_B</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">])</span>
-
-        <span class="k">if</span> <span class="n">lag_A</span> <span class="o">&lt;</span> <span class="n">lag_B</span><span class="p">:</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="p">{(</span><span class="n">_shift</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">lag_B</span><span class="p">),</span> <span class="n">status</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">status</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sepsets</span><span class="p">[</span><span class="n">var_B</span><span class="p">][(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">)]}</span>
-        <span class="k">elif</span> <span class="n">lag_A</span> <span class="o">&gt;</span> <span class="n">lag_B</span><span class="p">:</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="p">{(</span><span class="n">_shift</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">lag_A</span><span class="p">),</span> <span class="n">status</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">status</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sepsets</span><span class="p">[</span><span class="n">var_A</span><span class="p">][(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">-</span> <span class="n">lag_A</span><span class="p">)]}</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="p">{(</span><span class="n">_shift</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">lag_A</span><span class="p">),</span> <span class="n">status</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">status</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sepsets</span><span class="p">[</span><span class="nb">max</span><span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">var_B</span><span class="p">)][(</span><span class="nb">min</span><span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">var_B</span><span class="p">),</span> <span class="mi">0</span><span class="p">)]}</span>
-
-        <span class="k">return</span> <span class="n">out</span>
-
-
-    <span class="k">def</span> <span class="nf">_initialize_full_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        The function _get_na_pds_t() needs to know the future adjacencies of a given node, not only the non-future adjacencies that are</span>
-<span class="sd">        stored in self.graph_dict. To aid this, this function initializes the dictionary graph_full_dict:</span>
-
-<span class="sd">        self.graph_full_dict[j][(i, -tau_i)] contains all adjacencies of (j, 0), in particular those for which tau_i &lt; 0.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Build from an empty nested dictionary</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">graph_full_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)}</span>
-
-        <span class="c1"># Run through the entire nested dictionary self.graph_dict</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">),</span> <span class="n">link</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-
-                <span class="k">if</span> <span class="n">link</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-                    <span class="c1"># Add non-future adjacencies</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">graph_full_dict</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)]</span> <span class="o">=</span> <span class="n">link</span>
-
-                    <span class="c1"># Add the future adjacencies </span>
-                    <span class="k">if</span> <span class="n">lag</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">graph_full_dict</span><span class="p">[</span><span class="n">var</span><span class="p">][(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">link</span><span class="p">)</span>
-
-        <span class="c1"># Return nothing</span>
-        <span class="k">return</span> <span class="kc">None</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_pair_key_and_new_link</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">link_AB</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;The link from A to B takes the form link_AB. Bring this information into a form appropriate for the output of rule applications&quot;&quot;&quot;</span>
-
-        <span class="p">(</span><span class="n">var_A</span><span class="p">,</span> <span class="n">lag_A</span><span class="p">)</span> <span class="o">=</span> <span class="n">A</span>
-        <span class="p">(</span><span class="n">var_B</span><span class="p">,</span> <span class="n">lag_B</span><span class="p">)</span> <span class="o">=</span> <span class="n">B</span>
-
-        <span class="k">if</span> <span class="n">lag_A</span> <span class="o">&lt;=</span> <span class="n">lag_B</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">((</span><span class="n">var_A</span><span class="p">,</span> <span class="n">var_B</span><span class="p">,</span> <span class="n">lag_A</span> <span class="o">-</span> <span class="n">lag_B</span><span class="p">),</span> <span class="n">link_AB</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">lag_A</span> <span class="o">&gt;</span> <span class="n">lag_B</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">((</span><span class="n">var_B</span><span class="p">,</span> <span class="n">var_A</span><span class="p">,</span> <span class="n">lag_B</span> <span class="o">-</span> <span class="n">lag_A</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">link_AB</span><span class="p">))</span>
-
-
-    <span class="k">def</span> <span class="nf">_match_link</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pattern</span><span class="p">,</span> <span class="n">link</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Matches pattern including wildcards with link.&quot;&quot;&quot;</span>
-        
-        <span class="k">if</span> <span class="n">pattern</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span> <span class="ow">or</span> <span class="n">link</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">True</span> <span class="k">if</span> <span class="n">pattern</span> <span class="o">==</span> <span class="n">link</span> <span class="k">else</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">left_mark</span><span class="p">,</span> <span class="n">middle_mark</span><span class="p">,</span> <span class="n">right_mark</span> <span class="o">=</span> <span class="n">pattern</span>
-            <span class="k">if</span> <span class="n">left_mark</span> <span class="o">!=</span> <span class="s1">&#39;*&#39;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">left_mark</span> <span class="o">==</span> <span class="s1">&#39;+&#39;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;&lt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">]:</span> <span class="k">return</span> <span class="kc">False</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">left_mark</span><span class="p">:</span> <span class="k">return</span> <span class="kc">False</span>
-
-            <span class="k">if</span> <span class="n">right_mark</span> <span class="o">!=</span> <span class="s1">&#39;*&#39;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">right_mark</span> <span class="o">==</span> <span class="s1">&#39;+&#39;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">]:</span> <span class="k">return</span> <span class="kc">False</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">link</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="n">right_mark</span><span class="p">:</span> <span class="k">return</span> <span class="kc">False</span>    
-            
-            <span class="k">if</span> <span class="n">middle_mark</span> <span class="o">!=</span> <span class="s1">&#39;*&#39;</span> <span class="ow">and</span> <span class="n">link</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">middle_mark</span><span class="p">:</span> <span class="k">return</span> <span class="kc">False</span>    
-                       
-            <span class="k">return</span> <span class="kc">True</span>
-
-
-    <span class="k">def</span> <span class="nf">_dict2graph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert self.graph_dict to graph array of shape (N, N, self.tau_max + 1).&quot;&quot;&quot;</span>
-
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;U3&#39;</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">adj</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="o">=</span> <span class="n">adj</span>
-                <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag_i</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_dict</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">adj</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">graph</span>
-
-
-    <span class="k">def</span> <span class="nf">_find_adj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">node</span><span class="p">,</span> <span class="n">patterns</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignore_time_bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find adjacencies of node matching patterns.&quot;&quot;&quot;</span>
-        
-        <span class="c1"># Setup</span>
-        <span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">=</span> <span class="n">node</span>
-        <span class="k">if</span> <span class="n">exclude</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">exclude</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">patterns</span><span class="p">)</span> <span class="o">==</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="n">patterns</span> <span class="o">=</span> <span class="p">[</span><span class="n">patterns</span><span class="p">]</span>
-
-        <span class="c1"># Init</span>
-        <span class="n">adj</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="c1"># Find adjacencies going forward/contemp</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_ik</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:])):</span>  
-            <span class="n">matches</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">patt</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_ik</span><span class="p">])</span> <span class="k">for</span> <span class="n">patt</span> <span class="ow">in</span> <span class="n">patterns</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">matches</span><span class="p">):</span>
-                <span class="n">match</span> <span class="o">=</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">+</span> <span class="n">lag_ik</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">match</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">adj</span> <span class="ow">and</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">+</span> <span class="n">lag_ik</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclude</span> <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">lag_i</span> <span class="o">+</span> <span class="n">lag_ik</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">ignore_time_bounds</span><span class="p">):</span>
-                    <span class="n">adj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">match</span><span class="p">)</span>
-        
-        <span class="c1"># Find adjacencies going backward/contemp</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_ki</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span><span class="n">i</span><span class="p">,:])):</span>  
-            <span class="n">matches</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">patt</span><span class="p">),</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_ki</span><span class="p">])</span> <span class="k">for</span> <span class="n">patt</span> <span class="ow">in</span> <span class="n">patterns</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">matches</span><span class="p">):</span>
-                <span class="n">match</span> <span class="o">=</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">lag_ki</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">match</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">adj</span> <span class="ow">and</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">lag_ki</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclude</span> <span class="ow">and</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">lag_ki</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">ignore_time_bounds</span><span class="p">):</span>
-                    <span class="n">adj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">match</span><span class="p">)</span>
-     
-        <span class="k">return</span> <span class="n">adj</span>
-        
-
-    <span class="k">def</span> <span class="nf">_is_match</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pattern_ij</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check whether the link between X and Y agrees with pattern_ij&quot;&quot;&quot;</span>
-
-        <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="o">=</span> <span class="n">X</span>
-        <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">)</span> <span class="o">=</span> <span class="n">Y</span>
-        <span class="n">tauij</span> <span class="o">=</span> <span class="n">lag_j</span> <span class="o">-</span> <span class="n">lag_i</span>
-        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tauij</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]:</span>
-            <span class="k">return</span> <span class="kc">False</span>
-        <span class="k">return</span> <span class="p">((</span><span class="n">tauij</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern_ij</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tauij</span><span class="p">]))</span> <span class="ow">or</span>
-               <span class="p">(</span><span class="n">tauij</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse_link</span><span class="p">(</span><span class="n">pattern_ij</span><span class="p">),</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tauij</span><span class="p">)])))</span>
-
-
-    <span class="k">def</span> <span class="nf">_find_triples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pattern_ij</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find triples (i, lag_i), (j, lag_j), (k, lag_k) that match patterns.&quot;&quot;&quot;</span>
-  
-        <span class="c1"># Graph as array makes it easier to search forward AND backward in time</span>
-        <span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict2graph</span><span class="p">()</span>
-
-        <span class="c1"># print(graph[:,:,0])</span>
-        <span class="c1"># print(graph[:,:,1])</span>
-        <span class="c1"># print(&quot;matching &quot;, pattern_ij, pattern_jk, pattern_ik)</span>
-
-        <span class="n">matched_triples</span> <span class="o">=</span> <span class="p">[]</span>
-                
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="c1"># Set lag_i = 0 without loss of generality, will be adjusted at end</span>
-            <span class="n">lag_i</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">adjacencies_i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="n">pattern_ij</span><span class="p">)</span>
-            <span class="c1"># print(i, adjacencies_i)</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjacencies_i</span><span class="p">:</span>
-
-                <span class="n">adjacencies_j</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">),</span> <span class="n">pattern_jk</span><span class="p">,</span>
-                                          <span class="n">exclude</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)])</span>
-                <span class="c1"># print(j, adjacencies_j)</span>
-                <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjacencies_j</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_match</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">),</span> <span class="n">pattern_ik</span><span class="p">):</span>                            
-                        <span class="c1"># Now use stationarity and shift triple such that the right-most</span>
-                        <span class="c1"># node (on a line t=..., -2, -1, 0, 1, 2, ...) is at lag 0</span>
-                        <span class="n">righmost_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">lag_i</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">)</span>
-                        <span class="n">match</span> <span class="o">=</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">),</span> 
-                                 <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">),</span>
-                                 <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">))</span>
-                        <span class="n">largest_lag</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">lag_i</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">,</span> <span class="n">lag_j</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">,</span> <span class="n">lag_k</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">match</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">matched_triples</span> <span class="ow">and</span> \
-                            <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">largest_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">matched_triples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">match</span><span class="p">)</span>                       
-                
-        <span class="k">return</span> <span class="n">matched_triples</span>  
-
-
-    <span class="k">def</span> <span class="nf">_find_quadruples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pattern_ij</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="p">,</span> 
-                               <span class="n">pattern_il</span><span class="p">,</span> <span class="n">pattern_jl</span><span class="p">,</span> <span class="n">pattern_kl</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find quadruples (i, lag_i), (j, lag_j), (k, lag_k), (l, lag_l) that match patterns.&quot;&quot;&quot;</span>
-  
-        <span class="c1"># We assume this later</span>
-        <span class="k">assert</span> <span class="n">pattern_il</span> <span class="o">!=</span> <span class="s1">&#39;&#39;</span>
-
-        <span class="c1"># Graph as array makes it easier to search forward AND backward in time</span>
-        <span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict2graph</span><span class="p">()</span>
-
-        <span class="n">matched_quadruples</span> <span class="o">=</span> <span class="p">[]</span>
-                
-        <span class="c1"># First get triple ijk</span>
-        <span class="n">ijk_triples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples</span><span class="p">(</span><span class="n">pattern_ij</span><span class="p">,</span> <span class="n">pattern_jk</span><span class="p">,</span> <span class="n">pattern_ik</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">triple</span> <span class="ow">in</span> <span class="n">ijk_triples</span><span class="p">:</span>
-            <span class="c1"># Unpack triple</span>
-            <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">),</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">)</span> <span class="o">=</span> <span class="n">triple</span>
-
-            <span class="c1"># Search through adjacencies</span>
-            <span class="n">adjacencies</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="n">pattern_il</span><span class="p">,</span>
-                                          <span class="n">exclude</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">),</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">)]))</span>
-            <span class="k">if</span> <span class="n">pattern_jl</span> <span class="o">!=</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
-                <span class="n">adjacencies</span> <span class="o">=</span> <span class="n">adjacencies</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span>
-                                <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">),</span> <span class="n">pattern_jl</span><span class="p">,</span>
-                                          <span class="n">exclude</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">)])))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">adjacencies</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="n">adj</span> <span class="k">for</span> <span class="n">adj</span> <span class="ow">in</span> <span class="n">adjacencies</span> 
-                                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_match</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">),</span> <span class="n">adj</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)])</span>
-
-            <span class="k">if</span> <span class="n">pattern_kl</span> <span class="o">!=</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
-                <span class="n">adjacencies</span> <span class="o">=</span> <span class="n">adjacencies</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span>
-                                <span class="bp">self</span><span class="o">.</span><span class="n">_find_adj</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">),</span> <span class="n">pattern_kl</span><span class="p">,</span>
-                                          <span class="n">exclude</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">),</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">)])))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">adjacencies</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="n">adj</span> <span class="k">for</span> <span class="n">adj</span> <span class="ow">in</span> <span class="n">adjacencies</span> 
-                                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_match</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">),</span> <span class="n">adj</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)])</span>
-
-            <span class="k">for</span> <span class="n">adj</span> <span class="ow">in</span> <span class="n">adjacencies</span><span class="p">:</span>
-                <span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">lag_l</span><span class="p">)</span> <span class="o">=</span> <span class="n">adj</span>
-                    
-                <span class="c1"># Now use stationarity and shift quadruple such that the right-most</span>
-                <span class="c1"># node (on a line t=..., -2, -1, 0, 1, 2, ...) is at lag 0</span>
-                <span class="n">righmost_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">lag_i</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">,</span> <span class="n">lag_k</span><span class="p">,</span> <span class="n">lag_l</span><span class="p">)</span>
-                <span class="n">match</span> <span class="o">=</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">),</span> 
-                         <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">),</span>
-                         <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">lag_k</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">),</span>
-                         <span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">lag_l</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">),</span>
-                         <span class="p">)</span>
-                <span class="n">largest_lag</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">lag_i</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">,</span> 
-                                  <span class="n">lag_j</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">,</span> 
-                                  <span class="n">lag_k</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">,</span>
-                                  <span class="n">lag_l</span> <span class="o">-</span> <span class="n">righmost_lag</span><span class="p">,</span>
-                                  <span class="p">)</span>
-                <span class="k">if</span> <span class="n">match</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">matched_quadruples</span> <span class="ow">and</span> \
-                    <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;=</span> <span class="n">largest_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">matched_quadruples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">match</span><span class="p">)</span>                       
-                
-        <span class="k">return</span> <span class="n">matched_quadruples</span> 
-
-
-    <span class="k">def</span> <span class="nf">_get_R4_discriminating_paths</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">triple</span><span class="p">,</span> <span class="n">max_length</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find all discriminating paths starting from triple&quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">_search</span><span class="p">(</span><span class="n">path_taken</span><span class="p">,</span> <span class="n">max_length</span><span class="p">):</span>
-
-            <span class="c1"># Get the last visited node and its link to Y</span>
-            <span class="n">last_node</span> <span class="o">=</span> <span class="n">path_taken</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">link_to_Y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">last_node</span><span class="p">,</span> <span class="n">path_taken</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-            <span class="c1"># Base Case: If the current path is a discriminating path, return it as single entry of a list</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">path_taken</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">link_to_Y</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-                <span class="k">return</span> <span class="p">[</span><span class="n">path_taken</span><span class="p">]</span>            
-
-            <span class="c1"># If the current path is not a discriminating path, continue the path</span>
-            <span class="n">paths</span> <span class="o">=</span> <span class="p">[]</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">last_node</span><span class="p">,</span> <span class="n">path_taken</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&lt;&quot;</span> <span class="ow">and</span> <span class="n">link_to_Y</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">path_taken</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">max_length</span><span class="p">:</span>
-
-                <span class="c1"># Search through all adjacencies of the last node</span>
-                <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_full_dict</span><span class="p">[</span><span class="n">last_node</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-
-                    <span class="c1"># Build the next node and get its link to the previous</span>
-                    <span class="n">next_node</span> <span class="o">=</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">last_node</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-                    <span class="n">next_link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">next_node</span><span class="p">,</span> <span class="n">last_node</span><span class="p">)</span>
-
-                    <span class="c1"># Check whether this node can be visited</span>
-                    <span class="k">if</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="ow">and</span> <span class="n">next_node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">path_taken</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="s2">&quot;*-&gt;&quot;</span><span class="p">,</span> <span class="n">next_link</span><span class="p">):</span>
-
-                        <span class="c1"># Recursive call</span>
-                        <span class="n">paths</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">_search</span><span class="p">(</span><span class="n">path_taken</span><span class="p">[:]</span> <span class="o">+</span> <span class="p">[</span><span class="n">next_node</span><span class="p">],</span> <span class="n">max_length</span><span class="p">))</span>
-
-            <span class="c1"># Return the list of discriminating paths</span>
-            <span class="k">return</span> <span class="n">paths</span>
-
-        <span class="c1"># Unpack the triple</span>
-        <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="n">triple</span>
-
-        <span class="c1"># Return all discriminating paths starting at this triple</span>
-        <span class="k">return</span> <span class="n">_search</span><span class="p">([</span><span class="n">Y</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">W</span><span class="p">],</span> <span class="n">max_length</span><span class="p">)</span>
-
-
-    <span class="k">def</span> <span class="nf">_get_potentially_directed_uncovered_paths</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start_node</span><span class="p">,</span> <span class="n">end_node</span><span class="p">,</span> <span class="n">initial_allowed_patterns</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find all potentiall directed uncoverged paths from start_node to end_node whose first link takes one the forms specified by</span>
-<span class="sd">        initial_allowed_patters&quot;&quot;&quot;</span>
-
-        <span class="k">assert</span> <span class="n">start_node</span> <span class="o">!=</span> <span class="n">end_node</span>
-
-        <span class="c1"># Function for recursive search of potentially directed uncovered paths</span>
-        <span class="k">def</span> <span class="nf">_search</span><span class="p">(</span><span class="n">end_node</span><span class="p">,</span> <span class="n">path_taken</span><span class="p">,</span> <span class="n">allowed_patterns</span><span class="p">):</span>
-
-            <span class="c1"># List for outputting potentially directed uncovered paths</span>
-            <span class="n">paths</span> <span class="o">=</span> <span class="p">[]</span>
-
-            <span class="c1"># The last visited note becomes the new start_node</span>
-            <span class="n">start_node</span> <span class="o">=</span> <span class="n">path_taken</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-            <span class="c1"># Base case: End node has been reached</span>
-            <span class="k">if</span> <span class="n">start_node</span> <span class="o">==</span> <span class="n">end_node</span><span class="p">:</span>
-                <span class="n">paths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">path_taken</span><span class="p">)</span>
-
-            <span class="c1"># Recursive build case</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="c1"># Run through the adjacencies of start_node</span>
-                <span class="c1">#for next_node in self.graph_full_dict[start_node[0]]:</span>
-                <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph_full_dict</span><span class="p">[</span><span class="n">start_node</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-
-                    <span class="n">next_node</span> <span class="o">=</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">+</span> <span class="n">start_node</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-                    <span class="c1"># Consider only nodes that ...</span>
-                    <span class="c1"># ... are within the allowed time frame</span>
-                    <span class="k">if</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="ow">or</span> <span class="n">next_node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># ... have not been visited yet</span>
-                    <span class="k">if</span> <span class="n">next_node</span> <span class="ow">in</span> <span class="n">path_taken</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># ... are non-adjacent to the node before start_node</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">path_taken</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">path_taken</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="n">next_node</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-                        <span class="k">continue</span>
-                    <span class="c1"># ... whose link with start_node matches one of the allowed patters</span>
-                    <span class="n">link</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_link</span><span class="p">(</span><span class="n">start_node</span><span class="p">,</span> <span class="n">next_node</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span> <span class="o">=</span> <span class="n">pattern</span><span class="p">,</span> <span class="n">link</span> <span class="o">=</span> <span class="n">link</span><span class="p">)</span> <span class="k">for</span> <span class="n">pattern</span> <span class="ow">in</span> <span class="n">allowed_patterns</span><span class="p">]):</span>
-                        <span class="k">continue</span>
-
-                    <span class="c1"># Determine the allowed patters for the next recursive call</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;o*o&#39;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="n">link</span><span class="p">):</span>
-                        <span class="n">new_allowed_patters</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;o*o&quot;</span><span class="p">,</span> <span class="s2">&quot;o*&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;-*&gt;&quot;</span><span class="p">]</span>
-                    <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;o*&gt;&#39;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="n">link</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match_link</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;-*&gt;&#39;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="n">link</span><span class="p">):</span>
-                        <span class="n">new_allowed_patters</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;-*&gt;&quot;</span><span class="p">]</span>
-
-                    <span class="c1"># Determine the new path taken</span>
-                    <span class="n">new_path_taken</span> <span class="o">=</span> <span class="n">path_taken</span><span class="p">[:]</span> <span class="o">+</span> <span class="p">[</span><span class="n">next_node</span><span class="p">]</span>
-
-                    <span class="c1"># Recursive call</span>
-                    <span class="n">paths</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">_search</span><span class="p">(</span><span class="n">end_node</span><span class="p">,</span> <span class="n">new_path_taken</span><span class="p">,</span> <span class="n">new_allowed_patters</span><span class="p">))</span>
-
-            <span class="c1"># Output list of potentially directed uncovered paths</span>
-            <span class="k">return</span> <span class="n">paths</span>
-
-        <span class="c1"># end def _search(end_node, path_taken, allowed_patterns)</span>
-
-        <span class="c1"># Output potentially directed uncovered paths</span>
-        <span class="n">paths</span> <span class="o">=</span> <span class="n">_search</span><span class="p">(</span><span class="n">end_node</span><span class="p">,</span> <span class="p">[</span><span class="n">start_node</span><span class="p">],</span> <span class="n">initial_allowed_patterns</span><span class="p">)</span>
-        <span class="k">return</span> <span class="p">[</span><span class="n">path</span> <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">paths</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">]</span>
-
-
-    <span class="k">def</span> <span class="nf">_sort_search_set</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">search_set</span><span class="p">,</span> <span class="n">reference_node</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Sort the nodes in search_set by their values in self.pval_max_val with respect to the reference_node. Nodes with higher absolute</span>
-<span class="sd">        values appear earlier&quot;&quot;&quot;</span>
-
-        <span class="n">sort_by_potential_minus_infs</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_pval_max_val</span><span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">reference_node</span><span class="p">)</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">search_set</span><span class="p">]</span>
-        <span class="n">sort_by</span> <span class="o">=</span> <span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="k">if</span> <span class="n">value</span> <span class="o">!=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">sort_by_potential_minus_infs</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">sort_by</span><span class="p">,</span> <span class="n">search_set</span><span class="p">),</span> <span class="n">reverse</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)]</span>
-
-    <span class="k">def</span> <span class="nf">_get_pval_max_val</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the test statistic value of that independence test for X and Y which, among all such tests, has the largest p-value.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_val</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pval_max_val</span><span class="p">[</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">Y</span><span class="p">]</span>    
-
-    <span class="k">def</span> <span class="nf">_delete_sepsets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Delete all separating sets of X and Y. Y is assumed to be at lag 0&quot;&quot;&quot;</span>
-
-        <span class="c1"># Unpack X and Y</span>
-        <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_i</span><span class="p">)</span> <span class="o">=</span> <span class="n">X</span>
-        <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_j</span><span class="p">)</span> <span class="o">=</span> <span class="n">Y</span>
-
-        <span class="k">assert</span> <span class="n">lag_j</span> <span class="o">==</span> <span class="mi">0</span>
-
-        <span class="c1"># Save the sepset</span>
-        <span class="k">if</span> <span class="n">lag_i</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">sepsets</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">sepsets</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">Y</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-
-    <span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
-    <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
-    <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
-
-    <span class="c1"># Example process to play around with</span>
-    <span class="c1"># Each key refers to a variable and the incoming links are supplied</span>
-    <span class="c1"># as a list of format [((var, -lag), coeff, function), ...]</span>
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-    <span class="k">def</span> <span class="nf">nonlin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mf">5.</span> <span class="o">*</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mf">20.</span><span class="p">))</span>
-
-    <span class="n">links</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="o">-</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">1</span><span class="p">:</span> <span class="p">[((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.6</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">2</span><span class="p">:</span> <span class="p">[((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.6</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">3</span><span class="p">:</span> <span class="p">[],</span>
-             <span class="p">}</span>
-
-    <span class="n">full_data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span>
-                        <span class="n">T</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
-    
-    <span class="c1"># We now remove variable 3 which plays the role of a hidden confounder</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">full_data</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
-
-    <span class="c1"># Data must be array of shape (time, variables)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-    <span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
-    <span class="n">lpcmci</span> <span class="o">=</span> <span class="n">LPCMCI</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">)</span>
-    <span class="c1"># results = pcmci.run_lpcmci(tau_max=2, pc_alpha=0.01)</span>
-
-    <span class="c1"># # For a proper causal interpretation of the graph see the paper!</span>
-    <span class="c1"># print(results[&#39;graph&#39;])</span>
-    <span class="c1"># tp.plot_graph(graph=results[&#39;graph&#39;], val_matrix=results[&#39;val_matrix&#39;])</span>
-    <span class="c1"># plt.show()</span>
-
-    <span class="n">results</span> <span class="o">=</span> <span class="n">lpcmci</span><span class="o">.</span><span class="n">run_sliding_window_of</span><span class="p">(</span>
-        <span class="n">window_step</span><span class="o">=</span><span class="mi">499</span><span class="p">,</span> <span class="n">window_length</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-        <span class="n">method</span><span class="o">=</span><span class="s1">&#39;run_lpcmci&#39;</span><span class="p">,</span> <span class="n">method_args</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;tau_max&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">})</span>
-</pre></div>
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-  <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
-<span class="kn">import</span> <span class="nn">json</span><span class="o">,</span> <span class="nn">warnings</span><span class="o">,</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">pathlib</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">sklearn</span>
-<span class="kn">import</span> <span class="nn">sklearn.linear_model</span>
-<span class="kn">import</span> <span class="nn">networkx</span>
-<span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
-<span class="kn">from</span> <span class="nn">tigramite.pcmci</span> <span class="kn">import</span> <span class="n">PCMCI</span>
-
-<div class="viewcode-block" id="Models"><a class="viewcode-back" href="../../index.html#tigramite.models.Models">[docs]</a><span class="k">class</span> <span class="nc">Models</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Base class for time series models.</span>
-
-<span class="sd">    Allows to fit any model from sklearn to the parents of a target variable.</span>
-<span class="sd">    Also takes care of missing values, masking and preprocessing.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataframe : data object</span>
-<span class="sd">        Tigramite dataframe object. It must have the attributes dataframe.values</span>
-<span class="sd">        yielding a numpy array of shape (observations T, variables N) and</span>
-<span class="sd">        optionally a mask of the same shape and a missing values flag.</span>
-<span class="sd">    model : sklearn model object</span>
-<span class="sd">        For example, sklearn.linear_model.LinearRegression() for a linear</span>
-<span class="sd">        regression model.</span>
-<span class="sd">    conditional_model : sklearn model object, optional (default: None)</span>
-<span class="sd">        Used to fit conditional causal effects in nested regression. </span>
-<span class="sd">        If None, model is used.</span>
-<span class="sd">    data_transform : sklearn preprocessing object, optional (default: None)</span>
-<span class="sd">        Used to transform data prior to fitting. For example,</span>
-<span class="sd">        sklearn.preprocessing.StandardScaler for simple standardization. The</span>
-<span class="sd">        fitted parameters are stored. Note that the inverse_transform is then</span>
-<span class="sd">        applied to the predicted data.</span>
-<span class="sd">    mask_type : {None, &#39;y&#39;,&#39;x&#39;,&#39;z&#39;,&#39;xy&#39;,&#39;xz&#39;,&#39;yz&#39;,&#39;xyz&#39;}</span>
-<span class="sd">        Masking mode: Indicators for which variables in the dependence</span>
-<span class="sd">        measure I(X; Y | Z) the samples should be masked. If None, the mask</span>
-<span class="sd">        is not used. Explained in tutorial on masking and missing values.</span>
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">dataframe</span><span class="p">,</span>
-                 <span class="n">model</span><span class="p">,</span>
-                 <span class="n">conditional_model</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">data_transform</span><span class="o">=</span><span class="n">sklearn</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">StandardScaler</span><span class="p">(),</span>
-                 <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="c1"># Set the mask type and dataframe object</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span> <span class="o">=</span> <span class="n">mask_type</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">dataframe</span>
-        <span class="c1"># Get the number of nodes and length for this dataset</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">N</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">T</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">T</span>
-        <span class="c1"># Set the model to be used</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
-        <span class="k">if</span> <span class="n">conditional_model</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">conditional_model</span> <span class="o">=</span> <span class="n">model</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">conditional_model</span> <span class="o">=</span> <span class="n">conditional_model</span>
-        <span class="c1"># Set the data_transform object and verbosity</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span> <span class="o">=</span> <span class="n">data_transform</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
-        <span class="c1"># Initialize the object that will be set later</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="c1"># @profile    </span>
-<div class="viewcode-block" id="Models.get_general_fitted_model"><a class="viewcode-back" href="../../index.html#tigramite.models.Models.get_general_fitted_model">[docs]</a>    <span class="k">def</span> <span class="nf">get_general_fitted_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
-                <span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">conditions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;max_lag_or_tau_max&#39;</span><span class="p">,</span>
-                <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fit time series model.</span>
-
-<span class="sd">        For each variable in selected_variables, the sklearn model is fitted</span>
-<span class="sd">        with :math:`y` given by the target variable, and :math:`X` given by its</span>
-<span class="sd">        parents. The fitted model class is returned for later use.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : lists of tuples</span>
-<span class="sd">            List of variables for estimating model Y = f(X,Z)</span>
-<span class="sd">        conditions : list of tuples.</span>
-<span class="sd">            Conditions for estimating conditional causal effects.</span>
-<span class="sd">        tau_max : int, optional (default: None)</span>
-<span class="sd">            Maximum time lag. If None, the maximum lag in all_parents is used.</span>
-<span class="sd">        cut_off : {&#39;max_lag_or_tau_max&#39;, &#39;2xtau_max&#39;, &#39;max_lag&#39;}</span>
-<span class="sd">            How many samples to cutoff at the beginning. The default is</span>
-<span class="sd">            &#39;max_lag_or_tau_max&#39;, which uses the maximum of tau_max and the</span>
-<span class="sd">            conditions. This is useful to compare multiple models on the same</span>
-<span class="sd">            sample. Other options are &#39;2xtau_max&#39;, which guarantees that MCI</span>
-<span class="sd">            tests are all conducted on the same samples. Last, &#39;max_lag&#39; uses</span>
-<span class="sd">            as much samples as possible.</span>
-<span class="sd">        return_data : bool, optional (default: False)</span>
-<span class="sd">            Whether to save the data array.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        fit_results : dictionary of sklearn model objects for each variable</span>
-<span class="sd">            Returns the sklearn model after fitting. Also returns the data</span>
-<span class="sd">            transformation parameters.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">X</span> <span class="o">=</span> <span class="n">X</span> 
-        <span class="bp">self</span><span class="o">.</span><span class="n">Y</span> <span class="o">=</span> <span class="n">Y</span>
-
-        <span class="k">if</span> <span class="n">conditions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">conditions</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conditions</span> <span class="o">=</span> <span class="n">conditions</span>
-
-        <span class="k">if</span> <span class="n">Z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">Z</span> <span class="o">=</span> <span class="p">[</span><span class="n">z</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z</span> <span class="k">if</span> <span class="n">z</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">conditions</span><span class="p">]</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">cut_off</span> <span class="o">=</span> <span class="n">cut_off</span>
-
-        <span class="c1"># Find the maximal conditions lag</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">:</span>
-            <span class="n">this_lag</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">conditions</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">this_lag</span><span class="p">)</span>
-        <span class="c1"># Set the default tau max and check if it should be overwritten</span>
-        <span class="k">if</span> <span class="n">tau_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">max_lag</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_max</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;</span> <span class="n">max_lag</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;tau_max = </span><span class="si">%d</span><span class="s2">, but must be at least &quot;</span>
-                                 <span class="s2">&quot; max_lag = </span><span class="si">%d</span><span class="s2">&quot;</span>
-                                 <span class="s2">&quot;&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">))</span>
-
-        <span class="c1"># Initialize the fit results</span>
-        <span class="n">fit_results</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">:</span>
-
-            <span class="c1"># Construct array of shape (var, time)</span>
-            <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> \
-                <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="p">[</span><span class="n">y</span><span class="p">],</span>  
-                                               <span class="n">Z</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">conditions</span><span class="p">,</span>
-                                               <span class="n">extraZ</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Z</span><span class="p">,</span>
-                                               <span class="n">tau_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span>
-                                               <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                               <span class="n">cut_off</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cut_off</span><span class="p">,</span>
-                                               <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                               <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-            <span class="c1"># Transform the data if needed</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fitted_data_transform</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="c1"># Fit only X, Y, and S for later use in transforming input</span>
-                <span class="n">X_transform</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span><span class="p">)</span>
-                <span class="n">x_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="n">X_transform</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">x_indices</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">fitted_data_transform</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;X&#39;</span><span class="p">:</span> <span class="n">X_transform</span><span class="p">}</span>
-                <span class="n">Y_transform</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span><span class="p">)</span>
-                <span class="n">y_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="n">Y_transform</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">y_indices</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">fitted_data_transform</span><span class="p">[</span><span class="s1">&#39;Y&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Y_transform</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conditions</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">S_transform</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span><span class="p">)</span>
-                    <span class="n">s_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-                    <span class="n">S_transform</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">s_indices</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> 
-                    <span class="bp">self</span><span class="o">.</span><span class="n">fitted_data_transform</span><span class="p">[</span><span class="s1">&#39;S&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">S_transform</span>
-
-                <span class="c1"># Now transform whole array</span>
-                <span class="n">all_transform</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span><span class="p">)</span>
-                <span class="n">array</span> <span class="o">=</span> <span class="n">all_transform</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">array</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-
-            <span class="c1"># Fit the model </span>
-            <span class="c1"># Copy and fit the model</span>
-            <span class="n">a_model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
-
-            <span class="n">predictor_indices</span> <span class="o">=</span>  <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> \
-                               <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">3</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> \
-                               <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">predictor_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">predictor_indices</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span>
-            <span class="c1"># Target is only first entry of Y, ie [y]</span>
-            <span class="n">target_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="p">:]</span>
-
-            <span class="n">a_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">predictor_array</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">target_array</span><span class="p">)</span>
-            
-            <span class="c1"># Cache the results</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;observation_array&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">array</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;xyz&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">xyz</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">a_model</span>
-            <span class="c1"># Cache the data transform</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;fitted_data_transform&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fitted_data_transform</span>
-            <span class="c1"># # Cache the data if needed</span>
-            <span class="c1"># if return_data:</span>
-            <span class="c1">#     fit_results[y][&#39;data&#39;] = array</span>
-
-        <span class="c1"># Cache and return the fit results</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span> <span class="o">=</span> <span class="n">fit_results</span>
-        <span class="k">return</span> <span class="n">fit_results</span></div>
-
-    <span class="c1"># @profile</span>
-<div class="viewcode-block" id="Models.get_general_prediction"><a class="viewcode-back" href="../../index.html#tigramite.models.Models.get_general_prediction">[docs]</a>    <span class="k">def</span> <span class="nf">get_general_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                <span class="n">intervention_data</span><span class="p">,</span>
-                <span class="n">conditions_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">pred_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">transform_interventions_and_prediction</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">return_further_pred_results</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">aggregation_func</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span>
-                <span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Predict effect of intervention with fitted model.</span>
-
-<span class="sd">        Uses the model.predict() function of the sklearn model.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        intervention_data : numpy array</span>
-<span class="sd">            Numpy array of shape (time, len(X)) that contains the do(X) values.</span>
-<span class="sd">        conditions_data : data object, optional</span>
-<span class="sd">            Numpy array of shape (time, len(S)) that contains the S=s values.</span>
-<span class="sd">        pred_params : dict, optional</span>
-<span class="sd">            Optional parameters passed on to sklearn prediction function.</span>
-<span class="sd">        transform_interventions_and_prediction : bool (default: False)</span>
-<span class="sd">            Whether to perform the inverse data_transform on prediction results.</span>
-<span class="sd">        return_further_pred_results : bool, optional (default: False)</span>
-<span class="sd">            In case the predictor class returns more than just the expected value,</span>
-<span class="sd">            the entire results can be returned.</span>
-<span class="sd">        aggregation_func : callable</span>
-<span class="sd">            Callable applied to output of &#39;predict&#39;. Default is &#39;np.mean&#39;.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        Results from prediction.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">intervention_T</span><span class="p">,</span> <span class="n">lenX</span> <span class="o">=</span> <span class="n">intervention_data</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="n">intervention_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;intervention_data.shape[1] must be len(X).&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">conditions_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">conditions_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conditions</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;conditions_data.shape[1] must be len(S).&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">conditions_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">intervention_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;conditions_data.shape[0] must match intervention_data.shape[0].&quot;</span><span class="p">)</span>
-
-        <span class="n">lenS</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conditions</span><span class="p">)</span>
-        <span class="n">lenY</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">)</span>
-
-        <span class="c1"># predicted_array = np.zeros((intervention_T, lenY))</span>
-        <span class="n">pred_dict</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">iy</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Y</span><span class="p">):</span>
-            <span class="n">pred_dict</span><span class="p">[</span><span class="n">iy</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="c1"># Print message</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">## Predicting target </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-                <span class="k">if</span> <span class="n">pred_params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">pred_params</span><span class="p">):</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">pred_params</span><span class="p">[</span><span class="n">key</span><span class="p">]))</span>
-            <span class="c1"># Default value for pred_params</span>
-            <span class="k">if</span> <span class="n">pred_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">pred_params</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="c1"># Check this is a valid target</span>
-            <span class="k">if</span> <span class="n">y</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;y = </span><span class="si">%s</span><span class="s2"> not yet fitted&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-
-            <span class="c1"># Transform the data if needed</span>
-            <span class="n">fitted_data_transform</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;fitted_data_transform&#39;</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">transform_interventions_and_prediction</span> <span class="ow">and</span> <span class="n">fitted_data_transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">intervention_data</span> <span class="o">=</span> <span class="n">fitted_data_transform</span><span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">intervention_data</span><span class="p">)</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">conditions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">conditions_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">conditions_data</span> <span class="o">=</span> <span class="n">fitted_data_transform</span><span class="p">[</span><span class="s1">&#39;S&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">conditions_data</span><span class="p">)</span>
-
-            <span class="c1"># Extract observational Z from stored array</span>
-            <span class="n">z_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;xyz&#39;</span><span class="p">]</span><span class="o">==</span><span class="mi">3</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">z_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;observation_array&#39;</span><span class="p">][</span><span class="n">z_indices</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span>  
-            <span class="n">Tobs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;observation_array&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> 
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">conditions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">conditions_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">s_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;xyz&#39;</span><span class="p">]</span><span class="o">==</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="n">s_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;observation_array&#39;</span><span class="p">][</span><span class="n">s_indices</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span>  
-
-            <span class="c1"># Now iterate through interventions (and potentially S)</span>
-            <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">dox_vals</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">intervention_data</span><span class="p">):</span>
-                <span class="c1"># Construct XZS-array</span>
-                <span class="n">intervention_array</span> <span class="o">=</span> <span class="n">dox_vals</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">lenX</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">Tobs</span><span class="p">,</span> <span class="n">lenX</span><span class="p">))</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">conditions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">conditions_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">conditions_array</span> <span class="o">=</span> <span class="n">conditions_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">lenS</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">Tobs</span><span class="p">,</span> <span class="n">lenS</span><span class="p">))</span>  
-                    <span class="n">predictor_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">intervention_array</span><span class="p">,</span> <span class="n">z_array</span><span class="p">,</span> <span class="n">conditions_array</span><span class="p">))</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">predictor_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">intervention_array</span><span class="p">,</span> <span class="n">z_array</span><span class="p">))</span>
-
-                <span class="n">predicted_vals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-                <span class="n">X</span><span class="o">=</span><span class="n">predictor_array</span><span class="p">,</span> <span class="o">**</span><span class="n">pred_params</span><span class="p">)</span>
-
-                <span class="c1"># print(predicted_vals)</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">conditions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">conditions_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- 
-                    <span class="n">a_conditional_model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conditional_model</span><span class="p">)</span>
-                    
-                    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">predicted_vals</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
-                        <span class="n">predicted_vals_here</span> <span class="o">=</span> <span class="n">predicted_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">predicted_vals_here</span> <span class="o">=</span> <span class="n">predicted_vals</span>
-                    
-                    <span class="n">a_conditional_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">s_array</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">predicted_vals_here</span><span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="s1">&#39;conditional_model&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">a_conditional_model</span>
-
-                    <span class="n">predicted_vals</span> <span class="o">=</span> <span class="n">a_conditional_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-                        <span class="n">X</span><span class="o">=</span><span class="n">conditions_array</span><span class="p">,</span> <span class="o">**</span><span class="n">pred_params</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">transform_interventions_and_prediction</span> <span class="ow">and</span> <span class="n">fitted_data_transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">predicted_vals</span> <span class="o">=</span> <span class="n">fitted_data_transform</span><span class="p">[</span><span class="s1">&#39;Y&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">predicted_vals</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-                <span class="n">pred_dict</span><span class="p">[</span><span class="n">iy</span><span class="p">][</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">predicted_vals</span>
-
-                <span class="c1"># Apply aggregation function</span>
-                <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">predicted_vals</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
-                    <span class="n">aggregated_pred</span> <span class="o">=</span> <span class="n">aggregation_func</span><span class="p">(</span><span class="n">predicted_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">aggregated_pred</span> <span class="o">=</span> <span class="n">aggregation_func</span><span class="p">(</span><span class="n">predicted_vals</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">iy</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">index</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">predicted_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">intervention_T</span><span class="p">,</span> <span class="n">lenY</span><span class="p">,)</span> <span class="o">+</span> <span class="n">aggregated_pred</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> 
-                                            <span class="n">dtype</span><span class="o">=</span><span class="n">aggregated_pred</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-
-                <span class="n">predicted_array</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="n">iy</span><span class="p">]</span> <span class="o">=</span> <span class="n">aggregated_pred</span>
-
-                <span class="c1"># if fitted_data_transform is not None:</span>
-                <span class="c1">#     rescaled = fitted_data_transform[&#39;Y&#39;].inverse_transform(X=predicted_array[index, iy].reshape(-1, 1))</span>
-                <span class="c1">#     predicted_array[index, iy] = rescaled.squeeze()</span>
-
-        <span class="k">if</span> <span class="n">return_further_pred_results</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">predicted_array</span><span class="p">,</span> <span class="n">pred_dict</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">predicted_array</span></div>
-
-
-<div class="viewcode-block" id="Models.fit_full_model"><a class="viewcode-back" href="../../index.html#tigramite.models.Models.fit_full_model">[docs]</a>    <span class="k">def</span> <span class="nf">fit_full_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">all_parents</span><span class="p">,</span>
-                <span class="n">selected_variables</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;max_lag_or_tau_max&#39;</span><span class="p">,</span>
-                <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fit time series model.</span>
-
-<span class="sd">        For each variable in selected_variables, the sklearn model is fitted</span>
-<span class="sd">        with :math:`y` given by the target variable, and :math:`X` given by its</span>
-<span class="sd">        parents. The fitted model class is returned for later use.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        all_parents : dictionary</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, 0), ...], 1:[], ...} containing</span>
-<span class="sd">            the parents estimated with PCMCI.</span>
-<span class="sd">        selected_variables : list of integers, optional (default: range(N))</span>
-<span class="sd">            Specify to estimate parents only for selected variables. If None is</span>
-<span class="sd">            passed, parents are estimated for all variables.</span>
-<span class="sd">        tau_max : int, optional (default: None)</span>
-<span class="sd">            Maximum time lag. If None, the maximum lag in all_parents is used.</span>
-<span class="sd">        cut_off : {&#39;max_lag_or_tau_max&#39;, &#39;2xtau_max&#39;, &#39;max_lag&#39;}</span>
-<span class="sd">            How many samples to cutoff at the beginning. The default is</span>
-<span class="sd">            &#39;max_lag_or_tau_max&#39;, which uses the maximum of tau_max and the</span>
-<span class="sd">            conditions. This is useful to compare multiple models on the same</span>
-<span class="sd">            sample. Other options are &#39;2xtau_max&#39;, which guarantees that MCI</span>
-<span class="sd">            tests are all conducted on the same samples. Last, &#39;max_lag&#39; uses</span>
-<span class="sd">            as much samples as possible.</span>
-<span class="sd">        return_data : bool, optional (default: False)</span>
-<span class="sd">            Whether to save the data array.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        fit_results : dictionary of sklearn model objects for each variable</span>
-<span class="sd">            Returns the sklearn model after fitting. Also returns the data</span>
-<span class="sd">            transformation parameters.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Initialize the fit by setting the instance&#39;s all_parents attribute</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span> <span class="o">=</span> <span class="n">all_parents</span>
-        <span class="c1"># Set the default selected variables to all variables and check if this</span>
-        <span class="c1"># should be overwritten</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">selected_variables</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span> <span class="o">=</span> <span class="n">selected_variables</span>
-        <span class="c1"># Find the maximal parents lag</span>
-        <span class="n">max_parents_lag</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">all_parents</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="n">this_parent_lag</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">all_parents</span><span class="p">[</span><span class="n">j</span><span class="p">])[:,</span> <span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-                <span class="n">max_parents_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_parents_lag</span><span class="p">,</span> <span class="n">this_parent_lag</span><span class="p">)</span>
-        <span class="c1"># Set the default tau_max and check if it should be overwritten</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">max_parents_lag</span>
-        <span class="k">if</span> <span class="n">tau_max</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_max</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">&lt;</span> <span class="n">max_parents_lag</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;tau_max = </span><span class="si">%d</span><span class="s2">, but must be at least &quot;</span>
-                                 <span class="s2">&quot; max_parents_lag = </span><span class="si">%d</span><span class="s2">&quot;</span>
-                                 <span class="s2">&quot;&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">max_parents_lag</span><span class="p">))</span>
-        <span class="c1"># Initialize the fit results</span>
-        <span class="n">fit_results</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span><span class="p">:</span>
-            <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>  <span class="c1"># dummy</span>
-            <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
-            <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> \
-                <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span>
-                                               <span class="n">tau_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span>
-                                               <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                               <span class="n">cut_off</span><span class="o">=</span><span class="n">cut_off</span><span class="p">,</span>
-                                               <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                               <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-            <span class="c1"># Get the dimensions out of the constructed array</span>
-            <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-            <span class="n">dim_z</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">-</span> <span class="mi">2</span>
-            <span class="c1"># Transform the data if needed</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">array</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-            <span class="c1"># Cache the results</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="c1"># Cache the data transform</span>
-            <span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;data_transform&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">return_data</span><span class="p">:</span>
-                <span class="c1"># Cache the data if needed</span>
-                <span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;data&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">array</span>
-                <span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;used_indices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">use_indices_dataset_dict</span>
-            <span class="c1"># Fit the model if there are any parents for this variable to fit</span>
-            <span class="k">if</span> <span class="n">dim_z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="c1"># Copy and fit the model</span>
-                <span class="n">a_model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
-                <span class="n">a_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">array</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">array</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-                <span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">a_model</span>
-
-        <span class="c1"># Cache and return the fit results</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span> <span class="o">=</span> <span class="n">fit_results</span>
-        <span class="k">return</span> <span class="n">fit_results</span></div>
-
-<div class="viewcode-block" id="Models.get_coefs"><a class="viewcode-back" href="../../index.html#tigramite.models.Models.get_coefs">[docs]</a>    <span class="k">def</span> <span class="nf">get_coefs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns dictionary of coefficients for linear models.</span>
-
-<span class="sd">        Only for models from sklearn.linear_model</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        coeffs : dictionary</span>
-<span class="sd">            Dictionary of dictionaries for each variable with keys given by the</span>
-<span class="sd">            parents and the regression coefficients as values.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">coeffs</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span><span class="p">:</span>
-            <span class="n">coeffs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">ipar</span><span class="p">,</span> <span class="n">par</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span><span class="p">[</span><span class="n">j</span><span class="p">]):</span>
-                <span class="n">coeffs</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">par</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="n">ipar</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">coeffs</span></div>
-
-<div class="viewcode-block" id="Models.get_val_matrix"><a class="viewcode-back" href="../../index.html#tigramite.models.Models.get_val_matrix">[docs]</a>    <span class="k">def</span> <span class="nf">get_val_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the coefficient array for different lags for linear model.</span>
-
-<span class="sd">        Requires fit_model() before. An entry val_matrix[i,j,tau] gives the</span>
-<span class="sd">        coefficient of the link from i to j at lag tau, including tau=0.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val_matrix : array-like, shape (N, N, tau_max + 1)</span>
-<span class="sd">            Array of coefficients for each time lag, including lag-zero.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">coeffs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_coefs</span><span class="p">()</span>
-        <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">coeffs</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">par</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">coeffs</span><span class="p">[</span><span class="n">j</span><span class="p">]):</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">par</span>
-                <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">coeffs</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">par</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">val_matrix</span></div></div>
-
-<div class="viewcode-block" id="LinearMediation"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation">[docs]</a><span class="k">class</span> <span class="nc">LinearMediation</span><span class="p">(</span><span class="n">Models</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Linear mediation analysis for time series models.</span>
-
-<span class="sd">    Fits linear model to parents and provides functions to return measures such</span>
-<span class="sd">    as causal effect, mediated causal effect, average causal effect, etc. as</span>
-<span class="sd">    described in [4]_. Also allows for contemporaneous links.</span>
-
-<span class="sd">    For general linear and nonlinear causal effect analysis including latent</span>
-<span class="sd">    variables and further functionality use the CausalEffects class.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    This class implements the following causal mediation measures introduced in</span>
-<span class="sd">    [4]_:</span>
-
-<span class="sd">      * causal effect (CE)</span>
-<span class="sd">      * mediated causal effect (MCE)</span>
-<span class="sd">      * average causal effect (ACE)</span>
-<span class="sd">      * average causal susceptibility (ACS)</span>
-<span class="sd">      * average mediated causal effect (AMCE)</span>
-
-<span class="sd">    Consider a simple model of a causal chain as given in the Example with</span>
-
-<span class="sd">    .. math:: X_t &amp;= \eta^X_t \\</span>
-<span class="sd">              Y_t &amp;= 0.5 X_{t-1} +  \eta^Y_t \\</span>
-<span class="sd">              Z_t &amp;= 0.5 Y_{t-1} +  \eta^Z_t</span>
-
-<span class="sd">    Here the link coefficient of :math:`X_{t-2} \to Z_t` is zero while the</span>
-<span class="sd">    causal effect is 0.25. MCE through :math:`Y` is 0.25 implying that *all*</span>
-<span class="sd">    of the the CE is explained by :math:`Y`. ACE from :math:`X` is 0.37 since it</span>
-<span class="sd">    has CE 0.5 on :math:`Y` and 0.25 on :math:`Z`.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; links_coeffs = {0: [], 1: [((0, -1), 0.5)], 2: [((1, -1), 0.5)]}</span>
-<span class="sd">    &gt;&gt;&gt; data, true_parents = toys.var_process(links_coeffs, T=1000, seed=42)</span>
-<span class="sd">    &gt;&gt;&gt; dataframe = pp.DataFrame(data)</span>
-<span class="sd">    &gt;&gt;&gt; med = LinearMediation(dataframe=dataframe)</span>
-<span class="sd">    &gt;&gt;&gt; med.fit_model(all_parents=true_parents, tau_max=3)</span>
-<span class="sd">    &gt;&gt;&gt; print &quot;Link coefficient (0, -2) --&gt; 2: &quot;, med.get_coeff(</span>
-<span class="sd">    i=0, tau=-2, j=2)</span>
-<span class="sd">    &gt;&gt;&gt; print &quot;Causal effect (0, -2) --&gt; 2: &quot;, med.get_ce(i=0, tau=-2, j=2)</span>
-<span class="sd">    &gt;&gt;&gt; print &quot;Mediated Causal effect (0, -2) --&gt; 2 through 1: &quot;, med.get_mce(</span>
-<span class="sd">    i=0, tau=-2, j=2, k=1)</span>
-<span class="sd">    &gt;&gt;&gt; print &quot;Average Causal Effect: &quot;, med.get_all_ace()</span>
-<span class="sd">    &gt;&gt;&gt; print &quot;Average Causal Susceptibility: &quot;, med.get_all_acs()</span>
-<span class="sd">    &gt;&gt;&gt; print &quot;Average Mediated Causal Effect: &quot;, med.get_all_amce()</span>
-<span class="sd">    Link coefficient (0, -2) --&gt; 2:  0.0</span>
-<span class="sd">    Causal effect (0, -2) --&gt; 2:  0.250648072987</span>
-<span class="sd">    Mediated Causal effect (0, -2) --&gt; 2 through 1:  0.250648072987</span>
-<span class="sd">    Average Causal Effect:  [ 0.36897445  0.25718002  0.        ]</span>
-<span class="sd">    Average Causal Susceptibility:  [ 0.          0.24365041  0.38250406]</span>
-<span class="sd">    Average Mediated Causal Effect:  [ 0.          0.12532404  0.        ]</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [4]  J. Runge et al. (2015): Identifying causal gateways and mediators in</span>
-<span class="sd">            complex spatio-temporal systems.</span>
-<span class="sd">            Nature Communications, 6, 8502. http://doi.org/10.1038/ncomms9502</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataframe : data object</span>
-<span class="sd">        Tigramite dataframe object. It must have the attributes dataframe.values</span>
-<span class="sd">        yielding a numpy array of shape (observations T, variables N) and</span>
-<span class="sd">        optionally a mask of the same shape and a missing values flag.</span>
-<span class="sd">    model_params : dictionary, optional (default: None)</span>
-<span class="sd">        Optional parameters passed on to sklearn model</span>
-<span class="sd">    data_transform : sklearn preprocessing object, optional (default: StandardScaler)</span>
-<span class="sd">        Used to transform data prior to fitting. For example,</span>
-<span class="sd">        sklearn.preprocessing.StandardScaler for simple standardization. The</span>
-<span class="sd">        fitted parameters are stored.</span>
-<span class="sd">    mask_type : {None, &#39;y&#39;,&#39;x&#39;,&#39;z&#39;,&#39;xy&#39;,&#39;xz&#39;,&#39;yz&#39;,&#39;xyz&#39;}</span>
-<span class="sd">        Masking mode: Indicators for which variables in the dependence</span>
-<span class="sd">        measure I(X; Y | Z) the samples should be masked. If None, the mask</span>
-<span class="sd">        is not used. Explained in tutorial on masking and missing values.</span>
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">dataframe</span><span class="p">,</span>
-                 <span class="n">model_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">data_transform</span><span class="o">=</span><span class="n">sklearn</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">StandardScaler</span><span class="p">(),</span>
-                 <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="c1"># Initialize the member variables to None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">phi</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">dataframe</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span> <span class="o">=</span> <span class="n">mask_type</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span> <span class="o">=</span> <span class="n">data_transform</span>
-        <span class="k">if</span> <span class="n">model_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">model_params</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">model_params</span> <span class="o">=</span> <span class="n">model_params</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_available</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="c1"># Build the model using the parameters</span>
-        <span class="k">if</span> <span class="n">model_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">model_params</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">this_model</span> <span class="o">=</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">(</span><span class="o">**</span><span class="n">model_params</span><span class="p">)</span>
-        <span class="n">Models</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                        <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span>
-                        <span class="n">model</span><span class="o">=</span><span class="n">this_model</span><span class="p">,</span>
-                        <span class="n">data_transform</span><span class="o">=</span><span class="n">data_transform</span><span class="p">,</span>
-                        <span class="n">mask_type</span><span class="o">=</span><span class="n">mask_type</span><span class="p">,</span>
-                        <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
-
-<div class="viewcode-block" id="LinearMediation.fit_model"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.fit_model">[docs]</a>    <span class="k">def</span> <span class="nf">fit_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">all_parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fit linear time series model.</span>
-
-<span class="sd">        Fits a sklearn.linear_model.LinearRegression model to the parents of</span>
-<span class="sd">        each variable and computes the coefficient matrices :math:`\Phi` and</span>
-<span class="sd">        :math:`\Psi` as described in [4]_. Does accept contemporaneous links.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        all_parents : dictionary</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, 0), ...], 1:[], ...} containing</span>
-<span class="sd">            the parents estimated with PCMCI.</span>
-<span class="sd">        tau_max : int, optional (default: None)</span>
-<span class="sd">            Maximum time lag. If None, the maximum lag in all_parents is used.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Fit the model using the base class</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_full_model</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">all_parents</span><span class="p">,</span>
-                                        <span class="n">selected_variables</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                        <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
-        <span class="c1"># Cache the results in the member variables</span>
-        <span class="n">coeffs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_coefs</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">phi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_phi</span><span class="p">(</span><span class="n">coeffs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_psi</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_psi_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span> <span class="o">=</span> <span class="n">all_parents</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_max</span></div>
-
-<div class="viewcode-block" id="LinearMediation.fit_model_bootstrap"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.fit_model_bootstrap">[docs]</a>    <span class="k">def</span> <span class="nf">fit_model_bootstrap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
-            <span class="n">boot_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-            <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-            <span class="n">boot_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fits boostrap-versions of Phi, Psi, etc.</span>
-
-<span class="sd">        Random draws are generated</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        boot_blocklength : int, or in {&#39;cube_root&#39;, &#39;from_autocorrelation&#39;}</span>
-<span class="sd">            Block length for block-bootstrap, which only applies to</span>
-<span class="sd">            generate_noise_from=&#39;residuals&#39;. If &#39;from_autocorrelation&#39;, the block</span>
-<span class="sd">            length is determined from the decay of the autocovariance and</span>
-<span class="sd">            if &#39;cube_root&#39; it is the cube root of the time series length.</span>
-<span class="sd">        seed : int, optional(default = None)</span>
-<span class="sd">            Seed for RandomState (default_rng)</span>
-<span class="sd">        boot_samples : int</span>
-<span class="sd">            Number of bootstrap samples.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">phi_boots</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">boot_samples</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">psi_boots</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">boot_samples</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k_boots</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">boot_samples</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Generating bootstrap samples of Phi, Psi, etc &quot;</span>  <span class="o">+</span>
-                  <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span>
-                  <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">boot_samples = </span><span class="si">%s</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">boot_samples</span> <span class="o">+</span>
-                  <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">boot_blocklength = </span><span class="si">%s</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">boot_blocklength</span>
-                  <span class="p">)</span>
-
-
-        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">boot_samples</span><span class="p">):</span>
-            <span class="c1"># # Replace dataframe in method args by bootstrapped dataframe</span>
-            <span class="c1"># method_args_bootstrap[&#39;dataframe&#39;].bootstrap = boot_draw</span>
-            <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">+</span><span class="n">b</span><span class="p">)</span>
-
-            <span class="n">dataframe_here</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="p">)</span>
-
-            <span class="n">dataframe_here</span><span class="o">.</span><span class="n">bootstrap</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;boot_blocklength&#39;</span><span class="p">:</span><span class="n">boot_blocklength</span><span class="p">,</span>
-                                        <span class="s1">&#39;random_state&#39;</span><span class="p">:</span><span class="n">random_state</span><span class="p">}</span>
-
-            <span class="n">model</span> <span class="o">=</span> <span class="n">Models</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe_here</span><span class="p">,</span>
-                           <span class="n">model</span><span class="o">=</span><span class="n">sklearn</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">model_params</span><span class="p">),</span>
-                           <span class="n">data_transform</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span><span class="p">,</span>
-                           <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                           <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-            <span class="n">model</span><span class="o">.</span><span class="n">fit_full_model</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span><span class="p">,</span>
-                           <span class="n">tau_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
-            <span class="c1"># Cache the results in the member variables</span>
-            <span class="n">coeffs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_coefs</span><span class="p">()</span>
-            <span class="n">phi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_phi</span><span class="p">(</span><span class="n">coeffs</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">phi_boots</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="n">phi</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">psi_boots</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_psi</span><span class="p">(</span><span class="n">phi</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k_boots</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_psi_k</span><span class="p">(</span><span class="n">phi</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_available</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_bootstrap_of"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_bootstrap_of">[docs]</a>    <span class="k">def</span> <span class="nf">get_bootstrap_of</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">function</span><span class="p">,</span> <span class="n">function_args</span><span class="p">,</span> <span class="n">conf_lev</span><span class="o">=</span><span class="mf">0.9</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Applies bootstrap-versions of Phi, Psi, etc. to any function in </span>
-<span class="sd">        this class.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        function : string</span>
-<span class="sd">            Valid function from LinearMediation class</span>
-<span class="sd">        function_args : dict</span>
-<span class="sd">            Optional function arguments.</span>
-<span class="sd">        conf_lev : float</span>
-<span class="sd">            Confidence interval.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        Upper/Lower confidence interval of function.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">valid_functions</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="s1">&#39;get_coeff&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_ce&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_ce_max&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_joint_ce&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_joint_ce_matrix&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_mce&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_conditional_mce&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_joint_mce&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_ace&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_all_ace&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_acs&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_all_acs&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_amce&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_all_amce&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;get_val_matrix&#39;</span><span class="p">,</span>
-            <span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">function</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">valid_functions</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;function must be in </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span><span class="n">valid_functions</span><span class="p">)</span>
-
-        <span class="n">realizations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">phi_boots</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">original_phi</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">)</span>
-        <span class="n">original_psi</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">)</span>
-        <span class="n">original_all_psi_k</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">realizations</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">phi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">phi_boots</span><span class="p">[</span><span class="n">r</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">psi_boots</span><span class="p">[</span><span class="n">r</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k_boots</span><span class="p">[</span><span class="n">r</span><span class="p">]</span>
-
-            <span class="n">boot_effect</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">function</span><span class="p">)(</span><span class="o">**</span><span class="n">function_args</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">r</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">bootstrap_result</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">realizations</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">boot_effect</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-
-            <span class="n">bootstrap_result</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">=</span> <span class="n">boot_effect</span>
-
-        <span class="c1"># Confidence intervals for val_matrix; interval is two-sided</span>
-        <span class="n">c_int</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">conf_lev</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">)</span>
-        <span class="n">confidence_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span>
-                <span class="n">bootstrap_result</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">q</span> <span class="o">=</span> <span class="p">[</span><span class="mi">100</span><span class="o">*</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">c_int</span><span class="p">),</span> <span class="mi">100</span><span class="o">*</span><span class="n">c_int</span><span class="p">])</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">phi</span> <span class="o">=</span> <span class="n">original_phi</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="n">original_psi</span> 
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span> <span class="o">=</span> <span class="n">original_all_psi_k</span> 
-
-        <span class="k">return</span> <span class="n">confidence_interval</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_check_sanity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Checks validity of some parameters.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;X must be of form [(i, -tau)] and Y = [(j, 0)], &quot;</span>
-                             <span class="s2">&quot;but are X = </span><span class="si">%s</span><span class="s2">, Y=</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">))</span>
-
-        <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;X must be of form [(i, -tau)] with&quot;</span>
-                             <span class="s2">&quot; tau &lt;= tau_max&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">k</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="n">k</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">k</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;k must be in [0, N)&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_phi</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coeffs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the linear coefficient matrices for different lags.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        coeffs : dictionary</span>
-<span class="sd">            Dictionary of coefficients for each parent.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        phi : array-like, shape (tau_max + 1, N, N)</span>
-<span class="sd">            Matrices of coefficients for each time lag.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">phi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))</span>
-        <span class="c1"># phi[0] = np.identity(self.N)</span>
-
-        <span class="c1"># Also includes contemporaneous lags</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">coeffs</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">par</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">coeffs</span><span class="p">[</span><span class="n">j</span><span class="p">]):</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">par</span>
-                <span class="n">phi</span><span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">coeffs</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">par</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">phi</span>
-
-    <span class="k">def</span> <span class="nf">_get_psi</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">phi</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the linear causal effect matrices for different lags incl</span>
-<span class="sd">        lag zero.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        phi : array-like</span>
-<span class="sd">            Coefficient matrices at different lags.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        psi : array-like, shape (tau_max + 1, N, N)</span>
-<span class="sd">            Matrices of causal effects for each time lag incl contemporaneous links.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">psi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))</span>
-
-        <span class="n">psi</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="o">-</span> <span class="n">phi</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-        <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="c1"># psi[tau] = np.matmul(psi[0], np.matmul(phi[tau], psi[0]))</span>
-            <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="n">psi</span><span class="p">[</span><span class="n">tau</span><span class="p">]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">psi</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">phi</span><span class="p">[</span><span class="n">s</span><span class="p">],</span> <span class="n">psi</span><span class="p">[</span><span class="n">tau</span> <span class="o">-</span> <span class="n">s</span><span class="p">])</span> <span class="p">)</span> 
-
-        <span class="c1"># Lagged-only effects:</span>
-        <span class="c1"># psi = np.zeros((self.tau_max + 1, self.N, self.N))</span>
-
-        <span class="c1"># psi[0] = np.identity(self.N)</span>
-        <span class="c1"># for n in range(1, self.tau_max + 1):</span>
-        <span class="c1">#     psi[n] = np.zeros((self.N, self.N))</span>
-        <span class="c1">#     for s in range(1, n + 1):</span>
-        <span class="c1">#         psi[n] += np.dot(phi[s], psi[n - s])</span>
-
-        <span class="k">return</span> <span class="n">psi</span>
-
-    <span class="k">def</span> <span class="nf">_get_psi_k</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">phi</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the linear causal effect matrices excluding variable k.</span>
-
-<span class="sd">        Essentially, this blocks all path through parents of variable k</span>
-<span class="sd">        at any lag.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        phi : array-like</span>
-<span class="sd">            Coefficient matrices at different lags.</span>
-<span class="sd">        k : int or list of ints</span>
-<span class="sd">            Variable indices to exclude causal effects through.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        psi_k : array-like, shape (tau_max + 1, N, N)</span>
-<span class="sd">            Matrices of causal effects excluding k.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">psi_k</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))</span>
-        
-        <span class="n">phi_k</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">phi</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="n">phi_k</span><span class="p">[:,</span> <span class="n">k</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">0.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">k_here</span> <span class="ow">in</span> <span class="n">k</span><span class="p">:</span>
-                <span class="n">phi_k</span><span class="p">[:,</span> <span class="n">k_here</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">0.</span>
-
-
-        <span class="n">psi_k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="o">-</span> <span class="n">phi_k</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="c1"># psi_k[tau] = np.matmul(psi_k[0], np.matmul(phi_k[tau], psi_k[0]))</span>
-            <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="n">psi_k</span><span class="p">[</span><span class="n">tau</span><span class="p">]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">psi_k</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">phi_k</span><span class="p">[</span><span class="n">s</span><span class="p">],</span> <span class="n">psi_k</span><span class="p">[</span><span class="n">tau</span> <span class="o">-</span> <span class="n">s</span><span class="p">]))</span> 
-
-
-        <span class="c1"># psi_k[0] = np.identity(self.N)</span>
-        <span class="c1"># phi_k = np.copy(phi)</span>
-        <span class="c1"># phi_k[:, k, :] = 0.</span>
-        <span class="c1"># for n in range(1, self.tau_max + 1):</span>
-        <span class="c1">#     psi_k[n] = np.zeros((self.N, self.N))</span>
-        <span class="c1">#     for s in range(1, n + 1):</span>
-        <span class="c1">#         psi_k[n] += np.dot(phi_k[s], psi_k[n - s])</span>
-
-        <span class="k">return</span> <span class="n">psi_k</span>
-
-    <span class="k">def</span> <span class="nf">_get_all_psi_k</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">phi</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the linear causal effect matrices excluding variables.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        phi : array-like</span>
-<span class="sd">            Coefficient matrices at different lags.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        all_psi_k : array-like, shape (N, tau_max + 1, N, N)</span>
-<span class="sd">            Matrices of causal effects where for each row another variable is</span>
-<span class="sd">            excluded.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">all_psi_k</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">all_psi_k</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_psi_k</span><span class="p">(</span><span class="n">phi</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">all_psi_k</span>
-
-<div class="viewcode-block" id="LinearMediation.get_coeff"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_coeff">[docs]</a>    <span class="k">def</span> <span class="nf">get_coeff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns link coefficient.</span>
-
-<span class="sd">        This is the direct causal effect for a particular link (i, -tau) --&gt; j.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        tau : int</span>
-<span class="sd">            Lag of cause variable (incl lag zero).</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of effect variable.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        coeff : float</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_ce"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_ce">[docs]</a>    <span class="k">def</span> <span class="nf">get_ce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the causal effect.</span>
-
-<span class="sd">        This is the causal effect for  (i, -tau) -- --&gt; j.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        tau : int</span>
-<span class="sd">            Lag of cause variable (incl lag zero).</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of effect variable.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        ce : float</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_ce_max"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_ce_max">[docs]</a>    <span class="k">def</span> <span class="nf">get_ce_max</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the causal effect.</span>
-
-<span class="sd">        This is the maximum absolute causal effect for  i --&gt; j across all</span>
-<span class="sd">        lags (incl lag zero).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of effect variable.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        ce : float</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">argmax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">][</span><span class="n">argmax</span><span class="p">]</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_joint_ce"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_joint_ce">[docs]</a>    <span class="k">def</span> <span class="nf">get_joint_ce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the joint causal effect.</span>
-
-<span class="sd">        This is the causal effect from all lags [t, ..., t-tau_max]</span>
-<span class="sd">        of i on j at time t. Note that the joint effect does not</span>
-<span class="sd">        count links passing through parents of i itself.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of effect variable.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        joint_ce : array of shape (tau_max + 1)</span>
-<span class="sd">            Causal effect from each lag [t, ..., t-tau_max] of i on j.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">joint_ce</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">joint_ce</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_joint_ce_matrix"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_joint_ce_matrix">[docs]</a>    <span class="k">def</span> <span class="nf">get_joint_ce_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the joint causal effect matrix of i on j.</span>
-
-<span class="sd">        This is the causal effect from all lags [t, ..., t-tau_max]</span>
-<span class="sd">        of i on j at times [t, ..., t-tau_max]. Note that the joint effect does not</span>
-<span class="sd">        count links passing through parents of i itself.</span>
-
-<span class="sd">        An entry (taui, tauj) stands for the effect of i at t-taui on j at t-tauj.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of effect variable.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        joint_ce_matrix : 2d array of shape (tau_max + 1, tau_max + 1)</span>
-<span class="sd">            Causal effect matrix from each lag of i on each lag of j.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">joint_ce_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">tauj</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="n">joint_ce_matrix</span><span class="p">[</span><span class="n">tauj</span><span class="p">:,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">:,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">joint_ce_matrix</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_mce"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_mce">[docs]</a>    <span class="k">def</span> <span class="nf">get_mce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the mediated causal effect.</span>
-
-<span class="sd">        This is the causal effect for  i --&gt; j minus the causal effect not going</span>
-<span class="sd">        through k.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        tau : int</span>
-<span class="sd">            Lag of cause variable.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of effect variable.</span>
-<span class="sd">        k : int or list of ints</span>
-<span class="sd">            Indices of mediator variables.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        mce : float</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="n">effect_without_k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">effect_without_k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_psi_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">)[</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-
-        <span class="n">mce</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">effect_without_k</span>
-        <span class="k">return</span> <span class="n">mce</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_conditional_mce"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_conditional_mce">[docs]</a>    <span class="k">def</span> <span class="nf">get_conditional_mce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">notk</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the conditional mediated causal effect.</span>
-
-<span class="sd">        This is the causal effect for  i --&gt; j for all paths going through k, but not through notk.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        tau : int</span>
-<span class="sd">            Lag of cause variable.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of effect variable.</span>
-<span class="sd">        k : int or list of ints</span>
-<span class="sd">            Indices of mediator variables.</span>
-<span class="sd">        notk : int or list of ints</span>
-<span class="sd">            Indices of mediator variables to exclude.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        mce : float</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="n">k</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="n">k</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">k</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">notk</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="n">notk</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="n">notk</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">notk</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">notk</span><span class="p">)</span>
-
-        <span class="n">bothk</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">k</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">notk</span><span class="p">))</span>
-        <span class="n">notk</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">notk</span><span class="p">)</span>
-  
-        <span class="n">effect_without_bothk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_psi_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">bothk</span><span class="p">)[</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-        <span class="n">effect_without_notk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_psi_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">notk</span><span class="p">)[</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-
-        <span class="c1"># mce = self.psi[abs(tau), j, i] - effect_without_k</span>
-        <span class="n">mce</span> <span class="o">=</span> <span class="n">effect_without_notk</span> <span class="o">-</span> <span class="n">effect_without_bothk</span>
-
-        <span class="k">return</span> <span class="n">mce</span></div>
-
-
-<div class="viewcode-block" id="LinearMediation.get_joint_mce"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_joint_mce">[docs]</a>    <span class="k">def</span> <span class="nf">get_joint_mce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the joint causal effect mediated through k.</span>
-
-<span class="sd">        This is the mediated causal effect from all lags [t, ..., t-tau_max]</span>
-<span class="sd">        of i on j at time t for paths through k. Note that the joint effect</span>
-<span class="sd">        does not count links passing through parents of i itself.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of effect variable.</span>
-<span class="sd">        k : int or list of ints</span>
-<span class="sd">            Indices of mediator variables.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        joint_mce : array of shape (tau_max + 1)</span>
-<span class="sd">            Mediated causal effect from each lag [t, ..., t-tau_max] of i on j through k.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="n">k_here</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span><span class="p">]</span>
-
-        <span class="n">effect_without_k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_psi_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">k_here</span><span class="p">)</span>
-
-        <span class="n">joint_mce</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">effect_without_k</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">joint_mce</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_ace"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_ace">[docs]</a>    <span class="k">def</span> <span class="nf">get_ace</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag_mode</span><span class="o">=</span><span class="s1">&#39;absmax&#39;</span><span class="p">,</span> <span class="n">exclude_i</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the average causal effect.</span>
-
-<span class="sd">        This is the average causal effect (ACE) emanating from variable i to any</span>
-<span class="sd">        other variable. With lag_mode=&#39;absmax&#39; this is based on the lag of</span>
-<span class="sd">        maximum CE for each pair.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        lag_mode : {&#39;absmax&#39;, &#39;all_lags&#39;}</span>
-<span class="sd">            Lag mode. Either average across all lags between each pair or only</span>
-<span class="sd">            at the lag of maximum absolute causal effect.</span>
-<span class="sd">        exclude_i : bool, optional (default: True)</span>
-<span class="sd">            Whether to exclude causal effects on the variable itself at later</span>
-<span class="sd">            lags.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        ace :float</span>
-<span class="sd">            Average Causal Effect.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">all_but_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">exclude_i</span><span class="p">:</span>
-            <span class="n">all_but_i</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="k">if</span> <span class="n">lag_mode</span> <span class="o">==</span> <span class="s1">&#39;absmax&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">[:,</span> <span class="n">all_but_i</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="n">lag_mode</span> <span class="o">==</span> <span class="s1">&#39;all_lags&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">[:,</span> <span class="n">all_but_i</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;lag_mode = </span><span class="si">%s</span><span class="s2"> not implemented&quot;</span> <span class="o">%</span> <span class="n">lag_mode</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_all_ace"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_all_ace">[docs]</a>    <span class="k">def</span> <span class="nf">get_all_ace</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lag_mode</span><span class="o">=</span><span class="s1">&#39;absmax&#39;</span><span class="p">,</span> <span class="n">exclude_i</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the average causal effect for all variables.</span>
-
-<span class="sd">        This is the average causal effect (ACE) emanating from variable i to any</span>
-<span class="sd">        other variable. With lag_mode=&#39;absmax&#39; this is based on the lag of</span>
-<span class="sd">        maximum CE for each pair.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        lag_mode : {&#39;absmax&#39;, &#39;all_lags&#39;}</span>
-<span class="sd">            Lag mode. Either average across all lags between each pair or only</span>
-<span class="sd">            at the lag of maximum absolute causal effect.</span>
-<span class="sd">        exclude_i : bool, optional (default: True)</span>
-<span class="sd">            Whether to exclude causal effects on the variable itself at later</span>
-<span class="sd">            lags.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        ace : array of shape (N,)</span>
-<span class="sd">            Average Causal Effect for each variable.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">ace</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">ace</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_ace</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag_mode</span><span class="o">=</span><span class="n">lag_mode</span><span class="p">,</span> <span class="n">exclude_i</span><span class="o">=</span><span class="n">exclude_i</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">ace</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_acs"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_acs">[docs]</a>    <span class="k">def</span> <span class="nf">get_acs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">lag_mode</span><span class="o">=</span><span class="s1">&#39;absmax&#39;</span><span class="p">,</span> <span class="n">exclude_j</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the average causal susceptibility.</span>
-
-<span class="sd">        This is the Average Causal Susceptibility (ACS) affecting a variable j</span>
-<span class="sd">        from any other variable. With lag_mode=&#39;absmax&#39; this is based on the lag</span>
-<span class="sd">        of maximum CE for each pair.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of variable.</span>
-<span class="sd">        lag_mode : {&#39;absmax&#39;, &#39;all_lags&#39;}</span>
-<span class="sd">            Lag mode. Either average across all lags between each pair or only</span>
-<span class="sd">            at the lag of maximum absolute causal effect.</span>
-<span class="sd">        exclude_j : bool, optional (default: True)</span>
-<span class="sd">            Whether to exclude causal effects on the variable itself at previous</span>
-<span class="sd">            lags.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        acs : float</span>
-<span class="sd">            Average Causal Susceptibility.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">all_but_j</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">exclude_j</span><span class="p">:</span>
-            <span class="n">all_but_j</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="k">if</span> <span class="n">lag_mode</span> <span class="o">==</span> <span class="s1">&#39;absmax&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="n">all_but_j</span><span class="p">])</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="n">lag_mode</span> <span class="o">==</span> <span class="s1">&#39;all_lags&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="n">all_but_j</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;lag_mode = </span><span class="si">%s</span><span class="s2"> not implemented&quot;</span> <span class="o">%</span> <span class="n">lag_mode</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_all_acs"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_all_acs">[docs]</a>    <span class="k">def</span> <span class="nf">get_all_acs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lag_mode</span><span class="o">=</span><span class="s1">&#39;absmax&#39;</span><span class="p">,</span> <span class="n">exclude_j</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the average causal susceptibility.</span>
-
-<span class="sd">        This is the Average Causal Susceptibility (ACS) for each variable from</span>
-<span class="sd">        any other variable. With lag_mode=&#39;absmax&#39; this is based on the lag of</span>
-<span class="sd">        maximum CE for each pair.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        lag_mode : {&#39;absmax&#39;, &#39;all_lags&#39;}</span>
-<span class="sd">            Lag mode. Either average across all lags between each pair or only</span>
-<span class="sd">            at the lag of maximum absolute causal effect.</span>
-<span class="sd">        exclude_j : bool, optional (default: True)</span>
-<span class="sd">            Whether to exclude causal effects on the variable itself at previous</span>
-<span class="sd">            lags.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        acs : array of shape (N,)</span>
-<span class="sd">            Average Causal Susceptibility.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">acs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">acs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_acs</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">lag_mode</span><span class="o">=</span><span class="n">lag_mode</span><span class="p">,</span> <span class="n">exclude_j</span><span class="o">=</span><span class="n">exclude_j</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">acs</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_amce"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_amce">[docs]</a>    <span class="k">def</span> <span class="nf">get_amce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">lag_mode</span><span class="o">=</span><span class="s1">&#39;absmax&#39;</span><span class="p">,</span>
-                 <span class="n">exclude_k</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exclude_self_effects</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the average mediated causal effect.</span>
-
-<span class="sd">        This is the Average Mediated Causal Effect (AMCE) through a variable k</span>
-<span class="sd">        With lag_mode=&#39;absmax&#39; this is based on the lag of maximum CE for each</span>
-<span class="sd">        pair.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        k : int</span>
-<span class="sd">            Index of variable.</span>
-<span class="sd">        lag_mode : {&#39;absmax&#39;, &#39;all_lags&#39;}</span>
-<span class="sd">            Lag mode. Either average across all lags between each pair or only</span>
-<span class="sd">            at the lag of maximum absolute causal effect.</span>
-<span class="sd">        exclude_k : bool, optional (default: True)</span>
-<span class="sd">            Whether to exclude causal effects through the variable itself at</span>
-<span class="sd">            previous lags.</span>
-<span class="sd">        exclude_self_effects : bool, optional (default: True)</span>
-<span class="sd">            Whether to exclude causal self effects of variables on themselves.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        amce : float</span>
-<span class="sd">            Average Mediated Causal Effect.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">all_but_k</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">exclude_k</span><span class="p">:</span>
-            <span class="n">all_but_k</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="n">N_new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">N_new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span>
-
-        <span class="k">if</span> <span class="n">exclude_self_effects</span><span class="p">:</span>
-            <span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">N_new</span><span class="p">)</span> <span class="o">==</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">N_new</span><span class="p">,</span> <span class="n">N_new</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
-
-        <span class="c1"># if self.tau_max &lt; 2:</span>
-        <span class="c1">#     raise ValueError(&quot;Mediation only nonzero for tau_max &gt;= 2&quot;)</span>
-
-        <span class="n">all_mce</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="p">:]</span>
-        <span class="c1"># all_mce[:, range(self.N), range(self.N)] = 0.</span>
-
-        <span class="k">if</span> <span class="n">lag_mode</span> <span class="o">==</span> <span class="s1">&#39;absmax&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">all_mce</span><span class="p">[:,</span> <span class="n">all_but_k</span><span class="p">,</span> <span class="p">:]</span>
-                                     <span class="p">[:,</span> <span class="p">:,</span> <span class="n">all_but_k</span><span class="p">]</span>
-                                     <span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">lag_mode</span> <span class="o">==</span> <span class="s1">&#39;all_lags&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">all_mce</span><span class="p">[:,</span> <span class="n">all_but_k</span><span class="p">,</span> <span class="p">:][:,</span> <span class="p">:,</span> <span class="n">all_but_k</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;lag_mode = </span><span class="si">%s</span><span class="s2"> not implemented&quot;</span> <span class="o">%</span> <span class="n">lag_mode</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_all_amce"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_all_amce">[docs]</a>    <span class="k">def</span> <span class="nf">get_all_amce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lag_mode</span><span class="o">=</span><span class="s1">&#39;absmax&#39;</span><span class="p">,</span>
-                     <span class="n">exclude_k</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exclude_self_effects</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the average mediated causal effect.</span>
-
-<span class="sd">        This is the Average Mediated Causal Effect (AMCE) through all variables</span>
-<span class="sd">        With lag_mode=&#39;absmax&#39; this is based on the lag of maximum CE for each</span>
-<span class="sd">        pair.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        lag_mode : {&#39;absmax&#39;, &#39;all_lags&#39;}</span>
-<span class="sd">            Lag mode. Either average across all lags between each pair or only</span>
-<span class="sd">            at the lag of maximum absolute causal effect.</span>
-<span class="sd">        exclude_k : bool, optional (default: True)</span>
-<span class="sd">            Whether to exclude causal effects through the variable itself at</span>
-<span class="sd">            previous lags.</span>
-<span class="sd">        exclude_self_effects : bool, optional (default: True)</span>
-<span class="sd">            Whether to exclude causal self effects of variables on themselves.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        amce : array of shape (N,)</span>
-<span class="sd">            Average Mediated Causal Effect.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">amce</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">amce</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_amce</span><span class="p">(</span><span class="n">k</span><span class="p">,</span>
-                                    <span class="n">lag_mode</span><span class="o">=</span><span class="n">lag_mode</span><span class="p">,</span>
-                                    <span class="n">exclude_k</span><span class="o">=</span><span class="n">exclude_k</span><span class="p">,</span>
-                                    <span class="n">exclude_self_effects</span><span class="o">=</span><span class="n">exclude_self_effects</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">amce</span></div>
-
-
-<div class="viewcode-block" id="LinearMediation.get_val_matrix"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_val_matrix">[docs]</a>    <span class="k">def</span> <span class="nf">get_val_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">symmetrize</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the matrix of linear coefficients.</span>
-
-<span class="sd">        Requires fit_model() before. An entry val_matrix[i,j,tau] gives the</span>
-<span class="sd">        coefficient of the link from i to j at lag tau. Lag=0 is always set</span>
-<span class="sd">        to zero for LinearMediation, use Models class for contemporaneous </span>
-<span class="sd">        models.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        symmetrize : bool</span>
-<span class="sd">            If True, the lag-zero entries will be symmetrized such that</span>
-<span class="sd">            no zeros appear. Useful since other parts of tigramite </span>
-<span class="sd">            through an error for non-symmetric val_matrix, eg plotting.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val_matrix : array</span>
-<span class="sd">            Matrix of linear coefficients, shape (N, N, tau_max + 1).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="o">.</span><span class="n">transpose</span><span class="p">())</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">symmetrize</span><span class="p">:</span>
-            <span class="c1"># Symmetrize since otherwise other parts of tigramite through an error</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">:</span>
-                        <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">val_matrix</span></div>
-
-<div class="viewcode-block" id="LinearMediation.net_to_tsg"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.net_to_tsg">[docs]</a>    <span class="k">def</span> <span class="nf">net_to_tsg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">lag</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to translate from network to time series graph.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">row</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">lag</span></div>
-
-<div class="viewcode-block" id="LinearMediation.tsg_to_net"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.tsg_to_net">[docs]</a>    <span class="k">def</span> <span class="nf">tsg_to_net</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to translate from time series graph to network.&quot;&quot;&quot;</span>
-        <span class="n">row</span> <span class="o">=</span> <span class="n">node</span> <span class="o">//</span> <span class="n">max_lag</span>
-        <span class="n">lag</span> <span class="o">=</span> <span class="n">node</span> <span class="o">%</span> <span class="n">max_lag</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_tsg"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_tsg">[docs]</a>    <span class="k">def</span> <span class="nf">get_tsg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">link_matrix</span><span class="p">,</span> <span class="n">val_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">include_neighbors</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns time series graph matrix.</span>
-
-<span class="sd">        Constructs a matrix of shape (N*tau_max, N*tau_max) from link_matrix.</span>
-<span class="sd">        This matrix can be used for plotting the time series graph and analyzing</span>
-<span class="sd">        causal pathways.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        link_matrix : bool array-like, optional (default: None)</span>
-<span class="sd">            Matrix of significant links. Must be of same shape as val_matrix.</span>
-<span class="sd">            Either sig_thres or link_matrix has to be provided.</span>
-<span class="sd">        val_matrix : array_like</span>
-<span class="sd">            Matrix of shape (N, N, tau_max+1) containing test statistic values.</span>
-<span class="sd">        include_neighbors : bool, optional (default: False)</span>
-<span class="sd">            Whether to include causal paths emanating from neighbors of i</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        tsg : array of shape (N*tau_max, N*tau_max)</span>
-<span class="sd">            Time series graph matrix.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_matrix</span><span class="p">)</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="n">link_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
-
-        <span class="c1"># Create TSG</span>
-        <span class="n">tsg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">link_matrix</span><span class="p">)):</span>
-            <span class="c1"># if tau &gt; 0 or include_neighbors:</span>
-                <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_lag</span><span class="p">):</span>
-                    <span class="n">link_start</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net_to_tsg</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span>
-                    <span class="n">link_end</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net_to_tsg</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">link_start</span> <span class="ow">and</span>
-                            <span class="p">(</span><span class="n">link_start</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="p">(</span><span class="n">link_end</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">)):</span>
-                        <span class="k">if</span> <span class="n">val_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                            <span class="n">tsg</span><span class="p">[</span><span class="n">link_start</span><span class="p">,</span> <span class="n">link_end</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="n">tsg</span><span class="p">[</span><span class="n">link_start</span><span class="p">,</span> <span class="n">link_end</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">return</span> <span class="n">tsg</span></div>
-
-<div class="viewcode-block" id="LinearMediation.get_mediation_graph_data"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.get_mediation_graph_data">[docs]</a>    <span class="k">def</span> <span class="nf">get_mediation_graph_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">include_neighbors</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns link and node weights for mediation analysis.</span>
-
-<span class="sd">        Returns array with non-zero entries for links that are on causal</span>
-<span class="sd">        paths between :math:`i` and :math:`j` at lag :math:`\tau`.</span>
-<span class="sd">        ``path_val_matrix`` contains the corresponding path coefficients and</span>
-<span class="sd">        ``path_node_array`` the MCE values. ``tsg_path_val_matrix`` contains the</span>
-<span class="sd">        corresponding values in the time series graph format.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Index of cause variable.</span>
-<span class="sd">        tau : int</span>
-<span class="sd">            Lag of cause variable.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of effect variable.</span>
-<span class="sd">        include_neighbors : bool, optional (default: False)</span>
-<span class="sd">            Whether to include causal paths emanating from neighbors of i</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph_data : dictionary</span>
-<span class="sd">            Dictionary of matrices for coloring mediation graph plots.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">path_link_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">path_val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-
-        <span class="c1"># Get mediation of path variables</span>
-        <span class="n">path_node_array</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">psi</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-                           <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span><span class="p">)[:,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-
-        <span class="c1"># Get involved links</span>
-        <span class="n">val_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
-        <span class="n">link_matrix</span> <span class="o">=</span> <span class="n">val_matrix</span> <span class="o">!=</span> <span class="mf">0.</span>
-
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="n">link_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
-
-        <span class="c1"># include_neighbors = False because True would allow</span>
-        <span class="c1"># --&gt; o -- motifs in networkx.all_simple_paths as paths, but</span>
-        <span class="c1"># these are blocked...</span>
-        <span class="n">tsg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_tsg</span><span class="p">(</span><span class="n">link_matrix</span><span class="p">,</span> <span class="n">val_matrix</span><span class="o">=</span><span class="n">val_matrix</span><span class="p">,</span>
-                           <span class="n">include_neighbors</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">include_neighbors</span><span class="p">:</span>
-            <span class="c1"># Add contemporaneous links only at source node</span>
-            <span class="k">for</span> <span class="n">m</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">link_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])):</span>
-                <span class="c1"># print m,n</span>
-                <span class="k">if</span> <span class="n">m</span> <span class="o">!=</span> <span class="n">n</span><span class="p">:</span>
-                    <span class="n">tsg</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">net_to_tsg</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">),</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">net_to_tsg</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span>
-                    <span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">tsg_path_val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">tsg</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">networkx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">(</span><span class="n">tsg</span><span class="p">)</span>
-        <span class="n">pathways</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">networkx</span><span class="o">.</span><span class="n">all_simple_paths</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                                              <span class="n">source</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">net_to_tsg</span><span class="p">(</span><span class="n">i</span><span class="p">,</span>
-                                                                     <span class="n">max_lag</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span>
-                                                                     <span class="n">max_lag</span><span class="p">),</span>
-                                              <span class="n">target</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">net_to_tsg</span><span class="p">(</span><span class="n">j</span><span class="p">,</span>
-                                                                     <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">0</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span>
-                                                                     <span class="n">max_lag</span><span class="p">)):</span>
-            <span class="n">pathways</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">tsg_to_net</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">path</span><span class="p">])</span>
-            <span class="k">for</span> <span class="n">ip</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">path</span><span class="p">[</span><span class="mi">1</span><span class="p">:]):</span>
-                <span class="n">tsg_path_val_matrix</span><span class="p">[</span><span class="n">path</span><span class="p">[</span><span class="n">ip</span><span class="p">],</span> <span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">tsg</span><span class="p">[</span><span class="n">path</span><span class="p">[</span><span class="n">ip</span><span class="p">],</span> <span class="n">p</span><span class="p">]</span>
-
-                <span class="n">k</span><span class="p">,</span> <span class="n">tau_k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tsg_to_net</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span>
-                <span class="n">link_start</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tsg_to_net</span><span class="p">(</span><span class="n">path</span><span class="p">[</span><span class="n">ip</span><span class="p">],</span> <span class="n">max_lag</span><span class="p">)</span>
-                <span class="n">link_end</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tsg_to_net</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)</span>
-                <span class="n">delta_tau</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">link_end</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">link_start</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-                <span class="n">path_val_matrix</span><span class="p">[</span><span class="n">link_start</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-                                <span class="n">link_end</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-                                <span class="n">delta_tau</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">link_start</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-                                                        <span class="n">link_end</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-                                                        <span class="n">delta_tau</span><span class="p">]</span>
-
-        <span class="n">graph_data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;path_node_array&#39;</span><span class="p">:</span> <span class="n">path_node_array</span><span class="p">,</span>
-                      <span class="s1">&#39;path_val_matrix&#39;</span><span class="p">:</span> <span class="n">path_val_matrix</span><span class="p">,</span>
-                      <span class="s1">&#39;tsg_path_val_matrix&#39;</span><span class="p">:</span> <span class="n">tsg_path_val_matrix</span><span class="p">}</span>
-
-        <span class="k">return</span> <span class="n">graph_data</span></div></div>
-
-
-<div class="viewcode-block" id="Prediction"><a class="viewcode-back" href="../../index.html#tigramite.models.Prediction">[docs]</a><span class="k">class</span> <span class="nc">Prediction</span><span class="p">(</span><span class="n">Models</span><span class="p">,</span> <span class="n">PCMCI</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Prediction class for time series models.</span>
-
-<span class="sd">    Allows to fit and predict from any sklearn model. The optimal predictors can</span>
-<span class="sd">    be estimated using PCMCI. Also takes care of missing values, masking and</span>
-<span class="sd">    preprocessing.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataframe : data object</span>
-<span class="sd">        Tigramite dataframe object. It must have the attributes dataframe.values</span>
-<span class="sd">        yielding a numpy array of shape (observations T, variables N) and</span>
-<span class="sd">        optionally a mask of the same shape and a missing values flag.</span>
-<span class="sd">    train_indices : array-like</span>
-<span class="sd">        Either boolean array or time indices marking the training data.</span>
-<span class="sd">    test_indices : array-like</span>
-<span class="sd">        Either boolean array or time indices marking the test data.</span>
-<span class="sd">    prediction_model : sklearn model object</span>
-<span class="sd">        For example, sklearn.linear_model.LinearRegression() for a linear</span>
-<span class="sd">        regression model.</span>
-<span class="sd">    cond_ind_test : Conditional independence test object, optional</span>
-<span class="sd">        Only needed if predictors are estimated with causal algorithm.</span>
-<span class="sd">        The class will be initialized with masking set to the training data.</span>
-<span class="sd">    data_transform : sklearn preprocessing object, optional (default: None)</span>
-<span class="sd">        Used to transform data prior to fitting. For example,</span>
-<span class="sd">        sklearn.preprocessing.StandardScaler for simple standardization. The</span>
-<span class="sd">        fitted parameters are stored.</span>
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">dataframe</span><span class="p">,</span>
-                 <span class="n">train_indices</span><span class="p">,</span>
-                 <span class="n">test_indices</span><span class="p">,</span>
-                 <span class="n">prediction_model</span><span class="p">,</span>
-                 <span class="n">cond_ind_test</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">data_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-
-        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">!=</span> <span class="s1">&#39;single&#39;</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Prediction class currently only supports single &quot;</span>
-                             <span class="s2">&quot;datasets.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># dataframe.values = {0: dataframe.values[0]}</span>
-
-        <span class="c1"># Default value for the mask</span>
-        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span><span class="p">[</span><span class="mi">0</span><span class="p">]}</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)}</span>
-        <span class="c1"># Get the dataframe shape</span>
-        <span class="n">T</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="c1"># Have the default dataframe be the training data frame</span>
-        <span class="n">train_mask</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
-        <span class="n">train_mask</span><span class="p">[</span><span class="mi">0</span><span class="p">][[</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> <span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">train_indices</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">dataframe</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="n">train_mask</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">_initialized_from</span> <span class="o">=</span> <span class="s1">&#39;dict&#39;</span>
-                 <span class="c1"># = DataFrame(dataframe.values[0],</span>
-                 <span class="c1">#                   mask=train_mask,</span>
-                 <span class="c1">#                   missing_flag=dataframe.missing_flag)</span>
-        <span class="c1"># Initialize the models baseclass with the training dataframe</span>
-        <span class="n">Models</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                        <span class="n">dataframe</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="p">,</span>
-                        <span class="n">model</span><span class="o">=</span><span class="n">prediction_model</span><span class="p">,</span>
-                        <span class="n">data_transform</span><span class="o">=</span><span class="n">data_transform</span><span class="p">,</span>
-                        <span class="n">mask_type</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span>
-                        <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="c1"># Build the testing dataframe as well</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">test_mask</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">test_mask</span><span class="p">[</span><span class="mi">0</span><span class="p">][[</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> <span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">test_indices</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">train_indices</span> <span class="o">=</span> <span class="n">train_indices</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">test_indices</span> <span class="o">=</span> <span class="n">test_indices</span>
-
-        <span class="c1"># Setup the PCMCI instance</span>
-        <span class="k">if</span> <span class="n">cond_ind_test</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># Force the masking</span>
-            <span class="n">cond_ind_test</span><span class="o">.</span><span class="n">set_mask_type</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
-            <span class="n">cond_ind_test</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
-            <span class="n">PCMCI</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                           <span class="n">dataframe</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="p">,</span>
-                           <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">,</span>
-                           <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="c1"># Set the member variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">cond_ind_test</span>
-        <span class="c1"># Initialize member varialbes that are set outside</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">target_predictors</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fitted_model</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">test_array</span> <span class="o">=</span> <span class="kc">None</span>
-
-<div class="viewcode-block" id="Prediction.get_predictors"><a class="viewcode-back" href="../../index.html#tigramite.models.Prediction.get_predictors">[docs]</a>    <span class="k">def</span> <span class="nf">get_predictors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                       <span class="n">selected_targets</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                       <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                       <span class="n">steps_ahead</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                       <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                       <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-                       <span class="n">max_conds_dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                       <span class="n">max_combinations</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Estimate predictors using PC1 algorithm.</span>
-
-<span class="sd">        Wrapper around PCMCI.run_pc_stable that estimates causal predictors.</span>
-<span class="sd">        The lead time can be specified by ``steps_ahead``.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_targets : list of ints, optional (default: None)</span>
-<span class="sd">            List of variables to estimate predictors of. If None, predictors of</span>
-<span class="sd">            all variables are estimated.</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
-<span class="sd">            specifying whether only selected links should be tested. If None is</span>
-<span class="sd">            passed, all links are tested</span>
-<span class="sd">        steps_ahead : int, default: 1</span>
-<span class="sd">            Minimum time lag to test. Useful for multi-step ahead predictions.</span>
-<span class="sd">        tau_max : int, default: 1</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        pc_alpha : float or list of floats, default: 0.2</span>
-<span class="sd">            Significance level in algorithm. If a list or None is passed, the</span>
-<span class="sd">            pc_alpha level is optimized for every variable across the given</span>
-<span class="sd">            pc_alpha values using the score computed in</span>
-<span class="sd">            cond_ind_test.get_model_selection_criterion()</span>
-<span class="sd">        max_conds_dim : int or None</span>
-<span class="sd">            Maximum number of conditions to test. If None is passed, this number</span>
-<span class="sd">            is unrestricted.</span>
-<span class="sd">        max_combinations : int, default: 1</span>
-<span class="sd">            Maximum number of combinations of conditions of current cardinality</span>
-<span class="sd">            to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm</span>
-<span class="sd">            a larger number, such as 10, can be used.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        predictors : dict</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
-<span class="sd">            containing estimated predictors.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Ensure an independence model is given</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No cond_ind_test given!&quot;</span><span class="p">)</span>
-        <span class="c1"># Set the selected variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">selected_targets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span> <span class="o">=</span> <span class="n">selected_targets</span>
-        <span class="n">predictors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">selected_links</span><span class="o">=</span><span class="n">selected_links</span><span class="p">,</span>
-                                        <span class="n">tau_min</span><span class="o">=</span><span class="n">steps_ahead</span><span class="p">,</span>
-                                        <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                        <span class="n">save_iterations</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                        <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
-                                        <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                        <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">predictors</span></div>
-
-<div class="viewcode-block" id="Prediction.fit"><a class="viewcode-back" href="../../index.html#tigramite.models.Prediction.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target_predictors</span><span class="p">,</span>
-            <span class="n">selected_targets</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fit time series model.</span>
-
-<span class="sd">        Wrapper around ``Models.fit_full_model()``. To each variable in</span>
-<span class="sd">        ``selected_targets``, the sklearn model is fitted with :math:`y` given</span>
-<span class="sd">        by the target variable, and :math:`X` given by its predictors. The</span>
-<span class="sd">        fitted model class is returned for later use.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        target_predictors : dictionary</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing</span>
-<span class="sd">            the predictors estimated with PCMCI.</span>
-<span class="sd">        selected_targets : list of integers, optional (default: range(N))</span>
-<span class="sd">            Specify to fit model only for selected targets. If None is</span>
-<span class="sd">            passed, models are estimated for all variables.</span>
-<span class="sd">        tau_max : int, optional (default: None)</span>
-<span class="sd">            Maximum time lag. If None, the maximum lag in target_predictors is</span>
-<span class="sd">            used.</span>
-<span class="sd">        return_data : bool, optional (default: False)</span>
-<span class="sd">            Whether to save the data array.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self : instance of self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">tau_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># Find the maximal parents lag</span>
-            <span class="n">max_parents_lag</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">target_predictors</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="n">this_parent_lag</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">target_predictors</span><span class="p">[</span><span class="n">j</span><span class="p">])[:,</span> <span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-                    <span class="n">max_parents_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_parents_lag</span><span class="p">,</span> <span class="n">this_parent_lag</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">max_parents_lag</span> <span class="o">=</span> <span class="n">tau_max</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">test_indices</span><span class="p">)</span> <span class="o">-</span> <span class="n">max_parents_lag</span><span class="p">)</span>
-                <span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_indices</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;test_indices - maxlag(predictors) [or tau_max] &quot;</span>
-                <span class="s2">&quot;overlaps with train_indices: Choose test_indices &quot;</span>
-                <span class="s2">&quot;such that there is a gap of max_lag to train_indices!&quot;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">target_predictors</span> <span class="o">=</span> <span class="n">target_predictors</span>
-
-        <span class="k">if</span> <span class="n">selected_targets</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span> <span class="o">=</span> <span class="n">selected_targets</span>
-
-        <span class="k">for</span> <span class="n">target</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">target</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_predictors</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No predictors given for target </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">target</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">fitted_model</span> <span class="o">=</span> \
-            <span class="bp">self</span><span class="o">.</span><span class="n">fit_full_model</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_predictors</span><span class="p">,</span>
-                         <span class="n">selected_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span><span class="p">,</span>
-                         <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                         <span class="n">return_data</span><span class="o">=</span><span class="n">return_data</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-<div class="viewcode-block" id="Prediction.predict"><a class="viewcode-back" href="../../index.html#tigramite.models.Prediction.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span>
-                <span class="n">new_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">pred_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;max_lag_or_tau_max&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Predict target variable with fitted model.</span>
-
-<span class="sd">        Uses the model.predict() function of the sklearn model.</span>
-
-<span class="sd">        If target is an int, the predicted time series is returned. If target</span>
-<span class="sd">        is a list of integers, then a list of predicted time series is returned.</span>
-<span class="sd">        If the list of integers equals range(N), then an array of shape (T, N)</span>
-<span class="sd">        of the predicted series is returned.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        target : int or list of integers</span>
-<span class="sd">            Index or indices of target variable(s).</span>
-<span class="sd">        new_data : data object, optional</span>
-<span class="sd">            New Tigramite dataframe object with optional new mask. Note that</span>
-<span class="sd">            the data will be cut off according to cut_off, see parameter</span>
-<span class="sd">            `cut_off` below.</span>
-<span class="sd">        pred_params : dict, optional</span>
-<span class="sd">            Optional parameters passed on to sklearn prediction function.</span>
-<span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;}</span>
-<span class="sd">            How many samples to cutoff at the beginning. The default is</span>
-<span class="sd">            &#39;2xtau_max&#39;, which guarantees that MCI tests are all conducted on</span>
-<span class="sd">            the same samples.  For modeling, &#39;max_lag_or_tau_max&#39; can be used,</span>
-<span class="sd">            which uses the maximum of tau_max and the conditions, which is</span>
-<span class="sd">            useful to compare multiple models on the same sample. Last,</span>
-<span class="sd">            &#39;max_lag&#39; uses as much samples as possible.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        Results from prediction.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="n">target_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">target</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="n">target_list</span> <span class="o">=</span> <span class="n">target</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;target must be either int or list of integers &quot;</span>
-                             <span class="s2">&quot;indicating the index of the variables to &quot;</span>
-                             <span class="s2">&quot;predict.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">target_list</span> <span class="o">==</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">return_type</span> <span class="o">=</span> <span class="s1">&#39;array&#39;</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">target_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">return_type</span> <span class="o">=</span> <span class="s1">&#39;series&#39;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">return_type</span> <span class="o">=</span> <span class="s1">&#39;list&#39;</span>
-
-        <span class="n">pred_list</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">target_list</span><span class="p">:</span>
-            <span class="c1"># Print message</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Predicting target </span><span class="si">%s</span><span class="se">\n</span><span class="s2">##&quot;</span> <span class="o">%</span> <span class="n">target</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">pred_params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">pred_params</span><span class="p">):</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">pred_params</span><span class="p">[</span><span class="n">key</span><span class="p">]))</span>
-            <span class="c1"># Default value for pred_params</span>
-            <span class="k">if</span> <span class="n">pred_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">pred_params</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="c1"># Check this is a valid target</span>
-            <span class="k">if</span> <span class="n">target</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Target </span><span class="si">%s</span><span class="s2"> not yet fitted&quot;</span> <span class="o">%</span> <span class="n">target</span><span class="p">)</span>
-            <span class="c1"># Construct the array form of the data</span>
-            <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">target</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">target</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>  <span class="c1"># dummy</span>
-            <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_predictors</span><span class="p">[</span><span class="n">target</span><span class="p">]</span>
-            <span class="c1"># Check if we&#39;ve passed a new dataframe object</span>
-            <span class="n">test_array</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">if</span> <span class="n">new_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="c1"># if new_data.mask is None:</span>
-                <span class="c1">#     # if no mask is supplied, use the same mask as for the fitted array</span>
-                <span class="c1">#     new_data_mask = self.test_mask</span>
-                <span class="c1"># else:</span>
-                <span class="n">new_data_mask</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">mask</span>
-                <span class="n">test_array</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span>
-                                                         <span class="n">tau_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span>
-                                                         <span class="n">mask</span><span class="o">=</span><span class="n">new_data_mask</span><span class="p">,</span>
-                                                         <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                                         <span class="n">cut_off</span><span class="o">=</span><span class="n">cut_off</span><span class="p">,</span>
-                                                         <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                         <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-            <span class="c1"># Otherwise use the default values</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">test_array</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span>
-                                                   <span class="n">tau_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span>
-                                                   <span class="n">mask</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">test_mask</span><span class="p">,</span>
-                                                   <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
-                                                   <span class="n">cut_off</span><span class="o">=</span><span class="n">cut_off</span><span class="p">,</span>
-                                                   <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                   <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-            <span class="c1"># Transform the data if needed</span>
-            <span class="n">a_transform</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fitted_model</span><span class="p">[</span><span class="n">target</span><span class="p">][</span><span class="s1">&#39;data_transform&#39;</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">a_transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">test_array</span> <span class="o">=</span> <span class="n">a_transform</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">test_array</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-            <span class="c1"># Cache the test array</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">test_array</span> <span class="o">=</span> <span class="n">test_array</span>
-            <span class="c1"># Run the predictor</span>
-            <span class="n">pred_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fitted_model</span><span class="p">[</span><span class="n">target</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-                <span class="n">X</span><span class="o">=</span><span class="n">test_array</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">pred_params</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="n">return_type</span> <span class="o">==</span> <span class="s1">&#39;series&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pred_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="n">return_type</span> <span class="o">==</span> <span class="s1">&#39;list&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pred_list</span>
-        <span class="k">elif</span> <span class="n">return_type</span> <span class="o">==</span> <span class="s1">&#39;array&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">pred_list</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span></div>
-
-<div class="viewcode-block" id="Prediction.get_train_array"><a class="viewcode-back" href="../../index.html#tigramite.models.Prediction.get_train_array">[docs]</a>    <span class="k">def</span> <span class="nf">get_train_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns training array.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fitted_model</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;data&#39;</span><span class="p">]</span></div>
-
-<div class="viewcode-block" id="Prediction.get_test_array"><a class="viewcode-back" href="../../index.html#tigramite.models.Prediction.get_test_array">[docs]</a>    <span class="k">def</span> <span class="nf">get_test_array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns test array.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_array</span></div></div>
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-   
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
-    <span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span>
-    <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
-
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
- 
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    
-    <span class="n">links</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">1</span><span class="p">:</span> <span class="p">[((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">-</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">2</span><span class="p">:</span> <span class="p">[((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span>  <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">3</span><span class="p">:</span> <span class="p">[((</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span>  <span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">-</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)]</span>
-             <span class="p">}</span>
-    <span class="c1"># noises = [np.random.randn for j in links.keys()]</span>
-    <span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
-
-    <span class="n">missing_flag</span> <span class="o">=</span> <span class="mi">999</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">):</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">::</span><span class="mi">100</span><span class="p">]</span> <span class="o">=</span> <span class="n">missing_flag</span>
-
-    <span class="n">parents</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">_get_true_parent_neighbor_dict</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-    <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">missing_flag</span> <span class="o">=</span> <span class="n">missing_flag</span><span class="p">)</span>
-
-    <span class="n">med</span> <span class="o">=</span> <span class="n">LinearMediation</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
-        <span class="n">data_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-    <span class="n">med</span><span class="o">.</span><span class="n">fit_model</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-    <span class="n">med</span><span class="o">.</span><span class="n">fit_model_bootstrap</span><span class="p">(</span> 
-                <span class="n">boot_blocklength</span><span class="o">=</span><span class="s1">&#39;cube_root&#39;</span><span class="p">,</span>
-                <span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span><span class="p">,</span>
-                <span class="p">)</span>
-
-    <span class="c1"># print(med.get_val_matrix())</span>
-
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_ce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_bootstrap_of</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="s1">&#39;get_ce&#39;</span><span class="p">,</span> 
-        <span class="n">function_args</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;i&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;tau&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span>   <span class="s1">&#39;j&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">},</span> <span class="n">conf_lev</span><span class="o">=</span><span class="mf">0.9</span><span class="p">))</span>
-
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_coeff</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">j</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_ce_max</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">j</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_ce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_mce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_mce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">-</span> <span class="n">med</span><span class="o">.</span><span class="n">get_mce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_conditional_mce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">notk</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_bootstrap_of</span><span class="p">(</span><span class="s1">&#39;get_conditional_mce&#39;</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;i&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;tau&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">:[</span><span class="mi">2</span><span class="p">],</span> <span class="s1">&#39;notk&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;j&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">}))</span>
-
-    <span class="c1"># print(med.get_joint_ce(i=0, j=2))</span>
-    <span class="c1"># print(med.get_joint_mce(i=0, j=2, k=1))</span>
-
-    <span class="c1"># print(med.get_joint_ce_matrix(i=0, j=2))</span>
-
-    <span class="c1"># i=0; tau=4; j=2</span>
-    <span class="c1"># graph_data = med.get_mediation_graph_data(i=i, tau=tau, j=j)</span>
-    <span class="c1"># tp.plot_mediation_time_series_graph(</span>
-    <span class="c1">#     # var_names=var_names,</span>
-    <span class="c1">#     path_node_array=graph_data[&#39;path_node_array&#39;],</span>
-    <span class="c1">#     tsg_path_val_matrix=graph_data[&#39;tsg_path_val_matrix&#39;]</span>
-    <span class="c1">#     )</span>
-    <span class="c1"># tp.plot_mediation_graph(</span>
-    <span class="c1">#                     # var_names=var_names,</span>
-    <span class="c1">#                     path_val_matrix=graph_data[&#39;path_val_matrix&#39;], </span>
-    <span class="c1">#                     path_node_array=graph_data[&#39;path_node_array&#39;],</span>
-    <span class="c1">#                     ); </span>
-    <span class="c1"># plt.show()</span>
-
-    <span class="c1"># print (&quot;Average Causal Effect X=%.2f, Y=%.2f, Z=%.2f &quot; % tuple(med.get_all_ace()))</span>
-    <span class="c1"># print (&quot;Average Causal Susceptibility X=%.2f, Y=%.2f, Z=%.2f &quot; % tuple(med.get_all_acs()))</span>
-    <span class="c1"># print (&quot;Average Mediated Causal Effect X=%.2f, Y=%.2f, Z=%.2f &quot; % tuple(med.get_all_amce()))</span>
-    <span class="c1"># med = Models(dataframe=dataframe, model=sklearn.linear_model.LinearRegression(), data_transform=None)</span>
-    <span class="c1"># # Fit the model</span>
-    <span class="c1"># med.get_fit(all_parents=true_parents, tau_max=3)</span>
-
-    <span class="c1"># print(med.get_val_matrix())</span>
-
-    <span class="c1"># for j, i, tau, coeff in toys._iter_coeffs(links):</span>
-    <span class="c1">#     print(i, j, tau, coeff, med.get_coeff(i=i, tau=tau, j=j))</span>
-
-    <span class="c1"># for causal_coeff in [med.get_ce(i=0, tau=-2, j=2),</span>
-    <span class="c1">#                      med.get_mce(i=0, tau=-2, j=2, k=1)]:</span>
-    <span class="c1">#     print(causal_coeff)</span>
-
-
-    <span class="c1"># pred = Prediction(dataframe=dataframe,</span>
-    <span class="c1">#         cond_ind_test=ParCorr(),   #CMIknn ParCorr</span>
-    <span class="c1">#         prediction_model = sklearn.linear_model.LinearRegression(),</span>
-    <span class="c1"># #         prediction_model = sklearn.gaussian_process.GaussianProcessRegressor(),</span>
-    <span class="c1">#         # prediction_model = sklearn.neighbors.KNeighborsRegressor(),</span>
-    <span class="c1">#     data_transform=sklearn.preprocessing.StandardScaler(),</span>
-    <span class="c1">#     train_indices= list(range(int(0.8*T))),</span>
-    <span class="c1">#     test_indices= list(range(int(0.8*T), T)),</span>
-    <span class="c1">#     verbosity=0</span>
-    <span class="c1">#     )</span>
-
-    <span class="c1"># # predictors = pred.get_predictors(</span>
-    <span class="c1"># #                        selected_targets=[2],</span>
-    <span class="c1"># #                        selected_links=None,</span>
-    <span class="c1"># #                        steps_ahead=1,</span>
-    <span class="c1"># #                        tau_max=1,</span>
-    <span class="c1"># #                        pc_alpha=0.2,</span>
-    <span class="c1"># #                        max_conds_dim=None,</span>
-    <span class="c1"># #                        max_combinations=1)</span>
-    <span class="c1"># predictors = {0: [(0, -1)],</span>
-    <span class="c1">#              1: [(1, -1), (0, -1)],</span>
-    <span class="c1">#              2: [(2, -1), (1, 0)]}</span>
-    <span class="c1"># pred.fit(target_predictors=predictors,</span>
-    <span class="c1">#         selected_targets=None, tau_max=None, return_data=False)</span>
-
-    <span class="c1"># res = pred.predict(target=2,</span>
-    <span class="c1">#             new_data=None,</span>
-    <span class="c1">#             pred_params=None,</span>
-    <span class="c1">#             cut_off=&#39;max_lag_or_tau_max&#39;)</span>
-
-    <span class="c1"># print(data[:,2])</span>
-    <span class="c1"># print(res)</span>
-
-
-</pre></div>
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-  <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">import</span> <span class="nn">itertools</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
-<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">scipy.stats</span>
-
-<span class="kn">from</span> <span class="nn">.pcmci_base</span> <span class="kn">import</span> <span class="n">PCMCIbase</span>
-
-<span class="k">def</span> <span class="nf">_create_nested_dictionary</span><span class="p">(</span><span class="n">depth</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">lowest_type</span><span class="o">=</span><span class="nb">dict</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a series of nested dictionaries to a maximum depth.  The first</span>
-<span class="sd">    depth - 1 nested dictionaries are defaultdicts, the last is a normal</span>
-<span class="sd">    dictionary.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    depth : int</span>
-<span class="sd">        Maximum depth argument.</span>
-<span class="sd">    lowest_type: callable (optional)</span>
-<span class="sd">        Type contained in leaves of tree.  Ex: list, dict, tuple, int, float ...</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">new_depth</span> <span class="o">=</span> <span class="n">depth</span> <span class="o">-</span> <span class="mi">1</span>
-    <span class="k">if</span> <span class="n">new_depth</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">defaultdict</span><span class="p">(</span><span class="n">lowest_type</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">_create_nested_dictionary</span><span class="p">(</span><span class="n">new_depth</span><span class="p">))</span>
-
-
-<span class="k">def</span> <span class="nf">_nested_to_normal</span><span class="p">(</span><span class="n">nested_dict</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Transforms the nested default dictionary into a standard dictionaries</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    nested_dict : default dictionary of default dictionaries of ... etc.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">nested_dict</span><span class="p">,</span> <span class="n">defaultdict</span><span class="p">):</span>
-        <span class="n">nested_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">_nested_to_normal</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">nested_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-    <span class="k">return</span> <span class="n">nested_dict</span>
-
-
-<div class="viewcode-block" id="PCMCI"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI">[docs]</a><span class="k">class</span> <span class="nc">PCMCI</span><span class="p">(</span><span class="n">PCMCIbase</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;PCMCI causal discovery for time series datasets.</span>
-
-<span class="sd">    PCMCI is a causal discovery framework for large-scale time series</span>
-<span class="sd">    datasets. This class contains several methods. The standard PCMCI method</span>
-<span class="sd">    addresses time-lagged causal discovery and is described in [1]_ where</span>
-<span class="sd">    also further sub-variants are discussed. Lagged as well as contemporaneous</span>
-<span class="sd">    causal discovery is addressed with PCMCIplus and described in [5]_. See the</span>
-<span class="sd">    tutorials for guidance in applying these methods.</span>
-
-<span class="sd">    PCMCI has:</span>
-
-<span class="sd">    * different conditional independence tests adapted to linear or</span>
-<span class="sd">      nonlinear dependencies, and continuously-valued or discrete data (</span>
-<span class="sd">      implemented in ``tigramite.independence_tests``)</span>
-<span class="sd">    * (mostly) hyperparameter optimization</span>
-<span class="sd">    * easy parallelization (separate script)</span>
-<span class="sd">    * handling of masked time series data</span>
-<span class="sd">    * false discovery control and confidence interval estimation</span>
-
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    .. image:: mci_schematic.*</span>
-<span class="sd">       :width: 200pt</span>
-
-<span class="sd">    In the PCMCI framework, the dependency structure of a set of time series</span>
-<span class="sd">    variables is represented in a *time series graph* as shown in the Figure.</span>
-<span class="sd">    The nodes of a time series graph are defined as the variables at</span>
-<span class="sd">    different times and a link indicates a conditional dependency that can be</span>
-<span class="sd">    interpreted as a causal dependency under certain assumptions (see paper).</span>
-<span class="sd">    Assuming stationarity, the links are repeated in time. The parents</span>
-<span class="sd">    :math:`\mathcal{P}` of a variable are defined as the set of all nodes</span>
-<span class="sd">    with a link towards it (blue and red boxes in Figure).</span>
-
-<span class="sd">    The different PCMCI methods estimate causal links by iterative</span>
-<span class="sd">    conditional independence testing. PCMCI can be flexibly combined with</span>
-<span class="sd">    any kind of conditional independence test statistic adapted to the kind</span>
-<span class="sd">    of data (continuous or discrete) and its assumed dependency types.</span>
-<span class="sd">    These are available in ``tigramite.independence_tests``.</span>
-
-<span class="sd">    NOTE: MCI test statistic values define a particular measure of causal</span>
-<span class="sd">    strength depending on the test statistic used. For example, ParCorr()</span>
-<span class="sd">    results in normalized values between -1 and 1. However, if you are </span>
-<span class="sd">    interested in quantifying causal effects, i.e., the effect of</span>
-<span class="sd">    hypothetical interventions, you may better look at the causal effect </span>
-<span class="sd">    estimation functionality of Tigramite.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    .. [1] J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic,</span>
-<span class="sd">           Detecting and quantifying causal associations in large nonlinear time </span>
-<span class="sd">           series datasets. Sci. Adv. 5, eaau4996 (2019) </span>
-<span class="sd">           https://advances.sciencemag.org/content/5/11/eaau4996</span>
-
-<span class="sd">    .. [5] J. Runge,</span>
-<span class="sd">           Discovering contemporaneous and lagged causal relations in </span>
-<span class="sd">           autocorrelated nonlinear time series datasets</span>
-<span class="sd">           http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataframe : data object</span>
-<span class="sd">        This is the Tigramite dataframe object. Among others, it has the</span>
-<span class="sd">        attributes dataframe.values yielding a numpy array of shape (</span>
-<span class="sd">        observations T, variables N) and optionally a mask of the same shape.</span>
-<span class="sd">    cond_ind_test : conditional independence test object</span>
-<span class="sd">        This can be ParCorr or other classes from</span>
-<span class="sd">        ``tigramite.independence_tests`` or an external test passed as a</span>
-<span class="sd">        callable. This test can be based on the class</span>
-<span class="sd">        tigramite.independence_tests.CondIndTest.</span>
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Verbose levels 0, 1, ...</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    all_parents : dictionary</span>
-<span class="sd">        Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing</span>
-<span class="sd">        the conditioning-parents estimated with PC algorithm.</span>
-<span class="sd">    val_min : dictionary</span>
-<span class="sd">        Dictionary of form val_min[j][(i, -tau)] = float</span>
-<span class="sd">        containing the minimum test statistic value for each link estimated in</span>
-<span class="sd">        the PC algorithm.</span>
-<span class="sd">    pval_max : dictionary</span>
-<span class="sd">        Dictionary of form pval_max[j][(i, -tau)] = float containing the maximum</span>
-<span class="sd">        p-value for each link estimated in the PC algorithm.</span>
-<span class="sd">    iterations : dictionary</span>
-<span class="sd">        Dictionary containing further information on algorithm steps.</span>
-<span class="sd">    N : int</span>
-<span class="sd">        Number of variables.</span>
-<span class="sd">    T : dict</span>
-<span class="sd">        Time series sample length of dataset(s).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">,</span>
-                 <span class="n">cond_ind_test</span><span class="p">,</span>
-                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-
-        <span class="c1"># Init base class</span>
-        <span class="n">PCMCIbase</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
-                        <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">,</span>
-                        <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
-
-
-    <span class="k">def</span> <span class="nf">_iter_conditions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parent</span><span class="p">,</span> <span class="n">conds_dim</span><span class="p">,</span> <span class="n">all_parents</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Yield next condition.</span>
-
-<span class="sd">        Yields next condition from lexicographically ordered conditions.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        parent : tuple</span>
-<span class="sd">            Tuple of form (i, -tau).</span>
-<span class="sd">        conds_dim : int</span>
-<span class="sd">            Cardinality in current step.</span>
-<span class="sd">        all_parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...].</span>
-
-<span class="sd">        Yields</span>
-<span class="sd">        -------</span>
-<span class="sd">        cond :  list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...] for the next condition.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">all_parents_excl_current</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">all_parents</span> <span class="k">if</span> <span class="n">p</span> <span class="o">!=</span> <span class="n">parent</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">cond</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">all_parents_excl_current</span><span class="p">,</span> <span class="n">conds_dim</span><span class="p">):</span>
-            <span class="k">yield</span> <span class="nb">list</span><span class="p">(</span><span class="n">cond</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_sort_parents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parents_vals</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Sort current parents according to test statistic values.</span>
-
-<span class="sd">        Sorting is from strongest to weakest absolute values.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ---------</span>
-<span class="sd">        parents_vals : dict</span>
-<span class="sd">            Dictionary of form {(0, -1):float, ...} containing the minimum test</span>
-<span class="sd">            statistic value of a link.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...] containing sorted parents.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">    Sorting parents in decreasing order with &quot;</span>
-                  <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">    weight(i-tau-&gt;j) = min_</span><span class="si">{iterations}</span><span class="s2"> |val_</span><span class="si">{ij}</span><span class="s2">(tau)| &quot;</span><span class="p">)</span>
-        <span class="c1"># Get the absolute value for all the test statistics</span>
-        <span class="n">abs_values</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">parents_vals</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">parents_vals</span><span class="p">)}</span>
-        <span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">abs_values</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">abs_values</span><span class="o">.</span><span class="n">get</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_print_link_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">index_parent</span><span class="p">,</span> <span class="n">parent</span><span class="p">,</span> <span class="n">num_parents</span><span class="p">,</span>
-                         <span class="n">already_removed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print info about the current link being tested.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of current node being tested.</span>
-<span class="sd">        index_parent : int</span>
-<span class="sd">            Index of the current parent.</span>
-<span class="sd">        parent : tuple</span>
-<span class="sd">            Standard (i, tau) tuple of parent node id and time delay</span>
-<span class="sd">        num_parents : int</span>
-<span class="sd">            Total number of parents.</span>
-<span class="sd">        already_removed : bool</span>
-<span class="sd">            Whether parent was already removed.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">link_marker</span> <span class="o">=</span> <span class="p">{</span><span class="kc">True</span><span class="p">:</span><span class="s2">&quot;o?o&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">:</span><span class="s2">&quot;-?&gt;&quot;</span><span class="p">}</span>
-
-        <span class="n">abstau</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">parent</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">    Link (</span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">) </span><span class="si">%s</span><span class="s2"> </span><span class="si">%s</span><span class="s2"> (</span><span class="si">%d</span><span class="s2">/</span><span class="si">%d</span><span class="s2">):&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">parent</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">parent</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">link_marker</span><span class="p">[</span><span class="n">abstau</span><span class="o">==</span><span class="mi">0</span><span class="p">],</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                <span class="n">index_parent</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">num_parents</span><span class="p">))</span>
-
-            <span class="k">if</span> <span class="n">already_removed</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    Already removed.&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_print_cond_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">comb_index</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print info about the condition</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        Z : list</span>
-<span class="sd">            The current condition being tested.</span>
-<span class="sd">        comb_index : int</span>
-<span class="sd">            Index of the combination yielding this condition.</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value from this condition.</span>
-<span class="sd">        val : float</span>
-<span class="sd">            value from this condition.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">var_name_z</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">:</span>
-            <span class="n">var_name_z</span> <span class="o">+=</span> <span class="s2">&quot;(</span><span class="si">%s</span><span class="s2"> </span><span class="si">% .2s</span><span class="s2">) &quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tau</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">var_name_z</span> <span class="o">=</span> <span class="s2">&quot;()&quot;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    Subset </span><span class="si">%d</span><span class="s2">: </span><span class="si">%s</span><span class="s2"> gives pval = </span><span class="si">%.5f</span><span class="s2"> / val = </span><span class="si">% .3f</span><span class="s2">&quot;</span> <span class="o">%</span>
-              <span class="p">(</span><span class="n">comb_index</span><span class="p">,</span> <span class="n">var_name_z</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">))</span>
-
-    <span class="k">def</span> <span class="nf">_print_a_pc_result</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nonsig</span><span class="p">,</span> <span class="n">conds_dim</span><span class="p">,</span> <span class="n">max_combinations</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print the results from the current iteration of conditions.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        nonsig : bool</span>
-<span class="sd">            Indicate non-significance.</span>
-<span class="sd">        conds_dim : int</span>
-<span class="sd">            Cardinality of the current step.</span>
-<span class="sd">        max_combinations : int</span>
-<span class="sd">            Maximum number of combinations of conditions of current cardinality</span>
-<span class="sd">            to test.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Start with an indent</span>
-        <span class="n">print_str</span> <span class="o">=</span> <span class="s2">&quot;    &quot;</span>
-        <span class="c1"># Determine the body of the text</span>
-        <span class="k">if</span> <span class="n">nonsig</span><span class="p">:</span>
-            <span class="n">print_str</span> <span class="o">+=</span> <span class="s2">&quot;Non-significance detected.&quot;</span>
-        <span class="k">elif</span> <span class="n">conds_dim</span> <span class="o">&gt;</span> <span class="n">max_combinations</span><span class="p">:</span>
-            <span class="n">print_str</span> <span class="o">+=</span> <span class="s2">&quot;Still subsets of dimension&quot;</span> <span class="o">+</span> \
-                         <span class="s2">&quot; </span><span class="si">%d</span><span class="s2"> left,&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">conds_dim</span><span class="p">)</span> <span class="o">+</span> \
-                         <span class="s2">&quot; but q_max = </span><span class="si">%d</span><span class="s2"> reached.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">max_combinations</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">print_str</span> <span class="o">+=</span> <span class="s2">&quot;No conditions of dimension </span><span class="si">%d</span><span class="s2"> left.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">conds_dim</span><span class="p">)</span>
-        <span class="c1"># Print the message</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">print_str</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_print_converged_pc_single</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">converged</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">max_conds_dim</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Print statement about the convergence of the pc_stable_single algorithm.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        convergence : bool</span>
-<span class="sd">            true if convergence was reached.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Variable index.</span>
-<span class="sd">        max_conds_dim : int</span>
-<span class="sd">            Maximum number of conditions to test.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">converged</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Algorithm converged for variable </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span>
-                  <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span>
-                <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Algorithm not yet converged, but max_conds_dim = </span><span class="si">%d</span><span class="s2">&quot;</span>
-                <span class="s2">&quot; reached.&quot;</span> <span class="o">%</span> <span class="n">max_conds_dim</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_run_pc_stable_single</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span>
-                              <span class="n">link_assumptions_j</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                              <span class="n">tau_min</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                              <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                              <span class="n">save_iterations</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                              <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-                              <span class="n">max_conds_dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                              <span class="n">max_combinations</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Lagged PC algorithm for estimating lagged parents of single variable.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Variable index.</span>
-<span class="sd">        link_assumptions_j : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        tau_min : int, optional (default: 1)</span>
-<span class="sd">            Minimum time lag to test. Useful for variable selection in</span>
-<span class="sd">            multi-step ahead predictions. Must be greater zero.</span>
-<span class="sd">        tau_max : int, optional (default: 1)</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        save_iterations : bool, optional (default: False)</span>
-<span class="sd">            Whether to save iteration step results such as conditions used.</span>
-<span class="sd">        pc_alpha : float or None, optional (default: 0.2)</span>
-<span class="sd">            Significance level in algorithm. If a list is given, pc_alpha is</span>
-<span class="sd">            optimized using model selection criteria provided in the</span>
-<span class="sd">            cond_ind_test class as get_model_selection_criterion(). If None,</span>
-<span class="sd">            a default list of values is used.</span>
-<span class="sd">        max_conds_dim : int, optional (default: None)</span>
-<span class="sd">            Maximum number of conditions to test. If None is passed, this number</span>
-<span class="sd">            is unrestricted.</span>
-<span class="sd">        max_combinations : int, optional (default: 1)</span>
-<span class="sd">            Maximum number of combinations of conditions of current cardinality</span>
-<span class="sd">            to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm</span>
-<span class="sd">            a larger number, such as 10, can be used.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        parents : list</span>
-<span class="sd">            List of estimated parents.</span>
-<span class="sd">        val_min : dict</span>
-<span class="sd">            Dictionary of form {(0, -1):float, ...} containing the minimum test</span>
-<span class="sd">            statistic value of a link.</span>
-<span class="sd">        pval_max : dict</span>
-<span class="sd">            Dictionary of form {(0, -1):float, ...} containing the maximum</span>
-<span class="sd">            p-value of a link across different conditions.</span>
-<span class="sd">        iterations : dict</span>
-<span class="sd">            Dictionary containing further information on algorithm steps.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">pc_alpha</span> <span class="o">&lt;</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="n">pc_alpha</span> <span class="o">&gt;</span> <span class="mf">1.</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Choose 0 &lt;= pc_alpha &lt;= 1&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Initialize the dictionaries for the pval_max, val_min parents_values</span>
-        <span class="c1"># results</span>
-        <span class="n">pval_max</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="n">val_min</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="n">parents_values</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="c1"># Initialize the parents values from the selected links, copying to</span>
-        <span class="c1"># ensure this initial argument is unchanged.</span>
-        <span class="n">parents</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">itau</span> <span class="ow">in</span> <span class="n">link_assumptions_j</span><span class="p">:</span>
-            <span class="n">link_type</span> <span class="o">=</span> <span class="n">link_assumptions_j</span><span class="p">[</span><span class="n">itau</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">itau</span> <span class="o">!=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">link_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;?-&#39;</span><span class="p">]:</span>
-                <span class="n">parents</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itau</span><span class="p">)</span>
-
-        <span class="n">val_min</span> <span class="o">=</span> <span class="p">{(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">}</span>
-        <span class="n">pval_max</span> <span class="o">=</span> <span class="p">{(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">}</span>
-
-        <span class="c1"># Define a nested defaultdict of depth 4 to save all information about</span>
-        <span class="c1"># iterations</span>
-        <span class="n">iterations</span> <span class="o">=</span> <span class="n">_create_nested_dictionary</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-        <span class="c1"># Ensure tau_min is at least 1</span>
-        <span class="n">tau_min</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">)</span>
-
-        <span class="c1"># Loop over all possible condition dimensions</span>
-        <span class="n">max_conds_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                                    <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="c1"># Iteration through increasing number of conditions, i.e. from </span>
-        <span class="c1"># [0, max_conds_dim] inclusive</span>
-        <span class="n">converged</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">for</span> <span class="n">conds_dim</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_conds_dim</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="c1"># (Re)initialize the list of non-significant links</span>
-            <span class="n">nonsig_parents</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
-            <span class="c1"># Check if the algorithm has converged</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">&lt;</span> <span class="n">conds_dim</span><span class="p">:</span>
-                <span class="n">converged</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="k">break</span>
-            <span class="c1"># Print information about</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Testing condition sets of dimension </span><span class="si">%d</span><span class="s2">:&quot;</span> <span class="o">%</span> <span class="n">conds_dim</span><span class="p">)</span>
-
-            <span class="c1"># Iterate through all possible pairs (that have not converged yet)</span>
-            <span class="k">for</span> <span class="n">index_parent</span><span class="p">,</span> <span class="n">parent</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">parents</span><span class="p">):</span>
-                <span class="c1"># Print info about this link</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_print_link_info</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">index_parent</span><span class="p">,</span> <span class="n">parent</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents</span><span class="p">))</span>
-                <span class="c1"># Iterate through all possible combinations</span>
-                <span class="n">nonsig</span> <span class="o">=</span> <span class="kc">False</span>
-                <span class="k">for</span> <span class="n">comb_index</span><span class="p">,</span> <span class="n">Z</span> <span class="ow">in</span> \
-                        <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_iter_conditions</span><span class="p">(</span><span class="n">parent</span><span class="p">,</span> <span class="n">conds_dim</span><span class="p">,</span>
-                                                        <span class="n">parents</span><span class="p">)):</span>
-                    <span class="c1"># Break if we try too many combinations</span>
-                    <span class="k">if</span> <span class="n">comb_index</span> <span class="o">&gt;=</span> <span class="n">max_combinations</span><span class="p">:</span>
-                        <span class="k">break</span>
-                    <span class="c1"># Perform independence test</span>
-                    <span class="k">if</span> <span class="n">link_assumptions_j</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">:</span>
-                        <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span>
-                        <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[</span><span class="n">parent</span><span class="p">],</span>
-                                                    <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span>
-                                                    <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
-                                                    <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                    <span class="c1"># verbosity=self.verbosity</span>
-                                                    <span class="p">)</span>
-                    <span class="c1"># Print some information if needed</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_info</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">comb_index</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
-                    <span class="c1"># Keep track of maximum p-value and minimum estimated value</span>
-                    <span class="c1"># for each pair (across any condition)</span>
-                    <span class="n">parents_values</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> \
-                        <span class="nb">min</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val</span><span class="p">),</span> <span class="n">parents_values</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">parent</span><span class="p">,</span>
-                                                            <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">)))</span>
-
-                    <span class="k">if</span> <span class="n">pval_max</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pval_max</span><span class="p">[</span><span class="n">parent</span><span class="p">]:</span>
-                        <span class="n">pval_max</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
-                        <span class="n">val_min</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
-
-                    <span class="c1"># Save the iteration if we need to</span>
-                    <span class="k">if</span> <span class="n">save_iterations</span><span class="p">:</span>
-                        <span class="n">a_iter</span> <span class="o">=</span> <span class="n">iterations</span><span class="p">[</span><span class="s1">&#39;iterations&#39;</span><span class="p">][</span><span class="n">conds_dim</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span>
-                        <span class="n">a_iter</span><span class="p">[</span><span class="n">comb_index</span><span class="p">][</span><span class="s1">&#39;conds&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span>
-                        <span class="n">a_iter</span><span class="p">[</span><span class="n">comb_index</span><span class="p">][</span><span class="s1">&#39;val&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
-                        <span class="n">a_iter</span><span class="p">[</span><span class="n">comb_index</span><span class="p">][</span><span class="s1">&#39;pval&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
-                    <span class="c1"># Delete link later and break while-loop if non-significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pc_alpha</span><span class="p">:</span>
-                        <span class="n">nonsig_parents</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="n">parent</span><span class="p">))</span>
-                        <span class="n">nonsig</span> <span class="o">=</span> <span class="kc">True</span>
-                        <span class="k">break</span>
-
-                <span class="c1"># Print the results if needed</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_print_a_pc_result</span><span class="p">(</span><span class="n">nonsig</span><span class="p">,</span>
-                                            <span class="n">conds_dim</span><span class="p">,</span> <span class="n">max_combinations</span><span class="p">)</span>
-
-            <span class="c1"># Remove non-significant links</span>
-            <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">parent</span> <span class="ow">in</span> <span class="n">nonsig_parents</span><span class="p">:</span>
-                <span class="k">del</span> <span class="n">parents_values</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span>
-            <span class="c1"># Return the parents list sorted by the test metric so that the</span>
-            <span class="c1"># updated parents list is given to the next cond_dim loop</span>
-            <span class="n">parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sort_parents</span><span class="p">(</span><span class="n">parents_values</span><span class="p">)</span>
-            <span class="c1"># Print information about the change in possible parents</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Updating parents:&quot;</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_print_parents_single</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">parents_values</span><span class="p">,</span> <span class="n">pval_max</span><span class="p">)</span>
-
-        <span class="c1"># Print information about if convergence was reached</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_converged_pc_single</span><span class="p">(</span><span class="n">converged</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">max_conds_dim</span><span class="p">)</span>
-        <span class="c1"># Return the results</span>
-        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;parents&#39;</span><span class="p">:</span> <span class="n">parents</span><span class="p">,</span>
-                <span class="s1">&#39;val_min&#39;</span><span class="p">:</span> <span class="n">val_min</span><span class="p">,</span>
-                <span class="s1">&#39;pval_max&#39;</span><span class="p">:</span> <span class="n">pval_max</span><span class="p">,</span>
-                <span class="s1">&#39;iterations&#39;</span><span class="p">:</span> <span class="n">_nested_to_normal</span><span class="p">(</span><span class="n">iterations</span><span class="p">)}</span>
-
-    <span class="k">def</span> <span class="nf">_print_pc_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="p">,</span>
-                         <span class="n">max_conds_dim</span><span class="p">,</span> <span class="n">max_combinations</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print the setup of the current pc_stable run.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        link_assumptions : dict or None</span>
-<span class="sd">            Dictionary of form specifying which links should be tested.</span>
-<span class="sd">        tau_min : int, default: 1</span>
-<span class="sd">            Minimum time lag to test.</span>
-<span class="sd">        tau_max : int, default: 1</span>
-<span class="sd">            Maximum time lag to test.</span>
-<span class="sd">        pc_alpha : float or list of floats</span>
-<span class="sd">            Significance level in algorithm.</span>
-<span class="sd">        max_conds_dim : int</span>
-<span class="sd">            Maximum number of conditions to test.</span>
-<span class="sd">        max_combinations : int</span>
-<span class="sd">            Maximum number of combinations of conditions to test.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Step 1: PC1 algorithm with lagged conditions</span><span class="se">\n</span><span class="s2">##&quot;</span>
-              <span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Parameters:&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">link_assumptions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;link_assumptions = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">))</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;independence test = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">measure</span>
-              <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_min = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_min</span>
-              <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_max = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_max</span>
-              <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">pc_alpha = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">pc_alpha</span>
-              <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">max_conds_dim = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">max_conds_dim</span>
-              <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">max_combinations = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">max_combinations</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_print_pc_sel_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="p">,</span> <span class="n">results</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">optimal_alpha</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print the results from the pc_alpha selection.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        pc_alpha : list</span>
-<span class="sd">            Tested significance levels in algorithm.</span>
-<span class="sd">        results : dict</span>
-<span class="sd">            Results from the tested pc_alphas.</span>
-<span class="sd">        score : array of floats</span>
-<span class="sd">            scores from each pc_alpha.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of current variable.</span>
-<span class="sd">        optimal_alpha : float</span>
-<span class="sd">            Optimal value of pc_alpha.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2"># Condition selection results:&quot;</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">iscore</span><span class="p">,</span> <span class="n">pc_alpha_here</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pc_alpha</span><span class="p">):</span>
-            <span class="n">names_parents</span> <span class="o">=</span> <span class="s2">&quot;[ &quot;</span>
-            <span class="k">for</span> <span class="n">pari</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">pc_alpha_here</span><span class="p">][</span><span class="s1">&#39;parents&#39;</span><span class="p">]:</span>
-                <span class="n">names_parents</span> <span class="o">+=</span> <span class="s2">&quot;(</span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">) &quot;</span> <span class="o">%</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">pari</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">pari</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-            <span class="n">names_parents</span> <span class="o">+=</span> <span class="s2">&quot;]&quot;</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    pc_alpha=</span><span class="si">%s</span><span class="s2"> got score </span><span class="si">%.4f</span><span class="s2"> with parents </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span>
-                  <span class="p">(</span><span class="n">pc_alpha_here</span><span class="p">,</span> <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">],</span> <span class="n">names_parents</span><span class="p">))</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">==&gt; optimal pc_alpha for variable </span><span class="si">%s</span><span class="s2"> is </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span>
-              <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">optimal_alpha</span><span class="p">))</span>
-
-    <span class="k">def</span> <span class="nf">_check_tau_limits</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check the tau limits adhere to 0 &lt;= tau_min &lt;= tau_max.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        tau_min : float</span>
-<span class="sd">            Minimum tau value.</span>
-<span class="sd">        tau_max : float</span>
-<span class="sd">            Maximum tau value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">tau_min</span> <span class="o">&lt;=</span> <span class="n">tau_max</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;tau_max = </span><span class="si">%d</span><span class="s2">, &quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">tau_max</span><span class="p">)</span> <span class="o">+</span> \
-                             <span class="s2">&quot;tau_min = </span><span class="si">%d</span><span class="s2">, &quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">tau_min</span><span class="p">)</span> <span class="o">+</span> \
-                             <span class="s2">&quot;but 0 &lt;= tau_min &lt;= tau_max&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_set_max_condition_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_conds_dim</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Set the maximum dimension of the conditions. Defaults to self.N*tau_max.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        max_conds_dim : int</span>
-<span class="sd">            Input maximum condition dimension.</span>
-<span class="sd">        tau_max : int</span>
-<span class="sd">            Maximum tau.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        max_conds_dim : int</span>
-<span class="sd">            Input maximum condition dimension or default.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Check if an input was given</span>
-        <span class="k">if</span> <span class="n">max_conds_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">max_conds_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">*</span> <span class="p">(</span><span class="n">tau_max</span> <span class="o">-</span> <span class="n">tau_min</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="c1"># Check this is a valid</span>
-        <span class="k">if</span> <span class="n">max_conds_dim</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;maximum condition dimension must be &gt;= 0&quot;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">max_conds_dim</span>
-
-<div class="viewcode-block" id="PCMCI.run_pc_stable"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_pc_stable">[docs]</a>    <span class="k">def</span> <span class="nf">run_pc_stable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                      <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                      <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                      <span class="n">tau_min</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                      <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                      <span class="n">save_iterations</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                      <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-                      <span class="n">max_conds_dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                      <span class="n">max_combinations</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Lagged PC algorithm for estimating lagged parents of all variables.</span>
-
-<span class="sd">        Parents are made available as self.all_parents</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Deprecated, replaced by link_assumptions</span>
-<span class="sd">        link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        tau_min : int, default: 1</span>
-<span class="sd">            Minimum time lag to test. Useful for multi-step ahead predictions.</span>
-<span class="sd">            Must be greater zero.</span>
-<span class="sd">        tau_max : int, default: 1</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        save_iterations : bool, default: False</span>
-<span class="sd">            Whether to save iteration step results such as conditions used.</span>
-<span class="sd">        pc_alpha : float or list of floats, default: [0.05, 0.1, 0.2, ..., 0.5]</span>
-<span class="sd">            Significance level in algorithm. If a list or None is passed, the</span>
-<span class="sd">            pc_alpha level is optimized for every variable across the given</span>
-<span class="sd">            pc_alpha values using the score computed in</span>
-<span class="sd">            cond_ind_test.get_model_selection_criterion().</span>
-<span class="sd">        max_conds_dim : int or None</span>
-<span class="sd">            Maximum number of conditions to test. If None is passed, this number</span>
-<span class="sd">            is unrestricted.</span>
-<span class="sd">        max_combinations : int, default: 1</span>
-<span class="sd">            Maximum number of combinations of conditions of current cardinality</span>
-<span class="sd">            to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm</span>
-<span class="sd">            a larger number, such as 10, can be used.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        all_parents : dict</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
-<span class="sd">            containing estimated parents.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selected_links is DEPRECATED, use link_assumptions instead.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Create an internal copy of pc_alpha</span>
-        <span class="n">_int_pc_alpha</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">pc_alpha</span><span class="p">)</span>
-        <span class="c1"># Check if we are selecting an optimal alpha value</span>
-        <span class="n">select_optimal_alpha</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="c1"># Set the default values for pc_alpha</span>
-        <span class="k">if</span> <span class="n">_int_pc_alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">_int_pc_alpha</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">_int_pc_alpha</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
-            <span class="n">_int_pc_alpha</span> <span class="o">=</span> <span class="p">[</span><span class="n">_int_pc_alpha</span><span class="p">]</span>
-            <span class="n">select_optimal_alpha</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="c1"># Check the limits on tau_min</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_tau_limits</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="n">tau_min</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">)</span>
-        <span class="c1"># Check that the maximum combinations variable is correct</span>
-        <span class="k">if</span> <span class="n">max_combinations</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;max_combinations must be &gt; 0&quot;</span><span class="p">)</span>
-        <span class="c1"># Implement defaultdict for all pval_max, val_max, and iterations</span>
-        <span class="n">pval_max</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
-        <span class="n">val_min</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
-        <span class="n">iterations</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_pc_params</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span>
-                              <span class="n">_int_pc_alpha</span><span class="p">,</span> <span class="n">max_conds_dim</span><span class="p">,</span>
-                              <span class="n">max_combinations</span><span class="p">)</span>
-
-        <span class="c1"># Set the selected links</span>
-        <span class="c1"># _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max,</span>
-        <span class="c1">#                                      remove_contemp=True)</span>
-        <span class="n">_int_link_assumptions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_link_assumptions</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">,</span> 
-            <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">remove_contemp</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="c1"># Initialize all parents</span>
-        <span class="n">all_parents</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="c1"># Set the maximum condition dimension</span>
-        <span class="n">max_conds_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                                    <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="c1"># Loop through the selected variables</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="c1"># Print the status of this variable</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">## Variable </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Iterating through pc_alpha = </span><span class="si">%s</span><span class="s2">:&quot;</span> <span class="o">%</span> <span class="n">_int_pc_alpha</span><span class="p">)</span>
-            <span class="c1"># Initialize the scores for selecting the optimal alpha</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">_int_pc_alpha</span><span class="p">)</span>
-            <span class="c1"># Initialize the result</span>
-            <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">iscore</span><span class="p">,</span> <span class="n">pc_alpha_here</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">_int_pc_alpha</span><span class="p">):</span>
-                <span class="c1"># Print statement about the pc_alpha being tested</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2"># pc_alpha = </span><span class="si">%s</span><span class="s2"> (</span><span class="si">%d</span><span class="s2">/</span><span class="si">%d</span><span class="s2">):&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">pc_alpha_here</span><span class="p">,</span>
-                                                          <span class="n">iscore</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
-                                                          <span class="n">score</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-                <span class="c1"># Get the results for this alpha value</span>
-                <span class="n">results</span><span class="p">[</span><span class="n">pc_alpha_here</span><span class="p">]</span> <span class="o">=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_run_pc_stable_single</span><span class="p">(</span><span class="n">j</span><span class="p">,</span>
-                                               <span class="n">link_assumptions_j</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                                               <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-                                               <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                               <span class="n">save_iterations</span><span class="o">=</span><span class="n">save_iterations</span><span class="p">,</span>
-                                               <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha_here</span><span class="p">,</span>
-                                               <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                               <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">)</span>
-                <span class="c1"># Figure out the best score if there is more than one pc_alpha</span>
-                <span class="c1"># value</span>
-                <span class="k">if</span> <span class="n">select_optimal_alpha</span><span class="p">:</span>
-                    <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]</span> <span class="o">=</span> \
-                        <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span>
-                            <span class="n">j</span><span class="p">,</span> <span class="n">results</span><span class="p">[</span><span class="n">pc_alpha_here</span><span class="p">][</span><span class="s1">&#39;parents&#39;</span><span class="p">],</span> <span class="n">tau_max</span><span class="p">)</span>
-            <span class="c1"># Record the optimal alpha value</span>
-            <span class="n">optimal_alpha</span> <span class="o">=</span> <span class="n">_int_pc_alpha</span><span class="p">[</span><span class="n">score</span><span class="o">.</span><span class="n">argmin</span><span class="p">()]</span>
-            <span class="c1"># Only print the selection results if there is more than one</span>
-            <span class="c1"># pc_alpha</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">select_optimal_alpha</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_print_pc_sel_results</span><span class="p">(</span><span class="n">_int_pc_alpha</span><span class="p">,</span> <span class="n">results</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span>
-                                           <span class="n">score</span><span class="p">,</span> <span class="n">optimal_alpha</span><span class="p">)</span>
-            <span class="c1"># Record the results for this variable</span>
-            <span class="n">all_parents</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">optimal_alpha</span><span class="p">][</span><span class="s1">&#39;parents&#39;</span><span class="p">]</span>
-            <span class="n">val_min</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">optimal_alpha</span><span class="p">][</span><span class="s1">&#39;val_min&#39;</span><span class="p">]</span>
-            <span class="n">pval_max</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">optimal_alpha</span><span class="p">][</span><span class="s1">&#39;pval_max&#39;</span><span class="p">]</span>
-            <span class="n">iterations</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">optimal_alpha</span><span class="p">][</span><span class="s1">&#39;iterations&#39;</span><span class="p">]</span>
-            <span class="c1"># Only save the optimal alpha if there is more than one pc_alpha</span>
-            <span class="k">if</span> <span class="n">select_optimal_alpha</span><span class="p">:</span>
-                <span class="n">iterations</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;optimal_pc_alpha&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">optimal_alpha</span>
-        <span class="c1"># Save the results in the current status of the algorithm</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span> <span class="o">=</span> <span class="n">all_parents</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">val_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict_to_matrix</span><span class="p">(</span><span class="n">val_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> 
-                                               <span class="n">default</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict_to_matrix</span><span class="p">(</span><span class="n">pval_max</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span>
-                                            <span class="n">default</span><span class="o">=</span><span class="mf">1.</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">iterations</span> <span class="o">=</span> <span class="n">iterations</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">val_min</span> <span class="o">=</span> <span class="n">val_min</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pval_max</span> <span class="o">=</span> <span class="n">pval_max</span>
-        <span class="c1"># Print the results</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">## Resulting lagged parent (super)sets:&quot;</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_parents</span><span class="p">(</span><span class="n">all_parents</span><span class="p">,</span> <span class="n">val_min</span><span class="p">,</span> <span class="n">pval_max</span><span class="p">)</span>
-        <span class="c1"># Return the parents</span>
-        <span class="k">return</span> <span class="n">all_parents</span></div>
-
-    <span class="k">def</span> <span class="nf">_print_parents_single</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">val_min</span><span class="p">,</span> <span class="n">pval_max</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print current parents for variable j.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Index of current variable.</span>
-<span class="sd">        parents : list</span>
-<span class="sd">            List of form [(0, -1), (3, -2), ...].</span>
-<span class="sd">        val_min : dict</span>
-<span class="sd">            Dictionary of form {(0, -1):float, ...} containing the minimum test</span>
-<span class="sd">            statistic value of a link.</span>
-<span class="sd">        pval_max : dict</span>
-<span class="sd">            Dictionary of form {(0, -1):float, ...} containing the maximum</span>
-<span class="sd">            p-value of a link across different conditions.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">20</span> <span class="ow">or</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;iterations&#39;</span><span class="p">):</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">    Variable </span><span class="si">%s</span><span class="s2"> has </span><span class="si">%d</span><span class="s2"> link(s):&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents</span><span class="p">)))</span>
-            <span class="k">if</span> <span class="p">(</span><span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;iterations&#39;</span><span class="p">)</span>
-                    <span class="ow">and</span> <span class="s1">&#39;optimal_pc_alpha&#39;</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">iterations</span><span class="p">[</span><span class="n">j</span><span class="p">])):</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    [pc_alpha = </span><span class="si">%s</span><span class="s2">]&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">iterations</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;optimal_pc_alpha&#39;</span><span class="p">]))</span>
-            <span class="k">if</span> <span class="n">val_min</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">pval_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;        (</span><span class="si">%s</span><span class="s2"> % .d)&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;        (</span><span class="si">%s</span><span class="s2"> % .d): max_pval = </span><span class="si">%.5f</span><span class="s2">, min_val = </span><span class="si">% .3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">pval_max</span><span class="p">[</span><span class="n">p</span><span class="p">],</span>
-                        <span class="n">val_min</span><span class="p">[</span><span class="n">p</span><span class="p">]))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">    Variable </span><span class="si">%s</span><span class="s2"> has </span><span class="si">%d</span><span class="s2"> link(s):&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents</span><span class="p">)))</span>
-
-    <span class="k">def</span> <span class="nf">_print_parents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">all_parents</span><span class="p">,</span> <span class="n">val_min</span><span class="p">,</span> <span class="n">pval_max</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print current parents.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        all_parents : dictionary</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing</span>
-<span class="sd">            the conditioning-parents estimated with PC algorithm.</span>
-<span class="sd">        val_min : dict</span>
-<span class="sd">            Dictionary of form {0:{(0, -1):float, ...}} containing the minimum</span>
-<span class="sd">            test statistic value of a link.</span>
-<span class="sd">        pval_max : dict</span>
-<span class="sd">            Dictionary of form {0:{(0, -1):float, ...}} containing the maximum</span>
-<span class="sd">            p-value of a link across different conditions.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">all_parents</span><span class="p">)]:</span>
-            <span class="k">if</span> <span class="n">val_min</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">pval_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_print_parents_single</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">all_parents</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                                           <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_print_parents_single</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">all_parents</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                                           <span class="n">val_min</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">pval_max</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-
-    <span class="k">def</span> <span class="nf">_mci_condition_to_string</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">conds</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the list of conditions into a string.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        conds : list</span>
-<span class="sd">            List of conditions.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">cond_string</span> <span class="o">=</span> <span class="s2">&quot;[ &quot;</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">tau_k</span> <span class="ow">in</span> <span class="n">conds</span><span class="p">:</span>
-            <span class="n">cond_string</span> <span class="o">+=</span> <span class="s2">&quot;(</span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">) &quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">tau_k</span><span class="p">)</span>
-        <span class="n">cond_string</span> <span class="o">+=</span> <span class="s2">&quot;]&quot;</span>
-        <span class="k">return</span> <span class="n">cond_string</span>
-
-    <span class="k">def</span> <span class="nf">_print_mci_conditions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">conds_y</span><span class="p">,</span> <span class="n">conds_x_lagged</span><span class="p">,</span>
-                              <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">count</span><span class="p">,</span> <span class="n">n_parents</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print information about the conditions for the MCI algorithm.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        conds_y : list</span>
-<span class="sd">            Conditions on node.</span>
-<span class="sd">        conds_x_lagged : list</span>
-<span class="sd">            Conditions on parent.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Current node.</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Parent node.</span>
-<span class="sd">        tau : int</span>
-<span class="sd">            Parent time delay.</span>
-<span class="sd">        count : int</span>
-<span class="sd">            Index of current parent.</span>
-<span class="sd">        n_parents : int</span>
-<span class="sd">            Total number of parents.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Remove the current parent from the conditions</span>
-        <span class="n">conds_y_no_i</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">conds_y</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">)]</span>
-        <span class="c1"># Get the condition string for parent</span>
-        <span class="n">condy_str</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mci_condition_to_string</span><span class="p">(</span><span class="n">conds_y_no_i</span><span class="p">)</span>
-        <span class="c1"># Get the condition string for node</span>
-        <span class="n">condx_str</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mci_condition_to_string</span><span class="p">(</span><span class="n">conds_x_lagged</span><span class="p">)</span>
-        <span class="c1"># Formate and print the information</span>
-        <span class="n">link_marker</span> <span class="o">=</span> <span class="p">{</span><span class="kc">True</span><span class="p">:</span><span class="s2">&quot;o?o&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">:</span><span class="s2">&quot;-?&gt;&quot;</span><span class="p">}</span>
-        <span class="n">indent</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">        &quot;</span>
-        <span class="n">print_str</span> <span class="o">=</span> <span class="n">indent</span> <span class="o">+</span> <span class="s2">&quot;link (</span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">) &quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tau</span><span class="p">)</span>
-        <span class="n">print_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> </span><span class="si">%s</span><span class="s2"> (</span><span class="si">%d</span><span class="s2">/</span><span class="si">%d</span><span class="s2">):&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">link_marker</span><span class="p">[</span><span class="n">tau</span><span class="o">==</span><span class="mi">0</span><span class="p">],</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">count</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_parents</span><span class="p">)</span>
-        <span class="n">print_str</span> <span class="o">+=</span> <span class="n">indent</span> <span class="o">+</span> <span class="s2">&quot;with conds_y = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">condy_str</span><span class="p">)</span>
-        <span class="n">print_str</span> <span class="o">+=</span> <span class="n">indent</span> <span class="o">+</span> <span class="s2">&quot;with conds_x = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">condx_str</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">print_str</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_print_pcmciplus_conditions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">,</span>
-                                    <span class="n">max_conds_py</span><span class="p">,</span> <span class="n">max_conds_px</span><span class="p">,</span> 
-                                    <span class="n">max_conds_px_lagged</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print information about the conditions for PCMCIplus.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        lagged_parents : dictionary of lists</span>
-<span class="sd">            Dictionary of lagged parents for each node.</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Current node.</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Parent node.</span>
-<span class="sd">        abstau : int</span>
-<span class="sd">            Parent time delay.</span>
-<span class="sd">        max_conds_py : int</span>
-<span class="sd">            Max number of parents for node j.</span>
-<span class="sd">        max_conds_px : int</span>
-<span class="sd">            Max number of parents for lagged node i.</span>
-<span class="sd">        max_conds_px_lagged : int</span>
-<span class="sd">            Maximum number of lagged conditions of X when X is lagged in MCI </span>
-<span class="sd">            tests. If None is passed, this number is equal to max_conds_px.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">conds_y</span> <span class="o">=</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">j</span><span class="p">][:</span><span class="n">max_conds_py</span><span class="p">]</span>
-        <span class="n">conds_y_no_i</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">conds_y</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)]</span>
-        <span class="k">if</span> <span class="n">abstau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">conds_x</span> <span class="o">=</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">i</span><span class="p">][:</span><span class="n">max_conds_px</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">max_conds_px_lagged</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">conds_x</span> <span class="o">=</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">i</span><span class="p">][:</span><span class="n">max_conds_px</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">conds_x</span> <span class="o">=</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">i</span><span class="p">][:</span><span class="n">max_conds_px_lagged</span><span class="p">]</span>
-
-        <span class="c1"># Shift the conditions for X by tau</span>
-        <span class="n">conds_x_lagged</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span> <span class="o">+</span> <span class="n">k_tau</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">k_tau</span> <span class="ow">in</span> <span class="n">conds_x</span><span class="p">]</span>
-        <span class="n">condy_str</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mci_condition_to_string</span><span class="p">(</span><span class="n">conds_y_no_i</span><span class="p">)</span>
-        <span class="n">condx_str</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mci_condition_to_string</span><span class="p">(</span><span class="n">conds_x_lagged</span><span class="p">)</span>
-        <span class="n">print_str</span> <span class="o">=</span> <span class="s2">&quot;    with conds_y = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">condy_str</span><span class="p">)</span>
-        <span class="n">print_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">    with conds_x = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">condx_str</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">print_str</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_int_parents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parents</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the input parents dictionary.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        parents : dict or None</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
-<span class="sd">            specifying the conditions for each variable. If None is</span>
-<span class="sd">            passed, no conditions are used.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        int_parents : defaultdict of lists</span>
-<span class="sd">            Internal copy of parents, respecting default options</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">int_parents</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">parents</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">int_parents</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">int_parents</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">int_parents</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">int_parents</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">int_parents</span>
-
-    <span class="k">def</span> <span class="nf">_iter_indep_conds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                          <span class="n">parents</span><span class="p">,</span>
-                          <span class="n">_int_link_assumptions</span><span class="p">,</span>
-                          <span class="n">max_conds_py</span><span class="p">,</span>
-                          <span class="n">max_conds_px</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Iterate through the conditions dictated by the arguments, yielding</span>
-<span class="sd">        the needed arguments for conditional independence functions.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        parents : dict</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
-<span class="sd">            specifying the conditions for each variable.</span>
-<span class="sd">        _int_link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        max_conds_py : int</span>
-<span class="sd">            Maximum number of conditions of Y to use.</span>
-<span class="sd">        max_conds_px : int</span>
-<span class="sd">            Maximum number of conditions of Z to use.</span>
-
-<span class="sd">        Yields</span>
-<span class="sd">        ------</span>
-<span class="sd">        i, j, tau, Z : list of tuples</span>
-<span class="sd">            (i, tau) is the parent node, (j, 0) is the current node, and Z is of</span>
-<span class="sd">            the form [(var, tau + tau&#39;)] and specifies the condition to test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Loop over the selected variables</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="c1"># Get the conditions for node j</span>
-            <span class="n">conds_y</span> <span class="o">=</span> <span class="n">parents</span><span class="p">[</span><span class="n">j</span><span class="p">][:</span><span class="n">max_conds_py</span><span class="p">]</span>
-            <span class="c1"># Create a parent list from links seperated in time and by node</span>
-            <span class="c1"># parent_list = [(i, tau) for i, tau in _int_link_assumptions[j]</span>
-            <span class="c1">#                if (i, tau) != (j, 0)]</span>
-            <span class="n">parent_list</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">itau</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="n">link_type</span> <span class="o">=</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">itau</span><span class="p">]</span>
-                <span class="k">if</span> <span class="n">itau</span> <span class="o">!=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">link_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;?-&#39;</span><span class="p">]:</span>
-                    <span class="n">parent_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itau</span><span class="p">)</span>
-            <span class="c1"># Iterate through parents (except those in conditions)</span>
-            <span class="k">for</span> <span class="n">cnt</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">parent_list</span><span class="p">):</span>
-                <span class="c1"># Get the conditions for node i</span>
-                <span class="n">conds_x</span> <span class="o">=</span> <span class="n">parents</span><span class="p">[</span><span class="n">i</span><span class="p">][:</span><span class="n">max_conds_px</span><span class="p">]</span>
-                <span class="c1"># Shift the conditions for X by tau</span>
-                <span class="n">conds_x_lagged</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span><span class="p">,</span> <span class="n">tau</span> <span class="o">+</span> <span class="n">k_tau</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">k_tau</span> <span class="ow">in</span> <span class="n">conds_x</span><span class="p">]</span>
-                <span class="c1"># Print information about the mci conditions if requested</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_print_mci_conditions</span><span class="p">(</span><span class="n">conds_y</span><span class="p">,</span> <span class="n">conds_x_lagged</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span>
-                                               <span class="n">tau</span><span class="p">,</span> <span class="n">cnt</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">parent_list</span><span class="p">))</span>
-                <span class="c1"># Construct lists of tuples for estimating</span>
-                <span class="c1"># I(X_t-tau; Y_t | Z^Y_t, Z^X_t-tau)</span>
-                <span class="c1"># with conditions for X shifted by tau</span>
-                <span class="n">Z</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">conds_y</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">)]</span>
-                <span class="c1"># Remove overlapped nodes between conds_x_lagged and conds_y</span>
-                <span class="n">Z</span> <span class="o">+=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">conds_x_lagged</span> <span class="k">if</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">]</span>
-                <span class="c1"># Yield these list</span>
-                <span class="k">yield</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">Z</span>
-
-    <span class="k">def</span> <span class="nf">_run_mci_or_variants</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                             <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                             <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                             <span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                             <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                             <span class="n">parents</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                             <span class="n">max_conds_py</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                             <span class="n">max_conds_px</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                             <span class="n">val_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                             <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                             <span class="n">fdr_method</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Base function for MCI method and variants.</span>
-
-<span class="sd">        Returns the matrices of test statistic values, (optionally corrected) </span>
-<span class="sd">        p-values, and (optionally) confidence intervals. Also (new in 4.3)</span>
-<span class="sd">        returns graph based on alpha_level (and optional FDR-correction).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Deprecated, replaced by link_assumptions</span>
-<span class="sd">        link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        tau_min : int, default: 0</span>
-<span class="sd">            Minimum time lag to test. Note that zero-lags are undirected.</span>
-<span class="sd">        tau_max : int, default: 1</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        parents : dict or None</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
-<span class="sd">            specifying the conditions for each variable. If None is</span>
-<span class="sd">            passed, no conditions are used.</span>
-<span class="sd">        max_conds_py : int or None</span>
-<span class="sd">            Maximum number of conditions of Y to use. If None is passed, this</span>
-<span class="sd">            number is unrestricted.</span>
-<span class="sd">        max_conds_px : int or None</span>
-<span class="sd">            Maximum number of conditions of Z to use. If None is passed, this</span>
-<span class="sd">            number is unrestricted.</span>
-<span class="sd">        val_only : bool, default: False</span>
-<span class="sd">            Option to only compute dependencies and not p-values.</span>
-<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level at which the p_matrix is thresholded to </span>
-<span class="sd">            get graph.</span>
-<span class="sd">        fdr_method : str, optional (default: &#39;none&#39;)</span>
-<span class="sd">            Correction method, currently implemented is Benjamini-Hochberg</span>
-<span class="sd">            False Discovery Rate method (&#39;fdr_bh&#39;). </span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of test statistic values.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values, optionally adjusted if fdr_method is</span>
-<span class="sd">            not &#39;none&#39;.</span>
-<span class="sd">        conf_matrix : array of shape [N, N, tau_max+1,2]</span>
-<span class="sd">            Estimated matrix of confidence intervals of test statistic values.</span>
-<span class="sd">            Only computed if set in cond_ind_test, where also the percentiles</span>
-<span class="sd">            are set.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selected_links is DEPRECATED, use link_assumptions instead.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Check the limits on tau</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_tau_limits</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="c1"># Set the selected links</span>
-        <span class="c1"># _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)</span>
-        <span class="n">_int_link_assumptions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_link_assumptions</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="c1"># Set the maximum condition dimension for Y and X</span>
-        <span class="n">max_conds_py</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_py</span><span class="p">,</span>
-                                                   <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="n">max_conds_px</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_px</span><span class="p">,</span>
-                                                   <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="c1"># Get the parents that will be checked</span>
-        <span class="n">_int_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_int_parents</span><span class="p">(</span><span class="n">parents</span><span class="p">)</span>
-        <span class="c1"># Initialize the return values</span>
-        <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">p_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="c1"># Initialize the optional return of the confidance matrix</span>
-        <span class="n">conf_matrix</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">confidence</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">conf_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-
-        <span class="c1"># Get the conditions as implied by the input arguments</span>
-        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">Z</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_iter_indep_conds</span><span class="p">(</span><span class="n">_int_parents</span><span class="p">,</span>
-                                                   <span class="n">_int_link_assumptions</span><span class="p">,</span>
-                                                   <span class="n">max_conds_py</span><span class="p">,</span>
-                                                   <span class="n">max_conds_px</span><span class="p">):</span>
-            <span class="c1"># Set X and Y (for clarity of code)</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">)]</span>
-            <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-
-            <span class="k">if</span> <span class="n">val_only</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-                <span class="c1"># Run the independence tests and record the results</span>
-                <span class="k">if</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> 
-                     <span class="ow">and</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o-o&#39;</span><span class="p">]):</span>
-                    <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span> 
-                    <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
-                                                        <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                        <span class="c1"># verbosity=</span>
-                                                        <span class="c1"># self.verbosity</span>
-                                                        <span class="p">)</span>
-                <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">val</span>
-                <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">pval</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_measure</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
-                <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">val</span>
-
-            <span class="c1"># Get the confidence value, returns None if cond_ind_test.confidence</span>
-            <span class="c1"># is False</span>
-            <span class="n">conf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_confidence</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
-            <span class="c1"># Record the value if the conditional independence requires it</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">confidence</span><span class="p">:</span>
-                <span class="n">conf_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">conf</span>
-
-        <span class="k">if</span> <span class="n">val_only</span><span class="p">:</span>
-            <span class="n">results</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span><span class="n">val_matrix</span><span class="p">,</span>
-                       <span class="s1">&#39;conf_matrix&#39;</span><span class="p">:</span><span class="n">conf_matrix</span><span class="p">}</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="o">=</span> <span class="n">results</span>
-            <span class="k">return</span> <span class="n">results</span>
-
-        <span class="c1"># Correct the p_matrix if there is a fdr_method</span>
-        <span class="k">if</span> <span class="n">fdr_method</span> <span class="o">!=</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
-            <span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_corrected_pvalues</span><span class="p">(</span><span class="n">p_matrix</span><span class="o">=</span><span class="n">p_matrix</span><span class="p">,</span> <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
-                                                  <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
-                                                  <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span>
-                                                  <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
-
-        <span class="c1"># Threshold p_matrix to get graph</span>
-        <span class="n">final_graph</span> <span class="o">=</span> <span class="n">p_matrix</span> <span class="o">&lt;=</span> <span class="n">alpha_level</span>
-
-        <span class="c1"># Convert to string graph representation</span>
-        <span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_to_string_graph</span><span class="p">(</span><span class="n">final_graph</span><span class="p">)</span>
-
-        <span class="c1"># Symmetrize p_matrix and val_matrix</span>
-        <span class="n">symmetrized_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symmetrize_p_and_val_matrix</span><span class="p">(</span>
-                            <span class="n">p_matrix</span><span class="o">=</span><span class="n">p_matrix</span><span class="p">,</span> 
-                            <span class="n">val_matrix</span><span class="o">=</span><span class="n">val_matrix</span><span class="p">,</span> 
-                            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span>
-                            <span class="n">conf_matrix</span><span class="o">=</span><span class="n">conf_matrix</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">print_significant_links</span><span class="p">(</span>
-                    <span class="n">graph</span> <span class="o">=</span> <span class="n">graph</span><span class="p">,</span>
-                    <span class="n">p_matrix</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span> 
-                    <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span>
-                    <span class="n">conf_matrix</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;conf_matrix&#39;</span><span class="p">],</span>
-                    <span class="n">alpha_level</span> <span class="o">=</span> <span class="n">alpha_level</span><span class="p">)</span>
-
-        <span class="c1"># Return the values as a dictionary and store in class</span>
-        <span class="n">results</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-            <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span>
-            <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span>
-            <span class="s1">&#39;conf_matrix&#39;</span><span class="p">:</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;conf_matrix&#39;</span><span class="p">],</span>
-                   <span class="p">}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="o">=</span> <span class="n">results</span>
-        <span class="k">return</span> <span class="n">results</span>
-
-<div class="viewcode-block" id="PCMCI.run_mci"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_mci">[docs]</a>    <span class="k">def</span> <span class="nf">run_mci</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">parents</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">max_conds_py</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">max_conds_px</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">val_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                <span class="n">fdr_method</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI conditional independence tests.</span>
-
-<span class="sd">        Implements the MCI test (Algorithm 2 in [1]_). </span>
-
-<span class="sd">        Returns the matrices of test statistic values, (optionally corrected) </span>
-<span class="sd">        p-values, and (optionally) confidence intervals. Also (new in 4.3)</span>
-<span class="sd">        returns graph based on alpha_level (and optional FDR-correction).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Deprecated, replaced by link_assumptions</span>
-<span class="sd">        link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        tau_min : int, default: 0</span>
-<span class="sd">            Minimum time lag to test. Note that zero-lags are undirected.</span>
-<span class="sd">        tau_max : int, default: 1</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        parents : dict or None</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
-<span class="sd">            specifying the conditions for each variable. If None is</span>
-<span class="sd">            passed, no conditions are used.</span>
-<span class="sd">        max_conds_py : int or None</span>
-<span class="sd">            Maximum number of conditions of Y to use. If None is passed, this</span>
-<span class="sd">            number is unrestricted.</span>
-<span class="sd">        max_conds_px : int or None</span>
-<span class="sd">            Maximum number of conditions of Z to use. If None is passed, this</span>
-<span class="sd">            number is unrestricted.</span>
-<span class="sd">        val_only : bool, default: False</span>
-<span class="sd">            Option to only compute dependencies and not p-values.</span>
-<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level at which the p_matrix is thresholded to </span>
-<span class="sd">            get graph.</span>
-<span class="sd">        fdr_method : str, optional (default: &#39;none&#39;)</span>
-<span class="sd">            Correction method, currently implemented is Benjamini-Hochberg</span>
-<span class="sd">            False Discovery Rate method (&#39;fdr_bh&#39;). </span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of test statistic values.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values, optionally adjusted if fdr_method is</span>
-<span class="sd">            not &#39;none&#39;.</span>
-<span class="sd">        conf_matrix : array of shape [N, N, tau_max+1,2]</span>
-<span class="sd">            Estimated matrix of confidence intervals of test statistic values.</span>
-<span class="sd">            Only computed if set in cond_ind_test, where also the percentiles</span>
-<span class="sd">            are set.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selected_links is DEPRECATED, use link_assumptions instead.&quot;</span><span class="p">)</span>
-
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Step 2: MCI algorithm</span><span class="se">\n</span><span class="s2">##&quot;</span>
-                  <span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Parameters:&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">independence test = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">measure</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_min = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_min</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_max = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_max</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">max_conds_py = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">max_conds_py</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">max_conds_px = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">max_conds_px</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_mci_or_variants</span><span class="p">(</span>
-            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-            <span class="n">parents</span><span class="o">=</span><span class="n">parents</span><span class="p">,</span>
-            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
-            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
-            <span class="n">val_only</span><span class="o">=</span><span class="n">val_only</span><span class="p">,</span>
-            <span class="n">alpha_level</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">,</span>
-            <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="PCMCI.get_lagged_dependencies"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.get_lagged_dependencies">[docs]</a>    <span class="k">def</span> <span class="nf">get_lagged_dependencies</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                                <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                <span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                                <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                                <span class="n">val_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                                <span class="n">fdr_method</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Unconditional lagged independence tests.</span>
-
-<span class="sd">        Implements the unconditional lagged independence test (see [ 1]_).</span>
-<span class="sd">        </span>
-<span class="sd">        Returns the matrices of test statistic values, (optionally corrected) </span>
-<span class="sd">        p-values, and (optionally) confidence intervals. Also (new in 4.3)</span>
-<span class="sd">        returns graph based on alpha_level (and optional FDR-correction).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Deprecated, replaced by link_assumptions</span>
-<span class="sd">        link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        tau_min : int, default: 0</span>
-<span class="sd">            Minimum time lag to test. Note that zero-lags are undirected.</span>
-<span class="sd">        tau_max : int, default: 1</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        val_only : bool, default: False</span>
-<span class="sd">            Option to only compute dependencies and not p-values.</span>
-<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level at which the p_matrix is thresholded to </span>
-<span class="sd">            get graph.</span>
-<span class="sd">        fdr_method : str, optional (default: &#39;none&#39;)</span>
-<span class="sd">            Correction method, currently implemented is Benjamini-Hochberg</span>
-<span class="sd">            False Discovery Rate method (&#39;fdr_bh&#39;).  </span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of test statistic values.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values, optionally adjusted if fdr_method is</span>
-<span class="sd">            not &#39;none&#39;.</span>
-<span class="sd">        conf_matrix : array of shape [N, N, tau_max+1,2]</span>
-<span class="sd">            Estimated matrix of confidence intervals of test statistic values.</span>
-<span class="sd">            Only computed if set in cond_ind_test, where also the percentiles</span>
-<span class="sd">            are set.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selected_links is DEPRECATED, use link_assumptions instead.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Estimating lagged dependencies </span><span class="se">\n</span><span class="s2">##&quot;</span>
-                  <span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Parameters:&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">independence test = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">measure</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_min = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_min</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_max = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_mci_or_variants</span><span class="p">(</span>
-            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-            <span class="n">parents</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-            <span class="n">max_conds_py</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="n">max_conds_px</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="n">val_only</span><span class="o">=</span><span class="n">val_only</span><span class="p">,</span>
-            <span class="n">alpha_level</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">,</span>
-            <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="PCMCI.run_fullci"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_fullci">[docs]</a>    <span class="k">def</span> <span class="nf">run_fullci</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                   <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                   <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                   <span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                   <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                   <span class="n">val_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                   <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                   <span class="n">fdr_method</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;FullCI conditional independence tests.</span>
-
-<span class="sd">        Implements the FullCI test (see [1]_). </span>
-
-<span class="sd">        Returns the matrices of test statistic values, (optionally corrected) </span>
-<span class="sd">        p-values, and (optionally) confidence intervals. Also (new in 4.3)</span>
-<span class="sd">        returns graph based on alpha_level (and optional FDR-correction).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Deprecated, replaced by link_assumptions</span>
-<span class="sd">        link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        tau_min : int, default: 0</span>
-<span class="sd">            Minimum time lag to test. Note that zero-lags are undirected.</span>
-<span class="sd">        tau_max : int, default: 1</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        val_only : bool, default: False</span>
-<span class="sd">            Option to only compute dependencies and not p-values.</span>
-<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level at which the p_matrix is thresholded to </span>
-<span class="sd">            get graph.</span>
-<span class="sd">        fdr_method : str, optional (default: &#39;none&#39;)</span>
-<span class="sd">            Correction method, currently implemented is Benjamini-Hochberg</span>
-<span class="sd">            False Discovery Rate method (&#39;fdr_bh&#39;).  </span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of test statistic values.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values, optionally adjusted if fdr_method is</span>
-<span class="sd">            not &#39;none&#39;.</span>
-<span class="sd">        conf_matrix : array of shape [N, N, tau_max+1,2]</span>
-<span class="sd">            Estimated matrix of confidence intervals of test statistic values.</span>
-<span class="sd">            Only computed if set in cond_ind_test, where also the percentiles</span>
-<span class="sd">            are set.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selected_links is DEPRECATED, use link_assumptions instead.&quot;</span><span class="p">)</span>
-
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Running Tigramite FullCI algorithm</span><span class="se">\n</span><span class="s2">##&quot;</span>
-                  <span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Parameters:&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">independence test = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">measure</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_min = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_min</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_max = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="n">full_past</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">j</span><span class="p">,</span> <span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span>
-                               <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-                               <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
-                          <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)])</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_mci_or_variants</span><span class="p">(</span>
-            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-            <span class="n">parents</span><span class="o">=</span><span class="n">full_past</span><span class="p">,</span>
-            <span class="n">max_conds_py</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-            <span class="n">max_conds_px</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="n">val_only</span><span class="o">=</span><span class="n">val_only</span><span class="p">,</span>
-            <span class="n">alpha_level</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">,</span>
-            <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="PCMCI.run_bivci"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_bivci">[docs]</a>    <span class="k">def</span> <span class="nf">run_bivci</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                  <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                  <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                  <span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                  <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                  <span class="n">val_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                  <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                  <span class="n">fdr_method</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;BivCI conditional independence tests.</span>
-
-<span class="sd">        Implements the BivCI test (see [1]_). </span>
-
-<span class="sd">        Returns the matrices of test statistic values, (optionally corrected) </span>
-<span class="sd">        p-values, and (optionally) confidence intervals. Also (new in 4.3)</span>
-<span class="sd">        returns graph based on alpha_level (and optional FDR-correction).</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Deprecated, replaced by link_assumptions</span>
-<span class="sd">        link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        tau_min : int, default: 0</span>
-<span class="sd">            Minimum time lag to test. Note that zero-lags are undirected.</span>
-<span class="sd">        tau_max : int, default: 1</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        val_only : bool, default: False</span>
-<span class="sd">            Option to only compute dependencies and not p-values.</span>
-<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level at which the p_matrix is thresholded to </span>
-<span class="sd">            get graph.</span>
-<span class="sd">        fdr_method : str, optional (default: &#39;fdr_bh&#39;)</span>
-<span class="sd">            Correction method, currently implemented is Benjamini-Hochberg</span>
-<span class="sd">            False Discovery Rate method. </span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of test statistic values.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values, optionally adjusted if fdr_method is</span>
-<span class="sd">            not &#39;none&#39;.</span>
-<span class="sd">        conf_matrix : array of shape [N, N, tau_max+1,2]</span>
-<span class="sd">            Estimated matrix of confidence intervals of test statistic values.</span>
-<span class="sd">            Only computed if set in cond_ind_test, where also the percentiles</span>
-<span class="sd">            are set.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selected_links is DEPRECATED, use link_assumptions instead.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Running Tigramite BivCI algorithm</span><span class="se">\n</span><span class="s2">##&quot;</span>
-                  <span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Parameters:&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">independence test = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">measure</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_min = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_min</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_max = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="n">auto_past</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">j</span><span class="p">,</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span>
-                               <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
-                          <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)])</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_mci_or_variants</span><span class="p">(</span>
-            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-            <span class="n">parents</span><span class="o">=</span><span class="n">auto_past</span><span class="p">,</span>
-            <span class="n">max_conds_py</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-            <span class="n">max_conds_px</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="n">val_only</span><span class="o">=</span><span class="n">val_only</span><span class="p">,</span>
-            <span class="n">alpha_level</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">,</span>
-            <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="PCMCI.get_graph_from_pmatrix"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.get_graph_from_pmatrix">[docs]</a>    <span class="k">def</span> <span class="nf">get_graph_from_pmatrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p_matrix</span><span class="p">,</span> <span class="n">alpha_level</span><span class="p">,</span> 
-            <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Construct graph from thresholding the p_matrix at an alpha-level.</span>
-
-<span class="sd">        Allows to take into account link_assumptions.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values, optionally adjusted if fdr_method is</span>
-<span class="sd">            not &#39;none&#39;.</span>
-<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level at which the p_matrix is thresholded to </span>
-<span class="sd">            get graph.</span>
-<span class="sd">        tau_mix : int</span>
-<span class="sd">            Minimum time delay to test.</span>
-<span class="sd">        tau_max : int</span>
-<span class="sd">            Maximum time delay to test.</span>
-<span class="sd">        link_assumptions : dict or None</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-<span class="sd">        &quot;&quot;&quot;</span>  
-
-        <span class="c1"># _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)</span>
-        <span class="n">_int_link_assumptions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_link_assumptions</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">link_assumptions</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># Create a mask for these values</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
-            <span class="c1"># for node1, links_ in _int_sel_links.items():</span>
-            <span class="c1">#     for node2, lag in links_:</span>
-            <span class="c1">#         mask[node2, node1, abs(lag)] = True</span>
-            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">links_</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">lag</span> <span class="ow">in</span> <span class="n">links_</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">lag</span><span class="p">)]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;&lt;--&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;?-&quot;</span><span class="p">]:</span>
-                        <span class="n">mask</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">)]</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Create a mask for these values</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
-
-        <span class="c1"># Set all p-values of absent links to 1.</span>
-        <span class="n">p_matrix</span><span class="p">[</span><span class="n">mask</span><span class="o">==</span><span class="kc">False</span><span class="p">]</span> <span class="o">==</span> <span class="mf">1.</span>
-
-        <span class="c1"># Threshold p_matrix to get graph</span>
-        <span class="n">graph_bool</span> <span class="o">=</span> <span class="n">p_matrix</span> <span class="o">&lt;=</span> <span class="n">alpha_level</span>
-
-        <span class="c1"># Convert to string graph representation</span>
-        <span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_to_string_graph</span><span class="p">(</span><span class="n">graph_bool</span><span class="p">)</span>
-
-        <span class="c1"># Return the graph</span>
-        <span class="k">return</span> <span class="n">graph</span></div>
-
-<div class="viewcode-block" id="PCMCI.return_parents_dict"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.return_parents_dict">[docs]</a>    <span class="k">def</span> <span class="nf">return_parents_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span>
-                             <span class="n">val_matrix</span><span class="p">,</span>
-                             <span class="n">include_lagzero_parents</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns dictionary of parents sorted by val_matrix.</span>
-
-<span class="sd">        If parents are unclear (edgemarks with &#39;o&#39; or &#39;x&#39;, or middle mark &#39;?&#39;), </span>
-<span class="sd">        then no parent is returned. </span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array-like</span>
-<span class="sd">            Matrix of test statistic values. Must be of shape (N, N, tau_max +</span>
-<span class="sd">            1).</span>
-<span class="sd">        include_lagzero_parents : bool (default: False)</span>
-<span class="sd">            Whether the dictionary should also return parents at lag</span>
-<span class="sd">            zero. </span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        parents_dict : dict</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
-<span class="sd">            containing estimated parents.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Initialize the return value</span>
-        <span class="n">parents_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="c1"># Get the good links</span>
-            <span class="k">if</span> <span class="n">include_lagzero_parents</span><span class="p">:</span>
-                <span class="n">good_links</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">)</span>
-                <span class="c1"># Build a dictionary from these links to their values</span>
-                <span class="n">links</span> <span class="o">=</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">):</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)])</span>
-                         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="n">good_links</span><span class="p">}</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">good_links</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">:]</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">)</span>
-                <span class="c1"># Build a dictionary from these links to their values</span>
-                <span class="n">links</span> <span class="o">=</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-                         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="n">good_links</span><span class="p">}</span>
-            <span class="c1"># Sort by value</span>
-            <span class="n">parents_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">links</span><span class="o">.</span><span class="n">get</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        
-        <span class="k">return</span> <span class="n">parents_dict</span></div>
-               
-
-<div class="viewcode-block" id="PCMCI.return_significant_links"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.return_significant_links">[docs]</a>    <span class="k">def</span> <span class="nf">return_significant_links</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pq_matrix</span><span class="p">,</span>
-                                 <span class="n">val_matrix</span><span class="p">,</span>
-                                 <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                                 <span class="n">include_lagzero_links</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns list of significant links as well as a boolean matrix.</span>
-
-<span class="sd">        DEPRECATED. Will be removed in future.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;return_significant_links() is DEPRECATED: now run_pcmci(), &quot;</span>
-              <span class="s2">&quot; run_mci()&quot;</span>
-              <span class="s2">&quot; and all variants directly return the graph based on thresholding &quot;</span>
-              <span class="s2">&quot;the p_matrix at alpha_level. The graph can also be updated &quot;</span>
-              <span class="s2">&quot;based on a (potentially further adjusted) p_matrix using &quot;</span>
-              <span class="s2">&quot;get_graph_from_pmatrix(). &quot;</span>
-              <span class="s2">&quot;A dictionary of parents can be obtained &quot;</span>
-              <span class="s2">&quot;with return_parents_dict().&quot;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="kc">None</span></div>
-
-<div class="viewcode-block" id="PCMCI.print_significant_links"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.print_significant_links">[docs]</a>    <span class="k">def</span> <span class="nf">print_significant_links</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                                <span class="n">p_matrix</span><span class="p">,</span>
-                                <span class="n">val_matrix</span><span class="p">,</span>
-                                <span class="n">conf_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                <span class="n">graph</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                <span class="n">ambiguous_triples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Prints significant links.</span>
-
-<span class="sd">        Used for output of PCMCI and PCMCIplus. For the latter also information</span>
-<span class="sd">        on ambiguous links and conflicts is returned.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level.</span>
-<span class="sd">        p_matrix : array-like</span>
-<span class="sd">            Must be of shape (N, N, tau_max + 1).</span>
-<span class="sd">        val_matrix : array-like</span>
-<span class="sd">            Must be of shape (N, N, tau_max + 1).</span>
-<span class="sd">        conf_matrix : array-like, optional (default: None)</span>
-<span class="sd">            Matrix of confidence intervals of shape (N, N, tau_max+1, 2).</span>
-<span class="sd">        graph : array-like</span>
-<span class="sd">            Must be of shape (N, N, tau_max + 1).</span>
-<span class="sd">        ambiguous_triples : list</span>
-<span class="sd">            List of ambiguous triples.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">graph</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">sig_links</span> <span class="o">=</span> <span class="p">(</span><span class="n">graph</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">graph</span> <span class="o">!=</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">sig_links</span> <span class="o">=</span> <span class="p">(</span><span class="n">p_matrix</span> <span class="o">&lt;=</span> <span class="n">alpha_level</span><span class="p">)</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">## Significant links at alpha = </span><span class="si">%s</span><span class="s2">:&quot;</span> <span class="o">%</span> <span class="n">alpha_level</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">links</span> <span class="o">=</span> <span class="p">{(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
-                     <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">sig_links</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]))}</span>
-            <span class="c1"># Sort by value</span>
-            <span class="n">sorted_links</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">links</span><span class="o">.</span><span class="n">get</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="n">n_links</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-            <span class="n">string</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">    Variable </span><span class="si">%s</span><span class="s2"> has </span><span class="si">%d</span><span class="s2"> &quot;</span>
-                      <span class="s2">&quot;link(s):&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">n_links</span><span class="p">))</span>
-            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">sorted_links</span><span class="p">:</span>
-                <span class="n">string</span> <span class="o">+=</span> <span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">        (</span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">): pval = </span><span class="si">%.5f</span><span class="s2">&quot;</span> <span class="o">%</span>
-                           <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                            <span class="n">p_matrix</span><span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])]))</span>
-                <span class="n">string</span> <span class="o">+=</span> <span class="s2">&quot; | val = </span><span class="si">% .3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                    <span class="n">val_matrix</span><span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
-                <span class="k">if</span> <span class="n">conf_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">string</span> <span class="o">+=</span> <span class="s2">&quot; | conf = (</span><span class="si">%.3f</span><span class="s2">, </span><span class="si">%.3f</span><span class="s2">)&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                        <span class="n">conf_matrix</span><span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])][</span><span class="mi">0</span><span class="p">],</span>
-                        <span class="n">conf_matrix</span><span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])][</span><span class="mi">1</span><span class="p">])</span>
-                <span class="k">if</span> <span class="n">graph</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;o-o&quot;</span><span class="p">:</span>
-                        <span class="n">string</span> <span class="o">+=</span> <span class="s2">&quot; | unoriented link&quot;</span>
-                    <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">==</span> <span class="s2">&quot;x-x&quot;</span><span class="p">:</span>
-                        <span class="n">string</span> <span class="o">+=</span> <span class="s2">&quot; | unclear orientation due to conflict&quot;</span>
-            <span class="nb">print</span><span class="p">(</span><span class="n">string</span><span class="p">)</span>
-
-        <span class="c1"># link_marker = {True:&quot;o-o&quot;, False:&quot;--&gt;&quot;}</span>
-
-        <span class="k">if</span> <span class="n">ambiguous_triples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">ambiguous_triples</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">## Ambiguous triples (not used for orientation):</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">triple</span> <span class="ow">in</span> <span class="n">ambiguous_triples</span><span class="p">:</span>
-                <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">triple</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    [(</span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">), </span><span class="si">%s</span><span class="s2">, </span><span class="si">%s</span><span class="s2">]&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tau</span><span class="p">,</span> 
-                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span></div>
-
-<div class="viewcode-block" id="PCMCI.print_results"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.print_results">[docs]</a>    <span class="k">def</span> <span class="nf">print_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                      <span class="n">return_dict</span><span class="p">,</span>
-                      <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Prints significant parents from output of MCI or PCMCI algorithms.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        return_dict : dict</span>
-<span class="sd">            Dictionary of return values, containing keys</span>
-<span class="sd">                * &#39;p_matrix&#39;</span>
-<span class="sd">                * &#39;val_matrix&#39;</span>
-<span class="sd">                * &#39;conf_matrix&#39;</span>
-
-<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Check if conf_matrix is defined</span>
-        <span class="n">conf_matrix</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">conf_key</span> <span class="o">=</span> <span class="s1">&#39;conf_matrix&#39;</span>
-        <span class="k">if</span> <span class="n">conf_key</span> <span class="ow">in</span> <span class="n">return_dict</span><span class="p">:</span>
-            <span class="n">conf_matrix</span> <span class="o">=</span> <span class="n">return_dict</span><span class="p">[</span><span class="n">conf_key</span><span class="p">]</span>
-        <span class="c1"># Wrap the already defined function</span>
-        <span class="k">if</span> <span class="s1">&#39;graph&#39;</span> <span class="ow">in</span> <span class="n">return_dict</span><span class="p">:</span>
-            <span class="n">graph</span> <span class="o">=</span> <span class="n">return_dict</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">graph</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="s1">&#39;ambiguous_triples&#39;</span> <span class="ow">in</span> <span class="n">return_dict</span><span class="p">:</span>
-            <span class="n">ambiguous_triples</span> <span class="o">=</span> <span class="n">return_dict</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ambiguous_triples</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">print_significant_links</span><span class="p">(</span><span class="n">return_dict</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span>
-                                     <span class="n">return_dict</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span>
-                                     <span class="n">conf_matrix</span><span class="o">=</span><span class="n">conf_matrix</span><span class="p">,</span>
-                                     <span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span>
-                                     <span class="n">ambiguous_triples</span><span class="o">=</span><span class="n">ambiguous_triples</span><span class="p">,</span>
-                                     <span class="n">alpha_level</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="PCMCI.run_pcmci"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_pcmci">[docs]</a>    <span class="k">def</span> <span class="nf">run_pcmci</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                  <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                  <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                  <span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                  <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                  <span class="n">save_iterations</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                  <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                  <span class="n">max_conds_dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                  <span class="n">max_combinations</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                  <span class="n">max_conds_py</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                  <span class="n">max_conds_px</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                  <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                  <span class="n">fdr_method</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Runs PCMCI time-lagged causal discovery for time series.</span>
-
-<span class="sd">        Wrapper around PC-algorithm function and MCI function.</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-
-<span class="sd">        The PCMCI causal discovery method is comprehensively described in [</span>
-<span class="sd">        1]_, where also analytical and numerical results are presented. Here</span>
-<span class="sd">        we briefly summarize the method.</span>
-
-<span class="sd">        PCMCI estimates time-lagged causal links by a two-step procedure:</span>
-
-<span class="sd">        1.  Condition-selection: For each variable :math:`j`, estimate a</span>
-<span class="sd">            *superset* of parents :math:`\\tilde{\mathcal{P}}(X^j_t)` with the</span>
-<span class="sd">            iterative PC1 algorithm, implemented as ``run_pc_stable``. The</span>
-<span class="sd">            condition-selection step reduces the dimensionality and avoids</span>
-<span class="sd">            conditioning on irrelevant variables.</span>
-
-<span class="sd">        2.  *Momentary conditional independence* (MCI)</span>
-
-<span class="sd">        .. math:: X^i_{t-\\tau} \perp X^j_{t} | \\tilde{\\mathcal{P}}(</span>
-<span class="sd">                  X^j_t), \\tilde{\mathcal{P}}(X^i_{t-\\tau})</span>
-
-<span class="sd">        here implemented as ``run_mci``. This step estimates the p-values and</span>
-<span class="sd">        test statistic values for all links accounting for common drivers,</span>
-<span class="sd">        indirect links, and autocorrelation.</span>
-
-<span class="sd">        NOTE: MCI test statistic values define a particular measure of causal</span>
-<span class="sd">        strength depending on the test statistic used. For example, ParCorr()</span>
-<span class="sd">        results in normalized values between -1 and 1. However, if you are </span>
-<span class="sd">        interested in quantifying causal effects, i.e., the effect of</span>
-<span class="sd">        hypothetical interventions, you may better look at the causal effect </span>
-<span class="sd">        estimation functionality of Tigramite.</span>
-
-<span class="sd">        PCMCI can be flexibly combined with any kind of conditional</span>
-<span class="sd">        independence test statistic adapted to the kind of data (continuous</span>
-<span class="sd">        or discrete) and its assumed dependency types. These are available in</span>
-<span class="sd">        ``tigramite.independence_tests``.</span>
-
-<span class="sd">        The main free parameters of PCMCI (in addition to free parameters of</span>
-<span class="sd">        the conditional independence test statistic) are the maximum time</span>
-<span class="sd">        delay :math:`\\tau_{\\max}` (``tau_max``) and the significance</span>
-<span class="sd">        threshold in the condition-selection step :math:`\\alpha` (</span>
-<span class="sd">        ``pc_alpha``). The maximum time delay depends on the application and</span>
-<span class="sd">        should be chosen according to the maximum causal time lag expected in</span>
-<span class="sd">        the complex system. We recommend a rather large choice that includes</span>
-<span class="sd">        peaks in the ``get_lagged_dependencies`` function. :math:`\\alpha`</span>
-<span class="sd">        should not be seen as a significance test level in the</span>
-<span class="sd">        condition-selection step since the iterative hypothesis tests do not</span>
-<span class="sd">        allow for a precise assessment. :math:`\\alpha` rather takes the role</span>
-<span class="sd">        of a regularization parameter in model-selection techniques. If a</span>
-<span class="sd">        list of values is given or ``pc_alpha=None``, :math:`\\alpha` is</span>
-<span class="sd">        optimized using model selection criteria implemented in the respective</span>
-<span class="sd">        ``tigramite.independence_tests``.</span>
-
-<span class="sd">        Further optional parameters are discussed in [1]_.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; import numpy</span>
-<span class="sd">        &gt;&gt;&gt; from tigramite.pcmci import PCMCI</span>
-<span class="sd">        &gt;&gt;&gt; from tigramite.independence_tests import ParCorr</span>
-<span class="sd">        &gt;&gt;&gt; import tigramite.data_processing as pp</span>
-<span class="sd">        &gt;&gt;&gt; from tigramite.toymodels import structural_causal_processes as toys</span>
-<span class="sd">        &gt;&gt;&gt; numpy.random.seed(7)</span>
-<span class="sd">        &gt;&gt;&gt; # Example process to play around with</span>
-<span class="sd">        &gt;&gt;&gt; # Each key refers to a variable and the incoming links are supplied</span>
-<span class="sd">        &gt;&gt;&gt; # as a list of format [((driver, -lag), coeff), ...]</span>
-<span class="sd">        &gt;&gt;&gt; links_coeffs = {0: [((0, -1), 0.8)],</span>
-<span class="sd">                            1: [((1, -1), 0.8), ((0, -1), 0.5)],</span>
-<span class="sd">                            2: [((2, -1), 0.8), ((1, -2), -0.6)]}</span>
-<span class="sd">        &gt;&gt;&gt; data, _ = toys.var_process(links_coeffs, T=1000)</span>
-<span class="sd">        &gt;&gt;&gt; # Data must be array of shape (time, variables)</span>
-<span class="sd">        &gt;&gt;&gt; print (data.shape)</span>
-<span class="sd">        (1000, 3)</span>
-<span class="sd">        &gt;&gt;&gt; dataframe = pp.DataFrame(data)</span>
-<span class="sd">        &gt;&gt;&gt; cond_ind_test = ParCorr()</span>
-<span class="sd">        &gt;&gt;&gt; pcmci = PCMCI(dataframe=dataframe, cond_ind_test=cond_ind_test)</span>
-<span class="sd">        &gt;&gt;&gt; results = pcmci.run_pcmci(tau_max=2, pc_alpha=None)</span>
-<span class="sd">        &gt;&gt;&gt; pcmci.print_significant_links(p_matrix=results[&#39;p_matrix&#39;],</span>
-<span class="sd">                                         val_matrix=results[&#39;val_matrix&#39;],</span>
-<span class="sd">                                         alpha_level=0.05)</span>
-<span class="sd">        ## Significant parents at alpha = 0.05:</span>
-
-<span class="sd">            Variable 0 has 1 link(s):</span>
-<span class="sd">                (0 -1): pval = 0.00000 | val =  0.588</span>
-
-<span class="sd">            Variable 1 has 2 link(s):</span>
-<span class="sd">                (1 -1): pval = 0.00000 | val =  0.606</span>
-<span class="sd">                (0 -1): pval = 0.00000 | val =  0.447</span>
-
-<span class="sd">            Variable 2 has 2 link(s):</span>
-<span class="sd">                (2 -1): pval = 0.00000 | val =  0.618</span>
-<span class="sd">                (1 -2): pval = 0.00000 | val = -0.499</span>
-
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Deprecated, replaced by link_assumptions</span>
-<span class="sd">        link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        tau_min : int, optional (default: 0)</span>
-<span class="sd">            Minimum time lag to test. Note that zero-lags are undirected.</span>
-<span class="sd">        tau_max : int, optional (default: 1)</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        save_iterations : bool, optional (default: False)</span>
-<span class="sd">            Whether to save iteration step results such as conditions used.</span>
-<span class="sd">        pc_alpha : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level in algorithm.</span>
-<span class="sd">        max_conds_dim : int, optional (default: None)</span>
-<span class="sd">            Maximum number of conditions to test. If None is passed, this number</span>
-<span class="sd">            is unrestricted.</span>
-<span class="sd">        max_combinations : int, optional (default: 1)</span>
-<span class="sd">            Maximum number of combinations of conditions of current cardinality</span>
-<span class="sd">            to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm</span>
-<span class="sd">            a larger number, such as 10, can be used.</span>
-<span class="sd">        max_conds_py : int, optional (default: None)</span>
-<span class="sd">            Maximum number of conditions of Y to use. If None is passed, this</span>
-<span class="sd">            number is unrestricted.</span>
-<span class="sd">        max_conds_px : int, optional (default: None)</span>
-<span class="sd">            Maximum number of conditions of Z to use. If None is passed, this</span>
-<span class="sd">            number is unrestricted.</span>
-<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
-<span class="sd">            Significance level at which the p_matrix is thresholded to </span>
-<span class="sd">            get graph.</span>
-<span class="sd">        fdr_method : str, optional (default: &#39;fdr_bh&#39;)</span>
-<span class="sd">            Correction method, currently implemented is Benjamini-Hochberg</span>
-<span class="sd">            False Discovery Rate method. </span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of test statistic values.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values, optionally adjusted if fdr_method is</span>
-<span class="sd">            not &#39;none&#39;.</span>
-<span class="sd">        conf_matrix : array of shape [N, N, tau_max+1,2]</span>
-<span class="sd">            Estimated matrix of confidence intervals of test statistic values.</span>
-<span class="sd">            Only computed if set in cond_ind_test, where also the percentiles</span>
-<span class="sd">            are set.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selected_links is DEPRECATED, use link_assumptions instead.&quot;</span><span class="p">)</span>
-
-
-        <span class="c1"># Get the parents from run_pc_stable</span>
-        <span class="n">all_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-                                         <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-                                         <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                         <span class="n">save_iterations</span><span class="o">=</span><span class="n">save_iterations</span><span class="p">,</span>
-                                         <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
-                                         <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                         <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">)</span>
-
-        <span class="c1"># Get the results from run_mci, using the parents as the input</span>
-        <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_mci</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-                               <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-                               <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                               <span class="n">parents</span><span class="o">=</span><span class="n">all_parents</span><span class="p">,</span>
-                               <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
-                               <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
-                               <span class="n">alpha_level</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">,</span>
-                               <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
-    
-        <span class="c1"># Store the parents in the pcmci member</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span> <span class="o">=</span> <span class="n">all_parents</span>
-
-        <span class="c1"># Print the information</span>
-        <span class="c1"># if self.verbosity &gt; 0:</span>
-        <span class="c1">#     self.print_results(results)</span>
-        <span class="c1"># Return the dictionary</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="o">=</span> <span class="n">results</span>
-        <span class="k">return</span> <span class="n">results</span></div>
-
-<div class="viewcode-block" id="PCMCI.run_pcmciplus"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_pcmciplus">[docs]</a>    <span class="k">def</span> <span class="nf">run_pcmciplus</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                      <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                      <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                      <span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                      <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                      <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
-                      <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="s1">&#39;majority&#39;</span><span class="p">,</span>
-                      <span class="n">conflict_resolution</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                      <span class="n">reset_lagged_links</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                      <span class="n">max_conds_dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                      <span class="n">max_combinations</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                      <span class="n">max_conds_py</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                      <span class="n">max_conds_px</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                      <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                      <span class="n">fdr_method</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">,</span>
-                      <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Runs PCMCIplus time-lagged and contemporaneous causal discovery for</span>
-<span class="sd">        time series.</span>
-
-<span class="sd">        Method described in [5]_: </span>
-<span class="sd">        http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-
-<span class="sd">        The PCMCIplus causal discovery method is described in [5]_, where</span>
-<span class="sd">        also analytical and numerical results are presented. In contrast to</span>
-<span class="sd">        PCMCI, PCMCIplus can identify the full, lagged and contemporaneous,</span>
-<span class="sd">        causal graph (up to the Markov equivalence class for contemporaneous</span>
-<span class="sd">        links) under the standard assumptions of Causal Sufficiency,</span>
-<span class="sd">        Faithfulness and the Markov condition.</span>
-
-<span class="sd">        PCMCIplus estimates time-lagged and contemporaneous causal links by a</span>
-<span class="sd">        four-step procedure:</span>
-
-<span class="sd">        1.  Condition-selection (same as for PCMCI): For each variable</span>
-<span class="sd">        :math:`j`, estimate a *superset* of lagged parents :math:`\\widehat{</span>
-<span class="sd">        \\mathcal{B}}_t^-( X^j_t)` with the iterative PC1 algorithm,</span>
-<span class="sd">        implemented as ``run_pc_stable``. The condition-selection step</span>
-<span class="sd">        reduces the dimensionality and avoids conditioning on irrelevant</span>
-<span class="sd">        variables.</span>
-
-<span class="sd">        2.   PC skeleton phase with contemporaneous conditions and *Momentary</span>
-<span class="sd">        conditional independence* (MCI) tests: Iterate through subsets</span>
-<span class="sd">        :math:`\\mathcal{S}` of contemporaneous adjacencies and conduct MCI</span>
-<span class="sd">        conditional independence tests:</span>
-
-<span class="sd">        .. math:: X^i_{t-\\tau} ~\\perp~ X^j_{t} ~|~ \\mathcal{S},</span>
-<span class="sd">                  \\widehat{\\mathcal{B}}_t^-(X^j_t),</span>
-<span class="sd">                  \\widehat{\\mathcal{B}}_{t-\\tau}^-(X^i_{t-{\\tau}})</span>
-
-<span class="sd">        here implemented as ``run_pcalg``. This step estimates the p-values and</span>
-<span class="sd">        test statistic values for all lagged and contemporaneous adjacencies</span>
-<span class="sd">        accounting for common drivers, indirect links, and autocorrelation.</span>
-
-<span class="sd">        3.   PC collider orientation phase: Orient contemporaneous collider</span>
-<span class="sd">        motifs based on unshielded triples. Optionally apply conservative or</span>
-<span class="sd">        majority rule (also based on MCI tests).</span>
-
-<span class="sd">        4.   PC rule orientation phase: Orient remaining contemporaneous</span>
-<span class="sd">        links based on PC rules.</span>
-
-<span class="sd">        In contrast to PCMCI, the relevant output of PCMCIplus is the</span>
-<span class="sd">        array ``graph``. Its string entries are interpreted as follows:</span>
-
-<span class="sd">        * ``graph[i,j,tau]=--&gt;`` for :math:`\\tau&gt;0` denotes a directed, lagged</span>
-<span class="sd">          causal link from :math:`i` to :math:`j` at lag :math:`\\tau`</span>
-
-<span class="sd">        * ``graph[i,j,0]=--&gt;`` (and ``graph[j,i,0]=&lt;--``) denotes a directed,</span>
-<span class="sd">          contemporaneous causal link from :math:`i` to :math:`j`</span>
-
-<span class="sd">        * ``graph[i,j,0]=o-o`` (and ``graph[j,i,0]=o-o``) denotes an unoriented,</span>
-<span class="sd">          contemporaneous adjacency between :math:`i` and :math:`j` indicating</span>
-<span class="sd">          that the collider and orientation rules could not be applied (Markov</span>
-<span class="sd">          equivalence)</span>
-
-<span class="sd">        * ``graph[i,j,0]=x-x`` and (``graph[j,i,0]=x-x``) denotes a conflicting,</span>
-<span class="sd">          contemporaneous adjacency between :math:`i` and :math:`j` indicating</span>
-<span class="sd">          that the directionality is undecided due to conflicting orientation</span>
-<span class="sd">          rules</span>
-
-<span class="sd">        Importantly, ``p_matrix`` and ``val_matrix`` for PCMCIplus quantify</span>
-<span class="sd">        the uncertainty and strength, respectively, only for the</span>
-<span class="sd">        adjacencies, but not for the directionality of contemporaneous links.</span>
-<span class="sd">        Note that lagged links are always oriented due to time order.</span>
-
-<span class="sd">        PCMCIplus can be flexibly combined with any kind of conditional</span>
-<span class="sd">        independence test statistic adapted to the kind of data (continuous</span>
-<span class="sd">        or discrete) and its assumed dependency types. These are available in</span>
-<span class="sd">        ``tigramite.independence_tests``.</span>
-
-<span class="sd">        The main free parameters of PCMCIplus (in addition to free parameters of</span>
-<span class="sd">        the conditional independence tests) are the maximum time delay</span>
-<span class="sd">        :math:`\\tau_{\\max}` (``tau_max``) and the significance threshold</span>
-<span class="sd">        :math:`\\alpha` ( ``pc_alpha``). </span>
-
-<span class="sd">        If a list or None is passed for ``pc_alpha``, the significance level is</span>
-<span class="sd">        optimized for every graph across the given ``pc_alpha`` values using the</span>
-<span class="sd">        score computed in ``cond_ind_test.get_model_selection_criterion()``.</span>
-<span class="sd">        Since PCMCIplus outputs not a DAG, but an equivalence class of DAGs,</span>
-<span class="sd">        first one member of this class is computed and then the score is</span>
-<span class="sd">        computed as the average over all models fits for each variable in ``[0,</span>
-<span class="sd">        ..., N]`` for that member. The score is the same for all members of the</span>
-<span class="sd">        class.</span>
-
-<span class="sd">        The maximum time delay depends on the application and should be chosen</span>
-<span class="sd">        according to the maximum causal time lag expected in the complex system.</span>
-<span class="sd">        We recommend a rather large choice that includes peaks in the</span>
-<span class="sd">        ``get_lagged_dependencies`` function. Another important parameter is</span>
-<span class="sd">        ``contemp_collider_rule``. Only if set to ``majority`` or</span>
-<span class="sd">        ``conservative&#39;&#39; and together with ``conflict_resolution=True``,</span>
-<span class="sd">        PCMCIplus is fully *order independent* meaning that the order of the N</span>
-<span class="sd">        variables in the dataframe does not matter. Last, the default option</span>
-<span class="sd">        ``reset_lagged_links=False`` restricts the detection of lagged causal</span>
-<span class="sd">        links in Step 2 to the significant adjacencies found in Step 1, given by</span>
-<span class="sd">        :math:`\\widehat{ \\mathcal{B}}_t^-( X^j_t)`. For</span>
-<span class="sd">        ``reset_lagged_links=True``, *all* lagged links are considered again,</span>
-<span class="sd">        which improves detection power for lagged links, but also leads to</span>
-<span class="sd">        larger runtimes.</span>
-
-<span class="sd">        Further optional parameters are discussed in [5]_.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">        &gt;&gt;&gt; from tigramite.pcmci import PCMCI</span>
-<span class="sd">        &gt;&gt;&gt; from tigramite.independence_tests import ParCorr</span>
-<span class="sd">        &gt;&gt;&gt; import tigramite.data_processing as pp</span>
-<span class="sd">        &gt;&gt;&gt; from tigramite.toymodels import structural_causal_processes as toys</span>
-<span class="sd">        &gt;&gt;&gt; # Example process to play around with</span>
-<span class="sd">        &gt;&gt;&gt; # Each key refers to a variable and the incoming links are supplied</span>
-<span class="sd">        &gt;&gt;&gt; # as a list of format [((var, -lag), coeff, function), ...]</span>
-<span class="sd">        &gt;&gt;&gt; def lin_f(x): return x</span>
-<span class="sd">        &gt;&gt;&gt; links = {0: [((0, -1), 0.9, lin_f)],</span>
-<span class="sd">                     1: [((1, -1), 0.8, lin_f), ((0, -1), 0.8, lin_f)],</span>
-<span class="sd">                     2: [((2, -1), 0.7, lin_f), ((1, 0), 0.6, lin_f)],</span>
-<span class="sd">                     3: [((3, -1), 0.7, lin_f), ((2, 0), -0.5, lin_f)],</span>
-<span class="sd">                     }</span>
-<span class="sd">        &gt;&gt;&gt; data, nonstat = toys.structural_causal_process(links,</span>
-<span class="sd">                            T=1000, seed=7)</span>
-<span class="sd">        &gt;&gt;&gt; # Data must be array of shape (time, variables)</span>
-<span class="sd">        &gt;&gt;&gt; print (data.shape)</span>
-<span class="sd">        (1000, 4)</span>
-<span class="sd">        &gt;&gt;&gt; dataframe = pp.DataFrame(data)</span>
-<span class="sd">        &gt;&gt;&gt; cond_ind_test = ParCorr()</span>
-<span class="sd">        &gt;&gt;&gt; pcmci = PCMCI(dataframe=dataframe, cond_ind_test=cond_ind_test)</span>
-<span class="sd">        &gt;&gt;&gt; results = pcmci.run_pcmciplus(tau_min=0, tau_max=2, pc_alpha=0.01)</span>
-<span class="sd">        &gt;&gt;&gt; pcmci.print_results(results, alpha_level=0.01)</span>
-<span class="sd">            ## Significant links at alpha = 0.01:</span>
-
-<span class="sd">            Variable 0 has 1 link(s):</span>
-<span class="sd">                (0 -1): pval = 0.00000 | val =  0.676</span>
-
-<span class="sd">            Variable 1 has 2 link(s):</span>
-<span class="sd">                (1 -1): pval = 0.00000 | val =  0.602</span>
-<span class="sd">                (0 -1): pval = 0.00000 | val =  0.599</span>
-
-<span class="sd">            Variable 2 has 2 link(s):</span>
-<span class="sd">                (1  0): pval = 0.00000 | val =  0.486</span>
-<span class="sd">                (2 -1): pval = 0.00000 | val =  0.466</span>
-
-<span class="sd">            Variable 3 has 2 link(s):</span>
-<span class="sd">                (3 -1): pval = 0.00000 | val =  0.524</span>
-<span class="sd">                (2  0): pval = 0.00000 | val = -0.449 </span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Deprecated, replaced by link_assumptions</span>
-<span class="sd">        link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        tau_min : int, optional (default: 0)</span>
-<span class="sd">            Minimum time lag to test.</span>
-<span class="sd">        tau_max : int, optional (default: 1)</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        pc_alpha : float or list of floats, default: 0.01</span>
-<span class="sd">            Significance level in algorithm. If a list or None is passed, the</span>
-<span class="sd">            pc_alpha level is optimized for every graph across the given</span>
-<span class="sd">            pc_alpha values ([0.001, 0.005, 0.01, 0.025, 0.05] for None) using</span>
-<span class="sd">            the score computed in cond_ind_test.get_model_selection_criterion().</span>
-<span class="sd">        contemp_collider_rule : {&#39;majority&#39;, &#39;conservative&#39;, &#39;none&#39;}</span>
-<span class="sd">            Rule for collider phase to use. See the paper for details. Only</span>
-<span class="sd">            &#39;majority&#39; and &#39;conservative&#39; lead to an order-independent</span>
-<span class="sd">            algorithm.</span>
-<span class="sd">        conflict_resolution : bool, optional (default: True)</span>
-<span class="sd">            Whether to mark conflicts in orientation rules. Only for True</span>
-<span class="sd">            this leads to an order-independent algorithm.</span>
-<span class="sd">        reset_lagged_links : bool, optional (default: False)</span>
-<span class="sd">            Restricts the detection of lagged causal links in Step 2 to the</span>
-<span class="sd">            significant adjacencies found in the PC1 algorithm in Step 1. For</span>
-<span class="sd">            True, *all* lagged links are considered again, which improves</span>
-<span class="sd">            detection power for lagged links, but also leads to larger</span>
-<span class="sd">            runtimes.</span>
-<span class="sd">        max_conds_dim : int, optional (default: None)</span>
-<span class="sd">            Maximum number of conditions to test. If None is passed, this number</span>
-<span class="sd">            is unrestricted.</span>
-<span class="sd">        max_combinations : int, optional (default: 1)</span>
-<span class="sd">            Maximum number of combinations of conditions of current cardinality</span>
-<span class="sd">            to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm</span>
-<span class="sd">            a larger number, such as 10, can be used.</span>
-<span class="sd">        max_conds_py : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of Y to use in MCI tests. If</span>
-<span class="sd">            None is passed, this number is unrestricted.</span>
-<span class="sd">        max_conds_px : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of X to use in MCI tests. If</span>
-<span class="sd">            None is passed, this number is unrestricted.</span>
-<span class="sd">        max_conds_px_lagged : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of X when X is lagged in MCI </span>
-<span class="sd">            tests. If None is passed, this number is equal to max_conds_px.</span>
-<span class="sd">        fdr_method : str, optional (default: &#39;none&#39;)</span>
-<span class="sd">            Correction method, default is Benjamini-Hochberg False Discovery</span>
-<span class="sd">            Rate method.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Resulting causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
-<span class="sd">            Separating sets. See paper for details.</span>
-<span class="sd">        ambiguous_triples : list</span>
-<span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
-<span class="sd">            &#39;conservative&#39; rules, see paper for details.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selected_links is DEPRECATED, use link_assumptions instead.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Check if pc_alpha is chosen to optimze over a list</span>
-        <span class="k">if</span> <span class="n">pc_alpha</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pc_alpha</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
-            <span class="c1"># Call optimizer wrapper around run_pcmciplus()</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_optimize_pcmciplus_alpha</span><span class="p">(</span>
-                        <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-                        <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-                        <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                        <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
-                        <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">,</span>
-                        <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span>
-                        <span class="n">reset_lagged_links</span><span class="o">=</span><span class="n">reset_lagged_links</span><span class="p">,</span>
-                        <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                        <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">,</span>
-                        <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
-                        <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
-                        <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
-                        <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
-
-        <span class="c1"># else:</span>
-        <span class="c1">#     raise ValueError(&quot;pc_alpha=None not supported in PCMCIplus, choose&quot;</span>
-        <span class="c1">#                      &quot; 0 &lt; pc_alpha &lt; 1 (e.g., 0.01)&quot;)</span>
-
-        <span class="k">if</span> <span class="n">pc_alpha</span> <span class="o">&lt;</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="n">pc_alpha</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Choose 0 &lt;= pc_alpha &lt;= 1&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Check the limits on tau</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_tau_limits</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="c1"># Set the selected links</span>
-        <span class="c1"># _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)</span>
-        <span class="n">_int_link_assumptions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_link_assumptions</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="c1"># Step 1: Get a superset of lagged parents from run_pc_stable</span>
-        <span class="n">lagged_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-                                            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-                                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
-                                            <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                            <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">)</span>
-
-        <span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p_matrix</span>
-        <span class="n">val_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_matrix</span>
-
-        <span class="c1"># Step 2+3+4: PC algorithm with contemp. conditions and MCI tests</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Step 2: PC algorithm with contemp. conditions &quot;</span>
-                  <span class="s2">&quot;and MCI tests</span><span class="se">\n</span><span class="s2">##&quot;</span>
-                  <span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Parameters:&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">link_assumptions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">link_assumptions = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">_int_link_assumptions</span><span class="p">))</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">independence test = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">measure</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_min = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_min</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">tau_max = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">tau_max</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">pc_alpha = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">pc_alpha</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">contemp_collider_rule = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">contemp_collider_rule</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">conflict_resolution = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">conflict_resolution</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">reset_lagged_links = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">reset_lagged_links</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">max_conds_dim = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">max_conds_dim</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">max_conds_py = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">max_conds_py</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">max_conds_px = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">max_conds_px</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">max_conds_px_lagged = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">max_conds_px_lagged</span>
-                  <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">fdr_method = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">fdr_method</span>
-                  <span class="p">)</span>
-
-        <span class="c1"># Set the maximum condition dimension for Y and X</span>
-        <span class="n">max_conds_py</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_py</span><span class="p">,</span>
-                                                   <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="n">max_conds_px</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_px</span><span class="p">,</span>
-                                                   <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">reset_lagged_links</span><span class="p">:</span>
-            <span class="c1"># Run PCalg on full graph, ignoring that some lagged links</span>
-            <span class="c1"># were determined as non-significant in PC1 step</span>
-            <span class="n">links_for_pc</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">_int_link_assumptions</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Run PCalg only on lagged parents found with PC1 </span>
-            <span class="c1"># plus all contemporaneous links</span>
-            <span class="n">links_for_pc</span> <span class="o">=</span> <span class="p">{}</span>  <span class="c1">#deepcopy(lagged_parents)</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-                <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="k">if</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">]:</span>
-                        <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span>
-
-                <span class="c1"># Add contemporaneous links</span>
-                <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link</span>
-                    <span class="n">link_type</span> <span class="o">=</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">link</span><span class="p">]</span>
-                    <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">=</span> <span class="n">link_type</span>
-
-        <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pcalg</span><span class="p">(</span>
-            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">links_for_pc</span><span class="p">,</span>
-            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
-            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-            <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-            <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">,</span>
-            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
-            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
-            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
-            <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
-            <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;contemp_conds&#39;</span><span class="p">,</span>
-            <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">,</span>
-            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">)</span>
-
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span>
-
-        <span class="c1"># Update p_matrix and val_matrix with values from links_for_pc</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link</span>
-                <span class="k">if</span> <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">link</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;?-&#39;</span><span class="p">]:</span>
-                    <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
-                    <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> 
-                                                                       <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
-
-        <span class="c1"># Update p_matrix and val_matrix for indices of symmetrical links</span>
-        <span class="n">p_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
-        <span class="n">val_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">ambiguous</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span>
-
-        <span class="n">conf_matrix</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="c1"># TODO: implement confidence estimation, but how?</span>
-        <span class="c1"># if self.cond_ind_test.confidence is not False:</span>
-        <span class="c1">#     conf_matrix = results[&#39;conf_matrix&#39;]</span>
-
-        <span class="c1"># Correct the p_matrix if there is a fdr_method</span>
-        <span class="k">if</span> <span class="n">fdr_method</span> <span class="o">!=</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
-            <span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_corrected_pvalues</span><span class="p">(</span><span class="n">p_matrix</span><span class="o">=</span><span class="n">p_matrix</span><span class="p">,</span> <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
-                                                  <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
-                                                  <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span>
-                                                  <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
-
-        <span class="c1"># Store the parents in the pcmci member</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_lagged_parents</span> <span class="o">=</span> <span class="n">lagged_parents</span>
-
-        <span class="c1"># Cache the resulting values in the return dictionary</span>
-        <span class="n">return_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-                       <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">val_matrix</span><span class="p">,</span>
-                       <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">p_matrix</span><span class="p">,</span>
-                       <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">ambiguous</span><span class="p">,</span>
-                       <span class="s1">&#39;conf_matrix&#39;</span><span class="p">:</span> <span class="n">conf_matrix</span><span class="p">}</span>
-        <span class="c1"># Print the results</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">return_dict</span><span class="p">,</span> <span class="n">alpha_level</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">)</span>
-        <span class="c1"># Return the dictionary</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="o">=</span> <span class="n">return_dict</span>
-        <span class="k">return</span> <span class="n">return_dict</span></div>
-
-<div class="viewcode-block" id="PCMCI.run_pcalg"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_pcalg">[docs]</a>    <span class="k">def</span> <span class="nf">run_pcalg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
-                    <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                    <span class="n">link_assumptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                    <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> 
-                    <span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                    <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> 
-                    <span class="n">max_conds_dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                    <span class="n">max_combinations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                    <span class="n">lagged_parents</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                    <span class="n">max_conds_py</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                    <span class="n">max_conds_px</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                    <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                    <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;standard&#39;</span><span class="p">,</span> 
-                    <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="s1">&#39;majority&#39;</span><span class="p">,</span>
-                    <span class="n">conflict_resolution</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Runs PC algorithm for time-lagged and contemporaneous causal</span>
-<span class="sd">        discovery for time series.</span>
-
-<span class="sd">        For ``mode=&#39;contemp_conds&#39;`` this implements Steps 2-4 of the</span>
-<span class="sd">        PCMCIplus method described in [5]_. For ``mode=&#39;standard&#39;`` this</span>
-<span class="sd">        implements the standard PC algorithm adapted to time series.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        selected_links : dict or None</span>
-<span class="sd">            Deprecated, replaced by link_assumptions</span>
-<span class="sd">        link_assumptions : dict</span>
-<span class="sd">            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying</span>
-<span class="sd">            assumptions about links. This initializes the graph with entries</span>
-<span class="sd">            graph[i,j,tau] = link_type. For example, graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            implies that a directed link from i to j at lag 0 must exist.</span>
-<span class="sd">            Valid link types are &#39;o-o&#39;, &#39;--&gt;&#39;, &#39;&lt;--&#39;. In addition, the middle</span>
-<span class="sd">            mark can be &#39;?&#39; instead of &#39;-&#39;. Then &#39;-?&gt;&#39; implies that this link</span>
-<span class="sd">            may not exist, but if it exists, its orientation is &#39;--&gt;&#39;. Link</span>
-<span class="sd">            assumptions need to be consistent, i.e., graph[i,j,0] = &#39;--&gt;&#39;</span>
-<span class="sd">            requires graph[j,i,0] = &#39;&lt;--&#39; and acyclicity must hold. If a link</span>
-<span class="sd">            does not appear in the dictionary, it is assumed absent. That is,</span>
-<span class="sd">            if link_assumptions is not None, then all links have to be specified</span>
-<span class="sd">            or the links are assumed absent.</span>
-<span class="sd">        lagged_parents : dictionary</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing</span>
-<span class="sd">            additional conditions for each CI test. As part of PCMCIplus</span>
-<span class="sd">            these are the superset of lagged parents estimated with the PC1</span>
-<span class="sd">            algorithm.</span>
-<span class="sd">        mode : {&#39;standard&#39;, &#39;contemp_conds&#39;}</span>
-<span class="sd">            For ``mode=&#39;contemp_conds&#39;`` this implements Steps 2-4 of the</span>
-<span class="sd">            PCMCIplus method. For ``mode=&#39;standard&#39;`` this implements the</span>
-<span class="sd">            standard PC algorithm adapted to time series.</span>
-<span class="sd">        tau_min : int, optional (default: 0)</span>
-<span class="sd">            Minimum time lag to test.</span>
-<span class="sd">        tau_max : int, optional (default: 1)</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        pc_alpha : float, optional (default: 0.01)</span>
-<span class="sd">            Significance level.</span>
-<span class="sd">        contemp_collider_rule : {&#39;majority&#39;, &#39;conservative&#39;, &#39;none&#39;}</span>
-<span class="sd">            Rule for collider phase to use. See the paper for details. Only</span>
-<span class="sd">            &#39;majority&#39; and &#39;conservative&#39; lead to an order-independent</span>
-<span class="sd">            algorithm.</span>
-<span class="sd">        conflict_resolution : bool, optional (default: True)</span>
-<span class="sd">            Whether to mark conflicts in orientation rules. Only for True</span>
-<span class="sd">            this leads to an order-independent algorithm.</span>
-<span class="sd">        max_conds_dim : int, optional (default: None)</span>
-<span class="sd">            Maximum number of conditions to test. If None is passed, this number</span>
-<span class="sd">            is unrestricted.</span>
-<span class="sd">        max_combinations : int</span>
-<span class="sd">            Maximum number of combinations of conditions of current cardinality</span>
-<span class="sd">            to test.</span>
-<span class="sd">        max_conds_py : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of Y to use in MCI tests. If</span>
-<span class="sd">            None is passed, this number is unrestricted.</span>
-<span class="sd">        max_conds_px : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of X to use in MCI tests. If</span>
-<span class="sd">            None is passed, this number is unrestricted.</span>
-<span class="sd">        max_conds_px_lagged : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of X when X is lagged in MCI </span>
-<span class="sd">            tests. If None is passed, this number is equal to max_conds_px.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Resulting causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
-<span class="sd">            Separating sets. See paper for details.</span>
-<span class="sd">        ambiguous_triples : list</span>
-<span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
-<span class="sd">            &#39;conservative&#39; rules, see paper for details.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># TODO: save_iterations</span>
-
-        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;selected_links is DEPRECATED, use link_assumptions instead.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Sanity checks</span>
-        <span class="k">if</span> <span class="n">pc_alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;pc_alpha=None not supported in PC algorithm, &quot;</span>
-                             <span class="s2">&quot;choose 0 &lt; pc_alpha &lt; 1 (e.g., 0.01)&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">mode</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;contemp_conds&#39;</span><span class="p">,</span> <span class="s1">&#39;standard&#39;</span><span class="p">]:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;mode must be either &#39;contemp_conds&#39; or &quot;</span>
-                             <span class="s2">&quot;&#39;standard&#39;&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Check the limits on tau</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_tau_limits</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="c1"># Set the selected links</span>
-        <span class="c1"># _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)</span>
-        <span class="n">_int_link_assumptions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_link_assumptions</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">max_conds_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;standard&#39;</span><span class="p">:</span>
-                <span class="n">max_conds_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                                            <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;contemp_conds&#39;</span><span class="p">:</span>
-                <span class="n">max_conds_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span>
-
-        <span class="k">if</span> <span class="n">max_combinations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">max_combinations</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-
-        <span class="n">initial_graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict_to_graph</span><span class="p">(</span><span class="n">_int_link_assumptions</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="n">skeleton_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_skeleton</span><span class="p">(</span>
-            <span class="n">initial_graph</span><span class="o">=</span><span class="n">initial_graph</span><span class="p">,</span>
-            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
-            <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span>
-            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
-            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-            <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-            <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">,</span>
-            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
-            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
-            <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="n">skeleton_graph</span> <span class="o">=</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span>
-        <span class="n">sepset</span> <span class="o">=</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">]</span>
-
-        <span class="c1"># Now change assumed links mark</span>
-        <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;o?o&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;o-o&#39;</span>
-        <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;--&gt;&#39;</span>
-        <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;&lt;?-&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;--&#39;</span>
-
-        <span class="n">colliders_step_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_colliders</span><span class="p">(</span>
-            <span class="n">graph</span><span class="o">=</span><span class="n">skeleton_graph</span><span class="p">,</span>
-            <span class="n">sepset</span><span class="o">=</span><span class="n">sepset</span><span class="p">,</span>
-            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
-            <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span>
-            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
-            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
-            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
-            <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
-            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span>
-            <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">,</span>
-            <span class="p">)</span>
-
-        <span class="n">collider_graph</span> <span class="o">=</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span>
-        <span class="n">ambiguous_triples</span> <span class="o">=</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span>
-
-        <span class="n">final_graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_rules_timeseries</span><span class="p">(</span>
-            <span class="n">graph</span><span class="o">=</span><span class="n">collider_graph</span><span class="p">,</span>
-            <span class="n">ambiguous_triples</span><span class="o">=</span><span class="n">ambiguous_triples</span><span class="p">,</span>
-            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="c1"># Symmetrize p_matrix and val_matrix</span>
-        <span class="n">symmetrized_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symmetrize_p_and_val_matrix</span><span class="p">(</span>
-                            <span class="n">p_matrix</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span> 
-                            <span class="n">val_matrix</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span> 
-                            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span>
-                            <span class="n">conf_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-
-        <span class="c1"># Convert numerical graph matrix to string</span>
-        <span class="n">graph_str</span> <span class="o">=</span> <span class="n">final_graph</span> <span class="c1"># self.convert_to_string_graph(final_graph)</span>
-
-        <span class="n">pc_results</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph_str</span><span class="p">,</span>
-            <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span>
-            <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span>
-            <span class="s1">&#39;sepset&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">],</span>
-            <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">],</span>
-        <span class="p">}</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">-----------------------------&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;PCMCIplus algorithm finished.&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-----------------------------&quot;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">pc_results</span> <span class="o">=</span> <span class="n">pc_results</span>
-        <span class="k">return</span> <span class="n">pc_results</span></div>
-
-<div class="viewcode-block" id="PCMCI.run_pcalg_non_timeseries_data"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_pcalg_non_timeseries_data">[docs]</a>    <span class="k">def</span> <span class="nf">run_pcalg_non_timeseries_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
-                  <span class="n">max_conds_dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_combinations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                  <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="s1">&#39;majority&#39;</span><span class="p">,</span>
-                  <span class="n">conflict_resolution</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Runs PC algorithm for non-time series data.</span>
-
-<span class="sd">        Simply calls run_pcalg with tau_min = tau_max = 0.</span>
-<span class="sd">        Removes lags from output dictionaries.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        pc_alpha : float, optional (default: 0.01)</span>
-<span class="sd">            Significance level.</span>
-<span class="sd">        contemp_collider_rule : {&#39;majority&#39;, &#39;conservative&#39;, &#39;none&#39;}</span>
-<span class="sd">            Rule for collider phase to use. See the paper for details. Only</span>
-<span class="sd">            &#39;majority&#39; and &#39;conservative&#39; lead to an order-independent</span>
-<span class="sd">            algorithm.</span>
-<span class="sd">        conflict_resolution : bool, optional (default: True)</span>
-<span class="sd">            Whether to mark conflicts in orientation rules. Only for True</span>
-<span class="sd">            this leads to an order-independent algorithm.</span>
-<span class="sd">        max_conds_dim : int, optional (default: None)</span>
-<span class="sd">            Maximum number of conditions to test. If None is passed, this number</span>
-<span class="sd">            is unrestricted.</span>
-<span class="sd">        max_combinations : int</span>
-<span class="sd">            Maximum number of combinations of conditions of current cardinality</span>
-<span class="sd">            to test.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, 1]</span>
-<span class="sd">            Resulting causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, 1]</span>
-<span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
-<span class="sd">        p_matrix : array of shape [N, N, 1]</span>
-<span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
-<span class="sd">            Separating sets. See paper for details.</span>
-<span class="sd">        ambiguous_triples : list</span>
-<span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
-<span class="sd">            &#39;conservative&#39; rules, see paper for details.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pcalg</span><span class="p">(</span><span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span> <span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
-                    <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span> <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">,</span>
-                  <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;standard&#39;</span><span class="p">,</span> <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">,</span>
-                  <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">)</span>
-
-        <span class="c1"># Remove tau-dimension</span>
-        <span class="c1"># results[&#39;graph&#39;] = results[&#39;graph&#39;].squeeze()</span>
-        <span class="c1"># results[&#39;val_matrix&#39;] = results[&#39;val_matrix&#39;].squeeze()</span>
-        <span class="c1"># results[&#39;p_matrix&#39;] = results[&#39;p_matrix&#39;].squeeze()</span>
-        <span class="n">old_sepsets</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">old_sepset</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">:</span>
-           <span class="n">new_sepset</span> <span class="o">=</span> <span class="p">(</span><span class="n">old_sepset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">old_sepset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-           <span class="n">conds</span> <span class="o">=</span> <span class="p">[</span><span class="n">cond</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">cond</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">[</span><span class="n">old_sepset</span><span class="p">]]</span>
-
-           <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">][</span><span class="n">new_sepset</span><span class="p">]</span> <span class="o">=</span> <span class="n">conds</span>
-
-        <span class="n">ambiguous_triples</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">triple</span> <span class="ow">in</span> <span class="n">ambiguous_triples</span><span class="p">:</span>
-           <span class="n">new_triple</span> <span class="o">=</span> <span class="p">(</span><span class="n">triple</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">triple</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">triple</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-
-           <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_triple</span><span class="p">)</span>
-        
-        <span class="bp">self</span><span class="o">.</span><span class="n">pc_results</span> <span class="o">=</span> <span class="n">results</span>
-        <span class="k">return</span> <span class="n">results</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_run_pcalg_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span>
-                        <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI conditional independence tests within PCMCIplus or PC algorithm.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        graph : array</span>
-<span class="sd">            ...</span>
-<span class="sd">        i : int</span>
-<span class="sd">            Variable index.</span>
-<span class="sd">        abstau : int</span>
-<span class="sd">            Time lag (absolute value).</span>
-<span class="sd">        j : int</span>
-<span class="sd">            Variable index.</span>
-<span class="sd">        S : list</span>
-<span class="sd">            List of contemporaneous conditions.</span>
-<span class="sd">        lagged_parents : dictionary of lists</span>
-<span class="sd">            Dictionary of lagged parents for each node.</span>
-<span class="sd">        max_conds_py : int</span>
-<span class="sd">            Max number of lagged parents for node j.</span>
-<span class="sd">        max_conds_px : int</span>
-<span class="sd">            Max number of lagged parents for lagged node i.</span>
-<span class="sd">        max_conds_px_lagged : int</span>
-<span class="sd">            Maximum number of lagged conditions of X when X is lagged in MCI </span>
-<span class="sd">            tests. If None is passed, this number is equal to max_conds_px.</span>
-<span class="sd">        tau_max : int</span>
-<span class="sd">            Maximum time lag.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Test statistic value.</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            Test statistic p-value.</span>
-<span class="sd">        Z : list</span>
-<span class="sd">            List of conditions.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Perform independence test adding lagged parents</span>
-        <span class="k">if</span> <span class="n">lagged_parents</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">conds_y</span> <span class="o">=</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">j</span><span class="p">][:</span><span class="n">max_conds_py</span><span class="p">]</span>
-            <span class="c1"># Get the conditions for node i</span>
-            <span class="k">if</span> <span class="n">abstau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">conds_x</span> <span class="o">=</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">i</span><span class="p">][:</span><span class="n">max_conds_px</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">max_conds_px_lagged</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">conds_x</span> <span class="o">=</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">i</span><span class="p">][:</span><span class="n">max_conds_px</span><span class="p">]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">conds_x</span> <span class="o">=</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">i</span><span class="p">][:</span><span class="n">max_conds_px_lagged</span><span class="p">]</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">conds_y</span> <span class="o">=</span> <span class="n">conds_x</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="c1"># Shift the conditions for X by tau</span>
-        <span class="n">conds_x_lagged</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span> <span class="o">+</span> <span class="n">k_tau</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">k_tau</span> <span class="ow">in</span> <span class="n">conds_x</span><span class="p">]</span>
-
-        <span class="n">Z</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">S</span><span class="p">]</span>
-        <span class="n">Z</span> <span class="o">+=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">conds_y</span> <span class="k">if</span>
-              <span class="n">node</span> <span class="o">!=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)</span> <span class="ow">and</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">]</span>
-        <span class="c1"># Remove overlapping nodes between conds_x_lagged and conds_y</span>
-        <span class="n">Z</span> <span class="o">+=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">conds_x_lagged</span> <span class="k">if</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">]</span>
-
-        <span class="c1"># If middle mark is &#39;-&#39;, then set pval=0</span>
-        <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">abstau</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span> <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">abstau</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;-&#39;</span><span class="p">:</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span> 
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span>
-                                                <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                <span class="c1"># verbosity=self.verbosity</span>
-                                                <span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span>
-
-    <span class="k">def</span> <span class="nf">_print_triple_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">triple</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">n_triples</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Print info about the current triple being tested.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        triple : tuple</span>
-<span class="sd">            Standard ((i, tau), k, j) tuple of nodes and time delays.</span>
-<span class="sd">        index : int</span>
-<span class="sd">            Index of triple.</span>
-<span class="sd">        n_triples : int</span>
-<span class="sd">            Total number of triples.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">triple</span>
-        <span class="n">link_marker</span> <span class="o">=</span> <span class="p">{</span><span class="kc">True</span><span class="p">:</span><span class="s2">&quot;o-o&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">:</span><span class="s2">&quot;--&gt;&quot;</span><span class="p">}</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">    Triple (</span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">) </span><span class="si">%s</span><span class="s2"> </span><span class="si">%s</span><span class="s2"> o-o </span><span class="si">%s</span><span class="s2"> (</span><span class="si">%d</span><span class="s2">/</span><span class="si">%d</span><span class="s2">)&quot;</span> <span class="o">%</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tau</span><span class="p">,</span> <span class="n">link_marker</span><span class="p">[</span><span class="n">tau</span><span class="o">==</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_triples</span><span class="p">))</span>
-
-
-    <span class="k">def</span> <span class="nf">_tests_remaining</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">adjt</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function returning whether a certain pair still needs to be</span>
-<span class="sd">        tested.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span>
-            <span class="p">[</span><span class="n">a</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">adjt</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">if</span> <span class="n">a</span> <span class="o">!=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)])</span> <span class="o">&gt;=</span> <span class="n">p</span>
-
-    <span class="k">def</span> <span class="nf">_any_tests_remaining</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">adjt</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function returning whether any pair still needs to be</span>
-<span class="sd">        tested.&quot;&quot;&quot;</span>
-        <span class="n">remaining_pairs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_remaining_pairs</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">adjt</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span>
-                                                <span class="n">p</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">remaining_pairs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">True</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">False</span>
-
-    <span class="k">def</span> <span class="nf">_remaining_pairs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">adjt</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function returning the remaining pairs that still need to be</span>
-<span class="sd">        tested.&quot;&quot;&quot;</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">pairs</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)):</span>
-            <span class="k">for</span> <span class="n">abstau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span>
-                        <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span>
-                            <span class="p">[</span><span class="n">a</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">adjt</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">if</span> <span class="n">a</span> <span class="o">!=</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)])</span> <span class="o">&gt;=</span> <span class="n">p</span><span class="p">):</span>
-                    <span class="n">pairs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="n">pairs</span>
-
-    <span class="k">def</span> <span class="nf">_pcalg_skeleton</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                       <span class="n">initial_graph</span><span class="p">,</span>
-                       <span class="n">lagged_parents</span><span class="p">,</span>
-                       <span class="n">mode</span><span class="p">,</span>
-                       <span class="n">pc_alpha</span><span class="p">,</span>
-                       <span class="n">tau_min</span><span class="p">,</span>
-                       <span class="n">tau_max</span><span class="p">,</span>
-                       <span class="n">max_conds_dim</span><span class="p">,</span>
-                       <span class="n">max_combinations</span><span class="p">,</span>
-                       <span class="n">max_conds_py</span><span class="p">,</span>
-                       <span class="n">max_conds_px</span><span class="p">,</span>
-                       <span class="n">max_conds_px_lagged</span><span class="p">,</span>
-                       <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Implements the skeleton discovery step of the PC algorithm for</span>
-<span class="sd">        time series.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        initial_graph : array of shape (N, N, tau_max+1) or None</span>
-<span class="sd">            Initial graph.</span>
-<span class="sd">        lagged_parents : dictionary</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing</span>
-<span class="sd">            additional conditions for each CI test. As part of PCMCIplus</span>
-<span class="sd">            these are the superset of lagged parents estimated with the PC1</span>
-<span class="sd">            algorithm.</span>
-<span class="sd">        mode : {&#39;standard&#39;, &#39;contemp_conds&#39;}</span>
-<span class="sd">            For ``mode=&#39;contemp_conds&#39;`` this implements Steps 2-4 of the</span>
-<span class="sd">            PCMCIplus method. For ``mode=&#39;standard&#39;`` this implements the</span>
-<span class="sd">            standard PC algorithm adapted to time series.</span>
-<span class="sd">        tau_min : int, optional (default: 0)</span>
-<span class="sd">            Minimum time lag to test.</span>
-<span class="sd">        tau_max : int, optional (default: 1)</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        pc_alpha : float, optional (default: 0.01)</span>
-<span class="sd">            Significance level.</span>
-<span class="sd">        max_conds_dim : int, optional (default: None)</span>
-<span class="sd">            Maximum number of conditions to test. If None is passed, this number</span>
-<span class="sd">            is unrestricted.</span>
-<span class="sd">        max_combinations : int</span>
-<span class="sd">            Maximum number of combinations of conditions of current cardinality</span>
-<span class="sd">            to test.</span>
-<span class="sd">        max_conds_py : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of Y to use in MCI tests. If</span>
-<span class="sd">            None is passed, this number is unrestricted.</span>
-<span class="sd">        max_conds_px : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of X to use in MCI tests. If</span>
-<span class="sd">            None is passed, this number is unrestricted.</span>
-<span class="sd">        max_conds_px_lagged : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of X when X is lagged in MCI </span>
-<span class="sd">            tests. If None is passed, this number is equal to max_conds_px.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Resulting causal graph, see description above for interpretation.</span>
-<span class="sd">        val_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
-<span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
-<span class="sd">            Separating sets. See paper for details.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span>
-
-        <span class="c1"># Form complete graph</span>
-        <span class="k">if</span> <span class="n">initial_graph</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
-            <span class="n">graph</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;o?o&quot;</span>
-            <span class="n">graph</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">:]</span> <span class="o">=</span> <span class="s2">&quot;-?&gt;&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">graph</span> <span class="o">=</span> <span class="n">initial_graph</span>
-
-        <span class="c1"># Remove lag-zero self-loops</span>
-        <span class="n">graph</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-
-        <span class="c1"># Define adjacencies for standard and contemp_conds mode</span>
-        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;contemp_conds&#39;</span><span class="p">:</span>
-            <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series_contemp</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;standard&#39;</span><span class="p">:</span>
-            <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-
-        <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">val_min</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">val_min</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-                          <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">))}</span>
-
-        <span class="c1"># Initialize p-values. Set to 1 if there&#39;s no link in the initial graph</span>
-        <span class="n">pvalues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">pvalues</span><span class="p">[</span><span class="n">graph</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
-        <span class="n">pval_max</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">pval_max</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="mf">0.</span>
-                           <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">))}</span>
-
-        <span class="c1"># TODO: Remove sepset alltogether?</span>
-        <span class="c1"># Intialize sepsets that store the conditions that make i and j</span>
-        <span class="c1"># independent</span>
-        <span class="n">sepset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepset</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">--------------------------&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Skeleton discovery phase&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;--------------------------&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Start with zero cardinality conditions</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">while</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_any_tests_remaining</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">adjt</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span>
-                                         <span class="n">p</span><span class="p">)</span> <span class="ow">and</span> <span class="n">p</span> <span class="o">&lt;=</span> <span class="n">max_conds_dim</span><span class="p">):</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span>
-                    <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Testing contemporaneous condition sets of dimension &quot;</span>
-                    <span class="s2">&quot;</span><span class="si">%d</span><span class="s2">: &quot;</span> <span class="o">%</span> <span class="n">p</span><span class="p">)</span>
-
-            <span class="n">remaining_pairs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_remaining_pairs</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">adjt</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span>
-                                                    <span class="n">tau_max</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span>
-            <span class="n">n_remaining</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">remaining_pairs</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">ir</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">remaining_pairs</span><span class="p">):</span>
-                <span class="c1"># Check if link was not already removed (contemp links)</span>
-                <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_print_link_info</span><span class="p">(</span><span class="n">j</span><span class="o">=</span><span class="n">j</span><span class="p">,</span> <span class="n">index_parent</span><span class="o">=</span><span class="n">ir</span><span class="p">,</span>
-                                              <span class="n">parent</span><span class="o">=</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">),</span>
-                                              <span class="n">num_parents</span><span class="o">=</span><span class="n">n_remaining</span><span class="p">)</span>
-
-                    <span class="c1"># Generate all subsets of conditions of cardinality p</span>
-                    <span class="n">conditions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span>
-                        <span class="p">[(</span><span class="n">k</span><span class="p">,</span> <span class="n">tauk</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">tauk</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjt</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
-                         <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">k</span> <span class="o">==</span> <span class="n">i</span> <span class="ow">and</span> <span class="n">tauk</span> <span class="o">==</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)],</span> <span class="n">p</span><span class="p">))</span>
-
-                    <span class="n">n_conditions</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">conditions</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span>
-                            <span class="s2">&quot;    Iterate through </span><span class="si">%d</span><span class="s2"> subset(s) of conditions: &quot;</span>
-                            <span class="o">%</span> <span class="n">n_conditions</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">lagged_parents</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">_print_pcmciplus_conditions</span><span class="p">(</span><span class="n">lagged_parents</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span>
-                                                         <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">,</span>
-                                                         <span class="n">max_conds_py</span><span class="p">,</span>
-                                                         <span class="n">max_conds_px</span><span class="p">,</span>
-                                                         <span class="n">max_conds_px_lagged</span><span class="p">)</span>
-                    <span class="n">nonsig</span> <span class="o">=</span> <span class="kc">False</span>
-                    <span class="c1"># Iterate through condition sets</span>
-                    <span class="k">for</span> <span class="n">q</span><span class="p">,</span> <span class="n">S</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">conditions</span><span class="p">):</span>
-                        <span class="k">if</span> <span class="n">q</span> <span class="o">&gt;</span> <span class="n">max_combinations</span><span class="p">:</span>
-                            <span class="k">break</span>
-
-                        <span class="c1"># Run MCI test</span>
-                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                            <span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span>
-                            <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-
-                        <span class="c1"># Store minimum test statistic value for sorting adjt</span>
-                        <span class="c1"># (only internally used)</span>
-                        <span class="n">val_min</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val</span><span class="p">),</span>
-                                                       <span class="n">val_min</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
-                                                           <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)))</span>
-                        <span class="c1"># Store maximum p-value (only internally used)</span>
-                        <span class="n">pval_max</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">pval</span><span class="p">,</span>
-                                                        <span class="n">pval_max</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
-                                                            <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)))</span>
-
-                        <span class="c1"># Store max. p-value and corresponding value to return</span>
-                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;=</span> <span class="n">pvalues</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]:</span>
-                            <span class="n">pvalues</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
-                            <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
-
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_info</span><span class="p">(</span><span class="n">Z</span><span class="o">=</span><span class="n">S</span><span class="p">,</span> <span class="n">comb_index</span><span class="o">=</span><span class="n">q</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span>
-                                                  <span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
-
-                        <span class="c1"># If conditional independence is found, remove link</span>
-                        <span class="c1"># from graph and store sepset</span>
-                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pc_alpha</span><span class="p">:</span>
-                            <span class="n">nonsig</span> <span class="o">=</span> <span class="kc">True</span>
-                            <span class="k">if</span> <span class="n">abstau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="n">sepset</span><span class="p">[</span>
-                                    <span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
-                            <span class="k">else</span><span class="p">:</span>
-                                <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
-                            <span class="k">break</span>
-
-                    <span class="c1"># Print the results if needed</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_print_a_pc_result</span><span class="p">(</span><span class="n">nonsig</span><span class="p">,</span>
-                                                <span class="n">conds_dim</span><span class="o">=</span><span class="n">p</span><span class="p">,</span>
-                                                <span class="n">max_combinations</span><span class="o">=</span>
-                                                <span class="n">max_combinations</span><span class="p">)</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_print_link_info</span><span class="p">(</span><span class="n">j</span><span class="o">=</span><span class="n">j</span><span class="p">,</span> <span class="n">index_parent</span><span class="o">=</span><span class="n">ir</span><span class="p">,</span>
-                                          <span class="n">parent</span><span class="o">=</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">),</span>
-                                          <span class="n">num_parents</span><span class="o">=</span><span class="n">n_remaining</span><span class="p">,</span>
-                                          <span class="n">already_removed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-            <span class="c1"># Increase condition cardinality</span>
-            <span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span>
-
-            <span class="c1"># Re-compute adj and sort by minimum absolute test statistic value</span>
-            <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;contemp_conds&#39;</span><span class="p">:</span>
-                <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series_contemp</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">sort_by</span><span class="o">=</span><span class="n">val_min</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;standard&#39;</span><span class="p">:</span>
-                <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">sort_by</span><span class="o">=</span><span class="n">val_min</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Updated contemp. adjacencies:&quot;</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_print_parents</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">adjt</span><span class="p">,</span> <span class="n">val_min</span><span class="o">=</span><span class="n">val_min</span><span class="p">,</span>
-                                    <span class="n">pval_max</span><span class="o">=</span><span class="n">pval_max</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_any_tests_remaining</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">adjt</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span>
-                                              <span class="n">p</span><span class="p">)</span> <span class="ow">and</span> <span class="n">p</span> <span class="o">&lt;=</span> <span class="n">max_conds_dim</span><span class="p">):</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Algorithm converged at p = </span><span class="si">%d</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">p</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span>
-                    <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Algorithm not yet converged, but max_conds_dim = </span><span class="si">%d</span><span class="s2">&quot;</span>
-                    <span class="s2">&quot; reached.&quot;</span> <span class="o">%</span> <span class="n">max_conds_dim</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-                <span class="s1">&#39;sepset&#39;</span><span class="p">:</span> <span class="n">sepset</span><span class="p">,</span>
-                <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">pvalues</span><span class="p">,</span>
-                <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">val_matrix</span><span class="p">,</span>
-                <span class="p">}</span>
-
-    <span class="k">def</span> <span class="nf">_get_sepset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns initial sepset.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        tau_min : int, optional (default: 0)</span>
-<span class="sd">            Minimum time lag to test.</span>
-<span class="sd">        tau_max : int, optional (default: 1)</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        sepset : dict</span>
-<span class="sd">            Initialized sepset.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">sepset</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">),</span> <span class="p">[])</span>
-                       <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-                       <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-                       <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)])</span>
-
-        <span class="k">return</span> <span class="n">sepset</span>
-
-    <span class="k">def</span> <span class="nf">_find_unshielded_triples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find unshielded triples i_tau o-(&gt;) k_t o-o j_t with i_tau -/- j_t.</span>
-
-<span class="sd">        Excludes conflicting links.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        triples : list</span>
-<span class="sd">            List of triples.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">include_conflicts</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="c1"># Find unshielded triples</span>
-        <span class="c1"># Find triples i_tau o-(&gt;) k_t o-o j_t with i_tau -/- j_t</span>
-        <span class="n">triples</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">tauk</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjt</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="k">if</span> <span class="n">tauk</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;o-o&quot;</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">taui</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjt</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
-                        <span class="k">if</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">taui</span><span class="p">)</span> <span class="o">!=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> 
-                            <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="nb">abs</span><span class="p">(</span><span class="n">taui</span><span class="p">)]</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span>
-                            <span class="ow">and</span> <span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="nb">abs</span><span class="p">(</span><span class="n">taui</span><span class="p">)]</span> <span class="o">==</span> <span class="s2">&quot;o-o&quot;</span> 
-                                <span class="ow">or</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="nb">abs</span><span class="p">(</span><span class="n">taui</span><span class="p">)]</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">)):</span>
-                        <span class="c1"># if not (k == j or (</span>
-                        <span class="c1">#         taui == 0 and (i == k or i == j))):</span>
-                        <span class="c1">#     if ((taui == 0 and graph[i, j, 0] == &quot;&quot; and</span>
-                        <span class="c1">#          graph[j, i, 0] == &quot;&quot; and graph[j, k, 0] == &quot;o-o&quot;)</span>
-                        <span class="c1">#             or (taui &lt; 0 and graph[j, k, 0] == &quot;o-o&quot;</span>
-                        <span class="c1">#                 and graph[i, j, abs(taui)] == &quot;&quot;)):</span>
-                                <span class="n">triples</span><span class="o">.</span><span class="n">append</span><span class="p">(((</span><span class="n">i</span><span class="p">,</span> <span class="n">taui</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="n">triples</span>
-
-    <span class="k">def</span> <span class="nf">_pcalg_colliders</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                        <span class="n">graph</span><span class="p">,</span>
-                        <span class="n">sepset</span><span class="p">,</span>
-                        <span class="n">lagged_parents</span><span class="p">,</span>
-                        <span class="n">mode</span><span class="p">,</span>
-                        <span class="n">pc_alpha</span><span class="p">,</span>
-                        <span class="n">tau_max</span><span class="p">,</span>
-                        <span class="n">max_conds_py</span><span class="p">,</span>
-                        <span class="n">max_conds_px</span><span class="p">,</span>
-                        <span class="n">max_conds_px_lagged</span><span class="p">,</span>
-                        <span class="n">contemp_collider_rule</span><span class="p">,</span>
-                        <span class="n">conflict_resolution</span><span class="p">,</span>
-                        <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Implements the collider orientation step of the PC algorithm for</span>
-<span class="sd">        time series.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        graph : array of shape (N, N, tau_max+1)</span>
-<span class="sd">            Current graph.</span>
-<span class="sd">        sepset : dictionary</span>
-<span class="sd">            Separating sets. See paper for details.</span>
-<span class="sd">        lagged_parents : dictionary</span>
-<span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing</span>
-<span class="sd">            additional conditions for each CI test. As part of PCMCIplus</span>
-<span class="sd">            these are the superset of lagged parents estimated with the PC1</span>
-<span class="sd">            algorithm.</span>
-<span class="sd">        mode : {&#39;standard&#39;, &#39;contemp_conds&#39;}</span>
-<span class="sd">            For ``mode=&#39;contemp_conds&#39;`` this implements Steps 2-4 of the</span>
-<span class="sd">            PCMCIplus method. For ``mode=&#39;standard&#39;`` this implements the</span>
-<span class="sd">            standard PC algorithm adapted to time series.</span>
-<span class="sd">        pc_alpha : float, optional (default: 0.01)</span>
-<span class="sd">            Significance level.</span>
-<span class="sd">        tau_max : int, optional (default: 1)</span>
-<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
-<span class="sd">        max_conds_py : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of Y to use in MCI tests. If</span>
-<span class="sd">            None is passed, this number is unrestricted.</span>
-<span class="sd">        max_conds_px : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of X to use in MCI tests. If</span>
-<span class="sd">            None is passed, this number is unrestricted.</span>
-<span class="sd">        max_conds_px_lagged : int, optional (default: None)</span>
-<span class="sd">            Maximum number of lagged conditions of X when X is lagged in MCI </span>
-<span class="sd">            tests. If None is passed, this number is equal to max_conds_px.</span>
-<span class="sd">        contemp_collider_rule : {&#39;majority&#39;, &#39;conservative&#39;, &#39;none&#39;}</span>
-<span class="sd">            Rule for collider phase to use. See the paper for details. Only</span>
-<span class="sd">            &#39;majority&#39; and &#39;conservative&#39; lead to an order-independent</span>
-<span class="sd">            algorithm.</span>
-<span class="sd">        conflict_resolution : bool, optional (default: True)</span>
-<span class="sd">            Whether to mark conflicts in orientation rules. Only for True</span>
-<span class="sd">            this leads to an order-independent algorithm.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Resulting causal graph, see description above for interpretation.</span>
-<span class="sd">        sepset : dictionary</span>
-<span class="sd">            Separating sets. See paper for details.</span>
-<span class="sd">        ambiguous_triples : list</span>
-<span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
-<span class="sd">            &#39;conservative&#39; rules, see paper for details.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">----------------------------&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Collider orientation phase&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;----------------------------&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">contemp_collider_rule = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">contemp_collider_rule</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;conflict_resolution = </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">conflict_resolution</span><span class="p">)</span>
-
-        <span class="c1"># Check that no middle mark &#39;?&#39; exists</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="o">!=</span><span class="s1">&#39;&#39;</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;-&#39;</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Middle mark &#39;?&#39; exists!&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Find unshielded triples</span>
-        <span class="n">triples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_unshielded_triples</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-
-        <span class="n">v_structures</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">ambiguous_triples</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="k">if</span> <span class="n">contemp_collider_rule</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">contemp_collider_rule</span> <span class="o">==</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
-            <span class="c1"># Standard collider orientation rule of PC algorithm</span>
-            <span class="c1"># If k_t not in sepset(i_tau, j_t), then orient</span>
-            <span class="c1"># as i_tau --&gt; k_t &lt;-- j_t</span>
-            <span class="k">for</span> <span class="n">itaukj</span> <span class="ow">in</span> <span class="n">triples</span><span class="p">:</span>
-                <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">itaukj</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                    <span class="n">v_structures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itaukj</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Apply &#39;majority&#39; or &#39;conservative&#39; rule to orient colliders          </span>
-            <span class="c1"># Compute all (contemp) subsets of potential parents of i and all </span>
-            <span class="c1"># subsets of potential parents of j that make i and j independent</span>
-            <span class="k">def</span> <span class="nf">subsets</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="k">return</span> <span class="p">[]</span>
-                <span class="n">subsets</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">cardinality</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                    <span class="n">subsets</span> <span class="o">+=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">cardinality</span><span class="p">))</span>
-                <span class="n">subsets</span> <span class="o">=</span> <span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">sub</span><span class="p">)</span> <span class="k">for</span> <span class="n">sub</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">subsets</span><span class="p">))]</span>
-                <span class="k">return</span> <span class="n">subsets</span>
-
-            <span class="c1"># We only consider contemporaneous adjacencies because only these</span>
-            <span class="c1"># can include the (contemp) k. Furthermore, next to adjacencies of j,</span>
-            <span class="c1"># we only need to check adjacencies of i for tau=0</span>
-            <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;contemp_conds&#39;</span><span class="p">:</span>
-                <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series_contemp</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;standard&#39;</span><span class="p">:</span>
-                <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-
-            <span class="n">n_triples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">triples</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">ir</span><span class="p">,</span> <span class="n">itaukj</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">triples</span><span class="p">):</span>
-                <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">itaukj</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_print_triple_info</span><span class="p">(</span><span class="n">itaukj</span><span class="p">,</span> <span class="n">ir</span><span class="p">,</span> <span class="n">n_triples</span><span class="p">)</span>
-
-                <span class="n">neighbor_subsets_tmp</span> <span class="o">=</span> <span class="n">subsets</span><span class="p">(</span>
-                    <span class="p">[(</span><span class="n">l</span><span class="p">,</span> <span class="n">taul</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">taul</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjt</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
-                     <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">l</span> <span class="o">==</span> <span class="n">i</span> <span class="ow">and</span> <span class="n">tau</span> <span class="o">==</span> <span class="n">taul</span><span class="p">)])</span>
-                <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="c1"># Furthermore, we only need to check contemp. adjacencies</span>
-                    <span class="c1"># of i for tau=0</span>
-                    <span class="n">neighbor_subsets_tmp</span> <span class="o">+=</span> <span class="n">subsets</span><span class="p">(</span>
-                        <span class="p">[(</span><span class="n">l</span><span class="p">,</span> <span class="n">taul</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">taul</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjt</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                         <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">l</span> <span class="o">==</span> <span class="n">j</span> <span class="ow">and</span> <span class="n">taul</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)])</span>
-
-                <span class="c1"># Make unique</span>
-                <span class="n">neighbor_subsets</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">subset</span> <span class="ow">in</span> <span class="n">neighbor_subsets_tmp</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">subset</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">neighbor_subsets</span><span class="p">:</span>
-                        <span class="n">neighbor_subsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subset</span><span class="p">)</span>
-
-                <span class="n">n_neighbors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbor_subsets</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span>
-                        <span class="s2">&quot;    Iterate through </span><span class="si">%d</span><span class="s2"> condition subset(s) of &quot;</span>
-                        <span class="s2">&quot;neighbors: &quot;</span> <span class="o">%</span> <span class="n">n_neighbors</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">lagged_parents</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_print_pcmciplus_conditions</span><span class="p">(</span><span class="n">lagged_parents</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span>
-                                         <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">max_conds_py</span><span class="p">,</span> <span class="n">max_conds_px</span><span class="p">,</span>
-                                         <span class="n">max_conds_px_lagged</span><span class="p">)</span>
-
-                <span class="c1"># Test which neighbor subsets separate i and j</span>
-                <span class="n">neighbor_sepsets</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">iss</span><span class="p">,</span> <span class="n">S</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">neighbor_subsets</span><span class="p">):</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span>
-                        <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_info</span><span class="p">(</span><span class="n">Z</span><span class="o">=</span><span class="n">S</span><span class="p">,</span> <span class="n">comb_index</span><span class="o">=</span><span class="n">iss</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span>
-                                              <span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
-
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pc_alpha</span><span class="p">:</span>
-                        <span class="n">neighbor_sepsets</span> <span class="o">+=</span> <span class="p">[</span><span class="n">S</span><span class="p">]</span>
-
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbor_sepsets</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">fraction</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
-                        <span class="p">[(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">S</span> <span class="k">for</span> <span class="n">S</span> <span class="ow">in</span> <span class="n">neighbor_sepsets</span><span class="p">])</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span>
-                        <span class="nb">len</span><span class="p">(</span><span class="n">neighbor_sepsets</span><span class="p">))</span>
-
-                <span class="k">if</span> <span class="n">contemp_collider_rule</span> <span class="o">==</span> <span class="s1">&#39;conservative&#39;</span><span class="p">:</span>
-                    <span class="c1"># Triple is labeled as unambiguous if at least one</span>
-                    <span class="c1"># separating set is found and either k is in ALL</span>
-                    <span class="c1"># (fraction == 1) or NONE (fraction == 0) of them</span>
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbor_sepsets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span>
-                                <span class="s2">&quot;    No separating subsets --&gt; ambiguous &quot;</span>
-                                <span class="s2">&quot;triple found&quot;</span><span class="p">)</span>
-                        <span class="n">ambiguous_triples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itaukj</span><span class="p">)</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="n">fraction</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="c1"># If (k, 0) is in none of the neighbor_sepsets,</span>
-                            <span class="c1"># orient as collider</span>
-                            <span class="n">v_structures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itaukj</span><span class="p">)</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span>
-                                    <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
-                                    <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is = 0 --&gt; collider &quot;</span>
-                                    <span class="s2">&quot;found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also delete (k, 0) from sepset (if present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                        <span class="k">elif</span> <span class="n">fraction</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="c1"># If (k, 0) is in all of the neighbor_sepsets,</span>
-                            <span class="c1"># leave unoriented</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span>
-                                    <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
-                                    <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is = 1 --&gt; &quot;</span>
-                                    <span class="s2">&quot;non-collider found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also add (k, 0) to sepset (if not present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span>
-                                    <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
-                                    <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is = between 0 and 1 &quot;</span>
-                                    <span class="s2">&quot;--&gt; ambiguous triple found&quot;</span> <span class="o">%</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="n">ambiguous_triples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itaukj</span><span class="p">)</span>
-
-                <span class="k">elif</span> <span class="n">contemp_collider_rule</span> <span class="o">==</span> <span class="s1">&#39;majority&#39;</span><span class="p">:</span>
-
-                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbor_sepsets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span>
-                                <span class="s2">&quot;    No separating subsets --&gt; ambiguous &quot;</span>
-                                <span class="s2">&quot;triple found&quot;</span><span class="p">)</span>
-                        <span class="n">ambiguous_triples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itaukj</span><span class="p">)</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="n">fraction</span> <span class="o">==</span> <span class="mf">0.5</span><span class="p">:</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span>
-                                    <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
-                                    <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is = 0.5 --&gt; ambiguous &quot;</span>
-                                    <span class="s2">&quot;triple found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="n">ambiguous_triples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itaukj</span><span class="p">)</span>
-                        <span class="k">elif</span> <span class="n">fraction</span> <span class="o">&lt;</span> <span class="mf">0.5</span><span class="p">:</span>
-                            <span class="n">v_structures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itaukj</span><span class="p">)</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span>
-                                    <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
-                                    <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is &lt; 0.5 &quot;</span>
-                                    <span class="s2">&quot;--&gt; collider found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also delete (k, 0) from sepset (if present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                        <span class="k">elif</span> <span class="n">fraction</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">:</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span>
-                                    <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
-                                    <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is &gt; 0.5 &quot;</span>
-                                    <span class="s2">&quot;--&gt; non-collider found&quot;</span> <span class="o">%</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also add (k, 0) to sepset (if not present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-                            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">v_structures</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Orienting links among colliders:&quot;</span><span class="p">)</span>
-
-        <span class="n">link_marker</span> <span class="o">=</span> <span class="p">{</span><span class="kc">True</span><span class="p">:</span><span class="s2">&quot;o-o&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">:</span><span class="s2">&quot;--&gt;&quot;</span><span class="p">}</span>
-
-        <span class="c1"># Now go through list of v-structures and (optionally) detect conflicts</span>
-        <span class="n">oriented_links</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">itaukj</span> <span class="ow">in</span> <span class="n">v_structures</span><span class="p">:</span>
-            <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">itaukj</span>
-
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">    Collider (</span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">) </span><span class="si">%s</span><span class="s2"> </span><span class="si">%s</span><span class="s2"> o-o </span><span class="si">%s</span><span class="s2">:&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tau</span><span class="p">,</span> <span class="n">link_marker</span><span class="p">[</span>
-                        <span class="n">tau</span><span class="o">==</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
-
-            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span> <span class="ow">and</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;      Orient </span><span class="si">%s</span><span class="s2"> o-o </span><span class="si">%s</span><span class="s2"> as </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2"> &quot;</span> <span class="o">%</span> <span class="p">(</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
-                <span class="c1"># graph[k, j, 0] = 0</span>
-                <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span> <span class="c1">#0</span>
-                <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-
-                <span class="n">oriented_links</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">conflict_resolution</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;      Already oriented&quot;</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">conflict_resolution</span><span class="p">:</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span>
-                            <span class="s2">&quot;        Conflict since </span><span class="si">%s</span><span class="s2"> &lt;-- </span><span class="si">%s</span><span class="s2"> already &quot;</span>
-                            <span class="s2">&quot;oriented: Mark link as `2` in graph&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                                <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;x-x&quot;</span> <span class="c1">#2</span>
-
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span> <span class="ow">and</span> <span class="p">(</span>
-                        <span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;      Orient </span><span class="si">%s</span><span class="s2"> o-o </span><span class="si">%s</span><span class="s2"> as </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2"> &quot;</span> <span class="o">%</span> <span class="p">(</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span> <span class="c1">#0</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-
-                    <span class="n">oriented_links</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">))</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">conflict_resolution</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;      Already oriented&quot;</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">conflict_resolution</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span>
-                                <span class="s2">&quot;        Conflict since </span><span class="si">%s</span><span class="s2"> &lt;-- </span><span class="si">%s</span><span class="s2"> already &quot;</span>
-                                <span class="s2">&quot;oriented: Mark link as `2` in graph&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;x-x&quot;</span>  <span class="c1">#2</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Updated adjacencies:&quot;</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_parents</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">adjt</span><span class="p">,</span> <span class="n">val_min</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pval_max</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-                <span class="s1">&#39;sepset&#39;</span><span class="p">:</span> <span class="n">sepset</span><span class="p">,</span>
-                <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">ambiguous_triples</span><span class="p">,</span>
-                <span class="p">}</span>
-
-    <span class="k">def</span> <span class="nf">_find_triples_rule1</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find triples i_tau --&gt; k_t o-o j_t with i_tau -/- j_t.</span>
-
-<span class="sd">        Excludes conflicting links.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        triples : list</span>
-<span class="sd">            List of triples.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">include_conflicts</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">triples</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">tauk</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjt</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="k">if</span> <span class="n">tauk</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;o-o&#39;</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">taui</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjt</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
-                        <span class="k">if</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">taui</span><span class="p">)</span> <span class="o">!=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> 
-                            <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="nb">abs</span><span class="p">(</span><span class="n">taui</span><span class="p">)]</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span>
-                            <span class="ow">and</span> <span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="nb">abs</span><span class="p">(</span><span class="n">taui</span><span class="p">)]</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">)):</span>
-                                <span class="n">triples</span><span class="o">.</span><span class="n">append</span><span class="p">(((</span><span class="n">i</span><span class="p">,</span> <span class="n">taui</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">triples</span>
-
-    <span class="k">def</span> <span class="nf">_find_triples_rule2</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find triples i_t --&gt; k_t --&gt; j_t with i_t o-o j_t.</span>
-
-<span class="sd">        Excludes conflicting links.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        triples : list</span>
-<span class="sd">            List of triples.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">adjtcont</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series_contemp</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                                                     <span class="n">include_conflicts</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">triples</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">tauk</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjtcont</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">taui</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjtcont</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
-                        <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;--&gt;&#39;</span> <span class="ow">and</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">taui</span><span class="p">)</span> <span class="o">!=</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
-                            <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;o-o&#39;</span> <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;o-o&#39;</span><span class="p">:</span>
-                                <span class="n">triples</span><span class="o">.</span><span class="n">append</span><span class="p">(((</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">triples</span>
-
-    <span class="k">def</span> <span class="nf">_find_chains_rule3</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find chains i_t o-o k_t --&gt; j_t and i_t o-o l_t --&gt; j_t with</span>
-<span class="sd">           i_t o-o j_t and k_t -/- l_t.</span>
-
-<span class="sd">        Excludes conflicting links.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Causal graph, see description above for interpretation.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        chains : list</span>
-<span class="sd">            List of chains.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">adjtcont</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series_contemp</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                                                     <span class="n">include_conflicts</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="n">chains</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjtcont</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;o-o&#39;</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjtcont</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                        <span class="k">for</span> <span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">adjtcont</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                            <span class="k">if</span> <span class="p">((</span><span class="n">k</span> <span class="o">!=</span> <span class="n">l</span><span class="p">)</span> 
-                                <span class="ow">and</span> <span class="p">(</span><span class="n">k</span> <span class="o">!=</span> <span class="n">i</span><span class="p">)</span> 
-                                <span class="ow">and</span> <span class="p">(</span><span class="n">l</span> <span class="o">!=</span> <span class="n">i</span><span class="p">)</span>
-                                <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span>
-                                <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">l</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span>
-                                <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;o-o&quot;</span>
-                                <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">l</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;o-o&quot;</span>
-                                <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span><span class="n">l</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span>
-                                <span class="p">):</span>
-                                <span class="n">chains</span><span class="o">.</span><span class="n">append</span><span class="p">((((</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">),</span>
-                                               <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">l</span><span class="p">,</span> <span class="n">j</span><span class="p">)))</span>
-
-        <span class="k">return</span> <span class="n">chains</span>
-
-    <span class="k">def</span> <span class="nf">_pcalg_rules_timeseries</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                                <span class="n">graph</span><span class="p">,</span>
-                                <span class="n">ambiguous_triples</span><span class="p">,</span>
-                                <span class="n">conflict_resolution</span><span class="p">,</span>
-                                <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Implements the rule orientation step of the PC algorithm for</span>
-<span class="sd">        time series.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        graph : array of shape (N, N, tau_max+1)</span>
-<span class="sd">            Current graph.</span>
-<span class="sd">        ambiguous_triples : list</span>
-<span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
-<span class="sd">            &#39;conservative&#39; rules, see paper for details.</span>
-<span class="sd">        conflict_resolution : bool</span>
-<span class="sd">            Whether to mark conflicts in orientation rules. Only for True</span>
-<span class="sd">            this leads to an order-independent algorithm.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
-<span class="sd">            Resulting causal graph, see description above for interpretation.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">def</span> <span class="nf">rule1</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">oriented_links</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Find (unambiguous) triples i_tau --&gt; k_t o-o j_t with</span>
-<span class="sd">               i_tau -/- j_t and orient as i_tau --&gt; k_t --&gt; j_t.</span>
-<span class="sd">            &quot;&quot;&quot;</span>
-            <span class="n">triples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples_rule1</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-            <span class="n">triples_left</span> <span class="o">=</span> <span class="kc">False</span>
-
-            <span class="k">for</span> <span class="n">itaukj</span> <span class="ow">in</span> <span class="n">triples</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">itaukj</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ambiguous_triples</span><span class="p">:</span>
-                    <span class="n">triples_left</span> <span class="o">=</span> <span class="kc">True</span>
-                    <span class="c1"># Orient as i_tau --&gt; k_t --&gt; j_t</span>
-                    <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">itaukj</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span> <span class="ow">and</span> <span class="p">(</span>
-                            <span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span>
-                                <span class="s2">&quot;    R1: Found (</span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">) --&gt; </span><span class="si">%s</span><span class="s2"> o-o </span><span class="si">%s</span><span class="s2">, &quot;</span>
-                                <span class="s2">&quot;orient as </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tau</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
-                        <span class="c1"># graph[j, k, 0] = 0</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;--&gt;&#39;</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;--&#39;</span>  <span class="c1"># 0</span>
-
-                        <span class="n">oriented_links</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
-
-                    <span class="k">if</span> <span class="n">conflict_resolution</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span>
-                                    <span class="s2">&quot;        Conflict since </span><span class="si">%s</span><span class="s2"> &lt;-- </span><span class="si">%s</span><span class="s2"> already&quot;</span>
-                                    <span class="s2">&quot; oriented: Mark link as `2` in graph&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                                        <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
-                            <span class="c1"># graph[j, k, 0] = graph[k, j, 0] = 2</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;x-x&#39;</span>
-
-            <span class="k">return</span> <span class="n">triples_left</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">oriented_links</span>
-
-        <span class="k">def</span> <span class="nf">rule2</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">oriented_links</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Find (unambiguous) triples i_t --&gt; k_t --&gt; j_t with i_t o-o j_t</span>
-<span class="sd">               and orient as i_t --&gt; j_t.</span>
-<span class="sd">            &quot;&quot;&quot;</span>
-
-            <span class="n">triples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_triples_rule2</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-            <span class="n">triples_left</span> <span class="o">=</span> <span class="kc">False</span>
-
-            <span class="k">for</span> <span class="n">itaukj</span> <span class="ow">in</span> <span class="n">triples</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">itaukj</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ambiguous_triples</span><span class="p">:</span>
-                    <span class="c1"># TODO: CHeck whether this is actually needed</span>
-                    <span class="c1"># since ambiguous triples are always unshielded and here</span>
-                    <span class="c1"># we look for triples where i and j are connected</span>
-                    <span class="n">triples_left</span> <span class="o">=</span> <span class="kc">True</span>
-                    <span class="c1"># Orient as i_t --&gt; j_t</span>
-                    <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">itaukj</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span> <span class="ow">and</span> <span class="p">(</span>
-                            <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span>
-                                <span class="s2">&quot;    R2: Found </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2">  with  </span><span class="si">%s</span><span class="s2"> &quot;</span>
-                                <span class="s2">&quot;o-o </span><span class="si">%s</span><span class="s2">, orient as </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;--&gt;&#39;</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;--&#39;</span>  <span class="c1"># 0</span>
-
-                        <span class="n">oriented_links</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
-                    <span class="k">if</span> <span class="n">conflict_resolution</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span>
-                                    <span class="s2">&quot;        Conflict since </span><span class="si">%s</span><span class="s2"> &lt;-- </span><span class="si">%s</span><span class="s2"> already &quot;</span>
-                                    <span class="s2">&quot;oriented: Mark link as `2` in graph&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                                        <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
-                            <span class="c1"># graph[j, i, 0] = graph[i, j, 0] = 2</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;x-x&#39;</span>
-
-            <span class="k">return</span> <span class="n">triples_left</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">oriented_links</span>
-
-        <span class="k">def</span> <span class="nf">rule3</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">oriented_links</span><span class="p">):</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;Find (unambiguous) chains i_t o-o k_t --&gt; j_t</span>
-<span class="sd">               and i_t o-o l_t --&gt; j_t with i_t o-o j_t</span>
-<span class="sd">               and k_t -/- l_t: Orient as i_t --&gt; j_t.</span>
-<span class="sd">            &quot;&quot;&quot;</span>
-            <span class="c1"># First find all chains i_t -- k_t --&gt; j_t with i_t -- j_t</span>
-            <span class="c1"># and k_t -/- l_t</span>
-            <span class="n">chains</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find_chains_rule3</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-
-            <span class="n">chains_left</span> <span class="o">=</span> <span class="kc">False</span>
-
-            <span class="k">for</span> <span class="p">(</span><span class="n">itaukj</span><span class="p">,</span> <span class="n">itaulj</span><span class="p">)</span> <span class="ow">in</span> <span class="n">chains</span><span class="p">:</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">itaukj</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ambiguous_triples</span> <span class="ow">and</span>
-                        <span class="n">itaulj</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ambiguous_triples</span><span class="p">):</span>
-                    <span class="c1"># TODO: CHeck whether this is actually needed</span>
-                    <span class="c1"># since ambiguous triples are always unshielded and here</span>
-                    <span class="c1"># we look for triples where i and j are connected</span>
-                    <span class="n">chains_left</span> <span class="o">=</span> <span class="kc">True</span>
-                    <span class="c1"># Orient as i_t --&gt; j_t</span>
-                    <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">itaukj</span>
-                    <span class="n">_</span>       <span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">itaulj</span>
-
-                    <span class="k">if</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span> <span class="ow">and</span> <span class="p">(</span>
-                            <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">(</span>
-                                <span class="s2">&quot;    R3: Found </span><span class="si">%s</span><span class="s2"> o-o </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2"> and </span><span class="si">%s</span><span class="s2"> o-o &quot;</span>
-                                <span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2"> with </span><span class="si">%s</span><span class="s2"> o-o </span><span class="si">%s</span><span class="s2"> and </span><span class="si">%s</span><span class="s2"> -/- </span><span class="si">%s</span><span class="s2">, &quot;</span>
-                                <span class="s2">&quot;orient as </span><span class="si">%s</span><span class="s2"> --&gt; </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">l</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">l</span><span class="p">],</span>
-                                    <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;--&gt;&#39;</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;--&#39;</span>  <span class="c1"># 0</span>
-
-                        <span class="n">oriented_links</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
-                    <span class="k">if</span> <span class="n">conflict_resolution</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">oriented_links</span><span class="p">:</span>
-                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                                <span class="nb">print</span><span class="p">(</span>
-                                    <span class="s2">&quot;        Conflict since </span><span class="si">%s</span><span class="s2"> &lt;-- </span><span class="si">%s</span><span class="s2"> already &quot;</span>
-                                    <span class="s2">&quot;oriented: Mark link as `2` in graph&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                                        <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
-                            <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;x-x&#39;</span>
-
-            <span class="k">return</span> <span class="n">chains_left</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">oriented_links</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;----------------------------&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rule orientation phase&quot;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;----------------------------&quot;</span><span class="p">)</span>
-
-        <span class="n">oriented_links</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">graph_new</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-        <span class="n">any1</span> <span class="o">=</span> <span class="n">any2</span> <span class="o">=</span> <span class="n">any3</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">while</span> <span class="p">(</span><span class="n">any1</span> <span class="ow">or</span> <span class="n">any2</span> <span class="ow">or</span> <span class="n">any3</span><span class="p">):</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Try rule(s) </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                    <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">any1</span><span class="p">,</span> <span class="n">any2</span><span class="p">,</span> <span class="n">any3</span><span class="p">]))))</span>
-            <span class="n">any1</span><span class="p">,</span> <span class="n">graph_new</span><span class="p">,</span> <span class="n">oriented_links</span> <span class="o">=</span> <span class="n">rule1</span><span class="p">(</span><span class="n">graph_new</span><span class="p">,</span> <span class="n">oriented_links</span><span class="p">)</span>
-            <span class="n">any2</span><span class="p">,</span> <span class="n">graph_new</span><span class="p">,</span> <span class="n">oriented_links</span> <span class="o">=</span> <span class="n">rule2</span><span class="p">(</span><span class="n">graph_new</span><span class="p">,</span> <span class="n">oriented_links</span><span class="p">)</span>
-            <span class="n">any3</span><span class="p">,</span> <span class="n">graph_new</span><span class="p">,</span> <span class="n">oriented_links</span> <span class="o">=</span> <span class="n">rule3</span><span class="p">(</span><span class="n">graph_new</span><span class="p">,</span> <span class="n">oriented_links</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series</span><span class="p">(</span><span class="n">graph_new</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Updated adjacencies:&quot;</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_parents</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">adjt</span><span class="p">,</span> <span class="n">val_min</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pval_max</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">graph_new</span>
-
-    <span class="k">def</span> <span class="nf">_optimize_pcmciplus_alpha</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                      <span class="n">link_assumptions</span><span class="p">,</span>
-                      <span class="n">tau_min</span><span class="p">,</span>
-                      <span class="n">tau_max</span><span class="p">,</span>
-                      <span class="n">pc_alpha</span><span class="p">,</span>
-                      <span class="n">contemp_collider_rule</span><span class="p">,</span>
-                      <span class="n">conflict_resolution</span><span class="p">,</span>
-                      <span class="n">reset_lagged_links</span><span class="p">,</span>
-                      <span class="n">max_conds_dim</span><span class="p">,</span>
-                      <span class="n">max_combinations</span><span class="p">,</span>
-                      <span class="n">max_conds_py</span><span class="p">,</span>
-                      <span class="n">max_conds_px</span><span class="p">,</span>
-                      <span class="n">max_conds_px_lagged</span><span class="p">,</span>
-                      <span class="n">fdr_method</span><span class="p">,</span>
-                      <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Optimizes pc_alpha in PCMCIplus.</span>
-
-<span class="sd">        If a list or None is passed for ``pc_alpha``, the significance level is</span>
-<span class="sd">        optimized for every graph across the given ``pc_alpha`` values using the</span>
-<span class="sd">        score computed in ``cond_ind_test.get_model_selection_criterion()``</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        See those for run_pcmciplus()</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        Results for run_pcmciplus() for the optimal pc_alpha.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">pc_alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">pc_alpha_list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pc_alpha_list</span> <span class="o">=</span> <span class="n">pc_alpha</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Optimizing pc_alpha over &quot;</span> <span class="o">+</span> 
-                  <span class="s2">&quot;pc_alpha_list = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">pc_alpha_list</span><span class="p">)</span> <span class="o">+</span>
-                  <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##&quot;</span><span class="p">)</span>
-
-        <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">pc_alpha_list</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">iscore</span><span class="p">,</span> <span class="n">pc_alpha_here</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pc_alpha_list</span><span class="p">):</span>
-            <span class="c1"># Print statement about the pc_alpha being tested</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">## pc_alpha = </span><span class="si">%s</span><span class="s2"> (</span><span class="si">%d</span><span class="s2">/</span><span class="si">%d</span><span class="s2">):&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">pc_alpha_here</span><span class="p">,</span>
-                                                      <span class="n">iscore</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
-                                                      <span class="n">score</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-            <span class="c1"># Get the results for this alpha value</span>
-            <span class="n">results</span><span class="p">[</span><span class="n">pc_alpha_here</span><span class="p">]</span> <span class="o">=</span> \
-                <span class="bp">self</span><span class="o">.</span><span class="n">run_pcmciplus</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-                                    <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-                                    <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                    <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha_here</span><span class="p">,</span>
-                                    <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">,</span>
-                                    <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span>
-                                    <span class="n">reset_lagged_links</span><span class="o">=</span><span class="n">reset_lagged_links</span><span class="p">,</span>
-                                    <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                    <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">,</span>
-                                    <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
-                                    <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
-                                    <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
-                                    <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
-
-            <span class="c1"># Get one member of the Markov equivalence class of the result</span>
-            <span class="c1"># of PCMCIplus, which is a CPDAG</span>
-
-            <span class="c1"># First create order that is based on some feature of the variables</span>
-            <span class="c1"># to avoid order-dependence of DAG, i.e., it should not matter</span>
-            <span class="c1"># in which order the variables appear in dataframe</span>
-            <span class="c1"># Here we use the sum of absolute val_matrix values incident at j</span>
-            <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">pc_alpha_here</span><span class="p">][</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">]</span>
-            <span class="n">variable_order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span>
-                                <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">)))[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-            <span class="n">dag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dag_from_cpdag</span><span class="p">(</span>
-                            <span class="n">cpdag_graph</span><span class="o">=</span><span class="n">results</span><span class="p">[</span><span class="n">pc_alpha_here</span><span class="p">][</span><span class="s1">&#39;graph&#39;</span><span class="p">],</span>
-                            <span class="n">variable_order</span><span class="o">=</span><span class="n">variable_order</span><span class="p">)</span>
-            
-
-            <span class="c1"># Compute the best average score when the model selection</span>
-            <span class="c1"># is applied to all N variables</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-                <span class="n">parents</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">dag</span><span class="p">[:,</span><span class="n">j</span><span class="p">,:]</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">)):</span>
-                    <span class="n">parents</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]</span> <span class="o">+=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span>
-                        <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-            <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]</span> <span class="o">/=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-
-        <span class="c1"># Record the optimal alpha value</span>
-        <span class="n">optimal_alpha</span> <span class="o">=</span> <span class="n">pc_alpha_list</span><span class="p">[</span><span class="n">score</span><span class="o">.</span><span class="n">argmin</span><span class="p">()]</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##&quot;</span><span class="o">+</span>
-                  <span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">## Scores for individual pc_alpha values:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">iscore</span><span class="p">,</span> <span class="n">pc_alpha</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pc_alpha_list</span><span class="p">):</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;   pc_alpha = </span><span class="si">%7s</span><span class="s2"> yields score = </span><span class="si">%.5f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">pc_alpha</span><span class="p">,</span> 
-                                                                <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]))</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Results for optimal &quot;</span> <span class="o">+</span>
-                  <span class="s2">&quot;pc_alpha = </span><span class="si">%s</span><span class="se">\n</span><span class="s2">##&quot;</span> <span class="o">%</span> <span class="n">optimal_alpha</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">optimal_alpha</span><span class="p">],</span> <span class="n">alpha_level</span><span class="o">=</span><span class="n">optimal_alpha</span><span class="p">)</span>
-
-        <span class="n">optimal_results</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">optimal_alpha</span><span class="p">]</span>
-        <span class="n">optimal_results</span><span class="p">[</span><span class="s1">&#39;optimal_alpha&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">optimal_alpha</span>
-        <span class="k">return</span> <span class="n">optimal_results</span></div>
-
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span><span class="p">,</span> <span class="n">CMIknn</span><span class="p">,</span> <span class="n">ParCorrMult</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
-    <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
-    <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
-
-    <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">43</span><span class="p">)</span>
-    <span class="c1"># Example process to play around with</span>
-    <span class="c1"># Each key refers to a variable and the incoming links are supplied</span>
-    <span class="c1"># as a list of format [((var, -lag), coeff, function), ...]</span>
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-    <span class="k">def</span> <span class="nf">nonlin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mf">5.</span> <span class="o">*</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mf">20.</span><span class="p">))</span>
-
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">2000</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standar_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-    <span class="c1"># Simple sun</span>
-    <span class="n">data</span><span class="p">[:,</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">*</span><span class="mi">20</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.1</span><span class="o">*</span><span class="n">random_state</span><span class="o">.</span><span class="n">standar_normal</span><span class="p">((</span><span class="n">T</span><span class="p">))</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="mf">0.8</span>
-    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.4</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.4</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.6</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.3</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-    <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">var_names</span><span class="o">=</span><span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$X^0$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$X^1$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$X^2$&#39;</span><span class="p">,</span> <span class="s1">&#39;Sun&#39;</span><span class="p">])</span>
-    <span class="c1"># tp.plot_timeseries(dataframe); plt.show()</span>
-
-    <span class="n">parcorr</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
-    <span class="c1"># dataframe_nosun = pp.DataFrame(data[:,[0,1,2]], var_names=[r&#39;$X^0$&#39;, r&#39;$X^1$&#39;, r&#39;$X^2$&#39;])</span>
-    <span class="c1"># pcmci_parcorr = PCMCI(</span>
-    <span class="c1">#     dataframe=dataframe_nosun, </span>
-    <span class="c1">#     cond_ind_test=parcorr,</span>
-    <span class="c1">#     verbosity=0)</span>
-    <span class="n">tau_max</span> <span class="o">=</span> <span class="mi">2</span>
-    <span class="c1"># results = pcmci_parcorr.run_pcmci(tau_max=tau_max, pc_alpha=0.2, alpha_level = 0.01)</span>
-    <span class="c1"># Remove parents of variable 3</span>
-    <span class="c1"># Only estimate parents of variables 0, 1, 2</span>
-    <span class="n">link_assumptions</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">j</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]:</span>
-            <span class="c1"># Directed lagged links</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span><span class="p">):</span> <span class="s1">&#39;-?&gt;&#39;</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-                             <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)}</span>
-            <span class="c1"># Unoriented contemporaneous links</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span> <span class="s1">&#39;o?o&#39;</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">var</span> <span class="o">!=</span> <span class="n">j</span><span class="p">})</span>
-            <span class="c1"># Directed lagged and contemporaneous links from the sun (3)</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span><span class="p">):</span> <span class="s1">&#39;-?&gt;&#39;</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-                             <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)})</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">)</span>
-    <span class="n">pcmci_parcorr</span> <span class="o">=</span> <span class="n">PCMCI</span><span class="p">(</span>
-        <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
-        <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">parcorr</span><span class="p">,</span>
-        <span class="n">verbosity</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">results</span> <span class="o">=</span> <span class="n">pcmci_parcorr</span><span class="o">.</span><span class="n">run_pcmciplus</span><span class="p">(</span><span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> 
-                                      <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">)</span> <span class="c1">#, alpha_level = 0.01)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">][:,</span><span class="mi">3</span><span class="p">,:])</span>
-    <span class="c1"># Plot time series graph</span>
-    <span class="c1"># tp.plot_time_series_graph(</span>
-    <span class="c1">#     val_matrix=results[&#39;val_matrix&#39;],</span>
-    <span class="c1">#     graph=results[&#39;graph&#39;],</span>
-    <span class="c1">#     var_names=[r&#39;$X^0$&#39;, r&#39;$X^1$&#39;, r&#39;$X^2$&#39;, &#39;Sun&#39;],</span>
-    <span class="c1">#     link_colorbar_label=&#39;MCI&#39;,</span>
-    <span class="c1">#     ); plt.show()</span>
-
-    <span class="c1"># links_coeffs = {0: [((0, -1), 0.7, lin_f)],</span>
-    <span class="c1">#                 1: [((1, -1), 0.7, lin_f), ((0, 0), 0.2, lin_f), ((2, -2), 0.2, lin_f)],</span>
-    <span class="c1">#                 2: [((2, -1), 0.3, lin_f)],</span>
-    <span class="c1">#                 }</span>
-    <span class="c1"># T = 100     # time series length</span>
-    <span class="c1"># data, _ = toys.structural_causal_process(links_coeffs, T=T, seed=3)</span>
-    <span class="c1"># T, N = data.shape</span>
-
-    <span class="c1"># # Initialize dataframe object</span>
-    <span class="c1"># dataframe = pp.DataFrame(data)</span>
-    <span class="c1"># pcmci = PCMCI(</span>
-    <span class="c1">#     dataframe=dataframe, </span>
-    <span class="c1">#     cond_ind_test=ParCorr(),</span>
-    <span class="c1">#     verbosity=0)</span>
-
-    <span class="c1"># multidata[0][40:100, :] = 999.</span>
-
-    <span class="c1"># dataframe = pp.DataFrame(multidata, analysis_mode=&#39;multiple&#39;,</span>
-    <span class="c1">#         missing_flag = 999.,</span>
-    <span class="c1">#         time_offsets = {0:50, 1:0}</span>
-    <span class="c1">#          # reference_points=list(range(500, 1000))</span>
-    <span class="c1">#          ) </span>
-
-    <span class="c1"># pcmci = PCMCI(dataframe=dataframe, </span>
-    <span class="c1">#     cond_ind_test=ParCorr(verbosity=0), verbosity=0)</span>
-
-    <span class="c1"># # results = pcmci.run_pcmciplus(tau_max=1)</span>
-
-    <span class="c1"># results = pcmci.run_sliding_window_of(</span>
-    <span class="c1">#     window_step=499, window_length=500,</span>
-    <span class="c1">#     method=&#39;run_pcmciplus&#39;, method_args={&#39;tau_max&#39;:1, </span>
-    <span class="c1">#     &#39;link_assumptions&#39;:{</span>
-    <span class="c1">#     0: {(0, -1): &#39;--&gt;&#39;},</span>
-    <span class="c1">#     1: {(1, -1): &#39;--&gt;&#39;, (0, -1): &#39;-!&gt;&#39;},</span>
-    <span class="c1">#     }</span>
-    <span class="c1">#     })</span>
-
-    <span class="c1"># # tp.plot_graph(results[&#39;graph&#39;])</span>
-    <span class="c1"># print(multidata[0].shape, multidata[1].shape)</span>
-    <span class="c1"># print(results[&#39;window_results&#39;][&#39;val_matrix&#39;])</span>
-    <span class="c1"># print(results[&#39;window_results&#39;][&#39;val_matrix&#39;][0][0,1])</span>
-    <span class="c1"># print(results[&#39;window_results&#39;][&#39;val_matrix&#39;][1][0,1])</span>
-
-    <span class="c1"># plt.show()</span>
-
-</pre></div>
-
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-          
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-<h1 class="logo"><a href="../../index.html">Tigramite</a></h1>
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-  <h3 id="searchlabel">Quick search</h3>
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-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-            
-  <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite plotting package.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">json</span><span class="o">,</span> <span class="nn">warnings</span><span class="o">,</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">pathlib</span>
-<span class="kn">import</span> <span class="nn">matplotlib</span>
-<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
-<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
-<span class="kn">import</span> <span class="nn">matplotlib.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
-<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span><span class="p">,</span> <span class="n">ticker</span>
-<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">FormatStrFormatter</span>
-<span class="kn">import</span> <span class="nn">matplotlib.patches</span> <span class="k">as</span> <span class="nn">mpatches</span>
-<span class="kn">from</span> <span class="nn">matplotlib.collections</span> <span class="kn">import</span> <span class="n">PatchCollection</span>
-<span class="kn">from</span> <span class="nn">mpl_toolkits.axes_grid1</span> <span class="kn">import</span> <span class="n">make_axes_locatable</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">from</span> <span class="nn">operator</span> <span class="kn">import</span> <span class="n">sub</span>
-<span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
-<span class="kn">import</span> <span class="nn">matplotlib.path</span> <span class="k">as</span> <span class="nn">mpath</span>
-<span class="kn">import</span> <span class="nn">matplotlib.patheffects</span> <span class="k">as</span> <span class="nn">PathEffects</span>
-<span class="kn">from</span> <span class="nn">mpl_toolkits.axisartist.axislines</span> <span class="kn">import</span> <span class="n">Axes</span>
-<span class="kn">import</span> <span class="nn">csv</span>
-<span class="c1"># TODO: Add proper docstrings to internal functions...</span>
-
-
-<span class="k">def</span> <span class="nf">_par_corr_trafo</span><span class="p">(</span><span class="n">cmi</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Transformation of CMI to partial correlation scale.&quot;&quot;&quot;</span>
-
-    <span class="c1"># Set negative values to small positive number</span>
-    <span class="c1"># (zero would be interpreted as non-significant in some functions)</span>
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">cmi</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">cmi</span> <span class="o">&lt;</span> <span class="mf">0.0</span><span class="p">:</span>
-            <span class="n">cmi</span> <span class="o">=</span> <span class="mf">1e-8</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">cmi</span><span class="p">[</span><span class="n">cmi</span> <span class="o">&lt;</span> <span class="mf">0.0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1e-8</span>
-
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">cmi</span><span class="p">))</span>
-
-
-<span class="k">def</span> <span class="nf">_par_corr_to_cmi</span><span class="p">(</span><span class="n">par_corr</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Transformation of partial correlation to CMI scale.&quot;&quot;&quot;</span>
-
-    <span class="k">return</span> <span class="o">-</span><span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">par_corr</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_myround</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">round_mode</span><span class="o">=</span><span class="s2">&quot;updown&quot;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Rounds x to a float with precision base.&quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">round_mode</span> <span class="o">==</span> <span class="s2">&quot;updown&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">base</span> <span class="o">*</span> <span class="nb">round</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">base</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">round_mode</span> <span class="o">==</span> <span class="s2">&quot;down&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">base</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">base</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">round_mode</span> <span class="o">==</span> <span class="s2">&quot;up&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">base</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">base</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">base</span> <span class="o">*</span> <span class="nb">round</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">base</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_make_nice_axes</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skip</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Makes nice axes.&quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">where</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">where</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="s2">&quot;bottom&quot;</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">color</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">color</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;left&quot;</span><span class="p">:</span> <span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="s2">&quot;right&quot;</span><span class="p">:</span> <span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="s2">&quot;bottom&quot;</span><span class="p">:</span> <span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="s2">&quot;top&quot;</span><span class="p">:</span> <span class="s2">&quot;black&quot;</span><span class="p">}</span>
-
-    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">skip</span><span class="p">)</span> <span class="o">==</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="n">skip_x</span> <span class="o">=</span> <span class="n">skip_y</span> <span class="o">=</span> <span class="n">skip</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">skip_x</span> <span class="o">=</span> <span class="n">skip</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">skip_y</span> <span class="o">=</span> <span class="n">skip</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="k">for</span> <span class="n">loc</span><span class="p">,</span> <span class="n">spine</span> <span class="ow">in</span> <span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="k">if</span> <span class="n">loc</span> <span class="ow">in</span> <span class="n">where</span><span class="p">:</span>
-            <span class="n">spine</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s2">&quot;outward&quot;</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>  <span class="c1"># outward by 10 points</span>
-            <span class="n">spine</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="n">color</span><span class="p">[</span><span class="n">loc</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">loc</span> <span class="o">==</span> <span class="s2">&quot;left&quot;</span> <span class="ow">or</span> <span class="n">loc</span> <span class="o">==</span> <span class="s2">&quot;right&quot;</span><span class="p">:</span>
-                <span class="n">pyplot</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_yticklines</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">[</span><span class="n">loc</span><span class="p">])</span>
-                <span class="n">pyplot</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_yticklabels</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">[</span><span class="n">loc</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">loc</span> <span class="o">==</span> <span class="s2">&quot;top&quot;</span> <span class="ow">or</span> <span class="n">loc</span> <span class="o">==</span> <span class="s2">&quot;bottom&quot;</span><span class="p">:</span>
-                <span class="n">pyplot</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xticklines</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">[</span><span class="n">loc</span><span class="p">])</span>
-        <span class="k">elif</span> <span class="n">loc</span> <span class="ow">in</span> <span class="p">[</span>
-            <span class="n">item</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="s2">&quot;right&quot;</span><span class="p">,</span> <span class="s2">&quot;top&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="n">item</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">where</span>
-        <span class="p">]:</span>
-            <span class="n">spine</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>  <span class="c1"># don&#39;t draw spine</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;unknown spine location: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">loc</span><span class="p">)</span>
-
-    <span class="c1"># ax.xaxis.get_major_formatter().set_useOffset(False)</span>
-
-    <span class="c1"># turn off ticks where there is no spine</span>
-    <span class="k">if</span> <span class="s2">&quot;top&quot;</span> <span class="ow">in</span> <span class="n">where</span> <span class="ow">and</span> <span class="s2">&quot;bottom&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">where</span><span class="p">:</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s2">&quot;top&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">skip_x</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xticks</span><span class="p">()[::</span><span class="n">skip_x</span><span class="p">])</span>
-    <span class="k">elif</span> <span class="s2">&quot;bottom&quot;</span> <span class="ow">in</span> <span class="n">where</span><span class="p">:</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s2">&quot;bottom&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">skip_x</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xticks</span><span class="p">()[::</span><span class="n">skip_x</span><span class="p">])</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
-    <span class="k">if</span> <span class="s2">&quot;right&quot;</span> <span class="ow">in</span> <span class="n">where</span> <span class="ow">and</span> <span class="s2">&quot;left&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">where</span><span class="p">:</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">skip_y</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_yticks</span><span class="p">()[::</span><span class="n">skip_y</span><span class="p">])</span>
-    <span class="k">elif</span> <span class="s2">&quot;left&quot;</span> <span class="ow">in</span> <span class="n">where</span><span class="p">:</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s2">&quot;left&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">skip_y</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_yticks</span><span class="p">()[::</span><span class="n">skip_y</span><span class="p">])</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
-
-    <span class="n">ax</span><span class="o">.</span><span class="n">patch</span><span class="o">.</span><span class="n">set_alpha</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_get_absmax</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get value at absolute maximum in lag function array.</span>
-<span class="sd">    For an (N, N, tau)-array this comutes the lag of the absolute maximum</span>
-<span class="sd">    along the tau-axis and stores the (positive or negative) value in</span>
-<span class="sd">    the (N,N)-array absmax.&quot;&quot;&quot;</span>
-
-    <span class="n">absmax_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">)</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">indices</span><span class="p">(</span><span class="n">val_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span>
-
-    <span class="k">return</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">absmax_indices</span><span class="p">]</span>
-
-
-<span class="k">def</span> <span class="nf">_add_timeseries</span><span class="p">(</span>
-    <span class="n">dataframe</span><span class="p">,</span>
-    <span class="n">fig_axes</span><span class="p">,</span>
-    <span class="n">grey_masked_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">show_meanline</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">data_linewidth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-    <span class="n">alpha</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span>
-    <span class="n">grey_alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">selected_dataset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Adds a time series plot to an axis.</span>
-<span class="sd">    Plot of dataseries is added to axis. Allows for proper visualization of</span>
-<span class="sd">    masked data.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    fig : figure instance</span>
-<span class="sd">        Figure instance.</span>
-<span class="sd">    axes : axis instance</span>
-<span class="sd">        Either gridded axis object or single axis instance.</span>
-<span class="sd">    grey_masked_samples : bool, optional (default: False)</span>
-<span class="sd">        Whether to mark masked samples by grey fills (&#39;fill&#39;) or grey data</span>
-<span class="sd">        (&#39;data&#39;).</span>
-<span class="sd">    show_meanline : bool</span>
-<span class="sd">        Show mean of data as horizontal line.</span>
-<span class="sd">    data_linewidth : float, optional (default: 1.)</span>
-<span class="sd">        Linewidth.</span>
-<span class="sd">    color : str, optional (default: black)</span>
-<span class="sd">        Line color.</span>
-<span class="sd">    alpha : float</span>
-<span class="sd">        Alpha opacity.</span>
-<span class="sd">    grey_alpha : float, optional (default: 1.)</span>
-<span class="sd">        Opacity of fill_between.</span>
-<span class="sd">    selected_dataset : int, optional (default: 0)</span>
-<span class="sd">        In case of multiple datasets in dataframe, plot this one.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">fig_axes</span>
-
-    <span class="c1"># Read in all attributes from dataframe</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">selected_dataset</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">mask</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span><span class="p">[</span><span class="n">selected_dataset</span><span class="p">]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">mask</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="n">missing_flag</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">missing_flag</span>
-    <span class="n">time</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">datatime</span><span class="p">[</span><span class="n">selected_dataset</span><span class="p">]</span>
-    <span class="n">T</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">time</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
-        
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
-        <span class="n">dataseries</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:,</span><span class="n">j</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">missing_flag</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">dataseries_nomissing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">masked_where</span><span class="p">(</span>
-                <span class="n">dataseries</span> <span class="o">==</span> <span class="n">missing_flag</span><span class="p">,</span> <span class="n">dataseries</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">dataseries_nomissing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">masked_where</span><span class="p">(</span>
-                <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">dataseries</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">dataseries</span>
-            <span class="p">)</span>
-
-
-        <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">maskseries</span> <span class="o">=</span> <span class="n">mask</span><span class="p">[:,</span><span class="n">j</span><span class="p">]</span>
-
-            <span class="n">maskdata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">masked_where</span><span class="p">(</span><span class="n">maskseries</span><span class="p">,</span> <span class="n">dataseries_nomissing</span><span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">grey_masked_samples</span> <span class="o">==</span> <span class="s2">&quot;fill&quot;</span><span class="p">:</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span>
-                    <span class="n">time</span><span class="p">,</span>
-                    <span class="n">maskdata</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span>
-                    <span class="n">maskdata</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span>
-                    <span class="n">where</span><span class="o">=</span><span class="n">maskseries</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;grey&quot;</span><span class="p">,</span>
-                    <span class="n">interpolate</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                    <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
-                    <span class="n">alpha</span><span class="o">=</span><span class="n">grey_alpha</span><span class="p">,</span>
-                <span class="p">)</span>
-            <span class="k">elif</span> <span class="n">grey_masked_samples</span> <span class="o">==</span> <span class="s2">&quot;data&quot;</span><span class="p">:</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                    <span class="n">time</span><span class="p">,</span>
-                    <span class="n">dataseries_nomissing</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;grey&quot;</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span>
-                    <span class="n">markersize</span><span class="o">=</span><span class="n">data_linewidth</span><span class="p">,</span>
-                    <span class="n">linewidth</span><span class="o">=</span><span class="n">data_linewidth</span><span class="p">,</span>
-                    <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                    <span class="n">alpha</span><span class="o">=</span><span class="n">grey_alpha</span><span class="p">,</span>
-                <span class="p">)</span>
-            <span class="k">if</span> <span class="n">show_meanline</span><span class="p">:</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">maskdata</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="n">lw</span><span class="o">=</span><span class="n">data_linewidth</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
-
-            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                <span class="n">time</span><span class="p">,</span>
-                <span class="n">maskdata</span><span class="p">,</span>
-                <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                <span class="n">linewidth</span><span class="o">=</span><span class="n">data_linewidth</span><span class="p">,</span>
-                <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span>
-                <span class="n">markersize</span><span class="o">=</span><span class="n">data_linewidth</span><span class="p">,</span>
-                <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">show_meanline</span><span class="p">:</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">dataseries_nomissing</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="n">lw</span><span class="o">=</span><span class="n">data_linewidth</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
-
-            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                <span class="n">time</span><span class="p">,</span>
-                <span class="n">dataseries_nomissing</span><span class="p">,</span>
-                <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                <span class="n">linewidth</span><span class="o">=</span><span class="n">data_linewidth</span><span class="p">,</span>
-                <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                <span class="p">)</span>
-
-
-<div class="viewcode-block" id="plot_timeseries"><a class="viewcode-back" href="../../index.html#tigramite.plotting.plot_timeseries">[docs]</a><span class="k">def</span> <span class="nf">plot_timeseries</span><span class="p">(</span>
-    <span class="n">dataframe</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">save_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">fig_axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">var_units</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">time_label</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
-    <span class="n">grey_masked_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">show_meanline</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">data_linewidth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">skip_ticks_data_x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">skip_ticks_data_y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-    <span class="n">alpha</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span>
-    <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-    <span class="n">selected_dataset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">adjust_plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create and save figure of stacked panels with time series.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataframe : data object, optional</span>
-<span class="sd">        This is the Tigramite dataframe object. It has the attributes</span>
-<span class="sd">        dataframe.values yielding a np array of shape (observations T,</span>
-<span class="sd">        variables N) and optionally a mask of the same shape.</span>
-<span class="sd">    save_name : str, optional (default: None)</span>
-<span class="sd">        Name of figure file to save figure. If None, figure is shown in window.</span>
-<span class="sd">    fig_axes : subplots instance, optional (default: None)</span>
-<span class="sd">        Figure and axes instance. If None they are created as</span>
-<span class="sd">        fig, axes = pyplot.subplots(N,...)</span>
-<span class="sd">    figsize : tuple of floats, optional (default: None)</span>
-<span class="sd">        Figure size if new figure is created. If None, default pyplot figsize</span>
-<span class="sd">        is used.</span>
-<span class="sd">    var_units : list of str, optional (default: None)</span>
-<span class="sd">        Units of variables.</span>
-<span class="sd">    time_label : str, optional (default: &#39;&#39;)</span>
-<span class="sd">        Label of time axis.</span>
-<span class="sd">    grey_masked_samples : bool, optional (default: False)</span>
-<span class="sd">        Whether to mark masked samples by grey fills (&#39;fill&#39;) or grey data</span>
-<span class="sd">        (&#39;data&#39;).</span>
-<span class="sd">    show_meanline : bool, optional (default: False)</span>
-<span class="sd">        Whether to plot a horizontal line at the mean.</span>
-<span class="sd">    data_linewidth : float, optional (default: 1.)</span>
-<span class="sd">        Linewidth.</span>
-<span class="sd">    skip_ticks_data_x : int, optional (default: 1)</span>
-<span class="sd">        Skip every other tickmark.</span>
-<span class="sd">    skip_ticks_data_y : int, optional (default: 2)</span>
-<span class="sd">        Skip every other tickmark.</span>
-<span class="sd">    label_fontsize : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of variable labels.</span>
-<span class="sd">    tick_label_size : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of tick labels.</span>
-<span class="sd">    color : str, optional (default: black)</span>
-<span class="sd">        Line color.</span>
-<span class="sd">    alpha : float</span>
-<span class="sd">        Alpha opacity.</span>
-<span class="sd">    selected_dataset : int, optional (default: 0)</span>
-<span class="sd">        In case of multiple datasets in dataframe, plot this one.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">var_names</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">var_names</span>
-    <span class="n">time</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">datatime</span><span class="p">[</span><span class="n">selected_dataset</span><span class="p">]</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">N</span>
-
-    <span class="k">if</span> <span class="n">var_units</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">var_units</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)]</span>
-
-    <span class="k">if</span> <span class="n">fig_axes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">fig_axes</span>
-
-    <span class="k">if</span> <span class="n">adjust_plot</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-
-            <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-
-            <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="n">_make_nice_axes</span><span class="p">(</span>
-                    <span class="n">ax</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="s2">&quot;bottom&quot;</span><span class="p">],</span> <span class="n">skip</span><span class="o">=</span><span class="p">(</span><span class="n">skip_ticks_data_x</span><span class="p">,</span> <span class="n">skip_ticks_data_y</span><span class="p">)</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">time_label</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">_make_nice_axes</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">],</span> <span class="n">skip</span><span class="o">=</span><span class="p">(</span><span class="n">skip_ticks_data_x</span><span class="p">,</span> <span class="n">skip_ticks_data_y</span><span class="p">))</span>
-            <span class="c1"># ax.get_xaxis().get_major_formatter().set_useOffset(False)</span>
-
-            <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">FormatStrFormatter</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.0f</span><span class="s2">&quot;</span><span class="p">))</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">label_outer</span><span class="p">()</span>
-
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">time</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-
-            <span class="c1"># trans = transforms.blended_transform_factory(fig.transFigure, ax.transAxes)</span>
-            <span class="k">if</span> <span class="n">var_units</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> [</span><span class="si">%s</span><span class="s2">]&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">var_units</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">)</span>
-
-            <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;major&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">)</span>
-            <span class="c1"># ax.tick_params(axis=&#39;both&#39;, which=&#39;minor&#39;, labelsize=tick_label_size)</span>
-
-    <span class="n">_add_timeseries</span><span class="p">(</span>
-        <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span>
-        <span class="n">fig_axes</span> <span class="o">=</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span><span class="p">),</span>
-        <span class="n">grey_masked_samples</span><span class="o">=</span><span class="n">grey_masked_samples</span><span class="p">,</span>
-        <span class="n">show_meanline</span><span class="o">=</span><span class="n">show_meanline</span><span class="p">,</span>
-        <span class="n">data_linewidth</span><span class="o">=</span><span class="n">data_linewidth</span><span class="p">,</span>
-        <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-        <span class="n">selected_dataset</span><span class="o">=</span><span class="n">selected_dataset</span><span class="p">,</span>
-        <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-        <span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">adjust_plot</span><span class="p">:</span>
-        <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-
-    <span class="k">if</span> <span class="n">save_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_name</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span></div>
-
-
-<div class="viewcode-block" id="plot_lagfuncs"><a class="viewcode-back" href="../../index.html#tigramite.plotting.plot_lagfuncs">[docs]</a><span class="k">def</span> <span class="nf">plot_lagfuncs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">,</span> 
-                  <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                  <span class="n">setup_args</span><span class="o">=</span><span class="p">{},</span> 
-                  <span class="n">add_lagfunc_args</span><span class="o">=</span><span class="p">{}):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Wrapper helper function to plot lag functions.</span>
-<span class="sd">    Sets up the matrix object and plots the lagfunction, see parameters in</span>
-<span class="sd">    setup_matrix and add_lagfuncs.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    val_matrix : array_like</span>
-<span class="sd">        Matrix of shape (N, N, tau_max+1) containing test statistic values.</span>
-<span class="sd">    name : str, optional (default: None)</span>
-<span class="sd">        File name. If None, figure is shown in window.</span>
-<span class="sd">    setup_args : dict</span>
-<span class="sd">        Arguments for setting up the lag function matrix, see doc of</span>
-<span class="sd">        setup_matrix.</span>
-<span class="sd">    add_lagfunc_args : dict</span>
-<span class="sd">        Arguments for adding a lag function matrix, see doc of add_lagfuncs.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    matrix : object</span>
-<span class="sd">        Further lag functions can be overlaid using the</span>
-<span class="sd">        matrix.add_lagfuncs(val_matrix) function.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max_plusone</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="o">.</span><span class="n">shape</span>
-    <span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_max_plusone</span> <span class="o">-</span> <span class="mi">1</span>
-
-    <span class="n">matrix</span> <span class="o">=</span> <span class="n">setup_matrix</span><span class="p">(</span><span class="n">N</span><span class="o">=</span><span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="o">**</span><span class="n">setup_args</span><span class="p">)</span>
-    <span class="n">matrix</span><span class="o">.</span><span class="n">add_lagfuncs</span><span class="p">(</span><span class="n">val_matrix</span><span class="o">=</span><span class="n">val_matrix</span><span class="p">,</span> <span class="o">**</span><span class="n">add_lagfunc_args</span><span class="p">)</span>
-    <span class="n">matrix</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">matrix</span></div>
-
-
-<div class="viewcode-block" id="setup_matrix"><a class="viewcode-back" href="../../index.html#tigramite.plotting.setup_matrix">[docs]</a><span class="k">class</span> <span class="nc">setup_matrix</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create matrix of lag function panels.</span>
-<span class="sd">    Class to setup figure object. The function add_lagfuncs(...) allows to plot</span>
-<span class="sd">    the val_matrix of shape (N, N, tau_max+1). Multiple lagfunctions can be</span>
-<span class="sd">    overlaid for comparison.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    N : int</span>
-<span class="sd">        Number of variables</span>
-<span class="sd">    tau_max : int</span>
-<span class="sd">        Maximum time lag.</span>
-<span class="sd">    var_names : list, optional (default: None)</span>
-<span class="sd">        List of variable names. If None, range(N) is used.</span>
-<span class="sd">    figsize : tuple of floats, optional (default: None)</span>
-<span class="sd">        Figure size if new figure is created. If None, default pyplot figsize</span>
-<span class="sd">        is used.</span>
-<span class="sd">    minimum : int, optional (default: -1)</span>
-<span class="sd">        Lower y-axis limit.</span>
-<span class="sd">    maximum : int, optional (default: 1)</span>
-<span class="sd">        Upper y-axis limit.</span>
-<span class="sd">    label_space_left : float, optional (default: 0.1)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate left of plot for labels.</span>
-<span class="sd">    label_space_top : float, optional (default: 0.05)</span>
-<span class="sd">        Fraction of vertical figure space to allocate top of plot for labels.</span>
-<span class="sd">    legend_width : float, optional (default: 0.15)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate right of plot for</span>
-<span class="sd">        legend.</span>
-<span class="sd">    tick_label_size : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of tick labels.</span>
-<span class="sd">    x_base : float, optional (default: 1.)</span>
-<span class="sd">        x-tick intervals to show.</span>
-<span class="sd">    y_base : float, optional (default: .4)</span>
-<span class="sd">        y-tick intervals to show.</span>
-<span class="sd">    plot_gridlines : bool, optional (default: False)</span>
-<span class="sd">        Whether to show a grid.</span>
-<span class="sd">    lag_units : str, optional (default: &#39;&#39;)</span>
-<span class="sd">    lag_array : array, optional (default: None)</span>
-<span class="sd">        Optional specification of lags overwriting np.arange(0, tau_max+1)</span>
-<span class="sd">    label_fontsize : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of variable labels.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">N</span><span class="p">,</span>
-        <span class="n">tau_max</span><span class="p">,</span>
-        <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">minimum</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-        <span class="n">maximum</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-        <span class="n">label_space_left</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-        <span class="n">label_space_top</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-        <span class="n">legend_width</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span>
-        <span class="n">legend_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-        <span class="n">x_base</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-        <span class="n">y_base</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-        <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-        <span class="n">plot_gridlines</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">lag_units</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
-        <span class="n">lag_array</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_max</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lag_units</span> <span class="o">=</span> <span class="n">lag_units</span>
-        <span class="c1"># if lag_array is None:</span>
-        <span class="c1">#     self.lag_array = np.arange(0, self.tau_max + 1)</span>
-        <span class="c1"># else:</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lag_array</span> <span class="o">=</span> <span class="n">lag_array</span>
-        <span class="k">if</span> <span class="n">x_base</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">x_base</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">x_base</span> <span class="o">=</span> <span class="n">x_base</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">legend_width</span> <span class="o">=</span> <span class="n">legend_width</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">legend_fontsize</span> <span class="o">=</span> <span class="n">legend_fontsize</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_space_left</span> <span class="o">=</span> <span class="n">label_space_left</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_space_top</span> <span class="o">=</span> <span class="n">label_space_top</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_fontsize</span> <span class="o">=</span> <span class="n">label_fontsize</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">fig</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span> <span class="o">=</span> <span class="p">{}</span>
-
-        <span class="k">if</span> <span class="n">var_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">var_names</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-        <span class="n">plot_index</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">plot_index</span><span class="p">)</span>
-                <span class="c1"># Plot process labels</span>
-                <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">transFigure</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">transAxes</span>
-                    <span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                        <span class="mf">0.01</span><span class="p">,</span>
-                        <span class="mf">0.5</span><span class="p">,</span>
-                        <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span>
-                        <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
-                        <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                        <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">transFigure</span>
-                    <span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                        <span class="mf">0.5</span><span class="p">,</span>
-                        <span class="mf">0.99</span><span class="p">,</span>
-                        <span class="sa">r</span><span class="s2">&quot;${\to}$ &quot;</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]),</span>
-                        <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                        <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span>
-                        <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-                    <span class="p">)</span>
-
-                <span class="c1"># Make nice axis</span>
-                <span class="n">_make_nice_axes</span><span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)],</span> <span class="n">where</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="s2">&quot;bottom&quot;</span><span class="p">],</span> <span class="n">skip</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-                <span class="p">)</span>
-                <span class="k">if</span> <span class="n">x_base</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span>
-                        <span class="n">ticker</span><span class="o">.</span><span class="n">FixedLocator</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">x_base</span><span class="p">))</span>
-                    <span class="p">)</span>
-                    <span class="k">if</span> <span class="n">x_base</span> <span class="o">/</span> <span class="mf">2.0</span> <span class="o">%</span> <span class="mi">1</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span>
-                            <span class="n">ticker</span><span class="o">.</span><span class="n">FixedLocator</span><span class="p">(</span>
-                                <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">x_base</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">)</span>
-                            <span class="p">)</span>
-                        <span class="p">)</span>
-                <span class="k">if</span> <span class="n">y_base</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span>
-                        <span class="n">ticker</span><span class="o">.</span><span class="n">FixedLocator</span><span class="p">(</span>
-                            <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
-                                <span class="n">_myround</span><span class="p">(</span><span class="n">minimum</span><span class="p">,</span> <span class="n">y_base</span><span class="p">,</span> <span class="s2">&quot;down&quot;</span><span class="p">),</span>
-                                <span class="n">_myround</span><span class="p">(</span><span class="n">maximum</span><span class="p">,</span> <span class="n">y_base</span><span class="p">,</span> <span class="s2">&quot;up&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">y_base</span><span class="p">,</span>
-                                <span class="n">y_base</span><span class="p">,</span>
-                            <span class="p">)</span>
-                        <span class="p">)</span>
-                    <span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span>
-                        <span class="n">ticker</span><span class="o">.</span><span class="n">FixedLocator</span><span class="p">(</span>
-                            <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
-                                <span class="n">_myround</span><span class="p">(</span><span class="n">minimum</span><span class="p">,</span> <span class="n">y_base</span><span class="p">,</span> <span class="s2">&quot;down&quot;</span><span class="p">),</span>
-                                <span class="n">_myround</span><span class="p">(</span><span class="n">maximum</span><span class="p">,</span> <span class="n">y_base</span><span class="p">,</span> <span class="s2">&quot;up&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">y_base</span><span class="p">,</span>
-                                <span class="n">y_base</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">,</span>
-                            <span class="p">)</span>
-                        <span class="p">)</span>
-                    <span class="p">)</span>
-
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span>
-                        <span class="n">_myround</span><span class="p">(</span><span class="n">minimum</span><span class="p">,</span> <span class="n">y_base</span><span class="p">,</span> <span class="s2">&quot;down&quot;</span><span class="p">),</span>
-                        <span class="n">_myround</span><span class="p">(</span><span class="n">maximum</span><span class="p">,</span> <span class="n">y_base</span><span class="p">,</span> <span class="s2">&quot;up&quot;</span><span class="p">),</span>
-                    <span class="p">)</span>
-                <span class="k">if</span> <span class="n">j</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">plot_gridlines</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span>
-                        <span class="kc">True</span><span class="p">,</span>
-                        <span class="n">which</span><span class="o">=</span><span class="s2">&quot;major&quot;</span><span class="p">,</span>
-                        <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-                        <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dotted&quot;</span><span class="p">,</span>
-                        <span class="n">dashes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                        <span class="n">zorder</span><span class="o">=-</span><span class="mi">5</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;major&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;minor&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">)</span>
-
-                <span class="n">plot_index</span> <span class="o">+=</span> <span class="mi">1</span>
-
-<div class="viewcode-block" id="setup_matrix.add_lagfuncs"><a class="viewcode-back" href="../../index.html#tigramite.plotting.setup_matrix.add_lagfuncs">[docs]</a>    <span class="k">def</span> <span class="nf">add_lagfuncs</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">val_matrix</span><span class="p">,</span>
-        <span class="n">sig_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">conf_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-        <span class="n">label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">two_sided_thres</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span>
-        <span class="n">markersize</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-        <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Add lag function plot from val_matrix array.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        val_matrix : array_like</span>
-<span class="sd">            Matrix of shape (N, N, tau_max+1) containing test statistic values.</span>
-<span class="sd">        sig_thres : array-like, optional (default: None)</span>
-<span class="sd">            Matrix of significance thresholds. Must be of same shape as</span>
-<span class="sd">            val_matrix.</span>
-<span class="sd">        conf_matrix : array-like, optional (default: None)</span>
-<span class="sd">            Matrix of shape (, N, tau_max+1, 2) containing confidence bounds.</span>
-<span class="sd">        color : str, optional (default: &#39;black&#39;)</span>
-<span class="sd">            Line color.</span>
-<span class="sd">        label : str</span>
-<span class="sd">            Test statistic label.</span>
-<span class="sd">        two_sided_thres : bool, optional (default: True)</span>
-<span class="sd">            Whether to draw sig_thres for pos. and neg. values.</span>
-<span class="sd">        marker : matplotlib marker symbol, optional (default: &#39;.&#39;)</span>
-<span class="sd">            Marker.</span>
-<span class="sd">        markersize : int, optional (default: 5)</span>
-<span class="sd">            Marker size.</span>
-<span class="sd">        alpha : float, optional (default: 1.)</span>
-<span class="sd">            Opacity.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">marker</span><span class="p">,</span> <span class="n">markersize</span><span class="p">,</span> <span class="n">alpha</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">ij</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">):</span>
-            <span class="n">i</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">j</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">maskedres</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">)</span> <span class="p">:])</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">maskedres</span><span class="p">,</span>
-                <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
-                <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                <span class="n">marker</span><span class="o">=</span><span class="n">marker</span><span class="p">,</span>
-                <span class="n">markersize</span><span class="o">=</span><span class="n">markersize</span><span class="p">,</span>
-                <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="k">if</span> <span class="n">conf_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">maskedconfres</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">conf_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">)</span> <span class="p">:])</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                    <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">maskedconfres</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
-                    <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;_&quot;</span><span class="p">,</span>
-                    <span class="n">markersize</span><span class="o">=</span><span class="n">markersize</span> <span class="o">-</span> <span class="mi">2</span><span class="p">,</span>
-                    <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                    <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                    <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">maskedconfres</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
-                    <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;_&quot;</span><span class="p">,</span>
-                    <span class="n">markersize</span><span class="o">=</span><span class="n">markersize</span> <span class="o">-</span> <span class="mi">2</span><span class="p">,</span>
-                    <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                    <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="p">)</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">)),</span>
-                <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-                <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dotted&quot;</span><span class="p">,</span>
-                <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-            <span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">sig_thres</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">maskedsigres</span> <span class="o">=</span> <span class="n">sig_thres</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">)</span> <span class="p">:]</span>
-
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                    <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">maskedsigres</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                    <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;solid&quot;</span><span class="p">,</span>
-                    <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                    <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="k">if</span> <span class="n">two_sided_thres</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                        <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>
-                        <span class="o">-</span><span class="n">sig_thres</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">)</span> <span class="p">:],</span>
-                        <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                        <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;solid&quot;</span><span class="p">,</span>
-                        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                        <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                    <span class="p">)</span></div>
-        <span class="c1"># pyplot.tight_layout()</span>
-
-<div class="viewcode-block" id="setup_matrix.savefig"><a class="viewcode-back" href="../../index.html#tigramite.plotting.setup_matrix.savefig">[docs]</a>    <span class="k">def</span> <span class="nf">savefig</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Save matrix figure.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        name : str, optional (default: None)</span>
-<span class="sd">            File name. If None, figure is shown in window.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Trick to plot legend</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">axlegend</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;right&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;bottom&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;top&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
-
-            <span class="c1"># self.labels.append((label, color, marker, markersize, alpha))</span>
-            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">:</span>
-                <span class="n">label</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">color</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="n">marker</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                <span class="n">markersize</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-                <span class="n">alpha</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
-
-                <span class="n">axlegend</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                    <span class="p">[],</span>
-                    <span class="p">[],</span>
-                    <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="n">marker</span><span class="p">,</span>
-                    <span class="n">markersize</span><span class="o">=</span><span class="n">markersize</span><span class="p">,</span>
-                    <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
-                    <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                <span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
-                <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">,</span>
-                <span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.05</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
-                <span class="n">borderaxespad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">legend_fontsize</span><span class="p">,</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">draw_frame</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span>
-                <span class="n">left</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">label_space_left</span><span class="p">,</span>
-                <span class="n">right</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">legend_width</span><span class="p">,</span>
-                <span class="n">top</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_space_top</span><span class="p">,</span>
-                <span class="n">hspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-                <span class="n">wspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="n">pyplot</span><span class="o">.</span><span class="n">figtext</span><span class="p">(</span>
-                <span class="mf">0.5</span><span class="p">,</span>
-                <span class="mf">0.01</span><span class="p">,</span>
-                <span class="sa">r</span><span class="s2">&quot;lag $\tau$ [</span><span class="si">%s</span><span class="s2">]&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">lag_units</span><span class="p">,</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">label_fontsize</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span>
-                <span class="n">left</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">label_space_left</span><span class="p">,</span>
-                <span class="n">right</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
-                <span class="n">top</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_space_top</span><span class="p">,</span>
-                <span class="n">hspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-                <span class="n">wspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="n">pyplot</span><span class="o">.</span><span class="n">figtext</span><span class="p">(</span>
-                <span class="mf">0.55</span><span class="p">,</span>
-                <span class="mf">0.01</span><span class="p">,</span>
-                <span class="sa">r</span><span class="s2">&quot;lag $\tau$ [</span><span class="si">%s</span><span class="s2">]&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">lag_units</span><span class="p">,</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">label_fontsize</span><span class="p">,</span>
-            <span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lag_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">lag_array</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span>
-            <span class="k">for</span> <span class="n">ij</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">):</span>
-                <span class="n">i</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">j</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lag_array</span><span class="p">[::</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_base</span><span class="p">])</span>
-
-        <span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div></div>
-
-
-
-<div class="viewcode-block" id="plot_scatterplots"><a class="viewcode-back" href="../../index.html#tigramite.plotting.plot_scatterplots">[docs]</a><span class="k">def</span> <span class="nf">plot_scatterplots</span><span class="p">(</span><span class="n">dataframe</span><span class="p">,</span> 
-                      <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                      <span class="n">setup_args</span><span class="o">=</span><span class="p">{},</span> 
-                      <span class="n">add_scatterplot_args</span><span class="o">=</span><span class="p">{},</span>
-                      <span class="n">selected_dataset</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Wrapper helper function to plot scatter plots.</span>
-<span class="sd">    Sets up the matrix object and plots the scatter plots, see parameters in</span>
-<span class="sd">    setup_scatter_matrix and add_scatterplot.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataframe : data object</span>
-<span class="sd">        Tigramite dataframe object. It must have the attributes dataframe.values</span>
-<span class="sd">        yielding a numpy array of shape (observations T, variables N) and</span>
-<span class="sd">        optionally a mask of the same shape and a missing values flag.</span>
-<span class="sd">    name : str, optional (default: None)</span>
-<span class="sd">        File name. If None, figure is shown in window.</span>
-<span class="sd">    setup_args : dict</span>
-<span class="sd">        Arguments for setting up the scatter plot matrix, see doc of</span>
-<span class="sd">        setup_scatter_matrix.</span>
-<span class="sd">    add_scatterplot_args : dict</span>
-<span class="sd">        Arguments for adding a scatter plot matrix.</span>
-<span class="sd">    selected_dataset : int, optional (default: 0)</span>
-<span class="sd">        In case of multiple datasets in dataframe, plot this one.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    matrix : object</span>
-<span class="sd">        Further scatter plot can be overlaid using the</span>
-<span class="sd">        matrix.add_scatterplot function.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">N</span>
-
-    <span class="n">matrix</span> <span class="o">=</span> <span class="n">setup_scatter_matrix</span><span class="p">(</span><span class="n">N</span><span class="o">=</span><span class="n">N</span><span class="p">,</span> <span class="n">var_names</span><span class="o">=</span><span class="n">dataframe</span><span class="o">.</span><span class="n">var_names</span><span class="p">,</span> <span class="o">**</span><span class="n">setup_args</span><span class="p">)</span>
-    <span class="n">matrix</span><span class="o">.</span><span class="n">add_scatterplot</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">selected_dataset</span><span class="o">=</span><span class="n">selected_dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">add_scatterplot_args</span><span class="p">)</span>
-    <span class="n">matrix</span><span class="o">.</span><span class="n">adjustfig</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-   
-
-    <span class="k">return</span> <span class="n">matrix</span></div>
-
-
-<div class="viewcode-block" id="setup_scatter_matrix"><a class="viewcode-back" href="../../index.html#tigramite.plotting.setup_scatter_matrix">[docs]</a><span class="k">class</span> <span class="nc">setup_scatter_matrix</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create matrix of scatter plot panels.</span>
-<span class="sd">    Class to setup figure object. The function add_scatterplot allows to plot</span>
-<span class="sd">    scatterplots of variables in the dataframe. Multiple scatter plots can be</span>
-<span class="sd">    overlaid for comparison.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    N : int</span>
-<span class="sd">        Number of variables</span>
-<span class="sd">    var_names : list, optional (default: None)</span>
-<span class="sd">        List of variable names. If None, range(N) is used.</span>
-<span class="sd">    figsize : tuple of floats, optional (default: None)</span>
-<span class="sd">        Figure size if new figure is created. If None, default pyplot figsize</span>
-<span class="sd">        is used.</span>
-<span class="sd">    label_space_left : float, optional (default: 0.1)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate left of plot for labels.</span>
-<span class="sd">    label_space_top : float, optional (default: 0.05)</span>
-<span class="sd">        Fraction of vertical figure space to allocate top of plot for labels.</span>
-<span class="sd">    legend_width : float, optional (default: 0.15)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate right of plot for</span>
-<span class="sd">        legend.</span>
-<span class="sd">    tick_label_size : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of tick labels.</span>
-<span class="sd">    plot_gridlines : bool, optional (default: False)</span>
-<span class="sd">        Whether to show a grid.</span>
-<span class="sd">    label_fontsize : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of variable labels.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">N</span><span class="p">,</span>
-        <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">label_space_left</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-        <span class="n">label_space_top</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-        <span class="n">legend_width</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span>
-        <span class="n">legend_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-        <span class="n">plot_gridlines</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-        <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
-    
-        <span class="bp">self</span><span class="o">.</span><span class="n">legend_width</span> <span class="o">=</span> <span class="n">legend_width</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">legend_fontsize</span> <span class="o">=</span> <span class="n">legend_fontsize</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_space_left</span> <span class="o">=</span> <span class="n">label_space_left</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_space_top</span> <span class="o">=</span> <span class="n">label_space_top</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_fontsize</span> <span class="o">=</span> <span class="n">label_fontsize</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">fig</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span> <span class="o">=</span> <span class="p">{}</span>
-
-        <span class="k">if</span> <span class="n">var_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">var_names</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-        <span class="n">plot_index</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">plot_index</span><span class="p">,</span> <span class="n">axes_class</span><span class="o">=</span><span class="n">Axes</span><span class="p">)</span>
-                <span class="c1"># Plot process labels</span>
-                <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">transFigure</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">transAxes</span>
-                    <span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                        <span class="mf">0.01</span><span class="p">,</span>
-                        <span class="mf">0.5</span><span class="p">,</span>
-                        <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span>
-                        <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
-                        <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                        <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">transFigure</span>
-                    <span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                        <span class="mf">0.5</span><span class="p">,</span>
-                        <span class="mf">0.99</span><span class="p">,</span>
-                        <span class="sa">r</span><span class="s2">&quot;${\to}$ &quot;</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]),</span>
-                        <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                        <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span>
-                        <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-                    <span class="p">)</span>
-
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">axis</span><span class="p">[</span><span class="s2">&quot;right&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">axis</span><span class="p">[</span><span class="s2">&quot;top&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-
-                <span class="k">if</span> <span class="n">j</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
-                <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
-
-                <span class="k">if</span> <span class="n">plot_gridlines</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span>
-                        <span class="kc">True</span><span class="p">,</span>
-                        <span class="n">which</span><span class="o">=</span><span class="s2">&quot;major&quot;</span><span class="p">,</span>
-                        <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-                        <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dotted&quot;</span><span class="p">,</span>
-                        <span class="n">dashes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                        <span class="n">zorder</span><span class="o">=-</span><span class="mi">5</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;major&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">)</span>
-
-                <span class="n">plot_index</span> <span class="o">+=</span> <span class="mi">1</span>
-
-<div class="viewcode-block" id="setup_scatter_matrix.add_scatterplot"><a class="viewcode-back" href="../../index.html#tigramite.plotting.setup_scatter_matrix.add_scatterplot">[docs]</a>    <span class="k">def</span> <span class="nf">add_scatterplot</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">dataframe</span><span class="p">,</span>
-        <span class="n">matrix_lags</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-        <span class="n">label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span>
-        <span class="n">markersize</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-        <span class="n">alpha</span><span class="o">=</span><span class="mf">.2</span><span class="p">,</span>
-        <span class="n">selected_dataset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Add scatter plot.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        dataframe : data object</span>
-<span class="sd">            Tigramite dataframe object. It must have the attributes dataframe.values</span>
-<span class="sd">            yielding a numpy array of shape (observations T, variables N) and</span>
-<span class="sd">            optionally a mask of the same shape and a missing values flag.</span>
-<span class="sd">        matrix_lags : array</span>
-<span class="sd">            Lags to use in scatter plots. Either None or of shape (N, N). Then the</span>
-<span class="sd">            entry matrix_lags[i, j] = tau will depict the scatter plot of </span>
-<span class="sd">            time series (i, -tau) vs (j, 0). If None, tau = 0 for i != j and for i = j</span>
-<span class="sd">            tau = 1. </span>
-<span class="sd">        color : str, optional (default: &#39;black&#39;)</span>
-<span class="sd">            Line color.</span>
-<span class="sd">        label : str</span>
-<span class="sd">            Test statistic label.</span>
-<span class="sd">        marker : matplotlib marker symbol, optional (default: &#39;.&#39;)</span>
-<span class="sd">            Marker.</span>
-<span class="sd">        markersize : int, optional (default: 5)</span>
-<span class="sd">            Marker size.</span>
-<span class="sd">        alpha : float, optional (default: 1.)</span>
-<span class="sd">            Opacity.</span>
-<span class="sd">        selected_dataset : int, optional (default: 0)</span>
-<span class="sd">            In case of multiple datasets in dataframe, plot this one.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">matrix_lags</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">matrix_lags</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;matrix_lags must be non-negative!&quot;</span><span class="p">)</span>
-
-        <span class="n">data</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">selected_dataset</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span><span class="p">[</span><span class="n">selected_dataset</span><span class="p">]</span>
-
-        <span class="n">T</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="n">label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">marker</span><span class="p">,</span> <span class="n">markersize</span><span class="p">,</span> <span class="n">alpha</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">ij</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">):</span>                
-            <span class="n">i</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">j</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">matrix_lags</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">:</span>
-                    <span class="n">lag</span> <span class="o">=</span> <span class="mi">1</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">lag</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">lag</span> <span class="o">=</span> <span class="n">matrix_lags</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">data</span><span class="p">[:</span><span class="n">T</span><span class="o">-</span><span class="n">lag</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">:,</span> <span class="n">j</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">x</span><span class="p">[</span><span class="n">mask</span><span class="p">[:</span><span class="n">T</span><span class="o">-</span><span class="n">lag</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-                <span class="n">y</span><span class="p">[</span><span class="n">mask</span><span class="p">[</span><span class="n">lag</span><span class="p">:,</span> <span class="n">j</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-
-            <span class="c1"># print(i, j, lag, x.shape, y.shape)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span>
-                <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                <span class="n">marker</span><span class="o">=</span><span class="n">marker</span><span class="p">,</span>
-                <span class="n">s</span><span class="o">=</span><span class="n">markersize</span><span class="p">,</span>
-                <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">&quot;$\tau{=}</span><span class="si">%d</span><span class="s2">$&quot;</span> <span class="o">%</span><span class="n">lag</span><span class="p">,</span>
-            <span class="p">)</span></div>
-            <span class="c1"># self.axes_dict[(i, j)].text(0., 1., r&quot;$\tau{=}%d$&quot; %lag, </span>
-            <span class="c1">#     fontsize=self.legend_fontsize,</span>
-            <span class="c1">#     ha=&#39;left&#39;, va=&#39;top&#39;,</span>
-            <span class="c1">#     transform=self.axes_dict[(i, j)].transAxes)</span>
-
-
-<div class="viewcode-block" id="setup_scatter_matrix.adjustfig"><a class="viewcode-back" href="../../index.html#tigramite.plotting.setup_scatter_matrix.adjustfig">[docs]</a>    <span class="k">def</span> <span class="nf">adjustfig</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Adjust matrix figure.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        name : str, optional (default: None)</span>
-<span class="sd">            File name. If None, figure is shown in window.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Trick to plot legends</span>
-        <span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">:</span>
-            <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">for</span> <span class="n">ij</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">):</span>                
-            <span class="n">i</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">j</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-            <span class="n">leg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
-                <span class="c1"># loc=&quot;upper left&quot;,</span>
-                <span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="c1"># bbox_to_anchor=(1.05, 0.0, 0.1, 1.0),</span>
-                <span class="c1"># borderaxespad=0,</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">legend_fontsize</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span>
-                <span class="n">labelcolor</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
-                <span class="p">)</span><span class="o">.</span><span class="n">draw_frame</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-        
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">axlegend</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;right&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;bottom&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;top&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
-
-            <span class="c1"># self.labels.append((label, color, marker, markersize, alpha))</span>
-            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">:</span>
-                <span class="n">label</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">color</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="n">marker</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                <span class="n">markersize</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-                <span class="n">alpha</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
-
-                <span class="n">axlegend</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                    <span class="p">[],</span>
-                    <span class="p">[],</span>
-                    <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="n">marker</span><span class="p">,</span>
-                    <span class="n">markersize</span><span class="o">=</span><span class="n">markersize</span><span class="p">,</span>
-                    <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
-                    <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                <span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
-                <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">,</span>
-                <span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.05</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
-                <span class="n">borderaxespad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">legend_fontsize</span><span class="p">,</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">draw_frame</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span>
-                <span class="n">bottom</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                <span class="n">left</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">label_space_left</span><span class="p">,</span>
-                <span class="n">right</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">legend_width</span><span class="p">,</span>
-                <span class="n">top</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_space_top</span><span class="p">,</span>
-                <span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-                <span class="n">wspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-            <span class="p">)</span>
-      
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span>
-                <span class="n">left</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">label_space_left</span><span class="p">,</span>
-                <span class="n">bottom</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                <span class="n">right</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
-                <span class="n">top</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_space_top</span><span class="p">,</span>
-                <span class="n">hspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-                <span class="n">wspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-            <span class="p">)</span>
-       
-        <span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div></div>
-
-
-<div class="viewcode-block" id="plot_densityplots"><a class="viewcode-back" href="../../index.html#tigramite.plotting.plot_densityplots">[docs]</a><span class="k">def</span> <span class="nf">plot_densityplots</span><span class="p">(</span><span class="n">dataframe</span><span class="p">,</span> 
-                      <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                      <span class="n">setup_args</span><span class="o">=</span><span class="p">{},</span> 
-                      <span class="n">add_densityplot_args</span><span class="o">=</span><span class="p">{},</span>
-                      <span class="n">selected_dataset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                      <span class="n">show_marginal_densities_on_diagonal</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Wrapper helper function to plot density plots.</span>
-<span class="sd">    Sets up the matrix object and plots the density plots, see parameters in</span>
-<span class="sd">    setup_density_matrix and add_densityplot.</span>
-
-<span class="sd">    The diagonal shows the marginal densities. </span>
-
-<span class="sd">    Requires seaborn.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataframe : data object</span>
-<span class="sd">        Tigramite dataframe object. It must have the attributes dataframe.values</span>
-<span class="sd">        yielding a numpy array of shape (observations T, variables N) and</span>
-<span class="sd">        optionally a mask of the same shape and a missing values flag.</span>
-<span class="sd">    name : str, optional (default: None)</span>
-<span class="sd">        File name. If None, figure is shown in window.</span>
-<span class="sd">    setup_args : dict</span>
-<span class="sd">        Arguments for setting up the density plot matrix, see doc of</span>
-<span class="sd">        setup_density_matrix.</span>
-<span class="sd">    add_densityplot_args : dict</span>
-<span class="sd">        Arguments for adding a density plot matrix.</span>
-<span class="sd">    selected_dataset : int, optional (default: 0)</span>
-<span class="sd">        In case of multiple datasets in dataframe, plot this one.</span>
-<span class="sd">    show_marginal_densities_on_diagonal : bool, optional (default: True)</span>
-<span class="sd">        Flag to show marginal densities on the diagonal of the density plots</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    matrix : object</span>
-<span class="sd">        Further density plots can be overlaid using the</span>
-<span class="sd">        matrix.add_densityplot function.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">N</span>
-
-    <span class="n">matrix</span> <span class="o">=</span> <span class="n">setup_density_matrix</span><span class="p">(</span><span class="n">N</span><span class="o">=</span><span class="n">N</span><span class="p">,</span> <span class="n">var_names</span><span class="o">=</span><span class="n">dataframe</span><span class="o">.</span><span class="n">var_names</span><span class="p">,</span> <span class="o">**</span><span class="n">setup_args</span><span class="p">)</span>
-    <span class="n">matrix</span><span class="o">.</span><span class="n">add_densityplot</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">selected_dataset</span><span class="o">=</span><span class="n">selected_dataset</span><span class="p">,</span>
-                           <span class="n">show_marginal_densities_on_diagonal</span><span class="o">=</span><span class="n">show_marginal_densities_on_diagonal</span><span class="p">,</span> <span class="o">**</span><span class="n">add_densityplot_args</span><span class="p">)</span>
-    <span class="n">matrix</span><span class="o">.</span><span class="n">adjustfig</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-   
-
-    <span class="k">return</span> <span class="n">matrix</span></div>
-
-
-<div class="viewcode-block" id="setup_density_matrix"><a class="viewcode-back" href="../../index.html#tigramite.plotting.setup_density_matrix">[docs]</a><span class="k">class</span> <span class="nc">setup_density_matrix</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create matrix of density plot panels.</span>
-<span class="sd">    Class to setup figure object. The function add_densityplot allows to plot</span>
-<span class="sd">    density plots of variables in the dataframe. </span>
-
-<span class="sd">    Further density plots can be overlaid using the matrix.add_densityplot </span>
-<span class="sd">    function.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    N : int</span>
-<span class="sd">        Number of variables</span>
-<span class="sd">    var_names : list, optional (default: None)</span>
-<span class="sd">        List of variable names. If None, range(N) is used.</span>
-<span class="sd">    figsize : tuple of floats, optional (default: None)</span>
-<span class="sd">        Figure size if new figure is created. If None, default pyplot figsize</span>
-<span class="sd">        is used.</span>
-<span class="sd">    label_space_left : float, optional (default: 0.1)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate left of plot for labels.</span>
-<span class="sd">    label_space_top : float, optional (default: 0.05)</span>
-<span class="sd">        Fraction of vertical figure space to allocate top of plot for labels.</span>
-<span class="sd">    legend_width : float, optional (default: 0.15)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate right of plot for</span>
-<span class="sd">        legend.</span>
-<span class="sd">    tick_label_size : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of tick labels.</span>
-<span class="sd">    plot_gridlines : bool, optional (default: False)</span>
-<span class="sd">        Whether to show a grid.</span>
-<span class="sd">    label_fontsize : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of variable labels.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">N</span><span class="p">,</span>
-        <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">label_space_left</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span>
-        <span class="n">label_space_top</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-        <span class="n">legend_width</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span>
-        <span class="n">legend_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-        <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-        <span class="n">plot_gridlines</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
-    
-        <span class="bp">self</span><span class="o">.</span><span class="n">legend_width</span> <span class="o">=</span> <span class="n">legend_width</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">legend_fontsize</span> <span class="o">=</span> <span class="n">legend_fontsize</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_space_left</span> <span class="o">=</span> <span class="n">label_space_left</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_space_top</span> <span class="o">=</span> <span class="n">label_space_top</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_fontsize</span> <span class="o">=</span> <span class="n">label_fontsize</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">fig</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span> <span class="o">=</span> <span class="p">{}</span>
-
-        <span class="k">if</span> <span class="n">var_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">var_names</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-        <span class="n">plot_index</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">plot_index</span><span class="p">)</span>
-                <span class="c1"># Plot process labels</span>
-                <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">transFigure</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">transAxes</span>
-                    <span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                        <span class="mf">0.01</span><span class="p">,</span>
-                        <span class="mf">0.5</span><span class="p">,</span>
-                        <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span>
-                        <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
-                        <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                        <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">transFigure</span>
-                    <span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                        <span class="mf">0.5</span><span class="p">,</span>
-                        <span class="mf">0.99</span><span class="p">,</span>
-                        <span class="sa">r</span><span class="s2">&quot;${\to}$ &quot;</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">j</span><span class="p">]),</span>
-                        <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                        <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span>
-                        <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-                    <span class="p">)</span>
-
-                <span class="c1"># _make_nice_axes(self.axes_dict[(i, j)], where=[&quot;bottom&quot;], skip=(1, 1)             )</span>
-                <span class="c1"># self.axes_dict[(i, j)].axis[&quot;right&quot;].set_visible(False)</span>
-                <span class="c1"># self.axes_dict[(i, j)].axis[&quot;top&quot;].set_visible(False)</span>
-                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">:</span>
-                    <span class="c1"># self.axes_dict[(i, j)].axis[&quot;left&quot;].set_visible(False)</span>
-                    <span class="n">_make_nice_axes</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)],</span> <span class="n">where</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;bottom&quot;</span><span class="p">],</span> <span class="n">skip</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">_make_nice_axes</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)],</span> <span class="n">where</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="s2">&quot;bottom&quot;</span><span class="p">],</span> <span class="n">skip</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-                <span class="c1"># if j != 0:</span>
-                <span class="c1">#     self.axes_dict[(i, j)].get_yaxis().set_ticklabels([])</span>
-                <span class="c1"># if i != N - 1:</span>
-                <span class="c1">#     self.axes_dict[(i, j)].get_xaxis().set_ticklabels([])</span>
-
-                <span class="k">if</span> <span class="n">plot_gridlines</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span>
-                        <span class="kc">True</span><span class="p">,</span>
-                        <span class="n">which</span><span class="o">=</span><span class="s2">&quot;major&quot;</span><span class="p">,</span>
-                        <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-                        <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dotted&quot;</span><span class="p">,</span>
-                        <span class="n">dashes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                        <span class="n">zorder</span><span class="o">=-</span><span class="mi">5</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;major&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">)</span>
-                <span class="n">plot_index</span> <span class="o">+=</span> <span class="mi">1</span>
-
-<div class="viewcode-block" id="setup_density_matrix.add_densityplot"><a class="viewcode-back" href="../../index.html#tigramite.plotting.setup_density_matrix.add_densityplot">[docs]</a>    <span class="k">def</span> <span class="nf">add_densityplot</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">dataframe</span><span class="p">,</span>
-        <span class="n">matrix_lags</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">label_color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-        <span class="n">snskdeplot_args</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;cmap&#39;</span><span class="p">:</span><span class="s1">&#39;Greys&#39;</span><span class="p">},</span>
-        <span class="n">snskdeplot_diagonal_args</span> <span class="o">=</span> <span class="p">{},</span>
-        <span class="n">selected_dataset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-        <span class="n">show_marginal_densities_on_diagonal</span><span class="o">=</span><span class="kc">True</span>
-    <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Add density function plot.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        dataframe : data object</span>
-<span class="sd">            Tigramite dataframe object. It must have the attributes dataframe.values</span>
-<span class="sd">            yielding a numpy array of shape (observations T, variables N) and</span>
-<span class="sd">            optionally a mask of the same shape and a missing values flag.</span>
-<span class="sd">        matrix_lags : array</span>
-<span class="sd">            Lags to use in scatter plots. Either None or non-neg array of shape (N, N). Then the</span>
-<span class="sd">            entry matrix_lags[i, j] = tau will depict the scatter plot of </span>
-<span class="sd">            time series (i, -tau) vs (j, 0). If None, tau = 0 for i != j and for i = j</span>
-<span class="sd">            tau = 1. </span>
-<span class="sd">        snskdeplot_args : dict</span>
-<span class="sd">            Optional parameters to pass to sns.kdeplot() for i != j for off-diagonal plots.</span>
-<span class="sd">        snskdeplot_diagonal_args : dict</span>
-<span class="sd">            Optional parameters to pass to sns.kdeplot() for i == j on diagonal.</span>
-<span class="sd">        label : string</span>
-<span class="sd">            Label of this plot.</span>
-<span class="sd">        label_color : string</span>
-<span class="sd">            Color of line created just for legend.</span>
-<span class="sd">        selected_dataset : int, optional (default: 0)</span>
-<span class="sd">            In case of multiple datasets in dataframe, plot this one.</span>
-<span class="sd">        show_marginal_densities_on_diagonal : bool, optional (default: True)</span>
-<span class="sd">            Flag to show marginal densities on the diagonal of the density plots</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Use seaborn for this one</span>
-        <span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
-
-        <span class="c1"># set seaborn style</span>
-        <span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;white&quot;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">matrix_lags</span> <span class="o">=</span> <span class="n">matrix_lags</span>
-
-        <span class="k">if</span> <span class="n">matrix_lags</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">matrix_lags</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;matrix_lags must be non-negative!&quot;</span><span class="p">)</span>
-
-        <span class="n">data</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">selected_dataset</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span><span class="p">[</span><span class="n">selected_dataset</span><span class="p">]</span>
-
-        <span class="n">T</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># if label is not None:</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">label</span><span class="p">,</span> <span class="n">label_color</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">ij</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">):</span>  
-            <span class="n">i</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">j</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span>              
-            <span class="k">if</span> <span class="p">(</span><span class="n">matrix_lags</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">:</span>
-                    <span class="n">lag</span> <span class="o">=</span> <span class="mi">1</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">lag</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">lag</span> <span class="o">=</span> <span class="n">matrix_lags</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">data</span><span class="p">[:</span><span class="n">T</span><span class="o">-</span><span class="n">lag</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">:,</span> <span class="n">j</span><span class="p">])</span>
-            <span class="c1"># Data is set to NaN in dataframe init already</span>
-            <span class="c1"># if dataframe.missing_flag is not None:</span>
-            <span class="c1">#     x[x==dataframe.missing_flag] = np.nan</span>
-            <span class="c1">#     y[y==dataframe.missing_flag] = np.nan</span>
-            <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">x</span><span class="p">[</span><span class="n">mask</span><span class="p">[:</span><span class="n">T</span><span class="o">-</span><span class="n">lag</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-                <span class="n">y</span><span class="p">[</span><span class="n">mask</span><span class="p">[</span><span class="n">lag</span><span class="p">:,</span> <span class="n">j</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-
-            <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span> <span class="ow">and</span> <span class="n">show_marginal_densities_on_diagonal</span><span class="p">:</span>
-                <span class="n">sns</span><span class="o">.</span><span class="n">kdeplot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span>
-                    <span class="n">color</span> <span class="o">=</span> <span class="n">label_color</span><span class="p">,</span>
-                    <span class="c1"># label=r&quot;$\tau{=}%d$&quot; %lag,</span>
-                    <span class="o">**</span><span class="n">snskdeplot_diagonal_args</span><span class="p">,</span>
-                    <span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
-                <span class="c1"># ax.yaxis.set_ticks_position(&quot;none&quot;)</span>
-                <span class="c1"># ax.yaxis.set_ticklabels([])</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">sns</span><span class="o">.</span><span class="n">kdeplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> 
-                    <span class="c1">#label=r&quot;$\tau{=}%d$&quot; %lag,</span>
-                    <span class="o">**</span><span class="n">snskdeplot_args</span><span class="p">,</span>
-                    <span class="c1"># fill=True,</span>
-                    <span class="c1"># alpha=0.3,</span>
-                    <span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="setup_density_matrix.adjustfig"><a class="viewcode-back" href="../../index.html#tigramite.plotting.setup_density_matrix.adjustfig">[docs]</a>    <span class="k">def</span> <span class="nf">adjustfig</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Adjust matrix figure.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        name : str, optional (default: None)</span>
-<span class="sd">            File name. If None, figure is shown in window.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Trick to plot legends</span>
-        <span class="c1"># colors = []</span>
-        <span class="c1"># for item in self.labels:</span>
-        <span class="c1">#     colors.append(item[1])</span>
-        <span class="k">for</span> <span class="n">ij</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">):</span>                
-            <span class="n">i</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">j</span> <span class="o">=</span> <span class="n">ij</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix_lags</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">lag</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix_lags</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">:</span>
-                <span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">:</span>
-                    <span class="n">color</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                    <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">color</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">show_labels</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                            <span class="p">[],</span>
-                            <span class="p">[],</span>
-                            <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
-                            <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                            <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">&quot;$\tau{=}</span><span class="si">%d</span><span class="s2">$&quot;</span> <span class="o">%</span><span class="n">lag</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="c1"># print(&#39;here&#39;)</span>
-                <span class="n">leg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes_dict</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
-                    <span class="c1"># loc=&quot;best&quot;,</span>
-                    <span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                    <span class="c1"># bbox_to_anchor=(1.05, 0.0, 0.1, 1.0),</span>
-                    <span class="c1"># borderaxespad=0,</span>
-                    <span class="n">fontsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">legend_fontsize</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span>
-                    <span class="n">labelcolor</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
-                    <span class="p">)</span><span class="o">.</span><span class="n">draw_frame</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-        
-            <span class="c1"># if i == j:</span>
-            <span class="c1">#     # self.axes_dict[(i, j)].axis[&quot;left&quot;].set_visible(False)</span>
-            <span class="c1">#     _make_nice_axes(ax=self.axes_dict[(i, j)], where=[&quot;bottom&quot;], skip=(1, 1))</span>
-            <span class="c1"># else:</span>
-            <span class="c1"># _make_nice_axes(ax=self.axes_dict[(i, j)], where=[&quot;left&quot;, &quot;bottom&quot;], skip=(1, 1))</span>
-
-        <span class="k">if</span> <span class="n">show_labels</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">axlegend</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;right&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;bottom&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;top&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
-
-            <span class="c1"># self.labels.append((label, color, marker, markersize, alpha))</span>
-            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">:</span>
-                <span class="n">label</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">color</span> <span class="o">=</span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-                <span class="n">axlegend</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-                    <span class="p">[],</span>
-                    <span class="p">[],</span>
-                    <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;-&quot;</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
-                    <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
-                <span class="p">)</span>
-            <span class="n">axlegend</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
-                <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">,</span>
-                <span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.05</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
-                <span class="n">borderaxespad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">legend_fontsize</span><span class="p">,</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">draw_frame</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span>
-                <span class="n">bottom</span><span class="o">=</span><span class="mf">0.08</span><span class="p">,</span>
-                <span class="n">left</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">label_space_left</span><span class="p">,</span>
-                <span class="n">right</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">legend_width</span><span class="p">,</span>
-                <span class="n">top</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_space_top</span><span class="p">,</span>
-                <span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-                <span class="n">wspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-            <span class="p">)</span>
-      
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span>
-                <span class="n">left</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">label_space_left</span><span class="p">,</span>
-                <span class="n">bottom</span><span class="o">=</span><span class="mf">0.08</span><span class="p">,</span>
-                <span class="n">right</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
-                <span class="n">top</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_space_top</span><span class="p">,</span>
-                <span class="n">hspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-                <span class="n">wspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
-            <span class="p">)</span>
-       
-        <span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div></div>
-
-<span class="k">def</span> <span class="nf">_draw_network_with_curved_edges</span><span class="p">(</span>
-    <span class="n">fig</span><span class="p">,</span>
-    <span class="n">ax</span><span class="p">,</span>
-    <span class="n">G</span><span class="p">,</span>
-    <span class="n">pos</span><span class="p">,</span>
-    <span class="n">node_rings</span><span class="p">,</span>
-    <span class="n">node_labels</span><span class="p">,</span>
-    <span class="n">node_label_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">node_alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">standard_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-    <span class="n">node_aspect</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">standard_cmap</span><span class="o">=</span><span class="s2">&quot;OrRd&quot;</span><span class="p">,</span>
-    <span class="n">standard_color_links</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-    <span class="n">standard_color_nodes</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
-    <span class="n">log_sizes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">cmap_links</span><span class="o">=</span><span class="s2">&quot;YlOrRd&quot;</span><span class="p">,</span>
-    <span class="c1"># cmap_links_edges=&quot;YlOrRd&quot;,</span>
-    <span class="n">links_vmin</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
-    <span class="n">links_vmax</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">links_edges_vmin</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
-    <span class="n">links_edges_vmax</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">links_ticks</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">links_edges_ticks</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">link_label_fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-    <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;-&gt;, head_width=0.4, head_length=1&quot;</span><span class="p">,</span>
-    <span class="n">arrowhead_size</span><span class="o">=</span><span class="mf">3.0</span><span class="p">,</span>
-    <span class="n">curved_radius</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-    <span class="n">label_fraction</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-    <span class="n">link_colorbar_label</span><span class="o">=</span><span class="s2">&quot;link&quot;</span><span class="p">,</span>
-    <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-    <span class="c1"># link_edge_colorbar_label=&#39;link_edge&#39;,</span>
-    <span class="n">inner_edge_curved</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">inner_edge_style</span><span class="o">=</span><span class="s2">&quot;solid&quot;</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">network_left_bound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">show_colorbar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">special_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">autodep_sig_lags</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">show_autodependency_lags</span><span class="o">=</span><span class="kc">False</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to draw a network from networkx graph instance.</span>
-<span class="sd">    Various attributes are used to specify the graph&#39;s properties.</span>
-<span class="sd">    This function is just a beta-template for now that can be further</span>
-<span class="sd">    customized.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">FancyArrowPatch</span><span class="p">,</span> <span class="n">Circle</span><span class="p">,</span> <span class="n">Ellipse</span>
-
-    <span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;right&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;bottom&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;top&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
-
-    <span class="c1"># This fixes a positioning bug in matplotlib.</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">draw_edge</span><span class="p">(</span>
-        <span class="n">ax</span><span class="p">,</span>
-        <span class="n">u</span><span class="p">,</span>
-        <span class="n">v</span><span class="p">,</span>
-        <span class="n">d</span><span class="p">,</span>
-        <span class="n">seen</span><span class="p">,</span>
-        <span class="n">arrowstyle</span><span class="o">=</span> <span class="s2">&quot;Simple, head_width=2, head_length=2, tail_width=1&quot;</span><span class="p">,</span>
-        <span class="n">outer_edge</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="p">):</span>
-
-        <span class="c1"># avoiding attribute error raised by changes in networkx</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="s2">&quot;node&quot;</span><span class="p">):</span>
-            <span class="c1"># works with networkx 1.10</span>
-            <span class="n">n1</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="s2">&quot;patch&quot;</span><span class="p">]</span>
-            <span class="n">n2</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="s2">&quot;patch&quot;</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># works with networkx 2.4</span>
-            <span class="n">n1</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="s2">&quot;patch&quot;</span><span class="p">]</span>
-            <span class="n">n2</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="s2">&quot;patch&quot;</span><span class="p">]</span>
-
-        <span class="c1"># print(&quot;+++++++++++++++++++++++==cmap_links &quot;, cmap_links)</span>
-        <span class="k">if</span> <span class="n">outer_edge</span><span class="p">:</span>
-            <span class="n">rad</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span> <span class="o">*</span> <span class="n">curved_radius</span>
-            <span class="k">if</span> <span class="n">cmap_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">facecolor</span> <span class="o">=</span> <span class="n">data_to_rgb_links</span><span class="o">.</span><span class="n">to_rgba</span><span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">])</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">facecolor</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">facecolor</span> <span class="o">=</span> <span class="n">standard_color_links</span>
-
-            <span class="n">width</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge_width&quot;</span><span class="p">]</span>
-            <span class="n">alpha</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge_alpha&quot;</span><span class="p">]</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="n">seen</span><span class="p">:</span>
-                <span class="n">rad</span> <span class="o">=</span> <span class="n">seen</span><span class="o">.</span><span class="n">get</span><span class="p">((</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">))</span>
-                <span class="n">rad</span> <span class="o">=</span> <span class="p">(</span><span class="n">rad</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">rad</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.1</span><span class="p">)</span> <span class="o">*</span> <span class="o">-</span><span class="mf">1.0</span>
-            <span class="n">arrowstyle</span> <span class="o">=</span> <span class="n">arrowstyle</span>
-            <span class="c1"># link_edge = d[&#39;outer_edge_edge&#39;]</span>
-            <span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;solid&#39;</span> <span class="c1"># d.get(&quot;outer_edge_style&quot;)</span>
-
-            <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_attribute&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;spurious&quot;</span><span class="p">:</span>
-                <span class="n">facecolor</span> <span class="o">=</span> <span class="s2">&quot;grey&quot;</span>
-
-            <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;&lt;-o&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-x&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">]:</span>
-                <span class="n">n1</span><span class="p">,</span> <span class="n">n2</span> <span class="o">=</span> <span class="n">n2</span><span class="p">,</span> <span class="n">n1</span>
-
-            <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span>
-                <span class="s2">&quot;o-o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;---&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-o&quot;</span><span class="p">,</span>
-                <span class="c1"># &quot;+-&gt;&quot;,</span>
-                <span class="c1"># &quot;&lt;-+&quot;,</span>
-            <span class="p">]:</span>
-                <span class="n">arrowstyle</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span>
-                <span class="c1"># linewidth = width*factor</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">:</span>
-                <span class="c1"># arrowstyle = &quot;&lt;-&gt;, head_width=0.4, head_length=1&quot;</span>
-                <span class="n">arrowstyle</span> <span class="o">=</span> <span class="s2">&quot;Simple, head_width=2, head_length=2, tail_width=1&quot;</span> <span class="c1">#%float(width/20.)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;o-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-o&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-x&quot;</span><span class="p">,</span> <span class="s2">&quot;x-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">]:</span>
-                <span class="c1"># arrowstyle = &quot;-&gt;, head_width=0.4, head_length=1&quot;</span>
-                <span class="c1"># arrowstyle = &quot;-&gt;, head_width=0.4, head_length=1, width=10&quot;</span>
-                <span class="n">arrowstyle</span> <span class="o">=</span> <span class="s2">&quot;Simple, head_width=2, head_length=2, tail_width=1&quot;</span> <span class="c1">#%float(width/20.)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">arrowstyle</span> <span class="o">=</span> <span class="s2">&quot;Simple, head_width=2, head_length=2, tail_width=1&quot;</span> <span class="c1">#%float(width/20.)</span>
-                <span class="c1"># raise ValueError(&quot;edge type %s not valid.&quot; %d.get(&quot;outer_edge_type&quot;))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">rad</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span> <span class="o">*</span> <span class="n">inner_edge_curved</span> <span class="o">*</span> <span class="n">curved_radius</span>
-            <span class="k">if</span> <span class="n">cmap_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">facecolor</span> <span class="o">=</span> <span class="n">data_to_rgb_links</span><span class="o">.</span><span class="n">to_rgba</span><span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">])</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">facecolor</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="c1"># print(&quot;HERE&quot;)</span>
-                    <span class="n">facecolor</span> <span class="o">=</span> <span class="n">standard_color_links</span>
-
-            <span class="n">width</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge_width&quot;</span><span class="p">]</span>
-            <span class="n">alpha</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge_alpha&quot;</span><span class="p">]</span>
-
-            <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_attribute&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;spurious&quot;</span><span class="p">:</span>
-                <span class="n">facecolor</span> <span class="o">=</span> <span class="s2">&quot;grey&quot;</span>
-            <span class="c1"># print(d.get(&quot;inner_edge_type&quot;))</span>
-            <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;&lt;-o&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-x&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">]:</span>
-                <span class="n">n1</span><span class="p">,</span> <span class="n">n2</span> <span class="o">=</span> <span class="n">n2</span><span class="p">,</span> <span class="n">n1</span>
-
-            <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span>
-                <span class="s2">&quot;o-o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;---&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-o&quot;</span><span class="p">,</span>
-            <span class="p">]:</span>
-                <span class="n">arrowstyle</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">:</span>
-                <span class="c1"># arrowstyle = &quot;&lt;-&gt;, head_width=0.4, head_length=1&quot;</span>
-                <span class="n">arrowstyle</span> <span class="o">=</span> <span class="s2">&quot;Simple, head_width=2, head_length=2, tail_width=1&quot;</span> <span class="c1">#%float(width/20.)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;o-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-o&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;--&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-x&quot;</span><span class="p">,</span> <span class="s2">&quot;x-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">]:</span>
-                <span class="c1"># arrowstyle = &quot;-&gt;, head_width=0.4, head_length=1&quot;</span>
-                <span class="n">arrowstyle</span> <span class="o">=</span> <span class="s2">&quot;Simple, head_width=2, head_length=2, tail_width=1&quot;</span> <span class="c1">#%float(width/20.)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">arrowstyle</span> <span class="o">=</span> <span class="s2">&quot;Simple, head_width=2, head_length=2, tail_width=1&quot;</span> <span class="c1">#%float(width/20.)</span>
-
-            <span class="c1">#     raise ValueError(&quot;edge type %s not valid.&quot; %d.get(&quot;inner_edge_type&quot;))</span>
-
-            <span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;solid&#39;</span> <span class="c1">#d.get(&quot;inner_edge_style&quot;)</span>
-
-        <span class="n">coor1</span> <span class="o">=</span> <span class="n">n1</span><span class="o">.</span><span class="n">center</span>
-        <span class="n">coor2</span> <span class="o">=</span> <span class="n">n2</span><span class="o">.</span><span class="n">center</span>
-
-        <span class="n">marker_size</span> <span class="o">=</span> <span class="n">width</span> <span class="o">**</span> <span class="mi">2</span>
-        <span class="n">figuresize</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">get_size_inches</span><span class="p">()</span>
-
-        <span class="c1"># print(&quot;COLOR &quot;, facecolor)</span>
-        <span class="c1"># print(u, v, outer_edge, &quot;outer &quot;, d.get(&quot;outer_edge_type&quot;),  &quot;inner &quot;,  d.get(&quot;inner_edge_type&quot;), width, arrowstyle, linestyle)</span>
-        
-        <span class="k">if</span> <span class="p">((</span><span class="n">outer_edge</span> <span class="ow">is</span> <span class="kc">True</span> <span class="ow">and</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">)</span>
-           <span class="ow">or</span> <span class="p">(</span><span class="n">outer_edge</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">and</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">)):</span>
-            <span class="n">e_p</span> <span class="o">=</span> <span class="n">FancyArrowPatch</span><span class="p">(</span>
-                <span class="n">coor1</span><span class="p">,</span>
-                <span class="n">coor2</span><span class="p">,</span>
-                <span class="n">arrowstyle</span><span class="o">=</span><span class="n">arrowstyle</span><span class="p">,</span>
-                <span class="n">connectionstyle</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;arc3,rad=</span><span class="si">{</span><span class="n">rad</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
-                <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">1</span><span class="o">*</span><span class="n">width</span><span class="p">,</span>
-                <span class="n">lw</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="c1">#width / 2.,</span>
-                <span class="n">aa</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                <span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span><span class="p">,</span>
-                <span class="n">color</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">patchA</span><span class="o">=</span><span class="n">n1</span><span class="p">,</span>
-                <span class="n">patchB</span><span class="o">=</span><span class="n">n2</span><span class="p">,</span>
-                <span class="n">shrinkA</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
-                <span class="n">shrinkB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">zorder</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">capstyle</span><span class="o">=</span><span class="s2">&quot;butt&quot;</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">e_p</span><span class="p">)</span>
-
-            <span class="n">e_p_back</span> <span class="o">=</span> <span class="n">FancyArrowPatch</span><span class="p">(</span>
-              <span class="n">coor2</span><span class="p">,</span>
-              <span class="n">coor1</span><span class="p">,</span>
-              <span class="n">arrowstyle</span><span class="o">=</span><span class="n">arrowstyle</span><span class="p">,</span>
-              <span class="n">connectionstyle</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;arc3,rad=</span><span class="si">{</span><span class="o">-</span><span class="n">rad</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
-              <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">1</span><span class="o">*</span><span class="n">width</span><span class="p">,</span>
-              <span class="n">lw</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="c1">#width / 2.,</span>
-              <span class="n">aa</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-              <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-              <span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span><span class="p">,</span>
-              <span class="n">color</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-              <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-              <span class="n">patchA</span><span class="o">=</span><span class="n">n2</span><span class="p">,</span>
-              <span class="n">patchB</span><span class="o">=</span><span class="n">n1</span><span class="p">,</span>
-              <span class="n">shrinkA</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
-              <span class="n">shrinkB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-              <span class="n">zorder</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-              <span class="n">capstyle</span><span class="o">=</span><span class="s2">&quot;butt&quot;</span><span class="p">,</span>
-            <span class="p">)</span>  
-            <span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">e_p_back</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">arrowstyle</span> <span class="o">==</span> <span class="s1">&#39;-&#39;</span><span class="p">:</span>
-                <span class="n">lw</span> <span class="o">=</span> <span class="mi">1</span><span class="o">*</span><span class="n">width</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">lw</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="c1"># e_p = FancyArrowPatch(</span>
-            <span class="c1">#     coor1,</span>
-            <span class="c1">#     coor2,</span>
-            <span class="c1">#     arrowstyle=arrowstyle,</span>
-            <span class="c1">#     connectionstyle=f&quot;arc3,rad={rad}&quot;,</span>
-            <span class="c1">#     mutation_scale=np.sqrt(width)*2*1.1,</span>
-            <span class="c1">#     lw=lw*1.1, #width / 2.,</span>
-            <span class="c1">#     aa=True,</span>
-            <span class="c1">#     alpha=alpha,</span>
-            <span class="c1">#     linestyle=linestyle,</span>
-            <span class="c1">#     color=&#39;white&#39;,</span>
-            <span class="c1">#     clip_on=False,</span>
-            <span class="c1">#     patchA=n1,</span>
-            <span class="c1">#     patchB=n2,</span>
-            <span class="c1">#     shrinkA=0,</span>
-            <span class="c1">#     shrinkB=0,</span>
-            <span class="c1">#     zorder=-1,</span>
-            <span class="c1">#     capstyle=&quot;butt&quot;,</span>
-            <span class="c1"># )</span>
-            <span class="c1"># ax.add_artist(e_p)</span>
-            <span class="n">e_p</span> <span class="o">=</span> <span class="n">FancyArrowPatch</span><span class="p">(</span>
-                <span class="n">coor1</span><span class="p">,</span>
-                <span class="n">coor2</span><span class="p">,</span>
-                <span class="n">arrowstyle</span><span class="o">=</span><span class="n">arrowstyle</span><span class="p">,</span>
-                <span class="n">connectionstyle</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;arc3,rad=</span><span class="si">{</span><span class="n">rad</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
-                <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">1</span><span class="o">*</span><span class="n">width</span><span class="p">,</span>
-                <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="c1">#width / 2.,</span>
-                <span class="n">aa</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-                <span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span><span class="p">,</span>
-                <span class="n">color</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">patchA</span><span class="o">=</span><span class="n">n1</span><span class="p">,</span>
-                <span class="n">patchB</span><span class="o">=</span><span class="n">n2</span><span class="p">,</span>
-                <span class="n">shrinkA</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">shrinkB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">zorder</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">capstyle</span><span class="o">=</span><span class="s2">&quot;butt&quot;</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">e_p</span><span class="p">)</span>
-
-        <span class="n">e_p_marker</span> <span class="o">=</span> <span class="n">FancyArrowPatch</span><span class="p">(</span>
-                <span class="n">coor1</span><span class="p">,</span>
-                <span class="n">coor2</span><span class="p">,</span>
-                <span class="n">arrowstyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span>
-                <span class="n">connectionstyle</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;arc3,rad=</span><span class="si">{</span><span class="n">rad</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
-                <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">1</span><span class="o">*</span><span class="n">width</span><span class="p">,</span>
-                <span class="n">lw</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="c1">#width / 2.,</span>
-                <span class="n">aa</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                <span class="n">alpha</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span>
-                <span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span><span class="p">,</span>
-                <span class="n">color</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">patchA</span><span class="o">=</span><span class="n">n1</span><span class="p">,</span>
-                <span class="n">patchB</span><span class="o">=</span><span class="n">n2</span><span class="p">,</span>
-                <span class="n">shrinkA</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">shrinkB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">zorder</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span>
-                <span class="n">capstyle</span><span class="o">=</span><span class="s2">&quot;butt&quot;</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">e_p_marker</span><span class="p">)</span>
-
-        <span class="n">path</span> <span class="o">=</span> <span class="n">e_p_marker</span><span class="o">.</span><span class="n">get_path</span><span class="p">()</span>
-        <span class="n">vertices</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">vertices</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">m</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="n">vertices</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># print(vertices)</span>
-        <span class="n">start</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">end</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="c1"># This must be added to avoid rescaling of the plot, when no &#39;o&#39;</span>
-        <span class="c1"># or &#39;x&#39; is added to the graph.</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">start</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">outer_edge</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;o-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;o--&quot;</span><span class="p">]:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-o&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;--o&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;x--&quot;</span><span class="p">,</span> <span class="s2">&quot;x-&gt;&quot;</span><span class="p">]:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;+--&quot;</span><span class="p">,</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">]:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;P&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-x&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;P&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;--x&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;o-o&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;x-x&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;o-x&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;x-o&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;o-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;o--&quot;</span><span class="p">]:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-o&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;--o&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;x--&quot;</span><span class="p">,</span> <span class="s2">&quot;x-&gt;&quot;</span><span class="p">]:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;+--&quot;</span><span class="p">,</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">]:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;P&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-x&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;P&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;--x&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;o-o&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;x-x&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;o-x&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;x-o&quot;</span><span class="p">:</span>
-                <span class="n">circle_marker_start</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">start</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
-                <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-                    <span class="o">*</span><span class="n">end</span><span class="p">,</span>
-                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-                    <span class="n">s</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span>
-                    <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">outer_edge</span><span class="p">:</span>
-            <span class="c1"># Attach labels of lags</span>
-            <span class="n">trans</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># patch.get_transform()</span>
-            <span class="n">path</span> <span class="o">=</span> <span class="n">e_p</span><span class="o">.</span><span class="n">get_path</span><span class="p">()</span>
-            <span class="n">verts</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">to_polygons</span><span class="p">(</span><span class="n">trans</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">verts</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="n">label_vert</span> <span class="o">=</span> <span class="n">verts</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span>
-                <span class="n">l</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span>
-                <span class="n">string</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
-                <span class="n">txt</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                    <span class="n">label_vert</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-                    <span class="n">label_vert</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                    <span class="n">string</span><span class="p">,</span>
-                    <span class="n">fontsize</span><span class="o">=</span><span class="n">link_label_fontsize</span><span class="p">,</span>
-                    <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                    <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">txt</span><span class="o">.</span><span class="n">set_path_effects</span><span class="p">(</span>
-                    <span class="p">[</span><span class="n">PathEffects</span><span class="o">.</span><span class="n">withStroke</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">foreground</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)]</span>
-                <span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">rad</span>
-
-    <span class="c1"># Collect all edge weights to get color scale</span>
-    <span class="n">all_links_weights</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">all_links_edge_weights</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">u</span> <span class="o">!=</span> <span class="n">v</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">all_links_weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">all_links_weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">])</span>
-
-    <span class="k">if</span> <span class="n">cmap_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_links_weights</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">links_vmin</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">links_vmin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">all_links_weights</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">links_vmax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">links_vmax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">all_links_weights</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-        <span class="n">data_to_rgb_links</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span>
-            <span class="n">norm</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">pyplot</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap_links</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="n">data_to_rgb_links</span><span class="o">.</span><span class="n">set_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">all_links_weights</span><span class="p">))</span>
-        <span class="n">data_to_rgb_links</span><span class="o">.</span><span class="n">set_clim</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="n">links_vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">links_vmax</span><span class="p">)</span>
-        <span class="c1"># Create colorbars for links</span>
-
-        <span class="c1"># setup colorbar axes.</span>
-        <span class="k">if</span> <span class="n">show_colorbar</span><span class="p">:</span>
-            <span class="c1"># cax_e = pyplot.axes(</span>
-            <span class="c1">#     [</span>
-            <span class="c1">#         0.55,</span>
-            <span class="c1">#         ax.get_subplotspec().get_position(ax.figure).bounds[1] + 0.02,</span>
-            <span class="c1">#         0.4,</span>
-            <span class="c1">#         0.025 + (len(all_links_edge_weights) == 0) * 0.035,</span>
-            <span class="c1">#     ],</span>
-            <span class="c1">#     frameon=False,</span>
-            <span class="c1"># )</span>
-            <span class="n">bbox_ax</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span>
-            <span class="n">width</span> <span class="o">=</span> <span class="n">bbox_ax</span><span class="o">.</span><span class="n">xmax</span><span class="o">-</span><span class="n">bbox_ax</span><span class="o">.</span><span class="n">xmin</span>
-            <span class="n">height</span> <span class="o">=</span> <span class="n">bbox_ax</span><span class="o">.</span><span class="n">ymax</span><span class="o">-</span><span class="n">bbox_ax</span><span class="o">.</span><span class="n">ymin</span>
-            <span class="c1"># print(bbox_ax.xmin, bbox_ax.xmax, bbox_ax.ymin, bbox_ax.ymax) </span>
-            <span class="c1"># cax_e = fig.add_axes(</span>
-            <span class="c1">#     [</span>
-            <span class="c1">#         bbox_ax.xmax - width*0.45,</span>
-            <span class="c1">#         bbox_ax.ymin-0.075*height+network_lower_bound-0.15,</span>
-            <span class="c1">#         width*0.4,</span>
-            <span class="c1">#         0.075*height,   #0.025 + (len(all_links_edge_weights) == 0) * 0.035,</span>
-            <span class="c1">#     ],</span>
-            <span class="c1">#     frameon=False,</span>
-            <span class="c1"># )</span>
-            <span class="n">cax_e</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">inset_axes</span><span class="p">(</span> 
-                          <span class="p">[</span>
-                          <span class="mf">0.55</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.07</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.07</span>
-                    <span class="c1"># bbox_ax.xmax - width*0.45,</span>
-                    <span class="c1"># bbox_ax.ymin-0.075*height+network_lower_bound-0.15,</span>
-                    <span class="c1"># width*0.4,</span>
-                    <span class="c1"># 0.075*height,   #0.025 + (len(all_links_edge_weights) == 0) * 0.035,</span>
-                <span class="p">],</span>
-                <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,)</span>
-            <span class="c1"># divider = make_axes_locatable(ax)</span>
-
-            <span class="c1"># cax_e = divider.append_axes(&#39;bottom&#39;, size=&#39;5%&#39;, pad=0.05, frameon=False,)</span>
-
-            <span class="n">cb_e</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
-                <span class="n">data_to_rgb_links</span><span class="p">,</span> <span class="n">cax</span><span class="o">=</span><span class="n">cax_e</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;horizontal&quot;</span>
-            <span class="p">)</span>
-            <span class="c1"># try:</span>
-            <span class="n">ticks_here</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
-                    <span class="n">_myround</span><span class="p">(</span><span class="n">links_vmin</span><span class="p">,</span> <span class="n">links_ticks</span><span class="p">,</span> <span class="s2">&quot;down&quot;</span><span class="p">),</span>
-                    <span class="n">_myround</span><span class="p">(</span><span class="n">links_vmax</span><span class="p">,</span> <span class="n">links_ticks</span><span class="p">,</span> <span class="s2">&quot;up&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">links_ticks</span><span class="p">,</span>
-                    <span class="n">links_ticks</span><span class="p">,</span>
-                <span class="p">)</span>
-            <span class="n">cb_e</span><span class="o">.</span><span class="n">set_ticks</span><span class="p">(</span><span class="n">ticks_here</span><span class="p">[(</span><span class="n">links_vmin</span> <span class="o">&lt;=</span> <span class="n">ticks_here</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">ticks_here</span> <span class="o">&lt;=</span> <span class="n">links_vmax</span><span class="p">)])</span>
-            <span class="c1"># except:</span>
-            <span class="c1">#     print(&#39;no ticks given&#39;)</span>
-
-            <span class="n">cb_e</span><span class="o">.</span><span class="n">outline</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
-            <span class="n">cax_e</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span>
-                <span class="n">link_colorbar_label</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</span>
-            <span class="p">)</span>
-            <span class="n">cax_e</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;major&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">)</span>
-
-    <span class="c1">##</span>
-    <span class="c1"># Draw nodes</span>
-    <span class="c1">##</span>
-    <span class="n">node_sizes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">node_rings</span><span class="p">),</span> <span class="n">N</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">node_rings</span><span class="p">):</span>  <span class="c1"># iterate through to get all node sizes</span>
-        <span class="k">if</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;sizes&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">node_sizes</span><span class="p">[</span><span class="n">ring</span><span class="p">]</span> <span class="o">=</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;sizes&quot;</span><span class="p">]</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">node_sizes</span><span class="p">[</span><span class="n">ring</span><span class="p">]</span> <span class="o">=</span> <span class="n">standard_size</span>
-    <span class="n">max_sizes</span> <span class="o">=</span> <span class="n">node_sizes</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">total_max_size</span> <span class="o">=</span> <span class="n">node_sizes</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-    <span class="n">node_sizes</span> <span class="o">/=</span> <span class="n">total_max_size</span>
-    <span class="n">node_sizes</span> <span class="o">*=</span> <span class="n">standard_size</span>
-
-    <span class="k">def</span> <span class="nf">get_aspect</span><span class="p">(</span><span class="n">ax</span><span class="p">):</span>
-        <span class="c1"># Total figure size</span>
-        <span class="n">figW</span><span class="p">,</span> <span class="n">figH</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_figure</span><span class="p">()</span><span class="o">.</span><span class="n">get_size_inches</span><span class="p">()</span>
-        <span class="c1"># print(figW, figH)</span>
-        <span class="c1"># Axis size on figure</span>
-        <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span><span class="o">.</span><span class="n">bounds</span>
-        <span class="c1"># Ratio of display units</span>
-        <span class="c1"># print(w, h)</span>
-        <span class="n">disp_ratio</span> <span class="o">=</span> <span class="p">(</span><span class="n">figH</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">figW</span> <span class="o">*</span> <span class="n">w</span><span class="p">)</span>
-        <span class="c1"># Ratio of data units</span>
-        <span class="c1"># Negative over negative because of the order of subtraction</span>
-        <span class="n">data_ratio</span> <span class="o">=</span> <span class="n">sub</span><span class="p">(</span><span class="o">*</span><span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">())</span> <span class="o">/</span> <span class="n">sub</span><span class="p">(</span><span class="o">*</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">())</span>
-        <span class="c1"># print(data_ratio, disp_ratio)</span>
-        <span class="k">return</span> <span class="n">disp_ratio</span> <span class="o">/</span> <span class="n">data_ratio</span>
-
-    <span class="k">if</span> <span class="n">node_aspect</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">node_aspect</span> <span class="o">=</span> <span class="n">get_aspect</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
-
-    <span class="c1"># start drawing the outer ring first...</span>
-    <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">node_rings</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
-        <span class="c1">#        print ring</span>
-        <span class="c1"># dictionary of rings: {0:{&#39;sizes&#39;:(N,)-array, &#39;color_array&#39;:(N,)-array</span>
-        <span class="c1"># or None, &#39;cmap&#39;:string, &#39;vmin&#39;:float or None, &#39;vmax&#39;:float or None}}</span>
-        <span class="k">if</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;color_array&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">color_data</span> <span class="o">=</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;color_array&quot;</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;vmin&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">vmin</span> <span class="o">=</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;vmin&quot;</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">vmin</span> <span class="o">=</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;color_array&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
-            <span class="k">if</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;vmax&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">vmax</span> <span class="o">=</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;vmax&quot;</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">vmax</span> <span class="o">=</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;color_array&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-            <span class="k">if</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;cmap&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">cmap</span> <span class="o">=</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;cmap&quot;</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">cmap</span> <span class="o">=</span> <span class="n">standard_cmap</span>
-            <span class="n">data_to_rgb</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span>
-                <span class="n">norm</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">pyplot</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
-            <span class="p">)</span>
-            <span class="n">data_to_rgb</span><span class="o">.</span><span class="n">set_array</span><span class="p">(</span><span class="n">color_data</span><span class="p">)</span>
-            <span class="n">data_to_rgb</span><span class="o">.</span><span class="n">set_clim</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
-            <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="n">data_to_rgb</span><span class="o">.</span><span class="n">to_rgba</span><span class="p">(</span><span class="n">color_data</span><span class="p">[</span><span class="n">n</span><span class="p">])</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">G</span><span class="p">]</span>
-
-            <span class="k">if</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;colorbar&quot;</span><span class="p">]:</span>
-                <span class="c1"># Create colorbars for nodes</span>
-                <span class="c1"># cax_n = pyplot.axes([.8 + ring*0.11,</span>
-                <span class="c1"># ax.get_subplotspec().get_position(ax.figure).bounds[1]+0.05, 0.025, 0.35], frameon=False) #</span>
-                <span class="c1"># setup colorbar axes.</span>
-                <span class="c1"># setup colorbar axes.</span>
-                <span class="n">bbox_ax</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span>
-                <span class="c1"># print(bbox_ax.xmin, bbox_ax.xmax, bbox_ax.ymin, bbox_ax.ymax) </span>
-                <span class="n">cax_n</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">inset_axes</span><span class="p">(</span>
-                    <span class="p">[</span>
-                    <span class="mf">0.05</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.07</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.07</span>
-                        <span class="c1"># bbox_ax.xmin + width*0.05,</span>
-                        <span class="c1"># bbox_ax.ymin-0.075*height+network_lower_bound-0.15,</span>
-                        <span class="c1"># width*0.4,</span>
-                        <span class="c1"># 0.075*height,   #0.025 + (len(all_links_edge_weights) == 0) * 0.035,</span>
-                    <span class="p">],</span>
-                    <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">cb_n</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">data_to_rgb</span><span class="p">,</span> <span class="n">cax</span><span class="o">=</span><span class="n">cax_n</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;horizontal&quot;</span><span class="p">)</span>
-                <span class="c1"># try:</span>
-                <span class="n">ticks_here</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
-                    <span class="n">_myround</span><span class="p">(</span><span class="n">vmin</span><span class="p">,</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;ticks&quot;</span><span class="p">],</span> <span class="s2">&quot;down&quot;</span><span class="p">),</span>
-                    <span class="n">_myround</span><span class="p">(</span><span class="n">vmax</span><span class="p">,</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;ticks&quot;</span><span class="p">],</span> <span class="s2">&quot;up&quot;</span><span class="p">)</span>
-                    <span class="o">+</span> <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;ticks&quot;</span><span class="p">],</span>
-                    <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;ticks&quot;</span><span class="p">],</span>
-                <span class="p">)</span>
-                <span class="n">cb_n</span><span class="o">.</span><span class="n">set_ticks</span><span class="p">(</span><span class="n">ticks_here</span><span class="p">[(</span><span class="n">vmin</span> <span class="o">&lt;=</span> <span class="n">ticks_here</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">ticks_here</span> <span class="o">&lt;=</span> <span class="n">vmax</span><span class="p">)])</span>
-                <span class="c1"># except:</span>
-                <span class="c1">#     print (&#39;no ticks given&#39;)</span>
-                <span class="n">cb_n</span><span class="o">.</span><span class="n">outline</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
-                <span class="c1"># cb_n.set_ticks()</span>
-                <span class="n">cax_n</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span>
-                    <span class="n">node_rings</span><span class="p">[</span><span class="n">ring</span><span class="p">][</span><span class="s2">&quot;label&quot;</span><span class="p">],</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span>
-                <span class="p">)</span>
-                <span class="n">cax_n</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;major&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">colors</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="n">vmin</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="n">vmax</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">node_alpha</span><span class="p">)</span> <span class="o">==</span> <span class="nb">dict</span><span class="p">:</span>
-                <span class="n">alpha</span> <span class="o">=</span> <span class="n">node_alpha</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span>
-
-            <span class="k">if</span> <span class="n">special_nodes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">special_nodes</span><span class="p">:</span>
-                    <span class="n">color_here</span> <span class="o">=</span> <span class="n">special_nodes</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">color_here</span> <span class="o">=</span> <span class="s1">&#39;grey&#39;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">colors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">color_here</span> <span class="o">=</span> <span class="n">standard_color_nodes</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">color_here</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
-
-            <span class="n">c</span> <span class="o">=</span> <span class="n">Ellipse</span><span class="p">(</span>
-                <span class="n">pos</span><span class="p">[</span><span class="n">n</span><span class="p">],</span>
-                <span class="n">width</span><span class="o">=</span><span class="n">node_sizes</span><span class="p">[:</span> <span class="n">ring</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="n">node_aspect</span><span class="p">,</span>
-                <span class="n">height</span><span class="o">=</span><span class="n">node_sizes</span><span class="p">[:</span> <span class="n">ring</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="n">n</span><span class="p">],</span>
-                <span class="n">clip_on</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">facecolor</span><span class="o">=</span><span class="n">color_here</span><span class="p">,</span>
-                <span class="n">edgecolor</span><span class="o">=</span><span class="n">color_here</span><span class="p">,</span>
-                <span class="n">zorder</span><span class="o">=-</span><span class="n">ring</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span>
-            <span class="p">)</span>
-
-            <span class="c1"># else:</span>
-            <span class="c1">#     if special_nodes is not None and n in special_nodes:</span>
-            <span class="c1">#         color_here = special_nodes[n]</span>
-            <span class="c1">#     else:</span>
-            <span class="c1">#         color_here = colors[n]</span>
-            <span class="c1">#     c = Ellipse(</span>
-            <span class="c1">#         pos[n],</span>
-            <span class="c1">#         width=node_sizes[: ring + 1].sum(axis=0)[n] * node_aspect,</span>
-            <span class="c1">#         height=node_sizes[: ring + 1].sum(axis=0)[n],</span>
-            <span class="c1">#         clip_on=False,</span>
-            <span class="c1">#         facecolor=colors[n],</span>
-            <span class="c1">#         edgecolor=colors[n],</span>
-            <span class="c1">#         zorder=-ring - 1,</span>
-            <span class="c1">#     )</span>
-
-            <span class="n">ax</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-
-            <span class="c1"># avoiding attribute error raised by changes in networkx</span>
-            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="s2">&quot;node&quot;</span><span class="p">):</span>
-                <span class="c1"># works with networkx 1.10</span>
-                <span class="n">G</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="n">n</span><span class="p">][</span><span class="s2">&quot;patch&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="c1"># works with networkx 2.4</span>
-                <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="n">n</span><span class="p">][</span><span class="s2">&quot;patch&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span>
-
-            <span class="k">if</span> <span class="n">ring</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                    <span class="n">pos</span><span class="p">[</span><span class="n">n</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                    <span class="n">pos</span><span class="p">[</span><span class="n">n</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
-                    <span class="n">node_labels</span><span class="p">[</span><span class="n">n</span><span class="p">],</span>
-                    <span class="n">fontsize</span><span class="o">=</span><span class="n">node_label_size</span><span class="p">,</span>
-                    <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                    <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                    <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mf">5.</span>
-                <span class="p">)</span>
-                <span class="k">if</span> <span class="n">show_autodependency_lags</span><span class="p">:</span>
-                    <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                        <span class="n">pos</span><span class="p">[</span><span class="n">n</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                        <span class="n">pos</span><span class="p">[</span><span class="n">n</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
-                        <span class="n">autodep_sig_lags</span><span class="p">[</span><span class="n">n</span><span class="p">],</span>
-                        <span class="n">fontsize</span><span class="o">=</span><span class="n">link_label_fontsize</span><span class="p">,</span>
-                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                        <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                        <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-                        <span class="n">zorder</span><span class="o">=</span><span class="mf">5.</span>
-                    <span class="p">)</span>
-
-    <span class="c1"># Draw edges</span>
-    <span class="n">seen</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;no_links&quot;</span><span class="p">):</span>
-            <span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1e-8</span>
-            <span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1e-8</span>
-        <span class="k">if</span> <span class="n">u</span> <span class="o">!=</span> <span class="n">v</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]:</span>
-                <span class="n">seen</span><span class="p">[(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">)]</span> <span class="o">=</span> <span class="n">draw_edge</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">seen</span><span class="p">,</span> <span class="n">outer_edge</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]:</span>
-                <span class="n">seen</span><span class="p">[(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">)]</span> <span class="o">=</span> <span class="n">draw_edge</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">seen</span><span class="p">,</span> <span class="n">outer_edge</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-    <span class="c1"># if network_left_bound is not None:</span>
-    <span class="c1">#     network_right_bound = 0.98</span>
-    <span class="c1"># else:</span>
-    <span class="c1">#     network_right_bound = None</span>
-    <span class="c1"># fig.subplots_adjust(bottom=network_lower_bound, left=network_left_bound, right=network_right_bound) #, right=0.97)</span>
-
-
-<div class="viewcode-block" id="plot_graph"><a class="viewcode-back" href="../../index.html#tigramite.plotting.plot_graph">[docs]</a><span class="k">def</span> <span class="nf">plot_graph</span><span class="p">(</span>
-    <span class="n">graph</span><span class="p">,</span>
-    <span class="n">val_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">fig_ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">save_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_colorbar_label</span><span class="o">=</span><span class="s2">&quot;MCI&quot;</span><span class="p">,</span>
-    <span class="n">node_colorbar_label</span><span class="o">=</span><span class="s2">&quot;auto-MCI&quot;</span><span class="p">,</span>
-    <span class="n">link_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_attribute</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">node_pos</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">arrow_linewidth</span><span class="o">=</span><span class="mf">8.0</span><span class="p">,</span>
-    <span class="n">vmin_edges</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">vmax_edges</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">edge_ticks</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
-    <span class="n">cmap_edges</span><span class="o">=</span><span class="s2">&quot;RdBu_r&quot;</span><span class="p">,</span>
-    <span class="n">vmin_nodes</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">vmax_nodes</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">node_ticks</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
-    <span class="n">cmap_nodes</span><span class="o">=</span><span class="s2">&quot;RdBu_r&quot;</span><span class="p">,</span>
-    <span class="n">node_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
-    <span class="n">node_aspect</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">arrowhead_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
-    <span class="n">curved_radius</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-    <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">node_label_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">link_label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">lag_array</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">show_colorbar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">inner_edge_style</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span>
-    <span class="n">link_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">special_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">show_autodependency_lags</span><span class="o">=</span><span class="kc">False</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a network plot.</span>
-<span class="sd">    </span>
-<span class="sd">    This is still in beta. The network is defined from links in graph. Nodes</span>
-<span class="sd">    denote variables, straight links contemporaneous dependencies and curved</span>
-<span class="sd">    arrows lagged dependencies. The node color denotes the maximal absolute</span>
-<span class="sd">    auto-dependency and the link color the value at the lag with maximal</span>
-<span class="sd">    absolute cross-dependency. The link label lists the lags with significant</span>
-<span class="sd">    dependency in order of absolute magnitude. The network can also be</span>
-<span class="sd">    plotted over a map drawn before on the same axis. Then the node positions</span>
-<span class="sd">    can be supplied in appropriate axis coordinates via node_pos.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    graph : string or bool array-like, optional (default: None)</span>
-<span class="sd">        Either string matrix providing graph or bool array providing only adjacencies</span>
-<span class="sd">        Must be of same shape as val_matrix. </span>
-<span class="sd">    val_matrix : array_like</span>
-<span class="sd">        Matrix of shape (N, N, tau_max+1) containing test statistic values.</span>
-<span class="sd">    var_names : list, optional (default: None)</span>
-<span class="sd">        List of variable names. If None, range(N) is used.</span>
-<span class="sd">    fig_ax : tuple of figure and axis object, optional (default: None)</span>
-<span class="sd">        Figure and axes instance. If None they are created.</span>
-<span class="sd">    figsize : tuple</span>
-<span class="sd">        Size of figure.</span>
-<span class="sd">    save_name : str, optional (default: None)</span>
-<span class="sd">        Name of figure file to save figure. If None, figure is shown in window.</span>
-<span class="sd">    link_colorbar_label : str, optional (default: &#39;MCI&#39;)</span>
-<span class="sd">        Test statistic label.</span>
-<span class="sd">    node_colorbar_label : str, optional (default: &#39;auto-MCI&#39;)</span>
-<span class="sd">        Test statistic label for auto-dependencies.</span>
-<span class="sd">    link_width : array-like, optional (default: None)</span>
-<span class="sd">        Array of val_matrix.shape specifying relative link width with maximum</span>
-<span class="sd">        given by arrow_linewidth. If None, all links have same width.</span>
-<span class="sd">    link_attribute : array-like, optional (default: None)</span>
-<span class="sd">        String array of val_matrix.shape specifying link attributes.</span>
-<span class="sd">    node_pos : dictionary, optional (default: None)</span>
-<span class="sd">        Dictionary of node positions in axis coordinates of form</span>
-<span class="sd">        node_pos = {&#39;x&#39;:array of shape (N,), &#39;y&#39;:array of shape(N)}. These</span>
-<span class="sd">        coordinates could have been transformed before for basemap plots.</span>
-<span class="sd">    arrow_linewidth : float, optional (default: 30)</span>
-<span class="sd">        Linewidth.</span>
-<span class="sd">    vmin_edges : float, optional (default: -1)</span>
-<span class="sd">        Link colorbar scale lower bound.</span>
-<span class="sd">    vmax_edges : float, optional (default: 1)</span>
-<span class="sd">        Link colorbar scale upper bound.</span>
-<span class="sd">    edge_ticks : float, optional (default: 0.4)</span>
-<span class="sd">        Link tick mark interval.</span>
-<span class="sd">    cmap_edges : str, optional (default: &#39;RdBu_r&#39;)</span>
-<span class="sd">        Colormap for links.</span>
-<span class="sd">    vmin_nodes : float, optional (default: 0)</span>
-<span class="sd">        Node colorbar scale lower bound.</span>
-<span class="sd">    vmax_nodes : float, optional (default: 1)</span>
-<span class="sd">        Node colorbar scale upper bound.</span>
-<span class="sd">    node_ticks : float, optional (default: 0.4)</span>
-<span class="sd">        Node tick mark interval.</span>
-<span class="sd">    cmap_nodes : str, optional (default: &#39;OrRd&#39;)</span>
-<span class="sd">        Colormap for links.</span>
-<span class="sd">    node_size : int, optional (default: 0.3)</span>
-<span class="sd">        Node size.</span>
-<span class="sd">    node_aspect : float, optional (default: None)</span>
-<span class="sd">        Ratio between the heigth and width of the varible nodes.</span>
-<span class="sd">    arrowhead_size : int, optional (default: 20)</span>
-<span class="sd">        Size of link arrow head. Passed on to FancyArrowPatch object.</span>
-<span class="sd">    curved_radius, float, optional (default: 0.2)</span>
-<span class="sd">        Curvature of links. Passed on to FancyArrowPatch object.</span>
-<span class="sd">    label_fontsize : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of colorbar labels.</span>
-<span class="sd">    alpha : float, optional (default: 1.)</span>
-<span class="sd">        Opacity.</span>
-<span class="sd">    node_label_size : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of node labels.</span>
-<span class="sd">    link_label_fontsize : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of link labels.</span>
-<span class="sd">    tick_label_size : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of tick labels.</span>
-<span class="sd">    lag_array : array, optional (default: None)</span>
-<span class="sd">        Optional specification of lags overwriting np.arange(0, tau_max+1)</span>
-<span class="sd">    network_lower_bound : float, optional (default: 0.2)</span>
-<span class="sd">        Fraction of vertical space below graph plot.</span>
-<span class="sd">    show_colorbar : bool</span>
-<span class="sd">        Whether to show colorbars for links and nodes.</span>
-<span class="sd">    show_autodependency_lags : bool (default: False)</span>
-<span class="sd">        Shows significant autodependencies for a node.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">link_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;link_matrix is deprecated and replaced by graph array&quot;</span>
-                         <span class="s2">&quot; which is now returned by all methods.&quot;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">fig_ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">fig</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">frame_on</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">fig_ax</span>
-
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
-
-    <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Time series graph of shape (N,N,tau_max+1,tau_max+1) cannot be represented by plot_graph,&quot;</span>
-                         <span class="s2">&quot; use plot_time_series_graph instead.&quot;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="c1"># If a non-time series (N,N)-graph is given, insert a dummy dimension</span>
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">val_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">no_coloring</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="n">cmap_edges</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">cmap_nodes</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">no_coloring</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">,</span> <span class="n">link_width</span><span class="p">,</span> <span class="n">link_attribute</span><span class="p">)</span> <span class="o">=</span> <span class="n">_check_matrices</span><span class="p">(</span>
-        <span class="n">graph</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">,</span> <span class="n">link_width</span><span class="p">,</span> <span class="n">link_attribute</span><span class="p">)</span>
-    
-
-    <span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">dummy</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span>
-    <span class="n">tau_max</span> <span class="o">=</span> <span class="n">dummy</span> <span class="o">-</span> <span class="mi">1</span>
-    <span class="n">max_lag</span> <span class="o">=</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">graph</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span>
-    <span class="p">):</span>
-        <span class="n">diagonal</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">diagonal</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">graph</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="n">graph</span><span class="o">.</span><span class="n">size</span> <span class="ow">or</span> <span class="n">diagonal</span><span class="p">:</span>
-        <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;xxx&quot;</span>  <span class="c1"># Workaround, will not be plotted... </span>
-        <span class="n">no_links</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">no_links</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">if</span> <span class="n">var_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">var_names</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-    <span class="c1"># Define graph links by absolute maximum (positive or negative like for</span>
-    <span class="c1"># partial correlation)</span>
-    <span class="c1"># val_matrix[np.abs(val_matrix) &lt; sig_thres] = 0.</span>
-
-    <span class="c1"># Only draw link in one direction among contemp</span>
-    <span class="c1"># Remove lower triangle</span>
-    <span class="n">link_matrix_upper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-    <span class="n">link_matrix_upper</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">triu</span><span class="p">(</span><span class="n">link_matrix_upper</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])</span>
-
-    <span class="c1"># net = _get_absmax(link_matrix != &quot;&quot;)</span>
-    <span class="n">net</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">link_matrix_upper</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">(</span><span class="n">net</span><span class="p">)</span>
-    
-    <span class="c1"># This handels Graphs with no links.</span>
-    <span class="c1"># nx.draw(G, alpha=0, zorder=-10)</span>
-
-    <span class="n">node_color</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">N</span><span class="p">))</span>
-
-    <span class="k">if</span> <span class="n">show_autodependency_lags</span><span class="p">:</span>
-        <span class="n">autodep_sig_lags</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;object&#39;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">autodep_sig_lags</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="c1"># list of all strengths for color map</span>
-    <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="c1"># Add attributes, contemporaneous and lagged links are handled separately</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">dic</span><span class="p">)</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;no_links&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">no_links</span>
-        <span class="c1"># average lagfunc for link u --&gt; v ANDOR u -- v</span>
-        <span class="k">if</span> <span class="n">tau_max</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="c1"># argmax of absolute maximum where a link exists!</span>
-            <span class="n">links</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">link_matrix_upper</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">1</span><span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">argmax_links</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="mi">1</span><span class="p">:][</span><span class="n">links</span><span class="p">])</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span>
-                <span class="n">argmax</span> <span class="o">=</span> <span class="n">links</span><span class="p">[</span><span class="n">argmax_links</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">argmax</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">argmax</span> <span class="o">=</span> <span class="mi">0</span>
-
-        <span class="k">if</span> <span class="n">u</span> <span class="o">!=</span> <span class="n">v</span><span class="p">:</span>
-            <span class="c1"># For contemp links masking or finite samples can lead to different</span>
-            <span class="c1"># values for u--v and v--u</span>
-            <span class="c1"># Here we use the  maximum for the width and weight (=color)</span>
-            <span class="c1"># of the link</span>
-            <span class="c1"># Draw link if u--v OR v--u at lag 0 is nonzero</span>
-            <span class="c1"># dic[&#39;inner_edge&#39;] = ((np.abs(val_matrix[u, v][0]) &gt;=</span>
-            <span class="c1">#                       sig_thres[u, v][0]) or</span>
-            <span class="c1">#                      (np.abs(val_matrix[v, u][0]) &gt;=</span>
-            <span class="c1">#                       sig_thres[v, u][0]))</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">link_matrix_upper</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">link_matrix_upper</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
-            <span class="k">if</span> <span class="n">no_coloring</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-            <span class="c1"># # value at argmax of average</span>
-            <span class="c1"># if np.abs(val_matrix[u, v][0] - val_matrix[v, u][0]) &gt; .0001:</span>
-            <span class="c1">#     print(&quot;Contemporaneous I(%d; %d)=%.3f != I(%d; %d)=%.3f&quot; % (</span>
-            <span class="c1">#           u, v, val_matrix[u, v][0], v, u, val_matrix[v, u][0]) +</span>
-            <span class="c1">#           &quot; due to conditions, finite sample effects or &quot;</span>
-            <span class="c1">#           &quot;masking, here edge color = &quot;</span>
-            <span class="c1">#           &quot;larger (absolute) value.&quot;)</span>
-            <span class="c1"># dic[&#39;inner_edge_color&#39;] = _get_absmax(</span>
-            <span class="c1">#     np.array([[[val_matrix[u, v][0],</span>
-            <span class="c1">#                    val_matrix[v, u][0]]]])).squeeze()</span>
-
-            <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrow_linewidth</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="n">link_width</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">link_width</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="n">arrow_linewidth</span>
-                <span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">link_attribute</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">link_attribute</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-
-            <span class="c1">#     # fraction of nonzero values</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_style&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;solid&quot;</span>
-            <span class="c1"># else:</span>
-            <span class="c1"># dic[&#39;inner_edge_style&#39;] = link_style[</span>
-            <span class="c1">#         u, v, 0]</span>
-
-            <span class="n">all_strengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">])</span>
-
-            <span class="k">if</span> <span class="n">tau_max</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="c1"># True if ensemble mean at lags &gt; 0 is nonzero</span>
-                <span class="c1"># dic[&#39;outer_edge&#39;] = np.any(</span>
-                <span class="c1">#     np.abs(val_matrix[u, v][1:]) &gt;= sig_thres[u, v][1:])</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">link_matrix_upper</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">1</span><span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="c1"># print(u, v, dic[&quot;outer_edge&quot;], argmax, link_matrix_upper[u, v, :])</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">link_matrix_upper</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">argmax</span><span class="p">]</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
-            <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="c1"># fraction of nonzero values</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrow_linewidth</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="n">link_width</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">argmax</span><span class="p">]</span> <span class="o">/</span> <span class="n">link_width</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="n">arrow_linewidth</span>
-                <span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">link_attribute</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="c1"># fraction of nonzero values</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">link_attribute</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">argmax</span><span class="p">]</span>
-
-            <span class="c1"># value at argmax of average</span>
-            <span class="k">if</span> <span class="n">no_coloring</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="n">argmax</span><span class="p">]</span>
-            <span class="n">all_strengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">])</span>
-
-            <span class="c1"># Sorted list of significant lags (only if robust wrt</span>
-            <span class="c1"># d[&#39;min_ensemble_frac&#39;])</span>
-            <span class="k">if</span> <span class="n">tau_max</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">lags</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="mi">1</span><span class="p">:])</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
-                <span class="n">sig_lags</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">link_matrix_upper</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">1</span><span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">lags</span><span class="p">,</span> <span class="n">sig_lags</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="n">lag_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">([</span><span class="n">lag_array</span><span class="p">[</span><span class="n">l</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">lags</span> <span class="k">if</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">sig_lags</span><span class="p">])[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">([</span><span class="n">l</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">lags</span> <span class="k">if</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">sig_lags</span><span class="p">])[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Node color is max of average autodependency</span>
-            <span class="k">if</span> <span class="n">no_coloring</span><span class="p">:</span>
-                <span class="n">node_color</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">node_color</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="n">argmax</span><span class="p">]</span>
-
-            <span class="k">if</span> <span class="n">show_autodependency_lags</span><span class="p">:</span>
-                <span class="n">autodep_sig_lags</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="se">\n\n\n</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">link_matrix_upper</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">1</span><span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-                <span class="c1"># Lags upto tau_max</span>
-                <span class="c1">#autodep_lags = np.argsort(val_matrix[u, v][1:])[::-1]</span>
-                <span class="c1">#autodep_lags += 1</span>
-                <span class="c1">#autodeplags[u] = &quot;\n\n\n&quot; + &quot;,&quot;.join(str(i) for i in autodep_lags.tolist())</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="c1"># dic[&#39;outer_edge_edge&#39;] = False</span>
-        <span class="c1"># dic[&#39;outer_edge_edgecolor&#39;] = None</span>
-        <span class="c1"># dic[&#39;inner_edge_edge&#39;] = False</span>
-        <span class="c1"># dic[&#39;inner_edge_edgecolor&#39;] = None</span>
-
-    <span class="k">if</span> <span class="n">special_nodes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">special_nodes_draw</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">special_nodes</span><span class="p">:</span>
-            <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">node</span>
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="n">tau_max</span><span class="p">:</span>
-                <span class="n">special_nodes_draw</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">special_nodes</span><span class="p">[</span><span class="n">node</span><span class="p">]</span>
-        <span class="n">special_nodes</span> <span class="o">=</span> <span class="n">special_nodes_draw</span>
-    
-
-    <span class="c1"># If no links are present, set value to zero</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_strengths</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">]</span>
-
-    <span class="k">if</span> <span class="n">node_pos</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">circular_layout</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">G</span><span class="p">))</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">pos</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">pos</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">node_pos</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">node_pos</span><span class="p">[</span><span class="s2">&quot;y&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="k">if</span> <span class="n">cmap_nodes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">node_color</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="n">node_rings</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="mi">0</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;sizes&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
-            <span class="s2">&quot;color_array&quot;</span><span class="p">:</span> <span class="n">node_color</span><span class="p">,</span>
-            <span class="s2">&quot;cmap&quot;</span><span class="p">:</span> <span class="n">cmap_nodes</span><span class="p">,</span>
-            <span class="s2">&quot;vmin&quot;</span><span class="p">:</span> <span class="n">vmin_nodes</span><span class="p">,</span>
-            <span class="s2">&quot;vmax&quot;</span><span class="p">:</span> <span class="n">vmax_nodes</span><span class="p">,</span>
-            <span class="s2">&quot;ticks&quot;</span><span class="p">:</span> <span class="n">node_ticks</span><span class="p">,</span>
-            <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="n">node_colorbar_label</span><span class="p">,</span>
-            <span class="s2">&quot;colorbar&quot;</span><span class="p">:</span> <span class="n">show_colorbar</span><span class="p">,</span>
-        <span class="p">}</span>
-    <span class="p">}</span>
-
-    <span class="n">_draw_network_with_curved_edges</span><span class="p">(</span>
-        <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-        <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">G</span><span class="o">=</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">G</span><span class="p">),</span>
-        <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span>
-        <span class="c1"># dictionary of rings: {0:{&#39;sizes&#39;:(N,)-array, &#39;color_array&#39;:(N,)-array</span>
-        <span class="c1"># or None, &#39;cmap&#39;:string,</span>
-        <span class="n">node_rings</span><span class="o">=</span><span class="n">node_rings</span><span class="p">,</span>
-        <span class="c1"># &#39;vmin&#39;:float or None, &#39;vmax&#39;:float or None, &#39;label&#39;:string or None}}</span>
-        <span class="n">node_labels</span><span class="o">=</span><span class="n">var_names</span><span class="p">,</span>
-        <span class="n">node_label_size</span><span class="o">=</span><span class="n">node_label_size</span><span class="p">,</span>
-        <span class="n">node_alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-        <span class="n">standard_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
-        <span class="n">node_aspect</span><span class="o">=</span><span class="n">node_aspect</span><span class="p">,</span>
-        <span class="n">standard_cmap</span><span class="o">=</span><span class="s2">&quot;OrRd&quot;</span><span class="p">,</span>
-        <span class="n">standard_color_nodes</span><span class="o">=</span><span class="s2">&quot;lightgrey&quot;</span><span class="p">,</span>
-        <span class="n">standard_color_links</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-        <span class="n">log_sizes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">cmap_links</span><span class="o">=</span><span class="n">cmap_edges</span><span class="p">,</span>
-        <span class="n">links_vmin</span><span class="o">=</span><span class="n">vmin_edges</span><span class="p">,</span>
-        <span class="n">links_vmax</span><span class="o">=</span><span class="n">vmax_edges</span><span class="p">,</span>
-        <span class="n">links_ticks</span><span class="o">=</span><span class="n">edge_ticks</span><span class="p">,</span>
-        <span class="n">tick_label_size</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">,</span>
-        <span class="c1"># cmap_links_edges=&#39;YlOrRd&#39;, links_edges_vmin=-1., links_edges_vmax=1.,</span>
-        <span class="c1"># links_edges_ticks=.2, link_edge_colorbar_label=&#39;link_edge&#39;,</span>
-        <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;simple&quot;</span><span class="p">,</span>
-        <span class="n">arrowhead_size</span><span class="o">=</span><span class="n">arrowhead_size</span><span class="p">,</span>
-        <span class="n">curved_radius</span><span class="o">=</span><span class="n">curved_radius</span><span class="p">,</span>
-        <span class="n">label_fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-        <span class="n">link_label_fontsize</span><span class="o">=</span><span class="n">link_label_fontsize</span><span class="p">,</span>
-        <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span><span class="p">,</span>
-        <span class="n">show_colorbar</span><span class="o">=</span><span class="n">show_colorbar</span><span class="p">,</span>
-        <span class="c1"># label_fraction=label_fraction,</span>
-        <span class="n">special_nodes</span><span class="o">=</span><span class="n">special_nodes</span><span class="p">,</span>
-        <span class="n">autodep_sig_lags</span><span class="o">=</span><span class="n">autodep_sig_lags</span><span class="p">,</span>
-        <span class="n">show_autodependency_lags</span><span class="o">=</span><span class="n">show_autodependency_lags</span>
-    <span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">save_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_name</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
-
-
-<span class="k">def</span> <span class="nf">_reverse_patt</span><span class="p">(</span><span class="n">patt</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Inverts a link pattern&quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">patt</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="s2">&quot;&quot;</span>
-
-    <span class="n">left_mark</span><span class="p">,</span> <span class="n">middle_mark</span><span class="p">,</span> <span class="n">right_mark</span> <span class="o">=</span> <span class="n">patt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">patt</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">patt</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">left_mark</span> <span class="o">==</span> <span class="s2">&quot;&lt;&quot;</span><span class="p">:</span>
-        <span class="n">new_right_mark</span> <span class="o">=</span> <span class="s2">&quot;&gt;&quot;</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">new_right_mark</span> <span class="o">=</span> <span class="n">left_mark</span>
-    <span class="k">if</span> <span class="n">right_mark</span> <span class="o">==</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span>
-        <span class="n">new_left_mark</span> <span class="o">=</span> <span class="s2">&quot;&lt;&quot;</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">new_left_mark</span> <span class="o">=</span> <span class="n">right_mark</span>
-
-    <span class="k">return</span> <span class="n">new_left_mark</span> <span class="o">+</span> <span class="n">middle_mark</span> <span class="o">+</span> <span class="n">new_right_mark</span>
-
-    <span class="c1"># if patt in [&#39;---&#39;, &#39;o--&#39;, &#39;--o&#39;, &#39;o-o&#39;, &#39;&#39;]:</span>
-    <span class="c1">#     return patt[::-1]</span>
-    <span class="c1"># elif patt == &#39;&lt;-&gt;&#39;:</span>
-    <span class="c1">#     return &#39;&lt;-&gt;&#39;</span>
-    <span class="c1"># elif patt == &#39;o-&gt;&#39;:</span>
-    <span class="c1">#     return &#39;&lt;-o&#39;</span>
-    <span class="c1"># elif patt == &#39;&lt;-o&#39;:</span>
-    <span class="c1">#     return &#39;o-&gt;&#39;</span>
-    <span class="c1"># elif patt == &#39;--&gt;&#39;:</span>
-    <span class="c1">#     return &#39;&lt;--&#39;</span>
-    <span class="c1"># elif patt == &#39;&lt;--&#39;:</span>
-    <span class="c1">#     return &#39;--&gt;&#39;</span>
-
-
-<span class="k">def</span> <span class="nf">_check_matrices</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">,</span> <span class="n">link_width</span><span class="p">,</span> <span class="n">link_attribute</span><span class="p">):</span>
-
-    <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="s2">&quot;&lt;U3&quot;</span><span class="p">:</span>
-        <span class="c1"># Transform to new graph data type U3</span>
-        <span class="n">old_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">old_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;&lt;U3&quot;</span><span class="p">)</span>
-        <span class="n">graph</span><span class="p">[:]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">old_matrix</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">old_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;o-o&quot;</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;o-o&quot;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-    <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span>
-                <span class="s2">&quot;---&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o-o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o-&gt;&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;-o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--&gt;&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-&gt;&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;-+&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;+-&gt;&quot;</span><span class="p">,</span>
-            <span class="p">]:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid graph entry.&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">!=</span> <span class="n">_reverse_patt</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s2">&quot;graph needs to have consistent entries: &quot;</span>
-                    <span class="s2">&quot;graph[i, j, taui, tauj] == _reverse_patt(graph[j, i, tauj, taui])&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="p">(</span>
-                <span class="n">val_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                <span class="ow">and</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">!=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]</span>
-            <span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s2">&quot;val_matrix needs to have consistent entries: &quot;</span>
-                    <span class="s2">&quot;val_matrix[i, j, taui, tauj] == val_matrix[j, i, tauj, taui]&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="p">(</span>
-                <span class="n">link_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                <span class="ow">and</span> <span class="n">link_width</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">!=</span> <span class="n">link_width</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]</span>
-            <span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s2">&quot;link_width needs to have consistent entries: &quot;</span>
-                    <span class="s2">&quot;link_width[i, j, taui, tauj] == link_width[j, i, tauj, taui]&quot;</span><span class="p">)</span>            
-            <span class="k">if</span> <span class="p">(</span>
-                <span class="n">link_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                <span class="ow">and</span> <span class="n">link_attribute</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">!=</span> <span class="n">link_attribute</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tauj</span><span class="p">,</span> <span class="n">taui</span><span class="p">]</span>
-            <span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s2">&quot;link_attribute needs to have consistent entries: &quot;</span>
-                    <span class="s2">&quot;link_attribute[i, j, taui, tauj] == link_attribute[j, i, tauj, taui]&quot;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="c1"># print(graph[:,:,0])</span>
-        <span class="c1"># Assert that graph has valid and consistent lag-zero entries</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">_reverse_patt</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]):</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                        <span class="s2">&quot;graph needs to have consistent lag-zero links, but &quot;</span>
-                        <span class="s2">&quot; graph[</span><span class="si">%d</span><span class="s2">,</span><span class="si">%d</span><span class="s2">,0]=</span><span class="si">%s</span><span class="s2"> and graph[</span><span class="si">%d</span><span class="s2">,</span><span class="si">%d</span><span class="s2">,0]=</span><span class="si">%s</span><span class="s2">)&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
-                    <span class="p">)</span>
-                <span class="k">if</span> <span class="p">(</span>
-                    <span class="n">val_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                    <span class="ow">and</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-                <span class="p">):</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;val_matrix needs to be symmetric for lag-zero&quot;</span><span class="p">)</span>
-                <span class="k">if</span> <span class="p">(</span>
-                    <span class="n">link_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                    <span class="ow">and</span> <span class="n">link_width</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">link_width</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-                <span class="p">):</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;link_width needs to be symmetric for lag-zero&quot;</span><span class="p">)</span>
-                <span class="k">if</span> <span class="p">(</span>
-                    <span class="n">link_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                    <span class="ow">and</span> <span class="n">link_attribute</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">link_attribute</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-                <span class="p">):</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                        <span class="s2">&quot;link_attribute needs to be symmetric for lag-zero&quot;</span>
-                    <span class="p">)</span>
-
-            <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span>
-                <span class="s2">&quot;---&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o-o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o-&gt;&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;-o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--&gt;&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;-&gt;&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-o&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;o-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x--&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;--x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-&gt;&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;x-x&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;&lt;-+&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;+-&gt;&quot;</span><span class="p">,</span>
-            <span class="p">]:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid graph entry.&quot;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">val_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="c1"># if graph.ndim == 4:</span>
-        <span class="c1">#     val_matrix = (graph != &quot;&quot;).astype(&quot;int&quot;)</span>
-        <span class="c1"># else:</span>
-            <span class="n">val_matrix</span> <span class="o">=</span> <span class="p">(</span><span class="n">graph</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;int&quot;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">link_width</span> <span class="o">&gt;=</span> <span class="mf">0.0</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;link_width must be non-negative&quot;</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">graph</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">,</span> <span class="n">link_width</span><span class="p">,</span> <span class="n">link_attribute</span>
-
-
-<div class="viewcode-block" id="plot_time_series_graph"><a class="viewcode-back" href="../../index.html#tigramite.plotting.plot_time_series_graph">[docs]</a><span class="k">def</span> <span class="nf">plot_time_series_graph</span><span class="p">(</span>
-    <span class="n">graph</span><span class="p">,</span>
-    <span class="n">val_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">fig_ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_colorbar_label</span><span class="o">=</span><span class="s2">&quot;MCI&quot;</span><span class="p">,</span>
-    <span class="n">save_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_attribute</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">arrow_linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-    <span class="n">vmin_edges</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">vmax_edges</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">edge_ticks</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
-    <span class="n">cmap_edges</span><span class="o">=</span><span class="s2">&quot;RdBu_r&quot;</span><span class="p">,</span>
-    <span class="n">order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">node_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-    <span class="n">node_aspect</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">arrowhead_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
-    <span class="n">curved_radius</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-    <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">label_space_left</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-    <span class="n">label_space_top</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">inner_edge_style</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span>
-    <span class="n">link_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">special_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="c1"># aux_graph=None,</span>
-    <span class="n">standard_color_links</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-    <span class="n">standard_color_nodes</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a time series graph.</span>
-<span class="sd">    This is still in beta. The time series graph&#39;s links are colored by</span>
-<span class="sd">    val_matrix.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    graph : string or bool array-like, optional (default: None)</span>
-<span class="sd">        Either string matrix providing graph or bool array providing only adjacencies</span>
-<span class="sd">        Either of shape (N, N, tau_max + 1) or as auxiliary graph of dims </span>
-<span class="sd">        (N, N, tau_max+1, tau_max+1) describing auxADMG. </span>
-<span class="sd">    val_matrix : array_like</span>
-<span class="sd">        Matrix of same shape as graph containing test statistic values.</span>
-<span class="sd">    var_names : list, optional (default: None)</span>
-<span class="sd">        List of variable names. If None, range(N) is used.</span>
-<span class="sd">    fig_ax : tuple of figure and axis object, optional (default: None)</span>
-<span class="sd">        Figure and axes instance. If None they are created.</span>
-<span class="sd">    figsize : tuple</span>
-<span class="sd">        Size of figure.</span>
-<span class="sd">    save_name : str, optional (default: None)</span>
-<span class="sd">        Name of figure file to save figure. If None, figure is shown in window.</span>
-<span class="sd">    link_colorbar_label : str, optional (default: &#39;MCI&#39;)</span>
-<span class="sd">        Test statistic label.</span>
-<span class="sd">    link_width : array-like, optional (default: None)</span>
-<span class="sd">        Array of val_matrix.shape specifying relative link width with maximum</span>
-<span class="sd">        given by arrow_linewidth. If None, all links have same width.</span>
-<span class="sd">    link_attribute : array-like, optional (default: None)</span>
-<span class="sd">        Array of graph.shape specifying specific in drawing the graph (for internal use).</span>
-<span class="sd">    order : list, optional (default: None)</span>
-<span class="sd">        order of variables from top to bottom.</span>
-<span class="sd">    arrow_linewidth : float, optional (default: 30)</span>
-<span class="sd">        Linewidth.</span>
-<span class="sd">    vmin_edges : float, optional (default: -1)</span>
-<span class="sd">        Link colorbar scale lower bound.</span>
-<span class="sd">    vmax_edges : float, optional (default: 1)</span>
-<span class="sd">        Link colorbar scale upper bound.</span>
-<span class="sd">    edge_ticks : float, optional (default: 0.4)</span>
-<span class="sd">        Link tick mark interval.</span>
-<span class="sd">    cmap_edges : str, optional (default: &#39;RdBu_r&#39;)</span>
-<span class="sd">        Colormap for links.</span>
-<span class="sd">    node_size : int, optional (default: 0.1)</span>
-<span class="sd">        Node size.</span>
-<span class="sd">    node_aspect : float, optional (default: None)</span>
-<span class="sd">        Ratio between the heigth and width of the varible nodes.</span>
-<span class="sd">    arrowhead_size : int, optional (default: 20)</span>
-<span class="sd">        Size of link arrow head. Passed on to FancyArrowPatch object.</span>
-<span class="sd">    curved_radius, float, optional (default: 0.2)</span>
-<span class="sd">        Curvature of links. Passed on to FancyArrowPatch object.</span>
-<span class="sd">    label_fontsize : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of colorbar labels.</span>
-<span class="sd">    alpha : float, optional (default: 1.)</span>
-<span class="sd">        Opacity.</span>
-<span class="sd">    node_label_size : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of node labels.</span>
-<span class="sd">    link_label_fontsize : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of link labels.</span>
-<span class="sd">    tick_label_size : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of tick labels.</span>
-<span class="sd">    label_space_left : float, optional (default: 0.1)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate left of plot for labels.</span>
-<span class="sd">    label_space_top : float, optional (default: 0.)</span>
-<span class="sd">        Fraction of vertical figure space to allocate top of plot for labels.</span>
-<span class="sd">    network_lower_bound : float, optional (default: 0.2)</span>
-<span class="sd">        Fraction of vertical space below graph plot.</span>
-<span class="sd">    inner_edge_style : string, optional (default: &#39;dashed&#39;)</span>
-<span class="sd">        Style of inner_edge contemporaneous links.</span>
-<span class="sd">    special_nodes : dict</span>
-<span class="sd">        Dictionary of format {(i, -tau): &#39;blue&#39;, ...} to color special nodes.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">link_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;link_matrix is deprecated and replaced by graph array&quot;</span>
-                         <span class="s2">&quot; which is now returned by all methods.&quot;</span><span class="p">)</span>
-        
-    <span class="k">if</span> <span class="n">fig_ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">fig</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">frame_on</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">fig_ax</span>
-
-    <span class="k">if</span> <span class="n">val_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">no_coloring</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="n">cmap_edges</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">no_coloring</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">,</span> <span class="n">link_width</span><span class="p">,</span> <span class="n">link_attribute</span><span class="p">)</span> <span class="o">=</span> <span class="n">_check_matrices</span><span class="p">(</span>
-        <span class="n">graph</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">,</span> <span class="n">link_width</span><span class="p">,</span> <span class="n">link_attribute</span>
-    <span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
-        <span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">dummy</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">tau_max</span> <span class="o">=</span> <span class="n">dummy</span> <span class="o">-</span> <span class="mi">1</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">dummy</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">tau_max</span> <span class="o">=</span> <span class="n">dummy</span> <span class="o">-</span> <span class="mi">1</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">graph</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="n">graph</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
-            <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;---&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;---&quot;</span>
-        <span class="n">no_links</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">no_links</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">if</span> <span class="n">var_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">var_names</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">order</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">order</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;order must be a permutation of range(N)&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">translate</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">lag</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">row</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">lag</span>
-
-    <span class="c1"># Define graph links by absolute maximum (positive or negative like for</span>
-    <span class="c1"># partial correlation)</span>
-    <span class="n">tsg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">))</span>
-    <span class="n">tsg_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">))</span>
-    <span class="n">tsg_width</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">))</span>
-    <span class="n">tsg_style</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">graph</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">link_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">tsg_attr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">link_attribute</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
-        <span class="c1"># 4-dimensional graphs represent the finite-time window projection of stationary 3-d graphs</span>
-        <span class="c1"># They are internally created in some classes</span>
-        <span class="c1"># Only draw link in one direction</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">)):</span>
-            <span class="n">tau</span> <span class="o">=</span> <span class="n">taui</span> <span class="o">-</span> <span class="n">tauj</span>
-            <span class="c1"># if tau &lt;= 0 and j &lt;= i:</span>
-            <span class="k">if</span> <span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span>   <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">taui</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="n">tauj</span><span class="p">):</span>
-                <span class="k">continue</span>
-            <span class="c1"># print(max_lag, (i, -taui), (j, -tauj), aux_graph[i, j, taui, tauj])</span>
-            <span class="c1"># print(translate(i, max_lag - 1 - taui), translate(j, max_lag-1-tauj))</span>
-            <span class="n">tsg</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span>   <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">taui</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="n">tauj</span><span class="p">)]</span> <span class="o">=</span> <span class="mf">1.0</span>
-            <span class="n">tsg_val</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span>   <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">taui</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="n">tauj</span><span class="p">)]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span>
-            <span class="n">tsg_style</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span>   <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">taui</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="n">tauj</span><span class="p">)]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">tsg_width</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span>   <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">taui</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="n">tauj</span><span class="p">)]</span> <span class="o">=</span> <span class="n">link_width</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="o">/</span> <span class="n">link_width</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="n">arrow_linewidth</span>
-            <span class="k">if</span> <span class="n">link_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">tsg_attr</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span>   <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">taui</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="n">tauj</span><span class="p">)]</span> <span class="o">=</span> <span class="n">link_attribute</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">taui</span><span class="p">,</span> <span class="n">tauj</span><span class="p">]</span> <span class="c1">#&#39;spurious&#39;</span>
-        <span class="c1"># print(tsg_style)   </span>
-            <span class="c1"># print(tsg_style[translate(i,   max_lag - 1 - taui), translate(j, max_lag-1-tauj)] = graph[i, j, taui, tauj])    </span>
-            <span class="c1"># print(max_lag, (i, -taui), (j, -tauj), graph[i, j, taui, tauj], tsg_style[translate(i,   max_lag - 1 - taui), translate(j, max_lag-1-tauj)])</span>
- 
-
-    <span class="k">else</span><span class="p">:</span>
-      <span class="c1"># Only draw link in one direction</span>
-      <span class="c1"># Remove lower triangle</span>
-      <span class="n">link_matrix_tsg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-      <span class="n">link_matrix_tsg</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">triu</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])</span>
-
-      <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">link_matrix_tsg</span><span class="p">)):</span>
-        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_lag</span><span class="p">):</span>
-            <span class="k">if</span> <span class="p">(</span>
-                <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span>
-                <span class="ow">and</span> <span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span> <span class="o">%</span> <span class="n">max_lag</span> <span class="o">&lt;=</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span> <span class="o">%</span> <span class="n">max_lag</span>
-            <span class="p">):</span>
-
-                <span class="n">tsg</span><span class="p">[</span>
-                    <span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
-                <span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>  <span class="c1"># val_matrix[i, j, tau]</span>
-                <span class="n">tsg_val</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">)]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span>
-                <span class="n">tsg_style</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">)]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span>
-                    <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span>
-                <span class="p">]</span>
-                <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">tsg_width</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">)]</span> <span class="o">=</span> <span class="p">(</span>
-                        <span class="n">link_width</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">]</span> <span class="o">/</span> <span class="n">link_width</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="n">arrow_linewidth</span>
-                    <span class="p">)</span>
-                <span class="k">if</span> <span class="n">link_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">tsg_attr</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">),</span> <span class="n">translate</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">)]</span> <span class="o">=</span> <span class="n">link_attribute</span><span class="p">[</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span>
-                    <span class="p">]</span>
-
-
-    <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">(</span><span class="n">tsg</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">special_nodes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">special_nodes_tsg</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">special_nodes</span><span class="p">:</span>
-            <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">node</span>
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="n">tau_max</span><span class="p">:</span>
-                <span class="n">special_nodes_tsg</span><span class="p">[</span><span class="n">translate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">-</span><span class="mi">1</span> <span class="o">+</span> <span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">special_nodes</span><span class="p">[</span><span class="n">node</span><span class="p">]</span>
-
-        <span class="n">special_nodes</span> <span class="o">=</span> <span class="n">special_nodes_tsg</span>
-
-    <span class="c1"># node_color = np.zeros(N)</span>
-    <span class="c1"># list of all strengths for color map</span>
-    <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="c1"># Add attributes, contemporaneous and lagged links are handled separately</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">dic</span><span class="p">)</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;no_links&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">no_links</span>
-        <span class="k">if</span> <span class="n">u</span> <span class="o">!=</span> <span class="n">v</span><span class="p">:</span>
-            <span class="c1"># tau = np.abs((u - v) % max_lag)</span>
-            <span class="c1"># Determine neighbors in TSG</span>
-            <span class="n">i</span> <span class="o">=</span> <span class="n">u</span> <span class="o">//</span> <span class="n">max_lag</span>
-            <span class="n">taui</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">u</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">))</span>
-            <span class="n">j</span> <span class="o">=</span> <span class="n">v</span> <span class="o">//</span> <span class="n">max_lag</span>
-            <span class="n">tauj</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">v</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">))</span>
-
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">i</span><span class="o">-</span><span class="n">j</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">tauj</span><span class="o">-</span><span class="n">taui</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">inout</span> <span class="o">=</span> <span class="s1">&#39;inner&#39;</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">inout</span> <span class="o">=</span> <span class="s1">&#39;outer&#39;</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_type&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="n">tsg_style</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_alpha&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
-
-            <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="c1"># fraction of nonzero values</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_width&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_width&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrow_linewidth</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_width&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_width&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="n">tsg_width</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span>
-
-            <span class="k">if</span> <span class="n">link_attribute</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_attribute&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_attribute&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="n">tsg_attr</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span>
-
-            <span class="c1"># value at argmax of average</span>
-            <span class="k">if</span> <span class="n">no_coloring</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_color&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_color&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">]</span> <span class="o">=</span> <span class="n">tsg_val</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span>
-
-            <span class="n">all_strengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dic</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_edge_color&quot;</span> <span class="o">%</span> <span class="n">inout</span><span class="p">])</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="c1"># print(u, v, dic)</span>
-
-    <span class="c1"># If no links are present, set value to zero</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_strengths</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">]</span>
-
-    <span class="n">posarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">):</span>
-        <span class="n">posarray</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="n">i</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">),</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">i</span> <span class="o">//</span> <span class="n">max_lag</span><span class="p">)])</span>
-
-    <span class="n">pos_tmp</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">):</span>
-        <span class="c1"># for n in range(N):</span>
-        <span class="c1">#     for tau in range(max_lag):</span>
-        <span class="c1">#         i = n*N + tau</span>
-        <span class="n">pos_tmp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-            <span class="p">[</span>
-                <span class="p">((</span><span class="n">i</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">)</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="o">/</span> <span class="p">(</span><span class="n">posarray</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]),</span>
-                <span class="p">((</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">i</span> <span class="o">//</span> <span class="n">max_lag</span><span class="p">)</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
-                <span class="o">/</span> <span class="p">(</span><span class="n">posarray</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]),</span>
-            <span class="p">]</span>
-        <span class="p">)</span>
-        <span class="n">pos_tmp</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">pos_tmp</span><span class="p">[</span><span class="n">i</span><span class="p">])]</span> <span class="o">=</span> <span class="mf">0.0</span>
-
-    <span class="n">pos</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_lag</span><span class="p">):</span>
-            <span class="n">pos</span><span class="p">[</span><span class="n">n</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="n">pos_tmp</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">]</span>
-
-    <span class="n">node_rings</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="mi">0</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;sizes&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;color_array&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="s2">&quot;colorbar&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,}</span>
-    <span class="p">}</span>
-
-    <span class="n">node_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">)]</span>
-
-    <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span> <span class="ow">and</span> <span class="n">val_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">show_colorbar</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">show_colorbar</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="n">_draw_network_with_curved_edges</span><span class="p">(</span>
-        <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-        <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">G</span><span class="o">=</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">G</span><span class="p">),</span>
-        <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span>
-        <span class="n">node_rings</span><span class="o">=</span><span class="n">node_rings</span><span class="p">,</span>
-        <span class="n">node_labels</span><span class="o">=</span><span class="n">node_labels</span><span class="p">,</span>
-        <span class="c1"># node_label_size=node_label_size,</span>
-        <span class="n">node_alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-        <span class="n">standard_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
-        <span class="n">node_aspect</span><span class="o">=</span><span class="n">node_aspect</span><span class="p">,</span>
-        <span class="n">standard_cmap</span><span class="o">=</span><span class="s2">&quot;OrRd&quot;</span><span class="p">,</span>
-        <span class="n">standard_color_nodes</span><span class="o">=</span><span class="n">standard_color_nodes</span><span class="p">,</span>
-        <span class="n">standard_color_links</span><span class="o">=</span><span class="n">standard_color_links</span><span class="p">,</span>
-        <span class="n">log_sizes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">cmap_links</span><span class="o">=</span><span class="n">cmap_edges</span><span class="p">,</span>
-        <span class="n">links_vmin</span><span class="o">=</span><span class="n">vmin_edges</span><span class="p">,</span>
-        <span class="n">links_vmax</span><span class="o">=</span><span class="n">vmax_edges</span><span class="p">,</span>
-        <span class="n">links_ticks</span><span class="o">=</span><span class="n">edge_ticks</span><span class="p">,</span>
-        <span class="c1"># link_label_fontsize=link_label_fontsize,</span>
-        <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;simple&quot;</span><span class="p">,</span>
-        <span class="n">arrowhead_size</span><span class="o">=</span><span class="n">arrowhead_size</span><span class="p">,</span>
-        <span class="n">curved_radius</span><span class="o">=</span><span class="n">curved_radius</span><span class="p">,</span>
-        <span class="n">label_fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-        <span class="n">tick_label_size</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">,</span>
-        <span class="n">label_fraction</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-        <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
-        <span class="n">inner_edge_curved</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span><span class="p">,</span>
-        <span class="n">network_left_bound</span><span class="o">=</span><span class="n">label_space_left</span><span class="p">,</span>
-        <span class="n">inner_edge_style</span><span class="o">=</span><span class="n">inner_edge_style</span><span class="p">,</span>
-        <span class="n">special_nodes</span><span class="o">=</span><span class="n">special_nodes</span><span class="p">,</span>
-        <span class="n">show_colorbar</span><span class="o">=</span><span class="n">show_colorbar</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">transData</span><span class="p">)</span>
-        <span class="c1"># trans = transforms.blended_transform_factory(fig.transFigure, ax.transData)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-            <span class="mf">0.</span><span class="p">,</span>
-            <span class="n">pos</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
-            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">var_names</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
-            <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-            <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
-            <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-            <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-        <span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
-        <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">transData</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span>
-        <span class="c1"># trans = transforms.blended_transform_factory(ax.transData, fig.transFigure)</span>
-        <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span><span class="p">,</span> <span class="c1"># - label_space_top,</span>
-                <span class="sa">r</span><span class="s2">&quot;$t$&quot;</span><span class="p">,</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">label_fontsize</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">),</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span><span class="p">,</span> <span class="c1"># - label_space_top,</span>
-                <span class="sa">r</span><span class="s2">&quot;$t-</span><span class="si">%s</span><span class="s2">$&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">label_fontsize</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">),</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-            <span class="p">)</span>
-
-    <span class="c1"># pyplot.tight_layout()</span>
-    <span class="k">if</span> <span class="n">save_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_name</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
-
-
-<div class="viewcode-block" id="plot_mediation_time_series_graph"><a class="viewcode-back" href="../../index.html#tigramite.plotting.plot_mediation_time_series_graph">[docs]</a><span class="k">def</span> <span class="nf">plot_mediation_time_series_graph</span><span class="p">(</span>
-    <span class="n">path_node_array</span><span class="p">,</span>
-    <span class="n">tsg_path_val_matrix</span><span class="p">,</span>
-    <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">fig_ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_colorbar_label</span><span class="o">=</span><span class="s2">&quot;link coeff. (edge color)&quot;</span><span class="p">,</span>
-    <span class="n">node_colorbar_label</span><span class="o">=</span><span class="s2">&quot;MCE (node color)&quot;</span><span class="p">,</span>
-    <span class="n">save_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">arrow_linewidth</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-    <span class="n">vmin_edges</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">vmax_edges</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">edge_ticks</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
-    <span class="n">cmap_edges</span><span class="o">=</span><span class="s2">&quot;RdBu_r&quot;</span><span class="p">,</span>
-    <span class="n">order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">vmin_nodes</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">vmax_nodes</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">node_ticks</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
-    <span class="n">cmap_nodes</span><span class="o">=</span><span class="s2">&quot;RdBu_r&quot;</span><span class="p">,</span>
-    <span class="n">node_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-    <span class="n">node_aspect</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">arrowhead_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
-    <span class="n">curved_radius</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-    <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">node_label_size</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-    <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-    <span class="n">label_space_left</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-    <span class="n">label_space_top</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">standard_color_links</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-    <span class="n">standard_color_nodes</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a mediation time series graph plot.</span>
-<span class="sd">    This is still in beta. The time series graph&#39;s links are colored by</span>
-<span class="sd">    val_matrix.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    tsg_path_val_matrix : array_like</span>
-<span class="sd">        Matrix of shape (N*tau_max, N*tau_max) containing link weight values.</span>
-<span class="sd">    path_node_array: array_like</span>
-<span class="sd">        Array of shape (N,) containing node values.</span>
-<span class="sd">    var_names : list, optional (default: None)</span>
-<span class="sd">        List of variable names. If None, range(N) is used.</span>
-<span class="sd">    fig_ax : tuple of figure and axis object, optional (default: None)</span>
-<span class="sd">        Figure and axes instance. If None they are created.</span>
-<span class="sd">    figsize : tuple</span>
-<span class="sd">        Size of figure.</span>
-<span class="sd">    save_name : str, optional (default: None)</span>
-<span class="sd">        Name of figure file to save figure. If None, figure is shown in window.</span>
-<span class="sd">    link_colorbar_label : str, optional (default: &#39;link coeff. (edge color)&#39;)</span>
-<span class="sd">        Link colorbar label.</span>
-<span class="sd">    node_colorbar_label : str, optional (default: &#39;MCE (node color)&#39;)</span>
-<span class="sd">        Node colorbar label.</span>
-<span class="sd">    link_width : array-like, optional (default: None)</span>
-<span class="sd">        Array of val_matrix.shape specifying relative link width with maximum</span>
-<span class="sd">        given by arrow_linewidth. If None, all links have same width.</span>
-<span class="sd">    order : list, optional (default: None)</span>
-<span class="sd">        order of variables from top to bottom.</span>
-<span class="sd">    arrow_linewidth : float, optional (default: 30)</span>
-<span class="sd">        Linewidth.</span>
-<span class="sd">    vmin_edges : float, optional (default: -1)</span>
-<span class="sd">        Link colorbar scale lower bound.</span>
-<span class="sd">    vmax_edges : float, optional (default: 1)</span>
-<span class="sd">        Link colorbar scale upper bound.</span>
-<span class="sd">    edge_ticks : float, optional (default: 0.4)</span>
-<span class="sd">        Link tick mark interval.</span>
-<span class="sd">    cmap_edges : str, optional (default: &#39;RdBu_r&#39;)</span>
-<span class="sd">        Colormap for links.</span>
-<span class="sd">    vmin_nodes : float, optional (default: 0)</span>
-<span class="sd">        Node colorbar scale lower bound.</span>
-<span class="sd">    vmax_nodes : float, optional (default: 1)</span>
-<span class="sd">        Node colorbar scale upper bound.</span>
-<span class="sd">    node_ticks : float, optional (default: 0.4)</span>
-<span class="sd">        Node tick mark interval.</span>
-<span class="sd">    cmap_nodes : str, optional (default: &#39;OrRd&#39;)</span>
-<span class="sd">        Colormap for links.</span>
-<span class="sd">    node_size : int, optional (default: 0.1)</span>
-<span class="sd">        Node size.</span>
-<span class="sd">    node_aspect : float, optional (default: None)</span>
-<span class="sd">        Ratio between the heigth and width of the varible nodes.</span>
-<span class="sd">    arrowhead_size : int, optional (default: 20)</span>
-<span class="sd">        Size of link arrow head. Passed on to FancyArrowPatch object.</span>
-<span class="sd">    curved_radius, float, optional (default: 0.2)</span>
-<span class="sd">        Curvature of links. Passed on to FancyArrowPatch object.</span>
-<span class="sd">    label_fontsize : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of colorbar labels.</span>
-<span class="sd">    alpha : float, optional (default: 1.)</span>
-<span class="sd">        Opacity.</span>
-<span class="sd">    node_label_size : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of node labels.</span>
-<span class="sd">    link_label_fontsize : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of link labels.</span>
-<span class="sd">    label_space_left : float, optional (default: 0.1)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate left of plot for labels.</span>
-<span class="sd">    label_space_top : float, optional (default: 0.)</span>
-<span class="sd">        Fraction of vertical figure space to allocate top of plot for labels.</span>
-<span class="sd">    network_lower_bound : float, optional (default: 0.2)</span>
-<span class="sd">        Fraction of vertical space below graph plot.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">path_node_array</span><span class="p">)</span>
-    <span class="n">Nmaxlag</span> <span class="o">=</span> <span class="n">tsg_path_val_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">max_lag</span> <span class="o">=</span> <span class="n">Nmaxlag</span> <span class="o">//</span> <span class="n">N</span>
-
-    <span class="k">if</span> <span class="n">var_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">var_names</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">fig_ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">fig</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">frame_on</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">fig_ax</span>
-
-    <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">link_width</span> <span class="o">&gt;=</span> <span class="mf">0.0</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;link_width must be non-negative&quot;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">order</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">order</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;order must be a permutation of range(N)&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">translate</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">lag</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">row</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">lag</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">tsg_path_val_matrix</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="n">tsg_path_val_matrix</span><span class="p">)</span>
-    <span class="p">):</span>
-        <span class="n">diagonal</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">diagonal</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">tsg_path_val_matrix</span><span class="p">)</span> <span class="o">==</span> <span class="n">tsg_path_val_matrix</span><span class="o">.</span><span class="n">size</span> <span class="ow">or</span> <span class="n">diagonal</span><span class="p">:</span>
-        <span class="n">tsg_path_val_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">no_links</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">no_links</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="c1"># Define graph links by absolute maximum (positive or negative like for</span>
-    <span class="c1"># partial correlation)</span>
-    <span class="n">tsg</span> <span class="o">=</span> <span class="n">tsg_path_val_matrix</span>
-    <span class="n">tsg_attr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">))</span>
-
-    <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">(</span><span class="n">tsg</span><span class="p">)</span>
-
-    <span class="c1"># node_color = np.zeros(N)</span>
-    <span class="c1"># list of all strengths for color map</span>
-    <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="c1"># Add attributes, contemporaneous and lagged links are handled separately</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">dic</span><span class="p">)</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;no_links&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">no_links</span>
-        <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">if</span> <span class="n">u</span> <span class="o">!=</span> <span class="n">v</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="n">u</span> <span class="o">%</span> <span class="n">max_lag</span> <span class="o">==</span> <span class="n">v</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">_get_absmax</span><span class="p">(</span>
-                <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="n">tsg</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">],</span> <span class="n">tsg</span><span class="p">[</span><span class="n">v</span><span class="p">,</span> <span class="n">u</span><span class="p">]]]])</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrow_linewidth</span>
-            <span class="n">all_strengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">])</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrow_linewidth</span>
-
-            <span class="c1"># value at argmax of average</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tsg</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span>
-            <span class="n">all_strengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">])</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="c1"># dic[&#39;outer_edge_edge&#39;] = False</span>
-        <span class="c1"># dic[&#39;outer_edge_edgecolor&#39;] = None</span>
-        <span class="c1"># dic[&#39;inner_edge_edge&#39;] = False</span>
-        <span class="c1"># dic[&#39;inner_edge_edgecolor&#39;] = None</span>
-
-    <span class="c1"># If no links are present, set value to zero</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_strengths</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">]</span>
-
-    <span class="n">posarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">):</span>
-        <span class="n">posarray</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="n">i</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">),</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">i</span> <span class="o">//</span> <span class="n">max_lag</span><span class="p">)])</span>
-
-    <span class="n">pos_tmp</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">):</span>
-        <span class="c1"># for n in range(N):</span>
-        <span class="c1">#     for tau in range(max_lag):</span>
-        <span class="c1">#         i = n*N + tau</span>
-        <span class="n">pos_tmp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-            <span class="p">[</span>
-                <span class="p">((</span><span class="n">i</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">)</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="o">/</span> <span class="p">(</span><span class="n">posarray</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]),</span>
-                <span class="p">((</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">i</span> <span class="o">//</span> <span class="n">max_lag</span><span class="p">)</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
-                <span class="o">/</span> <span class="p">(</span><span class="n">posarray</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]),</span>
-            <span class="p">]</span>
-        <span class="p">)</span>
-        <span class="n">pos_tmp</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">pos_tmp</span><span class="p">[</span><span class="n">i</span><span class="p">])]</span> <span class="o">=</span> <span class="mf">0.0</span>
-
-    <span class="n">pos</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_lag</span><span class="p">):</span>
-            <span class="n">pos</span><span class="p">[</span><span class="n">n</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="n">pos_tmp</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">]</span>
-
-    <span class="n">node_color</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">inet</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">)):</span>
-        <span class="n">node_color</span><span class="p">[</span><span class="n">n</span> <span class="p">:</span> <span class="n">n</span> <span class="o">+</span> <span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">path_node_array</span><span class="p">[</span><span class="n">inet</span><span class="p">]</span>
-
-    <span class="c1"># node_rings = {0: {&#39;sizes&#39;: None, &#39;color_array&#39;: color_array,</span>
-    <span class="c1">#                   &#39;label&#39;: &#39;&#39;, &#39;colorbar&#39;: False,</span>
-    <span class="c1">#                   }</span>
-    <span class="c1">#               }</span>
-
-    <span class="n">node_rings</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="mi">0</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;sizes&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
-            <span class="s2">&quot;color_array&quot;</span><span class="p">:</span> <span class="n">node_color</span><span class="p">,</span>
-            <span class="s2">&quot;cmap&quot;</span><span class="p">:</span> <span class="n">cmap_nodes</span><span class="p">,</span>
-            <span class="s2">&quot;vmin&quot;</span><span class="p">:</span> <span class="n">vmin_nodes</span><span class="p">,</span>
-            <span class="s2">&quot;vmax&quot;</span><span class="p">:</span> <span class="n">vmax_nodes</span><span class="p">,</span>
-            <span class="s2">&quot;ticks&quot;</span><span class="p">:</span> <span class="n">node_ticks</span><span class="p">,</span>
-            <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="n">node_colorbar_label</span><span class="p">,</span>
-            <span class="s2">&quot;colorbar&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-        <span class="p">}</span>
-    <span class="p">}</span>
-
-    <span class="c1"># ] for v in range(max_lag)]</span>
-    <span class="n">node_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">)]</span>
-
-    <span class="n">_draw_network_with_curved_edges</span><span class="p">(</span>
-        <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-        <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">G</span><span class="o">=</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">G</span><span class="p">),</span>
-        <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span>
-        <span class="c1"># dictionary of rings: {0:{&#39;sizes&#39;:(N,)-array, &#39;color_array&#39;:(N,)-array</span>
-        <span class="c1"># or None, &#39;cmap&#39;:string,</span>
-        <span class="n">node_rings</span><span class="o">=</span><span class="n">node_rings</span><span class="p">,</span>
-        <span class="c1"># &#39;vmin&#39;:float or None, &#39;vmax&#39;:float or None, &#39;label&#39;:string or None}}</span>
-        <span class="n">node_labels</span><span class="o">=</span><span class="n">node_labels</span><span class="p">,</span>
-        <span class="n">node_label_size</span><span class="o">=</span><span class="n">node_label_size</span><span class="p">,</span>
-        <span class="n">node_alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-        <span class="n">standard_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
-        <span class="n">node_aspect</span><span class="o">=</span><span class="n">node_aspect</span><span class="p">,</span>
-        <span class="n">standard_cmap</span><span class="o">=</span><span class="s2">&quot;OrRd&quot;</span><span class="p">,</span>
-        <span class="n">standard_color_nodes</span><span class="o">=</span><span class="n">standard_color_nodes</span><span class="p">,</span>
-        <span class="n">standard_color_links</span><span class="o">=</span><span class="n">standard_color_links</span><span class="p">,</span>
-        <span class="n">log_sizes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">cmap_links</span><span class="o">=</span><span class="n">cmap_edges</span><span class="p">,</span>
-        <span class="n">links_vmin</span><span class="o">=</span><span class="n">vmin_edges</span><span class="p">,</span>
-        <span class="n">links_vmax</span><span class="o">=</span><span class="n">vmax_edges</span><span class="p">,</span>
-        <span class="n">links_ticks</span><span class="o">=</span><span class="n">edge_ticks</span><span class="p">,</span>
-        <span class="n">tick_label_size</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">,</span>
-        <span class="c1"># cmap_links_edges=&#39;YlOrRd&#39;, links_edges_vmin=-1., links_edges_vmax=1.,</span>
-        <span class="c1"># links_edges_ticks=.2, link_edge_colorbar_label=&#39;link_edge&#39;,</span>
-        <span class="n">arrowhead_size</span><span class="o">=</span><span class="n">arrowhead_size</span><span class="p">,</span>
-        <span class="n">curved_radius</span><span class="o">=</span><span class="n">curved_radius</span><span class="p">,</span>
-        <span class="n">label_fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-        <span class="n">label_fraction</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-        <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
-        <span class="n">inner_edge_curved</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span>
-        <span class="c1"># inner_edge_style=inner_edge_style</span>
-    <span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">transData</span><span class="p">)</span>
-        <span class="c1"># trans = transforms.blended_transform_factory(fig.transFigure, ax.transData)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-            <span class="n">label_space_left</span><span class="p">,</span>
-            <span class="n">pos</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
-            <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]]),</span>
-            <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-            <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
-            <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-            <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-        <span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
-        <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">transData</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span>
-        <span class="c1"># trans = transforms.blended_transform_factory(ax.transData, fig.transFigure)</span>
-        <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label_space_top</span><span class="p">,</span>
-                <span class="sa">r</span><span class="s2">&quot;$t$&quot;</span><span class="p">,</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label_space_top</span><span class="p">,</span>
-                <span class="sa">r</span><span class="s2">&quot;$t-</span><span class="si">%s</span><span class="s2">$&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-            <span class="p">)</span>
-
-    <span class="c1"># fig.subplots_adjust(left=0.1, right=.98, bottom=.25, top=.9)</span>
-    <span class="c1"># savestring = os.path.expanduser(save_name)</span>
-    <span class="k">if</span> <span class="n">save_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_name</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
-
-
-<div class="viewcode-block" id="plot_mediation_graph"><a class="viewcode-back" href="../../index.html#tigramite.plotting.plot_mediation_graph">[docs]</a><span class="k">def</span> <span class="nf">plot_mediation_graph</span><span class="p">(</span>
-    <span class="n">path_val_matrix</span><span class="p">,</span>
-    <span class="n">path_node_array</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">fig_ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">save_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_colorbar_label</span><span class="o">=</span><span class="s2">&quot;link coeff. (edge color)&quot;</span><span class="p">,</span>
-    <span class="n">node_colorbar_label</span><span class="o">=</span><span class="s2">&quot;MCE (node color)&quot;</span><span class="p">,</span>
-    <span class="n">link_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">node_pos</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">arrow_linewidth</span><span class="o">=</span><span class="mf">10.0</span><span class="p">,</span>
-    <span class="n">vmin_edges</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">vmax_edges</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">edge_ticks</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
-    <span class="n">cmap_edges</span><span class="o">=</span><span class="s2">&quot;RdBu_r&quot;</span><span class="p">,</span>
-    <span class="n">vmin_nodes</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">vmax_nodes</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">node_ticks</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
-    <span class="n">cmap_nodes</span><span class="o">=</span><span class="s2">&quot;RdBu_r&quot;</span><span class="p">,</span>
-    <span class="n">node_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
-    <span class="n">node_aspect</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">arrowhead_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
-    <span class="n">curved_radius</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-    <span class="n">lag_array</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">node_label_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">link_label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">standard_color_links</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-    <span class="n">standard_color_nodes</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a network plot visualizing the pathways of a mediation analysis.</span>
-<span class="sd">    This is still in beta. The network is defined from non-zero entries in</span>
-<span class="sd">    ``path_val_matrix``.  Nodes denote variables, straight links contemporaneous</span>
-<span class="sd">    dependencies and curved arrows lagged dependencies. The node color denotes</span>
-<span class="sd">    the mediated causal effect (MCE) and the link color the value at the lag</span>
-<span class="sd">    with maximal link coefficient. The link label lists the lags with</span>
-<span class="sd">    significant dependency in order of absolute magnitude. The network can also</span>
-<span class="sd">    be plotted over a map drawn before on the same axis. Then the node positions</span>
-<span class="sd">    can be supplied in appropriate axis coordinates via node_pos.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    path_val_matrix : array_like</span>
-<span class="sd">        Matrix of shape (N, N, tau_max+1) containing link weight values.</span>
-<span class="sd">    path_node_array: array_like</span>
-<span class="sd">        Array of shape (N,) containing node values.</span>
-<span class="sd">    var_names : list, optional (default: None)</span>
-<span class="sd">        List of variable names. If None, range(N) is used.</span>
-<span class="sd">    fig_ax : tuple of figure and axis object, optional (default: None)</span>
-<span class="sd">        Figure and axes instance. If None they are created.</span>
-<span class="sd">    figsize : tuple</span>
-<span class="sd">        Size of figure.</span>
-<span class="sd">    save_name : str, optional (default: None)</span>
-<span class="sd">        Name of figure file to save figure. If None, figure is shown in window.</span>
-<span class="sd">    link_colorbar_label : str, optional (default: &#39;link coeff. (edge color)&#39;)</span>
-<span class="sd">        Link colorbar label.</span>
-<span class="sd">    node_colorbar_label : str, optional (default: &#39;MCE (node color)&#39;)</span>
-<span class="sd">        Node colorbar label.</span>
-<span class="sd">    link_width : array-like, optional (default: None)</span>
-<span class="sd">        Array of val_matrix.shape specifying relative link width with maximum</span>
-<span class="sd">        given by arrow_linewidth. If None, all links have same width.</span>
-<span class="sd">    node_pos : dictionary, optional (default: None)</span>
-<span class="sd">        Dictionary of node positions in axis coordinates of form</span>
-<span class="sd">        node_pos = {&#39;x&#39;:array of shape (N,), &#39;y&#39;:array of shape(N)}. These</span>
-<span class="sd">        coordinates could have been transformed before for basemap plots.</span>
-<span class="sd">    arrow_linewidth : float, optional (default: 30)</span>
-<span class="sd">        Linewidth.</span>
-<span class="sd">    vmin_edges : float, optional (default: -1)</span>
-<span class="sd">        Link colorbar scale lower bound.</span>
-<span class="sd">    vmax_edges : float, optional (default: 1)</span>
-<span class="sd">        Link colorbar scale upper bound.</span>
-<span class="sd">    edge_ticks : float, optional (default: 0.4)</span>
-<span class="sd">        Link tick mark interval.</span>
-<span class="sd">    cmap_edges : str, optional (default: &#39;RdBu_r&#39;)</span>
-<span class="sd">        Colormap for links.</span>
-<span class="sd">    vmin_nodes : float, optional (default: 0)</span>
-<span class="sd">        Node colorbar scale lower bound.</span>
-<span class="sd">    vmax_nodes : float, optional (default: 1)</span>
-<span class="sd">        Node colorbar scale upper bound.</span>
-<span class="sd">    node_ticks : float, optional (default: 0.4)</span>
-<span class="sd">        Node tick mark interval.</span>
-<span class="sd">    cmap_nodes : str, optional (default: &#39;OrRd&#39;)</span>
-<span class="sd">        Colormap for links.</span>
-<span class="sd">    node_size : int, optional (default: 0.3)</span>
-<span class="sd">        Node size.</span>
-<span class="sd">    node_aspect : float, optional (default: None)</span>
-<span class="sd">        Ratio between the heigth and width of the varible nodes.</span>
-<span class="sd">    arrowhead_size : int, optional (default: 20)</span>
-<span class="sd">        Size of link arrow head. Passed on to FancyArrowPatch object.</span>
-<span class="sd">    curved_radius, float, optional (default: 0.2)</span>
-<span class="sd">        Curvature of links. Passed on to FancyArrowPatch object.</span>
-<span class="sd">    label_fontsize : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of colorbar labels.</span>
-<span class="sd">    alpha : float, optional (default: 1.)</span>
-<span class="sd">        Opacity.</span>
-<span class="sd">    node_label_size : int, optional (default: 10)</span>
-<span class="sd">        Fontsize of node labels.</span>
-<span class="sd">    link_label_fontsize : int, optional (default: 6)</span>
-<span class="sd">        Fontsize of link labels.</span>
-<span class="sd">    network_lower_bound : float, optional (default: 0.2)</span>
-<span class="sd">        Fraction of vertical space below graph plot.</span>
-<span class="sd">    lag_array : array, optional (default: None)</span>
-<span class="sd">        Optional specification of lags overwriting np.arange(0, tau_max+1)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">path_val_matrix</span>
-
-    <span class="k">if</span> <span class="n">fig_ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">fig</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">frame_on</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">fig_ax</span>
-
-    <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">link_width</span> <span class="o">&gt;=</span> <span class="mf">0.0</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;link_width must be non-negative&quot;</span><span class="p">)</span>
-
-    <span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">dummy</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="o">.</span><span class="n">shape</span>
-    <span class="n">tau_max</span> <span class="o">=</span> <span class="n">dummy</span> <span class="o">-</span> <span class="mi">1</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">)):</span>
-        <span class="n">diagonal</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">diagonal</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">)</span> <span class="o">==</span> <span class="n">val_matrix</span><span class="o">.</span><span class="n">size</span> <span class="ow">or</span> <span class="n">diagonal</span><span class="p">:</span>
-        <span class="n">val_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">no_links</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">no_links</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">if</span> <span class="n">var_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">var_names</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-    <span class="c1"># Define graph links by absolute maximum (positive or negative like for</span>
-    <span class="c1"># partial correlation)</span>
-    <span class="c1"># val_matrix[np.abs(val_matrix) &lt; sig_thres] = 0.</span>
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">val_matrix</span> <span class="o">!=</span> <span class="mf">0.0</span>
-    <span class="n">net</span> <span class="o">=</span> <span class="n">_get_absmax</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">)</span>
-    <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">(</span><span class="n">net</span><span class="p">)</span>
-
-    <span class="n">node_color</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-    <span class="c1"># list of all strengths for color map</span>
-    <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="c1"># Add attributes, contemporaneous and lagged links are handled separately</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">dic</span><span class="p">)</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;no_links&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">no_links</span>
-        <span class="c1"># average lagfunc for link u --&gt; v ANDOR u -- v</span>
-        <span class="k">if</span> <span class="n">tau_max</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="c1"># argmax of absolute maximum</span>
-            <span class="n">argmax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="mi">1</span><span class="p">:])</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">argmax</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">if</span> <span class="n">u</span> <span class="o">!=</span> <span class="n">v</span><span class="p">:</span>
-            <span class="c1"># For contemp links masking or finite samples can lead to different</span>
-            <span class="c1"># values for u--v and v--u</span>
-            <span class="c1"># Here we use the  maximum for the width and weight (=color)</span>
-            <span class="c1"># of the link</span>
-            <span class="c1"># Draw link if u--v OR v--u at lag 0 is nonzero</span>
-            <span class="c1"># dic[&#39;inner_edge&#39;] = ((np.abs(val_matrix[u, v][0]) &gt;=</span>
-            <span class="c1">#                       sig_thres[u, v][0]) or</span>
-            <span class="c1">#                      (np.abs(val_matrix[v, u][0]) &gt;=</span>
-            <span class="c1">#                       sig_thres[v, u][0]))</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="ow">or</span> <span class="n">graph</span><span class="p">[</span><span class="n">v</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
-            <span class="c1"># value at argmax of average</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">v</span><span class="p">,</span> <span class="n">u</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mf">0.0001</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span>
-                    <span class="s2">&quot;Contemporaneous I(</span><span class="si">%d</span><span class="s2">; </span><span class="si">%d</span><span class="s2">)=</span><span class="si">%.3f</span><span class="s2"> != I(</span><span class="si">%d</span><span class="s2">; </span><span class="si">%d</span><span class="s2">)=</span><span class="si">%.3f</span><span class="s2">&quot;</span>
-                    <span class="o">%</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">v</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">v</span><span class="p">,</span> <span class="n">u</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-                    <span class="o">+</span> <span class="s2">&quot; due to conditions, finite sample effects or &quot;</span>
-                    <span class="s2">&quot;masking, here edge color = &quot;</span>
-                    <span class="s2">&quot;larger (absolute) value.&quot;</span>
-                <span class="p">)</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">_get_absmax</span><span class="p">(</span>
-                <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">v</span><span class="p">,</span> <span class="n">u</span><span class="p">][</span><span class="mi">0</span><span class="p">]]]])</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-            <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrow_linewidth</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="n">link_width</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">link_width</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="n">arrow_linewidth</span>
-                <span class="p">)</span>
-
-            <span class="n">all_strengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">])</span>
-
-            <span class="k">if</span> <span class="n">tau_max</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="c1"># True if ensemble mean at lags &gt; 0 is nonzero</span>
-                <span class="c1"># dic[&#39;outer_edge&#39;] = np.any(</span>
-                <span class="c1">#     np.abs(val_matrix[u, v][1:]) &gt;= sig_thres[u, v][1:])</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">1</span><span class="p">:])</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
-            <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="c1"># fraction of nonzero values</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrow_linewidth</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="n">link_width</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">argmax</span><span class="p">]</span> <span class="o">/</span> <span class="n">link_width</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="n">arrow_linewidth</span>
-                <span class="p">)</span>
-
-            <span class="c1"># value at argmax of average</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="n">argmax</span><span class="p">]</span>
-            <span class="n">all_strengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">])</span>
-
-            <span class="c1"># Sorted list of significant lags (only if robust wrt</span>
-            <span class="c1"># d[&#39;min_ensemble_frac&#39;])</span>
-            <span class="k">if</span> <span class="n">tau_max</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">lags</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="mi">1</span><span class="p">:])</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
-                <span class="n">sig_lags</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="mi">1</span><span class="p">:])[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">lags</span><span class="p">,</span> <span class="n">sig_lags</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="n">lag_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">([</span><span class="n">lag_array</span><span class="p">[</span><span class="n">l</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">lags</span> <span class="k">if</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">sig_lags</span><span class="p">])[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">([</span><span class="n">l</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">lags</span> <span class="k">if</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">sig_lags</span><span class="p">])[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Node color is max of average autodependency</span>
-            <span class="n">node_color</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">][</span><span class="n">argmax</span><span class="p">]</span>
-
-        <span class="c1"># dic[&#39;outer_edge_edge&#39;] = False</span>
-        <span class="c1"># dic[&#39;outer_edge_edgecolor&#39;] = None</span>
-        <span class="c1"># dic[&#39;inner_edge_edge&#39;] = False</span>
-        <span class="c1"># dic[&#39;inner_edge_edgecolor&#39;] = None</span>
-
-    <span class="n">node_color</span> <span class="o">=</span> <span class="n">path_node_array</span>
-    <span class="c1"># print node_color</span>
-    <span class="c1"># If no links are present, set value to zero</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_strengths</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">]</span>
-
-    <span class="k">if</span> <span class="n">node_pos</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">circular_layout</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">G</span><span class="p">))</span>
-    <span class="c1">#            pos = nx.spring_layout(deepcopy(G))</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">pos</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="n">pos</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">node_pos</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">node_pos</span><span class="p">[</span><span class="s2">&quot;y&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="n">node_rings</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="mi">0</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;sizes&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
-            <span class="s2">&quot;color_array&quot;</span><span class="p">:</span> <span class="n">node_color</span><span class="p">,</span>
-            <span class="s2">&quot;cmap&quot;</span><span class="p">:</span> <span class="n">cmap_nodes</span><span class="p">,</span>
-            <span class="s2">&quot;vmin&quot;</span><span class="p">:</span> <span class="n">vmin_nodes</span><span class="p">,</span>
-            <span class="s2">&quot;vmax&quot;</span><span class="p">:</span> <span class="n">vmax_nodes</span><span class="p">,</span>
-            <span class="s2">&quot;ticks&quot;</span><span class="p">:</span> <span class="n">node_ticks</span><span class="p">,</span>
-            <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="n">node_colorbar_label</span><span class="p">,</span>
-            <span class="s2">&quot;colorbar&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-        <span class="p">}</span>
-    <span class="p">}</span>
-
-    <span class="n">_draw_network_with_curved_edges</span><span class="p">(</span>
-        <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-        <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">G</span><span class="o">=</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">G</span><span class="p">),</span>
-        <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span>
-        <span class="c1"># dictionary of rings: {0:{&#39;sizes&#39;:(N,)-array, &#39;color_array&#39;:(N,)-array</span>
-        <span class="c1"># or None, &#39;cmap&#39;:string,</span>
-        <span class="n">node_rings</span><span class="o">=</span><span class="n">node_rings</span><span class="p">,</span>
-        <span class="c1"># &#39;vmin&#39;:float or None, &#39;vmax&#39;:float or None, &#39;label&#39;:string or None}}</span>
-        <span class="n">node_labels</span><span class="o">=</span><span class="n">var_names</span><span class="p">,</span>
-        <span class="n">node_label_size</span><span class="o">=</span><span class="n">node_label_size</span><span class="p">,</span>
-        <span class="n">node_alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-        <span class="n">standard_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
-        <span class="n">node_aspect</span><span class="o">=</span><span class="n">node_aspect</span><span class="p">,</span>
-        <span class="n">standard_cmap</span><span class="o">=</span><span class="s2">&quot;OrRd&quot;</span><span class="p">,</span>
-        <span class="n">standard_color_nodes</span><span class="o">=</span><span class="n">standard_color_nodes</span><span class="p">,</span>
-        <span class="n">standard_color_links</span><span class="o">=</span><span class="n">standard_color_links</span><span class="p">,</span>
-        <span class="n">log_sizes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">cmap_links</span><span class="o">=</span><span class="n">cmap_edges</span><span class="p">,</span>
-        <span class="n">links_vmin</span><span class="o">=</span><span class="n">vmin_edges</span><span class="p">,</span>
-        <span class="n">links_vmax</span><span class="o">=</span><span class="n">vmax_edges</span><span class="p">,</span>
-        <span class="n">links_ticks</span><span class="o">=</span><span class="n">edge_ticks</span><span class="p">,</span>
-        <span class="n">tick_label_size</span><span class="o">=</span><span class="n">tick_label_size</span><span class="p">,</span>
-        <span class="c1"># cmap_links_edges=&#39;YlOrRd&#39;, links_edges_vmin=-1., links_edges_vmax=1.,</span>
-        <span class="c1"># links_edges_ticks=.2, link_edge_colorbar_label=&#39;link_edge&#39;,</span>
-        <span class="n">arrowhead_size</span><span class="o">=</span><span class="n">arrowhead_size</span><span class="p">,</span>
-        <span class="n">curved_radius</span><span class="o">=</span><span class="n">curved_radius</span><span class="p">,</span>
-        <span class="n">label_fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-        <span class="n">link_label_fontsize</span><span class="o">=</span><span class="n">link_label_fontsize</span><span class="p">,</span>
-        <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span><span class="p">,</span>
-        <span class="c1"># label_fraction=label_fraction,</span>
-        <span class="c1"># inner_edge_style=inner_edge_style</span>
-    <span class="p">)</span>
-
-    <span class="c1"># fig.subplots_adjust(left=0.1, right=.9, bottom=.25, top=.95)</span>
-    <span class="c1"># savestring = os.path.expanduser(save_name)</span>
-    <span class="k">if</span> <span class="n">save_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_name</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
-
-
-<span class="c1">#</span>
-<span class="c1">#  Functions to plot time series graphs from links including ancestors</span>
-<span class="c1">#</span>
-<div class="viewcode-block" id="plot_tsg"><a class="viewcode-back" href="../../index.html#tigramite.plotting.plot_tsg">[docs]</a><span class="k">def</span> <span class="nf">plot_tsg</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">anc_x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">anc_y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">anc_xy</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Plots TSG that is input in format (N*max_lag, N*max_lag).</span>
-<span class="sd">    Compared to the tigramite plotting function here links</span>
-<span class="sd">    X^i_{t-tau} --&gt; X^j_t can be missing for different t&#39;. Helpful to</span>
-<span class="sd">    visualize the conditioned TSG.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">varlag2node</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Translate from (var, lag) notation to node in TSG.</span>
-<span class="sd">        lag must be &lt;= 0.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">var</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">lag</span>
-
-    <span class="k">def</span> <span class="nf">node2varlag</span><span class="p">(</span><span class="n">node</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Translate from node in TSG to (var, -tau) notation.</span>
-<span class="sd">        Here tau is &lt;= 0.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">var</span> <span class="o">=</span> <span class="n">node</span> <span class="o">//</span> <span class="n">max_lag</span>
-        <span class="n">tau</span> <span class="o">=</span> <span class="n">node</span> <span class="o">%</span> <span class="p">(</span><span class="n">max_lag</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">var</span><span class="p">,</span> <span class="n">tau</span>
-
-    <span class="k">def</span> <span class="nf">_get_minmax_lag</span><span class="p">(</span><span class="n">links</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to retrieve tau_min and tau_max from links</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-        <span class="c1"># Get maximum time lag</span>
-        <span class="n">min_lag</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="c1"># func = link_props[2]</span>
-                <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">min_lag</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">min_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-                    <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">min_lag</span><span class="p">,</span> <span class="n">max_lag</span>
-
-    <span class="k">def</span> <span class="nf">_links_to_tsg</span><span class="p">(</span><span class="n">link_coeffs</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Transform link_coeffs to time series graph.</span>
-<span class="sd">        TSG is of shape (N*max_lag, N*max_lag).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_coeffs</span><span class="p">)</span>
-
-        <span class="c1"># Get maximum lag</span>
-        <span class="n">min_lag_links</span><span class="p">,</span> <span class="n">max_lag_links</span> <span class="o">=</span> <span class="n">_get_minmax_lag</span><span class="p">(</span><span class="n">link_coeffs</span><span class="p">)</span>
-
-        <span class="c1"># max_lag of TSG is max lag in links + 1 for the zero lag.</span>
-        <span class="k">if</span> <span class="n">max_lag</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="n">max_lag_links</span> <span class="o">+</span> <span class="mi">1</span>
-
-        <span class="n">tsg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">link_coeffs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="n">i</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">tau</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">)</span>
-                <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="c1"># func = link_props[2]</span>
-                <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.0</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_lag</span><span class="p">):</span>
-                        <span class="k">if</span> <span class="p">(</span>
-                            <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">varlag2node</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span>
-                            <span class="ow">and</span> <span class="n">varlag2node</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span> <span class="o">%</span> <span class="n">max_lag</span>
-                            <span class="o">&lt;=</span> <span class="n">varlag2node</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span> <span class="o">%</span> <span class="n">max_lag</span>
-                        <span class="p">):</span>
-                            <span class="n">tsg</span><span class="p">[</span><span class="n">varlag2node</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="o">-</span> <span class="n">tau</span><span class="p">),</span> <span class="n">varlag2node</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">)]</span> <span class="o">=</span> <span class="mf">1.0</span>
-
-        <span class="k">return</span> <span class="n">tsg</span>
-
-    <span class="n">color_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;lightgrey&quot;</span><span class="p">,</span> <span class="s2">&quot;grey&quot;</span><span class="p">,</span> <span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="s2">&quot;orange&quot;</span><span class="p">]</span>
-    <span class="n">listcmap</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">(</span><span class="n">color_list</span><span class="p">)</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-    <span class="n">min_lag_links</span><span class="p">,</span> <span class="n">max_lag_links</span> <span class="o">=</span> <span class="n">_get_minmax_lag</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-    <span class="n">max_lag</span> <span class="o">=</span> <span class="n">max_lag_links</span>
-
-    <span class="k">for</span> <span class="n">anc</span> <span class="ow">in</span> <span class="n">X</span> <span class="o">+</span> <span class="n">Y</span><span class="p">:</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">anc</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-    <span class="k">for</span> <span class="n">anc</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">:</span>
-        <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">anc</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-    <span class="k">if</span> <span class="n">Z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">anc</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">anc</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-
-    <span class="k">if</span> <span class="n">anc_x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">anc</span> <span class="ow">in</span> <span class="n">anc_x</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">anc</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-    <span class="k">if</span> <span class="n">anc_y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">anc</span> <span class="ow">in</span> <span class="n">anc_y</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">anc</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-    <span class="k">if</span> <span class="n">anc_xy</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">anc</span> <span class="ow">in</span> <span class="n">anc_xy</span><span class="p">:</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">anc</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-
-    <span class="n">max_lag</span> <span class="o">=</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span>
-
-    <span class="n">tsg</span> <span class="o">=</span> <span class="n">_links_to_tsg</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">max_lag</span><span class="p">)</span>
-
-    <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">(</span><span class="n">tsg</span><span class="p">)</span>
-
-    <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-    <span class="n">link_colorbar_label</span> <span class="o">=</span> <span class="s2">&quot;MCI&quot;</span>
-    <span class="n">arrow_linewidth</span> <span class="o">=</span> <span class="mf">8.0</span>
-    <span class="n">vmin_edges</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-    <span class="n">vmax_edges</span> <span class="o">=</span> <span class="mf">1.0</span>
-    <span class="n">edge_ticks</span> <span class="o">=</span> <span class="mf">0.4</span>
-    <span class="n">cmap_edges</span> <span class="o">=</span> <span class="s2">&quot;RdBu_r&quot;</span>
-    <span class="n">order</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">node_size</span> <span class="o">=</span> <span class="mf">.1</span>
-    <span class="n">arrowhead_size</span> <span class="o">=</span> <span class="mi">20</span>
-    <span class="n">curved_radius</span> <span class="o">=</span> <span class="mf">0.2</span>
-    <span class="n">label_fontsize</span> <span class="o">=</span> <span class="mi">10</span>
-    <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span>
-    <span class="n">node_label_size</span> <span class="o">=</span> <span class="mi">10</span>
-    <span class="n">label_space_left</span> <span class="o">=</span> <span class="mf">0.1</span>
-    <span class="n">label_space_top</span> <span class="o">=</span> <span class="mf">0.0</span>
-    <span class="n">network_lower_bound</span> <span class="o">=</span> <span class="mf">0.2</span>
-    <span class="n">inner_edge_style</span> <span class="o">=</span> <span class="s2">&quot;dashed&quot;</span>
-
-    <span class="n">node_color</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">)</span>  <span class="c1"># , dtype = &#39;object&#39;)</span>
-    <span class="n">node_color</span><span class="p">[:]</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">if</span> <span class="n">anc_x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="p">[</span><span class="n">varlag2node</span><span class="p">(</span><span class="n">itau</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">itau</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">itau</span> <span class="ow">in</span> <span class="n">anc_x</span><span class="p">]:</span>
-            <span class="n">node_color</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>
-    <span class="k">if</span> <span class="n">anc_y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="p">[</span><span class="n">varlag2node</span><span class="p">(</span><span class="n">itau</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">itau</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">itau</span> <span class="ow">in</span> <span class="n">anc_y</span><span class="p">]:</span>
-            <span class="n">node_color</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span>
-    <span class="k">if</span> <span class="n">anc_xy</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="p">[</span><span class="n">varlag2node</span><span class="p">(</span><span class="n">itau</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">itau</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">itau</span> <span class="ow">in</span> <span class="n">anc_xy</span><span class="p">]:</span>
-            <span class="n">node_color</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mi">5</span>
-
-    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">:</span>
-        <span class="n">node_color</span><span class="p">[</span><span class="n">varlag2node</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">2</span>
-    <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">Y</span><span class="p">:</span>
-        <span class="n">node_color</span><span class="p">[</span><span class="n">varlag2node</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">2</span>
-    <span class="k">if</span> <span class="n">Z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">Z</span><span class="p">:</span>
-            <span class="n">node_color</span><span class="p">[</span><span class="n">varlag2node</span><span class="p">(</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">1</span>
-
-    <span class="n">fig</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">frame_on</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">var_names</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-    <span class="n">order</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-    <span class="c1"># list of all strengths for color map</span>
-    <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="c1"># Add attributes, contemporaneous and lagged links are handled separately</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">dic</span><span class="p">)</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">u</span> <span class="o">!=</span> <span class="n">v</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">tsg</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="ow">and</span> <span class="n">tsg</span><span class="p">[</span><span class="n">v</span><span class="p">,</span> <span class="n">u</span><span class="p">]:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-                <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tsg</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span>
-
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrow_linewidth</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_attribute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-
-            <span class="n">all_strengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_color&quot;</span><span class="p">])</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_alpha&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;inner_edge_width&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrow_linewidth</span>
-
-            <span class="c1"># value at argmax of average</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tsg</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span>
-
-            <span class="n">all_strengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dic</span><span class="p">[</span><span class="s2">&quot;outer_edge_color&quot;</span><span class="p">])</span>
-            <span class="n">dic</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="c1"># If no links are present, set value to zero</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_strengths</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">all_strengths</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">]</span>
-
-    <span class="n">posarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">):</span>
-        <span class="n">posarray</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="n">i</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">),</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">i</span> <span class="o">//</span> <span class="n">max_lag</span><span class="p">)])</span>
-
-    <span class="n">pos_tmp</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">):</span>
-        <span class="n">pos_tmp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-            <span class="p">[</span>
-                <span class="p">((</span><span class="n">i</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">)</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="o">/</span> <span class="p">(</span><span class="n">posarray</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]),</span>
-                <span class="p">((</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">i</span> <span class="o">//</span> <span class="n">max_lag</span><span class="p">)</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
-                <span class="o">/</span> <span class="p">(</span><span class="n">posarray</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">posarray</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]),</span>
-            <span class="p">]</span>
-        <span class="p">)</span>
-        <span class="n">pos_tmp</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">pos_tmp</span><span class="p">[</span><span class="n">i</span><span class="p">])]</span> <span class="o">=</span> <span class="mf">0.0</span>
-
-    <span class="n">pos</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_lag</span><span class="p">):</span>
-            <span class="n">pos</span><span class="p">[</span><span class="n">n</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">]</span> <span class="o">=</span> <span class="n">pos_tmp</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_lag</span> <span class="o">+</span> <span class="n">tau</span><span class="p">]</span>
-
-    <span class="n">node_rings</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="mi">0</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;sizes&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
-            <span class="s2">&quot;color_array&quot;</span><span class="p">:</span> <span class="n">node_color</span><span class="p">,</span>
-            <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;colorbar&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-            <span class="s2">&quot;cmap&quot;</span><span class="p">:</span> <span class="n">listcmap</span><span class="p">,</span>
-            <span class="s2">&quot;vmin&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
-            <span class="s2">&quot;vmax&quot;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">color_list</span><span class="p">),</span>
-        <span class="p">}</span>
-    <span class="p">}</span>
-
-    <span class="n">node_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">)]</span>
-
-    <span class="n">_draw_network_with_curved_edges</span><span class="p">(</span>
-        <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-        <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">G</span><span class="o">=</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">G</span><span class="p">),</span>
-        <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span>
-        <span class="n">node_rings</span><span class="o">=</span><span class="n">node_rings</span><span class="p">,</span>
-        <span class="n">node_labels</span><span class="o">=</span><span class="n">node_labels</span><span class="p">,</span>
-        <span class="n">node_label_size</span><span class="o">=</span><span class="n">node_label_size</span><span class="p">,</span>
-        <span class="n">node_alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-        <span class="n">standard_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
-        <span class="n">node_aspect</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">standard_cmap</span><span class="o">=</span><span class="s2">&quot;OrRd&quot;</span><span class="p">,</span>
-        <span class="n">standard_color_links</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-        <span class="n">standard_color_nodes</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
-        <span class="n">log_sizes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">cmap_links</span><span class="o">=</span><span class="n">cmap_edges</span><span class="p">,</span>
-        <span class="n">links_vmin</span><span class="o">=</span><span class="n">vmin_edges</span><span class="p">,</span>
-        <span class="n">links_vmax</span><span class="o">=</span><span class="n">vmax_edges</span><span class="p">,</span>
-        <span class="n">links_ticks</span><span class="o">=</span><span class="n">edge_ticks</span><span class="p">,</span>
-        <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;simple&quot;</span><span class="p">,</span>
-        <span class="n">arrowhead_size</span><span class="o">=</span><span class="n">arrowhead_size</span><span class="p">,</span>
-        <span class="n">curved_radius</span><span class="o">=</span><span class="n">curved_radius</span><span class="p">,</span>
-        <span class="n">label_fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-        <span class="n">label_fraction</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-        <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
-        <span class="n">inner_edge_curved</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span><span class="p">,</span>
-        <span class="n">inner_edge_style</span><span class="o">=</span><span class="n">inner_edge_style</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">transData</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-            <span class="n">label_space_left</span><span class="p">,</span>
-            <span class="n">pos</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
-            <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]]),</span>
-            <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
-            <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
-            <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-            <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-        <span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
-        <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">transData</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label_space_top</span><span class="p">,</span>
-                <span class="sa">r</span><span class="s2">&quot;$t$&quot;</span><span class="p">,</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">label_fontsize</span> <span class="o">*</span> <span class="mf">0.7</span><span class="p">),</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-                <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label_space_top</span><span class="p">,</span>
-                <span class="sa">r</span><span class="s2">&quot;$t-</span><span class="si">%s</span><span class="s2">$&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">fontsize</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">label_fontsize</span> <span class="o">*</span> <span class="mf">0.7</span><span class="p">),</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
-                <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">trans</span><span class="p">,</span>
-            <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
-
-<div class="viewcode-block" id="write_csv"><a class="viewcode-back" href="../../index.html#tigramite.plotting.write_csv">[docs]</a><span class="k">def</span> <span class="nf">write_csv</span><span class="p">(</span>
-    <span class="n">graph</span><span class="p">,</span>
-    <span class="n">save_name</span><span class="p">,</span>
-    <span class="n">val_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">link_attribute</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">digits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Writes all links in a graph to a csv file.</span>
-<span class="sd">    </span>
-<span class="sd">    Format is each link in a row as &#39;Variable i&#39;, &#39;Variable j&#39;, &#39;Time lag of i&#39;, &#39;Link type i --- j&#39;,</span>
-<span class="sd">    with optional further columns for entries in [val_matrix link_attribute, link_width].</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    graph : string or bool array-like, optional (default: None)</span>
-<span class="sd">        Either string matrix providing graph or bool array providing only adjacencies</span>
-<span class="sd">        Must be of same shape as val_matrix. </span>
-<span class="sd">    save_name : str</span>
-<span class="sd">        Name of figure file to save figure. If None, figure is shown in window.</span>
-<span class="sd">    val_matrix : array_like</span>
-<span class="sd">        Matrix of shape (N, N, tau_max+1) containing test statistic values.</span>
-<span class="sd">    var_names : list, optional (default: None)</span>
-<span class="sd">        List of variable names. If None, range(N) is used.</span>
-<span class="sd">    link_width : array-like, optional (default: None)</span>
-<span class="sd">        Array of val_matrix.shape specifying relative link width with maximum</span>
-<span class="sd">        given by arrow_linewidth. If None, all links have same width.</span>
-<span class="sd">    link_attribute : array-like, optional (default: None)</span>
-<span class="sd">        String array of val_matrix.shape specifying link attributes.</span>
-<span class="sd">    digits : int</span>
-<span class="sd">        Number of significant digits for writing link value and width.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">val_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">val_matrix_exists</span> <span class="o">=</span> <span class="n">false</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">val_matrix_exists</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Time series graph of shape (N,N,tau_max+1,tau_max+1) cannot be represented by plot_graph,&quot;</span>
-                         <span class="s2">&quot; use plot_time_series_graph instead.&quot;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">graph</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="c1"># If a non-time series (N,N)-graph is given, insert a dummy dimension</span>
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
-
-    <span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">,</span> <span class="n">link_width</span><span class="p">,</span> <span class="n">link_attribute</span><span class="p">)</span> <span class="o">=</span> <span class="n">_check_matrices</span><span class="p">(</span>
-        <span class="n">graph</span><span class="p">,</span> <span class="n">val_matrix</span><span class="p">,</span> <span class="n">link_width</span><span class="p">,</span> <span class="n">link_attribute</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">var_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">var_names</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-
-    <span class="n">header</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Variable i&#39;</span><span class="p">,</span> <span class="s1">&#39;Variable j&#39;</span><span class="p">,</span> <span class="s1">&#39;Time lag of i&#39;</span><span class="p">,</span> <span class="s1">&#39;Link type i --- j&#39;</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">val_matrix_exists</span><span class="p">:</span>
-        <span class="n">header</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;Link value&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">link_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">header</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;Link attribute&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">header</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;Link width&#39;</span><span class="p">)</span>
-
-
-    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">save_name</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;UTF8&#39;</span><span class="p">,</span> <span class="n">newline</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
-        <span class="n">writer</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">writer</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
-
-        <span class="c1"># write the header</span>
-        <span class="n">writer</span><span class="o">.</span><span class="n">writerow</span><span class="p">(</span><span class="n">header</span><span class="p">)</span>
-
-        <span class="c1"># write the link data</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="o">!=</span><span class="s1">&#39;&#39;</span><span class="p">)):</span>
-            <span class="c1"># Only consider contemporaneous links once</span>
-            <span class="k">if</span> <span class="n">tau</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;=</span> <span class="n">j</span><span class="p">:</span>
-                <span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">tau</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">]]</span>
-                <span class="k">if</span> <span class="n">val_matrix_exists</span><span class="p">:</span>
-                    <span class="n">row</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">]</span><span class="si">:</span><span class="s2">.</span><span class="si">{</span><span class="n">digits</span><span class="si">}}</span><span class="s2">&quot;</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">link_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">row</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">link_attribute</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">])</span>
-                <span class="k">if</span> <span class="n">link_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">row</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">link_width</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">]</span><span class="si">:</span><span class="s2">.</span><span class="si">{</span><span class="n">digits</span><span class="si">}}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-                <span class="n">writer</span><span class="o">.</span><span class="n">writerow</span><span class="p">(</span><span class="n">row</span><span class="p">)</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="kn">import</span> <span class="nn">sys</span>
-    <span class="n">matplotlib</span><span class="o">.</span><span class="n">rc</span><span class="p">(</span><span class="s1">&#39;xtick&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span> 
-    <span class="n">matplotlib</span><span class="o">.</span><span class="n">rc</span><span class="p">(</span><span class="s1">&#39;ytick&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span> 
-
-    <span class="c1"># Consider some toy data</span>
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">import</span> <span class="nn">tigramite.toymodels.structural_causal_processes</span> <span class="k">as</span> <span class="nn">toys</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">from</span> <span class="nn">tigramite.causal_effects</span> <span class="kn">import</span> <span class="n">CausalEffects</span>
-
-
-    <span class="c1"># T = 1000</span>
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-    <span class="c1"># auto_coeff = 0.3</span>
-    <span class="c1"># coeff = 1.</span>
-    <span class="c1"># links = {</span>
-    <span class="c1">#         0: [((0, -1), auto_coeff, lin_f)], </span>
-    <span class="c1">#         1: [((1, -1), auto_coeff, lin_f), ((0, 0), coeff, lin_f)], </span>
-    <span class="c1">#         2: [((2, -1), auto_coeff, lin_f), ((1, 0), coeff, lin_f)],</span>
-    <span class="c1">#         }</span>
-    <span class="c1"># data, nonstat = toys.structural_causal_process(links, T=T, </span>
-    <span class="c1">#                             noises=None, seed=7)</span>
-    <span class="c1"># dataframe = pp.DataFrame(data, var_names=range(len(links)))</span>
-
-    <span class="c1"># links = {</span>
-    <span class="c1">#         0: [((0, -1), 1.5*auto_coeff, lin_f)], </span>
-    <span class="c1">#         1: [((1, -1), 1.5*auto_coeff, lin_f), ((0, 0), 1.5*coeff, lin_f)], </span>
-    <span class="c1">#         2: [((2, -1), 1.5*auto_coeff, lin_f), ((1, 0), 1.5*coeff, lin_f)],</span>
-    <span class="c1">#         }</span>
-    <span class="c1"># data2, nonstat = toys.structural_causal_process(links, T=T, </span>
-    <span class="c1">#                             noises=None, seed=7)</span>
-    <span class="c1"># dataframe2 = pp.DataFrame(data2, var_names=range(len(links)))</span>
-    <span class="c1"># plot_densityplots(dataframe, name=&#39;test.pdf&#39;)</span>
-
-    <span class="c1"># N = len(links)</span>
-
-
-    <span class="c1"># parcorr = ParCorr(significance=&#39;analytic&#39;)</span>
-    <span class="c1"># pcmci = PCMCI(</span>
-    <span class="c1">#     dataframe=dataframe, </span>
-    <span class="c1">#     cond_ind_test=parcorr,</span>
-    <span class="c1">#     verbosity=1)</span>
-
-
-    <span class="c1"># correlations = pcmci.get_lagged_dependencies(tau_max=20, val_only=True)[&#39;val_matrix&#39;]</span>
-    <span class="c1"># lag_func_matrix = plot_lagfuncs(val_matrix=correlations, setup_args={&#39;label_space_left&#39;:0.05, </span>
-    <span class="c1">#                                 &#39;x_base&#39;:5, &#39;y_base&#39;:.5})</span>
-    <span class="c1"># plt.show()</span>
-
-    
-    <span class="c1"># N = len(links)</span>
-    <span class="c1"># matrix = setup_density_matrix(N=N, var_names=dataframe.var_names)</span>
-    <span class="c1"># matrix.add_densityplot(dataframe=dataframe, </span>
-    <span class="c1">#     # selected_dataset=0, </span>
-    <span class="c1">#     **{</span>
-    <span class="c1">#     &#39;label&#39;:&#39;Weak&#39;,</span>
-    <span class="c1">#     &#39;label_color&#39;:&#39;blue&#39;,</span>
-    <span class="c1">#     &quot;snskdeplot_args&quot; : {&#39;cmap&#39;:&#39;Reds&#39;},</span>
-    <span class="c1">#     }), #{&#39;cmap&#39;:&#39;Blues&#39;, &#39;alpha&#39;:0.3}})</span>
-    <span class="c1"># matrix.add_densityplot(dataframe=dataframe2, selected_dataset=0, </span>
-    <span class="c1">#     **{&#39;label&#39;:&#39;Strong&#39;,</span>
-    <span class="c1">#     &#39;label_color&#39;:&#39;red&#39;,</span>
-    <span class="c1">#     &quot;snskdeplot_args&quot; : {&#39;cmap&#39;:&#39;Blues&#39;, &#39;alpha&#39;:0.3}})</span>
-    <span class="c1"># matrix.adjustfig(name=&#39;test.pdf&#39;)</span>
-
-    <span class="c1"># matrix = setup_scatter_matrix(N=dataframe.N, </span>
-    <span class="c1">#     var_names=dataframe.var_names)</span>
-    <span class="c1"># matrix_lags = np.ones((3, 3)).astype(&#39;int&#39;)</span>
-    <span class="c1"># matrix.add_scatterplot(dataframe=dataframe, matrix_lags=matrix_lags,</span>
-    <span class="c1">#             label=&#39;ones&#39;, alpha=0.4)</span>
-    <span class="c1"># matrix_lags = 2*np.ones((3, 3)).astype(&#39;int&#39;)</span>
-    <span class="c1"># matrix.add_scatterplot(dataframe=dataframe, matrix_lags=matrix_lags, </span>
-    <span class="c1">#     label=&#39;twos&#39;, color=&#39;red&#39;, alpha=0.4)</span>
-
-    <span class="c1"># matrix.savefig(name=&#39;scattertest.pdf&#39;)</span>
-    
-
-    <span class="c1"># pyplot.show()</span>
-    <span class="c1"># sys.exit(0)</span>
-
-
-    <span class="c1"># val_matrix = np.zeros((4, 4, 3))</span>
-
-    <span class="c1"># Complete test case</span>
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
-    <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="n">val_matrix</span><span class="p">[:,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.2</span>
-    <span class="n">graph</span><span class="p">[:]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-+&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;+-&gt;&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-
-    <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;+-&gt;&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;o-o&quot;</span>
-
-    <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-&gt;&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-&gt;&quot;</span>
-
-    <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;x-x&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;x-x&quot;</span>
-    <span class="n">nolinks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="c1"># nolinks[range(4), range(4), 1] = 1</span>
-
-    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="n">label_space_left</span> <span class="o">=</span> <span class="mf">0.2</span>
-    <span class="n">label_space_top</span> <span class="o">=</span> <span class="mf">0.</span>
-    <span class="n">network_lower_bound</span> <span class="o">=</span> <span class="mf">0.</span>
-    <span class="n">show_colorbar</span><span class="o">=</span><span class="kc">True</span>
-    <span class="n">plot_graph</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span>
-        <span class="c1"># fig_ax = (fig, axes),</span>
-        <span class="n">val_matrix</span><span class="o">=</span><span class="n">val_matrix</span><span class="p">,</span>
-        <span class="c1"># figsize=(5, 5),</span>
-        <span class="n">var_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Var </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">graph</span><span class="p">))],</span>
-        <span class="c1"># arrow_linewidth=6,</span>
-        <span class="c1"># label_space_left = label_space_left,</span>
-        <span class="c1"># label_space_top = label_space_top,</span>
-        <span class="c1"># # network_lower_bound=network_lower_bound,</span>
-        <span class="c1"># save_name=&quot;tsg_test.pdf&quot;</span>
-        <span class="p">)</span>
-
-    <span class="c1"># axes[0,0].scatter(np.random.randn(100), np.random.randn(100))</span>
-
-    <span class="c1"># plot_graph(graph=graph,</span>
-    <span class="c1">#     fig_ax = (fig, axes[0,0]),</span>
-    <span class="c1">#     val_matrix=val_matrix,</span>
-    <span class="c1">#     # figsize=(5, 5),</span>
-    <span class="c1">#     var_names = [&#39;Variable %s&#39; %i for i in range(len(graph))],</span>
-    <span class="c1">#     arrow_linewidth=6,</span>
-    <span class="c1">#     label_space_left = label_space_left,</span>
-    <span class="c1">#     label_space_top = label_space_top,</span>
-    <span class="c1">#     network_lower_bound=network_lower_bound,</span>
-    <span class="c1">#     # save_name=&quot;tsg_test.pdf&quot;</span>
-    <span class="c1">#     )</span>
-    <span class="c1"># plot_graph(graph=graph,</span>
-    <span class="c1">#     fig_ax = (fig, axes[0,1]),</span>
-    <span class="c1">#     val_matrix=val_matrix,</span>
-    <span class="c1">#     var_names = [&#39;Var %s&#39; %i for i in range(len(graph))],</span>
-    <span class="c1">#     arrow_linewidth=6,</span>
-    <span class="c1">#     label_space_left = label_space_left,</span>
-    <span class="c1">#     label_space_top = label_space_top,</span>
-    <span class="c1">#     network_lower_bound=network_lower_bound,</span>
-    <span class="c1">#     )</span>
-    <span class="c1"># plot_graph(graph=graph,</span>
-    <span class="c1">#     fig_ax = (fig, axes[1,0]),</span>
-    <span class="c1">#     val_matrix=val_matrix,</span>
-    <span class="c1">#     var_names = [&#39;Var %s&#39; %i for i in range(len(graph))],</span>
-    <span class="c1">#     arrow_linewidth=6,</span>
-    <span class="c1">#     label_space_left = label_space_left,</span>
-    <span class="c1">#     label_space_top = label_space_top,</span>
-    <span class="c1">#     network_lower_bound=network_lower_bound,</span>
-    <span class="c1">#     )</span>
-    <span class="c1"># plot_graph(graph=graph,</span>
-    <span class="c1">#     fig_ax = (fig, axes[1,1]),</span>
-    <span class="c1">#     val_matrix=val_matrix,</span>
-    <span class="c1">#     var_names = [&#39;Var %s&#39; %i for i in range(len(graph))],</span>
-    <span class="c1">#     arrow_linewidth=6,</span>
-    <span class="c1">#     label_space_left = label_space_left,</span>
-    <span class="c1">#     label_space_top = label_space_top,</span>
-    <span class="c1">#     network_lower_bound=network_lower_bound,</span>
-    <span class="c1">#     )</span>
-    <span class="c1"># pyplot.subplots_adjust(wspace=0.3, hspace=0.2)</span>
-    <span class="n">pyplot</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-    <span class="n">pyplot</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">&quot;test.pdf&quot;</span><span class="p">)</span>
-
-    <span class="c1"># def lin_f(x): return x</span>
-
-    <span class="c1"># links_coeffs = {0: [((0, -1), 0.3, lin_f)], #, ((1, -1), 0.5, lin_f)],</span>
-    <span class="c1">#             1: [((1, -1), 0.3, lin_f), ((0, 0), 0.7, lin_f), ((2, -1), 0.5, lin_f)],</span>
-    <span class="c1">#             2: [],</span>
-    <span class="c1">#             3: [((3, -1), 0., lin_f), ((2, 0), 0.6, lin_f),]</span>
-    <span class="c1">#             }</span>
-    <span class="c1"># graph = CausalEffects.get_graph_from_dict(links_coeffs, tau_max=None)</span>
-
-    <span class="c1"># val_matrix = np.random.randn(*graph.shape)</span>
-    <span class="c1"># val_matrix[:,:,0] = 0.</span>
-    <span class="c1"># write_csv(graph=graph,</span>
-    <span class="c1">#     val_matrix=val_matrix,</span>
-    <span class="c1">#     var_names=[&#39;s %d&#39; %i for i in range(graph.shape[0])],</span>
-    <span class="c1">#     link_width=np.ones(graph.shape),</span>
-    <span class="c1">#     link_attribute = np.ones(graph.shape, dtype=&#39;&lt;U10&#39;),</span>
-    <span class="c1">#     save_name=&#39;test.cv&#39;)</span>
-
-    <span class="c1"># # print(graph)</span>
-    <span class="c1"># X = [(0,-1)]</span>
-    <span class="c1"># Y = [(1,0)]</span>
-    <span class="c1"># causal_effects = CausalEffects(graph, graph_type=&#39;stationary_dag&#39;, X=X, Y=Y, S=None, </span>
-    <span class="c1">#                                hidden_variables=[(2, 0), (2, -1), (2, -2)], </span>
-    <span class="c1">#                                verbosity=0)</span>
-
-    <span class="c1"># pyplot.show()</span>
-</pre></div>
-
-          </div>
-          
-        </div>
-      </div>
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diff --git a/docs/_build/html/_modules/tigramite/toymodels/structural_causal_processes.html b/docs/_build/html/_modules/tigramite/toymodels/structural_causal_processes.html
deleted file mode 100644
index 8ecaa4a2..00000000
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-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.toymodels.structural_causal_processes &#8212; Tigramite 5.2 documentation</title>
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-            
-  <h1>Source code for tigramite.toymodels.structural_causal_processes</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite toymodels.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span><span class="p">,</span> <span class="n">OrderedDict</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">scipy.sparse</span>
-<span class="kn">import</span> <span class="nn">scipy.sparse.linalg</span>
-<span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
-<span class="kn">import</span> <span class="nn">itertools</span>
-
-<span class="k">def</span> <span class="nf">_generate_noise</span><span class="p">(</span><span class="n">covar_matrix</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">use_inverse</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Generate a multivariate normal distribution using correlated innovations.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    covar_matrix : array</span>
-<span class="sd">        Covariance matrix of the random variables</span>
-<span class="sd">    time : int</span>
-<span class="sd">        Sample size</span>
-<span class="sd">    use_inverse : bool, optional</span>
-<span class="sd">        Negate the off-diagonal elements and invert the covariance matrix</span>
-<span class="sd">        before use</span>
-
-<span class="sd">    return_eigenvectors</span>
-<span class="sd">    -------</span>
-<span class="sd">    noise : array</span>
-<span class="sd">        Random noise generated according to covar_matrix</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Pull out the number of nodes from the shape of the covar_matrix</span>
-    <span class="n">n_nodes</span> <span class="o">=</span> <span class="n">covar_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="c1"># Make a deep copy for use in the inverse case</span>
-    <span class="n">this_covar</span> <span class="o">=</span> <span class="n">covar_matrix</span>
-    <span class="c1"># Take the negative inverse if needed</span>
-    <span class="k">if</span> <span class="n">use_inverse</span><span class="p">:</span>
-        <span class="n">this_covar</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">covar_matrix</span><span class="p">)</span>
-        <span class="n">this_covar</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
-        <span class="n">this_covar</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">diag_indices_from</span><span class="p">(</span><span class="n">this_covar</span><span class="p">)]</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
-        <span class="n">this_covar</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">this_covar</span><span class="p">)</span>
-    <span class="c1"># Return the noise distribution</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_nodes</span><span class="p">),</span>
-                                            <span class="n">cov</span><span class="o">=</span><span class="n">this_covar</span><span class="p">,</span>
-                                            <span class="n">size</span><span class="o">=</span><span class="n">time</span><span class="p">)</span>
-
-<span class="k">def</span> <span class="nf">_check_stability</span><span class="p">(</span><span class="n">graph</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Raises an AssertionError if the input graph corresponds to a non-stationary</span>
-<span class="sd">    process.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    graph : array</span>
-<span class="sd">        Lagged connectivity matrices. Shape is (n_nodes, n_nodes, max_delay+1)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Get the shape from the input graph</span>
-    <span class="n">n_nodes</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">period</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span>
-    <span class="c1"># Set the top section as the horizontally stacked matrix of</span>
-    <span class="c1"># shape (n_nodes, n_nodes * period)</span>
-    <span class="n">stability_matrix</span> <span class="o">=</span> \
-        <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">lil_matrix</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">t_slice</span><span class="p">])</span>
-                             <span class="k">for</span> <span class="n">t_slice</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">period</span><span class="p">)])</span>
-    <span class="c1"># Extend an identity matrix of shape</span>
-    <span class="c1"># (n_nodes * (period - 1), n_nodes * (period - 1)) to shape</span>
-    <span class="c1"># (n_nodes * (period - 1), n_nodes * period) and stack the top section on</span>
-    <span class="c1"># top to make the stability matrix of shape</span>
-    <span class="c1"># (n_nodes * period, n_nodes * period)</span>
-    <span class="n">stability_matrix</span> <span class="o">=</span> \
-        <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">stability_matrix</span><span class="p">,</span>
-                             <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_nodes</span> <span class="o">*</span> <span class="p">(</span><span class="n">period</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
-                                              <span class="n">n_nodes</span> <span class="o">*</span> <span class="n">period</span><span class="p">)])</span>
-    <span class="c1"># Check the number of dimensions to see if we can afford to use a dense</span>
-    <span class="c1"># matrix</span>
-    <span class="n">n_eigs</span> <span class="o">=</span> <span class="n">stability_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">n_eigs</span> <span class="o">&lt;=</span> <span class="mi">25</span><span class="p">:</span>
-        <span class="c1"># If it is relatively low in dimensionality, use a dense array</span>
-        <span class="n">stability_matrix</span> <span class="o">=</span> <span class="n">stability_matrix</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
-        <span class="n">eigen_values</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">stability_matrix</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="c1"># If it is a large dimensionality, convert to a compressed row sorted</span>
-        <span class="c1"># matrix, as it may be easier for the linear algebra package</span>
-        <span class="n">stability_matrix</span> <span class="o">=</span> <span class="n">stability_matrix</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
-        <span class="c1"># Get the eigen values of the stability matrix</span>
-        <span class="n">eigen_values</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigs</span><span class="p">(</span><span class="n">stability_matrix</span><span class="p">,</span>
-                                                <span class="n">k</span><span class="o">=</span><span class="p">(</span><span class="n">n_eigs</span> <span class="o">-</span> <span class="mi">2</span><span class="p">),</span>
-                                                <span class="n">return_eigenvectors</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="c1"># Ensure they all have less than one magnitude</span>
-    <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">eigen_values</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">1.</span><span class="p">),</span> \
-        <span class="s2">&quot;Values given by time lagged connectivity matrix corresponds to a &quot;</span><span class="o">+</span>\
-        <span class="s2">&quot; non-stationary process!&quot;</span>
-
-<span class="k">def</span> <span class="nf">_check_initial_values</span><span class="p">(</span><span class="n">initial_values</span><span class="p">,</span> <span class="n">shape</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Raises a AssertionError if the input initial values:</span>
-<span class="sd">        * Are not a numpy array OR</span>
-<span class="sd">        * Do not have the shape (n_nodes, max_delay+1)</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    graph : array</span>
-<span class="sd">        Lagged connectivity matrices. Shape is (n_nodes, n_nodes, max_delay+1)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Ensure it is a numpy array</span>
-    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">initial_values</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">),</span>\
-        <span class="s2">&quot;User must provide initial_values as a numpy.ndarray&quot;</span>
-    <span class="c1"># Check the shape is correct</span>
-    <span class="k">assert</span> <span class="n">initial_values</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">shape</span><span class="p">,</span>\
-        <span class="s2">&quot;Initial values must be of shape (n_nodes, max_delay+1)&quot;</span><span class="o">+</span>\
-        <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> current shape : &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">initial_values</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">+</span>\
-        <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> desired shape : &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
-
-<span class="k">def</span> <span class="nf">_var_network</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                 <span class="n">add_noise</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">inno_cov</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">invert_inno</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">T</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-                 <span class="n">initial_values</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns a vector-autoregressive process with correlated innovations.</span>
-
-<span class="sd">    Useful for testing.</span>
-
-<span class="sd">    Example:</span>
-<span class="sd">        graph=numpy.array([[[0.2,0.,0.],[0.5,0.,0.]],</span>
-<span class="sd">                           [[0.,0.1,0. ],[0.3,0.,0.]]])</span>
-
-<span class="sd">        represents a process</span>
-
-<span class="sd">        X_1(t) = 0.2 X_1(t-1) + 0.5 X_2(t-1) + eps_1(t)</span>
-<span class="sd">        X_2(t) = 0.3 X_2(t-1) + 0.1 X_1(t-2) + eps_2(t)</span>
-
-<span class="sd">        with inv_inno_cov being the negative (except for diagonal) inverse</span>
-<span class="sd">        covariance matrix of (eps_1(t), eps_2(t)) OR inno_cov being</span>
-<span class="sd">        the covariance. Initial values can also be provided.</span>
-
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    graph : array</span>
-<span class="sd">        Lagged connectivity matrices. Shape is (n_nodes, n_nodes, max_delay+1)</span>
-<span class="sd">    add_noise : bool, optional (default: True)</span>
-<span class="sd">        Flag to add random noise or not</span>
-<span class="sd">    inno_cov : array, optional (default: None)</span>
-<span class="sd">        Covariance matrix of innovations.</span>
-<span class="sd">    invert_inno : bool, optional (defualt : False)</span>
-<span class="sd">        Flag to negate off-diagonal elements of inno_cov and invert it before</span>
-<span class="sd">        using it as the covariance matrix of innovations</span>
-<span class="sd">    T : int, optional (default: 100)</span>
-<span class="sd">        Sample size.</span>
-
-<span class="sd">    initial_values : array, optional (defult: None)</span>
-<span class="sd">        Initial values for each node. Shape is (n_nodes, max_delay+1), i.e. must</span>
-<span class="sd">        be of shape (graph.shape[1], graph.shape[2]).</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    X : array</span>
-<span class="sd">        Array of realization.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">n_nodes</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">period</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span>
-
-    <span class="n">time</span> <span class="o">=</span> <span class="n">T</span>
-    <span class="c1"># Test stability</span>
-    <span class="n">_check_stability</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-
-    <span class="c1"># Generate the returned data</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_nodes</span><span class="p">,</span> <span class="n">time</span><span class="p">)</span>
-    <span class="c1"># Load the initial values</span>
-    <span class="k">if</span> <span class="n">initial_values</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="c1"># Check the shape of the initial values</span>
-        <span class="n">_check_initial_values</span><span class="p">(</span><span class="n">initial_values</span><span class="p">,</span> <span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="n">period</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-        <span class="c1"># Input the initial values</span>
-        <span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="n">period</span><span class="p">]</span> <span class="o">=</span> <span class="n">initial_values</span>
-
-    <span class="c1"># Check if we are adding noise</span>
-    <span class="n">noise</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">if</span> <span class="n">add_noise</span><span class="p">:</span>
-        <span class="c1"># Use inno_cov if it was provided</span>
-        <span class="k">if</span> <span class="n">inno_cov</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">noise</span> <span class="o">=</span> <span class="n">_generate_noise</span><span class="p">(</span><span class="n">inno_cov</span><span class="p">,</span>
-                                    <span class="n">time</span><span class="o">=</span><span class="n">time</span><span class="p">,</span>
-                                    <span class="n">use_inverse</span><span class="o">=</span><span class="n">invert_inno</span><span class="p">)</span>
-        <span class="c1"># Otherwise just use uncorrelated random noise</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">noise</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">n_nodes</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">a_time</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">time</span><span class="p">):</span>
-        <span class="n">data_past</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span>
-            <span class="n">data</span><span class="p">[:,</span> <span class="n">a_time</span><span class="o">-</span><span class="n">period</span><span class="p">:</span><span class="n">a_time</span><span class="p">][:,</span> <span class="p">::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_nodes</span><span class="p">,</span> <span class="n">period</span><span class="p">),</span>
-            <span class="n">n_nodes</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">data</span><span class="p">[:,</span> <span class="n">a_time</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">data_past</span><span class="o">*</span><span class="n">graph</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">add_noise</span><span class="p">:</span>
-            <span class="n">data</span><span class="p">[:,</span> <span class="n">a_time</span><span class="p">]</span> <span class="o">+=</span> <span class="n">noise</span><span class="p">[</span><span class="n">a_time</span><span class="p">]</span>
-
-    <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
-
-<span class="k">def</span> <span class="nf">_iter_coeffs</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Iterator through the current parents_neighbors_coeffs structure.  Mainly to</span>
-<span class="sd">    save repeated code and make it easier to change this structure.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    parents_neighbors_coeffs : dict</span>
-<span class="sd">        Dictionary of format:</span>
-<span class="sd">        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for</span>
-<span class="sd">        all variables where vars must be in [0..N-1] and lags &lt;= 0 with number</span>
-<span class="sd">        of variables N.</span>
-
-<span class="sd">    Yields</span>
-<span class="sd">    -------</span>
-<span class="sd">    (node_id, parent_id, time_lag, coeff) : tuple</span>
-<span class="sd">        Tuple defining the relationship between nodes across time</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Iterate through all defined nodes</span>
-    <span class="k">for</span> <span class="n">node_id</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-        <span class="c1"># Iterate over parent nodes and unpack node and coeff</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">parent_id</span><span class="p">,</span> <span class="n">time_lag</span><span class="p">),</span> <span class="n">coeff</span> <span class="ow">in</span> <span class="n">parents_neighbors_coeffs</span><span class="p">[</span><span class="n">node_id</span><span class="p">]:</span>
-            <span class="c1"># Yield the entry</span>
-            <span class="k">yield</span> <span class="n">node_id</span><span class="p">,</span> <span class="n">parent_id</span><span class="p">,</span> <span class="n">time_lag</span><span class="p">,</span> <span class="n">coeff</span>
-
-<span class="k">def</span> <span class="nf">_check_parent_neighbor</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Checks to insure input parent-neighbor connectivity input is sane.  This</span>
-<span class="sd">    means that:</span>
-<span class="sd">        * all time lags are non-positive</span>
-<span class="sd">        * all parent nodes are included as nodes themselves</span>
-<span class="sd">        * all node indexing is contiguous</span>
-<span class="sd">        * all node indexing starts from zero</span>
-<span class="sd">    Raises a ValueError if any one of these conditions are not met.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    parents_neighbors_coeffs : dict</span>
-<span class="sd">        Dictionary of format:</span>
-<span class="sd">        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for</span>
-<span class="sd">        all variables where vars must be in [0..N-1] and lags &lt;= 0 with number</span>
-<span class="sd">        of variables N.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Initialize some lists for checking later</span>
-    <span class="n">all_nodes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-    <span class="n">all_parents</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-    <span class="c1"># Iterate through variables</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-        <span class="c1"># Cache all node ids to ensure they are contiguous</span>
-        <span class="n">all_nodes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">j</span><span class="p">)</span>
-    <span class="c1"># Iterate through all nodes</span>
-    <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">_iter_coeffs</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-        <span class="c1"># Check all time lags are equal to or less than zero</span>
-        <span class="k">if</span> <span class="n">tau</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Lag between parent </span><span class="si">{}</span><span class="s2"> and node </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span><span class="o">+</span>\
-                             <span class="s2">&quot; is </span><span class="si">{}</span><span class="s2"> &gt; 0, must be &lt;= 0!&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
-        <span class="c1"># Cache all parent ids to ensure they are mentioned as node ids</span>
-        <span class="n">all_parents</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-    <span class="c1"># Check that all nodes are contiguous from zero</span>
-    <span class="n">all_nodes_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">all_nodes</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">all_nodes_list</span> <span class="o">!=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">all_nodes_list</span><span class="p">))):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Node IDs in input dictionary must be contiguous&quot;</span><span class="o">+</span>\
-                         <span class="s2">&quot; and start from zero!</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">+</span>\
-                         <span class="s2">&quot; Found IDs : [&quot;</span> <span class="o">+</span>\
-                         <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">all_nodes_list</span><span class="p">))</span><span class="o">+</span> <span class="s2">&quot;]&quot;</span><span class="p">)</span>
-    <span class="c1"># Check that all parent nodes are mentioned as a node ID</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">all_parents</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="n">all_nodes</span><span class="p">):</span>
-        <span class="n">missing_nodes</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">all_parents</span> <span class="o">-</span> <span class="n">all_nodes</span><span class="p">))</span>
-        <span class="n">all_parents_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">all_parents</span><span class="p">))</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Parent IDs in input dictionary must also be in set&quot;</span><span class="o">+</span>\
-                         <span class="s2">&quot; of node IDs.&quot;</span><span class="o">+</span>\
-                         <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> Parent IDs &quot;</span><span class="o">+</span><span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">all_parents_list</span><span class="p">))</span><span class="o">+</span>\
-                         <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> Node IDs &quot;</span><span class="o">+</span><span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">all_nodes_list</span><span class="p">))</span> <span class="o">+</span>\
-                         <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> Missing IDs &quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">missing_nodes</span><span class="p">)))</span>
-
-<span class="k">def</span> <span class="nf">_check_symmetric_relations</span><span class="p">(</span><span class="n">a_matrix</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Check if the argument matrix is symmetric.  Raise a value error with details</span>
-<span class="sd">    about the offending elements if it is not.  This is useful for checking the</span>
-<span class="sd">    instantaneously linked nodes have the same link strength.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    a_matrix : 2D numpy array</span>
-<span class="sd">        Relationships between nodes at tau = 0. Indexed such that first index is</span>
-<span class="sd">        node and second is parent, i.e. node j with parent i has strength</span>
-<span class="sd">        a_matrix[j,i]</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Check it is symmetric</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">a_matrix</span><span class="p">,</span> <span class="n">a_matrix</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-10</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-10</span><span class="p">):</span>
-        <span class="c1"># Store the disagreement elements</span>
-        <span class="n">bad_elems</span> <span class="o">=</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">a_matrix</span><span class="p">,</span> <span class="n">a_matrix</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-10</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-10</span><span class="p">)</span>
-        <span class="n">bad_idxs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">bad_elems</span><span class="p">)</span>
-        <span class="n">error_message</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-        <span class="k">for</span> <span class="n">node</span><span class="p">,</span> <span class="n">parent</span> <span class="ow">in</span> <span class="n">bad_idxs</span><span class="p">:</span>
-            <span class="c1"># Check that we haven&#39;t already printed about this pair</span>
-            <span class="k">if</span> <span class="n">bad_elems</span><span class="p">[</span><span class="n">node</span><span class="p">,</span> <span class="n">parent</span><span class="p">]:</span>
-                <span class="n">error_message</span> <span class="o">+=</span> \
-                    <span class="s2">&quot;Parent </span><span class="si">{:d}</span><span class="s2"> of node </span><span class="si">{:d}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">parent</span><span class="p">,</span> <span class="n">node</span><span class="p">)</span><span class="o">+</span>\
-                    <span class="s2">&quot; has coefficient </span><span class="si">{:f}</span><span class="s2">.</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">a_matrix</span><span class="p">[</span><span class="n">node</span><span class="p">,</span> <span class="n">parent</span><span class="p">])</span><span class="o">+</span>\
-                    <span class="s2">&quot;Parent </span><span class="si">{:d}</span><span class="s2"> of node </span><span class="si">{:d}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">parent</span><span class="p">)</span><span class="o">+</span>\
-                    <span class="s2">&quot; has coefficient </span><span class="si">{:f}</span><span class="s2">.</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">a_matrix</span><span class="p">[</span><span class="n">parent</span><span class="p">,</span> <span class="n">node</span><span class="p">])</span>
-            <span class="c1"># Check if we already printed about this one</span>
-            <span class="n">bad_elems</span><span class="p">[</span><span class="n">node</span><span class="p">,</span> <span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="n">bad_elems</span><span class="p">[</span><span class="n">parent</span><span class="p">,</span> <span class="n">node</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Relationships between nodes at tau=0 are not&quot;</span><span class="o">+</span>\
-                         <span class="s2">&quot; symmetric!</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">+</span><span class="n">error_message</span><span class="p">)</span>
-
-<span class="k">def</span> <span class="nf">_find_max_time_lag_and_node_id</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Function to find the maximum time lag in the parent-neighbors-coefficients</span>
-<span class="sd">    object, as well as the largest node ID</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    parents_neighbors_coeffs : dict</span>
-<span class="sd">        Dictionary of format:</span>
-<span class="sd">        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for</span>
-<span class="sd">        all variables where vars must be in [0..N-1] and lags &lt;= 0 with number</span>
-<span class="sd">        of variables N.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    (max_time_lag, max_node_id) : tuple</span>
-<span class="sd">        Tuple of the maximum time lag and maximum node ID</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Default maximum lag and node ID</span>
-    <span class="n">max_time_lag</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">max_node_id</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="mi">1</span>
-    <span class="c1"># Iterate through the keys in parents_neighbors_coeffs</span>
-    <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">_iter_coeffs</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-        <span class="c1"># Find max lag time</span>
-        <span class="n">max_time_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_time_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
-        <span class="c1"># Find the max node ID</span>
-        <span class="c1"># max_node_id = max(max_node_id, j)</span>
-    <span class="c1"># Return these values</span>
-    <span class="k">return</span> <span class="n">max_time_lag</span><span class="p">,</span> <span class="n">max_node_id</span>
-
-<span class="k">def</span> <span class="nf">_get_true_parent_neighbor_dict</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Function to return the dictionary of true parent neighbor causal</span>
-<span class="sd">    connections in time.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    parents_neighbors_coeffs : dict</span>
-<span class="sd">        Dictionary of format:</span>
-<span class="sd">        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for</span>
-<span class="sd">        all variables where vars must be in [0..N-1] and lags &lt;= 0 with number</span>
-<span class="sd">        of variables N.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    true_parent_neighbor : dict</span>
-<span class="sd">        Dictionary of lists of tuples.  The dictionary is keyed by node ID, the</span>
-<span class="sd">        list stores the tuple values (parent_node_id, time_lag)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Initialize the returned dictionary of lists</span>
-    <span class="n">true_parents_neighbors</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">parents_neighbors_coeffs</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">parents_neighbors_coeffs</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-            <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="c1"># Add parent node id and lag if non-zero coeff</span>
-            <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                <span class="n">true_parents_neighbors</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">))</span>
-    <span class="c1"># Return the true relations</span>
-    <span class="k">return</span> <span class="n">true_parents_neighbors</span>
-
-<span class="k">def</span> <span class="nf">_get_covariance_matrix</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Determines the covariance matrix for correlated innovations</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    parents_neighbors_coeffs : dict</span>
-<span class="sd">        Dictionary of format:</span>
-<span class="sd">        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for</span>
-<span class="sd">        all variables where vars must be in [0..N-1] and lags &lt;= 0 with number</span>
-<span class="sd">        of variables N.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    covar_matrix : numpy array</span>
-<span class="sd">        Covariance matrix implied by the parents_neighbors_coeffs.  Used to</span>
-<span class="sd">        generate correlated innovations.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Get the total number of nodes</span>
-    <span class="n">_</span><span class="p">,</span> <span class="n">max_node_id</span> <span class="o">=</span> \
-            <span class="n">_find_max_time_lag_and_node_id</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">)</span>
-    <span class="n">n_nodes</span> <span class="o">=</span> <span class="n">max_node_id</span> <span class="o">+</span> <span class="mi">1</span>
-    <span class="c1"># Initialize the covariance matrix</span>
-    <span class="n">covar_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">n_nodes</span><span class="p">)</span>
-    <span class="c1"># Iterate through all the node connections</span>
-    <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">coeff</span> <span class="ow">in</span> <span class="n">_iter_coeffs</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-        <span class="c1"># Add to covar_matrix if node connection is instantaneous</span>
-        <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">covar_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">coeff</span>
-    <span class="k">return</span> <span class="n">covar_matrix</span>
-
-<span class="k">def</span> <span class="nf">_get_lag_connect_matrix</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Generates the lagged connectivity matrix from a parent-neighbor</span>
-<span class="sd">    connectivity dictionary.  Used to generate the input for _var_network</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    parents_neighbors_coeffs : dict</span>
-<span class="sd">        Dictionary of format:</span>
-<span class="sd">        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for</span>
-<span class="sd">        all variables where vars must be in [0..N-1] and lags &lt;= 0 with number</span>
-<span class="sd">        of variables N.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    connect_matrix : numpy array</span>
-<span class="sd">        Lagged connectivity matrix. Shape is (n_nodes, n_nodes, max_delay+1)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Get the total number of nodes and time lag</span>
-    <span class="n">max_time_lag</span><span class="p">,</span> <span class="n">max_node_id</span> <span class="o">=</span> \
-            <span class="n">_find_max_time_lag_and_node_id</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">)</span>
-    <span class="n">n_nodes</span> <span class="o">=</span> <span class="n">max_node_id</span> <span class="o">+</span> <span class="mi">1</span>
-    <span class="n">n_times</span> <span class="o">=</span> <span class="n">max_time_lag</span> <span class="o">+</span> <span class="mi">1</span>
-    <span class="c1"># Initialize full time graph</span>
-    <span class="n">connect_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_nodes</span><span class="p">,</span> <span class="n">n_nodes</span><span class="p">,</span> <span class="n">n_times</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">coeff</span> <span class="ow">in</span> <span class="n">_iter_coeffs</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
-        <span class="c1"># If there is a non-zero time lag, add the connection to the matrix</span>
-        <span class="k">if</span> <span class="n">tau</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">connect_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="p">(</span><span class="n">tau</span><span class="o">+</span><span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">coeff</span>
-    <span class="c1"># Return the connectivity matrix</span>
-    <span class="k">return</span> <span class="n">connect_matrix</span>
-
-<div class="viewcode-block" id="var_process"><a class="viewcode-back" href="../../../index.html#tigramite.toymodels.structural_causal_processes.var_process">[docs]</a><span class="k">def</span> <span class="nf">var_process</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">use</span><span class="o">=</span><span class="s1">&#39;inv_inno_cov&#39;</span><span class="p">,</span>
-                <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">initial_values</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a vector-autoregressive process with correlated innovations.</span>
-
-<span class="sd">    Wrapper around var_network with possibly more user-friendly input options.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    parents_neighbors_coeffs : dict</span>
-<span class="sd">        Dictionary of format: {..., j:[((var1, lag1), coef1), ((var2, lag2),</span>
-<span class="sd">        coef2), ...], ...} for all variables where vars must be in [0..N-1]</span>
-<span class="sd">        and lags &lt;= 0 with number of variables N. If lag=0, a nonzero value</span>
-<span class="sd">        in the covariance matrix (or its inverse) is implied. These should be</span>
-<span class="sd">        the same for (i, j) and (j, i).</span>
-<span class="sd">    use : str, optional (default: &#39;inv_inno_cov&#39;)</span>
-<span class="sd">        Specifier, either &#39;inno_cov&#39; or &#39;inv_inno_cov&#39;.</span>
-<span class="sd">        Any other specifier will result in non-correlated noise.</span>
-<span class="sd">        For debugging, &#39;no_noise&#39; can also be specified, in which case random</span>
-<span class="sd">        noise will be disabled.</span>
-<span class="sd">    T : int, optional (default: 1000)</span>
-<span class="sd">        Sample size.</span>
-<span class="sd">    verbosity : int, optional (default: 0)</span>
-<span class="sd">        Level of verbosity.</span>
-<span class="sd">    initial_values : array, optional (default: None)</span>
-<span class="sd">        Initial values for each node. Shape must be (N, max_delay+1)</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    data : array-like</span>
-<span class="sd">        Data generated from this process</span>
-<span class="sd">    true_parent_neighbor : dict</span>
-<span class="sd">        Dictionary of lists of tuples.  The dictionary is keyed by node ID, the</span>
-<span class="sd">        list stores the tuple values (parent_node_id, time_lag)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Check the input parents_neighbors_coeffs dictionary for sanity</span>
-    <span class="n">_check_parent_neighbor</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">)</span>
-    <span class="c1"># Generate the true parent neighbors graph</span>
-    <span class="n">true_parents_neighbors</span> <span class="o">=</span> \
-        <span class="n">_get_true_parent_neighbor_dict</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">)</span>
-    <span class="c1"># Generate the correlated innovations</span>
-    <span class="n">innos</span> <span class="o">=</span> <span class="n">_get_covariance_matrix</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">)</span>
-    <span class="c1"># Generate the lagged connectivity matrix for _var_network</span>
-    <span class="n">connect_matrix</span> <span class="o">=</span> <span class="n">_get_lag_connect_matrix</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">)</span>
-    <span class="c1"># Default values as per &#39;inno_cov&#39;</span>
-    <span class="n">add_noise</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="n">invert_inno</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="c1"># Use the correlated innovations</span>
-    <span class="k">if</span> <span class="n">use</span> <span class="o">==</span> <span class="s1">&#39;inno_cov&#39;</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Innovation Cov =</span><span class="se">\n</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">innos</span><span class="p">))</span>
-    <span class="c1"># Use the inverted correlated innovations</span>
-    <span class="k">elif</span> <span class="n">use</span> <span class="o">==</span> <span class="s1">&#39;inv_inno_cov&#39;</span><span class="p">:</span>
-        <span class="n">invert_inno</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Inverse Innovation Cov =</span><span class="se">\n</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">innos</span><span class="p">))</span>
-    <span class="c1"># Do not use any noise</span>
-    <span class="k">elif</span> <span class="n">use</span> <span class="o">==</span> <span class="s1">&#39;no_noise&#39;</span><span class="p">:</span>
-        <span class="n">add_noise</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Inverse Innovation Cov =</span><span class="se">\n</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">innos</span><span class="p">))</span>
-    <span class="c1"># Use decorrelated noise</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">innos</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="c1"># Ensure the innovation matrix is symmetric if it is used</span>
-    <span class="k">if</span> <span class="p">(</span><span class="n">innos</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="n">add_noise</span><span class="p">:</span>
-        <span class="n">_check_symmetric_relations</span><span class="p">(</span><span class="n">innos</span><span class="p">)</span>
-    <span class="c1"># Generate the data using _var_network</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">_var_network</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">connect_matrix</span><span class="p">,</span>
-                        <span class="n">add_noise</span><span class="o">=</span><span class="n">add_noise</span><span class="p">,</span>
-                        <span class="n">inno_cov</span><span class="o">=</span><span class="n">innos</span><span class="p">,</span>
-                        <span class="n">invert_inno</span><span class="o">=</span><span class="n">invert_inno</span><span class="p">,</span>
-                        <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span>
-                        <span class="n">initial_values</span><span class="o">=</span><span class="n">initial_values</span><span class="p">)</span>
-    <span class="c1"># Return the data</span>
-    <span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">true_parents_neighbors</span></div>
-
-<span class="k">class</span> <span class="nc">_Graph</span><span class="p">():</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Helper class to handle graph properties.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        List of nodes.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">vertices</span><span class="p">):</span> 
-        <span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span> 
-        <span class="bp">self</span><span class="o">.</span><span class="n">V</span> <span class="o">=</span> <span class="n">vertices</span> 
-  
-    <span class="k">def</span> <span class="nf">addEdge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">u</span><span class="p">,</span><span class="n">v</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Adding edge to graph.&quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">[</span><span class="n">u</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> 
-  
-    <span class="k">def</span> <span class="nf">isCyclicUtil</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">visited</span><span class="p">,</span> <span class="n">recStack</span><span class="p">):</span> 
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Utility function to return whether graph is cyclic.&quot;&quot;&quot;</span>
-        <span class="c1"># Mark current node as visited and</span>
-        <span class="c1"># adds to recursion stack </span>
-        <span class="n">visited</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="n">recStack</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-  
-        <span class="c1"># Recur for all neighbours </span>
-        <span class="c1"># if any neighbour is visited and in  </span>
-        <span class="c1"># recStack then graph is cyclic </span>
-        <span class="k">for</span> <span class="n">neighbour</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">[</span><span class="n">v</span><span class="p">]:</span> 
-            <span class="k">if</span> <span class="n">visited</span><span class="p">[</span><span class="n">neighbour</span><span class="p">]</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span> 
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isCyclicUtil</span><span class="p">(</span><span class="n">neighbour</span><span class="p">,</span> <span class="n">visited</span><span class="p">,</span> <span class="n">recStack</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span> 
-                    <span class="k">return</span> <span class="kc">True</span>
-            <span class="k">elif</span> <span class="n">recStack</span><span class="p">[</span><span class="n">neighbour</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span> 
-                <span class="k">return</span> <span class="kc">True</span>
-  
-        <span class="c1"># The node needs to be poped from  </span>
-        <span class="c1"># recursion stack before function ends </span>
-        <span class="n">recStack</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">return</span> <span class="kc">False</span>
-  
-    <span class="k">def</span> <span class="nf">isCyclic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns whether graph is cyclic.&quot;&quot;&quot;</span>
-        <span class="n">visited</span> <span class="o">=</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">V</span> 
-        <span class="n">recStack</span> <span class="o">=</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">V</span> 
-        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">V</span><span class="p">):</span> 
-            <span class="k">if</span> <span class="n">visited</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span> 
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isCyclicUtil</span><span class="p">(</span><span class="n">node</span><span class="p">,</span><span class="n">visited</span><span class="p">,</span><span class="n">recStack</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span> 
-                    <span class="k">return</span> <span class="kc">True</span>
-        <span class="k">return</span> <span class="kc">False</span>
-  
-    <span class="k">def</span> <span class="nf">topologicalSortUtil</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">v</span><span class="p">,</span><span class="n">visited</span><span class="p">,</span><span class="n">stack</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;A recursive function used by topologicalSort .&quot;&quot;&quot;</span>
-        <span class="c1"># Mark the current node as visited.</span>
-        <span class="n">visited</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="c1"># Recur for all the vertices adjacent to this vertex</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">[</span><span class="n">v</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="n">visited</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">topologicalSortUtil</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">visited</span><span class="p">,</span><span class="n">stack</span><span class="p">)</span>
-
-        <span class="c1"># Push current vertex to stack which stores result</span>
-        <span class="n">stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">v</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">topologicalSort</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;A sorting function. &quot;&quot;&quot;</span>
-        <span class="c1"># Mark all the vertices as not visited </span>
-        <span class="n">visited</span> <span class="o">=</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">V</span> 
-        <span class="n">stack</span> <span class="o">=</span><span class="p">[]</span> 
-
-        <span class="c1"># Call the recursive helper function to store Topological </span>
-        <span class="c1"># Sort starting from all vertices one by one </span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">V</span><span class="p">):</span> 
-          <span class="k">if</span> <span class="n">visited</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span> 
-              <span class="bp">self</span><span class="o">.</span><span class="n">topologicalSortUtil</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">visited</span><span class="p">,</span><span class="n">stack</span><span class="p">)</span> 
-
-        <span class="k">return</span> <span class="n">stack</span>
-
-<div class="viewcode-block" id="structural_causal_process"><a class="viewcode-back" href="../../../index.html#tigramite.toymodels.structural_causal_processes.structural_causal_process">[docs]</a><span class="k">def</span> <span class="nf">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
-                        <span class="n">intervention</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">intervention_type</span><span class="o">=</span><span class="s1">&#39;hard&#39;</span><span class="p">,</span>
-                        <span class="n">transient_fraction</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-                        <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a time series generated from a structural causal process.</span>
-
-<span class="sd">    Allows lagged and contemporaneous dependencies and includes the option</span>
-<span class="sd">    to have intervened variables or particular samples.</span>
-
-<span class="sd">    The interventional data is in particular useful for generating ground</span>
-<span class="sd">    truth for the CausalEffects class.</span>
-
-<span class="sd">    In more detail, the method implements a generalized additive noise model process of the form</span>
-
-<span class="sd">    .. math:: X^j_t = \\eta^j_t + \\sum_{X^i_{t-\\tau}\\in \\mathcal{P}(X^j_t)}</span>
-<span class="sd">              c^i_{\\tau} f^i_{\\tau}(X^i_{t-\\tau})</span>
-
-<span class="sd">    Links have the format ``{0:[((i, -tau), coeff, func),...], 1:[...],</span>
-<span class="sd">    ...}`` where ``func`` can be an arbitrary (nonlinear) function provided</span>
-<span class="sd">    as a python callable with one argument and coeff is the multiplication</span>
-<span class="sd">    factor. The noise distributions of :math:`\\eta^j` can be specified in</span>
-<span class="sd">    ``noises``.</span>
-
-<span class="sd">    Through the parameters ``intervention`` and ``intervention_type`` the model</span>
-<span class="sd">    can also be generated with intervened variables.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    links : dict</span>
-<span class="sd">        Dictionary of format: {0:[((i, -tau), coeff, func),...], 1:[...],</span>
-<span class="sd">        ...} for all variables where i must be in [0..N-1] and tau &gt;= 0 with</span>
-<span class="sd">        number of variables N. coeff must be a float and func a python</span>
-<span class="sd">        callable of one argument.</span>
-<span class="sd">    T : int</span>
-<span class="sd">        Sample size.</span>
-<span class="sd">    noises : list of callables or array, optional (default: &#39;np.random.randn&#39;)</span>
-<span class="sd">        Random distribution function that is called with noises[j](T). If an array,</span>
-<span class="sd">        it must be of shape ((transient_fraction + 1)*T, N).</span>
-<span class="sd">    intervention : dict</span>
-<span class="sd">        Dictionary of format: {1:np.array, ...} containing only keys of intervened</span>
-<span class="sd">        variables with the value being the array of length T with interventional values.</span>
-<span class="sd">        Set values to np.nan to leave specific time points of a variable un-intervened.</span>
-<span class="sd">    intervention_type : str or dict</span>
-<span class="sd">        Dictionary of format: {1:&#39;hard&#39;,  3:&#39;soft&#39;, ...} to specify whether intervention is </span>
-<span class="sd">        hard (set value) or soft (add value) for variable j. If str, all interventions have </span>
-<span class="sd">        the same type.</span>
-<span class="sd">    transient_fraction : float</span>
-<span class="sd">        Added percentage of T used as a transient. In total a realization of length</span>
-<span class="sd">        (transient_fraction + 1)*T will be generated, but then transient_fraction*T will be</span>
-<span class="sd">        cut off.</span>
-<span class="sd">    seed : int, optional (default: None)</span>
-<span class="sd">        Random seed.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    data : array-like</span>
-<span class="sd">        Data generated from this process, shape (T, N).</span>
-<span class="sd">    nonvalid : bool</span>
-<span class="sd">        Indicates whether data has NaNs or infinities.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-    <span class="k">if</span> <span class="n">noises</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">noises</span> <span class="o">=</span> <span class="p">[</span><span class="n">random_state</span><span class="o">.</span><span class="n">randn</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)]</span>
-
-    <span class="k">if</span> <span class="n">N</span> <span class="o">!=</span> <span class="nb">max</span><span class="p">(</span><span class="n">links</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">+</span><span class="mi">1</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;links keys must match N.&quot;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">noises</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">noises</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">(</span><span class="n">T</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">transient_fraction</span><span class="o">*</span><span class="n">T</span><span class="p">)),</span> <span class="n">N</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;noises.shape must match ((transient_fraction + 1)*T, N).&quot;</span><span class="p">)</span>            
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">N</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">noises</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;noises keys must match N.&quot;</span><span class="p">)</span>
-
-    <span class="c1"># Check parameters</span>
-    <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">contemp_dag</span> <span class="o">=</span> <span class="n">_Graph</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-            <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">func</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">lag</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">contemp</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="k">if</span> <span class="n">var</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;var must be in 0..</span><span class="si">{}</span><span class="s2">.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">N</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
-            <span class="k">if</span> <span class="s1">&#39;float&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">coeff</span><span class="p">)):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;coeff must be float.&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">lag</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">lag</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">int</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;lag must be non-positive int.&quot;</span><span class="p">)</span>
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-
-            <span class="c1"># Create contemp DAG</span>
-            <span class="k">if</span> <span class="n">var</span> <span class="o">!=</span> <span class="n">j</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">contemp_dag</span><span class="o">.</span><span class="n">addEdge</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">contemp_dag</span><span class="o">.</span><span class="n">isCyclic</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> 
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Contemporaneous links must not contain cycle.&quot;</span><span class="p">)</span>
-
-    <span class="n">causal_order</span> <span class="o">=</span> <span class="n">contemp_dag</span><span class="o">.</span><span class="n">topologicalSort</span><span class="p">()</span> 
-
-    <span class="k">if</span> <span class="n">intervention</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">intervention_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">intervention_type</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span><span class="s1">&#39;hard&#39;</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">intervention</span><span class="p">}</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention_type</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-            <span class="n">intervention_type</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span><span class="n">intervention_type</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">intervention</span><span class="p">}</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">intervention</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">intervention</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="o">!=</span> <span class="n">T</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;intervention array for j=</span><span class="si">%s</span><span class="s2"> must be of length T = </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">T</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">j</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">intervention_type</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>        
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;intervention_type dictionary must contain entry for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">j</span><span class="p">))</span>
-
-    <span class="n">transient</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">transient_fraction</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
-
-    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">T</span><span class="o">+</span><span class="n">transient</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">noises</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-            <span class="n">data</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">noises</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">data</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">noises</span><span class="p">[</span><span class="n">j</span><span class="p">](</span><span class="n">T</span><span class="o">+</span><span class="n">transient</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">T</span><span class="o">+</span><span class="n">transient</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">causal_order</span><span class="p">:</span>
-
-            <span class="k">if</span> <span class="p">(</span><span class="n">intervention</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">intervention</span> <span class="ow">and</span> <span class="n">t</span> <span class="o">&gt;=</span> <span class="n">transient</span>
-                <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">intervention</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">t</span> <span class="o">-</span> <span class="n">transient</span><span class="p">])</span> <span class="o">==</span> <span class="kc">False</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">intervention_type</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hard&#39;</span><span class="p">:</span>
-                    <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">intervention</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">t</span> <span class="o">-</span> <span class="n">transient</span><span class="p">]</span>
-                    <span class="c1"># Move to next j and skip link_props-loop from parents below </span>
-                    <span class="k">continue</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="n">intervention</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">t</span> <span class="o">-</span> <span class="n">transient</span><span class="p">]</span>
-
-            <span class="c1"># This loop is only entered if intervention_type != &#39;hard&#39;</span>
-            <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="n">func</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-                <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="n">coeff</span> <span class="o">*</span> <span class="n">func</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">+</span> <span class="n">lag</span><span class="p">,</span> <span class="n">var</span><span class="p">])</span>
-
-    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">transient</span><span class="p">:]</span>
-
-    <span class="n">nonvalid</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">data</span><span class="p">))</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="n">data</span><span class="p">)))</span>
-
-    <span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">nonvalid</span></div>
-
-<span class="k">def</span> <span class="nf">_get_minmax_lag</span><span class="p">(</span><span class="n">links</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper function to retrieve tau_min and tau_max from links.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-    <span class="c1"># Get maximum time lag</span>
-    <span class="n">min_lag</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-    <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_props</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="c1"># func = link_props[2]</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">coeff</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span> <span class="ow">or</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">min_lag</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">min_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-                    <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span>
-                <span class="n">min_lag</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">min_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-                <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>   
-
-    <span class="k">return</span> <span class="n">min_lag</span><span class="p">,</span> <span class="n">max_lag</span>
-
-<span class="k">def</span> <span class="nf">_get_parents</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">exclude_contemp</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper function to parents from links</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-    <span class="c1"># Get maximum time lag</span>
-    <span class="n">parents</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="n">parents</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-            <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="c1"># func = link_props[2]</span>
-            <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">exclude_contemp</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
-                    <span class="n">parents</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">parents</span>
-
-<span class="k">def</span> <span class="nf">_get_children</span><span class="p">(</span><span class="n">parents</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper function to children from parents</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents</span><span class="p">)</span>
-    <span class="n">children</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">j</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)])</span>
-
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">par</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-            <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">par</span>
-            <span class="n">children</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)))</span>
-
-    <span class="k">return</span> <span class="n">children</span>
-
-<div class="viewcode-block" id="links_to_graph"><a class="viewcode-back" href="../../../index.html#tigramite.toymodels.structural_causal_processes.links_to_graph">[docs]</a><span class="k">def</span> <span class="nf">links_to_graph</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper function to convert dictionary of links to graph array format.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    links : dict</span>
-<span class="sd">        Dictionary of form {0:[((0, -1), coeff, func), ...], 1:[...], ...}.</span>
-<span class="sd">        Also format {0:[(0, -1), ...], 1:[...], ...} is allowed.</span>
-<span class="sd">    tau_max : int or None</span>
-<span class="sd">        Maximum lag. If None, the maximum lag in links is used.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    graph : array of shape (N, N, tau_max+1)</span>
-<span class="sd">        Matrix format of graph with 1 for true links and 0 else.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-    <span class="c1"># Get maximum time lag</span>
-    <span class="n">min_lag</span><span class="p">,</span> <span class="n">max_lag</span> <span class="o">=</span> <span class="n">_get_minmax_lag</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-
-    <span class="c1"># Set maximum lag</span>
-    <span class="k">if</span> <span class="n">tau_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">tau_max</span> <span class="o">=</span> <span class="n">max_lag</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">max_lag</span> <span class="o">&gt;</span> <span class="n">tau_max</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;tau_max is smaller than maximum lag = </span><span class="si">%d</span><span class="s2"> &quot;</span>
-                             <span class="s2">&quot;found in links, use tau_max=None or larger &quot;</span>
-                             <span class="s2">&quot;value&quot;</span> <span class="o">%</span> <span class="n">max_lag</span><span class="p">)</span>
-
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">links</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-        <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">link_props</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">coeff</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="k">if</span> <span class="n">coeff</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">var</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">)]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-                    <span class="k">if</span> <span class="n">lag</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span>
-                <span class="n">graph</span><span class="p">[</span><span class="n">var</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">)]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
-                <span class="k">if</span> <span class="n">lag</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;--&quot;</span>
-
-    <span class="k">return</span> <span class="n">graph</span></div>
-
-<div class="viewcode-block" id="dag_to_links"><a class="viewcode-back" href="../../../index.html#tigramite.toymodels.structural_causal_processes.dag_to_links">[docs]</a><span class="k">def</span> <span class="nf">dag_to_links</span><span class="p">(</span><span class="n">dag</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper function to convert DAG graph to dictionary of parents.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    dag : array of shape (N, N, tau_max+1)</span>
-<span class="sd">        Matrix format of graph in string format. Must be DAG.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    parents : dict</span>
-<span class="sd">        Dictionary of form {0:[(0, -1), ...], 1:[...], ...}.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">N</span> <span class="o">=</span> <span class="n">dag</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="n">parents</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">j</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)])</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">dag</span><span class="o">==</span><span class="s1">&#39;o-o&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">dag</span><span class="o">==</span><span class="s1">&#39;x-x&#39;</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;graph must be DAG.&quot;</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">dag</span><span class="o">==</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">)):</span>
-        <span class="n">parents</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">parents</span></div>
-
-<div class="viewcode-block" id="generate_structural_causal_process"><a class="viewcode-back" href="../../../index.html#tigramite.toymodels.structural_causal_processes.generate_structural_causal_process">[docs]</a><span class="k">def</span> <span class="nf">generate_structural_causal_process</span><span class="p">(</span>
-                <span class="n">N</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> 
-                <span class="n">L</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> 
-                <span class="n">dependency_funcs</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;linear&#39;</span><span class="p">],</span> 
-                <span class="n">dependency_coeffs</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span> 
-                <span class="n">auto_coeffs</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> 
-                <span class="n">contemp_fraction</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span>
-                <span class="n">max_lag</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> 
-                <span class="n">noise_dists</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;gaussian&#39;</span><span class="p">],</span>
-                <span class="n">noise_means</span><span class="o">=</span><span class="p">[</span><span class="mf">0.</span><span class="p">],</span>
-                <span class="n">noise_sigmas</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
-                <span class="n">noise_seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;&quot;Randomly generates a structural causal process based on input characteristics.</span>
-
-<span class="sd">    The process has the form </span>
-
-<span class="sd">    .. math:: X^j_t = \\eta^j_t + a^j X^j_{t-1} + \\sum_{X^i_{t-\\tau}\\in pa(X^j_t)}</span>
-<span class="sd">              c^i_{\\tau} f^i_{\\tau}(X^i_{t-\\tau})</span>
-
-<span class="sd">    where ``j = 1, ..., N``. Here the properties of :math:`\\eta^j_t` are</span>
-<span class="sd">    randomly frawn from the noise parameters (see below), :math:`pa</span>
-<span class="sd">    (X^j_t)` are the causal parents drawn randomly such that in total ``L``</span>
-<span class="sd">    links occur out of which ``contemp_fraction`` are contemporaneous and</span>
-<span class="sd">    their time lags are drawn from ``[0 or 1..max_lag]``, the</span>
-<span class="sd">    coefficients :math:`c^i_{\\tau}` are drawn from</span>
-<span class="sd">    ``dependency_coeffs``, :math:`a^j` are drawn from ``auto_coeffs``,</span>
-<span class="sd">    and :math:`f^i_{\\tau}` are drawn from ``dependency_funcs``.</span>
-
-<span class="sd">    The returned dictionary links has the format </span>
-<span class="sd">    ``{0:[((i, -tau), coeff, func),...], 1:[...], ...}`` </span>
-<span class="sd">    where ``func`` can be an arbitrary (nonlinear) function provided</span>
-<span class="sd">    as a python callable with one argument and coeff is the multiplication</span>
-<span class="sd">    factor. The noise distributions of :math:`\\eta^j` are returned in</span>
-<span class="sd">    ``noises``, see specifics below.</span>
-
-<span class="sd">    The process might be non-stationary. In case of asymptotically linear</span>
-<span class="sd">    dependency functions and no contemporaneous links this can be checked with</span>
-<span class="sd">    ``check_stationarity(...)``. Otherwise check by generating a large sample</span>
-<span class="sd">    and test for np.inf.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    N : int</span>
-<span class="sd">        Number of variables.</span>
-<span class="sd">    L : int</span>
-<span class="sd">        Number of cross-links between two different variables.</span>
-<span class="sd">    dependency_funcs : list</span>
-<span class="sd">        List of callables or strings &#39;linear&#39; or &#39;nonlinear&#39; for a linear and a specific nonlinear function</span>
-<span class="sd">        that is asymptotically linear.</span>
-<span class="sd">    dependency_coeffs : list</span>
-<span class="sd">        List of floats from which the coupling coefficients are randomly drawn.</span>
-<span class="sd">    auto_coeffs : list</span>
-<span class="sd">        List of floats from which the lag-1 autodependencies are randomly drawn.</span>
-<span class="sd">    contemp_fraction : float [0., 1]</span>
-<span class="sd">        Fraction of the L links that are contemporaneous (lag zero).</span>
-<span class="sd">    max_lag : int</span>
-<span class="sd">        Maximum lag from which the time lags of links are drawn.</span>
-<span class="sd">    noise_dists : list</span>
-<span class="sd">        List of noise functions. Either in</span>
-<span class="sd">        {&#39;gaussian&#39;, &#39;weibull&#39;, &#39;uniform&#39;} or user-specified, in which case</span>
-<span class="sd">        it must be parametrized just by the size parameter. E.g. def beta</span>
-<span class="sd">        (T): return np.random.beta(a=1, b=0.5, T)</span>
-<span class="sd">    noise_means : list</span>
-<span class="sd">        Noise mean. Only used for noise in {&#39;gaussian&#39;, &#39;weibull&#39;, &#39;uniform&#39;}.</span>
-<span class="sd">    noise_sigmas : list</span>
-<span class="sd">        Noise standard deviation. Only used for noise in {&#39;gaussian&#39;, &#39;weibull&#39;, &#39;uniform&#39;}.   </span>
-<span class="sd">    seed : int</span>
-<span class="sd">        Random seed to draw the above random functions from.</span>
-<span class="sd">    noise_seed : int</span>
-<span class="sd">        Random seed for noise function random generator.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    links : dict</span>
-<span class="sd">        Dictionary of form {0:[((0, -1), coeff, func), ...], 1:[...], ...}.</span>
-<span class="sd">    noises : list</span>
-<span class="sd">        List of N noise functions to call by noise(T) where T is the time series length.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    
-    <span class="c1"># Init random states</span>
-    <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">random_state_noise</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="n">noise_seed</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">linear</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-    <span class="k">def</span> <span class="nf">nonlinear</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mf">5.</span> <span class="o">*</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="mf">20.</span><span class="p">))</span>
-
-    <span class="k">if</span> <span class="n">max_lag</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">contemp_fraction</span> <span class="o">=</span> <span class="mf">1.</span>
-
-    <span class="k">if</span> <span class="n">contemp_fraction</span> <span class="o">&gt;</span> <span class="mf">0.</span><span class="p">:</span>
-        <span class="n">ordered_pairs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
-        <span class="n">max_poss_links</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">L</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ordered_pairs</span><span class="p">))</span>
-        <span class="n">L_contemp</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">contemp_fraction</span><span class="o">*</span><span class="n">max_poss_links</span><span class="p">)</span>
-        <span class="n">L_lagged</span> <span class="o">=</span> <span class="n">max_poss_links</span> <span class="o">-</span> <span class="n">L_contemp</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">L_lagged</span> <span class="o">=</span> <span class="n">L</span>
-        <span class="n">L_contemp</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="c1"># Random order</span>
-    <span class="n">causal_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">random_state</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">N</span><span class="p">))</span>
-
-    <span class="c1"># Init link dict</span>
-    <span class="n">links</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)])</span>
-
-    <span class="c1"># Generate auto-dependencies at lag 1</span>
-    <span class="k">if</span> <span class="n">max_lag</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">causal_order</span><span class="p">:</span>
-            <span class="n">a</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">auto_coeffs</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">a</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                <span class="n">links</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(((</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="nb">float</span><span class="p">(</span><span class="n">a</span><span class="p">),</span> <span class="n">linear</span><span class="p">))</span>
-
-    <span class="c1"># Non-cyclic contemp random pairs of links such that</span>
-    <span class="c1"># index of cause &lt; index of effect</span>
-    <span class="c1"># Take up to (!) L_contemp links</span>
-    <span class="n">ordered_pairs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="n">random_state</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">ordered_pairs</span><span class="p">)</span>
-    <span class="n">contemp_links</span> <span class="o">=</span> <span class="p">[(</span><span class="n">causal_order</span><span class="p">[</span><span class="n">pair</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">causal_order</span><span class="p">[</span><span class="n">pair</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span> 
-                    <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">ordered_pairs</span><span class="p">[:</span><span class="n">L_contemp</span><span class="p">]]</span>
-
-    <span class="c1"># Possibly cyclic lagged random pairs of links </span>
-    <span class="c1"># where we remove already chosen contemp links</span>
-    <span class="c1"># Take up to (!) L_contemp links</span>
-    <span class="n">unordered_pairs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">permutations</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="n">unordered_pairs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">unordered_pairs</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">ordered_pairs</span><span class="p">[:</span><span class="n">L_contemp</span><span class="p">]))</span>
-    <span class="n">random_state</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">unordered_pairs</span><span class="p">)</span>
-    <span class="n">lagged_links</span> <span class="o">=</span> <span class="p">[(</span><span class="n">causal_order</span><span class="p">[</span><span class="n">pair</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">causal_order</span><span class="p">[</span><span class="n">pair</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span> 
-                    <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">unordered_pairs</span><span class="p">[:</span><span class="n">L_lagged</span><span class="p">]]</span>
-
-    <span class="n">chosen_links</span> <span class="o">=</span> <span class="n">lagged_links</span> <span class="o">+</span> <span class="n">contemp_links</span>
-
-    <span class="c1"># Populate links</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">chosen_links</span><span class="p">:</span>
-
-        <span class="c1"># Choose lag</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">contemp_links</span><span class="p">:</span>
-            <span class="n">tau</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">tau</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">random_state</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
-
-        <span class="c1"># Choose dependency</span>
-        <span class="n">c</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">dependency_coeffs</span><span class="p">))</span>
-        <span class="k">if</span> <span class="n">c</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">func</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">dependency_funcs</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">func</span> <span class="o">==</span> <span class="s1">&#39;linear&#39;</span><span class="p">:</span>
-                <span class="n">func</span> <span class="o">=</span> <span class="n">linear</span>
-            <span class="k">elif</span> <span class="n">func</span> <span class="o">==</span> <span class="s1">&#39;nonlinear&#39;</span><span class="p">:</span>
-                <span class="n">func</span> <span class="o">=</span> <span class="n">nonlinear</span>
-
-            <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(((</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="o">-</span><span class="n">tau</span><span class="p">),</span> <span class="n">c</span><span class="p">,</span> <span class="n">func</span><span class="p">))</span>
-
-    <span class="c1"># Now generate noise functions</span>
-    <span class="c1"># Either choose among pre-defined noise types or supply your own</span>
-    <span class="k">class</span> <span class="nc">NoiseModel</span><span class="p">:</span>
-        <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma</span>
-        <span class="k">def</span> <span class="nf">gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span>
-            <span class="c1"># Get zero-mean unit variance gaussian distribution</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="o">*</span><span class="n">random_state_noise</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
-        <span class="k">def</span> <span class="nf">weibull</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span> 
-            <span class="c1"># Get zero-mean sigma variance weibull distribution</span>
-            <span class="n">a</span> <span class="o">=</span> <span class="mi">2</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">gamma</span><span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="n">a</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">variance</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">gamma</span><span class="p">(</span><span class="mf">2.</span><span class="o">/</span><span class="n">a</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">gamma</span><span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="n">a</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="o">*</span><span class="p">(</span><span class="n">random_state_noise</span><span class="o">.</span><span class="n">weibull</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">a</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">T</span><span class="p">)</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">variance</span><span class="p">)</span>
-        <span class="k">def</span> <span class="nf">uniform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span> 
-            <span class="c1"># Get zero-mean sigma variance uniform distribution</span>
-            <span class="n">mean</span> <span class="o">=</span> <span class="mf">0.5</span>
-            <span class="n">variance</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="mf">12.</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="o">*</span><span class="p">(</span><span class="n">random_state_noise</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">T</span><span class="p">)</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">variance</span><span class="p">)</span>
-
-    <span class="n">noises</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">links</span><span class="p">:</span>
-        <span class="n">noise_dist</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">noise_dists</span><span class="p">)</span>
-        <span class="n">noise_mean</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">noise_means</span><span class="p">)</span>
-        <span class="n">noise_sigma</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">noise_sigmas</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">noise_dist</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="s1">&#39;weibull&#39;</span><span class="p">,</span> <span class="s1">&#39;uniform&#39;</span><span class="p">]:</span>
-            <span class="n">noise</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">NoiseModel</span><span class="p">(</span><span class="n">mean</span> <span class="o">=</span> <span class="n">noise_mean</span><span class="p">,</span> <span class="n">sigma</span> <span class="o">=</span> <span class="n">noise_sigma</span><span class="p">),</span> <span class="n">noise_dist</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">noise</span> <span class="o">=</span> <span class="n">noise_dist</span>
-        
-        <span class="n">noises</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">noise</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">links</span><span class="p">,</span> <span class="n">noises</span></div>
-
-<div class="viewcode-block" id="check_stationarity"><a class="viewcode-back" href="../../../index.html#tigramite.toymodels.structural_causal_processes.check_stationarity">[docs]</a><span class="k">def</span> <span class="nf">check_stationarity</span><span class="p">(</span><span class="n">links</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns stationarity according to a unit root test.</span>
-
-<span class="sd">    Assumes an at least asymptotically linear vector autoregressive process</span>
-<span class="sd">    without contemporaneous links.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    links : dict</span>
-<span class="sd">        Dictionary of form {0:[((0, -1), coeff, func), ...], 1:[...], ...}.</span>
-<span class="sd">        Also format {0:[(0, -1), ...], 1:[...], ...} is allowed.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    stationary : bool</span>
-<span class="sd">        True if VAR process is stationary.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
-    <span class="c1"># Check parameters</span>
-    <span class="n">max_lag</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-            <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="c1"># coeff = link_props[1]</span>
-            <span class="c1"># coupling = link_props[2]</span>
-
-            <span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">))</span>
-
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span><span class="n">N</span><span class="p">,</span><span class="n">max_lag</span><span class="p">))</span>
-    <span class="n">couplings</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">link_props</span> <span class="ow">in</span> <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-            <span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">coeff</span>    <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">coupling</span> <span class="o">=</span> <span class="n">link_props</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-            <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="n">var</span><span class="p">,</span><span class="nb">abs</span><span class="p">(</span><span class="n">lag</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">coeff</span>
-            <span class="n">couplings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">coupling</span><span class="p">)</span>
-
-    <span class="n">stabmat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="o">*</span><span class="n">max_lag</span><span class="p">,</span><span class="n">N</span><span class="o">*</span><span class="n">max_lag</span><span class="p">))</span>
-    <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">N</span><span class="o">*</span><span class="n">max_lag</span><span class="p">,</span><span class="n">N</span><span class="p">):</span>
-        <span class="n">stabmat</span><span class="p">[:</span><span class="n">N</span><span class="p">,</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">N</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[:,:,</span><span class="n">index</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">index</span> <span class="o">&lt;</span> <span class="n">max_lag</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span>
-            <span class="n">stabmat</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="n">N</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">N</span><span class="p">,</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">N</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-        <span class="n">index</span> <span class="o">+=</span> <span class="mi">1</span>
-
-    <span class="n">eig</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">stabmat</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">eig</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">1.</span><span class="p">):</span>
-        <span class="n">stationary</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">stationary</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">return</span> <span class="n">stationary</span></div>
-    <span class="c1"># if len(eig) == 0:</span>
-    <span class="c1">#     return stationary, 0.</span>
-    <span class="c1"># else:</span>
-    <span class="c1">#     return stationary, np.abs(eig).max()</span>
-
-<span class="k">class</span> <span class="nc">_Logger</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Class to append print output to a string which can be saved&quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">terminal</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>       <span class="c1"># open(&quot;log.dat&quot;, &quot;a&quot;)</span>
-
-    <span class="k">def</span> <span class="nf">write</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">message</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">terminal</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">message</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">+=</span> <span class="n">message</span>  <span class="c1"># .write(message)</span>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="c1">## Generate some time series from a structural causal process</span>
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-    <span class="k">def</span> <span class="nf">nonlin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mf">5.</span> <span class="o">*</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="mf">20.</span><span class="p">))</span>
-
-    <span class="n">links</span><span class="p">,</span> <span class="n">noises</span> <span class="o">=</span> <span class="n">generate_structural_causal_process</span><span class="p">()</span>
-    
-    <span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span>
-             <span class="n">T</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="n">noises</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-    <span class="c1"># links = {0: [((0, -1), 0.9, lin_f)],</span>
-    <span class="c1">#          1: [((1, -1), 0.8, lin_f), ((0, -1), 0.3, nonlin_f)],</span>
-    <span class="c1">#          2: [((2, -1), 0.7, lin_f), ((1, 0), -0.2, lin_f)],</span>
-    <span class="c1">#          }</span>
-    <span class="c1"># noises = [np.random.randn, np.random.randn, np.random.randn]</span>
-
-    <span class="c1"># data, nonstat = structural_causal_process(links,</span>
-    <span class="c1">#  T=100, noises=noises)</span>
-
-</pre></div>
-
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="../../../index.html">Tigramite</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../index.html">Documentation overview</a><ul>
-  <li><a href="../../index.html">Module code</a><ul>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
-
-
-
-
-
-
-
-
-        </div>
-      </div>
-      <div class="clearer"></div>
-    </div>
-    <div class="footer">
-      &copy;2023, Jakob Runge.
-      
-      |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
-      
-    </div>
-
-    
-
-    
-  </body>
-</html>
\ No newline at end of file
diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt
deleted file mode 100644
index 8227942e..00000000
--- a/docs/_build/html/_sources/index.rst.txt
+++ /dev/null
@@ -1,191 +0,0 @@
-.. Tigramite documentation master file, created by
-   sphinx-quickstart on Wed Oct  5 18:51:08 2023.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-Welcome to Tigramite's documentation!
-=====================================
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
-
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-
-.. Tigramite documentation master file, created by
-   sphinx-quickstart on Thu May 11 18:32:05 2017.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-TIGRAMITE
-=========
-
-`Github repo <https://github.com/jakobrunge/tigramite/>`_
-
-Tigramite is a causal time series analysis python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use these graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
-
-
-- PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019). https://advances.sciencemag.org/content/5/11/eaau4996
-
-- PCMCI+: J. Runge (2020): Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020,Toronto, Canada, 2019, AUAI Press, 2020. http://auai.org/uai2020/proceedings/579_main_paper.pdf
-
-- LPCMCI: Gerhardus, A. & Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders Advances in Neural Information Processing Systems, 2020, 33. https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html
-
-- Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310. https://aip.scitation.org/doi/10.1063/1.5025050
-
-- Nature Communications Perspective paper: https://www.nature.com/articles/s41467-019-10105-3
-
-- Causal effects: J. Runge, Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables, Advances in Neural Information Processing Systems, 2021, 34
-
-- Mediation class: J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. http://doi.org/10.1038/ncomms9502
-
-- Mediation class: J. Runge (2015): Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 62829. http://doi.org/10.1103/PhysRevE.92.062829
-
-- CMIknn: J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. http://proceedings.mlr.press/v84/runge18a.html
-
-
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
-
-.. autosummary::
-
-   tigramite.pcmci.PCMCI
-   tigramite.lpcmci.LPCMCI
-   tigramite.independence_tests.independence_tests_base.CondIndTest
-   tigramite.independence_tests.parcorr.ParCorr
-   tigramite.independence_tests.robust_parcorr.RobustParCorr
-   tigramite.independence_tests.gpdc.GPDC
-   tigramite.independence_tests.gpdc_torch.GPDCtorch
-   tigramite.independence_tests.cmiknn.CMIknn
-   tigramite.independence_tests.cmisymb.CMIsymb
-   tigramite.independence_tests.oracle_conditional_independence.OracleCI
-   tigramite.independence_tests.parcorr_mult.ParCorrMult
-   tigramite.independence_tests.gsquared.Gsquared
-   tigramite.independence_tests.parcorr_wls.ParCorrWLS
-   tigramite.independence_tests.regressionCI.RegressionCI
-   tigramite.causal_effects.CausalEffects
-   tigramite.models.Models
-   tigramite.models.LinearMediation
-   tigramite.models.Prediction
-   tigramite.data_processing
-   tigramite.toymodels.structural_causal_processes
-   tigramite.plotting
-
-
-:mod:`tigramite.pcmci`: PCMCI
-===========================================
-
-.. autoclass:: tigramite.pcmci.PCMCI
-   :members:
-
-
-:mod:`tigramite.lpcmci`: LPCMCI
-===========================================
-
-.. autoclass:: tigramite.lpcmci.LPCMCI
-   :members:
-
-
-:mod:`tigramite.independence_tests`: Conditional independence tests
-=================================================================================
-
-Base class:
-
-.. autoclass:: tigramite.independence_tests.independence_tests_base.CondIndTest
-   :members:
-
-Test statistics:
-
-.. autoclass:: tigramite.independence_tests.parcorr.ParCorr
-   :members:
-
-.. autoclass:: tigramite.independence_tests.robust_parcorr.RobustParCorr
-   :members:
-
-.. autoclass:: tigramite.independence_tests.gpdc.GPDC
-   :members:
-
-.. autoclass:: tigramite.independence_tests.gpdc_torch.GPDCtorch
-   :members:
-
-.. autoclass:: tigramite.independence_tests.cmiknn.CMIknn
-   :members:
-
-.. autoclass:: tigramite.independence_tests.cmisymb.CMIsymb
-   :members:
-
-.. autoclass:: tigramite.independence_tests.oracle_conditional_independence.OracleCI
-   :members:
-
-.. autoclass:: tigramite.independence_tests.parcorr_mult.ParCorrMult
-   :members:
-
-.. autoclass:: tigramite.independence_tests.gsquared.Gsquared
-   :members:
-
-.. autoclass:: tigramite.independence_tests.parcorr_wls.ParCorrWLS
-   :members:
-
-.. autoclass:: tigramite.independence_tests.regressionCI.RegressionCI
-   :members:
-
-:mod:`tigramite.causal_effects`: Causal Effect analysis
-===========================================================
-
-.. autoclass:: tigramite.causal_effects.CausalEffects
-   :members:
-
-
-:mod:`tigramite.models`: Time series modeling, mediation, and prediction
-========================================================================
-
-Base class:
-
-.. autoclass:: tigramite.models.Models
-   :members:
-
-Derived classes:
-
-.. autoclass:: tigramite.models.LinearMediation
-   :members:
-
-.. autoclass:: tigramite.models.Prediction
-   :members:
-
-
-:mod:`tigramite.data_processing`: Data processing functions
-===========================================================
-
-.. automodule:: tigramite.data_processing
-   :members:
-
-
-:mod:`tigramite.toymodels`: Toy model generators
-===========================================================
-
-.. automodule:: tigramite.toymodels.structural_causal_processes
-   :members:
-
-
-:mod:`tigramite.plotting`: Plotting functions
-=============================================
-
-.. automodule:: tigramite.plotting
-   :members:
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js
deleted file mode 100644
index 8549469d..00000000
--- a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js
+++ /dev/null
@@ -1,134 +0,0 @@
-/*
- * _sphinx_javascript_frameworks_compat.js
- * ~~~~~~~~~~
- *
- * Compatability shim for jQuery and underscores.js.
- *
- * WILL BE REMOVED IN Sphinx 6.0
- * xref RemovedInSphinx60Warning
- *
- */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-
-/**
- * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
- */
-jQuery.urldecode = function(x) {
-    if (!x) {
-        return x
-    }
-    return decodeURIComponent(x.replace(/\+/g, ' '));
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
-    if (typeof s === 'undefined')
-        s = document.location.search;
-    var parts = s.substr(s.indexOf('?') + 1).split('&');
-    var result = {};
-    for (var i = 0; i < parts.length; i++) {
-        var tmp = parts[i].split('=', 2);
-        var key = jQuery.urldecode(tmp[0]);
-        var value = jQuery.urldecode(tmp[1]);
-        if (key in result)
-            result[key].push(value);
-        else
-            result[key] = [value];
-    }
-    return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
-    function highlight(node, addItems) {
-        if (node.nodeType === 3) {
-            var val = node.nodeValue;
-            var pos = val.toLowerCase().indexOf(text);
-            if (pos >= 0 &&
-                !jQuery(node.parentNode).hasClass(className) &&
-                !jQuery(node.parentNode).hasClass("nohighlight")) {
-                var span;
-                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
-                if (isInSVG) {
-                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-                } else {
-                    span = document.createElement("span");
-                    span.className = className;
-                }
-                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
-                    document.createTextNode(val.substr(pos + text.length)),
-                    node.nextSibling));
-                node.nodeValue = val.substr(0, pos);
-                if (isInSVG) {
-                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
-                    var bbox = node.parentElement.getBBox();
-                    rect.x.baseVal.value = bbox.x;
-                    rect.y.baseVal.value = bbox.y;
-                    rect.width.baseVal.value = bbox.width;
-                    rect.height.baseVal.value = bbox.height;
-                    rect.setAttribute('class', className);
-                    addItems.push({
-                        "parent": node.parentNode,
-                        "target": rect});
-                }
-            }
-        }
-        else if (!jQuery(node).is("button, select, textarea")) {
-            jQuery.each(node.childNodes, function() {
-                highlight(this, addItems);
-            });
-        }
-    }
-    var addItems = [];
-    var result = this.each(function() {
-        highlight(this, addItems);
-    });
-    for (var i = 0; i < addItems.length; ++i) {
-        jQuery(addItems[i].parent).before(addItems[i].target);
-    }
-    return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
-    jQuery.uaMatch = function(ua) {
-        ua = ua.toLowerCase();
-
-        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
-            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
-            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
-            /(msie) ([\w.]+)/.exec(ua) ||
-            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
-            [];
-
-        return {
-            browser: match[ 1 ] || "",
-            version: match[ 2 ] || "0"
-        };
-    };
-    jQuery.browser = {};
-    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
diff --git a/docs/_build/html/_static/alabaster.css b/docs/_build/html/_static/alabaster.css
deleted file mode 100644
index 0eddaeb0..00000000
--- a/docs/_build/html/_static/alabaster.css
+++ /dev/null
@@ -1,701 +0,0 @@
-@import url("basic.css");
-
-/* -- page layout ----------------------------------------------------------- */
-
-body {
-    font-family: Georgia, serif;
-    font-size: 17px;
-    background-color: #fff;
-    color: #000;
-    margin: 0;
-    padding: 0;
-}
-
-
-div.document {
-    width: 940px;
-    margin: 30px auto 0 auto;
-}
-
-div.documentwrapper {
-    float: left;
-    width: 100%;
-}
-
-div.bodywrapper {
-    margin: 0 0 0 220px;
-}
-
-div.sphinxsidebar {
-    width: 220px;
-    font-size: 14px;
-    line-height: 1.5;
-}
-
-hr {
-    border: 1px solid #B1B4B6;
-}
-
-div.body {
-    background-color: #fff;
-    color: #3E4349;
-    padding: 0 30px 0 30px;
-}
-
-div.body > .section {
-    text-align: left;
-}
-
-div.footer {
-    width: 940px;
-    margin: 20px auto 30px auto;
-    font-size: 14px;
-    color: #888;
-    text-align: right;
-}
-
-div.footer a {
-    color: #888;
-}
-
-p.caption {
-    font-family: inherit;
-    font-size: inherit;
-}
-
-
-div.relations {
-    display: none;
-}
-
-
-div.sphinxsidebar a {
-    color: #444;
-    text-decoration: none;
-    border-bottom: 1px dotted #999;
-}
-
-div.sphinxsidebar a:hover {
-    border-bottom: 1px solid #999;
-}
-
-div.sphinxsidebarwrapper {
-    padding: 18px 10px;
-}
-
-div.sphinxsidebarwrapper p.logo {
-    padding: 0;
-    margin: -10px 0 0 0px;
-    text-align: center;
-}
-
-div.sphinxsidebarwrapper h1.logo {
-    margin-top: -10px;
-    text-align: center;
-    margin-bottom: 5px;
-    text-align: left;
-}
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-div.sphinxsidebarwrapper h1.logo-name {
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diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css
deleted file mode 100644
index 08896771..00000000
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diff --git a/docs/_build/html/_static/custom.css b/docs/_build/html/_static/custom.css
deleted file mode 100644
index 2a924f1d..00000000
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+++ /dev/null
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diff --git a/docs/_build/html/_static/doctools.js b/docs/_build/html/_static/doctools.js
deleted file mode 100644
index c3db08d1..00000000
--- a/docs/_build/html/_static/doctools.js
+++ /dev/null
@@ -1,264 +0,0 @@
-/*
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- * ~~~~~~~~~~~
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- * Base JavaScript utilities for all Sphinx HTML documentation.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
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- */
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-    Documentation.initDomainIndexTable();
-    Documentation.initOnKeyListeners();
-  },
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-            Documentation.gettext("Hide Search Matches") +
-            "</a></p>"
-        )
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-  /**
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-    window.history.replaceState({}, "", url);
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-    const blacklistedElements = new Set([
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-      switch (event.key) {
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-          Documentation.focusSearchBar();
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-};
-
-// quick alias for translations
-const _ = Documentation.gettext;
-
-_ready(Documentation.init);
diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js
deleted file mode 100644
index b5bf05a2..00000000
--- a/docs/_build/html/_static/documentation_options.js
+++ /dev/null
@@ -1,14 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '5.2',
-    LANGUAGE: 'en',
-    COLLAPSE_INDEX: false,
-    BUILDER: 'html',
-    FILE_SUFFIX: '.html',
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\ No newline at end of file
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diff --git a/docs/_build/html/_static/jquery.js b/docs/_build/html/_static/jquery.js
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index c4c6022f..00000000
--- a/docs/_build/html/_static/jquery.js
+++ /dev/null
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diff --git a/docs/_build/html/_static/language_data.js b/docs/_build/html/_static/language_data.js
deleted file mode 100644
index 2e22b06a..00000000
--- a/docs/_build/html/_static/language_data.js
+++ /dev/null
@@ -1,199 +0,0 @@
-/*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, is available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
-  var step2list = {
-    ational: 'ate',
-    tional: 'tion',
-    enci: 'ence',
-    anci: 'ance',
-    izer: 'ize',
-    bli: 'ble',
-    alli: 'al',
-    entli: 'ent',
-    eli: 'e',
-    ousli: 'ous',
-    ization: 'ize',
-    ation: 'ate',
-    ator: 'ate',
-    alism: 'al',
-    iveness: 'ive',
-    fulness: 'ful',
-    ousness: 'ous',
-    aliti: 'al',
-    iviti: 'ive',
-    biliti: 'ble',
-    logi: 'log'
-  };
-
-  var step3list = {
-    icate: 'ic',
-    ative: '',
-    alize: 'al',
-    iciti: 'ic',
-    ical: 'ic',
-    ful: '',
-    ness: ''
-  };
-
-  var c = "[^aeiou]";          // consonant
-  var v = "[aeiouy]";          // vowel
-  var C = c + "[^aeiouy]*";    // consonant sequence
-  var V = v + "[aeiou]*";      // vowel sequence
-
-  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
-  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
-  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
-  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
-
-  this.stemWord = function (w) {
-    var stem;
-    var suffix;
-    var firstch;
-    var origword = w;
-
-    if (w.length < 3)
-      return w;
-
-    var re;
-    var re2;
-    var re3;
-    var re4;
-
-    firstch = w.substr(0,1);
-    if (firstch == "y")
-      w = firstch.toUpperCase() + w.substr(1);
-
-    // Step 1a
-    re = /^(.+?)(ss|i)es$/;
-    re2 = /^(.+?)([^s])s$/;
-
-    if (re.test(w))
-      w = w.replace(re,"$1$2");
-    else if (re2.test(w))
-      w = w.replace(re2,"$1$2");
-
-    // Step 1b
-    re = /^(.+?)eed$/;
-    re2 = /^(.+?)(ed|ing)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      re = new RegExp(mgr0);
-      if (re.test(fp[1])) {
-        re = /.$/;
-        w = w.replace(re,"");
-      }
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1];
-      re2 = new RegExp(s_v);
-      if (re2.test(stem)) {
-        w = stem;
-        re2 = /(at|bl|iz)$/;
-        re3 = new RegExp("([^aeiouylsz])\\1$");
-        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-        if (re2.test(w))
-          w = w + "e";
-        else if (re3.test(w)) {
-          re = /.$/;
-          w = w.replace(re,"");
-        }
-        else if (re4.test(w))
-          w = w + "e";
-      }
-    }
-
-    // Step 1c
-    re = /^(.+?)y$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(s_v);
-      if (re.test(stem))
-        w = stem + "i";
-    }
-
-    // Step 2
-    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step2list[suffix];
-    }
-
-    // Step 3
-    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step3list[suffix];
-    }
-
-    // Step 4
-    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
-    re2 = /^(.+?)(s|t)(ion)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      if (re.test(stem))
-        w = stem;
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1] + fp[2];
-      re2 = new RegExp(mgr1);
-      if (re2.test(stem))
-        w = stem;
-    }
-
-    // Step 5
-    re = /^(.+?)e$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      re2 = new RegExp(meq1);
-      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
-        w = stem;
-    }
-    re = /ll$/;
-    re2 = new RegExp(mgr1);
-    if (re.test(w) && re2.test(w)) {
-      re = /.$/;
-      w = w.replace(re,"");
-    }
-
-    // and turn initial Y back to y
-    if (firstch == "y")
-      w = firstch.toLowerCase() + w.substr(1);
-    return w;
-  }
-}
-
diff --git a/docs/_build/html/_static/minus.png b/docs/_build/html/_static/minus.png
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deleted file mode 100644
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diff --git a/docs/_build/html/_static/pygments.css b/docs/_build/html/_static/pygments.css
deleted file mode 100644
index 87f8bd12..00000000
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-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-.highlight .hll { background-color: #ffffcc }
-.highlight { background: #f8f8f8; }
-.highlight .c { color: #8f5902; font-style: italic } /* Comment */
-.highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
-.highlight .g { color: #000000 } /* Generic */
-.highlight .k { color: #004461; font-weight: bold } /* Keyword */
-.highlight .l { color: #000000 } /* Literal */
-.highlight .n { color: #000000 } /* Name */
-.highlight .o { color: #582800 } /* Operator */
-.highlight .x { color: #000000 } /* Other */
-.highlight .p { color: #000000; font-weight: bold } /* Punctuation */
-.highlight .ch { color: #8f5902; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #8f5902; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #8f5902 } /* Comment.Preproc */
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diff --git a/docs/_build/html/_static/searchtools.js b/docs/_build/html/_static/searchtools.js
deleted file mode 100644
index ac4d5861..00000000
--- a/docs/_build/html/_static/searchtools.js
+++ /dev/null
@@ -1,531 +0,0 @@
-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
-  var Scorer = {
-    // Implement the following function to further tweak the score for each result
-    // The function takes a result array [docname, title, anchor, descr, score, filename]
-    // and returns the new score.
-    /*
-    score: result => {
-      const [docname, title, anchor, descr, score, filename] = result
-      return score
-    },
-    */
-
-    // query matches the full name of an object
-    objNameMatch: 11,
-    // or matches in the last dotted part of the object name
-    objPartialMatch: 6,
-    // Additive scores depending on the priority of the object
-    objPrio: {
-      0: 15, // used to be importantResults
-      1: 5, // used to be objectResults
-      2: -5, // used to be unimportantResults
-    },
-    //  Used when the priority is not in the mapping.
-    objPrioDefault: 0,
-
-    // query found in title
-    title: 15,
-    partialTitle: 7,
-    // query found in terms
-    term: 5,
-    partialTerm: 2,
-  };
-}
-
-const _removeChildren = (element) => {
-  while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
-  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, highlightTerms, searchTerms) => {
-  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
-  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-
-  const [docName, title, anchor, descr] = item;
-
-  let listItem = document.createElement("li");
-  let requestUrl;
-  let linkUrl;
-  if (docBuilder === "dirhtml") {
-    // dirhtml builder
-    let dirname = docName + "/";
-    if (dirname.match(/\/index\/$/))
-      dirname = dirname.substring(0, dirname.length - 6);
-    else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
-    linkUrl = requestUrl;
-  } else {
-    // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
-    linkUrl = docName + docLinkSuffix;
-  }
-  const params = new URLSearchParams();
-  params.set("highlight", [...highlightTerms].join(" "));
-  let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + "?" + params.toString() + anchor;
-  linkEl.innerHTML = title;
-  if (descr)
-    listItem.appendChild(document.createElement("span")).innerText =
-      " (" + descr + ")";
-  else if (showSearchSummary)
-    fetch(requestUrl)
-      .then((responseData) => responseData.text())
-      .then((data) => {
-        if (data)
-          listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, highlightTerms)
-          );
-      });
-  Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
-  Search.stopPulse();
-  Search.title.innerText = _("Search Results");
-  if (!resultCount)
-    Search.status.innerText = Documentation.gettext(
-      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
-    );
-  else
-    Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
-};
-const _displayNextItem = (
-  results,
-  resultCount,
-  highlightTerms,
-  searchTerms
-) => {
-  // results left, load the summary and display it
-  // this is intended to be dynamic (don't sub resultsCount)
-  if (results.length) {
-    _displayItem(results.pop(), highlightTerms, searchTerms);
-    setTimeout(
-      () => _displayNextItem(results, resultCount, highlightTerms, searchTerms),
-      5
-    );
-  }
-  // search finished, update title and status message
-  else _finishSearch(resultCount);
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
-  var splitQuery = (query) => query
-      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
-      .filter(term => term)  // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
-  _index: null,
-  _queued_query: null,
-  _pulse_status: -1,
-
-  htmlToText: (htmlString) => {
-    const htmlElement = document
-      .createRange()
-      .createContextualFragment(htmlString);
-    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
-    const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
-    console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
-    );
-    return "";
-  },
-
-  init: () => {
-    const query = new URLSearchParams(window.location.search).get("q");
-    document
-      .querySelectorAll('input[name="q"]')
-      .forEach((el) => (el.value = query));
-    if (query) Search.performSearch(query);
-  },
-
-  loadIndex: (url) =>
-    (document.body.appendChild(document.createElement("script")).src = url),
-
-  setIndex: (index) => {
-    Search._index = index;
-    if (Search._queued_query !== null) {
-      const query = Search._queued_query;
-      Search._queued_query = null;
-      Search.query(query);
-    }
-  },
-
-  hasIndex: () => Search._index !== null,
-
-  deferQuery: (query) => (Search._queued_query = query),
-
-  stopPulse: () => (Search._pulse_status = -1),
-
-  startPulse: () => {
-    if (Search._pulse_status >= 0) return;
-
-    const pulse = () => {
-      Search._pulse_status = (Search._pulse_status + 1) % 4;
-      Search.dots.innerText = ".".repeat(Search._pulse_status);
-      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
-    };
-    pulse();
-  },
-
-  /**
-   * perform a search for something (or wait until index is loaded)
-   */
-  performSearch: (query) => {
-    // create the required interface elements
-    const searchText = document.createElement("h2");
-    searchText.textContent = _("Searching");
-    const searchSummary = document.createElement("p");
-    searchSummary.classList.add("search-summary");
-    searchSummary.innerText = "";
-    const searchList = document.createElement("ul");
-    searchList.classList.add("search");
-
-    const out = document.getElementById("search-results");
-    Search.title = out.appendChild(searchText);
-    Search.dots = Search.title.appendChild(document.createElement("span"));
-    Search.status = out.appendChild(searchSummary);
-    Search.output = out.appendChild(searchList);
-
-    const searchProgress = document.getElementById("search-progress");
-    // Some themes don't use the search progress node
-    if (searchProgress) {
-      searchProgress.innerText = _("Preparing search...");
-    }
-    Search.startPulse();
-
-    // index already loaded, the browser was quick!
-    if (Search.hasIndex()) Search.query(query);
-    else Search.deferQuery(query);
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query: (query) => {
-    // stem the search terms and add them to the correct list
-    const stemmer = new Stemmer();
-    const searchTerms = new Set();
-    const excludedTerms = new Set();
-    const highlightTerms = new Set();
-    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
-    splitQuery(query.trim()).forEach((queryTerm) => {
-      const queryTermLower = queryTerm.toLowerCase();
-
-      // maybe skip this "word"
-      // stopwords array is from language_data.js
-      if (
-        stopwords.indexOf(queryTermLower) !== -1 ||
-        queryTerm.match(/^\d+$/)
-      )
-        return;
-
-      // stem the word
-      let word = stemmer.stemWord(queryTermLower);
-      // select the correct list
-      if (word[0] === "-") excludedTerms.add(word.substr(1));
-      else {
-        searchTerms.add(word);
-        highlightTerms.add(queryTermLower);
-      }
-    });
-
-    // console.debug("SEARCH: searching for:");
-    // console.info("required: ", [...searchTerms]);
-    // console.info("excluded: ", [...excludedTerms]);
-
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
-    _removeChildren(document.getElementById("search-progress"));
-
-    // lookup as object
-    objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
-    );
-
-    // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
-    // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
-        // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-      }
-      return leftScore > rightScore ? 1 : -1;
-    });
-
-    // remove duplicate search results
-    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
-    let seen = new Set();
-    results = results.reverse().reduce((acc, result) => {
-      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
-      if (!seen.has(resultStr)) {
-        acc.push(result);
-        seen.add(resultStr);
-      }
-      return acc;
-    }, []);
-
-    results = results.reverse();
-
-    // for debugging
-    //Search.lastresults = results.slice();  // a copy
-    // console.info("search results:", Search.lastresults);
-
-    // print the results
-    _displayNextItem(results, results.length, highlightTerms, searchTerms);
-  },
-
-  /**
-   * search for object names
-   */
-  performObjectSearch: (object, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const objects = Search._index.objects;
-    const objNames = Search._index.objnames;
-    const titles = Search._index.titles;
-
-    const results = [];
-
-    const objectSearchCallback = (prefix, match) => {
-      const name = match[4]
-      const fullname = (prefix ? prefix + "." : "") + name;
-      const fullnameLower = fullname.toLowerCase();
-      if (fullnameLower.indexOf(object) < 0) return;
-
-      let score = 0;
-      const parts = fullnameLower.split(".");
-
-      // check for different match types: exact matches of full name or
-      // "last name" (i.e. last dotted part)
-      if (fullnameLower === object || parts.slice(-1)[0] === object)
-        score += Scorer.objNameMatch;
-      else if (parts.slice(-1)[0].indexOf(object) > -1)
-        score += Scorer.objPartialMatch; // matches in last name
-
-      const objName = objNames[match[1]][2];
-      const title = titles[match[0]];
-
-      // If more than one term searched for, we require other words to be
-      // found in the name/title/description
-      const otherTerms = new Set(objectTerms);
-      otherTerms.delete(object);
-      if (otherTerms.size > 0) {
-        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
-        if (
-          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
-        )
-          return;
-      }
-
-      let anchor = match[3];
-      if (anchor === "") anchor = fullname;
-      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
-      const descr = objName + _(", in ") + title;
-
-      // add custom score for some objects according to scorer
-      if (Scorer.objPrio.hasOwnProperty(match[2]))
-        score += Scorer.objPrio[match[2]];
-      else score += Scorer.objPrioDefault;
-
-      results.push([
-        docNames[match[0]],
-        fullname,
-        "#" + anchor,
-        descr,
-        score,
-        filenames[match[0]],
-      ]);
-    };
-    Object.keys(objects).forEach((prefix) =>
-      objects[prefix].forEach((array) =>
-        objectSearchCallback(prefix, array)
-      )
-    );
-    return results;
-  },
-
-  /**
-   * search for full-text terms in the index
-   */
-  performTermsSearch: (searchTerms, excludedTerms) => {
-    // prepare search
-    const terms = Search._index.terms;
-    const titleTerms = Search._index.titleterms;
-    const docNames = Search._index.docnames;
-    const filenames = Search._index.filenames;
-    const titles = Search._index.titles;
-
-    const scoreMap = new Map();
-    const fileMap = new Map();
-
-    // perform the search on the required terms
-    searchTerms.forEach((word) => {
-      const files = [];
-      const arr = [
-        { files: terms[word], score: Scorer.term },
-        { files: titleTerms[word], score: Scorer.title },
-      ];
-      // add support for partial matches
-      if (word.length > 2) {
-        const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
-      }
-
-      // no match but word was a required one
-      if (arr.every((record) => record.files === undefined)) return;
-
-      // found search word in contents
-      arr.forEach((record) => {
-        if (record.files === undefined) return;
-
-        let recordFiles = record.files;
-        if (recordFiles.length === undefined) recordFiles = [recordFiles];
-        files.push(...recordFiles);
-
-        // set score for the word in each file
-        recordFiles.forEach((file) => {
-          if (!scoreMap.has(file)) scoreMap.set(file, {});
-          scoreMap.get(file)[word] = record.score;
-        });
-      });
-
-      // create the mapping
-      files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
-      });
-    });
-
-    // now check if the files don't contain excluded terms
-    const results = [];
-    for (const [file, wordList] of fileMap) {
-      // check if all requirements are matched
-
-      // as search terms with length < 3 are discarded
-      const filteredTermCount = [...searchTerms].filter(
-        (term) => term.length > 2
-      ).length;
-      if (
-        wordList.length !== searchTerms.size &&
-        wordList.length !== filteredTermCount
-      )
-        continue;
-
-      // ensure that none of the excluded terms is in the search result
-      if (
-        [...excludedTerms].some(
-          (term) =>
-            terms[term] === file ||
-            titleTerms[term] === file ||
-            (terms[term] || []).includes(file) ||
-            (titleTerms[term] || []).includes(file)
-        )
-      )
-        break;
-
-      // select one (max) score for the file.
-      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
-      // add result to the result list
-      results.push([
-        docNames[file],
-        titles[file],
-        "",
-        null,
-        score,
-        filenames[file],
-      ]);
-    }
-    return results;
-  },
-
-  /**
-   * helper function to return a node containing the
-   * search summary for a given text. keywords is a list
-   * of stemmed words, highlightWords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
-   */
-  makeSearchSummary: (htmlText, keywords, highlightWords) => {
-    const text = Search.htmlToText(htmlText).toLowerCase();
-    if (text === "") return null;
-
-    const actualStartPosition = [...keywords]
-      .map((k) => text.indexOf(k.toLowerCase()))
-      .filter((i) => i > -1)
-      .slice(-1)[0];
-    const startWithContext = Math.max(actualStartPosition - 120, 0);
-
-    const top = startWithContext === 0 ? "" : "...";
-    const tail = startWithContext + 240 < text.length ? "..." : "";
-
-    let summary = document.createElement("div");
-    summary.classList.add("context");
-    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
-
-    highlightWords.forEach((highlightWord) =>
-      _highlightText(summary, highlightWord, "highlighted")
-    );
-
-    return summary;
-  },
-};
-
-_ready(Search.init);
diff --git a/docs/_build/html/_static/sphinx_highlight.js b/docs/_build/html/_static/sphinx_highlight.js
deleted file mode 100644
index aae669d7..00000000
--- a/docs/_build/html/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,144 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
-
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
-    }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
-  }
-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
-  /**
-   * highlight the search words provided in localstorage in the text
-   */
-  highlightSearchWords: () => {
-    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
-
-    // get and clear terms from localstorage
-    const url = new URL(window.location);
-    const highlight =
-        localStorage.getItem("sphinx_highlight_terms")
-        || url.searchParams.get("highlight")
-        || "";
-    localStorage.removeItem("sphinx_highlight_terms")
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
-
-    // get individual terms from highlight string
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
-            _("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    localStorage.removeItem("sphinx_highlight_terms")
-  },
-
-  initEscapeListener: () => {
-    // only install a listener if it is really needed
-    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
-    document.addEventListener("keydown", (event) => {
-      // bail for input elements
-      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
-      // bail with special keys
-      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
-      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
-        SphinxHighlight.hideSearchWords();
-        event.preventDefault();
-      }
-    });
-  },
-};
-
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
diff --git a/docs/_build/html/_static/underscore-1.13.1.js b/docs/_build/html/_static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af9..00000000
--- a/docs/_build/html/_static/underscore-1.13.1.js
+++ /dev/null
@@ -1,2042 +0,0 @@
-(function (global, factory) {
-  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
-  typeof define === 'function' && define.amd ? define('underscore', factory) :
-  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
-    var current = global._;
-    var exports = global._ = factory();
-    exports.noConflict = function () { global._ = current; return exports; };
-  }()));
-}(this, (function () {
-  //     Underscore.js 1.13.1
-  //     https://underscorejs.org
-  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
-  //     Underscore may be freely distributed under the MIT license.
-
-  // Current version.
-  var VERSION = '1.13.1';
-
-  // Establish the root object, `window` (`self`) in the browser, `global`
-  // on the server, or `this` in some virtual machines. We use `self`
-  // instead of `window` for `WebWorker` support.
-  var root = typeof self == 'object' && self.self === self && self ||
-            typeof global == 'object' && global.global === global && global ||
-            Function('return this')() ||
-            {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
-  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var push = ArrayProto.push,
-      slice = ArrayProto.slice,
-      toString = ObjProto.toString,
-      hasOwnProperty = ObjProto.hasOwnProperty;
-
-  // Modern feature detection.
-  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
-      supportsDataView = typeof DataView !== 'undefined';
-
-  // All **ECMAScript 5+** native function implementations that we hope to use
-  // are declared here.
-  var nativeIsArray = Array.isArray,
-      nativeKeys = Object.keys,
-      nativeCreate = Object.create,
-      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
-  // Create references to these builtin functions because we override them.
-  var _isNaN = isNaN,
-      _isFinite = isFinite;
-
-  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
-  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
-  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
-    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
-  // The largest integer that can be represented exactly.
-  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
-  // Some functions take a variable number of arguments, or a few expected
-  // arguments at the beginning and then a variable number of values to operate
-  // on. This helper accumulates all remaining arguments past the function’s
-  // argument length (or an explicit `startIndex`), into an array that becomes
-  // the last argument. Similar to ES6’s "rest parameter".
-  function restArguments(func, startIndex) {
-    startIndex = startIndex == null ? func.length - 1 : +startIndex;
-    return function() {
-      var length = Math.max(arguments.length - startIndex, 0),
-          rest = Array(length),
-          index = 0;
-      for (; index < length; index++) {
-        rest[index] = arguments[index + startIndex];
-      }
-      switch (startIndex) {
-        case 0: return func.call(this, rest);
-        case 1: return func.call(this, arguments[0], rest);
-        case 2: return func.call(this, arguments[0], arguments[1], rest);
-      }
-      var args = Array(startIndex + 1);
-      for (index = 0; index < startIndex; index++) {
-        args[index] = arguments[index];
-      }
-      args[startIndex] = rest;
-      return func.apply(this, args);
-    };
-  }
-
-  // Is a given variable an object?
-  function isObject(obj) {
-    var type = typeof obj;
-    return type === 'function' || type === 'object' && !!obj;
-  }
-
-  // Is a given value equal to null?
-  function isNull(obj) {
-    return obj === null;
-  }
-
-  // Is a given variable undefined?
-  function isUndefined(obj) {
-    return obj === void 0;
-  }
-
-  // Is a given value a boolean?
-  function isBoolean(obj) {
-    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
-  }
-
-  // Is a given value a DOM element?
-  function isElement(obj) {
-    return !!(obj && obj.nodeType === 1);
-  }
-
-  // Internal function for creating a `toString`-based type tester.
-  function tagTester(name) {
-    var tag = '[object ' + name + ']';
-    return function(obj) {
-      return toString.call(obj) === tag;
-    };
-  }
-
-  var isString = tagTester('String');
-
-  var isNumber = tagTester('Number');
-
-  var isDate = tagTester('Date');
-
-  var isRegExp = tagTester('RegExp');
-
-  var isError = tagTester('Error');
-
-  var isSymbol = tagTester('Symbol');
-
-  var isArrayBuffer = tagTester('ArrayBuffer');
-
-  var isFunction = tagTester('Function');
-
-  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
-  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
-  var nodelist = root.document && root.document.childNodes;
-  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
-    isFunction = function(obj) {
-      return typeof obj == 'function' || false;
-    };
-  }
-
-  var isFunction$1 = isFunction;
-
-  var hasObjectTag = tagTester('Object');
-
-  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
-  // In IE 11, the most common among them, this problem also applies to
-  // `Map`, `WeakMap` and `Set`.
-  var hasStringTagBug = (
-        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
-      ),
-      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
-  var isDataView = tagTester('DataView');
-
-  // In IE 10 - Edge 13, we need a different heuristic
-  // to determine whether an object is a `DataView`.
-  function ie10IsDataView(obj) {
-    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
-  }
-
-  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native `Array.isArray`.
-  var isArray = nativeIsArray || tagTester('Array');
-
-  // Internal function to check whether `key` is an own property name of `obj`.
-  function has$1(obj, key) {
-    return obj != null && hasOwnProperty.call(obj, key);
-  }
-
-  var isArguments = tagTester('Arguments');
-
-  // Define a fallback version of the method in browsers (ahem, IE < 9), where
-  // there isn't any inspectable "Arguments" type.
-  (function() {
-    if (!isArguments(arguments)) {
-      isArguments = function(obj) {
-        return has$1(obj, 'callee');
-      };
-    }
-  }());
-
-  var isArguments$1 = isArguments;
-
-  // Is a given object a finite number?
-  function isFinite$1(obj) {
-    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
-  }
-
-  // Is the given value `NaN`?
-  function isNaN$1(obj) {
-    return isNumber(obj) && _isNaN(obj);
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function constant(value) {
-    return function() {
-      return value;
-    };
-  }
-
-  // Common internal logic for `isArrayLike` and `isBufferLike`.
-  function createSizePropertyCheck(getSizeProperty) {
-    return function(collection) {
-      var sizeProperty = getSizeProperty(collection);
-      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
-    }
-  }
-
-  // Internal helper to generate a function to obtain property `key` from `obj`.
-  function shallowProperty(key) {
-    return function(obj) {
-      return obj == null ? void 0 : obj[key];
-    };
-  }
-
-  // Internal helper to obtain the `byteLength` property of an object.
-  var getByteLength = shallowProperty('byteLength');
-
-  // Internal helper to determine whether we should spend extensive checks against
-  // `ArrayBuffer` et al.
-  var isBufferLike = createSizePropertyCheck(getByteLength);
-
-  // Is a given value a typed array?
-  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
-  function isTypedArray(obj) {
-    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
-    // Otherwise, fall back on the above regular expression.
-    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
-                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
-  }
-
-  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
-  // Internal helper to obtain the `length` property of an object.
-  var getLength = shallowProperty('length');
-
-  // Internal helper to create a simple lookup structure.
-  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
-  // circular imports. `emulatedSet` is a one-off solution that only works for
-  // arrays of strings.
-  function emulatedSet(keys) {
-    var hash = {};
-    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
-    return {
-      contains: function(key) { return hash[key]; },
-      push: function(key) {
-        hash[key] = true;
-        return keys.push(key);
-      }
-    };
-  }
-
-  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
-  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
-  // needed.
-  function collectNonEnumProps(obj, keys) {
-    keys = emulatedSet(keys);
-    var nonEnumIdx = nonEnumerableProps.length;
-    var constructor = obj.constructor;
-    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
-    // Constructor is a special case.
-    var prop = 'constructor';
-    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
-    while (nonEnumIdx--) {
-      prop = nonEnumerableProps[nonEnumIdx];
-      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
-        keys.push(prop);
-      }
-    }
-  }
-
-  // Retrieve the names of an object's own properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`.
-  function keys(obj) {
-    if (!isObject(obj)) return [];
-    if (nativeKeys) return nativeKeys(obj);
-    var keys = [];
-    for (var key in obj) if (has$1(obj, key)) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  function isEmpty(obj) {
-    if (obj == null) return true;
-    // Skip the more expensive `toString`-based type checks if `obj` has no
-    // `.length`.
-    var length = getLength(obj);
-    if (typeof length == 'number' && (
-      isArray(obj) || isString(obj) || isArguments$1(obj)
-    )) return length === 0;
-    return getLength(keys(obj)) === 0;
-  }
-
-  // Returns whether an object has a given set of `key:value` pairs.
-  function isMatch(object, attrs) {
-    var _keys = keys(attrs), length = _keys.length;
-    if (object == null) return !length;
-    var obj = Object(object);
-    for (var i = 0; i < length; i++) {
-      var key = _keys[i];
-      if (attrs[key] !== obj[key] || !(key in obj)) return false;
-    }
-    return true;
-  }
-
-  // If Underscore is called as a function, it returns a wrapped object that can
-  // be used OO-style. This wrapper holds altered versions of all functions added
-  // through `_.mixin`. Wrapped objects may be chained.
-  function _$1(obj) {
-    if (obj instanceof _$1) return obj;
-    if (!(this instanceof _$1)) return new _$1(obj);
-    this._wrapped = obj;
-  }
-
-  _$1.VERSION = VERSION;
-
-  // Extracts the result from a wrapped and chained object.
-  _$1.prototype.value = function() {
-    return this._wrapped;
-  };
-
-  // Provide unwrapping proxies for some methods used in engine operations
-  // such as arithmetic and JSON stringification.
-  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
-  _$1.prototype.toString = function() {
-    return String(this._wrapped);
-  };
-
-  // Internal function to wrap or shallow-copy an ArrayBuffer,
-  // typed array or DataView to a new view, reusing the buffer.
-  function toBufferView(bufferSource) {
-    return new Uint8Array(
-      bufferSource.buffer || bufferSource,
-      bufferSource.byteOffset || 0,
-      getByteLength(bufferSource)
-    );
-  }
-
-  // We use this string twice, so give it a name for minification.
-  var tagDataView = '[object DataView]';
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function eq(a, b, aStack, bStack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
-    if (a === b) return a !== 0 || 1 / a === 1 / b;
-    // `null` or `undefined` only equal to itself (strict comparison).
-    if (a == null || b == null) return false;
-    // `NaN`s are equivalent, but non-reflexive.
-    if (a !== a) return b !== b;
-    // Exhaust primitive checks
-    var type = typeof a;
-    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
-    return deepEq(a, b, aStack, bStack);
-  }
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function deepEq(a, b, aStack, bStack) {
-    // Unwrap any wrapped objects.
-    if (a instanceof _$1) a = a._wrapped;
-    if (b instanceof _$1) b = b._wrapped;
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className !== toString.call(b)) return false;
-    // Work around a bug in IE 10 - Edge 13.
-    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
-      if (!isDataView$1(b)) return false;
-      className = tagDataView;
-    }
-    switch (className) {
-      // These types are compared by value.
-      case '[object RegExp]':
-        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return '' + a === '' + b;
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive.
-        // Object(NaN) is equivalent to NaN.
-        if (+a !== +a) return +b !== +b;
-        // An `egal` comparison is performed for other numeric values.
-        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a === +b;
-      case '[object Symbol]':
-        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
-      case '[object ArrayBuffer]':
-      case tagDataView:
-        // Coerce to typed array so we can fall through.
-        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
-    }
-
-    var areArrays = className === '[object Array]';
-    if (!areArrays && isTypedArray$1(a)) {
-        var byteLength = getByteLength(a);
-        if (byteLength !== getByteLength(b)) return false;
-        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
-        areArrays = true;
-    }
-    if (!areArrays) {
-      if (typeof a != 'object' || typeof b != 'object') return false;
-
-      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
-      // from different frames are.
-      var aCtor = a.constructor, bCtor = b.constructor;
-      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
-                               isFunction$1(bCtor) && bCtor instanceof bCtor)
-                          && ('constructor' in a && 'constructor' in b)) {
-        return false;
-      }
-    }
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
-    // Initializing stack of traversed objects.
-    // It's done here since we only need them for objects and arrays comparison.
-    aStack = aStack || [];
-    bStack = bStack || [];
-    var length = aStack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (aStack[length] === a) return bStack[length] === b;
-    }
-
-    // Add the first object to the stack of traversed objects.
-    aStack.push(a);
-    bStack.push(b);
-
-    // Recursively compare objects and arrays.
-    if (areArrays) {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      length = a.length;
-      if (length !== b.length) return false;
-      // Deep compare the contents, ignoring non-numeric properties.
-      while (length--) {
-        if (!eq(a[length], b[length], aStack, bStack)) return false;
-      }
-    } else {
-      // Deep compare objects.
-      var _keys = keys(a), key;
-      length = _keys.length;
-      // Ensure that both objects contain the same number of properties before comparing deep equality.
-      if (keys(b).length !== length) return false;
-      while (length--) {
-        // Deep compare each member
-        key = _keys[length];
-        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    aStack.pop();
-    bStack.pop();
-    return true;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  function isEqual(a, b) {
-    return eq(a, b);
-  }
-
-  // Retrieve all the enumerable property names of an object.
-  function allKeys(obj) {
-    if (!isObject(obj)) return [];
-    var keys = [];
-    for (var key in obj) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Since the regular `Object.prototype.toString` type tests don't work for
-  // some types in IE 11, we use a fingerprinting heuristic instead, based
-  // on the methods. It's not great, but it's the best we got.
-  // The fingerprint method lists are defined below.
-  function ie11fingerprint(methods) {
-    var length = getLength(methods);
-    return function(obj) {
-      if (obj == null) return false;
-      // `Map`, `WeakMap` and `Set` have no enumerable keys.
-      var keys = allKeys(obj);
-      if (getLength(keys)) return false;
-      for (var i = 0; i < length; i++) {
-        if (!isFunction$1(obj[methods[i]])) return false;
-      }
-      // If we are testing against `WeakMap`, we need to ensure that
-      // `obj` doesn't have a `forEach` method in order to distinguish
-      // it from a regular `Map`.
-      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
-    };
-  }
-
-  // In the interest of compact minification, we write
-  // each string in the fingerprints only once.
-  var forEachName = 'forEach',
-      hasName = 'has',
-      commonInit = ['clear', 'delete'],
-      mapTail = ['get', hasName, 'set'];
-
-  // `Map`, `WeakMap` and `Set` each have slightly different
-  // combinations of the above sublists.
-  var mapMethods = commonInit.concat(forEachName, mapTail),
-      weakMapMethods = commonInit.concat(mapTail),
-      setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
-  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
-  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
-  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
-  var isWeakSet = tagTester('WeakSet');
-
-  // Retrieve the values of an object's properties.
-  function values(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var values = Array(length);
-    for (var i = 0; i < length; i++) {
-      values[i] = obj[_keys[i]];
-    }
-    return values;
-  }
-
-  // Convert an object into a list of `[key, value]` pairs.
-  // The opposite of `_.object` with one argument.
-  function pairs(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var pairs = Array(length);
-    for (var i = 0; i < length; i++) {
-      pairs[i] = [_keys[i], obj[_keys[i]]];
-    }
-    return pairs;
-  }
-
-  // Invert the keys and values of an object. The values must be serializable.
-  function invert(obj) {
-    var result = {};
-    var _keys = keys(obj);
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      result[obj[_keys[i]]] = _keys[i];
-    }
-    return result;
-  }
-
-  // Return a sorted list of the function names available on the object.
-  function functions(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (isFunction$1(obj[key])) names.push(key);
-    }
-    return names.sort();
-  }
-
-  // An internal function for creating assigner functions.
-  function createAssigner(keysFunc, defaults) {
-    return function(obj) {
-      var length = arguments.length;
-      if (defaults) obj = Object(obj);
-      if (length < 2 || obj == null) return obj;
-      for (var index = 1; index < length; index++) {
-        var source = arguments[index],
-            keys = keysFunc(source),
-            l = keys.length;
-        for (var i = 0; i < l; i++) {
-          var key = keys[i];
-          if (!defaults || obj[key] === void 0) obj[key] = source[key];
-        }
-      }
-      return obj;
-    };
-  }
-
-  // Extend a given object with all the properties in passed-in object(s).
-  var extend = createAssigner(allKeys);
-
-  // Assigns a given object with all the own properties in the passed-in
-  // object(s).
-  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
-  var extendOwn = createAssigner(keys);
-
-  // Fill in a given object with default properties.
-  var defaults = createAssigner(allKeys, true);
-
-  // Create a naked function reference for surrogate-prototype-swapping.
-  function ctor() {
-    return function(){};
-  }
-
-  // An internal function for creating a new object that inherits from another.
-  function baseCreate(prototype) {
-    if (!isObject(prototype)) return {};
-    if (nativeCreate) return nativeCreate(prototype);
-    var Ctor = ctor();
-    Ctor.prototype = prototype;
-    var result = new Ctor;
-    Ctor.prototype = null;
-    return result;
-  }
-
-  // Creates an object that inherits from the given prototype object.
-  // If additional properties are provided then they will be added to the
-  // created object.
-  function create(prototype, props) {
-    var result = baseCreate(prototype);
-    if (props) extendOwn(result, props);
-    return result;
-  }
-
-  // Create a (shallow-cloned) duplicate of an object.
-  function clone(obj) {
-    if (!isObject(obj)) return obj;
-    return isArray(obj) ? obj.slice() : extend({}, obj);
-  }
-
-  // Invokes `interceptor` with the `obj` and then returns `obj`.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  function tap(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  }
-
-  // Normalize a (deep) property `path` to array.
-  // Like `_.iteratee`, this function can be customized.
-  function toPath$1(path) {
-    return isArray(path) ? path : [path];
-  }
-  _$1.toPath = toPath$1;
-
-  // Internal wrapper for `_.toPath` to enable minification.
-  // Similar to `cb` for `_.iteratee`.
-  function toPath(path) {
-    return _$1.toPath(path);
-  }
-
-  // Internal function to obtain a nested property in `obj` along `path`.
-  function deepGet(obj, path) {
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      if (obj == null) return void 0;
-      obj = obj[path[i]];
-    }
-    return length ? obj : void 0;
-  }
-
-  // Get the value of the (deep) property on `path` from `object`.
-  // If any property in `path` does not exist or if the value is
-  // `undefined`, return `defaultValue` instead.
-  // The `path` is normalized through `_.toPath`.
-  function get(object, path, defaultValue) {
-    var value = deepGet(object, toPath(path));
-    return isUndefined(value) ? defaultValue : value;
-  }
-
-  // Shortcut function for checking if an object has a given property directly on
-  // itself (in other words, not on a prototype). Unlike the internal `has`
-  // function, this public version can also traverse nested properties.
-  function has(obj, path) {
-    path = toPath(path);
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      var key = path[i];
-      if (!has$1(obj, key)) return false;
-      obj = obj[key];
-    }
-    return !!length;
-  }
-
-  // Keep the identity function around for default iteratees.
-  function identity(value) {
-    return value;
-  }
-
-  // Returns a predicate for checking whether an object has a given set of
-  // `key:value` pairs.
-  function matcher(attrs) {
-    attrs = extendOwn({}, attrs);
-    return function(obj) {
-      return isMatch(obj, attrs);
-    };
-  }
-
-  // Creates a function that, when passed an object, will traverse that object’s
-  // properties down the given `path`, specified as an array of keys or indices.
-  function property(path) {
-    path = toPath(path);
-    return function(obj) {
-      return deepGet(obj, path);
-    };
-  }
-
-  // Internal function that returns an efficient (for current engines) version
-  // of the passed-in callback, to be repeatedly applied in other Underscore
-  // functions.
-  function optimizeCb(func, context, argCount) {
-    if (context === void 0) return func;
-    switch (argCount == null ? 3 : argCount) {
-      case 1: return function(value) {
-        return func.call(context, value);
-      };
-      // The 2-argument case is omitted because we’re not using it.
-      case 3: return function(value, index, collection) {
-        return func.call(context, value, index, collection);
-      };
-      case 4: return function(accumulator, value, index, collection) {
-        return func.call(context, accumulator, value, index, collection);
-      };
-    }
-    return function() {
-      return func.apply(context, arguments);
-    };
-  }
-
-  // An internal function to generate callbacks that can be applied to each
-  // element in a collection, returning the desired result — either `_.identity`,
-  // an arbitrary callback, a property matcher, or a property accessor.
-  function baseIteratee(value, context, argCount) {
-    if (value == null) return identity;
-    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
-    if (isObject(value) && !isArray(value)) return matcher(value);
-    return property(value);
-  }
-
-  // External wrapper for our callback generator. Users may customize
-  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
-  // This abstraction hides the internal-only `argCount` argument.
-  function iteratee(value, context) {
-    return baseIteratee(value, context, Infinity);
-  }
-  _$1.iteratee = iteratee;
-
-  // The function we call internally to generate a callback. It invokes
-  // `_.iteratee` if overridden, otherwise `baseIteratee`.
-  function cb(value, context, argCount) {
-    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
-    return baseIteratee(value, context, argCount);
-  }
-
-  // Returns the results of applying the `iteratee` to each element of `obj`.
-  // In contrast to `_.map` it returns an object.
-  function mapObject(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = keys(obj),
-        length = _keys.length,
-        results = {};
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys[index];
-      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function noop(){}
-
-  // Generates a function for a given object that returns a given property.
-  function propertyOf(obj) {
-    if (obj == null) return noop;
-    return function(path) {
-      return get(obj, path);
-    };
-  }
-
-  // Run a function **n** times.
-  function times(n, iteratee, context) {
-    var accum = Array(Math.max(0, n));
-    iteratee = optimizeCb(iteratee, context, 1);
-    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
-    return accum;
-  }
-
-  // Return a random integer between `min` and `max` (inclusive).
-  function random(min, max) {
-    if (max == null) {
-      max = min;
-      min = 0;
-    }
-    return min + Math.floor(Math.random() * (max - min + 1));
-  }
-
-  // A (possibly faster) way to get the current timestamp as an integer.
-  var now = Date.now || function() {
-    return new Date().getTime();
-  };
-
-  // Internal helper to generate functions for escaping and unescaping strings
-  // to/from HTML interpolation.
-  function createEscaper(map) {
-    var escaper = function(match) {
-      return map[match];
-    };
-    // Regexes for identifying a key that needs to be escaped.
-    var source = '(?:' + keys(map).join('|') + ')';
-    var testRegexp = RegExp(source);
-    var replaceRegexp = RegExp(source, 'g');
-    return function(string) {
-      string = string == null ? '' : '' + string;
-      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
-    };
-  }
-
-  // Internal list of HTML entities for escaping.
-  var escapeMap = {
-    '&': '&amp;',
-    '<': '&lt;',
-    '>': '&gt;',
-    '"': '&quot;',
-    "'": '&#x27;',
-    '`': '&#x60;'
-  };
-
-  // Function for escaping strings to HTML interpolation.
-  var _escape = createEscaper(escapeMap);
-
-  // Internal list of HTML entities for unescaping.
-  var unescapeMap = invert(escapeMap);
-
-  // Function for unescaping strings from HTML interpolation.
-  var _unescape = createEscaper(unescapeMap);
-
-  // By default, Underscore uses ERB-style template delimiters. Change the
-  // following template settings to use alternative delimiters.
-  var templateSettings = _$1.templateSettings = {
-    evaluate: /<%([\s\S]+?)%>/g,
-    interpolate: /<%=([\s\S]+?)%>/g,
-    escape: /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `_.templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /(.)^/;
-
-  // Certain characters need to be escaped so that they can be put into a
-  // string literal.
-  var escapes = {
-    "'": "'",
-    '\\': '\\',
-    '\r': 'r',
-    '\n': 'n',
-    '\u2028': 'u2028',
-    '\u2029': 'u2029'
-  };
-
-  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
-  function escapeChar(match) {
-    return '\\' + escapes[match];
-  }
-
-  // In order to prevent third-party code injection through
-  // `_.templateSettings.variable`, we test it against the following regular
-  // expression. It is intentionally a bit more liberal than just matching valid
-  // identifiers, but still prevents possible loopholes through defaults or
-  // destructuring assignment.
-  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  // NB: `oldSettings` only exists for backwards compatibility.
-  function template(text, settings, oldSettings) {
-    if (!settings && oldSettings) settings = oldSettings;
-    settings = defaults({}, settings, _$1.templateSettings);
-
-    // Combine delimiters into one regular expression via alternation.
-    var matcher = RegExp([
-      (settings.escape || noMatch).source,
-      (settings.interpolate || noMatch).source,
-      (settings.evaluate || noMatch).source
-    ].join('|') + '|$', 'g');
-
-    // Compile the template source, escaping string literals appropriately.
-    var index = 0;
-    var source = "__p+='";
-    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
-      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
-      index = offset + match.length;
-
-      if (escape) {
-        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
-      } else if (interpolate) {
-        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
-      } else if (evaluate) {
-        source += "';\n" + evaluate + "\n__p+='";
-      }
-
-      // Adobe VMs need the match returned to produce the correct offset.
-      return match;
-    });
-    source += "';\n";
-
-    var argument = settings.variable;
-    if (argument) {
-      // Insure against third-party code injection. (CVE-2021-23358)
-      if (!bareIdentifier.test(argument)) throw new Error(
-        'variable is not a bare identifier: ' + argument
-      );
-    } else {
-      // If a variable is not specified, place data values in local scope.
-      source = 'with(obj||{}){\n' + source + '}\n';
-      argument = 'obj';
-    }
-
-    source = "var __t,__p='',__j=Array.prototype.join," +
-      "print=function(){__p+=__j.call(arguments,'');};\n" +
-      source + 'return __p;\n';
-
-    var render;
-    try {
-      render = new Function(argument, '_', source);
-    } catch (e) {
-      e.source = source;
-      throw e;
-    }
-
-    var template = function(data) {
-      return render.call(this, data, _$1);
-    };
-
-    // Provide the compiled source as a convenience for precompilation.
-    template.source = 'function(' + argument + '){\n' + source + '}';
-
-    return template;
-  }
-
-  // Traverses the children of `obj` along `path`. If a child is a function, it
-  // is invoked with its parent as context. Returns the value of the final
-  // child, or `fallback` if any child is undefined.
-  function result(obj, path, fallback) {
-    path = toPath(path);
-    var length = path.length;
-    if (!length) {
-      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
-    }
-    for (var i = 0; i < length; i++) {
-      var prop = obj == null ? void 0 : obj[path[i]];
-      if (prop === void 0) {
-        prop = fallback;
-        i = length; // Ensure we don't continue iterating.
-      }
-      obj = isFunction$1(prop) ? prop.call(obj) : prop;
-    }
-    return obj;
-  }
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  function uniqueId(prefix) {
-    var id = ++idCounter + '';
-    return prefix ? prefix + id : id;
-  }
-
-  // Start chaining a wrapped Underscore object.
-  function chain(obj) {
-    var instance = _$1(obj);
-    instance._chain = true;
-    return instance;
-  }
-
-  // Internal function to execute `sourceFunc` bound to `context` with optional
-  // `args`. Determines whether to execute a function as a constructor or as a
-  // normal function.
-  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
-    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
-    var self = baseCreate(sourceFunc.prototype);
-    var result = sourceFunc.apply(self, args);
-    if (isObject(result)) return result;
-    return self;
-  }
-
-  // Partially apply a function by creating a version that has had some of its
-  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
-  // as a placeholder by default, allowing any combination of arguments to be
-  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
-  var partial = restArguments(function(func, boundArgs) {
-    var placeholder = partial.placeholder;
-    var bound = function() {
-      var position = 0, length = boundArgs.length;
-      var args = Array(length);
-      for (var i = 0; i < length; i++) {
-        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
-      }
-      while (position < arguments.length) args.push(arguments[position++]);
-      return executeBound(func, bound, this, this, args);
-    };
-    return bound;
-  });
-
-  partial.placeholder = _$1;
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally).
-  var bind = restArguments(function(func, context, args) {
-    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
-    var bound = restArguments(function(callArgs) {
-      return executeBound(func, bound, context, this, args.concat(callArgs));
-    });
-    return bound;
-  });
-
-  // Internal helper for collection methods to determine whether a collection
-  // should be iterated as an array or as an object.
-  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
-  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
-  var isArrayLike = createSizePropertyCheck(getLength);
-
-  // Internal implementation of a recursive `flatten` function.
-  function flatten$1(input, depth, strict, output) {
-    output = output || [];
-    if (!depth && depth !== 0) {
-      depth = Infinity;
-    } else if (depth <= 0) {
-      return output.concat(input);
-    }
-    var idx = output.length;
-    for (var i = 0, length = getLength(input); i < length; i++) {
-      var value = input[i];
-      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
-        // Flatten current level of array or arguments object.
-        if (depth > 1) {
-          flatten$1(value, depth - 1, strict, output);
-          idx = output.length;
-        } else {
-          var j = 0, len = value.length;
-          while (j < len) output[idx++] = value[j++];
-        }
-      } else if (!strict) {
-        output[idx++] = value;
-      }
-    }
-    return output;
-  }
-
-  // Bind a number of an object's methods to that object. Remaining arguments
-  // are the method names to be bound. Useful for ensuring that all callbacks
-  // defined on an object belong to it.
-  var bindAll = restArguments(function(obj, keys) {
-    keys = flatten$1(keys, false, false);
-    var index = keys.length;
-    if (index < 1) throw new Error('bindAll must be passed function names');
-    while (index--) {
-      var key = keys[index];
-      obj[key] = bind(obj[key], obj);
-    }
-    return obj;
-  });
-
-  // Memoize an expensive function by storing its results.
-  function memoize(func, hasher) {
-    var memoize = function(key) {
-      var cache = memoize.cache;
-      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
-      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
-      return cache[address];
-    };
-    memoize.cache = {};
-    return memoize;
-  }
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  var delay = restArguments(function(func, wait, args) {
-    return setTimeout(function() {
-      return func.apply(null, args);
-    }, wait);
-  });
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  var defer = partial(delay, _$1, 1);
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time. Normally, the throttled function will run
-  // as much as it can, without ever going more than once per `wait` duration;
-  // but if you'd like to disable the execution on the leading edge, pass
-  // `{leading: false}`. To disable execution on the trailing edge, ditto.
-  function throttle(func, wait, options) {
-    var timeout, context, args, result;
-    var previous = 0;
-    if (!options) options = {};
-
-    var later = function() {
-      previous = options.leading === false ? 0 : now();
-      timeout = null;
-      result = func.apply(context, args);
-      if (!timeout) context = args = null;
-    };
-
-    var throttled = function() {
-      var _now = now();
-      if (!previous && options.leading === false) previous = _now;
-      var remaining = wait - (_now - previous);
-      context = this;
-      args = arguments;
-      if (remaining <= 0 || remaining > wait) {
-        if (timeout) {
-          clearTimeout(timeout);
-          timeout = null;
-        }
-        previous = _now;
-        result = func.apply(context, args);
-        if (!timeout) context = args = null;
-      } else if (!timeout && options.trailing !== false) {
-        timeout = setTimeout(later, remaining);
-      }
-      return result;
-    };
-
-    throttled.cancel = function() {
-      clearTimeout(timeout);
-      previous = 0;
-      timeout = context = args = null;
-    };
-
-    return throttled;
-  }
-
-  // When a sequence of calls of the returned function ends, the argument
-  // function is triggered. The end of a sequence is defined by the `wait`
-  // parameter. If `immediate` is passed, the argument function will be
-  // triggered at the beginning of the sequence instead of at the end.
-  function debounce(func, wait, immediate) {
-    var timeout, previous, args, result, context;
-
-    var later = function() {
-      var passed = now() - previous;
-      if (wait > passed) {
-        timeout = setTimeout(later, wait - passed);
-      } else {
-        timeout = null;
-        if (!immediate) result = func.apply(context, args);
-        // This check is needed because `func` can recursively invoke `debounced`.
-        if (!timeout) args = context = null;
-      }
-    };
-
-    var debounced = restArguments(function(_args) {
-      context = this;
-      args = _args;
-      previous = now();
-      if (!timeout) {
-        timeout = setTimeout(later, wait);
-        if (immediate) result = func.apply(context, args);
-      }
-      return result;
-    });
-
-    debounced.cancel = function() {
-      clearTimeout(timeout);
-      timeout = args = context = null;
-    };
-
-    return debounced;
-  }
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  function wrap(func, wrapper) {
-    return partial(wrapper, func);
-  }
-
-  // Returns a negated version of the passed-in predicate.
-  function negate(predicate) {
-    return function() {
-      return !predicate.apply(this, arguments);
-    };
-  }
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  function compose() {
-    var args = arguments;
-    var start = args.length - 1;
-    return function() {
-      var i = start;
-      var result = args[start].apply(this, arguments);
-      while (i--) result = args[i].call(this, result);
-      return result;
-    };
-  }
-
-  // Returns a function that will only be executed on and after the Nth call.
-  function after(times, func) {
-    return function() {
-      if (--times < 1) {
-        return func.apply(this, arguments);
-      }
-    };
-  }
-
-  // Returns a function that will only be executed up to (but not including) the
-  // Nth call.
-  function before(times, func) {
-    var memo;
-    return function() {
-      if (--times > 0) {
-        memo = func.apply(this, arguments);
-      }
-      if (times <= 1) func = null;
-      return memo;
-    };
-  }
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  var once = partial(before, 2);
-
-  // Returns the first key on an object that passes a truth test.
-  function findKey(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = keys(obj), key;
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      key = _keys[i];
-      if (predicate(obj[key], key, obj)) return key;
-    }
-  }
-
-  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
-  function createPredicateIndexFinder(dir) {
-    return function(array, predicate, context) {
-      predicate = cb(predicate, context);
-      var length = getLength(array);
-      var index = dir > 0 ? 0 : length - 1;
-      for (; index >= 0 && index < length; index += dir) {
-        if (predicate(array[index], index, array)) return index;
-      }
-      return -1;
-    };
-  }
-
-  // Returns the first index on an array-like that passes a truth test.
-  var findIndex = createPredicateIndexFinder(1);
-
-  // Returns the last index on an array-like that passes a truth test.
-  var findLastIndex = createPredicateIndexFinder(-1);
-
-  // Use a comparator function to figure out the smallest index at which
-  // an object should be inserted so as to maintain order. Uses binary search.
-  function sortedIndex(array, obj, iteratee, context) {
-    iteratee = cb(iteratee, context, 1);
-    var value = iteratee(obj);
-    var low = 0, high = getLength(array);
-    while (low < high) {
-      var mid = Math.floor((low + high) / 2);
-      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
-    }
-    return low;
-  }
-
-  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
-  function createIndexFinder(dir, predicateFind, sortedIndex) {
-    return function(array, item, idx) {
-      var i = 0, length = getLength(array);
-      if (typeof idx == 'number') {
-        if (dir > 0) {
-          i = idx >= 0 ? idx : Math.max(idx + length, i);
-        } else {
-          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
-        }
-      } else if (sortedIndex && idx && length) {
-        idx = sortedIndex(array, item);
-        return array[idx] === item ? idx : -1;
-      }
-      if (item !== item) {
-        idx = predicateFind(slice.call(array, i, length), isNaN$1);
-        return idx >= 0 ? idx + i : -1;
-      }
-      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
-        if (array[idx] === item) return idx;
-      }
-      return -1;
-    };
-  }
-
-  // Return the position of the first occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
-  // Return the position of the last occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
-  // Return the first value which passes a truth test.
-  function find(obj, predicate, context) {
-    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
-    var key = keyFinder(obj, predicate, context);
-    if (key !== void 0 && key !== -1) return obj[key];
-  }
-
-  // Convenience version of a common use case of `_.find`: getting the first
-  // object containing specific `key:value` pairs.
-  function findWhere(obj, attrs) {
-    return find(obj, matcher(attrs));
-  }
-
-  // The cornerstone for collection functions, an `each`
-  // implementation, aka `forEach`.
-  // Handles raw objects in addition to array-likes. Treats all
-  // sparse array-likes as if they were dense.
-  function each(obj, iteratee, context) {
-    iteratee = optimizeCb(iteratee, context);
-    var i, length;
-    if (isArrayLike(obj)) {
-      for (i = 0, length = obj.length; i < length; i++) {
-        iteratee(obj[i], i, obj);
-      }
-    } else {
-      var _keys = keys(obj);
-      for (i = 0, length = _keys.length; i < length; i++) {
-        iteratee(obj[_keys[i]], _keys[i], obj);
-      }
-    }
-    return obj;
-  }
-
-  // Return the results of applying the iteratee to each element.
-  function map(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length,
-        results = Array(length);
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      results[index] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Internal helper to create a reducing function, iterating left or right.
-  function createReduce(dir) {
-    // Wrap code that reassigns argument variables in a separate function than
-    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
-    var reducer = function(obj, iteratee, memo, initial) {
-      var _keys = !isArrayLike(obj) && keys(obj),
-          length = (_keys || obj).length,
-          index = dir > 0 ? 0 : length - 1;
-      if (!initial) {
-        memo = obj[_keys ? _keys[index] : index];
-        index += dir;
-      }
-      for (; index >= 0 && index < length; index += dir) {
-        var currentKey = _keys ? _keys[index] : index;
-        memo = iteratee(memo, obj[currentKey], currentKey, obj);
-      }
-      return memo;
-    };
-
-    return function(obj, iteratee, memo, context) {
-      var initial = arguments.length >= 3;
-      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
-    };
-  }
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`.
-  var reduce = createReduce(1);
-
-  // The right-associative version of reduce, also known as `foldr`.
-  var reduceRight = createReduce(-1);
-
-  // Return all the elements that pass a truth test.
-  function filter(obj, predicate, context) {
-    var results = [];
-    predicate = cb(predicate, context);
-    each(obj, function(value, index, list) {
-      if (predicate(value, index, list)) results.push(value);
-    });
-    return results;
-  }
-
-  // Return all the elements for which a truth test fails.
-  function reject(obj, predicate, context) {
-    return filter(obj, negate(cb(predicate)), context);
-  }
-
-  // Determine whether all of the elements pass a truth test.
-  function every(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (!predicate(obj[currentKey], currentKey, obj)) return false;
-    }
-    return true;
-  }
-
-  // Determine if at least one element in the object passes a truth test.
-  function some(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (predicate(obj[currentKey], currentKey, obj)) return true;
-    }
-    return false;
-  }
-
-  // Determine if the array or object contains a given item (using `===`).
-  function contains(obj, item, fromIndex, guard) {
-    if (!isArrayLike(obj)) obj = values(obj);
-    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
-    return indexOf(obj, item, fromIndex) >= 0;
-  }
-
-  // Invoke a method (with arguments) on every item in a collection.
-  var invoke = restArguments(function(obj, path, args) {
-    var contextPath, func;
-    if (isFunction$1(path)) {
-      func = path;
-    } else {
-      path = toPath(path);
-      contextPath = path.slice(0, -1);
-      path = path[path.length - 1];
-    }
-    return map(obj, function(context) {
-      var method = func;
-      if (!method) {
-        if (contextPath && contextPath.length) {
-          context = deepGet(context, contextPath);
-        }
-        if (context == null) return void 0;
-        method = context[path];
-      }
-      return method == null ? method : method.apply(context, args);
-    });
-  });
-
-  // Convenience version of a common use case of `_.map`: fetching a property.
-  function pluck(obj, key) {
-    return map(obj, property(key));
-  }
-
-  // Convenience version of a common use case of `_.filter`: selecting only
-  // objects containing specific `key:value` pairs.
-  function where(obj, attrs) {
-    return filter(obj, matcher(attrs));
-  }
-
-  // Return the maximum element (or element-based computation).
-  function max(obj, iteratee, context) {
-    var result = -Infinity, lastComputed = -Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value > result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Return the minimum element (or element-based computation).
-  function min(obj, iteratee, context) {
-    var result = Infinity, lastComputed = Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value < result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed < lastComputed || computed === Infinity && result === Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Sample **n** random values from a collection using the modern version of the
-  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
-  // If **n** is not specified, returns a single random element.
-  // The internal `guard` argument allows it to work with `_.map`.
-  function sample(obj, n, guard) {
-    if (n == null || guard) {
-      if (!isArrayLike(obj)) obj = values(obj);
-      return obj[random(obj.length - 1)];
-    }
-    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
-    var length = getLength(sample);
-    n = Math.max(Math.min(n, length), 0);
-    var last = length - 1;
-    for (var index = 0; index < n; index++) {
-      var rand = random(index, last);
-      var temp = sample[index];
-      sample[index] = sample[rand];
-      sample[rand] = temp;
-    }
-    return sample.slice(0, n);
-  }
-
-  // Shuffle a collection.
-  function shuffle(obj) {
-    return sample(obj, Infinity);
-  }
-
-  // Sort the object's values by a criterion produced by an iteratee.
-  function sortBy(obj, iteratee, context) {
-    var index = 0;
-    iteratee = cb(iteratee, context);
-    return pluck(map(obj, function(value, key, list) {
-      return {
-        value: value,
-        index: index++,
-        criteria: iteratee(value, key, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria;
-      var b = right.criteria;
-      if (a !== b) {
-        if (a > b || a === void 0) return 1;
-        if (a < b || b === void 0) return -1;
-      }
-      return left.index - right.index;
-    }), 'value');
-  }
-
-  // An internal function used for aggregate "group by" operations.
-  function group(behavior, partition) {
-    return function(obj, iteratee, context) {
-      var result = partition ? [[], []] : {};
-      iteratee = cb(iteratee, context);
-      each(obj, function(value, index) {
-        var key = iteratee(value, index, obj);
-        behavior(result, value, key);
-      });
-      return result;
-    };
-  }
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  var groupBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
-  });
-
-  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
-  // when you know that your index values will be unique.
-  var indexBy = group(function(result, value, key) {
-    result[key] = value;
-  });
-
-  // Counts instances of an object that group by a certain criterion. Pass
-  // either a string attribute to count by, or a function that returns the
-  // criterion.
-  var countBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key]++; else result[key] = 1;
-  });
-
-  // Split a collection into two arrays: one whose elements all pass the given
-  // truth test, and one whose elements all do not pass the truth test.
-  var partition = group(function(result, value, pass) {
-    result[pass ? 0 : 1].push(value);
-  }, true);
-
-  // Safely create a real, live array from anything iterable.
-  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
-  function toArray(obj) {
-    if (!obj) return [];
-    if (isArray(obj)) return slice.call(obj);
-    if (isString(obj)) {
-      // Keep surrogate pair characters together.
-      return obj.match(reStrSymbol);
-    }
-    if (isArrayLike(obj)) return map(obj, identity);
-    return values(obj);
-  }
-
-  // Return the number of elements in a collection.
-  function size(obj) {
-    if (obj == null) return 0;
-    return isArrayLike(obj) ? obj.length : keys(obj).length;
-  }
-
-  // Internal `_.pick` helper function to determine whether `key` is an enumerable
-  // property name of `obj`.
-  function keyInObj(value, key, obj) {
-    return key in obj;
-  }
-
-  // Return a copy of the object only containing the allowed properties.
-  var pick = restArguments(function(obj, keys) {
-    var result = {}, iteratee = keys[0];
-    if (obj == null) return result;
-    if (isFunction$1(iteratee)) {
-      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
-      keys = allKeys(obj);
-    } else {
-      iteratee = keyInObj;
-      keys = flatten$1(keys, false, false);
-      obj = Object(obj);
-    }
-    for (var i = 0, length = keys.length; i < length; i++) {
-      var key = keys[i];
-      var value = obj[key];
-      if (iteratee(value, key, obj)) result[key] = value;
-    }
-    return result;
-  });
-
-  // Return a copy of the object without the disallowed properties.
-  var omit = restArguments(function(obj, keys) {
-    var iteratee = keys[0], context;
-    if (isFunction$1(iteratee)) {
-      iteratee = negate(iteratee);
-      if (keys.length > 1) context = keys[1];
-    } else {
-      keys = map(flatten$1(keys, false, false), String);
-      iteratee = function(value, key) {
-        return !contains(keys, key);
-      };
-    }
-    return pick(obj, iteratee, context);
-  });
-
-  // Returns everything but the last entry of the array. Especially useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N.
-  function initial(array, n, guard) {
-    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
-  }
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  function first(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[0];
-    return initial(array, array.length - n);
-  }
-
-  // Returns everything but the first entry of the `array`. Especially useful on
-  // the `arguments` object. Passing an **n** will return the rest N values in the
-  // `array`.
-  function rest(array, n, guard) {
-    return slice.call(array, n == null || guard ? 1 : n);
-  }
-
-  // Get the last element of an array. Passing **n** will return the last N
-  // values in the array.
-  function last(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[array.length - 1];
-    return rest(array, Math.max(0, array.length - n));
-  }
-
-  // Trim out all falsy values from an array.
-  function compact(array) {
-    return filter(array, Boolean);
-  }
-
-  // Flatten out an array, either recursively (by default), or up to `depth`.
-  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
-  function flatten(array, depth) {
-    return flatten$1(array, depth, false);
-  }
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  var difference = restArguments(function(array, rest) {
-    rest = flatten$1(rest, true, true);
-    return filter(array, function(value){
-      return !contains(rest, value);
-    });
-  });
-
-  // Return a version of the array that does not contain the specified value(s).
-  var without = restArguments(function(array, otherArrays) {
-    return difference(array, otherArrays);
-  });
-
-  // Produce a duplicate-free version of the array. If the array has already
-  // been sorted, you have the option of using a faster algorithm.
-  // The faster algorithm will not work with an iteratee if the iteratee
-  // is not a one-to-one function, so providing an iteratee will disable
-  // the faster algorithm.
-  function uniq(array, isSorted, iteratee, context) {
-    if (!isBoolean(isSorted)) {
-      context = iteratee;
-      iteratee = isSorted;
-      isSorted = false;
-    }
-    if (iteratee != null) iteratee = cb(iteratee, context);
-    var result = [];
-    var seen = [];
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var value = array[i],
-          computed = iteratee ? iteratee(value, i, array) : value;
-      if (isSorted && !iteratee) {
-        if (!i || seen !== computed) result.push(value);
-        seen = computed;
-      } else if (iteratee) {
-        if (!contains(seen, computed)) {
-          seen.push(computed);
-          result.push(value);
-        }
-      } else if (!contains(result, value)) {
-        result.push(value);
-      }
-    }
-    return result;
-  }
-
-  // Produce an array that contains the union: each distinct element from all of
-  // the passed-in arrays.
-  var union = restArguments(function(arrays) {
-    return uniq(flatten$1(arrays, true, true));
-  });
-
-  // Produce an array that contains every item shared between all the
-  // passed-in arrays.
-  function intersection(array) {
-    var result = [];
-    var argsLength = arguments.length;
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var item = array[i];
-      if (contains(result, item)) continue;
-      var j;
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diff --git a/docs/_build/html/_static/underscore.js b/docs/_build/html/_static/underscore.js
deleted file mode 100644
index cf177d42..00000000
--- a/docs/_build/html/_static/underscore.js
+++ /dev/null
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diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html
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-
-<!DOCTYPE html>
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-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Index &#8212; Tigramite 5.2 documentation</title>
-    <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="_static/alabaster.css" />
-    <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
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-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
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-  </head><body>
-  
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-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-
-<h1 id="index">Index</h1>
-
-<div class="genindex-jumpbox">
- <a href="#A"><strong>A</strong></a>
- | <a href="#C"><strong>C</strong></a>
- | <a href="#D"><strong>D</strong></a>
- | <a href="#F"><strong>F</strong></a>
- | <a href="#G"><strong>G</strong></a>
- | <a href="#I"><strong>I</strong></a>
- | <a href="#L"><strong>L</strong></a>
- | <a href="#M"><strong>M</strong></a>
- | <a href="#N"><strong>N</strong></a>
- | <a href="#O"><strong>O</strong></a>
- | <a href="#P"><strong>P</strong></a>
- | <a href="#Q"><strong>Q</strong></a>
- | <a href="#R"><strong>R</strong></a>
- | <a href="#S"><strong>S</strong></a>
- | <a href="#T"><strong>T</strong></a>
- | <a href="#V"><strong>V</strong></a>
- | <a href="#W"><strong>W</strong></a>
- 
-</div>
-<h2 id="A">A</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.plotting.setup_density_matrix.add_densityplot">add_densityplot() (tigramite.plotting.setup_density_matrix method)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.setup_matrix.add_lagfuncs">add_lagfuncs() (tigramite.plotting.setup_matrix method)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.setup_scatter_matrix.add_scatterplot">add_scatterplot() (tigramite.plotting.setup_scatter_matrix method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.plotting.setup_density_matrix.adjustfig">adjustfig() (tigramite.plotting.setup_density_matrix method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.plotting.setup_scatter_matrix.adjustfig">(tigramite.plotting.setup_scatter_matrix method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.all_parents">all_parents (tigramite.pcmci.PCMCI attribute)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="C">C</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects">CausalEffects (class in tigramite.causal_effects)</a>
-</li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.check_optimality">check_optimality() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.check_shortest_path">check_shortest_path() (tigramite.independence_tests.oracle_conditional_independence.OracleCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.toymodels.structural_causal_processes.check_stationarity">check_stationarity() (in module tigramite.toymodels.structural_causal_processes)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.check_XYS_paths">check_XYS_paths() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.cmiknn.CMIknn">CMIknn (class in tigramite.independence_tests.cmiknn)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.cmisymb.CMIsymb">CMIsymb (class in tigramite.independence_tests.cmisymb)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest">CondIndTest (class in tigramite.independence_tests.independence_tests_base)</a>
-</li>
-      <li><a href="index.html#tigramite.data_processing.DataFrame.construct_array">construct_array() (tigramite.data_processing.DataFrame method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="D">D</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.toymodels.structural_causal_processes.dag_to_links">dag_to_links() (in module tigramite.toymodels.structural_causal_processes)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.data_processing.DataFrame">DataFrame (class in tigramite.data_processing)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="F">F</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.models.Prediction.fit">fit() (tigramite.models.Prediction method)</a>
-</li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.fit_bootstrap_of">fit_bootstrap_of() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.Models.fit_full_model">fit_full_model() (tigramite.models.Models method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.models.LinearMediation.fit_model">fit_model() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.fit_model_bootstrap">fit_model_bootstrap() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.fit_total_effect">fit_total_effect() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.fit_wright_effect">fit_wright_effect() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="G">G</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC.generate_and_save_nulldists">generate_and_save_nulldists() (tigramite.independence_tests.gpdc.GPDC method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.generate_and_save_nulldists">(tigramite.independence_tests.gpdc_torch.GPDCtorch method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC.generate_nulldist">generate_nulldist() (tigramite.independence_tests.gpdc.GPDC method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.generate_nulldist">(tigramite.independence_tests.gpdc_torch.GPDCtorch method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.toymodels.structural_causal_processes.generate_structural_causal_process">generate_structural_causal_process() (in module tigramite.toymodels.structural_causal_processes)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_ace">get_ace() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.data_processing.get_acf">get_acf() (in module tigramite.data_processing)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_acs">get_acs() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_all_ace">get_all_ace() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_all_acs">get_all_acs() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_all_amce">get_all_amce() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_amce">get_amce() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_analytic_confidence">get_analytic_confidence() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.parcorr.ParCorr.get_analytic_confidence">(tigramite.independence_tests.parcorr.ParCorr method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_analytic_confidence">(tigramite.independence_tests.parcorr_mult.ParCorrMult method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_analytic_confidence">(tigramite.independence_tests.robust_parcorr.RobustParCorr method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC.get_analytic_significance">get_analytic_significance() (tigramite.independence_tests.gpdc.GPDC method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_analytic_significance">(tigramite.independence_tests.gpdc_torch.GPDCtorch method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.gsquared.Gsquared.get_analytic_significance">(tigramite.independence_tests.gsquared.Gsquared method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_analytic_significance">(tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr.ParCorr.get_analytic_significance">(tigramite.independence_tests.parcorr.ParCorr method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_analytic_significance">(tigramite.independence_tests.parcorr_mult.ParCorrMult method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.regressionCI.RegressionCI.get_analytic_significance">(tigramite.independence_tests.regressionCI.RegressionCI method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_analytic_significance">(tigramite.independence_tests.robust_parcorr.RobustParCorr method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.data_processing.get_block_length">get_block_length() (in module tigramite.data_processing)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_bootstrap_confidence">get_bootstrap_confidence() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_bootstrap_of">get_bootstrap_of() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_ce">get_ce() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_ce_max">get_ce_max() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_coeff">get_coeff() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.Models.get_coefs">get_coefs() (tigramite.models.Models method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.cmiknn.CMIknn.get_conditional_entropy">get_conditional_entropy() (tigramite.independence_tests.cmiknn.CMIknn method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_conditional_mce">get_conditional_mce() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_confidence">get_confidence() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_confidence">(tigramite.independence_tests.oracle_conditional_independence.OracleCI method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.cmiknn.CMIknn.get_dependence_measure">get_dependence_measure() (tigramite.independence_tests.cmiknn.CMIknn method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.cmisymb.CMIsymb.get_dependence_measure">(tigramite.independence_tests.cmisymb.CMIsymb method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC.get_dependence_measure">(tigramite.independence_tests.gpdc.GPDC method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_dependence_measure">(tigramite.independence_tests.gpdc_torch.GPDCtorch method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.gsquared.Gsquared.get_dependence_measure">(tigramite.independence_tests.gsquared.Gsquared method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_dependence_measure">(tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr.ParCorr.get_dependence_measure">(tigramite.independence_tests.parcorr.ParCorr method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_dependence_measure">(tigramite.independence_tests.parcorr_mult.ParCorrMult method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_dependence_measure">(tigramite.independence_tests.parcorr_wls.ParCorrWLS method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.regressionCI.RegressionCI.get_dependence_measure">(tigramite.independence_tests.regressionCI.RegressionCI method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_dependence_measure">(tigramite.independence_tests.robust_parcorr.RobustParCorr method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance">get_fixed_thres_significance() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.Models.get_general_fitted_model">get_general_fitted_model() (tigramite.models.Models method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.Models.get_general_prediction">get_general_prediction() (tigramite.models.Models method)</a>
-</li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.get_graph_from_dict">get_graph_from_dict() (tigramite.causal_effects.CausalEffects static method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_graph_from_links">get_graph_from_links() (tigramite.independence_tests.oracle_conditional_independence.OracleCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.get_graph_from_pmatrix">get_graph_from_pmatrix() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_joint_ce">get_joint_ce() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_joint_ce_matrix">get_joint_ce_matrix() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_joint_mce">get_joint_mce() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.get_lagged_dependencies">get_lagged_dependencies() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_links_from_graph">get_links_from_graph() (tigramite.independence_tests.oracle_conditional_independence.OracleCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_mce">get_mce() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_measure">get_measure() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_measure">(tigramite.independence_tests.oracle_conditional_independence.OracleCI method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_mediation_graph_data">get_mediation_graph_data() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.get_mediators">get_mediators() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC.get_model_selection_criterion">get_model_selection_criterion() (tigramite.independence_tests.gpdc.GPDC method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_model_selection_criterion">(tigramite.independence_tests.gpdc_torch.GPDCtorch method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_model_selection_criterion">(tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_model_selection_criterion">(tigramite.independence_tests.oracle_conditional_independence.OracleCI method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr.ParCorr.get_model_selection_criterion">(tigramite.independence_tests.parcorr.ParCorr method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_model_selection_criterion">(tigramite.independence_tests.parcorr_mult.ParCorrMult method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_model_selection_criterion">(tigramite.independence_tests.parcorr_wls.ParCorrWLS method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_model_selection_criterion">(tigramite.independence_tests.robust_parcorr.RobustParCorr method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.get_optimal_set">get_optimal_set() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.Prediction.get_predictors">get_predictors() (tigramite.models.Prediction method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.cmiknn.CMIknn.get_shuffle_significance">get_shuffle_significance() (tigramite.independence_tests.cmiknn.CMIknn method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.cmisymb.CMIsymb.get_shuffle_significance">(tigramite.independence_tests.cmisymb.CMIsymb method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC.get_shuffle_significance">(tigramite.independence_tests.gpdc.GPDC method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_shuffle_significance">(tigramite.independence_tests.gpdc_torch.GPDCtorch method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_shuffle_significance">(tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr.ParCorr.get_shuffle_significance">(tigramite.independence_tests.parcorr.ParCorr method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_shuffle_significance">(tigramite.independence_tests.parcorr_mult.ParCorrMult method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_shuffle_significance">(tigramite.independence_tests.parcorr_wls.ParCorrWLS method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_shuffle_significance">(tigramite.independence_tests.robust_parcorr.RobustParCorr method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance">get_significance() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.Prediction.get_test_array">get_test_array() (tigramite.models.Prediction method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.Prediction.get_train_array">get_train_array() (tigramite.models.Prediction method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_tsg">get_tsg() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.LinearMediation.get_val_matrix">get_val_matrix() (tigramite.models.LinearMediation method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.models.Models.get_val_matrix">(tigramite.models.Models method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC">GPDC (class in tigramite.independence_tests.gpdc)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch">GPDCtorch (class in tigramite.independence_tests.gpdc_torch)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.gsquared.Gsquared">Gsquared (class in tigramite.independence_tests.gsquared)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="I">I</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.iterations">iterations (tigramite.pcmci.PCMCI attribute)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="L">L</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.models.LinearMediation">LinearMediation (class in tigramite.models)</a>
-</li>
-      <li><a href="index.html#tigramite.toymodels.structural_causal_processes.links_to_graph">links_to_graph() (in module tigramite.toymodels.structural_causal_processes)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.data_processing.lowhighpass_filter">lowhighpass_filter() (in module tigramite.data_processing)</a>
-</li>
-      <li><a href="index.html#tigramite.lpcmci.LPCMCI">LPCMCI (class in tigramite.lpcmci)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="M">M</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.independence_tests.cmiknn.CMIknn.measure">measure (tigramite.independence_tests.cmiknn.CMIknn property)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.cmisymb.CMIsymb.measure">(tigramite.independence_tests.cmisymb.CMIsymb property)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC.measure">(tigramite.independence_tests.gpdc.GPDC property)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.measure">(tigramite.independence_tests.gpdc_torch.GPDCtorch property)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.gsquared.Gsquared.measure">(tigramite.independence_tests.gsquared.Gsquared property)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.measure">(tigramite.independence_tests.independence_tests_base.CondIndTest property)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.measure">(tigramite.independence_tests.oracle_conditional_independence.OracleCI property)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr.ParCorr.measure">(tigramite.independence_tests.parcorr.ParCorr property)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.measure">(tigramite.independence_tests.parcorr_mult.ParCorrMult property)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.regressionCI.RegressionCI.measure">(tigramite.independence_tests.regressionCI.RegressionCI property)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.measure">(tigramite.independence_tests.robust_parcorr.RobustParCorr property)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.models.Models">Models (class in tigramite.models)</a>
-</li>
-      <li>
-    module
-
-      <ul>
-        <li><a href="index.html#module-tigramite.data_processing">tigramite.data_processing</a>
-</li>
-        <li><a href="index.html#module-tigramite.plotting">tigramite.plotting</a>
-</li>
-        <li><a href="index.html#module-tigramite.toymodels.structural_causal_processes">tigramite.toymodels.structural_causal_processes</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult.mult_corr">mult_corr() (tigramite.independence_tests.parcorr_mult.ParCorrMult method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="N">N</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.N">N (tigramite.pcmci.PCMCI attribute)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.models.LinearMediation.net_to_tsg">net_to_tsg() (tigramite.models.LinearMediation method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="O">O</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI">OracleCI (class in tigramite.independence_tests.oracle_conditional_independence)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.data_processing.ordinal_patt_array">ordinal_patt_array() (in module tigramite.data_processing)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="P">P</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.independence_tests.parcorr.ParCorr">ParCorr (class in tigramite.independence_tests.parcorr)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.parcorr_mult.ParCorrMult">ParCorrMult (class in tigramite.independence_tests.parcorr_mult)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.parcorr_wls.ParCorrWLS">ParCorrWLS (class in tigramite.independence_tests.parcorr_wls)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI">PCMCI (class in tigramite.pcmci)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.plot_densityplots">plot_densityplots() (in module tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.plot_graph">plot_graph() (in module tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.plot_lagfuncs">plot_lagfuncs() (in module tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.plot_mediation_graph">plot_mediation_graph() (in module tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.plot_mediation_time_series_graph">plot_mediation_time_series_graph() (in module tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.plot_scatterplots">plot_scatterplots() (in module tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.plot_time_series_graph">plot_time_series_graph() (in module tigramite.plotting)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.plotting.plot_timeseries">plot_timeseries() (in module tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.plot_tsg">plot_tsg() (in module tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.models.Prediction.predict">predict() (tigramite.models.Prediction method)</a>
-</li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.predict_bootstrap_of">predict_bootstrap_of() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.predict_total_effect">predict_total_effect() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-      <li><a href="index.html#tigramite.causal_effects.CausalEffects.predict_wright_effect">predict_wright_effect() (tigramite.causal_effects.CausalEffects method)</a>
-</li>
-      <li><a href="index.html#tigramite.models.Prediction">Prediction (class in tigramite.models)</a>
-</li>
-      <li><a href="index.html#tigramite.data_processing.DataFrame.print_array_info">print_array_info() (tigramite.data_processing.DataFrame method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.print_info">print_info() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.print_results">print_results() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.print_significant_links">print_significant_links() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.pval_max">pval_max (tigramite.pcmci.PCMCI attribute)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="Q">Q</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.data_processing.quantile_bin_array">quantile_bin_array() (in module tigramite.data_processing)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="R">R</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.independence_tests.regressionCI.RegressionCI">RegressionCI (class in tigramite.independence_tests.regressionCI)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.return_parents_dict">return_parents_dict() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.return_significant_links">return_significant_links() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr">RobustParCorr (class in tigramite.independence_tests.robust_parcorr)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.run_bivci">run_bivci() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.run_fullci">run_fullci() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.lpcmci.LPCMCI.run_lpcmci">run_lpcmci() (tigramite.lpcmci.LPCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.run_mci">run_mci() (tigramite.pcmci.PCMCI method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.run_pc_stable">run_pc_stable() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.run_pcalg">run_pcalg() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.run_pcalg_non_timeseries_data">run_pcalg_non_timeseries_data() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.run_pcmci">run_pcmci() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.run_pcmciplus">run_pcmciplus() (tigramite.pcmci.PCMCI method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">run_test() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test">(tigramite.independence_tests.oracle_conditional_independence.OracleCI method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw">run_test_raw() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-  </ul></td>
-</tr></table>
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-<h2 id="S">S</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.plotting.setup_matrix.savefig">savefig() (tigramite.plotting.setup_matrix method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.set_dataframe">set_dataframe() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.set_dataframe">(tigramite.independence_tests.oracle_conditional_independence.OracleCI method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.regressionCI.RegressionCI.set_dataframe">(tigramite.independence_tests.regressionCI.RegressionCI method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.set_mask_type">set_mask_type() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.plotting.setup_density_matrix">setup_density_matrix (class in tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.setup_matrix">setup_matrix (class in tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.plotting.setup_scatter_matrix">setup_scatter_matrix (class in tigramite.plotting)</a>
-</li>
-      <li><a href="index.html#tigramite.data_processing.smooth">smooth() (in module tigramite.data_processing)</a>
-</li>
-      <li><a href="index.html#tigramite.data_processing.structural_causal_process">structural_causal_process() (in module tigramite.data_processing)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.toymodels.structural_causal_processes.structural_causal_process">(in module tigramite.toymodels.structural_causal_processes)</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
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-<h2 id="T">T</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.T">T (tigramite.pcmci.PCMCI attribute)</a>
-</li>
-      <li>
-    tigramite.data_processing
-
-      <ul>
-        <li><a href="index.html#module-tigramite.data_processing">module</a>
-</li>
-      </ul></li>
-      <li>
-    tigramite.plotting
-
-      <ul>
-        <li><a href="index.html#module-tigramite.plotting">module</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li>
-    tigramite.toymodels.structural_causal_processes
-
-      <ul>
-        <li><a href="index.html#module-tigramite.toymodels.structural_causal_processes">module</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.data_processing.time_bin_with_mask">time_bin_with_mask() (in module tigramite.data_processing)</a>
-</li>
-      <li><a href="index.html#tigramite.data_processing.trafo2normal">trafo2normal() (in module tigramite.data_processing)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.trafo2normal">(tigramite.independence_tests.robust_parcorr.RobustParCorr method)</a>
-</li>
-      </ul></li>
-      <li><a href="index.html#tigramite.models.LinearMediation.tsg_to_net">tsg_to_net() (tigramite.models.LinearMediation method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="V">V</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.pcmci.PCMCI.val_min">val_min (tigramite.pcmci.PCMCI attribute)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.data_processing.var_process">var_process() (in module tigramite.data_processing)</a>
-
-      <ul>
-        <li><a href="index.html#tigramite.toymodels.structural_causal_processes.var_process">(in module tigramite.toymodels.structural_causal_processes)</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
-
-<h2 id="W">W</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.data_processing.weighted_avg_and_std">weighted_avg_and_std() (in module tigramite.data_processing)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.plotting.write_csv">write_csv() (in module tigramite.plotting)</a>
-</li>
-  </ul></td>
-</tr></table>
-
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-<h1>Welcome to Tigramite’s documentation!<a class="headerlink" href="#welcome-to-tigramite-s-documentation" title="Permalink to this heading">¶</a></h1>
-<div class="toctree-wrapper compound">
-</div>
-</section>
-<section id="indices-and-tables">
-<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this heading">¶</a></h1>
-<ul class="simple">
-<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
-<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
-<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
-</ul>
-</section>
-<section id="tigramite">
-<h1>TIGRAMITE<a class="headerlink" href="#tigramite" title="Permalink to this heading">¶</a></h1>
-<p><a class="reference external" href="https://github.com/jakobrunge/tigramite/">Github repo</a></p>
-<p>Tigramite is a causal time series analysis python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use these graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:</p>
-<ul class="simple">
-<li><p>PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019). <a class="reference external" href="https://advances.sciencemag.org/content/5/11/eaau4996">https://advances.sciencemag.org/content/5/11/eaau4996</a></p></li>
-<li><p>PCMCI+: J. Runge (2020): Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020,Toronto, Canada, 2019, AUAI Press, 2020. <a class="reference external" href="http://auai.org/uai2020/proceedings/579_main_paper.pdf">http://auai.org/uai2020/proceedings/579_main_paper.pdf</a></p></li>
-<li><p>LPCMCI: Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders Advances in Neural Information Processing Systems, 2020, 33. <a class="reference external" href="https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html">https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</a></p></li>
-<li><p>Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310. <a class="reference external" href="https://aip.scitation.org/doi/10.1063/1.5025050">https://aip.scitation.org/doi/10.1063/1.5025050</a></p></li>
-<li><p>Nature Communications Perspective paper: <a class="reference external" href="https://www.nature.com/articles/s41467-019-10105-3">https://www.nature.com/articles/s41467-019-10105-3</a></p></li>
-<li><p>Causal effects: J. Runge, Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables, Advances in Neural Information Processing Systems, 2021, 34</p></li>
-<li><p>Mediation class: J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. <a class="reference external" href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></p></li>
-<li><p>Mediation class: J. Runge (2015): Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 62829. <a class="reference external" href="http://doi.org/10.1103/PhysRevE.92.062829">http://doi.org/10.1103/PhysRevE.92.062829</a></p></li>
-<li><p>CMIknn: J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. <a class="reference external" href="http://proceedings.mlr.press/v84/runge18a.html">http://proceedings.mlr.press/v84/runge18a.html</a></p></li>
-</ul>
-<div class="toctree-wrapper compound">
-</div>
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.pcmci.PCMCI" title="tigramite.pcmci.PCMCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.pcmci.PCMCI</span></code></a>(dataframe, cond_ind_test)</p></td>
-<td><p>PCMCI causal discovery for time series datasets.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.lpcmci.LPCMCI" title="tigramite.lpcmci.LPCMCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.lpcmci.LPCMCI</span></code></a>(dataframe, cond_ind_test)</p></td>
-<td><p>LPCMCI is an algorithm for causal discovery in large-scale times series that allows for latent confounders and learns lag-specific causal relationships.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.independence_tests_base.CondIndTest" title="tigramite.independence_tests.independence_tests_base.CondIndTest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.independence_tests_base.CondIndTest</span></code></a>([...])</p></td>
-<td><p>Base class of conditional independence tests.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.parcorr.ParCorr" title="tigramite.independence_tests.parcorr.ParCorr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.parcorr.ParCorr</span></code></a>(...)</p></td>
-<td><p>Partial correlation test.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr" title="tigramite.independence_tests.robust_parcorr.RobustParCorr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.robust_parcorr.RobustParCorr</span></code></a>(...)</p></td>
-<td><p>Robust partial correlation test based on non-paranormal models.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.gpdc.GPDC" title="tigramite.independence_tests.gpdc.GPDC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.gpdc.GPDC</span></code></a>([...])</p></td>
-<td><p>GPDC conditional independence test based on Gaussian processes and distance correlation.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch" title="tigramite.independence_tests.gpdc_torch.GPDCtorch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.gpdc_torch.GPDCtorch</span></code></a>([...])</p></td>
-<td><p>GPDC conditional independence test based on Gaussian processes and distance correlation.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.cmiknn.CMIknn" title="tigramite.independence_tests.cmiknn.CMIknn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.cmiknn.CMIknn</span></code></a>([...])</p></td>
-<td><p>Conditional mutual information test based on nearest-neighbor estimator.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.cmisymb.CMIsymb" title="tigramite.independence_tests.cmisymb.CMIsymb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.cmisymb.CMIsymb</span></code></a>([...])</p></td>
-<td><p>Conditional mutual information test for discrete/categorical data.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI" title="tigramite.independence_tests.oracle_conditional_independence.OracleCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.oracle_conditional_independence.OracleCI</span></code></a>([...])</p></td>
-<td><p>Oracle of conditional independence test X _|_ Y | Z given a graph.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult" title="tigramite.independence_tests.parcorr_mult.ParCorrMult"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.parcorr_mult.ParCorrMult</span></code></a>([...])</p></td>
-<td><p>Partial correlation test for multivariate X and Y.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.gsquared.Gsquared" title="tigramite.independence_tests.gsquared.Gsquared"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.gsquared.Gsquared</span></code></a>([...])</p></td>
-<td><p>G-squared conditional independence test for categorical data.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.parcorr_wls.ParCorrWLS" title="tigramite.independence_tests.parcorr_wls.ParCorrWLS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.parcorr_wls.ParCorrWLS</span></code></a>([...])</p></td>
-<td><p>Weighted partial correlation test.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.regressionCI.RegressionCI" title="tigramite.independence_tests.regressionCI.RegressionCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.regressionCI.RegressionCI</span></code></a>(...)</p></td>
-<td><p>Flexible parametric conditional independence tests for continuous, categorical, or mixed data.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.causal_effects.CausalEffects" title="tigramite.causal_effects.CausalEffects"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.causal_effects.CausalEffects</span></code></a>(...)</p></td>
-<td><p>Causal effect estimation.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.models.Models" title="tigramite.models.Models"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.Models</span></code></a>(dataframe, model[, ...])</p></td>
-<td><p>Base class for time series models.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.models.LinearMediation" title="tigramite.models.LinearMediation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.LinearMediation</span></code></a>(dataframe)</p></td>
-<td><p>Linear mediation analysis for time series models.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.models.Prediction" title="tigramite.models.Prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.Prediction</span></code></a>(dataframe, ...)</p></td>
-<td><p>Prediction class for time series models.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#module-tigramite.data_processing" title="tigramite.data_processing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.data_processing</span></code></a></p></td>
-<td><p>Tigramite data processing functions.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#module-tigramite.toymodels.structural_causal_processes" title="tigramite.toymodels.structural_causal_processes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.toymodels.structural_causal_processes</span></code></a></p></td>
-<td><p>Tigramite toymodels.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#module-tigramite.plotting" title="tigramite.plotting"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.plotting</span></code></a></p></td>
-<td><p>Tigramite plotting package.</p></td>
-</tr>
-</tbody>
-</table>
-</section>
-<section id="tigramite-pcmci-pcmci">
-<h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.pcmci</span></code>: PCMCI<a class="headerlink" href="#tigramite-pcmci-pcmci" title="Permalink to this heading">¶</a></h1>
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.pcmci.</span></span><span class="sig-name descname"><span class="pre">PCMCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cond_ind_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI" title="Permalink to this definition">¶</a></dt>
-<dd><p>PCMCI causal discovery for time series datasets.</p>
-<p>PCMCI is a causal discovery framework for large-scale time series
-datasets. This class contains several methods. The standard PCMCI method
-addresses time-lagged causal discovery and is described in <a class="footnote-reference brackets" href="#id3" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> where
-also further sub-variants are discussed. Lagged as well as contemporaneous
-causal discovery is addressed with PCMCIplus and described in <a class="footnote-reference brackets" href="#id4" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>. See the
-tutorials for guidance in applying these methods.</p>
-<p>PCMCI has:</p>
-<ul class="simple">
-<li><p>different conditional independence tests adapted to linear or
-nonlinear dependencies, and continuously-valued or discrete data (
-implemented in <code class="docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code>)</p></li>
-<li><p>(mostly) hyperparameter optimization</p></li>
-<li><p>easy parallelization (separate script)</p></li>
-<li><p>handling of masked time series data</p></li>
-<li><p>false discovery control and confidence interval estimation</p></li>
-</ul>
-<p class="rubric">Notes</p>
-<a class="reference internal image-reference" href="_images/mci_schematic.png"><img alt="_images/mci_schematic.png" src="_images/mci_schematic.png" style="width: 200pt;" /></a>
-<p>In the PCMCI framework, the dependency structure of a set of time series
-variables is represented in a <em>time series graph</em> as shown in the Figure.
-The nodes of a time series graph are defined as the variables at
-different times and a link indicates a conditional dependency that can be
-interpreted as a causal dependency under certain assumptions (see paper).
-Assuming stationarity, the links are repeated in time. The parents
-<img class="math" src="_images/math/fff7b4153a6590df59f8ed526be56220045b7f3b.png" alt="\mathcal{P}"/> of a variable are defined as the set of all nodes
-with a link towards it (blue and red boxes in Figure).</p>
-<p>The different PCMCI methods estimate causal links by iterative
-conditional independence testing. PCMCI can be flexibly combined with
-any kind of conditional independence test statistic adapted to the kind
-of data (continuous or discrete) and its assumed dependency types.
-These are available in <code class="docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code>.</p>
-<p>NOTE: MCI test statistic values define a particular measure of causal
-strength depending on the test statistic used. For example, ParCorr()
-results in normalized values between -1 and 1. However, if you are
-interested in quantifying causal effects, i.e., the effect of
-hypothetical interventions, you may better look at the causal effect
-estimation functionality of Tigramite.</p>
-<p class="rubric">References</p>
-<aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id3" role="note">
-<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
-<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id5">2</a>,<a role="doc-backlink" href="#id6">3</a>,<a role="doc-backlink" href="#id7">4</a>,<a role="doc-backlink" href="#id9">5</a>,<a role="doc-backlink" href="#id15">6</a>,<a role="doc-backlink" href="#id16">7</a>,<a role="doc-backlink" href="#id17">8</a>,<a role="doc-backlink" href="#id33">9</a>,<a role="doc-backlink" href="#id34">10</a>,<a role="doc-backlink" href="#id35">11</a>)</span>
-<p>J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic,
-Detecting and quantifying causal associations in large nonlinear time
-series datasets. Sci. Adv. 5, eaau4996 (2019)
-<a class="reference external" href="https://advances.sciencemag.org/content/5/11/eaau4996">https://advances.sciencemag.org/content/5/11/eaau4996</a></p>
-</aside>
-<aside class="footnote brackets" id="id4" role="note">
-<span class="label"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></span>
-<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id8">2</a>,<a role="doc-backlink" href="#id10">3</a>,<a role="doc-backlink" href="#id11">4</a>,<a role="doc-backlink" href="#id12">5</a>)</span>
-<p>J. Runge,
-Discovering contemporaneous and lagged causal relations in
-autocorrelated nonlinear time series datasets
-<a class="reference external" href="http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf">http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf</a></p>
-</aside>
-</aside>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – This is the Tigramite dataframe object. Among others, it has the
-attributes dataframe.values yielding a numpy array of shape (
-observations T, variables N) and optionally a mask of the same shape.</p></li>
-<li><p><strong>cond_ind_test</strong> (<em>conditional independence test object</em>) – This can be ParCorr or other classes from
-<code class="docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code> or an external test passed as a
-callable. This test can be based on the class
-tigramite.independence_tests.CondIndTest.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Verbose levels 0, 1, …</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py attribute">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.all_parents">
-<span class="sig-name descname"><span class="pre">all_parents</span></span><a class="headerlink" href="#tigramite.pcmci.PCMCI.all_parents" title="Permalink to this definition">¶</a></dt>
-<dd><p>Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …} containing
-the conditioning-parents estimated with PC algorithm.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dictionary</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.val_min">
-<span class="sig-name descname"><span class="pre">val_min</span></span><a class="headerlink" href="#tigramite.pcmci.PCMCI.val_min" title="Permalink to this definition">¶</a></dt>
-<dd><p>Dictionary of form val_min[j][(i, -tau)] = float
-containing the minimum test statistic value for each link estimated in
-the PC algorithm.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dictionary</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.pval_max">
-<span class="sig-name descname"><span class="pre">pval_max</span></span><a class="headerlink" href="#tigramite.pcmci.PCMCI.pval_max" title="Permalink to this definition">¶</a></dt>
-<dd><p>Dictionary of form pval_max[j][(i, -tau)] = float containing the maximum
-p-value for each link estimated in the PC algorithm.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dictionary</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.iterations">
-<span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#tigramite.pcmci.PCMCI.iterations" title="Permalink to this definition">¶</a></dt>
-<dd><p>Dictionary containing further information on algorithm steps.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dictionary</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.N">
-<span class="sig-name descname"><span class="pre">N</span></span><a class="headerlink" href="#tigramite.pcmci.PCMCI.N" title="Permalink to this definition">¶</a></dt>
-<dd><p>Number of variables.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>int</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.T">
-<span class="sig-name descname"><span class="pre">T</span></span><a class="headerlink" href="#tigramite.pcmci.PCMCI.T" title="Permalink to this definition">¶</a></dt>
-<dd><p>Time series sample length of dataset(s).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dict</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.get_graph_from_pmatrix">
-<span class="sig-name descname"><span class="pre">get_graph_from_pmatrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.get_graph_from_pmatrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.get_graph_from_pmatrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct graph from thresholding the p_matrix at an alpha-level.</p>
-<p>Allows to take into account link_assumptions.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>p_matrix</strong> (<em>array</em><em> of </em><em>shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
-not ‘none’.</p></li>
-<li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level at which the p_matrix is thresholded to
-get graph.</p></li>
-<li><p><strong>tau_mix</strong> (<em>int</em>) – Minimum time delay to test.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em>) – Maximum time delay to test.</p></li>
-<li><p><strong>link_assumptions</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {j:{(i, -tau): link_type, …}, …} specifying
-assumptions about links. This initializes the graph with entries
-graph[i,j,tau] = link_type. For example, graph[i,j,0] = ‘–&gt;’
-implies that a directed link from i to j at lag 0 must exist.
-Valid link types are ‘o-o’, ‘–&gt;’, ‘&lt;–’. In addition, the middle
-mark can be ‘?’ instead of ‘-’. Then ‘-?&gt;’ implies that this link
-may not exist, but if it exists, its orientation is ‘–&gt;’. Link
-assumptions need to be consistent, i.e., graph[i,j,0] = ‘–&gt;’
-requires graph[j,i,0] = ‘&lt;–’ and acyclicity must hold. If a link
-does not appear in the dictionary, it is assumed absent. That is,
-if link_assumptions is not None, then all links have to be specified
-or the links are assumed absent.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>graph</strong> – Causal graph, see description above for interpretation.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape [N, N, tau_max+1]</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.get_lagged_dependencies">
-<span class="sig-name descname"><span class="pre">get_lagged_dependencies</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fdr_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.get_lagged_dependencies"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.get_lagged_dependencies" title="Permalink to this definition">¶</a></dt>
-<dd><p>Unconditional lagged independence tests.</p>
-<p>Implements the unconditional lagged independence test (see [ 1]_).</p>
-<p>Returns the matrices of test statistic values, (optionally corrected)
-p-values, and (optionally) confidence intervals. Also (new in 4.3)
-returns graph based on alpha_level (and optional FDR-correction).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Deprecated, replaced by link_assumptions</p></li>
-<li><p><strong>link_assumptions</strong> (<em>dict</em>) – Dictionary of form {j:{(i, -tau): link_type, …}, …} specifying
-assumptions about links. This initializes the graph with entries
-graph[i,j,tau] = link_type. For example, graph[i,j,0] = ‘–&gt;’
-implies that a directed link from i to j at lag 0 must exist.
-Valid link types are ‘o-o’, ‘–&gt;’, ‘&lt;–’. In addition, the middle
-mark can be ‘?’ instead of ‘-’. Then ‘-?&gt;’ implies that this link
-may not exist, but if it exists, its orientation is ‘–&gt;’. Link
-assumptions need to be consistent, i.e., graph[i,j,0] = ‘–&gt;’
-requires graph[j,i,0] = ‘&lt;–’ and acyclicity must hold. If a link
-does not appear in the dictionary, it is assumed absent. That is,
-if link_assumptions is not None, then all links have to be specified
-or the links are assumed absent.</p></li>
-<li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 0</em>) – Minimum time lag to test. Note that zero-lags are undirected.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>val_only</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Option to only compute dependencies and not p-values.</p></li>
-<li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level at which the p_matrix is thresholded to
-get graph.</p></li>
-<li><p><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'none'</em><em>)</em>) – Correction method, currently implemented is Benjamini-Hochberg
-False Discovery Rate method (‘fdr_bh’).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Causal graph, see description above for interpretation.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values.</p></li>
-<li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
-not ‘none’.</p></li>
-<li><p><strong>conf_matrix</strong> (<em>array of shape [N, N, tau_max+1,2]</em>) – Estimated matrix of confidence intervals of test statistic values.
-Only computed if set in cond_ind_test, where also the percentiles
-are set.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.print_results">
-<span class="sig-name descname"><span class="pre">print_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.print_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.print_results" title="Permalink to this definition">¶</a></dt>
-<dd><p>Prints significant parents from output of MCI or PCMCI algorithms.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>return_dict</strong> (<em>dict</em>) – <dl class="simple">
-<dt>Dictionary of return values, containing keys</dt><dd><ul>
-<li><p>’p_matrix’</p></li>
-<li><p>’val_matrix’</p></li>
-<li><p>’conf_matrix’</p></li>
-</ul>
-</dd>
-</dl>
-</p></li>
-<li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.print_significant_links">
-<span class="sig-name descname"><span class="pre">print_significant_links</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ambiguous_triples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.print_significant_links"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.print_significant_links" title="Permalink to this definition">¶</a></dt>
-<dd><p>Prints significant links.</p>
-<p>Used for output of PCMCI and PCMCIplus. For the latter also information
-on ambiguous links and conflicts is returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level.</p></li>
-<li><p><strong>p_matrix</strong> (<em>array-like</em>) – Must be of shape (N, N, tau_max + 1).</p></li>
-<li><p><strong>val_matrix</strong> (<em>array-like</em>) – Must be of shape (N, N, tau_max + 1).</p></li>
-<li><p><strong>conf_matrix</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of confidence intervals of shape (N, N, tau_max+1, 2).</p></li>
-<li><p><strong>graph</strong> (<em>array-like</em>) – Must be of shape (N, N, tau_max + 1).</p></li>
-<li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.return_parents_dict">
-<span class="sig-name descname"><span class="pre">return_parents_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_lagzero_parents</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.return_parents_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.return_parents_dict" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns dictionary of parents sorted by val_matrix.</p>
-<p>If parents are unclear (edgemarks with ‘o’ or ‘x’, or middle mark ‘?’),
-then no parent is returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph, see description above for interpretation.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array-like</em>) – Matrix of test statistic values. Must be of shape (N, N, tau_max +
-1).</p></li>
-<li><p><strong>include_lagzero_parents</strong> (<em>bool</em><em> (</em><em>default: False</em><em>)</em>) – Whether the dictionary should also return parents at lag
-zero.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>parents_dict</strong> – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
-containing estimated parents.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dict</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.return_significant_links">
-<span class="sig-name descname"><span class="pre">return_significant_links</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pq_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_lagzero_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.return_significant_links"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.return_significant_links" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns list of significant links as well as a boolean matrix.</p>
-<p>DEPRECATED. Will be removed in future.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.run_bivci">
-<span class="sig-name descname"><span class="pre">run_bivci</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fdr_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_bivci"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_bivci" title="Permalink to this definition">¶</a></dt>
-<dd><p>BivCI conditional independence tests.</p>
-<p>Implements the BivCI test (see <a class="footnote-reference brackets" href="#id3" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>).</p>
-<p>Returns the matrices of test statistic values, (optionally corrected)
-p-values, and (optionally) confidence intervals. Also (new in 4.3)
-returns graph based on alpha_level (and optional FDR-correction).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Deprecated, replaced by link_assumptions</p></li>
-<li><p><strong>link_assumptions</strong> (<em>dict</em>) – Dictionary of form {j:{(i, -tau): link_type, …}, …} specifying
-assumptions about links. This initializes the graph with entries
-graph[i,j,tau] = link_type. For example, graph[i,j,0] = ‘–&gt;’
-implies that a directed link from i to j at lag 0 must exist.
-Valid link types are ‘o-o’, ‘–&gt;’, ‘&lt;–’. In addition, the middle
-mark can be ‘?’ instead of ‘-’. Then ‘-?&gt;’ implies that this link
-may not exist, but if it exists, its orientation is ‘–&gt;’. Link
-assumptions need to be consistent, i.e., graph[i,j,0] = ‘–&gt;’
-requires graph[j,i,0] = ‘&lt;–’ and acyclicity must hold. If a link
-does not appear in the dictionary, it is assumed absent. That is,
-if link_assumptions is not None, then all links have to be specified
-or the links are assumed absent.</p></li>
-<li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 0</em>) – Minimum time lag to test. Note that zero-lags are undirected.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>val_only</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Option to only compute dependencies and not p-values.</p></li>
-<li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level at which the p_matrix is thresholded to
-get graph.</p></li>
-<li><p><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'fdr_bh'</em><em>)</em>) – Correction method, currently implemented is Benjamini-Hochberg
-False Discovery Rate method.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Causal graph, see description above for interpretation.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values.</p></li>
-<li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
-not ‘none’.</p></li>
-<li><p><strong>conf_matrix</strong> (<em>array of shape [N, N, tau_max+1,2]</em>) – Estimated matrix of confidence intervals of test statistic values.
-Only computed if set in cond_ind_test, where also the percentiles
-are set.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.run_fullci">
-<span class="sig-name descname"><span class="pre">run_fullci</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fdr_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_fullci"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_fullci" title="Permalink to this definition">¶</a></dt>
-<dd><p>FullCI conditional independence tests.</p>
-<p>Implements the FullCI test (see <a class="footnote-reference brackets" href="#id3" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>).</p>
-<p>Returns the matrices of test statistic values, (optionally corrected)
-p-values, and (optionally) confidence intervals. Also (new in 4.3)
-returns graph based on alpha_level (and optional FDR-correction).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Deprecated, replaced by link_assumptions</p></li>
-<li><p><strong>link_assumptions</strong> (<em>dict</em>) – Dictionary of form {j:{(i, -tau): link_type, …}, …} specifying
-assumptions about links. This initializes the graph with entries
-graph[i,j,tau] = link_type. For example, graph[i,j,0] = ‘–&gt;’
-implies that a directed link from i to j at lag 0 must exist.
-Valid link types are ‘o-o’, ‘–&gt;’, ‘&lt;–’. In addition, the middle
-mark can be ‘?’ instead of ‘-’. Then ‘-?&gt;’ implies that this link
-may not exist, but if it exists, its orientation is ‘–&gt;’. Link
-assumptions need to be consistent, i.e., graph[i,j,0] = ‘–&gt;’
-requires graph[j,i,0] = ‘&lt;–’ and acyclicity must hold. If a link
-does not appear in the dictionary, it is assumed absent. That is,
-if link_assumptions is not None, then all links have to be specified
-or the links are assumed absent.</p></li>
-<li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 0</em>) – Minimum time lag to test. Note that zero-lags are undirected.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>val_only</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Option to only compute dependencies and not p-values.</p></li>
-<li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level at which the p_matrix is thresholded to
-get graph.</p></li>
-<li><p><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'none'</em><em>)</em>) – Correction method, currently implemented is Benjamini-Hochberg
-False Discovery Rate method (‘fdr_bh’).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Causal graph, see description above for interpretation.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values.</p></li>
-<li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
-not ‘none’.</p></li>
-<li><p><strong>conf_matrix</strong> (<em>array of shape [N, N, tau_max+1,2]</em>) – Estimated matrix of confidence intervals of test statistic values.
-Only computed if set in cond_ind_test, where also the percentiles
-are set.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.run_mci">
-<span class="sig-name descname"><span class="pre">run_mci</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_py</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_px</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fdr_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_mci"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_mci" title="Permalink to this definition">¶</a></dt>
-<dd><p>MCI conditional independence tests.</p>
-<p>Implements the MCI test (Algorithm 2 in <a class="footnote-reference brackets" href="#id3" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>).</p>
-<p>Returns the matrices of test statistic values, (optionally corrected)
-p-values, and (optionally) confidence intervals. Also (new in 4.3)
-returns graph based on alpha_level (and optional FDR-correction).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Deprecated, replaced by link_assumptions</p></li>
-<li><p><strong>link_assumptions</strong> (<em>dict</em>) – Dictionary of form {j:{(i, -tau): link_type, …}, …} specifying
-assumptions about links. This initializes the graph with entries
-graph[i,j,tau] = link_type. For example, graph[i,j,0] = ‘–&gt;’
-implies that a directed link from i to j at lag 0 must exist.
-Valid link types are ‘o-o’, ‘–&gt;’, ‘&lt;–’. In addition, the middle
-mark can be ‘?’ instead of ‘-’. Then ‘-?&gt;’ implies that this link
-may not exist, but if it exists, its orientation is ‘–&gt;’. Link
-assumptions need to be consistent, i.e., graph[i,j,0] = ‘–&gt;’
-requires graph[j,i,0] = ‘&lt;–’ and acyclicity must hold. If a link
-does not appear in the dictionary, it is assumed absent. That is,
-if link_assumptions is not None, then all links have to be specified
-or the links are assumed absent.</p></li>
-<li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 0</em>) – Minimum time lag to test. Note that zero-lags are undirected.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>parents</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
-specifying the conditions for each variable. If None is
-passed, no conditions are used.</p></li>
-<li><p><strong>max_conds_py</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions of Y to use. If None is passed, this
-number is unrestricted.</p></li>
-<li><p><strong>max_conds_px</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions of Z to use. If None is passed, this
-number is unrestricted.</p></li>
-<li><p><strong>val_only</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Option to only compute dependencies and not p-values.</p></li>
-<li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level at which the p_matrix is thresholded to
-get graph.</p></li>
-<li><p><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'none'</em><em>)</em>) – Correction method, currently implemented is Benjamini-Hochberg
-False Discovery Rate method (‘fdr_bh’).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Causal graph, see description above for interpretation.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values.</p></li>
-<li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
-not ‘none’.</p></li>
-<li><p><strong>conf_matrix</strong> (<em>array of shape [N, N, tau_max+1,2]</em>) – Estimated matrix of confidence intervals of test statistic values.
-Only computed if set in cond_ind_test, where also the percentiles
-are set.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.run_pc_stable">
-<span class="sig-name descname"><span class="pre">run_pc_stable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_combinations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_pc_stable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_pc_stable" title="Permalink to this definition">¶</a></dt>
-<dd><p>Lagged PC algorithm for estimating lagged parents of all variables.</p>
-<p>Parents are made available as self.all_parents</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Deprecated, replaced by link_assumptions</p></li>
-<li><p><strong>link_assumptions</strong> (<em>dict</em>) – Dictionary of form {j:{(i, -tau): link_type, …}, …} specifying
-assumptions about links. This initializes the graph with entries
-graph[i,j,tau] = link_type. For example, graph[i,j,0] = ‘–&gt;’
-implies that a directed link from i to j at lag 0 must exist.
-Valid link types are ‘o-o’, ‘–&gt;’, ‘&lt;–’. In addition, the middle
-mark can be ‘?’ instead of ‘-’. Then ‘-?&gt;’ implies that this link
-may not exist, but if it exists, its orientation is ‘–&gt;’. Link
-assumptions need to be consistent, i.e., graph[i,j,0] = ‘–&gt;’
-requires graph[j,i,0] = ‘&lt;–’ and acyclicity must hold. If a link
-does not appear in the dictionary, it is assumed absent. That is,
-if link_assumptions is not None, then all links have to be specified
-or the links are assumed absent.</p></li>
-<li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Minimum time lag to test. Useful for multi-step ahead predictions.
-Must be greater zero.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>save_iterations</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether to save iteration step results such as conditions used.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list</em><em> of </em><em>floats</em><em>, </em><em>default:</em><em> [</em><em>0.05</em><em>, </em><em>0.1</em><em>, </em><em>0.2</em><em>, </em><em>...</em><em>, </em><em>0.5</em><em>]</em>) – Significance level in algorithm. If a list or None is passed, the
-pc_alpha level is optimized for every variable across the given
-pc_alpha values using the score computed in
-cond_ind_test.get_model_selection_criterion().</p></li>
-<li><p><strong>max_conds_dim</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions to test. If None is passed, this number
-is unrestricted.</p></li>
-<li><p><strong>max_combinations</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum number of combinations of conditions of current cardinality
-to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
-a larger number, such as 10, can be used.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>all_parents</strong> – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
-containing estimated parents.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dict</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.run_pcalg">
-<span class="sig-name descname"><span class="pre">run_pcalg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_combinations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lagged_parents</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_py</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_px</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_px_lagged</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'standard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contemp_collider_rule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'majority'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conflict_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_pcalg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_pcalg" title="Permalink to this definition">¶</a></dt>
-<dd><p>Runs PC algorithm for time-lagged and contemporaneous causal
-discovery for time series.</p>
-<p>For <code class="docutils literal notranslate"><span class="pre">mode='contemp_conds'</span></code> this implements Steps 2-4 of the
-PCMCIplus method described in <a class="footnote-reference brackets" href="#id4" id="id8" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>. For <code class="docutils literal notranslate"><span class="pre">mode='standard'</span></code> this
-implements the standard PC algorithm adapted to time series.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Deprecated, replaced by link_assumptions</p></li>
-<li><p><strong>link_assumptions</strong> (<em>dict</em>) – Dictionary of form {j:{(i, -tau): link_type, …}, …} specifying
-assumptions about links. This initializes the graph with entries
-graph[i,j,tau] = link_type. For example, graph[i,j,0] = ‘–&gt;’
-implies that a directed link from i to j at lag 0 must exist.
-Valid link types are ‘o-o’, ‘–&gt;’, ‘&lt;–’. In addition, the middle
-mark can be ‘?’ instead of ‘-’. Then ‘-?&gt;’ implies that this link
-may not exist, but if it exists, its orientation is ‘–&gt;’. Link
-assumptions need to be consistent, i.e., graph[i,j,0] = ‘–&gt;’
-requires graph[j,i,0] = ‘&lt;–’ and acyclicity must hold. If a link
-does not appear in the dictionary, it is assumed absent. That is,
-if link_assumptions is not None, then all links have to be specified
-or the links are assumed absent.</p></li>
-<li><p><strong>lagged_parents</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …} containing
-additional conditions for each CI test. As part of PCMCIplus
-these are the superset of lagged parents estimated with the PC1
-algorithm.</p></li>
-<li><p><strong>mode</strong> (<em>{'standard'</em><em>, </em><em>'contemp_conds'}</em>) – For <code class="docutils literal notranslate"><span class="pre">mode='contemp_conds'</span></code> this implements Steps 2-4 of the
-PCMCIplus method. For <code class="docutils literal notranslate"><span class="pre">mode='standard'</span></code> this implements the
-standard PC algorithm adapted to time series.</p></li>
-<li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Minimum time lag to test.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.01</em><em>)</em>) – Significance level.</p></li>
-<li><p><strong>contemp_collider_rule</strong> (<em>{'majority'</em><em>, </em><em>'conservative'</em><em>, </em><em>'none'}</em>) – Rule for collider phase to use. See the paper for details. Only
-‘majority’ and ‘conservative’ lead to an order-independent
-algorithm.</p></li>
-<li><p><strong>conflict_resolution</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to mark conflicts in orientation rules. Only for True
-this leads to an order-independent algorithm.</p></li>
-<li><p><strong>max_conds_dim</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of conditions to test. If None is passed, this number
-is unrestricted.</p></li>
-<li><p><strong>max_combinations</strong> (<em>int</em>) – Maximum number of combinations of conditions of current cardinality
-to test.</p></li>
-<li><p><strong>max_conds_py</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of lagged conditions of Y to use in MCI tests. If
-None is passed, this number is unrestricted.</p></li>
-<li><p><strong>max_conds_px</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of lagged conditions of X to use in MCI tests. If
-None is passed, this number is unrestricted.</p></li>
-<li><p><strong>max_conds_px_lagged</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of lagged conditions of X when X is lagged in MCI
-tests. If None is passed, this number is equal to max_conds_px.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Resulting causal graph, see description above for interpretation.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values regarding adjacencies.</p></li>
-<li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values regarding adjacencies.</p></li>
-<li><p><strong>sepset</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
-<li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples, only relevant for ‘majority’ and
-‘conservative’ rules, see paper for details.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.run_pcalg_non_timeseries_data">
-<span class="sig-name descname"><span class="pre">run_pcalg_non_timeseries_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_combinations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contemp_collider_rule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'majority'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conflict_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_pcalg_non_timeseries_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_pcalg_non_timeseries_data" title="Permalink to this definition">¶</a></dt>
-<dd><p>Runs PC algorithm for non-time series data.</p>
-<p>Simply calls run_pcalg with tau_min = tau_max = 0.
-Removes lags from output dictionaries.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>pc_alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.01</em><em>)</em>) – Significance level.</p></li>
-<li><p><strong>contemp_collider_rule</strong> (<em>{'majority'</em><em>, </em><em>'conservative'</em><em>, </em><em>'none'}</em>) – Rule for collider phase to use. See the paper for details. Only
-‘majority’ and ‘conservative’ lead to an order-independent
-algorithm.</p></li>
-<li><p><strong>conflict_resolution</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to mark conflicts in orientation rules. Only for True
-this leads to an order-independent algorithm.</p></li>
-<li><p><strong>max_conds_dim</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of conditions to test. If None is passed, this number
-is unrestricted.</p></li>
-<li><p><strong>max_combinations</strong> (<em>int</em>) – Maximum number of combinations of conditions of current cardinality
-to test.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>graph</strong> (<em>array of shape [N, N, 1]</em>) – Resulting causal graph, see description above for interpretation.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array of shape [N, N, 1]</em>) – Estimated matrix of test statistic values regarding adjacencies.</p></li>
-<li><p><strong>p_matrix</strong> (<em>array of shape [N, N, 1]</em>) – Estimated matrix of p-values regarding adjacencies.</p></li>
-<li><p><strong>sepset</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
-<li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples, only relevant for ‘majority’ and
-‘conservative’ rules, see paper for details.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.run_pcmci">
-<span class="sig-name descname"><span class="pre">run_pcmci</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_combinations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_py</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_px</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fdr_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_pcmci"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_pcmci" title="Permalink to this definition">¶</a></dt>
-<dd><p>Runs PCMCI time-lagged causal discovery for time series.</p>
-<p>Wrapper around PC-algorithm function and MCI function.</p>
-<p class="rubric">Notes</p>
-<p>The PCMCI causal discovery method is comprehensively described in [
-1]_, where also analytical and numerical results are presented. Here
-we briefly summarize the method.</p>
-<p>PCMCI estimates time-lagged causal links by a two-step procedure:</p>
-<ol class="arabic simple">
-<li><p>Condition-selection: For each variable <img class="math" src="_images/math/e3fc28292267f066fee7718c64f4bbfece521f24.png" alt="j"/>, estimate a
-<em>superset</em> of parents <img class="math" src="_images/math/c4ed4f54ee00448249d2df22ad67f3e281df085b.png" alt="\tilde{\mathcal{P}}(X^j_t)"/> with the
-iterative PC1 algorithm, implemented as <code class="docutils literal notranslate"><span class="pre">run_pc_stable</span></code>. The
-condition-selection step reduces the dimensionality and avoids
-conditioning on irrelevant variables.</p></li>
-<li><p><em>Momentary conditional independence</em> (MCI)</p></li>
-</ol>
-<div class="math">
-<p><img src="_images/math/e676f7877e03a3707387149ce203c5e79ed8f9a9.png" alt="X^i_{t-\tau} \perp X^j_{t} | \tilde{\mathcal{P}}(
-X^j_t), \tilde{\mathcal{P}}(X^i_{t-\tau})"/></p>
-</div><p>here implemented as <code class="docutils literal notranslate"><span class="pre">run_mci</span></code>. This step estimates the p-values and
-test statistic values for all links accounting for common drivers,
-indirect links, and autocorrelation.</p>
-<p>NOTE: MCI test statistic values define a particular measure of causal
-strength depending on the test statistic used. For example, ParCorr()
-results in normalized values between -1 and 1. However, if you are
-interested in quantifying causal effects, i.e., the effect of
-hypothetical interventions, you may better look at the causal effect
-estimation functionality of Tigramite.</p>
-<p>PCMCI can be flexibly combined with any kind of conditional
-independence test statistic adapted to the kind of data (continuous
-or discrete) and its assumed dependency types. These are available in
-<code class="docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code>.</p>
-<p>The main free parameters of PCMCI (in addition to free parameters of
-the conditional independence test statistic) are the maximum time
-delay <img class="math" src="_images/math/2bf86ca220f43e569c6c7aefaf32742919222e6e.png" alt="\tau_{\max}"/> (<code class="docutils literal notranslate"><span class="pre">tau_max</span></code>) and the significance
-threshold in the condition-selection step <img class="math" src="_images/math/2f5aa019312e1bbc969deab8dca8b00f76025404.png" alt="\alpha"/> (
-<code class="docutils literal notranslate"><span class="pre">pc_alpha</span></code>). The maximum time delay depends on the application and
-should be chosen according to the maximum causal time lag expected in
-the complex system. We recommend a rather large choice that includes
-peaks in the <code class="docutils literal notranslate"><span class="pre">get_lagged_dependencies</span></code> function. <img class="math" src="_images/math/2f5aa019312e1bbc969deab8dca8b00f76025404.png" alt="\alpha"/>
-should not be seen as a significance test level in the
-condition-selection step since the iterative hypothesis tests do not
-allow for a precise assessment. <img class="math" src="_images/math/2f5aa019312e1bbc969deab8dca8b00f76025404.png" alt="\alpha"/> rather takes the role
-of a regularization parameter in model-selection techniques. If a
-list of values is given or <code class="docutils literal notranslate"><span class="pre">pc_alpha=None</span></code>, <img class="math" src="_images/math/2f5aa019312e1bbc969deab8dca8b00f76025404.png" alt="\alpha"/> is
-optimized using model selection criteria implemented in the respective
-<code class="docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code>.</p>
-<p>Further optional parameters are discussed in <a class="footnote-reference brackets" href="#id3" id="id9" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.pcmci</span> <span class="kn">import</span> <span class="n">PCMCI</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">7</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Example process to play around with</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Each key refers to a variable and the incoming links are supplied</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># as a list of format [((driver, -lag), coeff), ...]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">links_coeffs</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.8</span><span class="p">)],</span>
-<span class="go">                    1: [((1, -1), 0.8), ((0, -1), 0.5)],</span>
-<span class="go">                    2: [((2, -1), 0.8), ((1, -2), -0.6)]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">var_process</span><span class="p">(</span><span class="n">links_coeffs</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Data must be array of shape (time, variables)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-<span class="go">(1000, 3)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pcmci</span> <span class="o">=</span> <span class="n">PCMCI</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">results</span> <span class="o">=</span> <span class="n">pcmci</span><span class="o">.</span><span class="n">run_pcmci</span><span class="p">(</span><span class="n">tau_max</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pcmci</span><span class="o">.</span><span class="n">print_significant_links</span><span class="p">(</span><span class="n">p_matrix</span><span class="o">=</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span>
-<span class="go">                                 val_matrix=results[&#39;val_matrix&#39;],</span>
-<span class="go">                                 alpha_level=0.05)</span>
-<span class="go">## Significant parents at alpha = 0.05:</span>
-</pre></div>
-</div>
-<blockquote>
-<div><dl class="simple">
-<dt>Variable 0 has 1 link(s):</dt><dd><p>(0 -1): pval = 0.00000 | val =  0.588</p>
-</dd>
-<dt>Variable 1 has 2 link(s):</dt><dd><p>(1 -1): pval = 0.00000 | val =  0.606
-(0 -1): pval = 0.00000 | val =  0.447</p>
-</dd>
-<dt>Variable 2 has 2 link(s):</dt><dd><p>(2 -1): pval = 0.00000 | val =  0.618
-(1 -2): pval = 0.00000 | val = -0.499</p>
-</dd>
-</dl>
-</div></blockquote>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Deprecated, replaced by link_assumptions</p></li>
-<li><p><strong>link_assumptions</strong> (<em>dict</em>) – Dictionary of form {j:{(i, -tau): link_type, …}, …} specifying
-assumptions about links. This initializes the graph with entries
-graph[i,j,tau] = link_type. For example, graph[i,j,0] = ‘–&gt;’
-implies that a directed link from i to j at lag 0 must exist.
-Valid link types are ‘o-o’, ‘–&gt;’, ‘&lt;–’. In addition, the middle
-mark can be ‘?’ instead of ‘-’. Then ‘-?&gt;’ implies that this link
-may not exist, but if it exists, its orientation is ‘–&gt;’. Link
-assumptions need to be consistent, i.e., graph[i,j,0] = ‘–&gt;’
-requires graph[j,i,0] = ‘&lt;–’ and acyclicity must hold. If a link
-does not appear in the dictionary, it is assumed absent. That is,
-if link_assumptions is not None, then all links have to be specified
-or the links are assumed absent.</p></li>
-<li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Minimum time lag to test. Note that zero-lags are undirected.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>save_iterations</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to save iteration step results such as conditions used.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level in algorithm.</p></li>
-<li><p><strong>max_conds_dim</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of conditions to test. If None is passed, this number
-is unrestricted.</p></li>
-<li><p><strong>max_combinations</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum number of combinations of conditions of current cardinality
-to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
-a larger number, such as 10, can be used.</p></li>
-<li><p><strong>max_conds_py</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of conditions of Y to use. If None is passed, this
-number is unrestricted.</p></li>
-<li><p><strong>max_conds_px</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of conditions of Z to use. If None is passed, this
-number is unrestricted.</p></li>
-<li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level at which the p_matrix is thresholded to
-get graph.</p></li>
-<li><p><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'fdr_bh'</em><em>)</em>) – Correction method, currently implemented is Benjamini-Hochberg
-False Discovery Rate method.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Causal graph, see description above for interpretation.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values.</p></li>
-<li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
-not ‘none’.</p></li>
-<li><p><strong>conf_matrix</strong> (<em>array of shape [N, N, tau_max+1,2]</em>) – Estimated matrix of confidence intervals of test statistic values.
-Only computed if set in cond_ind_test, where also the percentiles
-are set.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.pcmci.PCMCI.run_pcmciplus">
-<span class="sig-name descname"><span class="pre">run_pcmciplus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contemp_collider_rule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'majority'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conflict_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reset_lagged_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_combinations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_py</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_px</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_px_lagged</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fdr_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_pcmciplus"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_pcmciplus" title="Permalink to this definition">¶</a></dt>
-<dd><p>Runs PCMCIplus time-lagged and contemporaneous causal discovery for
-time series.</p>
-<p>Method described in <a class="footnote-reference brackets" href="#id4" id="id10" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>:
-<a class="reference external" href="http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf">http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf</a></p>
-<p class="rubric">Notes</p>
-<p>The PCMCIplus causal discovery method is described in <a class="footnote-reference brackets" href="#id4" id="id11" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>, where
-also analytical and numerical results are presented. In contrast to
-PCMCI, PCMCIplus can identify the full, lagged and contemporaneous,
-causal graph (up to the Markov equivalence class for contemporaneous
-links) under the standard assumptions of Causal Sufficiency,
-Faithfulness and the Markov condition.</p>
-<p>PCMCIplus estimates time-lagged and contemporaneous causal links by a
-four-step procedure:</p>
-<p>1.  Condition-selection (same as for PCMCI): For each variable
-<img class="math" src="_images/math/e3fc28292267f066fee7718c64f4bbfece521f24.png" alt="j"/>, estimate a <em>superset</em> of lagged parents <img class="math" src="_images/math/15af846e07c93e1c0d4cdbba8f4cefca67a811cb.png" alt="\widehat{
-\mathcal{B}}_t^-( X^j_t)"/> with the iterative PC1 algorithm,
-implemented as <code class="docutils literal notranslate"><span class="pre">run_pc_stable</span></code>. The condition-selection step
-reduces the dimensionality and avoids conditioning on irrelevant
-variables.</p>
-<p>2.   PC skeleton phase with contemporaneous conditions and <em>Momentary
-conditional independence</em> (MCI) tests: Iterate through subsets
-<img class="math" src="_images/math/f852990c4a225a95d2c694a9f1351f54bac3ba86.png" alt="\mathcal{S}"/> of contemporaneous adjacencies and conduct MCI
-conditional independence tests:</p>
-<div class="math">
-<p><img src="_images/math/d4ac9b60213cb2ffe02c6d5dacac8796221c73ac.png" alt="X^i_{t-\tau} ~\perp~ X^j_{t} ~|~ \mathcal{S},
-\widehat{\mathcal{B}}_t^-(X^j_t),
-\widehat{\mathcal{B}}_{t-\tau}^-(X^i_{t-{\tau}})"/></p>
-</div><p>here implemented as <code class="docutils literal notranslate"><span class="pre">run_pcalg</span></code>. This step estimates the p-values and
-test statistic values for all lagged and contemporaneous adjacencies
-accounting for common drivers, indirect links, and autocorrelation.</p>
-<p>3.   PC collider orientation phase: Orient contemporaneous collider
-motifs based on unshielded triples. Optionally apply conservative or
-majority rule (also based on MCI tests).</p>
-<p>4.   PC rule orientation phase: Orient remaining contemporaneous
-links based on PC rules.</p>
-<p>In contrast to PCMCI, the relevant output of PCMCIplus is the
-array <code class="docutils literal notranslate"><span class="pre">graph</span></code>. Its string entries are interpreted as follows:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">graph[i,j,tau]=--&gt;</span></code> for <img class="math" src="_images/math/2882aa5d9b56c4f42679de5f902a74d8ae88a624.png" alt="\tau&gt;0"/> denotes a directed, lagged
-causal link from <img class="math" src="_images/math/5aa339d4daf45a810dda332e3c80a0698e526e04.png" alt="i"/> to <img class="math" src="_images/math/e3fc28292267f066fee7718c64f4bbfece521f24.png" alt="j"/> at lag <img class="math" src="_images/math/914b2d4b6659b86d3153d5510839dfb254dfc8a3.png" alt="\tau"/></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">graph[i,j,0]=--&gt;</span></code> (and <code class="docutils literal notranslate"><span class="pre">graph[j,i,0]=&lt;--</span></code>) denotes a directed,
-contemporaneous causal link from <img class="math" src="_images/math/5aa339d4daf45a810dda332e3c80a0698e526e04.png" alt="i"/> to <img class="math" src="_images/math/e3fc28292267f066fee7718c64f4bbfece521f24.png" alt="j"/></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">graph[i,j,0]=o-o</span></code> (and <code class="docutils literal notranslate"><span class="pre">graph[j,i,0]=o-o</span></code>) denotes an unoriented,
-contemporaneous adjacency between <img class="math" src="_images/math/5aa339d4daf45a810dda332e3c80a0698e526e04.png" alt="i"/> and <img class="math" src="_images/math/e3fc28292267f066fee7718c64f4bbfece521f24.png" alt="j"/> indicating
-that the collider and orientation rules could not be applied (Markov
-equivalence)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">graph[i,j,0]=x-x</span></code> and (<code class="docutils literal notranslate"><span class="pre">graph[j,i,0]=x-x</span></code>) denotes a conflicting,
-contemporaneous adjacency between <img class="math" src="_images/math/5aa339d4daf45a810dda332e3c80a0698e526e04.png" alt="i"/> and <img class="math" src="_images/math/e3fc28292267f066fee7718c64f4bbfece521f24.png" alt="j"/> indicating
-that the directionality is undecided due to conflicting orientation
-rules</p></li>
-</ul>
-<p>Importantly, <code class="docutils literal notranslate"><span class="pre">p_matrix</span></code> and <code class="docutils literal notranslate"><span class="pre">val_matrix</span></code> for PCMCIplus quantify
-the uncertainty and strength, respectively, only for the
-adjacencies, but not for the directionality of contemporaneous links.
-Note that lagged links are always oriented due to time order.</p>
-<p>PCMCIplus can be flexibly combined with any kind of conditional
-independence test statistic adapted to the kind of data (continuous
-or discrete) and its assumed dependency types. These are available in
-<code class="docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code>.</p>
-<p>The main free parameters of PCMCIplus (in addition to free parameters of
-the conditional independence tests) are the maximum time delay
-<img class="math" src="_images/math/2bf86ca220f43e569c6c7aefaf32742919222e6e.png" alt="\tau_{\max}"/> (<code class="docutils literal notranslate"><span class="pre">tau_max</span></code>) and the significance threshold
-<img class="math" src="_images/math/2f5aa019312e1bbc969deab8dca8b00f76025404.png" alt="\alpha"/> ( <code class="docutils literal notranslate"><span class="pre">pc_alpha</span></code>).</p>
-<p>If a list or None is passed for <code class="docutils literal notranslate"><span class="pre">pc_alpha</span></code>, the significance level is
-optimized for every graph across the given <code class="docutils literal notranslate"><span class="pre">pc_alpha</span></code> values using the
-score computed in <code class="docutils literal notranslate"><span class="pre">cond_ind_test.get_model_selection_criterion()</span></code>.
-Since PCMCIplus outputs not a DAG, but an equivalence class of DAGs,
-first one member of this class is computed and then the score is
-computed as the average over all models fits for each variable in <code class="docutils literal notranslate"><span class="pre">[0,</span>
-<span class="pre">...,</span> <span class="pre">N]</span></code> for that member. The score is the same for all members of the
-class.</p>
-<p>The maximum time delay depends on the application and should be chosen
-according to the maximum causal time lag expected in the complex system.
-We recommend a rather large choice that includes peaks in the
-<code class="docutils literal notranslate"><span class="pre">get_lagged_dependencies</span></code> function. Another important parameter is
-<code class="docutils literal notranslate"><span class="pre">contemp_collider_rule</span></code>. Only if set to <code class="docutils literal notranslate"><span class="pre">majority</span></code> or
-<code class="docutils literal notranslate"><span class="pre">conservative''</span> <span class="pre">and</span> <span class="pre">together</span> <span class="pre">with</span> <span class="pre">``conflict_resolution=True</span></code>,
-PCMCIplus is fully <em>order independent</em> meaning that the order of the N
-variables in the dataframe does not matter. Last, the default option
-<code class="docutils literal notranslate"><span class="pre">reset_lagged_links=False</span></code> restricts the detection of lagged causal
-links in Step 2 to the significant adjacencies found in Step 1, given by
-<img class="math" src="_images/math/6ecffdf6c6b31ddba452a834568233441411465b.png" alt="\widehat{ \mathcal{B}}_t^-( X^j_t)"/>. For
-<code class="docutils literal notranslate"><span class="pre">reset_lagged_links=True</span></code>, <em>all</em> lagged links are considered again,
-which improves detection power for lagged links, but also leads to
-larger runtimes.</p>
-<p>Further optional parameters are discussed in <a class="footnote-reference brackets" href="#id4" id="id12" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.pcmci</span> <span class="kn">import</span> <span class="n">PCMCI</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Example process to play around with</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Each key refers to a variable and the incoming links are supplied</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># as a list of format [((var, -lag), coeff, function), ...]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">links</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-<span class="go">             1: [((1, -1), 0.8, lin_f), ((0, -1), 0.8, lin_f)],</span>
-<span class="go">             2: [((2, -1), 0.7, lin_f), ((1, 0), 0.6, lin_f)],</span>
-<span class="go">             3: [((3, -1), 0.7, lin_f), ((2, 0), -0.5, lin_f)],</span>
-<span class="go">             }</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span>
-<span class="go">                    T=1000, seed=7)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Data must be array of shape (time, variables)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-<span class="go">(1000, 4)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pcmci</span> <span class="o">=</span> <span class="n">PCMCI</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">results</span> <span class="o">=</span> <span class="n">pcmci</span><span class="o">.</span><span class="n">run_pcmciplus</span><span class="p">(</span><span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pcmci</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
-<span class="go">    ## Significant links at alpha = 0.01:</span>
-</pre></div>
-</div>
-<blockquote>
-<div><dl class="simple">
-<dt>Variable 0 has 1 link(s):</dt><dd><p>(0 -1): pval = 0.00000 | val =  0.676</p>
-</dd>
-<dt>Variable 1 has 2 link(s):</dt><dd><p>(1 -1): pval = 0.00000 | val =  0.602
-(0 -1): pval = 0.00000 | val =  0.599</p>
-</dd>
-<dt>Variable 2 has 2 link(s):</dt><dd><p>(1  0): pval = 0.00000 | val =  0.486
-(2 -1): pval = 0.00000 | val =  0.466</p>
-</dd>
-<dt>Variable 3 has 2 link(s):</dt><dd><p>(3 -1): pval = 0.00000 | val =  0.524
-(2  0): pval = 0.00000 | val = -0.449</p>
-</dd>
-</dl>
-</div></blockquote>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Deprecated, replaced by link_assumptions</p></li>
-<li><p><strong>link_assumptions</strong> (<em>dict</em>) – Dictionary of form {j:{(i, -tau): link_type, …}, …} specifying
-assumptions about links. This initializes the graph with entries
-graph[i,j,tau] = link_type. For example, graph[i,j,0] = ‘–&gt;’
-implies that a directed link from i to j at lag 0 must exist.
-Valid link types are ‘o-o’, ‘–&gt;’, ‘&lt;–’. In addition, the middle
-mark can be ‘?’ instead of ‘-’. Then ‘-?&gt;’ implies that this link
-may not exist, but if it exists, its orientation is ‘–&gt;’. Link
-assumptions need to be consistent, i.e., graph[i,j,0] = ‘–&gt;’
-requires graph[j,i,0] = ‘&lt;–’ and acyclicity must hold. If a link
-does not appear in the dictionary, it is assumed absent. That is,
-if link_assumptions is not None, then all links have to be specified
-or the links are assumed absent.</p></li>
-<li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Minimum time lag to test.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list</em><em> of </em><em>floats</em><em>, </em><em>default: 0.01</em>) – Significance level in algorithm. If a list or None is passed, the
-pc_alpha level is optimized for every graph across the given
-pc_alpha values ([0.001, 0.005, 0.01, 0.025, 0.05] for None) using
-the score computed in cond_ind_test.get_model_selection_criterion().</p></li>
-<li><p><strong>contemp_collider_rule</strong> (<em>{'majority'</em><em>, </em><em>'conservative'</em><em>, </em><em>'none'}</em>) – Rule for collider phase to use. See the paper for details. Only
-‘majority’ and ‘conservative’ lead to an order-independent
-algorithm.</p></li>
-<li><p><strong>conflict_resolution</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to mark conflicts in orientation rules. Only for True
-this leads to an order-independent algorithm.</p></li>
-<li><p><strong>reset_lagged_links</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Restricts the detection of lagged causal links in Step 2 to the
-significant adjacencies found in the PC1 algorithm in Step 1. For
-True, <em>all</em> lagged links are considered again, which improves
-detection power for lagged links, but also leads to larger
-runtimes.</p></li>
-<li><p><strong>max_conds_dim</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of conditions to test. If None is passed, this number
-is unrestricted.</p></li>
-<li><p><strong>max_combinations</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum number of combinations of conditions of current cardinality
-to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
-a larger number, such as 10, can be used.</p></li>
-<li><p><strong>max_conds_py</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of lagged conditions of Y to use in MCI tests. If
-None is passed, this number is unrestricted.</p></li>
-<li><p><strong>max_conds_px</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of lagged conditions of X to use in MCI tests. If
-None is passed, this number is unrestricted.</p></li>
-<li><p><strong>max_conds_px_lagged</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of lagged conditions of X when X is lagged in MCI
-tests. If None is passed, this number is equal to max_conds_px.</p></li>
-<li><p><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'none'</em><em>)</em>) – Correction method, default is Benjamini-Hochberg False Discovery
-Rate method.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Resulting causal graph, see description above for interpretation.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values regarding adjacencies.</p></li>
-<li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values regarding adjacencies.</p></li>
-<li><p><strong>sepset</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
-<li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples, only relevant for ‘majority’ and
-‘conservative’ rules, see paper for details.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="tigramite-lpcmci-lpcmci">
-<h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.lpcmci</span></code>: LPCMCI<a class="headerlink" href="#tigramite-lpcmci-lpcmci" title="Permalink to this heading">¶</a></h1>
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.lpcmci.LPCMCI">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.lpcmci.</span></span><span class="sig-name descname"><span class="pre">LPCMCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cond_ind_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/lpcmci.html#LPCMCI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.lpcmci.LPCMCI" title="Permalink to this definition">¶</a></dt>
-<dd><p>LPCMCI is an algorithm for causal discovery in large-scale times series that allows for latent confounders and
-learns lag-specific causal relationships. The algorithm is introduced and explained in:
-[1] Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders.
-Advances in Neural Information Processing Systems, 2020, 33.
-<a class="reference external" href="https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html">https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</a>
-NOTE: This method is still EXPERIMENTAL since the default settings of hyperparameters are still being fine-tuned.
-We actually invite feedback on which work best in applications and numerical experiments.
-The main function, which applies the algorithm, is ‘run_lpcmci’.</p>
-<p>Parameters passed to the constructor:
-- dataframe:</p>
-<blockquote>
-<div><p>Tigramite dataframe object that contains the the time series dataset old{X}</p>
-</div></blockquote>
-<ul class="simple">
-<li><dl class="simple">
-<dt>cond_ind_test:</dt><dd><p>A conditional independence test object that specifies which conditional independence test CI is to be used</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>verbosity:</dt><dd><p>Controls the verbose output self.run_lpcmci() and the function it calls.</p>
-</dd>
-</dl>
-</li>
-</ul>
-<p>Parameters passed to self.run_lpcmci():
-Note: The default values are still being tuned and some parameters might be removed in the future.
-- link_assumptions: dict or None</p>
-<blockquote>
-<div><p>Two-level nested dictionary such that link_assumptions[j][(i, lag_i)], where 0 &lt;= j, i &lt;= N-1 (with N the number of component
-time series) and -tau_max &lt;= lag_i &lt;= -tau_min, is a string which specifies background knowledge about the link from X^i_{t+lag_i} to
-X^j_t. These are the possibilities for this string and the corresponding claim:</p>
-<blockquote>
-<div><p>‘-?&gt;’   : X^i_{t+lag_i} is an ancestor of X^j_t.
-‘–&gt;’   : X^i_{t+lag_i} is an ancestor of X^j_t, and there is a link between X^i_{t+lag_i} and X^j_t
-‘&lt;?-’   : Only allowed for lag_i = 0. X^j_t is an ancestor of X^i_t.
-‘&lt;–’   : Only allowed for lag_i = 0. X^j_t is an ancestor of X^i_t, and there is a link between X^i_t and X^j_t
-‘&lt;?&gt;’   : Neither X^i_{t+lag_i} is an ancestor of X^j_t nor the other way around
-‘&lt;-&gt;’   : Neither X^i_{t+lag_i} is an ancestor of X^j_t nor the other way around, and there is a link between X^i_{t+lag_i}</p>
-<blockquote>
-<div><p>and X^j_t</p>
-</div></blockquote>
-<dl class="simple">
-<dt>‘o?&gt;’<span class="classifier">X^j_t is not an ancestor of X^i_{t+lag_i} (for lag_i &lt; 0 this background knowledge is (for the default settings of</span></dt><dd><p>self.run_lpcmci()) imposed automatically)</p>
-</dd>
-</dl>
-<p>‘o-&gt;’   : X^j_t is not an ancestor of X^i_{t+lag_i}, and there is a link between X^i_{t+lag_i} and X^j_t
-‘&lt;?o’   : Only allowed for lag_i = 0. X^i_t is not an ancestor of X^j_t
-‘&lt;-o’   : Only allowed for lag_i = 0. X^i_t is not an ancestor of X^j_t, and there is a link between X^i_t and X^j_t
-‘o-o’   : Only allowed for lag_i = 0. There is a link between X^i_t and X^j_t
-‘o?o’   : Only allowed for lag_i = 0. No claim is made
-‘’      : There is no link between X^i_{t+lag_i} and X^j_t.</p>
-</div></blockquote>
-<p>Another way to specify the absent link is if the form of the link between (i, lag_i) and (j, 0) is not specified by the dictionary, that is, if either
-link_assumptions[j] does not exist or link_assumptions[j] does exist but link_assumptions[j][(i, lag_i)] does
-not exist, then the link between (i, lag_i) and (j, 0) is assumed to be absent.</p>
-</div></blockquote>
-<ul class="simple">
-<li><dl class="simple">
-<dt>tau_min:</dt><dd><p>The assumed minimum time lag, i.e., links with a lag smaller than tau_min are assumed to be absent.</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>tau_max:</dt><dd><p>The maximum considered time lag, i.e., the algorithm learns a DPAG on a time window [t- aumax, t] with  au_max + 1 time steps.
-It is <em>not</em> assumed that in the underlying time series DAG there are no links with a lag larger than    au_max.</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>pc_alpha:</dt><dd><p>The significance level of conditional independence tests</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>n_preliminary_iterations:</dt><dd><p>Determines the number of iterations in the preliminary phase of LPCMCI, corresponding to the ‘k’ in LPCMCI(k) in [1].</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>max_cond_px:</dt><dd><p>Consider a pair of variables (X^i_{t-   au}, X^j_t) with        au &gt; 0. In Algorithm S2 in [1] (here this is
-self._run_ancestral_removal_phase()), the algorithm does not test for conditional independence given subsets of
-apds_t(X^i_{t-  au}, X^j_t, C(G)) of cardinality higher than max_cond_px. In Algorithm S3 in [1] (here this is
-self._run_non_ancestral_removal_phase()), the algorithm does not test for conditional independence given subsets of
-napds_t(X^i_{t- au}, X^j_t, C(G)) of cardinality higher than max_cond_px.</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>max_p_global:</dt><dd><p>Restricts all conditional independence tests to conditioning sets with cardinality smaller or equal to max_p_global</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>max_p_non_ancestral:</dt><dd><p>Restricts all conditional independence tests in the second removal phase (here this is self._run_dsep_removal_phase()) to
-conditioning sets with cardinality smaller or equal to max_p_global</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>max_q_global:</dt><dd><p>For each ordered pair (X^i_{t-  au}, X^j_t) of adjacent variables and for each cardinality of the conditioning sets test at most
-max_q_global many conditioning sets (when summing over all tested cardinalities more than max_q_global tests may be made)</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>max_pds_set:</dt><dd><p>In Algorithm S3 (here this is self._run_non_ancestral_removal_phase()), the algorithm tests for conditional independence given
-subsets of the relevant napds_t sets. If for a given link the set napds_t(X^j_t, X^i_{t-        au}, C(G)) has more than max_pds_set many
-elements (or, if the link is also tested in the opposite directed, if napds_t(X^i_{t-   au}, X^j_t, C(G)) has more than max_pds_set
-elements), this link is not tested.</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>prelim_with_collider_rules:</dt><dd><p>If True: As in pseudocode
-If False: Line 22 of Algorithm S2 in [1] is replaced by line 18 of Algorithm S2 when Algorithm S2 is called from the preliminary
-phase (not in the last application of Algorithm S2 directly before Algorithm S3 is applied)</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>parents_of_lagged:</dt><dd><p>If True: As in pseudocode
-If False: The default conditioning set is pa(X^j_t, C(G)) rather than pa({X^j_t, X^i_{t-        au}, C(G)) for tau &gt; 0</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>prelim_only:</dt><dd><p>If True, stop after the preliminary phase. Can be used for detailed performance analysis</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>break_once_separated:</dt><dd><p>If True: As in pseudocode
-If False: The break commands are removed from Algorithms S2 and S3 in in [1]</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>no_non_ancestral_phase:</dt><dd><p>If True, do not execute Algorithm S3. Can be used for detailed performance analysis</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>use_a_pds_t_for_majority:</dt><dd><p>If True: As in pseudocode
-If False: The search for separating sets instructed by the majority rule is made given subsets adj(X^j_t, C(G)) rather than
-subsets of apds_t(X^j_t, X^i_{t-        au}, C(G))</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>orient_contemp:</dt><dd><p>If orient_contemp == 1: As in pseudocode of Algorithm S2 in [1]
-If orient_contemp == 2: Also orient contemporaneous links in line 18 of Algorithm S2
-If orient_comtemp == 0: Also not orient contemporaneous links in line 22 of Algorithm S2</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>update_middle_marks:</dt><dd><p>If True: As in pseudoce of Algorithms S2 and S3 in [1]
-If False: The MMR rule is not applied</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>prelim_rules:</dt><dd><p>If prelim_rules == 1: As in pseudocode of Algorithm S2 in [1]
-If prelim_rules == 0: Exclude rules R9^prime and R10^prime from line 18 in Algorithm S2</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>fix_all_edges_before_final_orientation:</dt><dd><p>When one of max_p_global, max_p_non_ancestral, max_q_global or max_pds_set is not np.inf, the algorithm may terminate although not
-all middle marks are empty. All orientation rules are nevertheless sound, since the rules always check for the appropriate middle
-marks. If fix_all_edges_before_final_orientation is True, all middle marks are set to the empty middle mark by force, followed by
-another application of the rules.</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>auto_first:</dt><dd><p>If True: As in pseudcode of Algorithms S2 and S3 in [1]
-If False: Autodependency links are not prioritized even before contemporaneous links</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>remember_only_parents:</dt><dd><p>If True: As in pseudocode of Algorithm 1
-If False: If X^i_{t-    au} has been marked as ancestor of X^j_t at any point of a preliminary iteration but the link between
-X^i_{t- au} and X^j_t was removed later, the link is nevertheless initialized with a tail at X^i_{t-    au} in the re-initialization</p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>no_apr:</dt><dd><p>If no_apr == 0: As in pseudcode of Algorithms S2 and S3 in [1]
-If no_apr == 1: The APR is not applied by Algorithm S2, except in line 22 of its last call directly before the call of Algorithm S3
-If no_apr == 2: The APR is never applied</p>
-</dd>
-</dl>
-</li>
-</ul>
-<dl class="simple">
-<dt>Return value of self.run_lpcmci():</dt><dd><dl class="simple">
-<dt>graph<span class="classifier">array of shape (N, N, tau_max+1)</span></dt><dd><p>Resulting DPAG, representing the learned causal relationships.</p>
-</dd>
-<dt>val_matrix<span class="classifier">array of shape (N, N, tau_max+1)</span></dt><dd><p>Estimated matrix of test statistic values regarding adjacencies.</p>
-</dd>
-<dt>p_matrix<span class="classifier">array of shape [N, N, tau_max+1]</span></dt><dd><p>Estimated matrix of p-values regarding adjacencies.</p>
-</dd>
-</dl>
-</dd>
-<dt>A note on middle marks:</dt><dd><p>For convenience (to have strings of the same lengths) we here internally denote the empty middle mark by ‘-’.  For post-processing
-purposes all middle marks are set to the empty middle mark (here ‘-‘).</p>
-</dd>
-<dt>A note on wildcards:</dt><dd><p>The middle mark wildcard st and the edge mark wildcard are here represented as <a href="#id13"><span class="problematic" id="id14">*</span></a>, the edge mark wildcard star as +</p>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.lpcmci.LPCMCI.run_lpcmci">
-<span class="sig-name descname"><span class="pre">run_lpcmci</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">link_assumptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_preliminary_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cond_px</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_p_global</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_p_non_ancestral</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_q_global</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pds_set</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prelim_with_collider_rules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents_of_lagged</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prelim_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">break_once_separated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_non_ancestral_phase</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_a_pds_t_for_majority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orient_contemp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_middle_marks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prelim_rules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fix_all_edges_before_final_orientation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remember_only_parents</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_apr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/lpcmci.html#LPCMCI.run_lpcmci"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.lpcmci.LPCMCI.run_lpcmci" title="Permalink to this definition">¶</a></dt>
-<dd><p>Run LPCMCI on the dataset and with the conditional independence test passed to the class constructor and with the
-options passed to this function.</p>
-</dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="tigramite-independence-tests-conditional-independence-tests">
-<h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code>: Conditional independence tests<a class="headerlink" href="#tigramite-independence-tests-conditional-independence-tests" title="Permalink to this heading">¶</a></h1>
-<p>Base class:</p>
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.independence_tests_base.</span></span><span class="sig-name descname"><span class="pre">CondIndTest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'analytic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">confidence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recycle_residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest" title="Permalink to this definition">¶</a></dt>
-<dd><p>Base class of conditional independence tests.</p>
-<p>Provides useful general functions for different independence tests such as
-shuffle significance testing and bootstrap confidence estimation. Also
-handles masked samples. Other test classes can inherit from this class.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>(</em><em>default = 42</em><em>)</em>) – Seed for RandomState (default_rng)</p></li>
-<li><p><strong>mask_type</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default = None</em><em>)</em>) – Must be in {None, ‘y’,’x’,’z’,’xy’,’xz’,’yz’,’xyz’}
-Masking mode: Indicators for which variables in the dependence measure
-I(X; Y | Z) the samples should be masked. If None, the mask is not used.
-Explained in tutorial on masking and missing values.</p></li>
-<li><p><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'analytic'</em><em>)</em>) – Type of significance test to use. In this package ‘analytic’,
-‘fixed_thres’ and ‘shuffle_test’ are available.</p></li>
-<li><p><strong>fixed_thres</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – If significance is ‘fixed_thres’, this specifies the threshold for the
-absolute value of the dependence measure.</p></li>
-<li><p><strong>sig_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 500</em><em>)</em>) – Number of samples for shuffle significance test.</p></li>
-<li><p><strong>sig_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Block length for block-shuffle significance test. If None, the
-block length is determined from the decay of the autocovariance as
-explained in <a class="footnote-reference brackets" href="#id3" id="id15" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p></li>
-<li><p><strong>confidence</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Specify type of confidence estimation. If False, numpy.nan is returned.
-‘bootstrap’ can be used with any test, for ParCorr also ‘analytic’ is
-implemented.</p></li>
-<li><p><strong>conf_lev</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.9</em><em>)</em>) – Two-sided confidence interval.</p></li>
-<li><p><strong>conf_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 100</em><em>)</em>) – Number of samples for bootstrap.</p></li>
-<li><p><strong>conf_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Block length for block-bootstrap. If None, the block length is
-determined from the decay of the autocovariance as explained in <a class="footnote-reference brackets" href="#id3" id="id16" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p></li>
-<li><p><strong>recycle_residuals</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Specifies whether residuals should be stored. This may be faster, but
-can cost considerable memory.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_analytic_confidence">
-<span class="sig-name descname"><span class="pre">get_analytic_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_analytic_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Base class assumption that this is not implemented.  Concrete classes
-should override when possible.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_analytic_significance">
-<span class="sig-name descname"><span class="pre">get_analytic_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_analytic_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Base class assumption that this is not implemented.  Concrete classes
-should override when possible.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_bootstrap_confidence">
-<span class="sig-name descname"><span class="pre">get_bootstrap_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependence_measure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_bootstrap_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_bootstrap_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Perform bootstrap confidence interval estimation.</p>
-<blockquote>
-<div><p>With conf_blocklength &gt; 1 or None a block-bootstrap is performed.</p>
-<dl class="simple">
-<dt>array<span class="classifier">array-like</span></dt><dd><p>data array with X, Y, Z in rows and observations in columns</p>
-</dd>
-<dt>xyz<span class="classifier">array of ints</span></dt><dd><p>XYZ identifier array of shape (dim,).</p>
-</dd>
-<dt>dependence_measure<span class="classifier">function (default = self.get_dependence_measure)</span></dt><dd><p>Dependence measure function must be of form
-dependence_measure(array, xyz) and return a numeric value</p>
-</dd>
-<dt>conf_lev<span class="classifier">float, optional (default: 0.9)</span></dt><dd><p>Two-sided confidence interval.</p>
-</dd>
-<dt>conf_samples<span class="classifier">int, optional (default: 100)</span></dt><dd><p>Number of samples for bootstrap.</p>
-</dd>
-<dt>conf_blocklength<span class="classifier">int, optional (default: None)</span></dt><dd><p>Block length for block-bootstrap. If None, the block length is
-determined from the decay of the autocovariance as explained in
-<a class="footnote-reference brackets" href="#id3" id="id17" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
-</dd>
-</dl>
-</div></blockquote>
-<dl>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
-<div><p>Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.</p>
-</div></blockquote>
-<dl class="simple">
-<dt>verbosity<span class="classifier">int, optional (default: 0)</span></dt><dd><p>Level of verbosity.</p>
-</dd>
-</dl>
-<dl class="simple">
-<dt>(conf_lower, conf_upper)<span class="classifier">Tuple of floats</span></dt><dd><p>Upper and lower confidence bound of confidence interval.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_confidence">
-<span class="sig-name descname"><span class="pre">get_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Perform confidence interval estimation.</p>
-<blockquote>
-<div><p>Calls the dependence measure and confidence test functions. The child
-classes can specify a function get_dependence_measure and
-get_analytic_confidence or get_bootstrap_confidence. If confidence is
-False, (numpy.nan, numpy.nan) is returned.</p>
-<dl class="simple">
-<dt>X, Y, Z<span class="classifier">list of tuples</span></dt><dd><p>X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index and tau the time lag.</p>
-</dd>
-<dt>tau_max<span class="classifier">int, optional (default: 0)</span></dt><dd><p>Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</p>
-</dd>
-</dl>
-</div></blockquote>
-<dl>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
-<div><p>Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.</p>
-</div></blockquote>
-<dl class="simple">
-<dt>(conf_lower, conf_upper)<span class="classifier">Tuple of floats</span></dt><dd><p>Upper and lower confidence bound of confidence interval.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_dependence_measure">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Abstract function that all concrete classes must instantiate.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance">
-<span class="sig-name descname"><span class="pre">get_fixed_thres_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_thres</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_fixed_thres_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns signficance for thresholding test.</p>
-<p>Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1 else.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-<li><p><strong>fixed_thres</strong> (<em>number</em>) – Fixed threshold, is made positive.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1
-else.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>bool</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_measure">
-<span class="sig-name descname"><span class="pre">get_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Estimate dependence measure.</p>
-<blockquote>
-<div><p>Calls the dependence measure function. The child classes must specify
-a function get_dependence_measure.</p>
-<dl class="simple">
-<dt>X, Y [, Z]<span class="classifier">list of tuples</span></dt><dd><p>X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index and tau the time lag.</p>
-</dd>
-<dt>tau_max<span class="classifier">int, optional (default: 0)</span></dt><dd><p>Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</p>
-</dd>
-</dl>
-</div></blockquote>
-<dl>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
-<div><p>Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.</p>
-</div></blockquote>
-<dl class="simple">
-<dt>val<span class="classifier">float</span></dt><dd><p>The test statistic value.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_model_selection_criterion">
-<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Base class assumption that this is not implemented.  Concrete classes
-should override when possible.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Base class assumption that this is not implemented.  Concrete classes
-should override when possible.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance">
-<span class="sig-name descname"><span class="pre">get_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance" title="Permalink to this definition">¶</a></dt>
-<dd><blockquote>
-<div><p>Returns the p-value from whichever significance function is specified
-for this test.  If an override is used, then it will call a different
-function then specified by self.significance</p>
-<dl class="simple">
-<dt>val<span class="classifier">float</span></dt><dd><p>Test statistic value.</p>
-</dd>
-<dt>array<span class="classifier">array-like</span></dt><dd><p>data array with X, Y, Z in rows and observations in columns</p>
-</dd>
-<dt>xyz<span class="classifier">array of ints</span></dt><dd><p>XYZ identifier array of shape (dim,).</p>
-</dd>
-<dt>T<span class="classifier">int</span></dt><dd><p>Sample length</p>
-</dd>
-<dt>dim<span class="classifier">int</span></dt><dd><p>Dimensionality, ie, number of features.</p>
-</dd>
-</dl>
-</div></blockquote>
-<dl>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
-<div><p>Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.</p>
-</div></blockquote>
-<dl class="simple">
-<dt>sig_override<span class="classifier">string</span></dt><dd><p>Must be in ‘analytic’, ‘shuffle_test’, ‘fixed_thres’</p>
-</dd>
-</dl>
-<dl class="simple">
-<dt>pval<span class="classifier">float or numpy.nan</span></dt><dd><p>P-value.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.measure">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Abstract property to store the type of independence test.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.print_info">
-<span class="sig-name descname"><span class="pre">print_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.print_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.print_info" title="Permalink to this definition">¶</a></dt>
-<dd><p>Print information about the conditional independence test parameters</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">
-<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test" title="Permalink to this definition">¶</a></dt>
-<dd><p>Perform conditional independence test.</p>
-<p>Calls the dependence measure and signficicance test functions. The child
-classes must specify a function get_dependence_measure and either or
-both functions get_analytic_significance and  get_shuffle_significance.
-If recycle_residuals is True, also _get_single_residuals must be
-available.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index and tau the time lag.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index and tau the time lag.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index and tau the time lag.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</p></li>
-<li><p><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) – How many samples to cutoff at the beginning. The default is
-‘2xtau_max’, which guarantees that MCI tests are all conducted on
-the same samples. For modeling, ‘max_lag_or_tau_max’ can be used,
-which uses the maximum of tau_max and the conditions, which is
-useful to compare multiple models on the same sample.  Last,
-‘max_lag’ uses as much samples as possible.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val, pval</strong> – The test statistic value and the p-value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of floats</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw">
-<span class="sig-name descname"><span class="pre">run_test_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test_raw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw" title="Permalink to this definition">¶</a></dt>
-<dd><p>Perform conditional independence test directly on input arrays x, y, z.</p>
-<p>Calls the dependence measure and signficicance test functions. The child
-classes must specify a function get_dependence_measure and either or
-both functions get_analytic_significance and  get_shuffle_significance.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>x</strong> (<em>arrays</em>) – x,y,z are of the form (samples, dimension).</p></li>
-<li><p><strong>y</strong> (<em>arrays</em>) – x,y,z are of the form (samples, dimension).</p></li>
-<li><p><strong>z</strong> (<em>arrays</em>) – x,y,z are of the form (samples, dimension).</p></li>
-<li><p><strong>x_type</strong> (<em>array-like</em>) – data arrays of same shape as x, y and z respectively, which describes whether variables
-are continuous or discrete: 0s for continuous variables and
-1s for discrete variables</p></li>
-<li><p><strong>y_type</strong> (<em>array-like</em>) – data arrays of same shape as x, y and z respectively, which describes whether variables
-are continuous or discrete: 0s for continuous variables and
-1s for discrete variables</p></li>
-<li><p><strong>z_type</strong> (<em>array-like</em>) – data arrays of same shape as x, y and z respectively, which describes whether variables
-are continuous or discrete: 0s for continuous variables and
-1s for discrete variables</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val, pval</strong> – The test statistic value and the p-value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of floats</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.set_dataframe">
-<span class="sig-name descname"><span class="pre">set_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.set_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.set_dataframe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize and check the dataframe.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>dataframe</strong> (<em>data object</em>) – Set tigramite dataframe object. It must have the attributes
-dataframe.values yielding a numpy array of shape (observations T,
-variables N) and optionally a mask of the same shape and a missing
-values flag.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.set_mask_type">
-<span class="sig-name descname"><span class="pre">set_mask_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.set_mask_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.set_mask_type" title="Permalink to this definition">¶</a></dt>
-<dd><p>Setter for mask type to ensure that this option does not clash with
-recycle_residuals.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>mask_type</strong> (<em>str</em>) – Must be in {None, ‘y’,’x’,’z’,’xy’,’xz’,’yz’,’xyz’}
-Masking mode: Indicators for which variables in the dependence measure
-I(X; Y | Z) the samples should be masked. If None, the mask is not used.
-Explained in tutorial on masking and missing values.</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<p>Test statistics:</p>
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr.ParCorr">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.parcorr.</span></span><span class="sig-name descname"><span class="pre">ParCorr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr.html#ParCorr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr.ParCorr" title="Permalink to this definition">¶</a></dt>
-<dd><p>Partial correlation test.</p>
-<p>Partial correlation is estimated through linear ordinary least squares (OLS)
-regression and a test for non-zero linear Pearson correlation on the
-residuals.</p>
-<p class="rubric">Notes</p>
-<p>To test <img class="math" src="_images/math/667eda4bf3d5ce33b6cc785cadfef79bb95741ca.png" alt="X \perp Y | Z"/>, first <img class="math" src="_images/math/95f028ab2b20b895fa12d986e0d9f40f7b6e52d3.png" alt="Z"/> is regressed out from
-<img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> and <img class="math" src="_images/math/7daf0d4815e763eb90f0d5f1dc406f668c1e21db.png" alt="Y"/> assuming the  model</p>
-<div class="math">
-<p><img src="_images/math/78b6e77cc610bf56f5c64cb6dcc6d6ee49f886f9.png" alt="X &amp; =  Z \beta_X + \epsilon_{X} \\
-Y &amp; =  Z \beta_Y + \epsilon_{Y}"/></p>
-</div><p>using OLS regression. Then the dependency of the residuals is tested with
-the Pearson correlation test.</p>
-<div class="math">
-<p><img src="_images/math/789735c1db036ea36cd0aa25a3af4b2528ed3abe.png" alt="\rho\left(r_X, r_Y\right)"/></p>
-</div><p>For the <code class="docutils literal notranslate"><span class="pre">significance='analytic'</span></code> Student’s-<em>t</em> distribution with
-<img class="math" src="_images/math/9c353382eebb42a8a9dec3a426d346d4842bd39d.png" alt="T-D_Z-2"/> degrees of freedom is implemented.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>**kwargs</strong> – Arguments passed on to Parent class CondIndTest.</p>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr.ParCorr.get_analytic_confidence">
-<span class="sig-name descname"><span class="pre">get_analytic_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr.html#ParCorr.get_analytic_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr.ParCorr.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns analytic confidence interval for correlation coefficient.</p>
-<p>Based on Student’s t-distribution.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
-<li><p><strong>df</strong> (<em>int</em>) – degrees of freedom of the test</p></li>
-<li><p><strong>conf_lev</strong> (<em>float</em>) – Confidence interval, eg, 0.9</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>(conf_lower, conf_upper)</strong> – Upper and lower confidence bound of confidence interval.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of floats</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr.ParCorr.get_analytic_significance">
-<span class="sig-name descname"><span class="pre">get_analytic_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr.html#ParCorr.get_analytic_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr.ParCorr.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns analytic p-value from Student’s t-test for the Pearson
-correlation coefficient.</p>
-<p>Assumes two-sided correlation. If the degrees of freedom are less than
-1, numpy.nan is returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
-<li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
-<li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – P-value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float or numpy.nan</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr.ParCorr.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr.html#ParCorr.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr.ParCorr.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return partial correlation.</p>
-<p>Estimated as the Pearson correlation of the residuals of a linear
-OLS regression.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – Partial correlation coefficient.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr.ParCorr.get_model_selection_criterion">
-<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">corrected_aic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr.html#ParCorr.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr.ParCorr.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns Akaike’s Information criterion modulo constants.</p>
-<p>Fits a linear model of the parents to variable j and returns the
-score. Leave-one-out cross-validation is asymptotically equivalent to
-AIC for ordinary linear regression models. Here used to determine
-optimal hyperparameters in PCMCI, in particular the pc_alpha value.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>j</strong> (<em>int</em>) – Index of target variable in data array.</p></li>
-<li><p><strong>parents</strong> (<em>list</em>) – List of form [(0, -1), (3, -2), …] containing parents.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</p></li>
-<li><p><strong>Returns</strong> – </p></li>
-<li><p><strong>score</strong> (<em>float</em>) – Model score.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr.ParCorr.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr.html#ParCorr.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr.ParCorr.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for shuffle significance test.</p>
-<p>For residual-based test statistics only the residuals are shuffled.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr.ParCorr.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.parcorr.ParCorr.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.robust_parcorr.RobustParCorr">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.robust_parcorr.</span></span><span class="sig-name descname"><span class="pre">RobustParCorr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/robust_parcorr.html#RobustParCorr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr" title="Permalink to this definition">¶</a></dt>
-<dd><p>Robust partial correlation test based on non-paranormal models.</p>
-<p>Partial correlation is estimated through transformation to standard
-normal marginals, ordinary least squares (OLS) regression, and a test for
-non-zero linear Pearson correlation on the residuals.</p>
-<p class="rubric">Notes</p>
-<p>To test <img class="math" src="_images/math/667eda4bf3d5ce33b6cc785cadfef79bb95741ca.png" alt="X \perp Y | Z"/>, firstly, each marginal is transformed to be
-standard normally distributed. For that, the transform
-<img class="math" src="_images/math/12404a5b5e25ce89d718023e0444f48e6c472c3b.png" alt="\Phi^{-1}\circ\hat{F}"/> is used. Here, <img class="math" src="_images/math/2bf7d59fe2ecd30074d48feb7da561ea838ea61f.png" alt="\Phi^{-1}"/> is the</p>
-<blockquote>
-<div><p>quantile function of a standard normal distribution and
-<img class="math" src="_images/math/f08158476cb55e03cc8643afc1e10a56da446a3a.png" alt="\hat{F}"/> is the empirical distribution function for the respective
-marginal.</p>
-</div></blockquote>
-<p>This idea stems from the literature on nonparanormal models, see:</p>
-<ul class="simple">
-<li><p>Han Liu, John Lafferty, and Larry Wasserman. The nonparanormal:
-semiparametric estimation of high dimensional undirected graphs. J.
-Mach. Learn. Res., 10:2295–2328, 2009.</p></li>
-<li><p>Han Liu, Fang Han, Ming Yuan, John Lafferty, and Larry Wasserman.
-High-dimensional semiparametric Gaussian copula graphical models. Ann.
-Statist., 40(4):2293–2326, 2012a.</p></li>
-<li><p>Naftali Harris, Mathias Drton. PC Algorithm for Nonparanormal Graphical
-Models. Journal of Machine Learning Research, 14: 3365-3383, 2013.</p></li>
-</ul>
-<p>Afterwards (where Z, X, and Y are now assumed to be transformed to the
-standard normal scale):</p>
-<p><img class="math" src="_images/math/95f028ab2b20b895fa12d986e0d9f40f7b6e52d3.png" alt="Z"/> is regressed out from
-<img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> and <img class="math" src="_images/math/7daf0d4815e763eb90f0d5f1dc406f668c1e21db.png" alt="Y"/> assuming the  model</p>
-<div class="math">
-<p><img src="_images/math/78b6e77cc610bf56f5c64cb6dcc6d6ee49f886f9.png" alt="X &amp; =  Z \beta_X + \epsilon_{X} \\
-Y &amp; =  Z \beta_Y + \epsilon_{Y}"/></p>
-</div><p>using OLS regression. Then the dependency of the residuals is tested with
-the Pearson correlation test.</p>
-<div class="math">
-<p><img src="_images/math/789735c1db036ea36cd0aa25a3af4b2528ed3abe.png" alt="\rho\left(r_X, r_Y\right)"/></p>
-</div><p>For the <code class="docutils literal notranslate"><span class="pre">significance='analytic'</span></code> Student’s-<em>t</em> distribution with
-<img class="math" src="_images/math/9c353382eebb42a8a9dec3a426d346d4842bd39d.png" alt="T-D_Z-2"/> degrees of freedom is implemented.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>**kwargs</strong> – Arguments passed on to Parent class CondIndTest.</p>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.robust_parcorr.RobustParCorr.get_analytic_confidence">
-<span class="sig-name descname"><span class="pre">get_analytic_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/robust_parcorr.html#RobustParCorr.get_analytic_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns analytic confidence interval for correlation coefficient.</p>
-<p>Based on Student’s t-distribution.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
-<li><p><strong>df</strong> (<em>int</em>) – degrees of freedom of the test</p></li>
-<li><p><strong>conf_lev</strong> (<em>float</em>) – Confidence interval, eg, 0.9</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>(conf_lower, conf_upper)</strong> – Upper and lower confidence bound of confidence interval.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of floats</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.robust_parcorr.RobustParCorr.get_analytic_significance">
-<span class="sig-name descname"><span class="pre">get_analytic_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/robust_parcorr.html#RobustParCorr.get_analytic_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns analytic p-value from Student’s t-test for the Pearson
-correlation coefficient.</p>
-<p>Assumes two-sided correlation. If the degrees of freedom are less than
-1, numpy.nan is returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
-<li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
-<li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – P-value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float or numpy.nan</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.robust_parcorr.RobustParCorr.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/robust_parcorr.html#RobustParCorr.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return partial correlation.</p>
-<p>Marginals are firstly transformed to standard normal scale. Dependence
-Measure is then estimated as the Pearson correlation of the residuals
-of a linear OLS regression.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – Partial correlation coefficient.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.robust_parcorr.RobustParCorr.get_model_selection_criterion">
-<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">corrected_aic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/robust_parcorr.html#RobustParCorr.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns Akaike’s Information criterion modulo constants.</p>
-<p>First of all, each marginal is transformed to the standard normal
-scale. For this, each marginal is transformed to the uniform scale
-using the empirical distribution function and then, transformed to
-the standard normal scale by applying the quantile function of a
-standard normal. Afterwards, fits a linear model of the parents to
-variable j and returns the score. Leave-one-out cross-validation is
-asymptotically equivalent to AIC for ordinary linear regression
-models. Here used to determine optimal hyperparameters in
-PCMCI(plus), in particular the pc_alpha value.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>j</strong> (<em>int</em>) – Index of target variable in data array.</p></li>
-<li><p><strong>parents</strong> (<em>list</em>) – List of form [(0, -1), (3, -2), …] containing parents.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</p></li>
-<li><p><strong>Returns</strong> – </p></li>
-<li><p><strong>score</strong> (<em>float</em>) – Model score.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.robust_parcorr.RobustParCorr.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/robust_parcorr.html#RobustParCorr.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for shuffle significance test.</p>
-<p>Firstly, each marginal is transformed to the standard normal scale.
-For residual-based test statistics only the residuals are shuffled.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.robust_parcorr.RobustParCorr.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.robust_parcorr.RobustParCorr.trafo2normal">
-<span class="sig-name descname"><span class="pre">trafo2normal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/robust_parcorr.html#RobustParCorr.trafo2normal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr.trafo2normal" title="Permalink to this definition">¶</a></dt>
-<dd><p>Transforms input array to standard normal marginals.</p>
-<p>For that, the code first transforms to uniform <img class="math" src="_images/math/a7b17d1c3442224393b5a845ae344dbe542593d7.png" alt="[0,1]"/> marginals
-using the empirical distribution function, and then transforms to
-normal marginals by applying the quantile function of a standard
-normal. Assumes x.shape = (dim, T)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>x</strong> (<em>array-like</em>) – Input array.</p></li>
-<li><p><strong>thres</strong> (<em>float</em>) – Small number between 0 and 1; after transformation to the uniform
-scale, all values that are too close to zero are replaced by thres,
-similarly, all values that are too close to one, are replaced by
-1-thres. This avoids NaNs.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>normal</strong> – array with normal marginals.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array-like</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc.GPDC">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.gpdc.</span></span><span class="sig-name descname"><span class="pre">GPDC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">null_dist_filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gp_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc.html#GPDC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc.GPDC" title="Permalink to this definition">¶</a></dt>
-<dd><p>GPDC conditional independence test based on Gaussian processes and distance correlation.</p>
-<p>GPDC is based on a Gaussian process (GP) regression and a distance
-correlation test on the residuals <a class="footnote-reference brackets" href="#id20" id="id18" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. GP is estimated with scikit-learn
-and allows to flexibly specify kernels and hyperparameters or let them be
-optimized automatically. The distance correlation test is implemented with
-cython. Here the null distribution is not analytically available, but can be
-precomputed with the function generate_and_save_nulldists(…) which saves a
-*.npz file containing the null distribution for different sample sizes.
-This file can then be supplied as null_dist_filename.</p>
-<p class="rubric">Notes</p>
-<p>GPDC is based on a Gaussian process (GP) regression and a distance
-correlation test on the residuals. Distance correlation is described in
-<a class="footnote-reference brackets" href="#id20" id="id19" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. To test <img class="math" src="_images/math/667eda4bf3d5ce33b6cc785cadfef79bb95741ca.png" alt="X \perp Y | Z"/>, first <img class="math" src="_images/math/95f028ab2b20b895fa12d986e0d9f40f7b6e52d3.png" alt="Z"/> is regressed out from
-<img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> and <img class="math" src="_images/math/7daf0d4815e763eb90f0d5f1dc406f668c1e21db.png" alt="Y"/> assuming the  model</p>
-<div class="math">
-<p><img src="_images/math/db3d34854a6f48587cf5b9a41df90ad1c5e332d6.png" alt="X &amp; =  f_X(Z) + \epsilon_{X} \\
-Y &amp; =  f_Y(Z) + \epsilon_{Y}  \\
-\epsilon_{X,Y} &amp;\sim \mathcal{N}(0, \sigma^2)"/></p>
-</div><p>using GP regression. Here <img class="math" src="_images/math/5406eadc281dbd20de843b0034c8497320dae5cb.png" alt="\sigma^2"/> and the kernel bandwidth are
-optimzed using <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>. Then the residuals  are transformed to uniform
-marginals yielding <img class="math" src="_images/math/be07aa32325c7a6161c0cef04f9b702054873211.png" alt="r_X,r_Y"/> and their dependency is tested with</p>
-<div class="math">
-<p><img src="_images/math/5f8c562c89b6bf12d27dc6cdc9dc090f7bb78e9c.png" alt="\mathcal{R}\left(r_X, r_Y\right)"/></p>
-</div><p>The null distribution of the distance correlation should be pre-computed.
-Otherwise it is computed during runtime.</p>
-<p class="rubric">References</p>
-<aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id20" role="note">
-<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
-<span class="backrefs">(<a role="doc-backlink" href="#id18">1</a>,<a role="doc-backlink" href="#id19">2</a>,<a role="doc-backlink" href="#id21">3</a>,<a role="doc-backlink" href="#id22">4</a>)</span>
-<p>Gabor J. Szekely, Maria L. Rizzo, and Nail K. Bakirov: Measuring and
-testing dependence by correlation of distances,
-<a class="reference external" href="https://arxiv.org/abs/0803.4101">https://arxiv.org/abs/0803.4101</a></p>
-</aside>
-</aside>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>null_dist_filename</strong> (<em>str</em><em>, </em><em>otional</em><em> (</em><em>default: None</em><em>)</em>) – Path to file containing null distribution.</p></li>
-<li><p><strong>gp_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Dictionary with parameters for <code class="docutils literal notranslate"><span class="pre">GaussianProcessRegressor</span></code>.</p></li>
-<li><p><strong>**kwargs</strong> – Arguments passed on to parent class GaussProcReg.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc.GPDC.generate_and_save_nulldists">
-<span class="sig-name descname"><span class="pre">generate_and_save_nulldists</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">null_dist_filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc.html#GPDC.generate_and_save_nulldists"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc.GPDC.generate_and_save_nulldists" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generates and saves null distribution for pairwise independence
-tests.</p>
-<p>Generates the null distribution for different sample sizes. Calls
-generate_nulldist. Null dists are saved to disk as
-self.null_dist_filename.npz. Also adds the null distributions to
-self.gauss_pr.null_dists.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>sample_sizes</strong> (<em>list</em>) – List of sample sizes.</p></li>
-<li><p><strong>null_dist_filename</strong> (<em>str</em>) – Name to save file containing null distributions.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc.GPDC.generate_nulldist">
-<span class="sig-name descname"><span class="pre">generate_nulldist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_to_null_dists</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc.html#GPDC.generate_nulldist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc.GPDC.generate_nulldist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generates null distribution for pairwise independence tests.</p>
-<p>Generates the null distribution for sample size df. Assumes pairwise
-samples transformed to uniform marginals. Uses get_dependence_measure
-available in class and generates self.sig_samples random samples. Adds
-the null distributions to self.gauss_pr.null_dists.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>df</strong> (<em>int</em>) – Degrees of freedom / sample size to generate null distribution for.</p></li>
-<li><p><strong>add_to_null_dists</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to add the null dist to the dictionary of null dists or
-just return it.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>null_dist</strong> – Only returned,if add_to_null_dists is False.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape [df,]</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc.GPDC.get_analytic_significance">
-<span class="sig-name descname"><span class="pre">get_analytic_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc.html#GPDC.get_analytic_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc.GPDC.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for the distance correlation coefficient.</p>
-<p>The null distribution for necessary degrees of freedom (df) is loaded.
-If not available, the null distribution is generated with the function
-generate_nulldist(). It is recommended to generate the nulldists for a
-wide range of sample sizes beforehand with the function
-generate_and_save_nulldists(…). The distance correlation coefficient
-is one-sided. If the degrees of freedom are less than 1, numpy.nan is
-returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
-<li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
-<li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float or numpy.nan</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc.GPDC.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc.html#GPDC.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc.GPDC.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return GPDC measure.</p>
-<p>Estimated as the distance correlation of the residuals of a GP
-regression.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – GPDC test statistic.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc.GPDC.get_model_selection_criterion">
-<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc.html#GPDC.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc.GPDC.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns log marginal likelihood for GP regression.</p>
-<p>Fits a GP model of the parents to variable j and returns the negative
-log marginal likelihood as a model selection score. Is used to determine
-optimal hyperparameters in PCMCI, in particular the pc_alpha value.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>j</strong> (<em>int</em>) – Index of target variable in data array.</p></li>
-<li><p><strong>parents</strong> (<em>list</em>) – List of form [(0, -1), (3, -2), …] containing parents.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</p></li>
-<li><p><strong>Returns</strong> – </p></li>
-<li><p><strong>score</strong> (<em>float</em>) – Model score.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc.GPDC.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc.html#GPDC.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc.GPDC.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for shuffle significance test.</p>
-<p>For residual-based test statistics only the residuals are shuffled.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc.GPDC.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.gpdc.GPDC.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc_torch.GPDCtorch">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.gpdc_torch.</span></span><span class="sig-name descname"><span class="pre">GPDCtorch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">null_dist_filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc_torch.html#GPDCtorch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch" title="Permalink to this definition">¶</a></dt>
-<dd><p>GPDC conditional independence test based on Gaussian processes and distance correlation. Here with gpytorch implementation.</p>
-<p>GPDC is based on a Gaussian process (GP) regression and a distance
-correlation test on the residuals <a class="footnote-reference brackets" href="#id20" id="id21" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. GP is estimated with gpytorch.
-The distance correlation test is implemented with the dcor package available
-from pip. Here the null distribution is not analytically available, but can be
-precomputed with the function generate_and_save_nulldists(…) which saves a
-*.npz file containing the null distribution for different sample sizes.
-This file can then be supplied as null_dist_filename.</p>
-<p class="rubric">Notes</p>
-<p>GPDC is based on a Gaussian process (GP) regression and a distance
-correlation test on the residuals. Distance correlation is described in
-<a class="footnote-reference brackets" href="#id20" id="id22" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. To test <img class="math" src="_images/math/667eda4bf3d5ce33b6cc785cadfef79bb95741ca.png" alt="X \perp Y | Z"/>, first <img class="math" src="_images/math/95f028ab2b20b895fa12d986e0d9f40f7b6e52d3.png" alt="Z"/> is regressed out from
-<img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> and <img class="math" src="_images/math/7daf0d4815e763eb90f0d5f1dc406f668c1e21db.png" alt="Y"/> assuming the  model</p>
-<div class="math">
-<p><img src="_images/math/db3d34854a6f48587cf5b9a41df90ad1c5e332d6.png" alt="X &amp; =  f_X(Z) + \epsilon_{X} \\
-Y &amp; =  f_Y(Z) + \epsilon_{Y}  \\
-\epsilon_{X,Y} &amp;\sim \mathcal{N}(0, \sigma^2)"/></p>
-</div><p>using GP regression. Here <img class="math" src="_images/math/5406eadc281dbd20de843b0034c8497320dae5cb.png" alt="\sigma^2"/> and the kernel bandwidth are
-optimzed using <code class="docutils literal notranslate"><span class="pre">gpytorch</span></code>. Then the residuals  are transformed to uniform
-marginals yielding <img class="math" src="_images/math/be07aa32325c7a6161c0cef04f9b702054873211.png" alt="r_X,r_Y"/> and their dependency is tested with</p>
-<div class="math">
-<p><img src="_images/math/5f8c562c89b6bf12d27dc6cdc9dc090f7bb78e9c.png" alt="\mathcal{R}\left(r_X, r_Y\right)"/></p>
-</div><p>The null distribution of the distance correlation should be pre-computed.
-Otherwise it is computed during runtime.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>null_dist_filename</strong> (<em>str</em><em>, </em><em>otional</em><em> (</em><em>default: None</em><em>)</em>) – Path to file containing null distribution.</p></li>
-<li><p><strong>**kwargs</strong> – Arguments passed on to parent class GaussProcRegTorch.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc_torch.GPDCtorch.generate_and_save_nulldists">
-<span class="sig-name descname"><span class="pre">generate_and_save_nulldists</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">null_dist_filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc_torch.html#GPDCtorch.generate_and_save_nulldists"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch.generate_and_save_nulldists" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generates and saves null distribution for pairwise independence
-tests.</p>
-<p>Generates the null distribution for different sample sizes. Calls
-generate_nulldist. Null dists are saved to disk as
-self.null_dist_filename.npz. Also adds the null distributions to
-self.gauss_pr.null_dists.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>sample_sizes</strong> (<em>list</em>) – List of sample sizes.</p></li>
-<li><p><strong>null_dist_filename</strong> (<em>str</em>) – Name to save file containing null distributions.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc_torch.GPDCtorch.generate_nulldist">
-<span class="sig-name descname"><span class="pre">generate_nulldist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_to_null_dists</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc_torch.html#GPDCtorch.generate_nulldist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch.generate_nulldist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generates null distribution for pairwise independence tests.</p>
-<p>Generates the null distribution for sample size df. Assumes pairwise
-samples transformed to uniform marginals. Uses get_dependence_measure
-available in class and generates self.sig_samples random samples. Adds
-the null distributions to self.gauss_pr.null_dists.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>df</strong> (<em>int</em>) – Degrees of freedom / sample size to generate null distribution for.</p></li>
-<li><p><strong>add_to_null_dists</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to add the null dist to the dictionary of null dists or
-just return it.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>null_dist</strong> – Only returned,if add_to_null_dists is False.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape [df,]</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc_torch.GPDCtorch.get_analytic_significance">
-<span class="sig-name descname"><span class="pre">get_analytic_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc_torch.html#GPDCtorch.get_analytic_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for the distance correlation coefficient.</p>
-<p>The null distribution for necessary degrees of freedom (df) is loaded.
-If not available, the null distribution is generated with the function
-generate_nulldist(). It is recommended to generate the nulldists for a
-wide range of sample sizes beforehand with the function
-generate_and_save_nulldists(…). The distance correlation coefficient
-is one-sided. If the degrees of freedom are less than 1, numpy.nan is
-returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
-<li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
-<li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float or numpy.nan</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc_torch.GPDCtorch.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc_torch.html#GPDCtorch.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return GPDC measure.</p>
-<p>Estimated as the distance correlation of the residuals of a GP
-regression.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – GPDC test statistic.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc_torch.GPDCtorch.get_model_selection_criterion">
-<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc_torch.html#GPDCtorch.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns log marginal likelihood for GP regression.</p>
-<p>Fits a GP model of the parents to variable j and returns the negative
-log marginal likelihood as a model selection score. Is used to determine
-optimal hyperparameters in PCMCI, in particular the pc_alpha value.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>j</strong> (<em>int</em>) – Index of target variable in data array.</p></li>
-<li><p><strong>parents</strong> (<em>list</em>) – List of form [(0, -1), (3, -2), …] containing parents.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</p></li>
-<li><p><strong>Returns</strong> – </p></li>
-<li><p><strong>score</strong> (<em>float</em>) – Model score.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc_torch.GPDCtorch.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gpdc_torch.html#GPDCtorch.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for shuffle significance test.</p>
-<p>For residual-based test statistics only the residuals are shuffled.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gpdc_torch.GPDCtorch.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.cmiknn.</span></span><span class="sig-name descname"><span class="pre">CMIknn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">knn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'shuffle_test'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ranks'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn" title="Permalink to this definition">¶</a></dt>
-<dd><p>Conditional mutual information test based on nearest-neighbor estimator.</p>
-<p>Conditional mutual information is the most general dependency measure coming
-from an information-theoretic framework. It makes no assumptions about the
-parametric form of the dependencies by directly estimating the underlying
-joint density. The test here is based on the estimator in  S. Frenzel and B.
-Pompe, Phys. Rev. Lett. 99, 204101 (2007), combined with a shuffle test to
-generate  the distribution under the null hypothesis of independence first
-used in <a class="footnote-reference brackets" href="#id24" id="id23" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>. The knn-estimator is suitable only for variables taking a
-continuous range of values. For discrete variables use the CMIsymb class.</p>
-<p class="rubric">Notes</p>
-<p>CMI is given by</p>
-<div class="math">
-<p><img src="_images/math/b62df33f46595ed60c51f255186ee346c1fcc0cb.png" alt="I(X;Y|Z) &amp;= \int p(z)  \iint  p(x,y|z) \log
-\frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)} \,dx dy dz"/></p>
-</div><p>Its knn-estimator is given by</p>
-<div class="math">
-<p><img src="_images/math/42858b59a7270363c15ca14b0d5fc56d33af1f8a.png" alt="\widehat{I}(X;Y|Z)  &amp;=   \psi (k) + \frac{1}{T} \sum_{t=1}^T
-\left[ \psi(k_{Z,t}) - \psi(k_{XZ,t}) - \psi(k_{YZ,t}) \right]"/></p>
-</div><p>where <img class="math" src="_images/math/b6c245d487949782a89cab9ee83504a62fdc2337.png" alt="\psi"/> is the Digamma function.  This estimator has as a
-parameter the number of nearest-neighbors <img class="math" src="_images/math/9630132210b904754c9ab272b61cb527d12263ca.png" alt="k"/> which determines the
-size of hyper-cubes around each (high-dimensional) sample point. Then
-<img class="math" src="_images/math/26b09a13f97c2e89eb7687980b95a54839775fc8.png" alt="k_{Z,},k_{XZ},k_{YZ}"/> are the numbers of neighbors in the respective
-subspaces.</p>
-<p><img class="math" src="_images/math/9630132210b904754c9ab272b61cb527d12263ca.png" alt="k"/> can be viewed as a density smoothing parameter (although it is
-data-adaptive unlike fixed-bandwidth estimators). For large <img class="math" src="_images/math/9630132210b904754c9ab272b61cb527d12263ca.png" alt="k"/>, the
-underlying dependencies are more smoothed and CMI has a larger bias,
-but lower variance, which is more important for significance testing. Note
-that the estimated CMI values can be slightly negative while CMI is a non-
-negative quantity.</p>
-<p>This method requires the scipy.spatial.cKDTree package.</p>
-<p class="rubric">References</p>
-<aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id24" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id23">3</a><span class="fn-bracket">]</span></span>
-<p>J. Runge (2018): Conditional Independence Testing Based on a
-Nearest-Neighbor Estimator of Conditional Mutual Information.
-In Proceedings of the 21st International Conference on Artificial
-Intelligence and Statistics.
-<a class="reference external" href="http://proceedings.mlr.press/v84/runge18a.html">http://proceedings.mlr.press/v84/runge18a.html</a></p>
-</aside>
-</aside>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>knn</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Number of nearest-neighbors which determines the size of hyper-cubes
-around each (high-dimensional) sample point. If smaller than 1, this is
-computed as a fraction of T, hence knn=knn*T. For knn larger or equal to
-1, this is the absolute number.</p></li>
-<li><p><strong>shuffle_neighbors</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 5</em><em>)</em>) – Number of nearest-neighbors within Z for the shuffle surrogates which
-determines the size of hyper-cubes around each (high-dimensional) sample
-point.</p></li>
-<li><p><strong>transform</strong> (<em>{'ranks'</em><em>, </em><em>'standardize'</em><em>,  </em><em>'uniform'</em><em>, </em><em>False}</em><em>, </em><em>optional</em>) – (default: ‘ranks’)
-Whether to transform the array beforehand by standardizing
-or transforming to uniform marginals.</p></li>
-<li><p><strong>workers</strong> (<em>int</em><em> (</em><em>optional</em><em>, </em><em>default = -1</em><em>)</em>) – Number of workers to use for parallel processing. If -1 is given
-all processors are used. Default: -1.</p></li>
-<li><p><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'shuffle_test'</em><em>)</em>) – Type of significance test to use. For CMIknn only ‘fixed_thres’ and
-‘shuffle_test’ are available.</p></li>
-<li><p><strong>**kwargs</strong> – Arguments passed on to parent class CondIndTest.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn.get_conditional_entropy">
-<span class="sig-name descname"><span class="pre">get_conditional_entropy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn.get_conditional_entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn.get_conditional_entropy" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the nearest-neighbor conditional entropy estimate of H(X|Y).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,). Here only uses 0 for X and
-1 for Y.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – Entropy estimate.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns CMI estimate as described in Frenzel and Pompe PRL (2007).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – Conditional mutual information estimate.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for nearest-neighbor shuffle significance test.</p>
-<p>For non-empty Z, overwrites get_shuffle_significance from the parent
-class  which is a block shuffle test, which does not preserve
-dependencies of X and Y with Z. Here the parameter shuffle_neighbors is
-used to permute only those values <img class="math" src="_images/math/c67734af70861b2bd4dedf5c41c9aad231466f84.png" alt="x_i"/> and <img class="math" src="_images/math/ab9afdaf786ce53318d75d81f050af8560822fcd.png" alt="x_j"/> for which
-<img class="math" src="_images/math/1a2da53015c9d2f2c52257723f812bf512b6818e.png" alt="z_j"/> is among the nearest niehgbors of <img class="math" src="_images/math/c0d0f97cd9bb4e6571e2689163f9f2989b304f55.png" alt="z_i"/>. If Z is
-empty, the block-shuffle test is used.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.cmisymb.CMIsymb">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.cmisymb.</span></span><span class="sig-name descname"><span class="pre">CMIsymb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_symbs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'shuffle_test'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmisymb.html#CMIsymb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmisymb.CMIsymb" title="Permalink to this definition">¶</a></dt>
-<dd><p>Conditional mutual information test for discrete/categorical data.</p>
-<p>Conditional mutual information is the most general dependency measure
-coming from an information-theoretic framework. It makes no assumptions
-about the parametric form of the dependencies by directly estimating the
-underlying joint density. The test here is based on directly estimating
-the joint distribution assuming symbolic input, combined with a
-local shuffle test to generate  the distribution under the null hypothesis of
-independence. This estimator is suitable only for discrete variables.
-For continuous variables use the CMIknn class and for mixed-variable
-datasets the CMIknnMixed class (including mixed-type variables).</p>
-<p>Allows for multi-dimensional X, Y.</p>
-<p class="rubric">Notes</p>
-<p>CMI and its estimator are given by</p>
-<div class="math">
-<p><img src="_images/math/d226d17c031c4b264fb8f59f953381951cc2e9b4.png" alt="I(X;Y|Z) &amp;= \sum p(z)  \sum \sum  p(x,y|z) \log
-\frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)} \,dx dy dz"/></p>
-</div><dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>n_symbs</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Number of symbols in input data. Should be at least as large as the
-maximum array entry + 1. If None, n_symbs is inferred by scipy’s crosstab.</p></li>
-<li><p><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'shuffle_test'</em><em>)</em>) – Type of significance test to use. For CMIsymb only ‘fixed_thres’ and
-‘shuffle_test’ are available.</p></li>
-<li><p><strong>sig_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Block length for block-shuffle significance test.</p></li>
-<li><p><strong>conf_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Block length for block-bootstrap.</p></li>
-<li><p><strong>**kwargs</strong> – Arguments passed on to parent class CondIndTest.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.cmisymb.CMIsymb.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmisymb.html#CMIsymb.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmisymb.CMIsymb.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns CMI estimate based on contingency table from scipy’s crosstab
-to approximate probability mass.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – Conditional mutual information estimate.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.cmisymb.CMIsymb.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmisymb.html#CMIsymb.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmisymb.CMIsymb.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for shuffle significance test.</p>
-<p>Performes a local permutation test: x_i values are only permuted with
-those x_j for which z_i = z_j. Samples are drawn without replacement
-as much as possible.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for original (unshuffled) estimate.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.cmisymb.CMIsymb.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.cmisymb.CMIsymb.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.oracle_conditional_independence.</span></span><span class="sig-name descname"><span class="pre">OracleCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observed_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_is_mag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI" title="Permalink to this definition">¶</a></dt>
-<dd><p>Oracle of conditional independence test X _|_ Y | Z given a graph.</p>
-<p>Class around link_coeff causal ground truth. X _|_ Y | Z is based on
-assessing whether X and Y are d-separated given Z in the graph.</p>
-<p>Class can be used just like a Tigramite conditional independence class
-(e.g., ParCorr). The main use is for unit testing of PCMCI methods.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph.</p></li>
-<li><p><strong>links</strong> (<em>dict</em>) – Dictionary of form {0:[(0, -1), …], 1:[…], …}.
-Alternatively can also digest {0: [((0, -1), coeff, func)], …}.</p></li>
-<li><p><strong>observed_vars</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Subset of keys in links definining which variables are
-observed. If None, then all variables are observed.</p></li>
-<li><p><strong>selection_vars</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Subset of keys in links definining which variables are
-selected (= always conditioned on at every time lag).
-If None, then no variables are selected.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.check_shortest_path">
-<span class="sig-name descname"><span class="pre">check_shortest_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">starts_with</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ends_with</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forbidden_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">directed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">only_non_causal_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_optimality_cond</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimality_cond_des_YM</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimality_cond_Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.check_shortest_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.check_shortest_path" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns path between X and Y given Z in the graph.</p>
-<p>X, Y, Z are of the form (var, lag) for lag &lt;= 0. D-separation is
-based on:</p>
-<p>1. Assessing maximum time lag max_lag of last ancestor of any X, Y, Z
-with non-blocked (by Z), non-repeating directed path towards X, Y, Z
-in the graph. ‘non_repeating’ means that an ancestor X^i_{ t-   au_i}
-with link X^i_{t-       au_i} –&gt; X^j_{ t-      au_j} is only included if
-X^i_{t’-        au_i} –&gt; X^j_{ t’-     au_j} for t’!=t is not already part of
-the ancestors.</p>
-<p>2. Using the time series graph truncated at max_lag we then test
-d-separation between X and Y conditional on Z using breadth-first
-search of non-blocked paths according to d-separation rules including
-selection variables.</p>
-<p>Optionally only considers paths starting/ending with specific marks)
-and makes available the ancestors up to max_lag of X, Y, Z. This may take
-a very long time, however.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of variables chosen for testing paths.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of variables chosen for testing paths.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of variables chosen for testing paths.</p></li>
-<li><p><strong>max_lag</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used here to constrain the has_path function to the graph
-truncated at max_lag instead of identifying the max_lag from
-ancestral search.</p></li>
-<li><p><strong>compute_ancestors</strong> (<em>bool</em>) – Whether to also make available the ancestors for X, Y, Z as
-self.anc_all_x, self.anc_all_y, and self.anc_all_z, respectively.</p></li>
-<li><p><strong>starts_with</strong> (<em>{None</em><em>, </em><em>'tail'</em><em>, </em><em>'arrohead'}</em>) – Whether to only consider paths starting with particular mark at X.</p></li>
-<li><p><strong>ends_with</strong> (<em>{None</em><em>, </em><em>'tail'</em><em>, </em><em>'arrohead'}</em>) – Whether to only consider paths ending with particular mark at Y.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>path</strong> – Returns path or False if no path exists.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>list or False</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_confidence">
-<span class="sig-name descname"><span class="pre">get_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.get_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>For compatibility with PCMCI.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>None</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_graph_from_links">
-<span class="sig-name descname"><span class="pre">get_graph_from_links</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.get_graph_from_links"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_graph_from_links" title="Permalink to this definition">¶</a></dt>
-<dd><p>Constructs graph (DAG or MAG or ADMG) from links, observed_vars,
-and selection_vars.</p>
-<p>For ADMGs uses the Latent projection operation (Pearl 2009).</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_links_from_graph">
-<span class="sig-name descname"><span class="pre">get_links_from_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.get_links_from_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_links_from_graph" title="Permalink to this definition">¶</a></dt>
-<dd><p>Constructs links_coeffs dictionary, observed_vars,
-and selection_vars from graph array (MAG or DAG).</p>
-<p>In the case of MAGs, for every &lt;-&gt; or — link further
-latent and selection variables, respectively, are added.
-This corresponds to a canonical DAG (Richardson Spirtes 2002).</p>
-<p>For ADMGs “—” are not supported, but also links of type “+-&gt;”
-exist, which corresponds to having both “–&gt;” and “&lt;-&gt;”.</p>
-<p>Can be used to evaluate d-separation in MAG/DAGs.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_measure">
-<span class="sig-name descname"><span class="pre">get_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.get_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns dependence measure.</p>
-<p>Returns 0 if X and Y are d-separated given Z in the graph and 1 else.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>[</strong> (<em>Y</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>Z</strong><strong>]</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – The test statistic value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_model_selection_criterion">
-<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Base class assumption that this is not implemented.  Concrete classes
-should override when possible.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test">
-<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test" title="Permalink to this definition">¶</a></dt>
-<dd><p>Perform oracle conditional independence test.</p>
-<p>Calls the d-separation function.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
-variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Not used here.</p></li>
-<li><p><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) – Not used here.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val, pval</strong> – The test statistic value and the p-value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of floats</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.set_dataframe">
-<span class="sig-name descname"><span class="pre">set_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.set_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.set_dataframe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Dummy function.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_mult.ParCorrMult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.parcorr_mult.</span></span><span class="sig-name descname"><span class="pre">ParCorrMult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">correlation_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max_corr'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_mult.html#ParCorrMult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult" title="Permalink to this definition">¶</a></dt>
-<dd><p>Partial correlation test for multivariate X and Y.</p>
-<p>Multivariate partial correlation is estimated through ordinary least squares (OLS)
-regression and some test for multivariate dependency among the residuals.</p>
-<p class="rubric">Notes</p>
-<p>To test <img class="math" src="_images/math/667eda4bf3d5ce33b6cc785cadfef79bb95741ca.png" alt="X \perp Y | Z"/>, first <img class="math" src="_images/math/95f028ab2b20b895fa12d986e0d9f40f7b6e52d3.png" alt="Z"/> is regressed out from
-<img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> and <img class="math" src="_images/math/7daf0d4815e763eb90f0d5f1dc406f668c1e21db.png" alt="Y"/> assuming the  model</p>
-<div class="math">
-<p><img src="_images/math/78b6e77cc610bf56f5c64cb6dcc6d6ee49f886f9.png" alt="X &amp; =  Z \beta_X + \epsilon_{X} \\
-Y &amp; =  Z \beta_Y + \epsilon_{Y}"/></p>
-</div><p>using OLS regression. Then different measures for the dependency among the residuals
-can be used. Currently only a test for zero correlation on the maximum of the residuals’
-correlation is performed.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>correlation_type</strong> (<em>{'max_corr'}</em>) – Which dependency measure to use on residuals.</p></li>
-<li><p><strong>**kwargs</strong> – Arguments passed on to Parent class CondIndTest.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_mult.ParCorrMult.get_analytic_confidence">
-<span class="sig-name descname"><span class="pre">get_analytic_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_mult.html#ParCorrMult.get_analytic_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Base class assumption that this is not implemented.  Concrete classes
-should override when possible.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_mult.ParCorrMult.get_analytic_significance">
-<span class="sig-name descname"><span class="pre">get_analytic_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_mult.html#ParCorrMult.get_analytic_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns analytic p-value depending on correlation_type.</p>
-<p>Assumes two-sided correlation. If the degrees of freedom are less than
-1, numpy.nan is returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
-<li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
-<li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – P-value.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float or numpy.nan</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_mult.ParCorrMult.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_mult.html#ParCorrMult.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return multivariate kernel correlation coefficient.</p>
-<p>Estimated as some dependency measure on the
-residuals of a linear OLS regression.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – Partial correlation coefficient.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_mult.ParCorrMult.get_model_selection_criterion">
-<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_mult.html#ParCorrMult.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Base class assumption that this is not implemented.  Concrete classes
-should override when possible.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_mult.ParCorrMult.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_mult.html#ParCorrMult.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for shuffle significance test.</p>
-<p>For residual-based test statistics only the residuals are shuffled.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_mult.ParCorrMult.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_mult.ParCorrMult.mult_corr">
-<span class="sig-name descname"><span class="pre">mult_corr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standardize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_mult.html#ParCorrMult.mult_corr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult.mult_corr" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return multivariate dependency measure.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>standardize</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to standardize the array beforehand. Must be used for
-partial correlation.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – Multivariate dependency measure.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gsquared.Gsquared">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.gsquared.</span></span><span class="sig-name descname"><span class="pre">Gsquared</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_symbs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gsquared.html#Gsquared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gsquared.Gsquared" title="Permalink to this definition">¶</a></dt>
-<dd><p>G-squared conditional independence test for categorical data.</p>
-<p>Uses Chi2 as the null distribution and the method from <a class="footnote-reference brackets" href="#id26" id="id25" role="doc-noteref"><span class="fn-bracket">[</span>7<span class="fn-bracket">]</span></a> to
-adjust the degrees of freedom. Valid only asymptotically, recommended are
-above 1000-2000 samples (depends on data). For smaller sample sizes use the
-CMIsymb class which includes a local permutation test.</p>
-<p>Assumes one-dimensional X, Y.</p>
-<p>This method requires the scipy.stats package.</p>
-<p class="rubric">Notes</p>
-<p>The general formula is</p>
-<div class="math">
-<p><img src="_images/math/93468ec117fc04c2589757eb61fc11c15d27bc1b.png" alt="G(X;Y|Z) &amp;= 2 n \sum p(z)  \sum \sum  p(x,y|z) \log
-\frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)}"/></p>
-</div><p>where <img class="math" src="_images/math/5a939c5280da7202ca4531f175a7780ad5e1f80a.png" alt="n"/> is the sample size. This is simply <img class="math" src="_images/math/88da5b28f5e08d9d8ed693d334e710f4adadbcfd.png" alt="2 n CMI(X;Y|Z)"/>.</p>
-<p class="rubric">References</p>
-<aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id26" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id25">7</a><span class="fn-bracket">]</span></span>
-<p>Bishop, Y.M.M., Fienberg, S.E. and Holland, P.W. (1975) Discrete
-Multivariate Analysis: Theory and Practice. MIT Press, Cambridge.</p>
-</aside>
-</aside>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>n_symbs</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Number of symbols in input data. Should be at least as large as the
-maximum array entry + 1. If None, n_symbs is inferred by scipy’s crosstab</p></li>
-<li><p><strong>**kwargs</strong> – Arguments passed on to parent class CondIndTest.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gsquared.Gsquared.get_analytic_significance">
-<span class="sig-name descname"><span class="pre">get_analytic_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gsquared.html#Gsquared.get_analytic_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gsquared.Gsquared.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the p_value of test statistic value ‘value’, according to a
-chi-square distribution with ‘dof’ degrees of freedom.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gsquared.Gsquared.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/gsquared.html#Gsquared.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.gsquared.Gsquared.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns Gsquared/G-test test statistic.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – G-squared estimate.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.gsquared.Gsquared.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.gsquared.Gsquared.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_wls.ParCorrWLS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.parcorr_wls.</span></span><span class="sig-name descname"><span class="pre">ParCorrWLS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gt_std_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expert_knowledge</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'time-dependent</span> <span class="pre">heteroskedasticity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">robustify</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_wls.html#ParCorrWLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_wls.ParCorrWLS" title="Permalink to this definition">¶</a></dt>
-<dd><p>Weighted partial correlation test.</p>
-<p>Partial correlation is estimated through linear weighted least squares (WLS)
-regression and a test for non-zero linear Pearson correlation on the
-residuals.
-Either the variances, i.e. weights, are known, or they can be estimated using non-parametric regression
-(using k nearest neighbour).</p>
-<p class="rubric">Notes</p>
-<p>To test <img class="math" src="_images/math/667eda4bf3d5ce33b6cc785cadfef79bb95741ca.png" alt="X \perp Y | Z"/>, first <img class="math" src="_images/math/95f028ab2b20b895fa12d986e0d9f40f7b6e52d3.png" alt="Z"/> is regressed out from
-<img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> and <img class="math" src="_images/math/7daf0d4815e763eb90f0d5f1dc406f668c1e21db.png" alt="Y"/> assuming the  model</p>
-<div class="math">
-<p><img src="_images/math/78b6e77cc610bf56f5c64cb6dcc6d6ee49f886f9.png" alt="X &amp; =  Z \beta_X + \epsilon_{X} \\
-Y &amp; =  Z \beta_Y + \epsilon_{Y}"/></p>
-</div><p>using WLS regression. Here, we do not assume homoskedasticity of the error terms.
-Then the dependency of the residuals is tested with
-the Pearson correlation test.</p>
-<div class="math">
-<p><img src="_images/math/789735c1db036ea36cd0aa25a3af4b2528ed3abe.png" alt="\rho\left(r_X, r_Y\right)"/></p>
-</div><p>For the <code class="docutils literal notranslate"><span class="pre">significance='analytic'</span></code> Student’s-<em>t</em> distribution with
-<img class="math" src="_images/math/9c353382eebb42a8a9dec3a426d346d4842bd39d.png" alt="T-D_Z-2"/> degrees of freedom is implemented.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>gt_std_matrix</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Standard deviations of the noise of shape (T, nb_nodes)</p></li>
-<li><p><strong>expert_knowledge</strong> (<em>string</em><em> or </em><em>dict</em><em> (</em><em>default: time-dependent heteroskedasticity</em><em>)</em>) – Either string “time-dependent heteroskedasticity” meaning that every variable only has time-dependent
-heteroskedasticity, or string “homoskedasticity” where we assume homoskedasticity for all variables, or
-dictionary containing expert knowledge about heteroskedastic relationships as list of tuples or strings.</p></li>
-<li><p><strong>window_size</strong> (<em>int</em><em> (</em><em>default: 10</em><em>)</em>) – Number of nearest neighbours that we are using for estimating the variance function.</p></li>
-<li><p><strong>robustify</strong> (<em>bool</em><em> (</em><em>default: False</em><em>)</em>) – Indicates whether the robust partial correlation test should be used, i.e. whether the data should be
-transformed to normal marginals before testing</p></li>
-<li><p><strong>**kwargs</strong> – Arguments passed on to Parent class ParCorr.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_wls.html#ParCorrWLS.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return partial correlation.</p>
-<p>Estimated as the Pearson correlation of the residuals of a linear
-OLS regression.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – Partial correlation coefficient.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_model_selection_criterion">
-<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">corrected_aic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_wls.html#ParCorrWLS.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns Akaike’s Information criterion modulo constants.</p>
-<p>Fits a linear model of the parents to variable j and returns the
-score. Leave-one-out cross-validation is asymptotically equivalent to
-AIC for ordinary linear regression models. Here used to determine
-optimal hyperparameters in PCMCI, in particular the pc_alpha value.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>j</strong> (<em>int</em>) – Index of target variable in data array.</p></li>
-<li><p><strong>parents</strong> (<em>list</em>) – List of form [(0, -1), (3, -2), …] containing parents.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</p></li>
-<li><p><strong>Returns</strong> – </p></li>
-<li><p><strong>score</strong> (<em>float</em>) – Model score.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/parcorr_wls.html#ParCorrWLS.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.parcorr_wls.ParCorrWLS.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for shuffle significance test.</p>
-<p>For residual-based test statistics only the residuals are shuffled.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – p-value</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.independence_tests.regressionCI.RegressionCI">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.regressionCI.</span></span><span class="sig-name descname"><span class="pre">RegressionCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/regressionCI.html#RegressionCI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.regressionCI.RegressionCI" title="Permalink to this definition">¶</a></dt>
-<dd><p>Flexible parametric conditional independence tests for continuous, categorical, or mixed data.</p>
-<p>Assumes one-dimensional X, Y.</p>
-<p class="rubric">Notes</p>
-<p>To test <img class="math" src="_images/math/667eda4bf3d5ce33b6cc785cadfef79bb95741ca.png" alt="X \perp Y | Z"/>, the regressions Y|XZ vs Y|Z, or, depending
-on certain criteria, X|YZ vs X|Z are compared. For that, the notion of
-the deviance is employed. If the fits of the respective regressions do
-not differ significantly (measured using the deviance), the null
-hypotheses of conditional independence is “accepted”. This approach
-assumes that X and Y are univariate, and Z can be either empty,
-univariate or multivariate. Moreover, this approach works for all
-combinations of “discrete” and “continuous” X, Y and respective columns
-of Z; depending on the case, linear regression or multinomial regression
-is employed.</p>
-<p>Assumes one-dimensional X, Y.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>**kwargs</strong> – Arguments passed on to parent class CondIndTest.</p>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.regressionCI.RegressionCI.get_analytic_significance">
-<span class="sig-name descname"><span class="pre">get_analytic_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/regressionCI.html#RegressionCI.get_analytic_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.regressionCI.RegressionCI.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the p_value of test statistic.</p>
-<p>According to a chi-square distribution with ‘dof’ degrees of freedom.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.regressionCI.RegressionCI.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/regressionCI.html#RegressionCI.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.regressionCI.RegressionCI.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns test statistic.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>type_mask</strong> (<em>array-like</em>) – array of same shape as array which describes whether samples
-are continuous or discrete: 0s for continuous and
-1s for discrete</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val</strong> – test estimate.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="tigramite.independence_tests.regressionCI.RegressionCI.measure">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.regressionCI.RegressionCI.measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Concrete property to return the measure of the independence test</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.regressionCI.RegressionCI.set_dataframe">
-<span class="sig-name descname"><span class="pre">set_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/regressionCI.html#RegressionCI.set_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.regressionCI.RegressionCI.set_dataframe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize and check the dataframe.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>dataframe</strong> (<em>data object</em>) – Set tigramite dataframe object. It must have the attributes
-dataframe.values yielding a numpy array of shape (observations T,
-variables N) and optionally a mask of the same shape and a missing
-values flag.</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="tigramite-causal-effects-causal-effect-analysis">
-<h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.causal_effects</span></code>: Causal Effect analysis<a class="headerlink" href="#tigramite-causal-effects-causal-effect-analysis" title="Permalink to this heading">¶</a></h1>
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.causal_effects.</span></span><span class="sig-name descname"><span class="pre">CausalEffects</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">S</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_variables</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_SM_overlap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects" title="Permalink to this definition">¶</a></dt>
-<dd><p>Causal effect estimation.</p>
-<p>Methods for the estimation of linear or non-parametric causal effects
-between (potentially multivariate) X and Y (potentially conditional
-on S) by (generalized) backdoor adjustment. Various graph types are
-supported, also including hidden variables.</p>
-<p>Linear and non-parametric estimators are based on sklearn. For the
-linear case without hidden variables also an efficient estimation
-based on Wright’s path coefficients is available. This estimator
-also allows to estimate mediation effects.</p>
-<p>See the corresponding paper <a class="footnote-reference brackets" href="#id28" id="id27" role="doc-noteref"><span class="fn-bracket">[</span>6<span class="fn-bracket">]</span></a> and tigramite tutorial for an
-in-depth introduction.</p>
-<p class="rubric">References</p>
-<aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id28" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id27">6</a><span class="fn-bracket">]</span></span>
-<p>J. Runge, Necessary and sufficient graphical conditions for
-optimal adjustment sets in causal graphical models with
-hidden variables, Advances in Neural Information Processing
-Systems, 2021, 34
-<a class="reference external" href="https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html">https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html</a></p>
-</aside>
-</aside>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>either shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>]</em><em>, </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em><em>, or </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>, </em><em>tau_max+1</em><em>]</em>) – Different graph types are supported, see tutorial.</p></li>
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of tuples [(i, -tau), …] containing cause variables.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of tuples [(j, 0), …] containing effect variables.</p></li>
-<li><p><strong>S</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of tuples [(i, -tau), …] containing conditioned variables.</p></li>
-<li><p><strong>graph_type</strong> (<em>str</em>) – Type of graph.</p></li>
-<li><p><strong>hidden_variables</strong> (<em>list</em><em> of </em><em>tuples</em>) – Hidden variables in format [(i, -tau), …]. The internal graph is
-constructed by a latent projection.</p></li>
-<li><p><strong>check_SM_overlap</strong> (<em>bool</em>) – Whether to check whether S overlaps with M.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.check_XYS_paths">
-<span class="sig-name descname"><span class="pre">check_XYS_paths</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.check_XYS_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.check_XYS_paths" title="Permalink to this definition">¶</a></dt>
-<dd><p>Check whether one can remove nodes from X and Y with no proper causal paths.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>X, Y</strong></p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>cleaned lists of X and Y with irrelevant nodes removed.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.check_optimality">
-<span class="sig-name descname"><span class="pre">check_optimality</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.check_optimality"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.check_optimality" title="Permalink to this definition">¶</a></dt>
-<dd><p>Check whether optimal adjustment set exists according to Thm. 3 in Runge NeurIPS 2021.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>optimality</strong> – Returns True if an optimal adjustment set exists, otherwise False.</p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>bool</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.fit_bootstrap_of">
-<span class="sig-name descname"><span class="pre">fit_bootstrap_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boot_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boot_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.fit_bootstrap_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.fit_bootstrap_of" title="Permalink to this definition">¶</a></dt>
-<dd><p>Runs chosen method on bootstrap samples drawn from DataFrame.</p>
-<p>Bootstraps for tau=0 are drawn from [max_lag, …, T] and all lagged
-variables constructed in DataFrame.construct_array are consistently
-shifted with respect to this bootsrap sample to ensure that lagged
-relations in the bootstrap sample are preserved.</p>
-<p>This function fits the models, predict_bootstrap_of can then be used
-to get confidence intervals for the effect of interventions.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>method</strong> (<em>str</em>) – Chosen method among valid functions in this class.</p></li>
-<li><p><strong>method_args</strong> (<em>dict</em>) – Arguments passed to method.</p></li>
-<li><p><strong>boot_samples</strong> (<em>int</em>) – Number of bootstrap samples to draw.</p></li>
-<li><p><strong>boot_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Block length for block-bootstrap.</p></li>
-<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>(</em><em>default = None</em><em>)</em>) – Seed for RandomState (default_rng)</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.fit_total_effect">
-<span class="sig-name descname"><span class="pre">fit_total_effect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adjustment_set</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'optimal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conditional_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_identifiability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.fit_total_effect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.fit_total_effect" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="simple">
-<dt>Returns a fitted model for the total causal effect of X on Y</dt><dd><p>conditional on S.</p>
-</dd>
-</dl>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
-yielding a numpy array of shape (observations T, variables N) and
-optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>estimator</strong> (<em>sklearn model object</em>) – For example, sklearn.linear_model.LinearRegression() for a linear
-regression model.</p></li>
-<li><p><strong>adjustment_set</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>tuples</em>) – If ‘optimal’ the Oset is used, if ‘minimized_optimal’ the minimized Oset,
-and if ‘colliders_minimized_optimal’, the colliders-minimized Oset.
-If a list of tuples is passed, this set is used.</p></li>
-<li><p><strong>conditional_estimator</strong> (<em>sklearn model object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to fit conditional causal effects in nested regression.
-If None, the same model as for estimator is used.</p></li>
-<li><p><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to transform data prior to fitting. For example,
-sklearn.preprocessing.StandardScaler for simple standardization. The
-fitted parameters are stored.</p></li>
-<li><p><strong>mask_type</strong> (<em>{None</em><em>, </em><em>'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
-measure I(X; Y | Z) the samples should be masked. If None, the mask
-is not used. Explained in tutorial on masking and missing values.</p></li>
-<li><p><strong>ignore_identifiability</strong> (<em>bool</em>) – Only applies to adjustment sets supplied by user. Ignores if that
-set leads to a non-identifiable effect.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.fit_wright_effect">
-<span class="sig-name descname"><span class="pre">fit_wright_effect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mediation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'parents'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">links_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.fit_wright_effect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.fit_wright_effect" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="simple">
-<dt>Returns a fitted model for the total or mediated causal effect of X on Y</dt><dd><p>potentially through mediator variables.</p>
-</dd>
-</dl>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
-yielding a numpy array of shape (observations T, variables N) and
-optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>mediation</strong> (<em>None</em><em>, </em><em>'direct'</em><em>, or </em><em>list</em><em> of </em><em>tuples</em>) – If None, total effect is estimated, if ‘direct’ then only the direct effect is estimated,
-else only those causal paths are considerd that pass at least through one of these mediator nodes.</p></li>
-<li><p><strong>method</strong> (<em>{'parents'</em><em>, </em><em>'links_coeffs'</em><em>, </em><em>'optimal'}</em>) – Method to use for estimating Wright’s path coefficients. If ‘optimal’,
-the Oset is used, if ‘links_coeffs’, the coefficients in links_coeffs are used,
-if ‘parents’, the parents are used (only valid for DAGs).</p></li>
-<li><p><strong>links_coeffs</strong> (<em>dict</em>) – Only used if method = ‘links_coeffs’.
-Dictionary of format: {0:[((i, -tau), coeff),…], 1:[…],
-…} for all variables where i must be in [0..N-1] and tau &gt;= 0 with
-number of variables N. coeff must be a float.</p></li>
-<li><p><strong>data_transform</strong> (<em>None</em>) – Not implemented for Wright estimator. Complicated for missing samples.</p></li>
-<li><p><strong>mask_type</strong> (<em>{None</em><em>, </em><em>'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
-measure I(X; Y | Z) the samples should be masked. If None, the mask
-is not used. Explained in tutorial on masking and missing values.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.get_graph_from_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_graph_from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">links</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.get_graph_from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.get_graph_from_dict" title="Permalink to this definition">¶</a></dt>
-<dd><p>Helper function to convert dictionary of links to graph array format.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>links</strong> (<em>dict</em>) – Dictionary of form {0:[((0, -1), coeff, func), …], 1:[…], …}.
-Also format {0:[(0, -1), …], 1:[…], …} is allowed.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum lag. If None, the maximum lag in links is used.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>graph</strong> – Matrix format of graph with 1 for true links and 0 else.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape (N, N, tau_max+1)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.get_mediators">
-<span class="sig-name descname"><span class="pre">get_mediators</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.get_mediators"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.get_mediators" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns mediator variables on proper causal paths.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>start</strong> (<em>set</em>) – Set of start nodes.</p></li>
-<li><p><strong>end</strong> (<em>set</em>) – Set of end nodes.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>mediators</strong> – Mediators on causal paths from start to end.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>set</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.get_optimal_set">
-<span class="sig-name descname"><span class="pre">get_optimal_set</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alternative_conditions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minimize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_separate_sets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.get_optimal_set"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.get_optimal_set" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns optimal adjustment set.</p>
-<p>See Runge NeurIPS 2021.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>alternative_conditions</strong> (<em>set</em><em> of </em><em>tuples</em>) – Used only internally in optimality theorem. If None, self.S is used.</p></li>
-<li><p><strong>minimize</strong> (<em>{False</em><em>, </em><em>True</em><em>, </em><em>'colliders_only'}</em>) – Minimize optimal set. If True, minimize such that no subset
-can be removed without making it invalid. If ‘colliders_only’,
-only colliders are minimized.</p></li>
-<li><p><strong>return_separate_sets</strong> (<em>bool</em>) – Whether to return tuple of parents, colliders, collider_parents, and S.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>Oset_S</strong> – Returns optimal adjustment set if a valid set exists, otherwise False.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>False or list or tuple of lists</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.predict_bootstrap_of">
-<span class="sig-name descname"><span class="pre">predict_bootstrap_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_individual_bootstrap_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.predict_bootstrap_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.predict_bootstrap_of" title="Permalink to this definition">¶</a></dt>
-<dd><p>Predicts with fitted bootstraps.</p>
-<p>To be used after fitting with fit_bootstrap_of. Only uses the
-expected values of the predict function, not potential other output.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>method</strong> (<em>str</em>) – Chosen method among valid functions in this class.</p></li>
-<li><p><strong>method_args</strong> (<em>dict</em>) – Arguments passed to method.</p></li>
-<li><p><strong>conf_lev</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.9</em><em>)</em>) – Two-sided confidence interval.</p></li>
-<li><p><strong>return_individual_bootstrap_results</strong> (<em>bool</em>) – Returns the individual bootstrap predictions.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>confidence_intervals</strong></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>numpy array of</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.predict_total_effect">
-<span class="sig-name descname"><span class="pre">predict_total_effect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">intervention_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conditions_data=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_params=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_further_pred_results=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregation_func=&lt;function</span> <span class="pre">mean&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_interventions_and_prediction=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.predict_total_effect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.predict_total_effect" title="Permalink to this definition">¶</a></dt>
-<dd><p>Predict effect of intervention with fitted model.</p>
-<p>Uses the model.predict() function of the sklearn model.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>intervention_data</strong> (<em>numpy array</em>) – Numpy array of shape (time, len(X)) that contains the do(X) values.</p></li>
-<li><p><strong>conditions_data</strong> (<em>data object</em><em>, </em><em>optional</em>) – Numpy array of shape (time, len(S)) that contains the S=s values.</p></li>
-<li><p><strong>pred_params</strong> (<em>dict</em><em>, </em><em>optional</em>) – Optional parameters passed on to sklearn prediction function.</p></li>
-<li><p><strong>return_further_pred_results</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – In case the predictor class returns more than just the expected value,
-the entire results can be returned.</p></li>
-<li><p><strong>aggregation_func</strong> (<em>callable</em>) – Callable applied to output of ‘predict’. Default is ‘np.mean’.</p></li>
-<li><p><strong>transform_interventions_and_prediction</strong> (<em>bool</em><em> (</em><em>default: False</em><em>)</em>) – Whether to perform the inverse data_transform on prediction results.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>Results from prediction</strong> (<em>an array of shape  (time, len(Y)).</em>)</p></li>
-<li><p><em>If estimate_confidence = True, then a tuple is returned.</em></p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.predict_wright_effect">
-<span class="sig-name descname"><span class="pre">predict_wright_effect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">intervention_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.predict_wright_effect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.predict_wright_effect" title="Permalink to this definition">¶</a></dt>
-<dd><p>Predict linear effect of intervention with fitted Wright-model.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>intervention_data</strong> (<em>numpy array</em>) – Numpy array of shape (time, len(X)) that contains the do(X) values.</p></li>
-<li><p><strong>pred_params</strong> (<em>dict</em><em>, </em><em>optional</em>) – Optional parameters passed on to sklearn prediction function.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>Results from prediction</strong></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>an array of shape  (time, len(Y)).</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="tigramite-models-time-series-modeling-mediation-and-prediction">
-<h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.models</span></code>: Time series modeling, mediation, and prediction<a class="headerlink" href="#tigramite-models-time-series-modeling-mediation-and-prediction" title="Permalink to this heading">¶</a></h1>
-<p>Base class:</p>
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.models.Models">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.models.</span></span><span class="sig-name descname"><span class="pre">Models</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conditional_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">StandardScaler()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models" title="Permalink to this definition">¶</a></dt>
-<dd><p>Base class for time series models.</p>
-<p>Allows to fit any model from sklearn to the parents of a target variable.
-Also takes care of missing values, masking and preprocessing.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
-yielding a numpy array of shape (observations T, variables N) and
-optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>model</strong> (<em>sklearn model object</em>) – For example, sklearn.linear_model.LinearRegression() for a linear
-regression model.</p></li>
-<li><p><strong>conditional_model</strong> (<em>sklearn model object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to fit conditional causal effects in nested regression.
-If None, model is used.</p></li>
-<li><p><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to transform data prior to fitting. For example,
-sklearn.preprocessing.StandardScaler for simple standardization. The
-fitted parameters are stored. Note that the inverse_transform is then
-applied to the predicted data.</p></li>
-<li><p><strong>mask_type</strong> (<em>{None</em><em>, </em><em>'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
-measure I(X; Y | Z) the samples should be masked. If None, the mask
-is not used. Explained in tutorial on masking and missing values.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Models.fit_full_model">
-<span class="sig-name descname"><span class="pre">fit_full_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">all_parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_variables</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max_lag_or_tau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.fit_full_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models.fit_full_model" title="Permalink to this definition">¶</a></dt>
-<dd><p>Fit time series model.</p>
-<p>For each variable in selected_variables, the sklearn model is fitted
-with <img class="math" src="_images/math/1b5e577d6216dca3af7d87aa122a0b9b360d6cb3.png" alt="y"/> given by the target variable, and <img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> given by its
-parents. The fitted model class is returned for later use.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>all_parents</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, 0), …], 1:[], …} containing
-the parents estimated with PCMCI.</p></li>
-<li><p><strong>selected_variables</strong> (<em>list</em><em> of </em><em>integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to estimate parents only for selected variables. If None is
-passed, parents are estimated for all variables.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in all_parents is used.</p></li>
-<li><p><strong>cut_off</strong> (<em>{'max_lag_or_tau_max'</em><em>, </em><em>'2xtau_max'</em><em>, </em><em>'max_lag'}</em>) – How many samples to cutoff at the beginning. The default is
-‘max_lag_or_tau_max’, which uses the maximum of tau_max and the
-conditions. This is useful to compare multiple models on the same
-sample. Other options are ‘2xtau_max’, which guarantees that MCI
-tests are all conducted on the same samples. Last, ‘max_lag’ uses
-as much samples as possible.</p></li>
-<li><p><strong>return_data</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to save the data array.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>fit_results</strong> – Returns the sklearn model after fitting. Also returns the data
-transformation parameters.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dictionary of sklearn model objects for each variable</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Models.get_coefs">
-<span class="sig-name descname"><span class="pre">get_coefs</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.get_coefs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models.get_coefs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns dictionary of coefficients for linear models.</p>
-<p>Only for models from sklearn.linear_model</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>coeffs</strong> – Dictionary of dictionaries for each variable with keys given by the
-parents and the regression coefficients as values.</p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>dictionary</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Models.get_general_fitted_model">
-<span class="sig-name descname"><span class="pre">get_general_fitted_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conditions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max_lag_or_tau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.get_general_fitted_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models.get_general_fitted_model" title="Permalink to this definition">¶</a></dt>
-<dd><p>Fit time series model.</p>
-<p>For each variable in selected_variables, the sklearn model is fitted
-with <img class="math" src="_images/math/1b5e577d6216dca3af7d87aa122a0b9b360d6cb3.png" alt="y"/> given by the target variable, and <img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> given by its
-parents. The fitted model class is returned for later use.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>lists</em><em> of </em><em>tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
-<li><p><strong>Y</strong> (<em>lists</em><em> of </em><em>tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
-<li><p><strong>Z</strong> (<em>lists</em><em> of </em><em>tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
-<li><p><strong>conditions</strong> (<em>list</em><em> of </em><em>tuples.</em>) – Conditions for estimating conditional causal effects.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in all_parents is used.</p></li>
-<li><p><strong>cut_off</strong> (<em>{'max_lag_or_tau_max'</em><em>, </em><em>'2xtau_max'</em><em>, </em><em>'max_lag'}</em>) – How many samples to cutoff at the beginning. The default is
-‘max_lag_or_tau_max’, which uses the maximum of tau_max and the
-conditions. This is useful to compare multiple models on the same
-sample. Other options are ‘2xtau_max’, which guarantees that MCI
-tests are all conducted on the same samples. Last, ‘max_lag’ uses
-as much samples as possible.</p></li>
-<li><p><strong>return_data</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to save the data array.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>fit_results</strong> – Returns the sklearn model after fitting. Also returns the data
-transformation parameters.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dictionary of sklearn model objects for each variable</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Models.get_general_prediction">
-<span class="sig-name descname"><span class="pre">get_general_prediction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">intervention_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conditions_data=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_params=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_interventions_and_prediction=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_further_pred_results=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregation_func=&lt;function</span> <span class="pre">mean&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.get_general_prediction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models.get_general_prediction" title="Permalink to this definition">¶</a></dt>
-<dd><p>Predict effect of intervention with fitted model.</p>
-<p>Uses the model.predict() function of the sklearn model.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>intervention_data</strong> (<em>numpy array</em>) – Numpy array of shape (time, len(X)) that contains the do(X) values.</p></li>
-<li><p><strong>conditions_data</strong> (<em>data object</em><em>, </em><em>optional</em>) – Numpy array of shape (time, len(S)) that contains the S=s values.</p></li>
-<li><p><strong>pred_params</strong> (<em>dict</em><em>, </em><em>optional</em>) – Optional parameters passed on to sklearn prediction function.</p></li>
-<li><p><strong>transform_interventions_and_prediction</strong> (<em>bool</em><em> (</em><em>default: False</em><em>)</em>) – Whether to perform the inverse data_transform on prediction results.</p></li>
-<li><p><strong>return_further_pred_results</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – In case the predictor class returns more than just the expected value,
-the entire results can be returned.</p></li>
-<li><p><strong>aggregation_func</strong> (<em>callable</em>) – Callable applied to output of ‘predict’. Default is ‘np.mean’.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>Results from prediction.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Models.get_val_matrix">
-<span class="sig-name descname"><span class="pre">get_val_matrix</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.get_val_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models.get_val_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the coefficient array for different lags for linear model.</p>
-<p>Requires fit_model() before. An entry val_matrix[i,j,tau] gives the
-coefficient of the link from i to j at lag tau, including tau=0.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>val_matrix</strong> – Array of coefficients for each time lag, including lag-zero.</p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>array-like, shape (N, N, tau_max + 1)</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<p>Derived classes:</p>
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.models.</span></span><span class="sig-name descname"><span class="pre">LinearMediation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">StandardScaler()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation" title="Permalink to this definition">¶</a></dt>
-<dd><p>Linear mediation analysis for time series models.</p>
-<p>Fits linear model to parents and provides functions to return measures such
-as causal effect, mediated causal effect, average causal effect, etc. as
-described in <a class="footnote-reference brackets" href="#id31" id="id29" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>. Also allows for contemporaneous links.</p>
-<p>For general linear and nonlinear causal effect analysis including latent
-variables and further functionality use the CausalEffects class.</p>
-<p class="rubric">Notes</p>
-<p>This class implements the following causal mediation measures introduced in
-<a class="footnote-reference brackets" href="#id31" id="id30" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>:</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>causal effect (CE)</p></li>
-<li><p>mediated causal effect (MCE)</p></li>
-<li><p>average causal effect (ACE)</p></li>
-<li><p>average causal susceptibility (ACS)</p></li>
-<li><p>average mediated causal effect (AMCE)</p></li>
-</ul>
-</div></blockquote>
-<p>Consider a simple model of a causal chain as given in the Example with</p>
-<div class="math">
-<p><img src="_images/math/7b5b384bfd47bd6e8d707b3189aaab3a46c5ed04.png" alt="X_t &amp;= \eta^X_t \\
-Y_t &amp;= 0.5 X_{t-1} +  \eta^Y_t \\
-Z_t &amp;= 0.5 Y_{t-1} +  \eta^Z_t"/></p>
-</div><p>Here the link coefficient of <img class="math" src="_images/math/64932505aadaa1eac6316ff09c2c3b101b068168.png" alt="X_{t-2} \to Z_t"/> is zero while the
-causal effect is 0.25. MCE through <img class="math" src="_images/math/7daf0d4815e763eb90f0d5f1dc406f668c1e21db.png" alt="Y"/> is 0.25 implying that <em>all</em>
-of the the CE is explained by <img class="math" src="_images/math/7daf0d4815e763eb90f0d5f1dc406f668c1e21db.png" alt="Y"/>. ACE from <img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> is 0.37 since it
-has CE 0.5 on <img class="math" src="_images/math/7daf0d4815e763eb90f0d5f1dc406f668c1e21db.png" alt="Y"/> and 0.25 on <img class="math" src="_images/math/95f028ab2b20b895fa12d986e0d9f40f7b6e52d3.png" alt="Z"/>.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">links_coeffs</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">)],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">)]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="p">,</span> <span class="n">true_parents</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">var_process</span><span class="p">(</span><span class="n">links_coeffs</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">med</span> <span class="o">=</span> <span class="n">LinearMediation</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">med</span><span class="o">.</span><span class="n">fit_model</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">true_parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s2">&quot;Link coefficient (0, -2) --&gt; 2: &quot;</span><span class="p">,</span> <span class="n">med</span><span class="o">.</span><span class="n">get_coeff</span><span class="p">(</span>
-<span class="go">i=0, tau=-2, j=2)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s2">&quot;Causal effect (0, -2) --&gt; 2: &quot;</span><span class="p">,</span> <span class="n">med</span><span class="o">.</span><span class="n">get_ce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">j</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s2">&quot;Mediated Causal effect (0, -2) --&gt; 2 through 1: &quot;</span><span class="p">,</span> <span class="n">med</span><span class="o">.</span><span class="n">get_mce</span><span class="p">(</span>
-<span class="go">i=0, tau=-2, j=2, k=1)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s2">&quot;Average Causal Effect: &quot;</span><span class="p">,</span> <span class="n">med</span><span class="o">.</span><span class="n">get_all_ace</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s2">&quot;Average Causal Susceptibility: &quot;</span><span class="p">,</span> <span class="n">med</span><span class="o">.</span><span class="n">get_all_acs</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s2">&quot;Average Mediated Causal Effect: &quot;</span><span class="p">,</span> <span class="n">med</span><span class="o">.</span><span class="n">get_all_amce</span><span class="p">()</span>
-<span class="go">Link coefficient (0, -2) --&gt; 2:  0.0</span>
-<span class="go">Causal effect (0, -2) --&gt; 2:  0.250648072987</span>
-<span class="go">Mediated Causal effect (0, -2) --&gt; 2 through 1:  0.250648072987</span>
-<span class="go">Average Causal Effect:  [ 0.36897445  0.25718002  0.        ]</span>
-<span class="go">Average Causal Susceptibility:  [ 0.          0.24365041  0.38250406]</span>
-<span class="go">Average Mediated Causal Effect:  [ 0.          0.12532404  0.        ]</span>
-</pre></div>
-</div>
-<p class="rubric">References</p>
-<aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id31" role="note">
-<span class="label"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></span>
-<span class="backrefs">(<a role="doc-backlink" href="#id29">1</a>,<a role="doc-backlink" href="#id30">2</a>,<a role="doc-backlink" href="#id32">3</a>)</span>
-<p>J. Runge et al. (2015): Identifying causal gateways and mediators in
-complex spatio-temporal systems.
-Nature Communications, 6, 8502. <a class="reference external" href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></p>
-</aside>
-</aside>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
-yielding a numpy array of shape (observations T, variables N) and
-optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>model_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional parameters passed on to sklearn model</p></li>
-<li><p><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: StandardScaler</em><em>)</em>) – Used to transform data prior to fitting. For example,
-sklearn.preprocessing.StandardScaler for simple standardization. The
-fitted parameters are stored.</p></li>
-<li><p><strong>mask_type</strong> (<em>{None</em><em>, </em><em>'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
-measure I(X; Y | Z) the samples should be masked. If None, the mask
-is not used. Explained in tutorial on masking and missing values.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.fit_model">
-<span class="sig-name descname"><span class="pre">fit_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">all_parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.fit_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.fit_model" title="Permalink to this definition">¶</a></dt>
-<dd><p>Fit linear time series model.</p>
-<p>Fits a sklearn.linear_model.LinearRegression model to the parents of
-each variable and computes the coefficient matrices <img class="math" src="_images/math/d8ee85ac4d75924cdf4b18f5fb3b46550932fc26.png" alt="\Phi"/> and
-<img class="math" src="_images/math/20582dab63cb7f6604f5bf70224030ad3411ae16.png" alt="\Psi"/> as described in <a class="footnote-reference brackets" href="#id31" id="id32" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>. Does accept contemporaneous links.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>all_parents</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, 0), …], 1:[], …} containing
-the parents estimated with PCMCI.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in all_parents is used.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.fit_model_bootstrap">
-<span class="sig-name descname"><span class="pre">fit_model_bootstrap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">boot_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boot_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.fit_model_bootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.fit_model_bootstrap" title="Permalink to this definition">¶</a></dt>
-<dd><p>Fits boostrap-versions of Phi, Psi, etc.</p>
-<p>Random draws are generated</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>boot_blocklength</strong> (<em>int</em><em>, or </em><em>in {'cube_root'</em><em>, </em><em>'from_autocorrelation'}</em>) – Block length for block-bootstrap, which only applies to
-generate_noise_from=’residuals’. If ‘from_autocorrelation’, the block
-length is determined from the decay of the autocovariance and
-if ‘cube_root’ it is the cube root of the time series length.</p></li>
-<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>(</em><em>default = None</em><em>)</em>) – Seed for RandomState (default_rng)</p></li>
-<li><p><strong>boot_samples</strong> (<em>int</em>) – Number of bootstrap samples.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_ace">
-<span class="sig-name descname"><span class="pre">get_ace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'absmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_i</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_ace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_ace" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the average causal effect.</p>
-<p>This is the average causal effect (ACE) emanating from variable i to any
-other variable. With lag_mode=’absmax’ this is based on the lag of
-maximum CE for each pair.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
-at the lag of maximum absolute causal effect.</p></li>
-<li><p><strong>exclude_i</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects on the variable itself at later
-lags.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>ace</strong> – Average Causal Effect.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_acs">
-<span class="sig-name descname"><span class="pre">get_acs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'absmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_j</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_acs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_acs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the average causal susceptibility.</p>
-<p>This is the Average Causal Susceptibility (ACS) affecting a variable j
-from any other variable. With lag_mode=’absmax’ this is based on the lag
-of maximum CE for each pair.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>j</strong> (<em>int</em>) – Index of variable.</p></li>
-<li><p><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
-at the lag of maximum absolute causal effect.</p></li>
-<li><p><strong>exclude_j</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects on the variable itself at previous
-lags.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>acs</strong> – Average Causal Susceptibility.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_all_ace">
-<span class="sig-name descname"><span class="pre">get_all_ace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lag_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'absmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_i</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_all_ace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_all_ace" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the average causal effect for all variables.</p>
-<p>This is the average causal effect (ACE) emanating from variable i to any
-other variable. With lag_mode=’absmax’ this is based on the lag of
-maximum CE for each pair.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
-at the lag of maximum absolute causal effect.</p></li>
-<li><p><strong>exclude_i</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects on the variable itself at later
-lags.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>ace</strong> – Average Causal Effect for each variable.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape (N,)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_all_acs">
-<span class="sig-name descname"><span class="pre">get_all_acs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lag_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'absmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_j</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_all_acs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_all_acs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the average causal susceptibility.</p>
-<p>This is the Average Causal Susceptibility (ACS) for each variable from
-any other variable. With lag_mode=’absmax’ this is based on the lag of
-maximum CE for each pair.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
-at the lag of maximum absolute causal effect.</p></li>
-<li><p><strong>exclude_j</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects on the variable itself at previous
-lags.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>acs</strong> – Average Causal Susceptibility.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape (N,)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_all_amce">
-<span class="sig-name descname"><span class="pre">get_all_amce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lag_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'absmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_self_effects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_all_amce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_all_amce" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the average mediated causal effect.</p>
-<p>This is the Average Mediated Causal Effect (AMCE) through all variables
-With lag_mode=’absmax’ this is based on the lag of maximum CE for each
-pair.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
-at the lag of maximum absolute causal effect.</p></li>
-<li><p><strong>exclude_k</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects through the variable itself at
-previous lags.</p></li>
-<li><p><strong>exclude_self_effects</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal self effects of variables on themselves.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>amce</strong> – Average Mediated Causal Effect.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape (N,)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_amce">
-<span class="sig-name descname"><span class="pre">get_amce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'absmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_self_effects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_amce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_amce" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the average mediated causal effect.</p>
-<p>This is the Average Mediated Causal Effect (AMCE) through a variable k
-With lag_mode=’absmax’ this is based on the lag of maximum CE for each
-pair.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>k</strong> (<em>int</em>) – Index of variable.</p></li>
-<li><p><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
-at the lag of maximum absolute causal effect.</p></li>
-<li><p><strong>exclude_k</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects through the variable itself at
-previous lags.</p></li>
-<li><p><strong>exclude_self_effects</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal self effects of variables on themselves.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>amce</strong> – Average Mediated Causal Effect.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_bootstrap_of">
-<span class="sig-name descname"><span class="pre">get_bootstrap_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_bootstrap_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_bootstrap_of" title="Permalink to this definition">¶</a></dt>
-<dd><p>Applies bootstrap-versions of Phi, Psi, etc. to any function in
-this class.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>function</strong> (<em>string</em>) – Valid function from LinearMediation class</p></li>
-<li><p><strong>function_args</strong> (<em>dict</em>) – Optional function arguments.</p></li>
-<li><p><strong>conf_lev</strong> (<em>float</em>) – Confidence interval.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>Upper/Lower confidence interval of function.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_ce">
-<span class="sig-name descname"><span class="pre">get_ce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">j</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_ce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_ce" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the causal effect.</p>
-<p>This is the causal effect for  (i, -tau) – –&gt; j.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>tau</strong> (<em>int</em>) – Lag of cause variable (incl lag zero).</p></li>
-<li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>ce</strong></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_ce_max">
-<span class="sig-name descname"><span class="pre">get_ce_max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">j</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_ce_max"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_ce_max" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the causal effect.</p>
-<p>This is the maximum absolute causal effect for  i –&gt; j across all
-lags (incl lag zero).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>ce</strong></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_coeff">
-<span class="sig-name descname"><span class="pre">get_coeff</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">j</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_coeff"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_coeff" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns link coefficient.</p>
-<p>This is the direct causal effect for a particular link (i, -tau) –&gt; j.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>tau</strong> (<em>int</em>) – Lag of cause variable (incl lag zero).</p></li>
-<li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>coeff</strong></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_conditional_mce">
-<span class="sig-name descname"><span class="pre">get_conditional_mce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">notk</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_conditional_mce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_conditional_mce" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the conditional mediated causal effect.</p>
-<p>This is the causal effect for  i –&gt; j for all paths going through k, but not through notk.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</p></li>
-<li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables.</p></li>
-<li><p><strong>notk</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables to exclude.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>mce</strong></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_joint_ce">
-<span class="sig-name descname"><span class="pre">get_joint_ce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">j</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_joint_ce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_joint_ce" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the joint causal effect.</p>
-<p>This is the causal effect from all lags [t, …, t-tau_max]
-of i on j at time t. Note that the joint effect does not
-count links passing through parents of i itself.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>joint_ce</strong> – Causal effect from each lag [t, …, t-tau_max] of i on j.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape (tau_max + 1)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_joint_ce_matrix">
-<span class="sig-name descname"><span class="pre">get_joint_ce_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">j</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_joint_ce_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_joint_ce_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the joint causal effect matrix of i on j.</p>
-<p>This is the causal effect from all lags [t, …, t-tau_max]
-of i on j at times [t, …, t-tau_max]. Note that the joint effect does not
-count links passing through parents of i itself.</p>
-<p>An entry (taui, tauj) stands for the effect of i at t-taui on j at t-tauj.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>joint_ce_matrix</strong> – Causal effect matrix from each lag of i on each lag of j.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>2d array of shape (tau_max + 1, tau_max + 1)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_joint_mce">
-<span class="sig-name descname"><span class="pre">get_joint_mce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_joint_mce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_joint_mce" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the joint causal effect mediated through k.</p>
-<p>This is the mediated causal effect from all lags [t, …, t-tau_max]
-of i on j at time t for paths through k. Note that the joint effect
-does not count links passing through parents of i itself.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>joint_mce</strong> – Mediated causal effect from each lag [t, …, t-tau_max] of i on j through k.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape (tau_max + 1)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_mce">
-<span class="sig-name descname"><span class="pre">get_mce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_mce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_mce" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the mediated causal effect.</p>
-<p>This is the causal effect for  i –&gt; j minus the causal effect not going
-through k.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</p></li>
-<li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>mce</strong></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_mediation_graph_data">
-<span class="sig-name descname"><span class="pre">get_mediation_graph_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_mediation_graph_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_mediation_graph_data" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns link and node weights for mediation analysis.</p>
-<p>Returns array with non-zero entries for links that are on causal
-paths between <img class="math" src="_images/math/5aa339d4daf45a810dda332e3c80a0698e526e04.png" alt="i"/> and <img class="math" src="_images/math/e3fc28292267f066fee7718c64f4bbfece521f24.png" alt="j"/> at lag <img class="math" src="_images/math/914b2d4b6659b86d3153d5510839dfb254dfc8a3.png" alt="\tau"/>.
-<code class="docutils literal notranslate"><span class="pre">path_val_matrix</span></code> contains the corresponding path coefficients and
-<code class="docutils literal notranslate"><span class="pre">path_node_array</span></code> the MCE values. <code class="docutils literal notranslate"><span class="pre">tsg_path_val_matrix</span></code> contains the
-corresponding values in the time series graph format.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
-<li><p><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</p></li>
-<li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>include_neighbors</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to include causal paths emanating from neighbors of i</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>graph_data</strong> – Dictionary of matrices for coloring mediation graph plots.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dictionary</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_tsg">
-<span class="sig-name descname"><span class="pre">get_tsg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_tsg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_tsg" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns time series graph matrix.</p>
-<p>Constructs a matrix of shape (N*tau_max, N*tau_max) from link_matrix.
-This matrix can be used for plotting the time series graph and analyzing
-causal pathways.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>link_matrix</strong> (<em>bool array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of significant links. Must be of same shape as val_matrix.
-Either sig_thres or link_matrix has to be provided.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</p></li>
-<li><p><strong>include_neighbors</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to include causal paths emanating from neighbors of i</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>tsg</strong> – Time series graph matrix.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape (N*tau_max, N*tau_max)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.get_val_matrix">
-<span class="sig-name descname"><span class="pre">get_val_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symmetrize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_val_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_val_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the matrix of linear coefficients.</p>
-<p>Requires fit_model() before. An entry val_matrix[i,j,tau] gives the
-coefficient of the link from i to j at lag tau. Lag=0 is always set
-to zero for LinearMediation, use Models class for contemporaneous
-models.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>symmetrize</strong> (<em>bool</em>) – If True, the lag-zero entries will be symmetrized such that
-no zeros appear. Useful since other parts of tigramite
-through an error for non-symmetric val_matrix, eg plotting.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val_matrix</strong> – Matrix of linear coefficients, shape (N, N, tau_max + 1).</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.net_to_tsg">
-<span class="sig-name descname"><span class="pre">net_to_tsg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">row</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lag</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.net_to_tsg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.net_to_tsg" title="Permalink to this definition">¶</a></dt>
-<dd><p>Helper function to translate from network to time series graph.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.LinearMediation.tsg_to_net">
-<span class="sig-name descname"><span class="pre">tsg_to_net</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lag</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.tsg_to_net"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.LinearMediation.tsg_to_net" title="Permalink to this definition">¶</a></dt>
-<dd><p>Helper function to translate from time series graph to network.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.models.Prediction">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.models.</span></span><span class="sig-name descname"><span class="pre">Prediction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cond_ind_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Prediction" title="Permalink to this definition">¶</a></dt>
-<dd><p>Prediction class for time series models.</p>
-<p>Allows to fit and predict from any sklearn model. The optimal predictors can
-be estimated using PCMCI. Also takes care of missing values, masking and
-preprocessing.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
-yielding a numpy array of shape (observations T, variables N) and
-optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>train_indices</strong> (<em>array-like</em>) – Either boolean array or time indices marking the training data.</p></li>
-<li><p><strong>test_indices</strong> (<em>array-like</em>) – Either boolean array or time indices marking the test data.</p></li>
-<li><p><strong>prediction_model</strong> (<em>sklearn model object</em>) – For example, sklearn.linear_model.LinearRegression() for a linear
-regression model.</p></li>
-<li><p><strong>cond_ind_test</strong> (<em>Conditional independence test object</em><em>, </em><em>optional</em>) – Only needed if predictors are estimated with causal algorithm.
-The class will be initialized with masking set to the training data.</p></li>
-<li><p><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to transform data prior to fitting. For example,
-sklearn.preprocessing.StandardScaler for simple standardization. The
-fitted parameters are stored.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Prediction.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_predictors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Prediction.fit" title="Permalink to this definition">¶</a></dt>
-<dd><p>Fit time series model.</p>
-<p>Wrapper around <code class="docutils literal notranslate"><span class="pre">Models.fit_full_model()</span></code>. To each variable in
-<code class="docutils literal notranslate"><span class="pre">selected_targets</span></code>, the sklearn model is fitted with <img class="math" src="_images/math/1b5e577d6216dca3af7d87aa122a0b9b360d6cb3.png" alt="y"/> given
-by the target variable, and <img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> given by its predictors. The
-fitted model class is returned for later use.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>target_predictors</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …} containing
-the predictors estimated with PCMCI.</p></li>
-<li><p><strong>selected_targets</strong> (<em>list</em><em> of </em><em>integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to fit model only for selected targets. If None is
-passed, models are estimated for all variables.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in target_predictors is
-used.</p></li>
-<li><p><strong>return_data</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to save the data array.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>self</strong></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>instance of self</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Prediction.get_predictors">
-<span class="sig-name descname"><span class="pre">get_predictors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps_ahead</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_conds_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_combinations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.get_predictors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Prediction.get_predictors" title="Permalink to this definition">¶</a></dt>
-<dd><p>Estimate predictors using PC1 algorithm.</p>
-<p>Wrapper around PCMCI.run_pc_stable that estimates causal predictors.
-The lead time can be specified by <code class="docutils literal notranslate"><span class="pre">steps_ahead</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_targets</strong> (<em>list</em><em> of </em><em>ints</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variables to estimate predictors of. If None, predictors of
-all variables are estimated.</p></li>
-<li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
-specifying whether only selected links should be tested. If None is
-passed, all links are tested</p></li>
-<li><p><strong>steps_ahead</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Minimum time lag to test. Useful for multi-step ahead predictions.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list</em><em> of </em><em>floats</em><em>, </em><em>default: 0.2</em>) – Significance level in algorithm. If a list or None is passed, the
-pc_alpha level is optimized for every variable across the given
-pc_alpha values using the score computed in
-cond_ind_test.get_model_selection_criterion()</p></li>
-<li><p><strong>max_conds_dim</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions to test. If None is passed, this number
-is unrestricted.</p></li>
-<li><p><strong>max_combinations</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum number of combinations of conditions of current cardinality
-to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
-a larger number, such as 10, can be used.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>predictors</strong> – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
-containing estimated predictors.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dict</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Prediction.get_test_array">
-<span class="sig-name descname"><span class="pre">get_test_array</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.get_test_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Prediction.get_test_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns test array.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Prediction.get_train_array">
-<span class="sig-name descname"><span class="pre">get_train_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.get_train_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Prediction.get_train_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns training array.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.models.Prediction.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max_lag_or_tau_max'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Prediction.predict" title="Permalink to this definition">¶</a></dt>
-<dd><p>Predict target variable with fitted model.</p>
-<p>Uses the model.predict() function of the sklearn model.</p>
-<p>If target is an int, the predicted time series is returned. If target
-is a list of integers, then a list of predicted time series is returned.
-If the list of integers equals range(N), then an array of shape (T, N)
-of the predicted series is returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>target</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>integers</em>) – Index or indices of target variable(s).</p></li>
-<li><p><strong>new_data</strong> (<em>data object</em><em>, </em><em>optional</em>) – New Tigramite dataframe object with optional new mask. Note that
-the data will be cut off according to cut_off, see parameter
-<cite>cut_off</cite> below.</p></li>
-<li><p><strong>pred_params</strong> (<em>dict</em><em>, </em><em>optional</em>) – Optional parameters passed on to sklearn prediction function.</p></li>
-<li><p><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) – How many samples to cutoff at the beginning. The default is
-‘2xtau_max’, which guarantees that MCI tests are all conducted on
-the same samples.  For modeling, ‘max_lag_or_tau_max’ can be used,
-which uses the maximum of tau_max and the conditions, which is
-useful to compare multiple models on the same sample. Last,
-‘max_lag’ uses as much samples as possible.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>Results from prediction.</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="module-tigramite.data_processing">
-<span id="tigramite-data-processing-data-processing-functions"></span><h1><a class="reference internal" href="#module-tigramite.data_processing" title="tigramite.data_processing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.data_processing</span></code></a>: Data processing functions<a class="headerlink" href="#module-tigramite.data_processing" title="Permalink to this heading">¶</a></h1>
-<p>Tigramite data processing functions.</p>
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.data_processing.DataFrame">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">DataFrame</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_flag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vector_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datatime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analysis_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'single'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_missing_upto_maxlag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame" title="Permalink to this definition">¶</a></dt>
-<dd><dl>
-<dt>Data object containing single or multiple time series arrays and optional</dt><dd><p>mask.</p>
-<dl class="simple">
-<dt>data<span class="classifier">array-like</span></dt><dd><dl class="simple">
-<dt>if analysis_mode == ‘single’:</dt><dd><p>1) Numpy array of shape (observations T, variables N)
-OR
-2) Dictionary with a single entry whose value is a numpy array of
-shape (observations T, variables N)</p>
-</dd>
-<dt>if analysis_mode == ‘multiple’:</dt><dd><p>1) Numpy array of shape (multiple datasets M, observations T,
-variables N)
-OR
-2) Dictionary whose values are numpy arrays of shape
-(observations T_i, variables N), where the number of observations
-T_i may vary across the multiple datasets but the number of variables
-N is fixed.</p>
-</dd>
-</dl>
-</dd>
-<dt>mask<span class="classifier">array-like, optional (default: None)</span></dt><dd><p>Optional mask array, must be of same format and shape as data.</p>
-</dd>
-</dl>
-</dd>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
-<div><p>Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.</p>
-</div></blockquote>
-<dl class="simple">
-<dt>missing_flag<span class="classifier">number, optional (default: None)</span></dt><dd><p>Flag for missing values in dataframe. Dismisses all time slices of
-samples where missing values occur in any variable. For
-remove_missing_upto_maxlag=True also flags samples for all lags up to
-2*tau_max (more precisely, this depends on the cut_off argument in
-self.construct_array(), see further below). This avoids biases, see
-section on masking in Supplement of <a class="footnote-reference brackets" href="#id3" id="id33" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
-</dd>
-<dt>vector_vars<span class="classifier">dict</span></dt><dd><p>Dictionary of vector variables of the form,
-Eg. {0: [(0, 0), (1, 0)], 1: [(2, 0)], 2: [(3, 0)], 3: [(4, 0)]}
-The keys are the new vectorized variables and respective tuple values
-are the individual components of the vector variables. In the method of
-construct_array(), the individual components are parsed from vector_vars
-and added (accounting for lags) to the list that creates X, Y and Z for
-conditional independence test.</p>
-</dd>
-<dt>var_names<span class="classifier">list of strings, optional (default: range(N))</span></dt><dd><p>Names of variables, must match the number of variables. If None is
-passed, variables are enumerated as [0, 1, …]</p>
-</dd>
-<dt>datatime<span class="classifier">array-like, optional (default: None)</span></dt><dd><p>Timelabel array. If None, range(T) is used.</p>
-</dd>
-<dt>remove_missing_upto_maxlag<span class="classifier">bool, optional (default: False)</span></dt><dd><p>Whether to remove not only missing samples, but also all neighboring
-samples up to max_lag (as given by cut_off in construct_array).</p>
-</dd>
-<dt>analysis_mode<span class="classifier">string, optional (default: ‘single’)</span></dt><dd><p>Must be ‘single’ or ‘multiple’.
-Determines whether data contains a single (potentially multivariate)
-time series (–&gt; ‘single’) or multiple time series (–&gt; ‘multiple’).</p>
-</dd>
-<dt>reference_points<span class="classifier">None, int, or list (or 1D array) of integers,</span></dt><dd><p>optional (default:None)
-Determines the time steps — relative to the shared time axis as
-defined by the optional time_offset argument (see below) — that are
-used to create samples for conditional independence testing.
-Set to [0, 1, …, T_max-1] if None is passed, where T_max is
-self.largest_time_step, see below.
-All values smaller than 0 and bigger than T_max-1 will be ignored.
-At least one value must be in [0, 1, …, T_max-1].</p>
-</dd>
-<dt>time_offsets<span class="classifier">None or dict, optional (default: None)</span></dt><dd><dl class="simple">
-<dt>if analysis_mode == ‘single’:</dt><dd><p>Must be None.
-Shared time axis defined by the time indices of the single time series</p>
-</dd>
-<dt>if analysis_mode == ‘multiple’ and data is numpy array:</dt><dd><p>Must be None.
-All datasets are assumed to be already aligned in time with
-respect to a shared time axis, which is the time axis of data</p>
-</dd>
-<dt>if analysis_mode == ‘multiple’ and data is dictionary:</dt><dd><p>Must be dictionary of the form {key(m): time_offset(m), …} whose
-set of keys agrees with the set of keys of data and whose values are
-non-negative integers, at least one of which is 0. The value
-time_offset(m) defines the time offset of dataset m with
-respect to a shared time axis.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl>
-<dt>self._initialized_from<span class="classifier">string</span></dt><dd><p>Specifies the data format in which data was given at instantiation.
-Possible values: ‘2d numpy array’, ‘3d numpy array’, ‘dict’.</p>
-</dd>
-<dt>self.values<span class="classifier">dictionary</span></dt><dd><p>Dictionary holding the observations given by data internally mapped to a
-dictionary representation as follows:
-If analysis_mode == ‘single’:</p>
-<blockquote>
-<div><dl class="simple">
-<dt>If self._initialized_from == ‘2d numpy array’:</dt><dd><p>Is {0: data}</p>
-</dd>
-<dt>If self._initialized_from == ‘dict’:</dt><dd><p>Is data</p>
-</dd>
-</dl>
-</div></blockquote>
-<dl class="simple">
-<dt>If analysis_mode == ‘multiple’:</dt><dd><dl class="simple">
-<dt>If self._initialized_from == ‘3d numpy array’:</dt><dd><p>Is {m: data[m, :, :] for m in range(data.shape[0])}</p>
-</dd>
-<dt>If self._initialized_from == ‘dict’:</dt><dd><p>Is data</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd>
-<dt>self.datasets: list</dt><dd><p>List of the keys identifiying the multiple datasets, i.e.,
-list(self.values.keys())</p>
-</dd>
-<dt>self.mask<span class="classifier">dictionary</span></dt><dd><p>Mask internally mapped to a dictionary representation in the same way as
-data is mapped to self.values</p>
-</dd>
-<dt>self.type_mask<span class="classifier">array-like</span></dt><dd><p>Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.</p>
-</dd>
-<dt>self.missing_flag:</dt><dd><p>Is missing_flag</p>
-</dd>
-<dt>self.var_names:</dt><dd><dl class="simple">
-<dt>If var_names is not None:</dt><dd><p>Is var_names</p>
-</dd>
-<dt>If var_names is None:</dt><dd><p>Is {i: i for i in range(self.N)}</p>
-</dd>
-</dl>
-</dd>
-<dt>self.datatime<span class="classifier">dictionary</span></dt><dd><p>Time axis for each of the multiple datasets.</p>
-</dd>
-<dt>self.analysis_mode<span class="classifier">string</span></dt><dd><p>Is analysis_mode</p>
-</dd>
-<dt>self.reference_points: array-like</dt><dd><dl class="simple">
-<dt>If reference_points is not None:</dt><dd><p>1D numpy array holding all specified reference_points, less those
-smaller than 0 and larger than self.largest_time_step-1</p>
-</dd>
-<dt>If reference_points is None:</dt><dd><p>Is np.array(range(self.largest_time_step))</p>
-</dd>
-</dl>
-</dd>
-<dt>self.time_offsets<span class="classifier">dictionary</span></dt><dd><dl class="simple">
-<dt>If time_offsets is not None:</dt><dd><p>Is time_offsets</p>
-</dd>
-<dt>If time_offsets is None:</dt><dd><p>Is {key: 0 for key in self.values.keys()}</p>
-</dd>
-</dl>
-</dd>
-<dt>self.M<span class="classifier">int</span></dt><dd><p>Number of datasets</p>
-</dd>
-<dt>self.N<span class="classifier">int</span></dt><dd><p>Number of variables (constant across datasets)</p>
-</dd>
-<dt>self.T<span class="classifier">dictionary</span></dt><dd><p>Dictionary {key(m): T(m), …}, where T(m) is the time length of
-datasets m and key(m) its identifier as in self.values</p>
-</dd>
-<dt>self.largest_time_step<span class="classifier">int</span></dt><dd><p>max_{0 &lt;= m &lt;= M} [ T(m) + time_offset(m)], i.e., the largest (latest)
-time step relative to the shared time axis for which at least one
-observation exists in the dataset.</p>
-</dd>
-<dt>self.bootstrap<span class="classifier">dictionary</span></dt><dd><p>Whether to use bootstrap. Must be a dictionary with keys random_state,
-boot_samples, and boot_blocklength.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.data_processing.DataFrame.construct_array">
-<span class="sig-name descname"><span class="pre">construct_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extraZ</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_cleaned_xyz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_checks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_overlaps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame.construct_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame.construct_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Constructs array from variables X, Y, Z from data.
-Data is of shape (T, N) if analysis_mode == ‘single’, where T is the
-time series length and N the number of variables, and of (n_ens, T, N)
-if analysis_mode == ‘multiple’.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
-the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
-has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
-the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
-has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
-the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
-has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>extraZ</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
-the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
-has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em>) – Maximum time lag. This may be used to make sure that estimates for
-different lags in X and Z all have the same sample size.</p></li>
-<li><p><strong>mask</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional mask array, must be of same shape as data.  If it is set,
-then it overrides the self.mask assigned to the dataframe. If it is
-None, then the self.mask is used, if it exists.</p></li>
-<li><p><strong>mask_type</strong> (<em>{None</em><em>, </em><em>'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
-measure I(X; Y | Z) the samples should be masked. If None, the mask
-is not used. Explained in tutorial on masking and missing values.</p></li>
-<li><p><strong>type_mask</strong> (<em>array-like</em>) – Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.
-If it is set, then it overrides the self.type_mask assigned to the dataframe.</p></li>
-<li><p><strong>return_cleaned_xyz</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to return cleaned X,Y,Z, where possible duplicates are
-removed.</p></li>
-<li><p><strong>do_checks</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to perform sanity checks on input X,Y,Z</p></li>
-<li><p><strong>remove_overlaps</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to remove variables from Z/extraZ if they overlap with X or Y.</p></li>
-<li><p><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'tau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'</em><em>,</em>) – <p>2xtau_max_future}
-If cut_off == ‘2xtau_max’:</p>
-<blockquote>
-<div><ul>
-<li><p>2*tau_max samples are cut off at the beginning of the time</p></li>
-</ul>
-<p>series (‘beginning’ here refers to the temporally first time
-steps). This guarantees that (as long as no mask is used) all
-MCI tests are conducted on the same samples, independent of X,
-Y, and Z.
-- If at time step t_missing a data value is missing, then the
-time steps t_missing, …, t_missing + 2*tau_max are cut out.
-The latter part only holds if remove_missing_upto_maxlag=True.</p>
-</div></blockquote>
-<dl>
-<dt>If cut_off ==  ‘max_lag’:</dt><dd><ul>
-<li><p>max_lag(X, Y, Z) samples are cut off at the beginning of the</p></li>
-</ul>
-<p>time series, where max_lag(X, Y, Z) is the maximum lag of all
-nodes in X, Y, and Z. These are all samples that can in
-principle be used.
-- If at time step t_missing a data value is missing, then the
-time steps t_missing, …, t_missing + max_lag(X, Y, Z) are cut
-out.
-The latter part only holds if remove_missing_upto_maxlag=True.</p>
-</dd>
-<dt>If cut_off == ‘max_lag_or_tau_max’:</dt><dd><ul>
-<li><p>max(max_lag(X, Y, Z), tau_max) are cut off at the beginning.</p></li>
-</ul>
-<p>This may be useful for modeling by comparing multiple models on
-the same samples.
-- If at time step t_missing a data value is missing, then the
-time steps
-t_missing, …, t_missing + max(max_lag(X, Y, Z), tau_max)
-are cut out.
-The latter part only holds if remove_missing_upto_maxlag=True.</p>
-</dd>
-<dt>If cut_off == ‘tau_max’:</dt><dd><ul>
-<li><p>tau_max samples are cut off at the beginning.</p></li>
-</ul>
-<p>This may be useful for modeling by comparing multiple models on
-the same samples.
-- If at time step t_missing a data value is missing, then the
-time steps
-t_missing, …, t_missing + max(max_lag(X, Y, Z), tau_max)
-are cut out.
-The latter part only holds if remove_missing_upto_maxlag=True.</p>
-</dd>
-<dt>If cut_off == ‘2xtau_max_future’:</dt><dd><p>First, the relevant time steps are determined as for cut_off ==
-‘max_lag’. Then, the temporally latest time steps are removed
-such that the same number of time steps remains as there would
-be for cut_off == ‘2xtau_max’. This may be useful when one is
-mostly interested in the temporally first time steps and would
-like all MCI tests to be performed on the same <em>number</em> of
-samples. Note, however, that while the <em>number</em> of samples is
-the same for all MCI tests, the samples themselves may be
-different.</p>
-</dd>
-</dl>
-</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>array, xyz [,XYZ], type_mask</strong> – xyz identifier array of shape (dim,) identifying which row in array
-corresponds to X, Y, and Z, and the type mask that indicates which samples
-are continuous or discrete. For example:: X = [(0, -1)],
-Y = [(1, 0)], Z = [(1, -1), (0, -2)] yields an array of shape
-(4, n_samples) and xyz is xyz = numpy.array([0,1,2,2]). If
-return_cleaned_xyz is True, also outputs the cleaned XYZ lists.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of data array of shape (dim, n_samples),</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.data_processing.DataFrame.print_array_info">
-<span class="sig-name descname"><span class="pre">print_array_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_flag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extraZ</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame.print_array_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame.print_array_info" title="Permalink to this definition">¶</a></dt>
-<dd><p>Print info about the constructed array</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>Data array</em><em> of </em><em>shape</em><em> (</em><em>dim</em><em>, </em><em>T</em><em>)</em>) – Data array.</p></li>
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
-where var specifies the variable index. X typically is of the form
-[(varX, -tau)] with tau denoting the time lag and Z can be
-multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
-where var specifies the variable index. X typically is of the form
-[(varX, -tau)] with tau denoting the time lag and Z can be
-multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
-where var specifies the variable index. X typically is of the form
-[(varX, -tau)] with tau denoting the time lag and Z can be
-multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>extraZ</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
-where var specifies the variable index. X typically is of the form
-[(varX, -tau)] with tau denoting the time lag and Z can be
-multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>missing_flag</strong> (<em>number</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Flag for missing values. Dismisses all time slices of samples where
-missing values occur in any variable and also flags samples for all
-lags up to 2*tau_max. This avoids biases, see section on masking in
-Supplement of <a class="footnote-reference brackets" href="#id3" id="id34" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p></li>
-<li><p><strong>mask_type</strong> (<em>{'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
-measure I(X; Y | Z) the samples should be masked. If None, the mask
-is not used. Explained in tutorial on masking and missing values.</p></li>
-<li><p><strong>type_mask</strong> (<em>array-like</em>) – Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.get_acf">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">get_acf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">series</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#get_acf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.get_acf" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns autocorrelation function.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>series</strong> (<em>1D-array</em>) – data series to compute autocorrelation from</p></li>
-<li><p><strong>max_lag</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – maximum lag for autocorrelation function. If None is passed, 10% of
-the data series length are used.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>autocorr</strong> – Autocorrelation function.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape (max_lag + 1,)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.get_block_length">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">get_block_length</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#get_block_length"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.get_block_length" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns optimal block length for significance and confidence tests.</p>
-<p>Determine block length using approach in Mader (2013) [Eq. (6)] which
-improves the method of Pfeifer (2005) with non-overlapping blocks In
-case of multidimensional X, the max is used. Further details in <a class="footnote-reference brackets" href="#id3" id="id35" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
-Two modes are available. For mode=’significance’, only the indices
-corresponding to X are shuffled in array. For mode=’confidence’ all
-variables are jointly shuffled. If the autocorrelation curve fit fails,
-a block length of 5% of T is used. The block length is limited to a
-maximum of 10% of T.</p>
-<p>Mader et al., Journal of Neuroscience Methods,
-Volume 219, Issue 2, 15 October 2013, Pages 285-291</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>mode</strong> (<em>str</em>) – Which mode to use.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>block_len</strong> – Optimal block length.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>int</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.lowhighpass_filter">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">lowhighpass_filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutperiod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pass_periods</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'low'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#lowhighpass_filter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.lowhighpass_filter" title="Permalink to this definition">¶</a></dt>
-<dd><p>Butterworth low- or high pass filter.</p>
-<p>This function applies a linear filter twice, once forward and once
-backwards. The combined filter has linear phase.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>data</strong> (<em>array</em>) – Data array of shape (time, variables).</p></li>
-<li><p><strong>cutperiod</strong> (<em>int</em>) – Period of cutoff.</p></li>
-<li><p><strong>pass_periods</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'low'</em><em>)</em>) – Either ‘low’ or ‘high’ to act as a low- or high-pass filter</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>data</strong> – Filtered data array.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.ordinal_patt_array">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">ordinal_patt_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#ordinal_patt_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.ordinal_patt_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns symbolified array of ordinal patterns.</p>
-<p>Each data vector (X_t, …, X_t+(dim-1)*step) is converted to its rank
-vector. E.g., (0.2, -.6, 1.2) –&gt; (1,0,2) which is then assigned to a
-unique integer (see Article). There are faculty(dim) possible rank vectors.</p>
-<p>Note that the symb_array is step*(dim-1) shorter than the original array!</p>
-<p>Reference: B. Pompe and J. Runge (2011). Momentary information transfer as
-a coupling measure of time series. Phys. Rev. E, 83(5), 1-12.
-doi:10.1103/PhysRevE.83.051122</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>array-like</em>) – Data array of shape (time, variables).</p></li>
-<li><p><strong>array_mask</strong> (<em>bool array</em>) – Data mask where True labels masked samples.</p></li>
-<li><p><strong>dim</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 2</em><em>)</em>) – Pattern dimension</p></li>
-<li><p><strong>step</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Delay of pattern embedding vector.</p></li>
-<li><p><strong>weights</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to return array of variances of embedding vectors as weights.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>patt, patt_mask [, patt_time]</strong> – Tuple of converted pattern array and new length</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>tuple of arrays</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.quantile_bin_array">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">quantile_bin_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#quantile_bin_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.quantile_bin_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns symbolified array with equal-quantile binning.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>data</strong> (<em>array</em>) – Data array of shape (time, variables).</p></li>
-<li><p><strong>bins</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Number of bins.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>symb_array</strong> – Converted data of integer type.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.smooth">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">smooth</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth_width</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#smooth"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.smooth" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns either smoothed time series or its residuals.</p>
-<p>the difference between the original and the smoothed time series
-(=residuals) of a kernel smoothing with gaussian (smoothing kernel width =
-twice the sigma!) or heaviside window, equivalent to a running mean.</p>
-<p>Assumes data of shape (T, N) or (T,)
-:rtype: array
-:returns: smoothed/residual data</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>data</strong> (<em>array</em>) – Data array of shape (time, variables).</p></li>
-<li><p><strong>smooth_width</strong> (<em>float</em>) – Window width of smoothing, 2*sigma for a gaussian.</p></li>
-<li><p><strong>kernel</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'gaussian'</em><em>)</em>) – Smoothing kernel, ‘gaussian’ or ‘heaviside’ for a running mean.</p></li>
-<li><p><strong>mask</strong> (<em>bool array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Data mask where True labels masked samples.</p></li>
-<li><p><strong>residuals</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – True if residuals should be returned instead of smoothed data.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>data</strong> – Smoothed/residual data.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array-like</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.structural_causal_process">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">structural_causal_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">links</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noises</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervention</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervention_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#structural_causal_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.structural_causal_process" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a structural causal process with contemporaneous and lagged
-dependencies.</p>
-<p>DEPRECATED. Will be removed in future.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.time_bin_with_mask">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">time_bin_with_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_bin_length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#time_bin_with_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.time_bin_with_mask" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns time binned data where only about non-masked values is averaged.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>data</strong> (<em>array</em>) – Data array of shape (time, variables).</p></li>
-<li><p><strong>time_bin_length</strong> (<em>int</em>) – Length of time bin.</p></li>
-<li><p><strong>mask</strong> (<em>bool array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Data mask where True labels masked samples.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>(bindata, T)</strong> – Tuple of time-binned data array and new length of array.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>tuple of array and int</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.trafo2normal">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">trafo2normal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#trafo2normal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.trafo2normal" title="Permalink to this definition">¶</a></dt>
-<dd><p>Transforms input data to standard normal marginals.</p>
-<p>Assumes data.shape = (T, dim)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>data</strong> (<em>array</em>) – Data array of shape (time, variables).</p></li>
-<li><p><strong>thres</strong> (<em>float</em>) – Set outer points in CDF to this value.</p></li>
-<li><p><strong>mask</strong> (<em>bool array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Data mask where True labels masked samples.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>normal_data</strong> – data with standard normal marginals.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array-like</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.var_process">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">var_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parents_neighbors_coeffs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'inv_inno_cov'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#var_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.var_process" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a vector-autoregressive process with correlated innovations.</p>
-<p>Wrapper around var_network with possibly more user-friendly input options.</p>
-<p>DEPRECATED. Will be removed in future.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.data_processing.weighted_avg_and_std">
-<span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">weighted_avg_and_std</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#weighted_avg_and_std"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.weighted_avg_and_std" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the weighted average and standard deviation.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>values</strong> (<em>array</em>) – Data array of shape (time, variables).</p></li>
-<li><p><strong>axis</strong> (<em>int</em>) – Axis to average/std about</p></li>
-<li><p><strong>weights</strong> (<em>array</em>) – Weight array of shape (time, variables).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>(average, std)</strong> – Tuple of weighted average and standard deviation along axis.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>tuple of arrays</p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
-<section id="module-tigramite.toymodels.structural_causal_processes">
-<span id="tigramite-toymodels-toy-model-generators"></span><h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.toymodels</span></code>: Toy model generators<a class="headerlink" href="#module-tigramite.toymodels.structural_causal_processes" title="Permalink to this heading">¶</a></h1>
-<p>Tigramite toymodels.</p>
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.toymodels.structural_causal_processes.check_stationarity">
-<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">check_stationarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">links</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#check_stationarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.check_stationarity" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns stationarity according to a unit root test.</p>
-<p>Assumes an at least asymptotically linear vector autoregressive process
-without contemporaneous links.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>links</strong> (<em>dict</em>) – Dictionary of form {0:[((0, -1), coeff, func), …], 1:[…], …}.
-Also format {0:[(0, -1), …], 1:[…], …} is allowed.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>stationary</strong> – True if VAR process is stationary.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>bool</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.toymodels.structural_causal_processes.dag_to_links">
-<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">dag_to_links</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dag</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#dag_to_links"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.dag_to_links" title="Permalink to this definition">¶</a></dt>
-<dd><p>Helper function to convert DAG graph to dictionary of parents.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>dag</strong> (<em>array</em><em> of </em><em>shape</em><em> (</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>)</em>) – Matrix format of graph in string format. Must be DAG.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>parents</strong> – Dictionary of form {0:[(0, -1), …], 1:[…], …}.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dict</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.toymodels.structural_causal_processes.generate_structural_causal_process">
-<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">generate_structural_causal_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">L</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_funcs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['linear']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[-0.5,</span> <span class="pre">0.5]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">0.7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contemp_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_dists</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['gaussian']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_means</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_sigmas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">2.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#generate_structural_causal_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.generate_structural_causal_process" title="Permalink to this definition">¶</a></dt>
-<dd><p>“Randomly generates a structural causal process based on input characteristics.</p>
-<p>The process has the form</p>
-<div class="math">
-<p><img src="_images/math/c7568cb2be91af3efd87bd543226444672c4126e.png" alt="X^j_t = \eta^j_t + a^j X^j_{t-1} + \sum_{X^i_{t-\tau}\in pa(X^j_t)}
-c^i_{\tau} f^i_{\tau}(X^i_{t-\tau})"/></p>
-</div><p>where <code class="docutils literal notranslate"><span class="pre">j</span> <span class="pre">=</span> <span class="pre">1,</span> <span class="pre">...,</span> <span class="pre">N</span></code>. Here the properties of <img class="math" src="_images/math/aebf5e0fc00c3cf8bdbef4581708d03703b5dca7.png" alt="\eta^j_t"/> are
-randomly frawn from the noise parameters (see below), <img class="math" src="_images/math/5d71aa22c129c9a9fefccd42a37ecfff31311645.png" alt="pa
-(X^j_t)"/> are the causal parents drawn randomly such that in total <code class="docutils literal notranslate"><span class="pre">L</span></code>
-links occur out of which <code class="docutils literal notranslate"><span class="pre">contemp_fraction</span></code> are contemporaneous and
-their time lags are drawn from <code class="docutils literal notranslate"><span class="pre">[0</span> <span class="pre">or</span> <span class="pre">1..max_lag]</span></code>, the
-coefficients <img class="math" src="_images/math/20efd09fac35da0e767657f265a363a8697a6c8d.png" alt="c^i_{\tau}"/> are drawn from
-<code class="docutils literal notranslate"><span class="pre">dependency_coeffs</span></code>, <img class="math" src="_images/math/25bc6fbe0a632d2f173d16ac6bc4f11d108e7fe6.png" alt="a^j"/> are drawn from <code class="docutils literal notranslate"><span class="pre">auto_coeffs</span></code>,
-and <img class="math" src="_images/math/8b11c561ae4561d1808b1c7d4a0321745aa3a134.png" alt="f^i_{\tau}"/> are drawn from <code class="docutils literal notranslate"><span class="pre">dependency_funcs</span></code>.</p>
-<p>The returned dictionary links has the format
-<code class="docutils literal notranslate"><span class="pre">{0:[((i,</span> <span class="pre">-tau),</span> <span class="pre">coeff,</span> <span class="pre">func),...],</span> <span class="pre">1:[...],</span> <span class="pre">...}</span></code>
-where <code class="docutils literal notranslate"><span class="pre">func</span></code> can be an arbitrary (nonlinear) function provided
-as a python callable with one argument and coeff is the multiplication
-factor. The noise distributions of <img class="math" src="_images/math/be38eb4004ec8179f569f36692f9132289acf8b1.png" alt="\eta^j"/> are returned in
-<code class="docutils literal notranslate"><span class="pre">noises</span></code>, see specifics below.</p>
-<p>The process might be non-stationary. In case of asymptotically linear
-dependency functions and no contemporaneous links this can be checked with
-<code class="docutils literal notranslate"><span class="pre">check_stationarity(...)</span></code>. Otherwise check by generating a large sample
-and test for np.inf.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>N</strong> (<em>int</em>) – Number of variables.</p></li>
-<li><p><strong>L</strong> (<em>int</em>) – Number of cross-links between two different variables.</p></li>
-<li><p><strong>dependency_funcs</strong> (<em>list</em>) – List of callables or strings ‘linear’ or ‘nonlinear’ for a linear and a specific nonlinear function
-that is asymptotically linear.</p></li>
-<li><p><strong>dependency_coeffs</strong> (<em>list</em>) – List of floats from which the coupling coefficients are randomly drawn.</p></li>
-<li><p><strong>auto_coeffs</strong> (<em>list</em>) – List of floats from which the lag-1 autodependencies are randomly drawn.</p></li>
-<li><p><strong>contemp_fraction</strong> (<em>float</em><em> [</em><em>0.</em><em>, </em><em>1</em><em>]</em>) – Fraction of the L links that are contemporaneous (lag zero).</p></li>
-<li><p><strong>max_lag</strong> (<em>int</em>) – Maximum lag from which the time lags of links are drawn.</p></li>
-<li><p><strong>noise_dists</strong> (<em>list</em>) – List of noise functions. Either in
-{‘gaussian’, ‘weibull’, ‘uniform’} or user-specified, in which case
-it must be parametrized just by the size parameter. E.g. def beta
-(T): return np.random.beta(a=1, b=0.5, T)</p></li>
-<li><p><strong>noise_means</strong> (<em>list</em>) – Noise mean. Only used for noise in {‘gaussian’, ‘weibull’, ‘uniform’}.</p></li>
-<li><p><strong>noise_sigmas</strong> (<em>list</em>) – Noise standard deviation. Only used for noise in {‘gaussian’, ‘weibull’, ‘uniform’}.</p></li>
-<li><p><strong>seed</strong> (<em>int</em>) – Random seed to draw the above random functions from.</p></li>
-<li><p><strong>noise_seed</strong> (<em>int</em>) – Random seed for noise function random generator.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>links</strong> (<em>dict</em>) – Dictionary of form {0:[((0, -1), coeff, func), …], 1:[…], …}.</p></li>
-<li><p><strong>noises</strong> (<em>list</em>) – List of N noise functions to call by noise(T) where T is the time series length.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.toymodels.structural_causal_processes.links_to_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">links_to_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">links</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#links_to_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.links_to_graph" title="Permalink to this definition">¶</a></dt>
-<dd><p>Helper function to convert dictionary of links to graph array format.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>links</strong> (<em>dict</em>) – Dictionary of form {0:[((0, -1), coeff, func), …], 1:[…], …}.
-Also format {0:[(0, -1), …], 1:[…], …} is allowed.</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum lag. If None, the maximum lag in links is used.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>graph</strong> – Matrix format of graph with 1 for true links and 0 else.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>array of shape (N, N, tau_max+1)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.toymodels.structural_causal_processes.structural_causal_process">
-<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">structural_causal_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">links</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noises</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervention</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervention_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transient_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#structural_causal_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.structural_causal_process" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a time series generated from a structural causal process.</p>
-<p>Allows lagged and contemporaneous dependencies and includes the option
-to have intervened variables or particular samples.</p>
-<p>The interventional data is in particular useful for generating ground
-truth for the CausalEffects class.</p>
-<p>In more detail, the method implements a generalized additive noise model process of the form</p>
-<div class="math">
-<p><img src="_images/math/04effbe50a9c1421627153afe77c1c013193dd90.png" alt="X^j_t = \eta^j_t + \sum_{X^i_{t-\tau}\in \mathcal{P}(X^j_t)}
-c^i_{\tau} f^i_{\tau}(X^i_{t-\tau})"/></p>
-</div><p>Links have the format <code class="docutils literal notranslate"><span class="pre">{0:[((i,</span> <span class="pre">-tau),</span> <span class="pre">coeff,</span> <span class="pre">func),...],</span> <span class="pre">1:[...],</span>
-<span class="pre">...}</span></code> where <code class="docutils literal notranslate"><span class="pre">func</span></code> can be an arbitrary (nonlinear) function provided
-as a python callable with one argument and coeff is the multiplication
-factor. The noise distributions of <img class="math" src="_images/math/be38eb4004ec8179f569f36692f9132289acf8b1.png" alt="\eta^j"/> can be specified in
-<code class="docutils literal notranslate"><span class="pre">noises</span></code>.</p>
-<p>Through the parameters <code class="docutils literal notranslate"><span class="pre">intervention</span></code> and <code class="docutils literal notranslate"><span class="pre">intervention_type</span></code> the model
-can also be generated with intervened variables.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>links</strong> (<em>dict</em>) – Dictionary of format: {0:[((i, -tau), coeff, func),…], 1:[…],
-…} for all variables where i must be in [0..N-1] and tau &gt;= 0 with
-number of variables N. coeff must be a float and func a python
-callable of one argument.</p></li>
-<li><p><strong>T</strong> (<em>int</em>) – Sample size.</p></li>
-<li><p><strong>noises</strong> (<em>list</em><em> of </em><em>callables</em><em> or </em><em>array</em><em>, </em><em>optional</em><em> (</em><em>default: 'np.random.randn'</em><em>)</em>) – Random distribution function that is called with noises[j](T). If an array,
-it must be of shape ((transient_fraction + 1)*T, N).</p></li>
-<li><p><strong>intervention</strong> (<em>dict</em>) – Dictionary of format: {1:np.array, …} containing only keys of intervened
-variables with the value being the array of length T with interventional values.
-Set values to np.nan to leave specific time points of a variable un-intervened.</p></li>
-<li><p><strong>intervention_type</strong> (<em>str</em><em> or </em><em>dict</em>) – Dictionary of format: {1:’hard’,  3:’soft’, …} to specify whether intervention is
-hard (set value) or soft (add value) for variable j. If str, all interventions have
-the same type.</p></li>
-<li><p><strong>transient_fraction</strong> (<em>float</em>) – Added percentage of T used as a transient. In total a realization of length
-(transient_fraction + 1)*T will be generated, but then transient_fraction*T will be
-cut off.</p></li>
-<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Random seed.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>data</strong> (<em>array-like</em>) – Data generated from this process, shape (T, N).</p></li>
-<li><p><strong>nonvalid</strong> (<em>bool</em>) – Indicates whether data has NaNs or infinities.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.toymodels.structural_causal_processes.var_process">
-<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">var_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parents_neighbors_coeffs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'inv_inno_cov'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#var_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.var_process" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a vector-autoregressive process with correlated innovations.</p>
-<p>Wrapper around var_network with possibly more user-friendly input options.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>parents_neighbors_coeffs</strong> (<em>dict</em>) – Dictionary of format: {…, j:[((var1, lag1), coef1), ((var2, lag2),
-coef2), …], …} for all variables where vars must be in [0..N-1]
-and lags &lt;= 0 with number of variables N. If lag=0, a nonzero value
-in the covariance matrix (or its inverse) is implied. These should be
-the same for (i, j) and (j, i).</p></li>
-<li><p><strong>use</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'inv_inno_cov'</em><em>)</em>) – Specifier, either ‘inno_cov’ or ‘inv_inno_cov’.
-Any other specifier will result in non-correlated noise.
-For debugging, ‘no_noise’ can also be specified, in which case random
-noise will be disabled.</p></li>
-<li><p><strong>T</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1000</em><em>)</em>) – Sample size.</p></li>
-<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
-<li><p><strong>initial_values</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Initial values for each node. Shape must be (N, max_delay+1)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>data</strong> (<em>array-like</em>) – Data generated from this process</p></li>
-<li><p><strong>true_parent_neighbor</strong> (<em>dict</em>) – Dictionary of lists of tuples.  The dictionary is keyed by node ID, the
-list stores the tuple values (parent_node_id, time_lag)</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
-<section id="module-tigramite.plotting">
-<span id="tigramite-plotting-plotting-functions"></span><h1><a class="reference internal" href="#module-tigramite.plotting" title="tigramite.plotting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.plotting</span></code></a>: Plotting functions<a class="headerlink" href="#module-tigramite.plotting" title="Permalink to this heading">¶</a></h1>
-<p>Tigramite plotting package.</p>
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.plot_densityplots">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_densityplots</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_densityplot_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_marginal_densities_on_diagonal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_densityplots"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_densityplots" title="Permalink to this definition">¶</a></dt>
-<dd><p>Wrapper helper function to plot density plots.
-Sets up the matrix object and plots the density plots, see parameters in
-setup_density_matrix and add_densityplot.</p>
-<p>The diagonal shows the marginal densities.</p>
-<p>Requires seaborn.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
-yielding a numpy array of shape (observations T, variables N) and
-optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – File name. If None, figure is shown in window.</p></li>
-<li><p><strong>setup_args</strong> (<em>dict</em>) – Arguments for setting up the density plot matrix, see doc of
-setup_density_matrix.</p></li>
-<li><p><strong>add_densityplot_args</strong> (<em>dict</em>) – Arguments for adding a density plot matrix.</p></li>
-<li><p><strong>selected_dataset</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – In case of multiple datasets in dataframe, plot this one.</p></li>
-<li><p><strong>show_marginal_densities_on_diagonal</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Flag to show marginal densities on the diagonal of the density plots</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>matrix</strong> – Further density plots can be overlaid using the
-matrix.add_densityplot function.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>object</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.plot_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto-MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">network_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_autodependency_lags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_graph" title="Permalink to this definition">¶</a></dt>
-<dd><p>Creates a network plot.</p>
-<p>This is still in beta. The network is defined from links in graph. Nodes
-denote variables, straight links contemporaneous dependencies and curved
-arrows lagged dependencies. The node color denotes the maximal absolute
-auto-dependency and the link color the value at the lag with maximal
-absolute cross-dependency. The link label lists the lags with significant
-dependency in order of absolute magnitude. The network can also be
-plotted over a map drawn before on the same axis. Then the node positions
-can be supplied in appropriate axis coordinates via node_pos.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>string</em><em> or </em><em>bool array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Either string matrix providing graph or bool array providing only adjacencies
-Must be of same shape as val_matrix.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</p></li>
-<li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
-<li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
-<li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) – Test statistic label.</p></li>
-<li><p><strong>node_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'auto-MCI'</em><em>)</em>) – Test statistic label for auto-dependencies.</p></li>
-<li><p><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of val_matrix.shape specifying relative link width with maximum
-given by arrow_linewidth. If None, all links have same width.</p></li>
-<li><p><strong>link_attribute</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – String array of val_matrix.shape specifying link attributes.</p></li>
-<li><p><strong>node_pos</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Dictionary of node positions in axis coordinates of form
-node_pos = {‘x’:array of shape (N,), ‘y’:array of shape(N)}. These
-coordinates could have been transformed before for basemap plots.</p></li>
-<li><p><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</p></li>
-<li><p><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</p></li>
-<li><p><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</p></li>
-<li><p><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Link tick mark interval.</p></li>
-<li><p><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) – Colormap for links.</p></li>
-<li><p><strong>vmin_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Node colorbar scale lower bound.</p></li>
-<li><p><strong>vmax_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Node colorbar scale upper bound.</p></li>
-<li><p><strong>node_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Node tick mark interval.</p></li>
-<li><p><strong>cmap_nodes</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'OrRd'</em><em>)</em>) – Colormap for links.</p></li>
-<li><p><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0.3</em><em>)</em>) – Node size.</p></li>
-<li><p><strong>node_aspect</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Ratio between the heigth and width of the varible nodes.</p></li>
-<li><p><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Size of link arrow head. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>curved_radius</strong> (<em>0.2</em><em>)</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>float</strong> (<em>0.2</em><em>)</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>(</strong><strong>default</strong> (<em>optional</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of colorbar labels.</p></li>
-<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</p></li>
-<li><p><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</p></li>
-<li><p><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</p></li>
-<li><p><strong>tick_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of tick labels.</p></li>
-<li><p><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional specification of lags overwriting np.arange(0, tau_max+1)</p></li>
-<li><p><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</p></li>
-<li><p><strong>show_colorbar</strong> (<em>bool</em>) – Whether to show colorbars for links and nodes.</p></li>
-<li><p><strong>show_autodependency_lags</strong> (<em>bool</em><em> (</em><em>default: False</em><em>)</em>) – Shows significant autodependencies for a node.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.plot_lagfuncs">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_lagfuncs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_lagfunc_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_lagfuncs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_lagfuncs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Wrapper helper function to plot lag functions.
-Sets up the matrix object and plots the lagfunction, see parameters in
-setup_matrix and add_lagfuncs.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</p></li>
-<li><p><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – File name. If None, figure is shown in window.</p></li>
-<li><p><strong>setup_args</strong> (<em>dict</em>) – Arguments for setting up the lag function matrix, see doc of
-setup_matrix.</p></li>
-<li><p><strong>add_lagfunc_args</strong> (<em>dict</em>) – Arguments for adding a lag function matrix, see doc of add_lagfuncs.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>matrix</strong> – Further lag functions can be overlaid using the
-matrix.add_lagfuncs(val_matrix) function.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>object</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.plot_mediation_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">network_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_graph" title="Permalink to this definition">¶</a></dt>
-<dd><p>Creates a network plot visualizing the pathways of a mediation analysis.
-This is still in beta. The network is defined from non-zero entries in
-<code class="docutils literal notranslate"><span class="pre">path_val_matrix</span></code>.  Nodes denote variables, straight links contemporaneous
-dependencies and curved arrows lagged dependencies. The node color denotes
-the mediated causal effect (MCE) and the link color the value at the lag
-with maximal link coefficient. The link label lists the lags with
-significant dependency in order of absolute magnitude. The network can also
-be plotted over a map drawn before on the same axis. Then the node positions
-can be supplied in appropriate axis coordinates via node_pos.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>path_val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing link weight values.</p></li>
-<li><p><strong>path_node_array</strong> (<em>array_like</em>) – Array of shape (N,) containing node values.</p></li>
-<li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
-<li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
-<li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) – Link colorbar label.</p></li>
-<li><p><strong>node_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCE</em><em> (</em><em>node color</em><em>)</em><em>'</em><em>)</em>) – Node colorbar label.</p></li>
-<li><p><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of val_matrix.shape specifying relative link width with maximum
-given by arrow_linewidth. If None, all links have same width.</p></li>
-<li><p><strong>node_pos</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Dictionary of node positions in axis coordinates of form
-node_pos = {‘x’:array of shape (N,), ‘y’:array of shape(N)}. These
-coordinates could have been transformed before for basemap plots.</p></li>
-<li><p><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</p></li>
-<li><p><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</p></li>
-<li><p><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</p></li>
-<li><p><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Link tick mark interval.</p></li>
-<li><p><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) – Colormap for links.</p></li>
-<li><p><strong>vmin_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Node colorbar scale lower bound.</p></li>
-<li><p><strong>vmax_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Node colorbar scale upper bound.</p></li>
-<li><p><strong>node_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Node tick mark interval.</p></li>
-<li><p><strong>cmap_nodes</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'OrRd'</em><em>)</em>) – Colormap for links.</p></li>
-<li><p><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0.3</em><em>)</em>) – Node size.</p></li>
-<li><p><strong>node_aspect</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Ratio between the heigth and width of the varible nodes.</p></li>
-<li><p><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Size of link arrow head. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>curved_radius</strong> (<em>0.2</em><em>)</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>float</strong> (<em>0.2</em><em>)</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>(</strong><strong>default</strong> (<em>optional</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of colorbar labels.</p></li>
-<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</p></li>
-<li><p><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</p></li>
-<li><p><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</p></li>
-<li><p><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</p></li>
-<li><p><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional specification of lags overwriting np.arange(0, tau_max+1)</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.plot_mediation_time_series_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tsg_path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">network_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_time_series_graph" title="Permalink to this definition">¶</a></dt>
-<dd><p>Creates a mediation time series graph plot.
-This is still in beta. The time series graph’s links are colored by
-val_matrix.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>tsg_path_val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N*tau_max, N*tau_max) containing link weight values.</p></li>
-<li><p><strong>path_node_array</strong> (<em>array_like</em>) – Array of shape (N,) containing node values.</p></li>
-<li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
-<li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
-<li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) – Link colorbar label.</p></li>
-<li><p><strong>node_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCE</em><em> (</em><em>node color</em><em>)</em><em>'</em><em>)</em>) – Node colorbar label.</p></li>
-<li><p><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of val_matrix.shape specifying relative link width with maximum
-given by arrow_linewidth. If None, all links have same width.</p></li>
-<li><p><strong>order</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – order of variables from top to bottom.</p></li>
-<li><p><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</p></li>
-<li><p><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</p></li>
-<li><p><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</p></li>
-<li><p><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Link tick mark interval.</p></li>
-<li><p><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) – Colormap for links.</p></li>
-<li><p><strong>vmin_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Node colorbar scale lower bound.</p></li>
-<li><p><strong>vmax_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Node colorbar scale upper bound.</p></li>
-<li><p><strong>node_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Node tick mark interval.</p></li>
-<li><p><strong>cmap_nodes</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'OrRd'</em><em>)</em>) – Colormap for links.</p></li>
-<li><p><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Node size.</p></li>
-<li><p><strong>node_aspect</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Ratio between the heigth and width of the varible nodes.</p></li>
-<li><p><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Size of link arrow head. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>curved_radius</strong> (<em>0.2</em><em>)</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>float</strong> (<em>0.2</em><em>)</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>(</strong><strong>default</strong> (<em>optional</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of colorbar labels.</p></li>
-<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</p></li>
-<li><p><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</p></li>
-<li><p><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</p></li>
-<li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
-<li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
-<li><p><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.plot_scatterplots">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_scatterplots</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_scatterplot_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_scatterplots"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_scatterplots" title="Permalink to this definition">¶</a></dt>
-<dd><p>Wrapper helper function to plot scatter plots.
-Sets up the matrix object and plots the scatter plots, see parameters in
-setup_scatter_matrix and add_scatterplot.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
-yielding a numpy array of shape (observations T, variables N) and
-optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – File name. If None, figure is shown in window.</p></li>
-<li><p><strong>setup_args</strong> (<em>dict</em>) – Arguments for setting up the scatter plot matrix, see doc of
-setup_scatter_matrix.</p></li>
-<li><p><strong>add_scatterplot_args</strong> (<em>dict</em>) – Arguments for adding a scatter plot matrix.</p></li>
-<li><p><strong>selected_dataset</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – In case of multiple datasets in dataframe, plot this one.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>matrix</strong> – Further scatter plot can be overlaid using the
-matrix.add_scatterplot function.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>object</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.plot_time_series_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">network_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_time_series_graph" title="Permalink to this definition">¶</a></dt>
-<dd><p>Creates a time series graph.
-This is still in beta. The time series graph’s links are colored by
-val_matrix.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>string</em><em> or </em><em>bool array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Either string matrix providing graph or bool array providing only adjacencies
-Either of shape (N, N, tau_max + 1) or as auxiliary graph of dims
-(N, N, tau_max+1, tau_max+1) describing auxADMG.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of same shape as graph containing test statistic values.</p></li>
-<li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
-<li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
-<li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) – Test statistic label.</p></li>
-<li><p><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of val_matrix.shape specifying relative link width with maximum
-given by arrow_linewidth. If None, all links have same width.</p></li>
-<li><p><strong>link_attribute</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of graph.shape specifying specific in drawing the graph (for internal use).</p></li>
-<li><p><strong>order</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – order of variables from top to bottom.</p></li>
-<li><p><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</p></li>
-<li><p><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</p></li>
-<li><p><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</p></li>
-<li><p><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Link tick mark interval.</p></li>
-<li><p><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) – Colormap for links.</p></li>
-<li><p><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Node size.</p></li>
-<li><p><strong>node_aspect</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Ratio between the heigth and width of the varible nodes.</p></li>
-<li><p><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Size of link arrow head. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>curved_radius</strong> (<em>0.2</em><em>)</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>float</strong> (<em>0.2</em><em>)</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>(</strong><strong>default</strong> (<em>optional</em>) – Curvature of links. Passed on to FancyArrowPatch object.</p></li>
-<li><p><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of colorbar labels.</p></li>
-<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</p></li>
-<li><p><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</p></li>
-<li><p><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</p></li>
-<li><p><strong>tick_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of tick labels.</p></li>
-<li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
-<li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
-<li><p><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</p></li>
-<li><p><strong>inner_edge_style</strong> (<em>string</em><em>, </em><em>optional</em><em> (</em><em>default: 'dashed'</em><em>)</em>) – Style of inner_edge contemporaneous links.</p></li>
-<li><p><strong>special_nodes</strong> (<em>dict</em>) – Dictionary of format {(i, -tau): ‘blue’, …} to color special nodes.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.plot_timeseries">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_timeseries</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_axes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grey_masked_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_meanline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_ticks_data_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_ticks_data_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adjust_plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_timeseries"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_timeseries" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create and save figure of stacked panels with time series.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em><em>, </em><em>optional</em>) – This is the Tigramite dataframe object. It has the attributes
-dataframe.values yielding a np array of shape (observations T,
-variables N) and optionally a mask of the same shape.</p></li>
-<li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
-<li><p><strong>fig_axes</strong> (<em>subplots instance</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created as
-fig, axes = pyplot.subplots(N,…)</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
-is used.</p></li>
-<li><p><strong>var_units</strong> (<em>list</em><em> of </em><em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Units of variables.</p></li>
-<li><p><strong>time_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: ''</em><em>)</em>) – Label of time axis.</p></li>
-<li><p><strong>grey_masked_samples</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to mark masked samples by grey fills (‘fill’) or grey data
-(‘data’).</p></li>
-<li><p><strong>show_meanline</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to plot a horizontal line at the mean.</p></li>
-<li><p><strong>data_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Linewidth.</p></li>
-<li><p><strong>skip_ticks_data_x</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Skip every other tickmark.</p></li>
-<li><p><strong>skip_ticks_data_y</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 2</em><em>)</em>) – Skip every other tickmark.</p></li>
-<li><p><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of variable labels.</p></li>
-<li><p><strong>tick_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of tick labels.</p></li>
-<li><p><strong>color</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: black</em><em>)</em>) – Line color.</p></li>
-<li><p><strong>alpha</strong> (<em>float</em>) – Alpha opacity.</p></li>
-<li><p><strong>selected_dataset</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – In case of multiple datasets in dataframe, plot this one.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.plot_tsg">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_tsg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">links</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anc_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anc_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anc_xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_tsg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_tsg" title="Permalink to this definition">¶</a></dt>
-<dd><p>Plots TSG that is input in format (N*max_lag, N*max_lag).
-Compared to the tigramite plotting function here links
-X^i_{t-tau} –&gt; X^j_t can be missing for different t’. Helpful to
-visualize the conditioned TSG.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.plotting.setup_density_matrix">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">setup_density_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_gridlines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_density_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_density_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create matrix of density plot panels.
-Class to setup figure object. The function add_densityplot allows to plot
-density plots of variables in the dataframe.</p>
-<p>Further density plots can be overlaid using the matrix.add_densityplot
-function.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>N</strong> (<em>int</em>) – Number of variables</p></li>
-<li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
-is used.</p></li>
-<li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
-<li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
-<li><p><strong>legend_width</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.15</em><em>)</em>) – Fraction of horizontal figure space to allocate right of plot for
-legend.</p></li>
-<li><p><strong>tick_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of tick labels.</p></li>
-<li><p><strong>plot_gridlines</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to show a grid.</p></li>
-<li><p><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of variable labels.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.plotting.setup_density_matrix.add_densityplot">
-<span class="sig-name descname"><span class="pre">add_densityplot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">matrix_lags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snskdeplot_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{'cmap':</span> <span class="pre">'Greys'}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snskdeplot_diagonal_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_marginal_densities_on_diagonal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_density_matrix.add_densityplot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_density_matrix.add_densityplot" title="Permalink to this definition">¶</a></dt>
-<dd><p>Add density function plot.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
-yielding a numpy array of shape (observations T, variables N) and
-optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>matrix_lags</strong> (<em>array</em>) – Lags to use in scatter plots. Either None or non-neg array of shape (N, N). Then the
-entry matrix_lags[i, j] = tau will depict the scatter plot of
-time series (i, -tau) vs (j, 0). If None, tau = 0 for i != j and for i = j
-tau = 1.</p></li>
-<li><p><strong>snskdeplot_args</strong> (<em>dict</em>) – Optional parameters to pass to sns.kdeplot() for i != j for off-diagonal plots.</p></li>
-<li><p><strong>snskdeplot_diagonal_args</strong> (<em>dict</em>) – Optional parameters to pass to sns.kdeplot() for i == j on diagonal.</p></li>
-<li><p><strong>label</strong> (<em>string</em>) – Label of this plot.</p></li>
-<li><p><strong>label_color</strong> (<em>string</em>) – Color of line created just for legend.</p></li>
-<li><p><strong>selected_dataset</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – In case of multiple datasets in dataframe, plot this one.</p></li>
-<li><p><strong>show_marginal_densities_on_diagonal</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Flag to show marginal densities on the diagonal of the density plots</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.plotting.setup_density_matrix.adjustfig">
-<span class="sig-name descname"><span class="pre">adjustfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_density_matrix.adjustfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_density_matrix.adjustfig" title="Permalink to this definition">¶</a></dt>
-<dd><p>Adjust matrix figure.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – File name. If None, figure is shown in window.</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.plotting.setup_matrix">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">setup_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minimum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_gridlines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create matrix of lag function panels.
-Class to setup figure object. The function add_lagfuncs(…) allows to plot
-the val_matrix of shape (N, N, tau_max+1). Multiple lagfunctions can be
-overlaid for comparison.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>N</strong> (<em>int</em>) – Number of variables</p></li>
-<li><p><strong>tau_max</strong> (<em>int</em>) – Maximum time lag.</p></li>
-<li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
-is used.</p></li>
-<li><p><strong>minimum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Lower y-axis limit.</p></li>
-<li><p><strong>maximum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Upper y-axis limit.</p></li>
-<li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
-<li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
-<li><p><strong>legend_width</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.15</em><em>)</em>) – Fraction of horizontal figure space to allocate right of plot for
-legend.</p></li>
-<li><p><strong>tick_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of tick labels.</p></li>
-<li><p><strong>x_base</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – x-tick intervals to show.</p></li>
-<li><p><strong>y_base</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: .4</em><em>)</em>) – y-tick intervals to show.</p></li>
-<li><p><strong>plot_gridlines</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to show a grid.</p></li>
-<li><p><strong>lag_units</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: ''</em><em>)</em>) – </p></li>
-<li><p><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional specification of lags overwriting np.arange(0, tau_max+1)</p></li>
-<li><p><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of variable labels.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.plotting.setup_matrix.add_lagfuncs">
-<span class="sig-name descname"><span class="pre">add_lagfuncs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">two_sided_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">marker</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'.'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">markersize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix.add_lagfuncs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix.add_lagfuncs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Add lag function plot from val_matrix array.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</p></li>
-<li><p><strong>sig_thres</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of significance thresholds. Must be of same shape as
-val_matrix.</p></li>
-<li><p><strong>conf_matrix</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of shape (, N, tau_max+1, 2) containing confidence bounds.</p></li>
-<li><p><strong>color</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'black'</em><em>)</em>) – Line color.</p></li>
-<li><p><strong>label</strong> (<em>str</em>) – Test statistic label.</p></li>
-<li><p><strong>two_sided_thres</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to draw sig_thres for pos. and neg. values.</p></li>
-<li><p><strong>marker</strong> (<em>matplotlib marker symbol</em><em>, </em><em>optional</em><em> (</em><em>default: '.'</em><em>)</em>) – Marker.</p></li>
-<li><p><strong>markersize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 5</em><em>)</em>) – Marker size.</p></li>
-<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.plotting.setup_matrix.savefig">
-<span class="sig-name descname"><span class="pre">savefig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix.savefig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix.savefig" title="Permalink to this definition">¶</a></dt>
-<dd><p>Save matrix figure.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – File name. If None, figure is shown in window.</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="tigramite.plotting.setup_scatter_matrix">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">setup_scatter_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_gridlines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_scatter_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_scatter_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create matrix of scatter plot panels.
-Class to setup figure object. The function add_scatterplot allows to plot
-scatterplots of variables in the dataframe. Multiple scatter plots can be
-overlaid for comparison.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>N</strong> (<em>int</em>) – Number of variables</p></li>
-<li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
-is used.</p></li>
-<li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
-<li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
-<li><p><strong>legend_width</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.15</em><em>)</em>) – Fraction of horizontal figure space to allocate right of plot for
-legend.</p></li>
-<li><p><strong>tick_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of tick labels.</p></li>
-<li><p><strong>plot_gridlines</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to show a grid.</p></li>
-<li><p><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of variable labels.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.plotting.setup_scatter_matrix.add_scatterplot">
-<span class="sig-name descname"><span class="pre">add_scatterplot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">matrix_lags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">marker</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'.'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">markersize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_scatter_matrix.add_scatterplot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_scatter_matrix.add_scatterplot" title="Permalink to this definition">¶</a></dt>
-<dd><p>Add scatter plot.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
-yielding a numpy array of shape (observations T, variables N) and
-optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>matrix_lags</strong> (<em>array</em>) – Lags to use in scatter plots. Either None or of shape (N, N). Then the
-entry matrix_lags[i, j] = tau will depict the scatter plot of
-time series (i, -tau) vs (j, 0). If None, tau = 0 for i != j and for i = j
-tau = 1.</p></li>
-<li><p><strong>color</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'black'</em><em>)</em>) – Line color.</p></li>
-<li><p><strong>label</strong> (<em>str</em>) – Test statistic label.</p></li>
-<li><p><strong>marker</strong> (<em>matplotlib marker symbol</em><em>, </em><em>optional</em><em> (</em><em>default: '.'</em><em>)</em>) – Marker.</p></li>
-<li><p><strong>markersize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 5</em><em>)</em>) – Marker size.</p></li>
-<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</p></li>
-<li><p><strong>selected_dataset</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – In case of multiple datasets in dataframe, plot this one.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.plotting.setup_scatter_matrix.adjustfig">
-<span class="sig-name descname"><span class="pre">adjustfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_scatter_matrix.adjustfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_scatter_matrix.adjustfig" title="Permalink to this definition">¶</a></dt>
-<dd><p>Adjust matrix figure.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – File name. If None, figure is shown in window.</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="tigramite.plotting.write_csv">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">write_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">digits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#write_csv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.write_csv" title="Permalink to this definition">¶</a></dt>
-<dd><p>Writes all links in a graph to a csv file.</p>
-<p>Format is each link in a row as ‘Variable i’, ‘Variable j’, ‘Time lag of i’, ‘Link type i — j’,
-with optional further columns for entries in [val_matrix link_attribute, link_width].</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>string</em><em> or </em><em>bool array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Either string matrix providing graph or bool array providing only adjacencies
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-<li><p><strong>save_name</strong> (<em>str</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
-<li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</p></li>
-<li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of val_matrix.shape specifying relative link width with maximum
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-<li><p><strong>link_attribute</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – String array of val_matrix.shape specifying link attributes.</p></li>
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-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
-<section id="id36">
-<h1>Indices and tables<a class="headerlink" href="#id36" title="Permalink to this heading">¶</a></h1>
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"tigramite.plotting.plot_graph"]], "plot_lagfuncs() (in module tigramite.plotting)": [[0, "tigramite.plotting.plot_lagfuncs"]], "plot_mediation_graph() (in module tigramite.plotting)": [[0, "tigramite.plotting.plot_mediation_graph"]], "plot_mediation_time_series_graph() (in module tigramite.plotting)": [[0, "tigramite.plotting.plot_mediation_time_series_graph"]], "plot_scatterplots() (in module tigramite.plotting)": [[0, "tigramite.plotting.plot_scatterplots"]], "plot_time_series_graph() (in module tigramite.plotting)": [[0, "tigramite.plotting.plot_time_series_graph"]], "plot_timeseries() (in module tigramite.plotting)": [[0, "tigramite.plotting.plot_timeseries"]], "plot_tsg() (in module tigramite.plotting)": [[0, "tigramite.plotting.plot_tsg"]], "predict() (tigramite.models.prediction method)": [[0, "tigramite.models.Prediction.predict"]], "predict_bootstrap_of() (tigramite.causal_effects.causaleffects method)": [[0, "tigramite.causal_effects.CausalEffects.predict_bootstrap_of"]], "predict_total_effect() (tigramite.causal_effects.causaleffects method)": [[0, "tigramite.causal_effects.CausalEffects.predict_total_effect"]], "predict_wright_effect() (tigramite.causal_effects.causaleffects method)": [[0, "tigramite.causal_effects.CausalEffects.predict_wright_effect"]], "print_array_info() (tigramite.data_processing.dataframe method)": [[0, "tigramite.data_processing.DataFrame.print_array_info"]], "print_info() (tigramite.independence_tests.independence_tests_base.condindtest method)": [[0, "tigramite.independence_tests.independence_tests_base.CondIndTest.print_info"]], "print_results() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.print_results"]], "print_significant_links() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.print_significant_links"]], "pval_max (tigramite.pcmci.pcmci attribute)": [[0, "tigramite.pcmci.PCMCI.pval_max"]], "quantile_bin_array() (in module tigramite.data_processing)": [[0, "tigramite.data_processing.quantile_bin_array"]], "return_parents_dict() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.return_parents_dict"]], "return_significant_links() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.return_significant_links"]], "run_bivci() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.run_bivci"]], "run_fullci() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.run_fullci"]], "run_lpcmci() (tigramite.lpcmci.lpcmci method)": [[0, "tigramite.lpcmci.LPCMCI.run_lpcmci"]], "run_mci() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.run_mci"]], "run_pc_stable() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.run_pc_stable"]], "run_pcalg() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.run_pcalg"]], "run_pcalg_non_timeseries_data() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.run_pcalg_non_timeseries_data"]], "run_pcmci() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.run_pcmci"]], "run_pcmciplus() (tigramite.pcmci.pcmci method)": [[0, "tigramite.pcmci.PCMCI.run_pcmciplus"]], "run_test() (tigramite.independence_tests.independence_tests_base.condindtest method)": [[0, "tigramite.independence_tests.independence_tests_base.CondIndTest.run_test"]], "run_test() (tigramite.independence_tests.oracle_conditional_independence.oracleci method)": [[0, "tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test"]], "run_test_raw() (tigramite.independence_tests.independence_tests_base.condindtest method)": [[0, "tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw"]], "savefig() (tigramite.plotting.setup_matrix method)": [[0, "tigramite.plotting.setup_matrix.savefig"]], "set_dataframe() (tigramite.independence_tests.independence_tests_base.condindtest method)": [[0, "tigramite.independence_tests.independence_tests_base.CondIndTest.set_dataframe"]], "set_dataframe() (tigramite.independence_tests.oracle_conditional_independence.oracleci method)": [[0, "tigramite.independence_tests.oracle_conditional_independence.OracleCI.set_dataframe"]], "set_dataframe() (tigramite.independence_tests.regressionci.regressionci method)": [[0, "tigramite.independence_tests.regressionCI.RegressionCI.set_dataframe"]], "set_mask_type() (tigramite.independence_tests.independence_tests_base.condindtest method)": [[0, "tigramite.independence_tests.independence_tests_base.CondIndTest.set_mask_type"]], "setup_density_matrix (class in tigramite.plotting)": [[0, "tigramite.plotting.setup_density_matrix"]], "setup_matrix (class in tigramite.plotting)": [[0, "tigramite.plotting.setup_matrix"]], "setup_scatter_matrix (class in tigramite.plotting)": [[0, "tigramite.plotting.setup_scatter_matrix"]], "smooth() (in module tigramite.data_processing)": [[0, "tigramite.data_processing.smooth"]], "structural_causal_process() (in module tigramite.data_processing)": [[0, "tigramite.data_processing.structural_causal_process"]], "structural_causal_process() (in module tigramite.toymodels.structural_causal_processes)": [[0, "tigramite.toymodels.structural_causal_processes.structural_causal_process"]], "tigramite.data_processing": [[0, "module-tigramite.data_processing"]], "tigramite.plotting": [[0, "module-tigramite.plotting"]], "tigramite.toymodels.structural_causal_processes": [[0, "module-tigramite.toymodels.structural_causal_processes"]], "time_bin_with_mask() (in module tigramite.data_processing)": [[0, "tigramite.data_processing.time_bin_with_mask"]], "trafo2normal() (in module tigramite.data_processing)": [[0, "tigramite.data_processing.trafo2normal"]], "trafo2normal() (tigramite.independence_tests.robust_parcorr.robustparcorr method)": [[0, "tigramite.independence_tests.robust_parcorr.RobustParCorr.trafo2normal"]], "tsg_to_net() (tigramite.models.linearmediation method)": [[0, "tigramite.models.LinearMediation.tsg_to_net"]], "val_min (tigramite.pcmci.pcmci attribute)": [[0, "tigramite.pcmci.PCMCI.val_min"]], "var_process() (in module tigramite.data_processing)": [[0, "tigramite.data_processing.var_process"]], "var_process() (in module tigramite.toymodels.structural_causal_processes)": [[0, "tigramite.toymodels.structural_causal_processes.var_process"]], "weighted_avg_and_std() (in module tigramite.data_processing)": [[0, "tigramite.data_processing.weighted_avg_and_std"]], "write_csv() (in module tigramite.plotting)": [[0, "tigramite.plotting.write_csv"]]}})
\ No newline at end of file
diff --git a/docs/_build/index.html b/docs/_build/index.html
index d23db7a6..38987cf5 100644
--- a/docs/_build/index.html
+++ b/docs/_build/index.html
@@ -4,14 +4,16 @@
 <html lang="en">
   <head>
     <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
     <title>Welcome to Tigramite’s documentation! &#8212; Tigramite 5.2 documentation</title>
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     <link rel="stylesheet" type="text/css" href="_static/alabaster.css" />
     <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+    <script src="_static/jquery.js"></script>
+    <script src="_static/underscore.js"></script>
+    <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="_static/doctools.js"></script>
-    <script src="_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
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@@ -48,6 +50,7 @@ <h1>TIGRAMITE<a class="headerlink" href="#tigramite" title="Permalink to this he
 <p><a class="reference external" href="https://github.com/jakobrunge/tigramite/">Github repo</a></p>
 <p>Tigramite is a causal time series analysis python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use these graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:</p>
 <ul class="simple">
+<li><p>Overview: Runge, J., Gerhardus, A., Varando, G. et al. Causal inference for time series. Nat Rev Earth Environ (2023). <a class="reference external" href="https://doi.org/10.1038/s43017-023-00431-y">https://doi.org/10.1038/s43017-023-00431-y</a></p></li>
 <li><p>PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019). <a class="reference external" href="https://advances.sciencemag.org/content/5/11/eaau4996">https://advances.sciencemag.org/content/5/11/eaau4996</a></p></li>
 <li><p>PCMCI+: J. Runge (2020): Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020,Toronto, Canada, 2019, AUAI Press, 2020. <a class="reference external" href="http://auai.org/uai2020/proceedings/579_main_paper.pdf">http://auai.org/uai2020/proceedings/579_main_paper.pdf</a></p></li>
 <li><p>LPCMCI: Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders Advances in Neural Information Processing Systems, 2020, 33. <a class="reference external" href="https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html">https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</a></p></li>
@@ -176,7 +179,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 hypothetical interventions, you may better look at the causal effect
 estimation functionality of Tigramite.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id3" role="note">
 <span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
 <span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id5">2</a>,<a role="doc-backlink" href="#id6">3</a>,<a role="doc-backlink" href="#id7">4</a>,<a role="doc-backlink" href="#id9">5</a>,<a role="doc-backlink" href="#id15">6</a>,<a role="doc-backlink" href="#id16">7</a>,<a role="doc-backlink" href="#id17">8</a>,<a role="doc-backlink" href="#id33">9</a>,<a role="doc-backlink" href="#id34">10</a>)</span>
@@ -193,7 +195,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 autocorrelated nonlinear time series datasets
 <a class="reference external" href="http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf">http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -286,7 +287,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>p_matrix</strong> (<em>array</em><em> of </em><em>shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
+<li><p><strong>p_matrix</strong> (<em>array of shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
 not ‘none’.</p></li>
 <li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level at which the p_matrix is thresholded to
 get graph.</p></li>
@@ -414,7 +415,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph, see description above for interpretation.</p></li>
+<li><p><strong>graph</strong> (<em>array of shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph, see description above for interpretation.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array-like</em>) – Matrix of test statistic values. Must be of shape (N, N, tau_max +
 1).</p></li>
 <li><p><strong>include_lagzero_parents</strong> (<em>bool</em><em> (</em><em>default: False</em><em>)</em>) – Whether the dictionary should also return parents at lag
@@ -614,7 +615,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Must be greater zero.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
 <li><p><strong>save_iterations</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether to save iteration step results such as conditions used.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list</em><em> of </em><em>floats</em><em>, </em><em>default:</em><em> [</em><em>0.05</em><em>, </em><em>0.1</em><em>, </em><em>0.2</em><em>, </em><em>...</em><em>, </em><em>0.5</em><em>]</em>) – Significance level in algorithm. If a list or None is passed, the
+<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default:</em><em> [</em><em>0.05</em><em>, </em><em>0.1</em><em>, </em><em>0.2</em><em>, </em><em>...</em><em>, </em><em>0.5</em><em>]</em>) – Significance level in algorithm. If a list or None is passed, the
 pc_alpha level is optimized for every variable across the given
 pc_alpha values using the score computed in
 cond_ind_test.get_model_selection_criterion().</p></li>
@@ -691,7 +692,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Resulting causal graph, see description above for interpretation.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values regarding adjacencies.</p></li>
 <li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values regarding adjacencies.</p></li>
-<li><p><strong>sepset</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
+<li><p><strong>sepsets</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
 <li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples, only relevant for ‘majority’ and
 ‘conservative’ rules, see paper for details.</p></li>
 </ul>
@@ -726,7 +727,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>graph</strong> (<em>array of shape [N, N, 1]</em>) – Resulting causal graph, see description above for interpretation.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array of shape [N, N, 1]</em>) – Estimated matrix of test statistic values regarding adjacencies.</p></li>
 <li><p><strong>p_matrix</strong> (<em>array of shape [N, N, 1]</em>) – Estimated matrix of p-values regarding adjacencies.</p></li>
-<li><p><strong>sepset</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
+<li><p><strong>sepsets</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
 <li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples, only relevant for ‘majority’ and
 ‘conservative’ rules, see paper for details.</p></li>
 </ul>
@@ -981,7 +982,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 or the links are assumed absent.</p></li>
 <li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Minimum time lag to test.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list</em><em> of </em><em>floats</em><em>, </em><em>default: 0.01</em>) – Significance level in algorithm. If a list or None is passed, the
+<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default: 0.01</em>) – Significance level in algorithm. If a list or None is passed, the
 pc_alpha level is optimized for every graph across the given
 pc_alpha values ([0.001, 0.005, 0.01, 0.025, 0.05] for None) using
 the score computed in cond_ind_test.get_model_selection_criterion().</p></li>
@@ -1015,7 +1016,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Resulting causal graph, see description above for interpretation.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values regarding adjacencies.</p></li>
 <li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values regarding adjacencies.</p></li>
-<li><p><strong>sepset</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
+<li><p><strong>sepsets</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
 <li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples, only relevant for ‘majority’ and
 ‘conservative’ rules, see paper for details.</p></li>
 </ul>
@@ -1033,11 +1034,11 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="sig sig-object py" id="tigramite.lpcmci.LPCMCI">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.lpcmci.</span></span><span class="sig-name descname"><span class="pre">LPCMCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cond_ind_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/lpcmci.html#LPCMCI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.lpcmci.LPCMCI" title="Permalink to this definition">¶</a></dt>
 <dd><p>LPCMCI is an algorithm for causal discovery in large-scale times series that allows for latent confounders and
-learns lag-specific causal relationships. The algorithm is introduced and explained in:
-[1] Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders.
+learns lag-specific causal relationships. The algorithm is introduced and explained in:</p>
+<p>[1] Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders.
 Advances in Neural Information Processing Systems, 2020, 33.
-<a class="reference external" href="https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html">https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</a>
-NOTE: This method is still EXPERIMENTAL since the default settings of hyperparameters are still being fine-tuned.
+<a class="reference external" href="https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html">https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</a></p>
+<p>NOTE: This method is still EXPERIMENTAL since the default settings of hyperparameters are still being fine-tuned.
 We actually invite feedback on which work best in applications and numerical experiments.
 The main function, which applies the algorithm, is ‘run_lpcmci’.</p>
 <p>Parameters passed to the constructor:
@@ -1259,7 +1260,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.rpcmci</span></code>: RPCMCI<a class="headerlink" href="#tigramite-rpcmci-rpcmci" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="tigramite.rpcmci.RPCMCI">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.rpcmci.</span></span><span class="sig-name descname"><span class="pre">RPCMCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cond_ind_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/rpcmci.html#RPCMCI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.rpcmci.RPCMCI" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.rpcmci.</span></span><span class="sig-name descname"><span class="pre">RPCMCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cond_ind_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/rpcmci.html#RPCMCI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.rpcmci.RPCMCI" title="Permalink to this definition">¶</a></dt>
 <dd><p>RPCMCI class for extracting causal regimes and the associated graphs from
 time series data.</p>
 <p class="rubric">Notes</p>
@@ -1295,7 +1296,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.rpcmci.RPCMCI.run_rpcmci">
-<span class="sig-name descname"><span class="pre">run_rpcmci</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_regimes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_transitions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">switch_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_anneal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/rpcmci.html#RPCMCI.run_rpcmci"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.rpcmci.RPCMCI.run_rpcmci" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_rpcmci</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_regimes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_transitions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">switch_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_anneal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/rpcmci.html#RPCMCI.run_rpcmci"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.rpcmci.RPCMCI.run_rpcmci" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Run RPCMCI method for extracting causal regimes and the associated graphs from</dt><dd><p>time series data.</p>
 </dd>
@@ -1338,7 +1339,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p>Base class:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.independence_tests_base.</span></span><span class="sig-name descname"><span class="pre">CondIndTest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'analytic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">confidence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recycle_residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.independence_tests_base.</span></span><span class="sig-name descname"><span class="pre">CondIndTest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'analytic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">confidence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recycle_residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class of conditional independence tests.</p>
 <p>Provides useful general functions for different independence tests such as
 shuffle significance testing and bootstrap confidence estimation. Also
@@ -1353,8 +1354,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Explained in tutorial on masking and missing values.</p></li>
 <li><p><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'analytic'</em><em>)</em>) – Type of significance test to use. In this package ‘analytic’,
 ‘fixed_thres’ and ‘shuffle_test’ are available.</p></li>
-<li><p><strong>fixed_thres</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – If significance is ‘fixed_thres’, this specifies the threshold for the
-absolute value of the dependence measure.</p></li>
+<li><p><strong>fixed_thres</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Deprecated.</p></li>
 <li><p><strong>sig_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 500</em><em>)</em>) – Number of samples for shuffle significance test.</p></li>
 <li><p><strong>sig_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Block length for block-shuffle significance test. If None, the
 block length is determined from the decay of the autocovariance as
@@ -1469,23 +1469,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance">
 <span class="sig-name descname"><span class="pre">get_fixed_thres_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_thres</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_fixed_thres_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns signficance for thresholding test.</p>
-<p>Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1 else.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-<li><p><strong>fixed_thres</strong> (<em>number</em>) – Fixed threshold, is made positive.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1
-else.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>bool</p>
-</dd>
-</dl>
+<dd><p>DEPRECATED Returns signficance for thresholding test.</p>
 </dd></dl>
 
 <dl class="py method">
@@ -1532,44 +1516,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 should override when possible.</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance">
-<span class="sig-name descname"><span class="pre">get_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance" title="Permalink to this definition">¶</a></dt>
-<dd><blockquote>
-<div><p>Returns the p-value from whichever significance function is specified
-for this test.  If an override is used, then it will call a different
-function then specified by self.significance</p>
-<dl class="simple">
-<dt>val<span class="classifier">float</span></dt><dd><p>Test statistic value.</p>
-</dd>
-<dt>array<span class="classifier">array-like</span></dt><dd><p>data array with X, Y, Z in rows and observations in columns</p>
-</dd>
-<dt>xyz<span class="classifier">array of ints</span></dt><dd><p>XYZ identifier array of shape (dim,).</p>
-</dd>
-<dt>T<span class="classifier">int</span></dt><dd><p>Sample length</p>
-</dd>
-<dt>dim<span class="classifier">int</span></dt><dd><p>Dimensionality, ie, number of features.</p>
-</dd>
-</dl>
-</div></blockquote>
-<dl>
-<dt>data_type<span class="classifier">array-like</span></dt><dd><blockquote>
-<div><p>Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.</p>
-</div></blockquote>
-<dl class="simple">
-<dt>sig_override<span class="classifier">string</span></dt><dd><p>Must be in ‘analytic’, ‘shuffle_test’, ‘fixed_thres’</p>
-</dd>
-</dl>
-<dl class="simple">
-<dt>pval<span class="classifier">float or numpy.nan</span></dt><dd><p>P-value.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py property">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.measure">
 <em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.measure" title="Permalink to this definition">¶</a></dt>
@@ -1584,7 +1530,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">
-<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_or_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform conditional independence test.</p>
 <p>Calls the dependence measure and signficicance test functions. The child
 classes must specify a function get_dependence_measure and either or
@@ -1594,11 +1540,11 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>X</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index and tau the time lag.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index and tau the time lag.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index and tau the time lag.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
 different lags in X, Z, all have the same sample size.</p></li>
@@ -1608,20 +1554,24 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 which uses the maximum of tau_max and the conditions, which is
 useful to compare multiple models on the same sample.  Last,
 ‘max_lag’ uses as much samples as possible.</p></li>
+<li><p><strong>alpha_or_thres</strong> (<em>float</em><em> (</em><em>optional</em><em>)</em>) – Significance level (if significance=’analytic’ or ‘shuffle_test’) or
+threshold (if significance=’fixed_thres’). If given, run_test returns
+the test decision dependent=True/False.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val, pval</strong> – The test statistic value and the p-value.</p>
+<dd class="field-even"><p><strong>val, pval, [dependent]</strong> – The test statistic value and the p-value. If alpha_or_thres is
+given, run_test also returns the test decision dependent=True/False.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of floats</p>
+<dd class="field-odd"><p>Tuple of floats and bool</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw">
-<span class="sig-name descname"><span class="pre">run_test_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test_raw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_test_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_or_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test_raw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform conditional independence test directly on input arrays x, y, z.</p>
 <p>Calls the dependence measure and signficicance test functions. The child
 classes must specify a function get_dependence_measure and either or
@@ -1641,13 +1591,17 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>z_type</strong> (<em>array-like</em>) – data arrays of same shape as x, y and z respectively, which describes whether variables
 are continuous or discrete: 0s for continuous variables and
 1s for discrete variables</p></li>
+<li><p><strong>alpha_or_thres</strong> (<em>float</em><em> (</em><em>optional</em><em>)</em>) – Significance level (if significance=’analytic’ or ‘shuffle_test’) or
+threshold (if significance=’fixed_thres’). If given, run_test returns
+the test decision dependent=True/False.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val, pval</strong> – The test statistic value and the p-value.</p>
+<dd class="field-even"><p><strong>val, pval, [dependent]</strong> – The test statistic value and the p-value. If alpha_or_thres is
+given, run_test also returns the test decision dependent=True/False.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of floats</p>
+<dd class="field-odd"><p>Tuple of floats and bool</p>
 </dd>
 </dl>
 </dd></dl>
@@ -1743,7 +1697,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1765,7 +1719,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1808,7 +1762,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -1909,7 +1863,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1932,7 +1886,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1981,7 +1935,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2058,7 +2012,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </div><p>The null distribution of the distance correlation should be pre-computed.
 Otherwise it is computed during runtime.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id20" role="note">
 <span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
 <span class="backrefs">(<a role="doc-backlink" href="#id18">1</a>,<a role="doc-backlink" href="#id19">2</a>,<a role="doc-backlink" href="#id21">3</a>,<a role="doc-backlink" href="#id22">4</a>)</span>
@@ -2066,7 +2019,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 testing dependence by correlation of distances,
 <a class="reference external" href="https://arxiv.org/abs/0803.4101">https://arxiv.org/abs/0803.4101</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -2137,7 +2089,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2159,7 +2111,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2201,7 +2153,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2318,7 +2270,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2340,7 +2292,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2382,7 +2334,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2405,7 +2357,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py class">
 <dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.cmiknn.</span></span><span class="sig-name descname"><span class="pre">CMIknn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">knn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'shuffle_test'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ranks'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.cmiknn.</span></span><span class="sig-name descname"><span class="pre">CMIknn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">knn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'shuffle_test'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ranks'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_selection_folds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conditional mutual information test based on nearest-neighbor estimator.</p>
 <p>Conditional mutual information is the most general dependency measure coming
 from an information-theoretic framework. It makes no assumptions about the
@@ -2437,7 +2389,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 negative quantity.</p>
 <p>This method requires the scipy.spatial.cKDTree package.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id24" role="note">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id23">3</a><span class="fn-bracket">]</span></span>
 <p>J. Runge (2018): Conditional Independence Testing Based on a
@@ -2446,7 +2397,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Intelligence and Statistics.
 <a class="reference external" href="http://proceedings.mlr.press/v84/runge18a.html">http://proceedings.mlr.press/v84/runge18a.html</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -2462,6 +2412,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 or transforming to uniform marginals.</p></li>
 <li><p><strong>workers</strong> (<em>int</em><em> (</em><em>optional</em><em>, </em><em>default = -1</em><em>)</em>) – Number of workers to use for parallel processing. If -1 is given
 all processors are used. Default: -1.</p></li>
+<li><p><strong>model_selection_folds</strong> (<em>int</em>) – Number of folds in cross-validation used in model selection.</p></li>
 <li><p><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'shuffle_test'</em><em>)</em>) – Type of significance test to use. For CMIknn only ‘fixed_thres’ and
 ‘shuffle_test’ are available.</p></li>
 <li><p><strong>**kwargs</strong> – Arguments passed on to parent class CondIndTest.</p></li>
@@ -2476,7 +2427,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,). Here only uses 0 for X and
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,). Here only uses 0 for X and
 1 for Y.</p></li>
 </ul>
 </dd>
@@ -2497,7 +2448,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2509,6 +2460,27 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn.get_model_selection_criterion">
+<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a cross-validation-based score for nearest-neighbor estimates.</p>
+<p>Fits a nearest-neighbor model of the parents to variable j and returns
+the score. The lower, the better the fit. Here used to determine
+optimal hyperparameters in PCMCI(pc_alpha or fixed thres).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>j</strong> (<em>int</em>) – Index of target variable in data array.</p></li>
+<li><p><strong>parents</strong> (<em>list</em>) – List of form [(0, -1), (3, -2), …] containing parents.</p></li>
+<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
+different lags in X, Z, all have the same sample size.</p></li>
+<li><p><strong>Returns</strong> – </p></li>
+<li><p><strong>score</strong> (<em>float</em>) – Model score.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn.get_shuffle_significance">
 <span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
@@ -2523,7 +2495,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2585,7 +2557,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2608,7 +2580,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for original (unshuffled) estimate.</p></li>
 </ul>
 </dd>
@@ -2640,7 +2612,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph.</p></li>
+<li><p><strong>graph</strong> (<em>array of shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph.</p></li>
 <li><p><strong>links</strong> (<em>dict</em>) – Dictionary of form {0:[(0, -1), …], 1:[…], …}.
 Alternatively can also digest {0: [((0, -1), coeff, func)], …}.</p></li>
 <li><p><strong>observed_vars</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Subset of keys in links definining which variables are
@@ -2674,9 +2646,9 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of variables chosen for testing paths.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of variables chosen for testing paths.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of variables chosen for testing paths.</p></li>
+<li><p><strong>X</strong> (<em>list of tuples</em>) – List of variables chosen for testing paths.</p></li>
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – List of variables chosen for testing paths.</p></li>
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – List of variables chosen for testing paths.</p></li>
 <li><p><strong>max_lag</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used here to constrain the has_path function to the graph
 truncated at max_lag instead of identifying the max_lag from
 ancestral search.</p></li>
@@ -2735,11 +2707,11 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>X</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
 <li><p><strong>[</strong> (<em>Y</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>Z</strong><strong>]</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Z</strong><strong>]</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
 different lags in X, Z, all have the same sample size.</p></li>
@@ -2769,20 +2741,21 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test">
-<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_or_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform oracle conditional independence test.</p>
 <p>Calls the d-separation function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>X</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Not used here.</p></li>
 <li><p><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) – Not used here.</p></li>
+<li><p><strong>alpha_or_thres</strong> (<em>float</em>) – Not used here.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2844,7 +2817,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2866,7 +2839,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2894,7 +2867,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2921,7 +2894,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>standardize</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to standardize the array beforehand. Must be used for
 partial correlation.</p></li>
 </ul>
@@ -2954,13 +2927,11 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 \frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)}"/></p>
 </div><p>where <img class="math" src="_images/math/5a939c5280da7202ca4531f175a7780ad5e1f80a.png" alt="n"/> is the sample size. This is simply <img class="math" src="_images/math/88da5b28f5e08d9d8ed693d334e710f4adadbcfd.png" alt="2 n CMI(X;Y|Z)"/>.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id26" role="note">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id25">7</a><span class="fn-bracket">]</span></span>
 <p>Bishop, Y.M.M., Fienberg, S.E. and Holland, P.W. (1975) Discrete
 Multivariate Analysis: Theory and Practice. MIT Press, Cambridge.</p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -2985,7 +2956,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -3051,7 +3022,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -3094,7 +3065,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -3146,7 +3117,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>data_type</strong> (<em>array-like</em>) – array of same shape as array which describes whether samples
 are continuous or discrete: 0s for continuous and
 1s for discrete</p></li>
@@ -3201,7 +3172,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p>See the corresponding paper <a class="footnote-reference brackets" href="#id28" id="id27" role="doc-noteref"><span class="fn-bracket">[</span>6<span class="fn-bracket">]</span></a> and tigramite tutorial for an
 in-depth introduction.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id28" role="note">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id27">6</a><span class="fn-bracket">]</span></span>
 <p>J. Runge, Necessary and sufficient graphical conditions for
@@ -3210,16 +3180,15 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Systems, 2021, 34
 <a class="reference external" href="https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html">https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>either shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>]</em><em>, </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em><em>, or </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>, </em><em>tau_max+1</em><em>]</em>) – Different graph types are supported, see tutorial.</p></li>
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of tuples [(i, -tau), …] containing cause variables.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of tuples [(j, 0), …] containing effect variables.</p></li>
-<li><p><strong>S</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of tuples [(i, -tau), …] containing conditioned variables.</p></li>
+<li><p><strong>graph</strong> (<em>array of either shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>]</em><em>, </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em><em>, or </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>, </em><em>tau_max+1</em><em>]</em>) – Different graph types are supported, see tutorial.</p></li>
+<li><p><strong>X</strong> (<em>list of tuples</em>) – List of tuples [(i, -tau), …] containing cause variables.</p></li>
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – List of tuples [(j, 0), …] containing effect variables.</p></li>
+<li><p><strong>S</strong> (<em>list of tuples</em>) – List of tuples [(i, -tau), …] containing conditioned variables.</p></li>
 <li><p><strong>graph_type</strong> (<em>str</em>) – Type of graph.</p></li>
-<li><p><strong>hidden_variables</strong> (<em>list</em><em> of </em><em>tuples</em>) – Hidden variables in format [(i, -tau), …]. The internal graph is
+<li><p><strong>hidden_variables</strong> (<em>list of tuples</em>) – Hidden variables in format [(i, -tau), …]. The internal graph is
 constructed by a latent projection.</p></li>
 <li><p><strong>check_SM_overlap</strong> (<em>bool</em>) – Whether to check whether S overlaps with M.</p></li>
 <li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
@@ -3292,7 +3261,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 optionally a mask of the same shape and a missing values flag.</p></li>
 <li><p><strong>estimator</strong> (<em>sklearn model object</em>) – For example, sklearn.linear_model.LinearRegression() for a linear
 regression model.</p></li>
-<li><p><strong>adjustment_set</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>tuples</em>) – If ‘optimal’ the Oset is used, if ‘minimized_optimal’ the minimized Oset,
+<li><p><strong>adjustment_set</strong> (<em>str</em><em> or </em><em>list of tuples</em>) – If ‘optimal’ the Oset is used, if ‘minimized_optimal’ the minimized Oset,
 and if ‘colliders_minimized_optimal’, the colliders-minimized Oset.
 If a list of tuples is passed, this set is used.</p></li>
 <li><p><strong>conditional_estimator</strong> (<em>sklearn model object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to fit conditional causal effects in nested regression.
@@ -3323,7 +3292,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
 yielding a numpy array of shape (observations T, variables N) and
 optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>mediation</strong> (<em>None</em><em>, </em><em>'direct'</em><em>, or </em><em>list</em><em> of </em><em>tuples</em>) – If None, total effect is estimated, if ‘direct’ then only the direct effect is estimated,
+<li><p><strong>mediation</strong> (<em>None</em><em>, </em><em>'direct'</em><em>, or </em><em>list of tuples</em>) – If None, total effect is estimated, if ‘direct’ then only the direct effect is estimated,
 else only those causal paths are considerd that pass at least through one of these mediator nodes.</p></li>
 <li><p><strong>method</strong> (<em>{'parents'</em><em>, </em><em>'links_coeffs'</em><em>, </em><em>'optimal'}</em>) – Method to use for estimating Wright’s path coefficients. If ‘optimal’,
 the Oset is used, if ‘links_coeffs’, the coefficients in links_coeffs are used,
@@ -3348,7 +3317,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>shape</em><em> (</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>)</em>) – Matrix format of graph in string format.</p></li>
+<li><p><strong>graph</strong> (<em>array of shape</em><em> (</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>)</em>) – Matrix format of graph in string format.</p></li>
 <li><p><strong>parents_only</strong> (<em>bool</em>) – Whether to only return parents (’–&gt;’ in graph)</p></li>
 </ul>
 </dd>
@@ -3410,7 +3379,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>alternative_conditions</strong> (<em>set</em><em> of </em><em>tuples</em>) – Used only internally in optimality theorem. If None, self.S is used.</p></li>
+<li><p><strong>alternative_conditions</strong> (<em>set of tuples</em>) – Used only internally in optimality theorem. If None, self.S is used.</p></li>
 <li><p><strong>minimize</strong> (<em>{False</em><em>, </em><em>True</em><em>, </em><em>'colliders_only'}</em>) – Minimize optimal set. If True, minimize such that no subset
 can be removed without making it invalid. If ‘colliders_only’,
 only colliders are minimized.</p></li>
@@ -3542,7 +3511,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>all_parents</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, 0), …], 1:[], …} containing
 the parents estimated with PCMCI.</p></li>
-<li><p><strong>selected_variables</strong> (<em>list</em><em> of </em><em>integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to estimate parents only for selected variables. If None is
+<li><p><strong>selected_variables</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to estimate parents only for selected variables. If None is
 passed, parents are estimated for all variables.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in all_parents is used.</p></li>
 <li><p><strong>cut_off</strong> (<em>{'max_lag_or_tau_max'</em><em>, </em><em>'2xtau_max'</em><em>, </em><em>'max_lag'}</em>) – How many samples to cutoff at the beginning. The default is
@@ -3591,10 +3560,10 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>lists</em><em> of </em><em>tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
-<li><p><strong>Y</strong> (<em>lists</em><em> of </em><em>tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
-<li><p><strong>Z</strong> (<em>lists</em><em> of </em><em>tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
-<li><p><strong>conditions</strong> (<em>list</em><em> of </em><em>tuples.</em>) – Conditions for estimating conditional causal effects.</p></li>
+<li><p><strong>X</strong> (<em>lists of tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
+<li><p><strong>Y</strong> (<em>lists of tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
+<li><p><strong>Z</strong> (<em>lists of tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
+<li><p><strong>conditions</strong> (<em>list of tuples.</em>) – Conditions for estimating conditional causal effects.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in all_parents is used.</p></li>
 <li><p><strong>cut_off</strong> (<em>{'max_lag_or_tau_max'</em><em>, </em><em>'2xtau_max'</em><em>, </em><em>'max_lag'}</em>) – How many samples to cutoff at the beginning. The default is
 ‘max_lag_or_tau_max’, which uses the maximum of tau_max and the
@@ -3711,7 +3680,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </pre></div>
 </div>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id31" role="note">
 <span class="label"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></span>
 <span class="backrefs">(<a role="doc-backlink" href="#id29">1</a>,<a role="doc-backlink" href="#id30">2</a>,<a role="doc-backlink" href="#id32">3</a>)</span>
@@ -3719,7 +3687,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 complex spatio-temporal systems.
 Nature Communications, 6, 8502. <a class="reference external" href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -4025,8 +3992,8 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
 <li><p><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</p></li>
 <li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables.</p></li>
-<li><p><strong>notk</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables to exclude.</p></li>
+<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list of ints</em>) – Indices of mediator variables.</p></li>
+<li><p><strong>notk</strong> (<em>int</em><em> or </em><em>list of ints</em>) – Indices of mediator variables to exclude.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -4097,7 +4064,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
 <li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables.</p></li>
+<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list of ints</em>) – Indices of mediator variables.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -4121,7 +4088,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
 <li><p><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</p></li>
 <li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables.</p></li>
+<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list of ints</em>) – Indices of mediator variables.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -4261,7 +4228,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>target_predictors</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …} containing
 the predictors estimated with PCMCI.</p></li>
-<li><p><strong>selected_targets</strong> (<em>list</em><em> of </em><em>integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to fit model only for selected targets. If None is
+<li><p><strong>selected_targets</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to fit model only for selected targets. If None is
 passed, models are estimated for all variables.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in target_predictors is
 used.</p></li>
@@ -4286,14 +4253,14 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_targets</strong> (<em>list</em><em> of </em><em>ints</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variables to estimate predictors of. If None, predictors of
+<li><p><strong>selected_targets</strong> (<em>list of ints</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variables to estimate predictors of. If None, predictors of
 all variables are estimated.</p></li>
 <li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
 specifying whether only selected links should be tested. If None is
 passed, all links are tested</p></li>
 <li><p><strong>steps_ahead</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Minimum time lag to test. Useful for multi-step ahead predictions.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list</em><em> of </em><em>floats</em><em>, </em><em>default: 0.2</em>) – Significance level in algorithm. If a list or None is passed, the
+<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default: 0.2</em>) – Significance level in algorithm. If a list or None is passed, the
 pc_alpha level is optimized for every variable across the given
 pc_alpha values using the score computed in
 cond_ind_test.get_model_selection_criterion()</p></li>
@@ -4338,7 +4305,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>target</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>integers</em>) – Index or indices of target variable(s).</p></li>
+<li><p><strong>target</strong> (<em>int</em><em> or </em><em>list of integers</em>) – Index or indices of target variable(s).</p></li>
 <li><p><strong>new_data</strong> (<em>data object</em><em>, </em><em>optional</em>) – New Tigramite dataframe object with optional new mask. Note that
 the data will be cut off according to cut_off, see parameter
 <cite>cut_off</cite> below.</p></li>
@@ -4367,7 +4334,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="sig sig-object py" id="tigramite.data_processing.DataFrame">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">DataFrame</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_flag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vector_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datatime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analysis_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'single'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_missing_upto_maxlag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame" title="Permalink to this definition">¶</a></dt>
 <dd><dl>
-<dt>Data object containing single or multiple time series arrays and optional</dt><dd><p>mask.</p>
+<dt>Data object containing single or multiple time series arrays and optional</dt><dd><p>mask, as well as variable definitions.</p>
 <dl class="simple">
 <dt>data<span class="classifier">array-like</span></dt><dd><dl class="simple">
 <dt>if analysis_mode == ‘single’:</dt><dd><p>1) Numpy array of shape (observations T, variables N)
@@ -4500,7 +4467,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt>If reference_points is not None:</dt><dd><p>1D numpy array holding all specified reference_points, less those
 smaller than 0 and larger than self.largest_time_step-1</p>
 </dd>
-<dt>If reference_points is None:</dt><dd><p>Is np.array(range(self.largest_time_step))</p>
+<dt>If reference_points is None:</dt><dd><p>Is np.array(self.largest_time_step)</p>
 </dd>
 </dl>
 </dd>
@@ -4538,16 +4505,16 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
+<li><p><strong>X</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
 the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
 has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
 the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
 has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
 the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
 has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>extraZ</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
+<li><p><strong>extraZ</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
 the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
 has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em>) – Maximum time lag. This may be used to make sure that estimates for
@@ -4632,7 +4599,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>array, xyz [,XYZ], data_type</strong> – xyz identifier array of shape (dim,) identifying which row in array
 corresponds to X, Y, and Z, and the type mask that indicates which samples
-are continuous or discrete. For example:: X = [(0, -1)],
+are continuous or discrete. For example: X = [(0, -1)],
 Y = [(1, 0)], Z = [(1, -1), (0, -2)] yields an array of shape
 (4, n_samples) and xyz is xyz = numpy.array([0,1,2,2]). If
 return_cleaned_xyz is True, also outputs the cleaned XYZ lists.</p>
@@ -4650,20 +4617,20 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>Data array</em><em> of </em><em>shape</em><em> (</em><em>dim</em><em>, </em><em>T</em><em>)</em>) – Data array.</p></li>
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+<li><p><strong>array</strong> (<em>Data array of shape</em><em> (</em><em>dim</em><em>, </em><em>T</em><em>)</em>) – Data array.</p></li>
+<li><p><strong>X</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
 where var specifies the variable index. X typically is of the form
 [(varX, -tau)] with tau denoting the time lag and Z can be
 multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
 where var specifies the variable index. X typically is of the form
 [(varX, -tau)] with tau denoting the time lag and Z can be
 multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
 where var specifies the variable index. X typically is of the form
 [(varX, -tau)] with tau denoting the time lag and Z can be
 multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>extraZ</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+<li><p><strong>extraZ</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
 where var specifies the variable index. X typically is of the form
 [(varX, -tau)] with tau denoting the time lag and Z can be
 multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
@@ -4723,7 +4690,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>mode</strong> (<em>str</em>) – Which mode to use.</p></li>
 </ul>
 </dd>
@@ -4950,7 +4917,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd><p>Helper function to convert DAG graph to dictionary of parents.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>dag</strong> (<em>array</em><em> of </em><em>shape</em><em> (</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>)</em>) – Matrix format of graph in string format. Must be DAG.</p>
+<dd class="field-odd"><p><strong>dag</strong> (<em>array of shape</em><em> (</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>)</em>) – Matrix format of graph in string format. Must be DAG.</p>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>parents</strong> – Dictionary of form {0:[(0, -1), …], 1:[…], …}.</p>
@@ -4963,7 +4930,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.toymodels.structural_causal_processes.generate_structural_causal_process">
-<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">generate_structural_causal_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">L</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_funcs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['linear']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[-0.5,</span> <span class="pre">0.5]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">0.7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contemp_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_dists</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['gaussian']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_means</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_sigmas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">2.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#generate_structural_causal_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.generate_structural_causal_process" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">generate_structural_causal_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">L</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_funcs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['linear']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[-</span> <span class="pre">0.5,</span> <span class="pre">0.5]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">0.7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contemp_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_dists</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['gaussian']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_means</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_sigmas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">2.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#generate_structural_causal_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.generate_structural_causal_process" title="Permalink to this definition">¶</a></dt>
 <dd><p>“Randomly generates a structural causal process based on input characteristics.</p>
 <p>The process has the form</p>
 <div class="math">
@@ -5066,7 +5033,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 number of variables N. coeff must be a float and func a python
 callable of one argument.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample size.</p></li>
-<li><p><strong>noises</strong> (<em>list</em><em> of </em><em>callables</em><em> or </em><em>array</em><em>, </em><em>optional</em><em> (</em><em>default: 'np.random.randn'</em><em>)</em>) – Random distribution function that is called with noises[j](T). If an array,
+<li><p><strong>noises</strong> (<em>list of callables</em><em> or </em><em>array</em><em>, </em><em>optional</em><em> (</em><em>default: 'np.random.randn'</em><em>)</em>) – Random distribution function that is called with noises[j](T). If an array,
 it must be of shape ((transient_fraction + 1)*T, N).</p></li>
 <li><p><strong>intervention</strong> (<em>dict</em>) – Dictionary of format: {1:np.array, …} containing only keys of intervened
 variables with the value being the array of length T with interventional values.
@@ -5161,7 +5128,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.plotting.plot_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto-MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_autodependency_lags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto-MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_autodependency_lags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a network plot.</p>
 <p>This is still in beta. The network is defined from links in graph. Nodes
 denote variables, straight links contemporaneous dependencies and curved
@@ -5178,7 +5145,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Must be of same shape as val_matrix.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
+<li><p><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
 <li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) – Test statistic label.</p></li>
@@ -5247,7 +5214,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.plotting.plot_mediation_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a network plot visualizing the pathways of a mediation analysis.
 This is still in beta. The network is defined from non-zero entries in
 <code class="docutils literal notranslate"><span class="pre">path_val_matrix</span></code>.  Nodes denote variables, straight links contemporaneous
@@ -5263,7 +5230,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>path_val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing link weight values.</p></li>
 <li><p><strong>path_node_array</strong> (<em>array_like</em>) – Array of shape (N,) containing node values.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
+<li><p><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
 <li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) – Link colorbar label.</p></li>
@@ -5302,7 +5269,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.plotting.plot_mediation_time_series_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tsg_path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_time_series_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tsg_path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_time_series_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a mediation time series graph plot.
 This is still in beta. The time series graph’s links are colored by
 val_matrix.</p>
@@ -5312,7 +5279,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>tsg_path_val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N*tau_max, N*tau_max) containing link weight values.</p></li>
 <li><p><strong>path_node_array</strong> (<em>array_like</em>) – Array of shape (N,) containing node values.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
+<li><p><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
 <li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) – Link colorbar label.</p></li>
@@ -5375,7 +5342,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.plotting.plot_time_series_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_time_series_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_time_series_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a time series graph.
 This is still in beta. The time series graph’s links are colored by
 val_matrix.</p>
@@ -5387,7 +5354,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 (N, N, tau_max+1, tau_max+1) describing auxADMG.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of same shape as graph containing test statistic values.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
+<li><p><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
 <li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) – Test statistic label.</p></li>
@@ -5431,9 +5398,9 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>fig_axes</strong> (<em>subplots instance</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created as
 fig, axes = pyplot.subplots(N,…)</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
+<li><p><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</p></li>
-<li><p><strong>var_units</strong> (<em>list</em><em> of </em><em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Units of variables.</p></li>
+<li><p><strong>var_units</strong> (<em>list of str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Units of variables.</p></li>
 <li><p><strong>time_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: ''</em><em>)</em>) – Label of time axis.</p></li>
 <li><p><strong>grey_masked_samples</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to mark masked samples by grey fills (‘fill’) or grey data
 (‘data’).</p></li>
@@ -5473,7 +5440,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>N</strong> (<em>int</em>) – Number of variables</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
+<li><p><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</p></li>
 <li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
 <li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
@@ -5525,7 +5492,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py class">
 <dt class="sig sig-object py" id="tigramite.plotting.setup_matrix">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">setup_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minimum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_gridlines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">setup_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minimum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_gridlines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create matrix of lag function panels.
 Class to setup figure object. The function add_lagfuncs(…) allows to plot
 the val_matrix of shape (N, N, tau_max+1). Multiple lagfunctions can be
@@ -5536,7 +5503,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>N</strong> (<em>int</em>) – Number of variables</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em>) – Maximum time lag.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
+<li><p><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</p></li>
 <li><p><strong>minimum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Lower y-axis limit.</p></li>
 <li><p><strong>maximum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Upper y-axis limit.</p></li>
@@ -5601,7 +5568,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>N</strong> (<em>int</em>) – Number of variables</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
+<li><p><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</p></li>
 <li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
 <li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
@@ -5735,8 +5702,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
       |
       <a href="_sources/index.rst.txt"
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diff --git a/docs/_modules/abc.html b/docs/_modules/abc.html
index 40278c42..78f38697 100644
--- a/docs/_modules/abc.html
+++ b/docs/_modules/abc.html
@@ -39,7 +39,7 @@ <h1>Source code for abc</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">abstractmethod</span><span class="p">(</span><span class="n">funcobj</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A decorator indicating abstract methods.</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A decorator indicating abstract methods.</span>
 
 <span class="sd">    Requires that the metaclass is ABCMeta or derived from it.  A</span>
 <span class="sd">    class that has a metaclass derived from ABCMeta cannot be</span>
@@ -60,7 +60,7 @@ <h1>Source code for abc</h1><div class="highlight"><pre>
 
 
 <span class="k">class</span> <span class="nc">abstractclassmethod</span><span class="p">(</span><span class="nb">classmethod</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A decorator indicating abstract classmethods.</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A decorator indicating abstract classmethods.</span>
 
 <span class="sd">    Deprecated, use &#39;classmethod&#39; with &#39;abstractmethod&#39; instead:</span>
 
@@ -74,13 +74,13 @@ <h1>Source code for abc</h1><div class="highlight"><pre>
 
     <span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callable</span><span class="p">):</span>
-        <span class="n">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">callable</span><span class="p">)</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">callable</span><span class="p">):</span>
+        <span class="nb">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="nb">callable</span><span class="p">)</span>
 
 
 <span class="k">class</span> <span class="nc">abstractstaticmethod</span><span class="p">(</span><span class="nb">staticmethod</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A decorator indicating abstract staticmethods.</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A decorator indicating abstract staticmethods.</span>
 
 <span class="sd">    Deprecated, use &#39;staticmethod&#39; with &#39;abstractmethod&#39; instead:</span>
 
@@ -94,13 +94,13 @@ <h1>Source code for abc</h1><div class="highlight"><pre>
 
     <span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callable</span><span class="p">):</span>
-        <span class="n">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">callable</span><span class="p">)</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">callable</span><span class="p">):</span>
+        <span class="nb">callable</span><span class="o">.</span><span class="n">__isabstractmethod__</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="nb">callable</span><span class="p">)</span>
 
 
 <span class="k">class</span> <span class="nc">abstractproperty</span><span class="p">(</span><span class="nb">property</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A decorator indicating abstract properties.</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A decorator indicating abstract properties.</span>
 
 <span class="sd">    Deprecated, use &#39;property&#39; with &#39;abstractmethod&#39; instead:</span>
 
@@ -124,7 +124,7 @@ <h1>Source code for abc</h1><div class="highlight"><pre>
     <span class="n">ABCMeta</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="s1">&#39;abc&#39;</span>
 <span class="k">else</span><span class="p">:</span>
     <span class="k">class</span> <span class="nc">ABCMeta</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Metaclass for defining Abstract Base Classes (ABCs).</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Metaclass for defining Abstract Base Classes (ABCs).</span>
 
 <span class="sd">        Use this metaclass to create an ABC.  An ABC can be subclassed</span>
 <span class="sd">        directly, and then acts as a mix-in class.  You can also register</span>
@@ -142,22 +142,22 @@ <h1>Source code for abc</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="bp">cls</span>
 
         <span class="k">def</span> <span class="nf">register</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">subclass</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Register a virtual subclass of an ABC.</span>
+<span class="w">            </span><span class="sd">&quot;&quot;&quot;Register a virtual subclass of an ABC.</span>
 
 <span class="sd">            Returns the subclass, to allow usage as a class decorator.</span>
 <span class="sd">            &quot;&quot;&quot;</span>
             <span class="k">return</span> <span class="n">_abc_register</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">subclass</span><span class="p">)</span>
 
         <span class="k">def</span> <span class="fm">__instancecheck__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">instance</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Override for isinstance(instance, cls).&quot;&quot;&quot;</span>
+<span class="w">            </span><span class="sd">&quot;&quot;&quot;Override for isinstance(instance, cls).&quot;&quot;&quot;</span>
             <span class="k">return</span> <span class="n">_abc_instancecheck</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">instance</span><span class="p">)</span>
 
         <span class="k">def</span> <span class="fm">__subclasscheck__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">subclass</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Override for issubclass(subclass, cls).&quot;&quot;&quot;</span>
+<span class="w">            </span><span class="sd">&quot;&quot;&quot;Override for issubclass(subclass, cls).&quot;&quot;&quot;</span>
             <span class="k">return</span> <span class="n">_abc_subclasscheck</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">subclass</span><span class="p">)</span>
 
         <span class="k">def</span> <span class="nf">_dump_registry</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Debug helper to print the ABC registry.&quot;&quot;&quot;</span>
+<span class="w">            </span><span class="sd">&quot;&quot;&quot;Debug helper to print the ABC registry.&quot;&quot;&quot;</span>
             <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Class: </span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__module__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__qualname__</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">file</span><span class="p">)</span>
             <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Inv. counter: </span><span class="si">{</span><span class="n">get_cache_token</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">file</span><span class="p">)</span>
             <span class="p">(</span><span class="n">_abc_registry</span><span class="p">,</span> <span class="n">_abc_cache</span><span class="p">,</span> <span class="n">_abc_negative_cache</span><span class="p">,</span>
@@ -169,16 +169,16 @@ <h1>Source code for abc</h1><div class="highlight"><pre>
                   <span class="n">file</span><span class="o">=</span><span class="n">file</span><span class="p">)</span>
 
         <span class="k">def</span> <span class="nf">_abc_registry_clear</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Clear the registry (for debugging or testing).&quot;&quot;&quot;</span>
+<span class="w">            </span><span class="sd">&quot;&quot;&quot;Clear the registry (for debugging or testing).&quot;&quot;&quot;</span>
             <span class="n">_reset_registry</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
 
         <span class="k">def</span> <span class="nf">_abc_caches_clear</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;Clear the caches (for debugging or testing).&quot;&quot;&quot;</span>
+<span class="w">            </span><span class="sd">&quot;&quot;&quot;Clear the caches (for debugging or testing).&quot;&quot;&quot;</span>
             <span class="n">_reset_caches</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
 
 
 <span class="k">class</span> <span class="nc">ABC</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Helper class that provides a standard way to create an ABC using</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper class that provides a standard way to create an ABC using</span>
 <span class="sd">    inheritance.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="vm">__slots__</span> <span class="o">=</span> <span class="p">()</span>
@@ -233,7 +233,7 @@ <h3 id="searchlabel">Quick search</h3>
       <div class="clearer"></div>
     </div>
     <div class="footer">
-      &copy;2022, Jakob Runge.
+      &copy;2023, Jakob Runge.
       
       |
       Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index 104a1a54..fca319d3 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../_static/alabaster.css" />
     <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+    <script src="../_static/jquery.js"></script>
+    <script src="../_static/underscore.js"></script>
+    <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
    
@@ -49,6 +51,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="tigramite/models.html">tigramite.models</a></li>
 <li><a href="tigramite/pcmci.html">tigramite.pcmci</a></li>
 <li><a href="tigramite/plotting.html">tigramite.plotting</a></li>
+<li><a href="tigramite/rpcmci.html">tigramite.rpcmci</a></li>
 <li><a href="tigramite/toymodels/structural_causal_processes.html">tigramite.toymodels.structural_causal_processes</a></li>
 </ul>
 
@@ -102,8 +105,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/causal_effects.html b/docs/_modules/tigramite/causal_effects.html
index d1224f12..7840b799 100644
--- a/docs/_modules/tigramite/causal_effects.html
+++ b/docs/_modules/tigramite/causal_effects.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -30,7 +32,7 @@
           <div class="body" role="main">
             
   <h1>Source code for tigramite.causal_effects</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
+<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal inference for time series.&quot;&quot;&quot;</span>
 
 <span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
 <span class="c1">#</span>
@@ -2396,6 +2398,35 @@ <h1>Source code for tigramite.causal_effects</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">confidence_interval</span></div>
 
+<div class="viewcode-block" id="CausalEffects.get_dict_from_graph"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.get_dict_from_graph">[docs]</a>    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">get_dict_from_graph</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">parents_only</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to convert graph to dictionary of links.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ---------</span>
+<span class="sd">        graph : array of shape (N, N, tau_max+1)</span>
+<span class="sd">            Matrix format of graph in string format.</span>
+
+<span class="sd">        parents_only : bool</span>
+<span class="sd">            Whether to only return parents (&#39;--&gt;&#39; in graph)</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        links : dict</span>
+<span class="sd">            Dictionary of form {0:{(0, -1): o-o, ...}, 1:{...}, ...}.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">N</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">links</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">j</span><span class="p">,</span> <span class="p">{})</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)])</span>
+
+        <span class="k">if</span> <span class="n">parents_only</span><span class="p">:</span>
+            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="o">==</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">)):</span>
+                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="o">!=</span><span class="s1">&#39;&#39;</span><span class="p">)):</span>
+                <span class="n">links</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">links</span></div>
 
 <div class="viewcode-block" id="CausalEffects.get_graph_from_dict"><a class="viewcode-back" href="../../index.html#tigramite.causal_effects.CausalEffects.get_graph_from_dict">[docs]</a>    <span class="nd">@staticmethod</span>
     <span class="k">def</span> <span class="nf">get_graph_from_dict</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@@ -2487,46 +2518,94 @@ <h1>Source code for tigramite.causal_effects</h1><div class="highlight"><pre>
     <span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
 
 
-    <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-    <span class="n">coeff</span> <span class="o">=</span> <span class="mf">.5</span>
+    <span class="c1"># def lin_f(x): return x</span>
+    <span class="c1"># coeff = .5</span>
  
-    <span class="n">links_coeffs</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">1</span><span class="p">:</span> <span class="p">[((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">2</span><span class="p">:</span> <span class="p">[((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)]</span>
-             <span class="p">}</span>
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    <span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">structural_causal_process</span><span class="p">(</span>
-        <span class="n">links_coeffs</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
-    <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">CausalEffects</span><span class="o">.</span><span class="n">get_graph_from_dict</span><span class="p">(</span><span class="n">links_coeffs</span><span class="p">)</span>
-
-    <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)]</span>
-    <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-
-    <span class="c1"># Initialize class as `stationary_dag`</span>
-    <span class="n">causal_effects</span> <span class="o">=</span> <span class="n">CausalEffects</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">graph_type</span><span class="o">=</span><span class="s1">&#39;stationary_dag&#39;</span><span class="p">,</span> 
+    <span class="c1"># links_coeffs = {0: [((0, -1), 0.5, lin_f)],</span>
+    <span class="c1">#          1: [((1, -1), 0.5, lin_f), ((0, -1), 0.5, lin_f)],</span>
+    <span class="c1">#          2: [((2, -1), 0.5, lin_f), ((1, 0), 0.5, lin_f)]</span>
+    <span class="c1">#          }</span>
+    <span class="c1"># T = 1000</span>
+    <span class="c1"># data, nonstat = toys.structural_causal_process(</span>
+    <span class="c1">#     links_coeffs, T=T, noises=None, seed=7)</span>
+    <span class="c1"># dataframe = pp.DataFrame(data)</span>
+
+    <span class="c1"># graph = CausalEffects.get_graph_from_dict(links_coeffs)</span>
+
+    <span class="n">original_graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">]],</span>
+
+       <span class="p">[[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">]],</span>
+
+       <span class="p">[[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">]],</span>
+
+       <span class="p">[[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+        <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
+    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">original_graph</span><span class="p">)</span>
+
+    <span class="c1"># Add T &lt;-&gt; Reco and T </span>
+    <span class="n">graph</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;+-&gt;&#39;</span> <span class="p">;</span> <span class="n">graph</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;-+&#39;</span>
+    <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;-&gt;&#39;</span> <span class="c1">#; graph[2,1,0] = &#39;&lt;--&#39;</span>
+
+    <span class="n">added</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
+    <span class="n">added</span><span class="p">[:]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">added</span> <span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+
+    <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+    <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+
+    <span class="c1"># # Initialize class as `stationary_dag`</span>
+    <span class="n">causal_effects</span> <span class="o">=</span> <span class="n">CausalEffects</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">graph_type</span><span class="o">=</span><span class="s1">&#39;stationary_admg&#39;</span><span class="p">,</span> 
                                 <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">S</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
                                 <span class="n">hidden_variables</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
                                 <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 
-    <span class="n">causal_effects</span><span class="o">.</span><span class="n">fit_wright_effect</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
-                            <span class="c1"># links_coeffs = links_coeffs,</span>
-                            <span class="c1"># mediation = [(1, 0), (1, -1), (1, -2)]</span>
-                            <span class="p">)</span>
-
-    <span class="n">intervention_data</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="n">y1</span> <span class="o">=</span> <span class="n">causal_effects</span><span class="o">.</span><span class="n">predict_wright_effect</span><span class="p">(</span> 
-            <span class="n">intervention_data</span><span class="o">=</span><span class="n">intervention_data</span><span class="p">,</span>
-            <span class="p">)</span>
-
-    <span class="n">intervention_data</span> <span class="o">=</span> <span class="mf">0.</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="n">y2</span> <span class="o">=</span> <span class="n">causal_effects</span><span class="o">.</span><span class="n">predict_wright_effect</span><span class="p">(</span> 
-            <span class="n">intervention_data</span><span class="o">=</span><span class="n">intervention_data</span><span class="p">,</span>
-            <span class="p">)</span>
-
-    <span class="n">beta</span> <span class="o">=</span> <span class="p">(</span><span class="n">y1</span> <span class="o">-</span> <span class="n">y2</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Causal effect is </span><span class="si">%.5f</span><span class="s2">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">beta</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">causal_effects</span><span class="o">.</span><span class="n">get_optimal_set</span><span class="p">())</span>
+
+    <span class="n">tp</span><span class="o">.</span><span class="n">plot_time_series_graph</span><span class="p">(</span>
+        <span class="n">graph</span> <span class="o">=</span> <span class="n">graph</span><span class="p">,</span>
+        <span class="n">save_name</span><span class="o">=</span><span class="s1">&#39;Example_graph_in.pdf&#39;</span><span class="p">,</span>
+        <span class="c1"># special_nodes=special_nodes,</span>
+        <span class="c1"># var_names=var_names,</span>
+        <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+        <span class="p">)</span>
+
+    <span class="n">tp</span><span class="o">.</span><span class="n">plot_time_series_graph</span><span class="p">(</span>
+        <span class="n">graph</span> <span class="o">=</span> <span class="n">causal_effects</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span>
+        <span class="n">save_name</span><span class="o">=</span><span class="s1">&#39;Example_graph_out.pdf&#39;</span><span class="p">,</span>
+        <span class="c1"># special_nodes=special_nodes,</span>
+        <span class="c1"># var_names=var_names,</span>
+        <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+        <span class="p">)</span>
+
+    <span class="c1"># causal_effects.fit_wright_effect(dataframe=dataframe, </span>
+    <span class="c1">#                         # links_coeffs = links_coeffs,</span>
+    <span class="c1">#                         # mediation = [(1, 0), (1, -1), (1, -2)]</span>
+    <span class="c1">#                         )</span>
+
+    <span class="c1"># intervention_data = 1.*np.ones((1, 1))</span>
+    <span class="c1"># y1 = causal_effects.predict_wright_effect( </span>
+    <span class="c1">#         intervention_data=intervention_data,</span>
+    <span class="c1">#         )</span>
+
+    <span class="c1"># intervention_data = 0.*np.ones((1, 1))</span>
+    <span class="c1"># y2 = causal_effects.predict_wright_effect( </span>
+    <span class="c1">#         intervention_data=intervention_data,</span>
+    <span class="c1">#         )</span>
+
+    <span class="c1"># beta = (y1 - y2)</span>
+    <span class="c1"># print(&quot;Causal effect is %.5f&quot; %(beta))</span>
 
     <span class="c1"># tp.plot_time_series_graph(</span>
     <span class="c1">#     graph = causal_effects.graph,</span>
@@ -2693,8 +2772,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/data_processing.html b/docs/_modules/tigramite/data_processing.html
index e80751ac..fe5e4e3f 100644
--- a/docs/_modules/tigramite/data_processing.html
+++ b/docs/_modules/tigramite/data_processing.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -50,7 +52,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="DataFrame"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.DataFrame">[docs]</a><span class="k">class</span> <span class="nc">DataFrame</span><span class="p">():</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Data object containing single or multiple time series arrays and optional </span>
-<span class="sd">    mask.</span>
+<span class="sd">    mask, as well as variable definitions.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -70,7 +72,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 <span class="sd">            N is fixed. </span>
 <span class="sd">    mask : array-like, optional (default: None)</span>
 <span class="sd">        Optional mask array, must be of same format and shape as data.</span>
-<span class="sd">   type_mask : array-like</span>
+<span class="sd">   data_type : array-like</span>
 <span class="sd">        Binary data array of same shape as array which describes whether </span>
 <span class="sd">        individual samples in a variable (or all samples) are continuous </span>
 <span class="sd">        or discrete: 0s for continuous variables and 1s for discrete variables.</span>
@@ -80,7 +82,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 <span class="sd">        remove_missing_upto_maxlag=True also flags samples for all lags up to</span>
 <span class="sd">        2*tau_max (more precisely, this depends on the cut_off argument in</span>
 <span class="sd">        self.construct_array(), see further below). This avoids biases, see</span>
-<span class="sd">        section on masking in Supplement of [1]_.</span>
+<span class="sd">        section on masking in Supplement of Runge et al. SciAdv (2019).</span>
 <span class="sd">    vector_vars : dict</span>
 <span class="sd">        Dictionary of vector variables of the form,</span>
 <span class="sd">        Eg. {0: [(0, 0), (1, 0)], 1: [(2, 0)], 2: [(3, 0)], 3: [(4, 0)]}</span>
@@ -149,7 +151,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 <span class="sd">    self.mask : dictionary</span>
 <span class="sd">        Mask internally mapped to a dictionary representation in the same way as</span>
 <span class="sd">        data is mapped to self.values</span>
-<span class="sd">    self.type_mask : array-like</span>
+<span class="sd">    self.data_type : array-like</span>
 <span class="sd">        Binary data array of same shape as array which describes whether </span>
 <span class="sd">        individual samples in a variable (or all samples) are continuous </span>
 <span class="sd">        or discrete: 0s for continuous variables and 1s for discrete variables.</span>
@@ -169,7 +171,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 <span class="sd">            1D numpy array holding all specified reference_points, less those</span>
 <span class="sd">            smaller than 0 and larger than self.largest_time_step-1</span>
 <span class="sd">        If reference_points is None:</span>
-<span class="sd">            Is np.array(range(self.largest_time_step))</span>
+<span class="sd">            Is np.array(self.largest_time_step)</span>
 <span class="sd">    self.time_offsets : dictionary</span>
 <span class="sd">        If time_offsets is not None:</span>
 <span class="sd">            Is time_offsets</span>
@@ -192,7 +194,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">missing_flag</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vector_vars</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">var_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">datatime</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">analysis_mode</span> <span class="o">=</span><span class="s1">&#39;single&#39;</span><span class="p">,</span> <span class="n">reference_points</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">datatime</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">analysis_mode</span> <span class="o">=</span><span class="s1">&#39;single&#39;</span><span class="p">,</span> <span class="n">reference_points</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
         <span class="n">time_offsets</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">remove_missing_upto_maxlag</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 
         <span class="c1"># Check that a valid analysis mode, specified by the argument</span>
@@ -332,6 +334,11 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Ndata</span><span class="p">),</span> <span class="p">[[(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> 
                                 <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Ndata</span><span class="p">)]))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">has_vector_data</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">has_vector_data</span> <span class="o">=</span> <span class="kc">True</span>
+
+
         <span class="c1"># TODO: check vector_vars!</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vector_vars</span><span class="p">)</span>
 
@@ -356,9 +363,9 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="p">)</span>
             
-        <span class="bp">self</span><span class="o">.</span><span class="n">type_mask</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="n">type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">,</span> <span class="n">check_type_mask</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_type</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">data_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">check_data_type</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
         <span class="c1"># Check and prepare the time offsets</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_set_time_offsets</span><span class="p">(</span><span class="n">time_offsets</span><span class="p">)</span>
@@ -394,7 +401,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap</span> <span class="o">=</span> <span class="kc">None</span>
 
 
-    <span class="k">def</span> <span class="nf">_check_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">check_type_mask</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_check_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">check_data_type</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Checks that the mask is:</span>
 <span class="sd">            * The same shape as the data</span>
 <span class="sd">            * Is an numpy ndarray (or subtype)</span>
@@ -449,7 +456,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
                 <span class="k">if</span> <span class="n">_use_mask_dict_data</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">dataset_data</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
                     <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">_use_mask_dict_data</span><span class="p">))</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
                         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;NaNs in the data mask&quot;</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">check_type_mask</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">check_data_type</span><span class="p">:</span>
                         <span class="k">if</span> <span class="ow">not</span> <span class="nb">set</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">_use_mask_dict_data</span><span class="p">))</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])):</span>
                             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type mask contains other values than 0 and 1&quot;</span><span class="p">)</span>
                 <span class="k">else</span><span class="p">:</span>
@@ -530,7 +537,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">reference_points</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="c1"># If no reference point is specified, use as many reference points</span>
             <span class="c1"># as possible</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">largest_time_step</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">reference_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">largest_time_step</span><span class="p">)</span>
 
         <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reference_points</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
             <span class="c1"># If a single reference point is specified as an int, convert it to</span>
@@ -581,7 +588,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
                         <span class="n">extraZ</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                         <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                         <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                        <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                         <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                         <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                         <span class="n">remove_overlaps</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
@@ -609,11 +616,11 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 <span class="sd">            Masking mode: Indicators for which variables in the dependence</span>
 <span class="sd">            measure I(X; Y | Z) the samples should be masked. If None, the mask</span>
 <span class="sd">            is not used. Explained in tutorial on masking and missing values.</span>
-<span class="sd">        type_mask : array-like</span>
+<span class="sd">        data_type : array-like</span>
 <span class="sd">            Binary data array of same shape as array which describes whether </span>
 <span class="sd">            individual samples in a variable (or all samples) are continuous </span>
 <span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
-<span class="sd">            If it is set, then it overrides the self.type_mask assigned to the dataframe.</span>
+<span class="sd">            If it is set, then it overrides the self.data_type assigned to the dataframe.</span>
 <span class="sd">        return_cleaned_xyz : bool, optional (default: False)</span>
 <span class="sd">            Whether to return cleaned X,Y,Z, where possible duplicates are</span>
 <span class="sd">            removed.</span>
@@ -674,10 +681,10 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        array, xyz [,XYZ], type_mask : Tuple of data array of shape (dim, n_samples),</span>
+<span class="sd">        array, xyz [,XYZ], data_type : Tuple of data array of shape (dim, n_samples),</span>
 <span class="sd">            xyz identifier array of shape (dim,) identifying which row in array</span>
 <span class="sd">            corresponds to X, Y, and Z, and the type mask that indicates which samples</span>
-<span class="sd">            are continuous or discrete. For example:: X = [(0, -1)],</span>
+<span class="sd">            are continuous or discrete. For example: X = [(0, -1)],</span>
 <span class="sd">            Y = [(1, 0)], Z = [(1, -1), (0, -2)] yields an array of shape</span>
 <span class="sd">            (4, n_samples) and xyz is xyz = numpy.array([0,1,2,2]). If</span>
 <span class="sd">            return_cleaned_xyz is True, also outputs the cleaned XYZ lists.</span>
@@ -732,11 +739,11 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">_mask</span><span class="p">)</span>
             
-        <span class="n">_type_mask</span> <span class="o">=</span> <span class="n">type_mask</span>
-        <span class="k">if</span> <span class="n">_type_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">_type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">type_mask</span>
+        <span class="n">_data_type</span> <span class="o">=</span> <span class="n">data_type</span>
+        <span class="k">if</span> <span class="n">_data_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">_data_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_type</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">_type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">_type_mask</span><span class="p">,</span> <span class="n">check_type_mask</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">_data_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">mask</span> <span class="o">=</span> <span class="n">_data_type</span><span class="p">,</span> <span class="n">check_data_type</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
         <span class="c1"># Figure out what cut off we will be using</span>
         <span class="k">if</span> <span class="n">cut_off</span> <span class="o">==</span> <span class="s1">&#39;2xtau_max&#39;</span><span class="p">:</span>
@@ -766,7 +773,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
         <span class="c1"># Run through all datasets and fill a dictionary holding the</span>
         <span class="c1"># samples taken from the individual datasets</span>
         <span class="n">samples_datasets</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="n">type_masks</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+        <span class="n">data_types</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">use_indices_dataset_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
 
         <span class="k">for</span> <span class="n">dataset_key</span><span class="p">,</span> <span class="n">dataset_data</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
@@ -860,11 +867,11 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
             <span class="n">use_indices_dataset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;int&#39;</span><span class="p">)</span>
 
             <span class="c1"># Build the type mask array corresponding to this dataset</span>
-            <span class="k">if</span> <span class="n">_type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">type_mask_dataset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;bool&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">_data_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">data_type_dataset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_points_here</span><span class="p">)),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;bool&#39;</span><span class="p">)</span>
                 <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">XYZ</span><span class="p">):</span>
-                    <span class="n">type_mask_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">_type_mask</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">][</span><span class="n">ref_points_here</span> <span class="o">+</span> <span class="n">lag</span><span class="p">,</span> <span class="n">var</span><span class="p">]</span>
-                <span class="n">type_masks</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">type_mask_dataset</span>
+                    <span class="n">data_type_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">_data_type</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">][</span><span class="n">ref_points_here</span> <span class="o">+</span> <span class="n">lag</span><span class="p">,</span> <span class="n">var</span><span class="p">]</span>
+                <span class="n">data_types</span><span class="p">[</span><span class="n">dataset_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_type_dataset</span>
             
             <span class="c1"># Remove all values that have missing value flag, and optionally as well the time</span>
             <span class="c1"># slices that occur up to max_lag after</span>
@@ -907,8 +914,8 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 
         <span class="c1"># Concatenate the arrays of all datasets</span>
         <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">samples_datasets</span><span class="o">.</span><span class="n">values</span><span class="p">()),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">_type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">type_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">type_masks</span><span class="o">.</span><span class="n">values</span><span class="p">()),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">_data_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">type_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">data_types</span><span class="o">.</span><span class="n">values</span><span class="p">()),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">type_array</span> <span class="o">=</span> <span class="kc">None</span>
         
@@ -963,7 +970,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
         <span class="c1">#     raise ValueError(&quot;Y-nodes are %s, &quot; % str(Y) +</span>
         <span class="c1">#                      &quot;but one of the Y-nodes must have zero lag&quot;)</span>
 
-<div class="viewcode-block" id="DataFrame.print_array_info"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.DataFrame.print_array_info">[docs]</a>    <span class="k">def</span> <span class="nf">print_array_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">missing_flag</span><span class="p">,</span> <span class="n">mask_type</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">extraZ</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="DataFrame.print_array_info"><a class="viewcode-back" href="../../index.html#tigramite.data_processing.DataFrame.print_array_info">[docs]</a>    <span class="k">def</span> <span class="nf">print_array_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">missing_flag</span><span class="p">,</span> <span class="n">mask_type</span><span class="p">,</span> <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">extraZ</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Print info about the constructed array</span>
 
@@ -985,7 +992,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 <span class="sd">            Masking mode: Indicators for which variables in the dependence</span>
 <span class="sd">            measure I(X; Y | Z) the samples should be masked. If None, the mask</span>
 <span class="sd">            is not used. Explained in tutorial on masking and missing values.</span>
-<span class="sd">        type_mask : array-like</span>
+<span class="sd">        data_type : array-like</span>
 <span class="sd">            Binary data array of same shape as array which describes whether </span>
 <span class="sd">            individual samples in a variable (or all samples) are continuous </span>
 <span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
@@ -1001,8 +1008,8 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
             <span class="nb">print</span><span class="p">(</span><span class="n">indt</span> <span class="o">+</span> <span class="s2">&quot;extraZ = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">extraZ</span><span class="p">))</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">mask_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="n">indt</span><span class="o">+</span><span class="s2">&quot;with masked samples in </span><span class="si">%s</span><span class="s2"> removed&quot;</span> <span class="o">%</span> <span class="n">mask_type</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="n">indt</span><span class="o">+</span><span class="s2">&quot;with </span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">iscrete values&quot;</span> <span class="o">%</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span><span class="o">/</span><span class="n">type_mask</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">indt</span><span class="o">+</span><span class="s2">&quot;with </span><span class="si">%s</span><span class="s2"> </span><span class="si">% d</span><span class="s2">iscrete values&quot;</span> <span class="o">%</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">data_type</span><span class="p">)</span><span class="o">/</span><span class="n">data_type</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="n">indt</span><span class="o">+</span><span class="s2">&quot;with missing values = </span><span class="si">%s</span><span class="s2"> removed&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span><span class="p">)</span></div></div>
 
@@ -1631,8 +1638,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/cmiknn.html b/docs/_modules/tigramite/independence_tests/cmiknn.html
index 68c29513..dce8d776 100644
--- a/docs/_modules/tigramite/independence_tests/cmiknn.html
+++ b/docs/_modules/tigramite/independence_tests/cmiknn.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -43,6 +45,7 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
 <span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
 <span class="kn">import</span> <span class="nn">warnings</span>
 
+
 <div class="viewcode-block" id="CMIknn"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknn.CMIknn">[docs]</a><span class="k">class</span> <span class="nc">CMIknn</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Conditional mutual information test based on nearest-neighbor estimator.</span>
 
@@ -113,6 +116,9 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
 <span class="sd">        Number of workers to use for parallel processing. If -1 is given</span>
 <span class="sd">        all processors are used. Default: -1.</span>
 
+<span class="sd">    model_selection_folds : int</span>
+<span class="sd">        Number of folds in cross-validation used in model selection.</span>
+
 <span class="sd">    significance : str, optional (default: &#39;shuffle_test&#39;)</span>
 <span class="sd">        Type of significance test to use. For CMIknn only &#39;fixed_thres&#39; and</span>
 <span class="sd">        &#39;shuffle_test&#39; are available.</span>
@@ -133,6 +139,7 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
                  <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
                  <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;ranks&#39;</span><span class="p">,</span>
                  <span class="n">workers</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                 <span class="n">model_selection_folds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="c1"># Set the member variables</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">=</span> <span class="n">knn</span>
@@ -143,6 +150,7 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
         <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">workers</span> <span class="o">=</span> <span class="n">workers</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_selection_folds</span> <span class="o">=</span> <span class="n">model_selection_folds</span>
         <span class="c1"># Call the parent constructor</span>
         <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">significance</span><span class="o">=</span><span class="n">significance</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="c1"># Print some information about construction</span>
@@ -201,7 +209,7 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># FIXME: If the time series is constant, return nan rather than</span>
             <span class="c1"># raising Exception</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
                 <span class="c1"># raise ValueError(&quot;nans after standardizing, &quot;</span>
                 <span class="c1">#                  &quot;possibly constant array!&quot;)</span>
@@ -420,7 +428,7 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># FIXME: If the time series is constant, return nan rather than</span>
             <span class="c1"># raising Exception</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
             <span class="c1">#     raise ValueError(&quot;nans after standardizing, &quot;</span>
@@ -482,8 +490,80 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
             <span class="n">restricted_permutation</span><span class="p">[</span><span class="n">sample_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">use</span>
             <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">used</span><span class="p">,</span> <span class="n">use</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">restricted_permutation</span></div>
+        <span class="k">return</span> <span class="n">restricted_permutation</span>
+
+<div class="viewcode-block" id="CMIknn.get_model_selection_criterion"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknn.CMIknn.get_model_selection_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_selection_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a cross-validation-based score for nearest-neighbor estimates.</span>
+
+<span class="sd">        Fits a nearest-neighbor model of the parents to variable j and returns</span>
+<span class="sd">        the score. The lower, the better the fit. Here used to determine</span>
+<span class="sd">        optimal hyperparameters in PCMCI(pc_alpha or fixed thres).</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        j : int</span>
+<span class="sd">            Index of target variable in data array.</span>
+
+<span class="sd">        parents : list</span>
+<span class="sd">            List of form [(0, -1), (3, -2), ...] containing parents.</span>
+
+<span class="sd">        tau_max : int, optional (default: 0)</span>
+<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
+<span class="sd">            different lags in X, Z, all have the same sample size.</span>
 
+<span class="sd">        Returns:</span>
+<span class="sd">        score : float</span>
+<span class="sd">            Model score.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="kn">import</span> <span class="nn">sklearn</span>
+        <span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsRegressor</span>
+        <span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
+
+        <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>   <span class="c1"># dummy variable here</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="n">parents</span>
+        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
+                                                    <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
+                                                    <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
+                                                    <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                    <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                                    <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
+        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="c1"># Standardize</span>
+        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+        <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
+                <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
+            <span class="c1"># raise ValueError(&quot;nans after standardizing, &quot;</span>
+            <span class="c1">#                  &quot;possibly constant array!&quot;)</span>
+
+        <span class="n">predictor_indices</span> <span class="o">=</span>  <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">predictor_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">predictor_indices</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">T</span>
+        <span class="c1"># Target is only first entry of Y, ie [y]</span>
+        <span class="n">target_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="p">:]</span>
+
+        <span class="k">if</span> <span class="n">predictor_array</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="c1"># Regressing on ones if empty parents</span>
+            <span class="n">predictor_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">knn_here</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">knn_here</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">))</span>
+
+        <span class="n">knn_model</span> <span class="o">=</span> <span class="n">KNeighborsRegressor</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="n">knn_here</span><span class="p">)</span>
+        
+        <span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">knn_model</span><span class="p">,</span> 
+            <span class="n">X</span><span class="o">=</span><span class="n">predictor_array</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">target_array</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_selection_folds</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">)</span>
+        
+        <span class="c1"># print(scores)</span>
+        <span class="k">return</span> <span class="o">-</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div></div>
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
     
@@ -494,8 +574,8 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
 
     <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
     <span class="n">cmi</span> <span class="o">=</span> <span class="n">CMIknn</span><span class="p">(</span><span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                   <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
-                   <span class="n">fixed_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                   <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;fixed_thres&#39;</span><span class="p">,</span>
+                   <span class="n">fixed_thres</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
                    <span class="n">sig_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
                    <span class="n">sig_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                    <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">,</span>
@@ -505,15 +585,28 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
     <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
     <span class="n">dimz</span> <span class="o">=</span> <span class="mi">1</span>
 
+    <span class="c1"># # Continuous data</span>
+    <span class="c1"># z = random_state.standard_normal((T, dimz))</span>
+    <span class="c1"># x = (1.*z[:,0] + random_state.standard_normal(T)).reshape(T, 1)</span>
+    <span class="c1"># y = (1.*z[:,0] + random_state.standard_normal(T)).reshape(T, 1)</span>
+
+    <span class="c1"># print(&#39;X _|_ Y&#39;)</span>
+    <span class="c1"># print(cmi.run_test_raw(x, y, z=None))</span>
+    <span class="c1"># print(&#39;X _|_ Y | Z&#39;)</span>
+    <span class="c1"># print(cmi.run_test_raw(x, y, z=z))</span>
+
     <span class="c1"># Continuous data</span>
     <span class="n">z</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.8</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.8</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;X _|_ Y&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;X _|_ Y | Z&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">))</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mf">1.</span><span class="o">*</span><span class="n">x</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>
+    <span class="nb">print</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">dataframe</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">cmi</span><span class="o">.</span><span class="n">set_dataframe</span><span class="p">(</span><span class="n">dataframe</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span><span class="n">j</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="p">[],</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">folds</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span><span class="n">j</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">folds</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span><span class="n">j</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">folds</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
 </pre></div>
 
           </div>
@@ -568,8 +661,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/cmiknnmixed.html b/docs/_modules/tigramite/independence_tests/cmiknnmixed.html
deleted file mode 100644
index ab67cc88..00000000
--- a/docs/_modules/tigramite/independence_tests/cmiknnmixed.html
+++ /dev/null
@@ -1,1597 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>tigramite.independence_tests.cmiknnmixed &#8212; Tigramite 5.2 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
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-    <script src="../../../_static/sphinx_highlight.js"></script>
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-   
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-  
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-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for tigramite.independence_tests.cmiknnmixed</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
-
-<span class="c1"># Author: Oana Popescu, Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: GNU General Public License v3.0</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">special</span><span class="p">,</span> <span class="n">spatial</span>
-<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">BallTree</span><span class="p">,</span> <span class="n">NearestNeighbors</span>
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">OneHotEncoder</span><span class="p">,</span> <span class="n">MinMaxScaler</span>
-<span class="kn">from</span> <span class="nn">sklearn.utils.extmath</span> <span class="kn">import</span> <span class="n">cartesian</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
-<span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="c1"># profiling</span>
-<span class="kn">import</span> <span class="nn">cProfile</span><span class="o">,</span> <span class="nn">pstats</span><span class="o">,</span> <span class="nn">io</span>
-<span class="kn">from</span> <span class="nn">pstats</span> <span class="kn">import</span> <span class="n">SortKey</span>
-
-
-<div class="viewcode-block" id="CMIknnMixed"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed">[docs]</a><span class="k">class</span> <span class="nc">CMIknnMixed</span><span class="p">(</span><span class="n">CondIndTest</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Conditional mutual information test based on nearest-neighbor estimator.</span>
-
-<span class="sd">    Conditional mutual information is the most general dependency measure coming</span>
-<span class="sd">    from an information-theoretic framework. It makes no assumptions about the</span>
-<span class="sd">    parametric form of the dependencies by directly estimating the underlying</span>
-<span class="sd">    joint density. The test here is based on the estimator in  S. Frenzel and B.</span>
-<span class="sd">    Pompe, Phys. Rev. Lett. 99, 204101 (2007), combined with a shuffle test to</span>
-<span class="sd">    generate  the distribution under the null hypothesis of independence first</span>
-<span class="sd">    used in the reference below. The knn-estimator is suitable only for variables taking a</span>
-<span class="sd">    continuous range of values. For discrete variables use the CMIsymb class.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    CMI is given by</span>
-
-<span class="sd">    .. math:: I(X;Y|Z) &amp;= \int p(z)  \iint  p(x,y|z) \log</span>
-<span class="sd">                \frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)} \,dx dy dz</span>
-
-<span class="sd">    Its knn-estimator is given by</span>
-
-<span class="sd">    .. math:: \widehat{I}(X;Y|Z)  &amp;=   \psi (k) + \frac{1}{T} \sum_{t=1}^T</span>
-<span class="sd">            \left[ \psi(k_{Z,t}) - \psi(k_{XZ,t}) - \psi(k_{YZ,t}) \right]</span>
-
-<span class="sd">    where :math:`\psi` is the Digamma function.  This estimator has as a</span>
-<span class="sd">    parameter the number of nearest-neighbors :math:`k` which determines the</span>
-<span class="sd">    size of hyper-cubes around each (high-dimensional) sample point. Then</span>
-<span class="sd">    :math:`k_{Z,},k_{XZ},k_{YZ}` are the numbers of neighbors in the respective</span>
-<span class="sd">    subspaces.</span>
-
-<span class="sd">    :math:`k` can be viewed as a density smoothing parameter (although it is</span>
-<span class="sd">    data-adaptive unlike fixed-bandwidth estimators). For large :math:`k`, the</span>
-<span class="sd">    underlying dependencies are more smoothed and CMI has a larger bias,</span>
-<span class="sd">    but lower variance, which is more important for significance testing. Note</span>
-<span class="sd">    that the estimated CMI values can be slightly negative while CMI is a non-</span>
-<span class="sd">    negative quantity.</span>
-<span class="sd">    </span>
-<span class="sd">    For the case of mixed variables, the distance metric changes from the L-inf </span>
-<span class="sd">    norm to ...</span>
-
-<span class="sd">    This method requires the scikit-learn package.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    J. Runge (2018): Conditional Independence Testing Based on a</span>
-<span class="sd">           Nearest-Neighbor Estimator of Conditional Mutual Information.</span>
-<span class="sd">           In Proceedings of the 21st International Conference on Artificial</span>
-<span class="sd">           Intelligence and Statistics.</span>
-<span class="sd">           http://proceedings.mlr.press/v84/runge18a.html</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    knn : int or float, optional (default: 0.2)</span>
-<span class="sd">        Number of nearest-neighbors which determines the size of hyper-cubes</span>
-<span class="sd">        around each (high-dimensional) sample point. If smaller than 1, this is</span>
-<span class="sd">        computed as a fraction of T, hence knn=knn*T. For knn larger or equal to</span>
-<span class="sd">        1, this is the absolute number.</span>
-<span class="sd">        </span>
-<span class="sd">    estimator : string, optional (default: &#39;MS&#39;)</span>
-<span class="sd">        The type of estimator to be used. Three options are available:</span>
-<span class="sd">        Mesner and Shalizi (2021): &#39;MS&#39;, Frenzel and Pompe (2007) with </span>
-<span class="sd">        infinite distance for points from different categories: &#39;FPinf&#39;,</span>
-<span class="sd">        and Zao et.al. (2022) where entropies are computed conditional on </span>
-<span class="sd">        the discrete dimensions of X,Y and Z. </span>
-
-<span class="sd">    shuffle_neighbors : int, optional (default: 5)</span>
-<span class="sd">        Number of nearest-neighbors within Z for the shuffle surrogates which</span>
-<span class="sd">        determines the size of hyper-cubes around each (high-dimensional) sample</span>
-<span class="sd">        point.</span>
-
-<span class="sd">    transform : {&#39;ranks&#39;, &#39;standardize&#39;,  &#39;uniform&#39;, False}, optional</span>
-<span class="sd">        (default: &#39;ranks&#39;)</span>
-<span class="sd">        Whether to transform the array beforehand by standardizing</span>
-<span class="sd">        or transforming to uniform marginals.</span>
-
-<span class="sd">    workers : int (optional, default = -1)</span>
-<span class="sd">        Number of workers to use for parallel processing. If -1 is given</span>
-<span class="sd">        all processors are used. Default: -1.</span>
-<span class="sd">        </span>
-<span class="sd">    rho: list of float, optional (default: [np.inf])</span>
-<span class="sd">        Hyperparameters used for weighting the discrete variable distances. </span>
-<span class="sd">        If not initialized, the distance will be set to np.inf, such that discrete</span>
-<span class="sd">        variables with different values will never be considered neighbors. </span>
-<span class="sd">        Otherwise the rho</span>
-<span class="sd">        ...</span>
-
-<span class="sd">    significance : str, optional (default: &#39;shuffle_test&#39;)</span>
-<span class="sd">        Type of significance test to use. For CMIknn only &#39;fixed_thres&#39; and</span>
-<span class="sd">        &#39;shuffle_test&#39; are available.</span>
-
-<span class="sd">    **kwargs :</span>
-<span class="sd">        Arguments passed on to parent class CondIndTest.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concrete property to return the measure of the independence test</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">knn</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                 <span class="n">estimator</span><span class="o">=</span><span class="s1">&#39;MS&#39;</span><span class="p">,</span>
-                 <span class="n">use_local_knn</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">shuffle_neighbors</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-                 <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;standardize&#39;</span><span class="p">,</span>
-                 <span class="n">scale_range</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                 <span class="n">perc</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">workers</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># Set the member variables</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">=</span> <span class="n">knn</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">=</span> <span class="n">estimator</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span> <span class="o">=</span> <span class="n">use_local_knn</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span> <span class="o">=</span> <span class="n">shuffle_neighbors</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
-        <span class="k">if</span> <span class="n">perc</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">perc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">perc</span> <span class="o">=</span> <span class="n">perc</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">scale_range</span> <span class="o">=</span> <span class="n">scale_range</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_measure</span> <span class="o">=</span> <span class="s1">&#39;cmi_knn_mixed&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_based</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">workers</span> <span class="o">=</span> <span class="n">workers</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="mf">1e-5</span>
-            
-        <span class="c1"># Call the parent constructor</span>
-        <span class="n">CondIndTest</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">significance</span><span class="o">=</span><span class="n">significance</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="c1"># Print some information about construction</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;knn/T = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;knn = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;shuffle_neighbors = </span><span class="si">%d</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">)</span>
-            
-    <span class="k">def</span> <span class="nf">_standardize_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">dim</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Standardizes a given array with dimensions dim.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        dim: int</span>
-<span class="sd">            number of dimensions of the data.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            The standardized array.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">array</span> <span class="o">-=</span> <span class="n">array</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">std</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
-                <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
-        <span class="c1"># FIXME: If the time series is constant, return nan rather than</span>
-        <span class="c1"># raising Exception</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
-            <span class="c1"># raise ValueError(&quot;nans after standardizing, &quot;</span>
-            <span class="c1">#                  &quot;possibly constant array!&quot;)</span>
-        <span class="k">return</span> <span class="n">array</span>
-    
-    <span class="k">def</span> <span class="nf">_scale_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">minmax</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)):</span>
-        <span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">(</span><span class="n">minmax</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-            
-    <span class="k">def</span> <span class="nf">_transform_mixed_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">add_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Applies data transformations to the continuous dimensions of the given data.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        add_noise : bool (default False)</span>
-<span class="sd">            Defines whether to add small normal noise to the continuous data.</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            The array with the continuous data transformed. </span>
-<span class="sd">            </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">continuous_idxs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>  
-        <span class="n">cont_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">continuous_idxs</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">add_noise</span><span class="p">:</span>
-            <span class="c1"># Add noise to destroy ties</span>
-            <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1E-6</span> <span class="o">*</span> <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">cont_dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-                  <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;standardize&#39;</span><span class="p">:</span>
-            <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_standardize_array</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">],</span> <span class="n">cont_dim</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;scale&#39;</span><span class="p">:</span>
-            <span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scale_array</span><span class="p">(</span><span class="n">array</span><span class="p">[</span><span class="n">continuous_idxs</span><span class="p">],</span> <span class="n">minmax</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">scale_range</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Unknown transform&#39;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">array</span>
-         
-    
-    <span class="k">def</span> <span class="nf">_transform_to_one_hot_mixed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                                    <span class="n">type_mask</span><span class="p">,</span>
-                                    <span class="n">zero_inf</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        
-        <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="n">mixed_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-        <span class="n">narray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">nxyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">xyz</span><span class="p">)</span>
-        <span class="n">ntype_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span>
-
-        <span class="n">appended_columns</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">)):</span>
-            <span class="c1"># print(i)</span>
-            <span class="k">if</span> <span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">encoder</span> <span class="o">=</span> <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
-                <span class="n">i</span> <span class="o">+=</span> <span class="n">appended_columns</span>
-                <span class="n">data</span> <span class="o">=</span> <span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span>
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">encoder_df</span> <span class="o">=</span> <span class="n">encoder</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
-                <span class="k">if</span> <span class="n">zero_inf</span><span class="p">:</span>
-                    <span class="n">encoder_df</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">encoder_df</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">9999999</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="p">[</span><span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">*</span> <span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-                <span class="n">narray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">narray</span><span class="p">[:,</span> <span class="p">:</span><span class="n">i</span><span class="p">],</span> <span class="n">encoder_df</span><span class="p">,</span> <span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-                <span class="n">nxyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">nxyz</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span> <span class="n">xyz_val</span><span class="p">,</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]])</span>
-                <span class="n">ntype_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">ntype_mask</span><span class="p">[:,</span> <span class="p">:</span><span class="n">i</span><span class="p">],</span>
-                                             <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> 
-                                             <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]],</span> 
-                                            <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-                <span class="n">appended_columns</span> <span class="o">+=</span> <span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
-
-            <span class="k">elif</span> <span class="n">mixed_idx_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">i</span> <span class="o">+=</span> <span class="n">appended_columns</span>
-                <span class="n">data</span> <span class="o">=</span> <span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span>
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-
-                <span class="c1"># print(i, narray[:, i], ntype_mask[:, i])</span>
-                <span class="c1"># find categories </span>
-                <span class="n">categories</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
-                <span class="n">cont_vars</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]))</span>
-
-                <span class="n">encoder</span> <span class="o">=</span> <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="n">categories</span><span class="p">],</span> <span class="n">handle_unknown</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">encoder_df</span> <span class="o">=</span> <span class="n">encoder</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
-                <span class="k">if</span> <span class="n">zero_inf</span><span class="p">:</span>
-                    <span class="n">encoder_df</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">encoder_df</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">9999999</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">cont_vars</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
-
-                <span class="n">xyz_val</span> <span class="o">=</span> <span class="p">[</span><span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">*</span> <span class="p">(</span><span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-                <span class="n">cont_column</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-                <span class="n">narray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">narray</span><span class="p">[:,</span> <span class="p">:</span><span class="n">i</span><span class="p">],</span> <span class="n">cont_column</span><span class="p">,</span> <span class="n">encoder_df</span><span class="p">,</span> <span class="n">narray</span><span class="p">[:,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-                <span class="n">nxyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">nxyz</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span> <span class="n">xyz_val</span><span class="p">,</span> <span class="n">nxyz</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]])</span>
-                <span class="n">ntype_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">ntype_mask</span><span class="p">[:,</span> <span class="p">:</span><span class="n">i</span><span class="p">],</span> 
-                                             <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">cont_column</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> 
-                                             <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> 
-                                             <span class="n">ntype_mask</span><span class="p">[:,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]],</span> 
-                                            <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-                <span class="n">appended_columns</span> <span class="o">+=</span> <span class="n">encoder_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="n">ndiscrete_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">ntype_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">narray</span><span class="p">,</span> <span class="n">nxyz</span><span class="p">,</span> <span class="n">ntype_mask</span><span class="p">,</span> <span class="n">ndiscrete_idx_list</span>         
-
-        
-
-<div class="viewcode-block" id="CMIknnMixed.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test.</span>
-
-<span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
-<span class="sd">        classes must specify a function get_dependence_measure and either or</span>
-<span class="sd">        both functions get_analytic_significance and  get_shuffle_significance.</span>
-<span class="sd">        If recycle_residuals is True, also _get_single_residuals must be</span>
-<span class="sd">        available.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X, Y, Z : list of tuples</span>
-<span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
-<span class="sd">            variable index and tau the time lag.</span>
-
-<span class="sd">        tau_max : int, optional (default: 0)</span>
-<span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
-<span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
-<span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;}</span>
-<span class="sd">            How many samples to cutoff at the beginning. The default is</span>
-<span class="sd">            &#39;2xtau_max&#39;, which guarantees that MCI tests are all conducted on</span>
-<span class="sd">            the same samples. For modeling, &#39;max_lag_or_tau_max&#39; can be used,</span>
-<span class="sd">            which uses the maximum of tau_max and the conditions, which is</span>
-<span class="sd">            useful to compare multiple models on the same sample.  Last,</span>
-<span class="sd">            &#39;max_lag&#39; uses as much samples as possible.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-<span class="sd">            The test statistic value and the p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Get the array to test on</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">cut_off</span><span class="p">)</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">XYZ</span>
-
-        <span class="c1"># Record the dimensions</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Ensure it is a valid array</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
-
-        <span class="n">combined_hash</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array_hash</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">combined_hash</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-            <span class="n">cached</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">cached</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="c1"># Get the dependence measure, reycling residuals if need be</span>
-            <span class="n">val</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                              <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span> 
-            <span class="c1"># Get the p-value</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span>
-                                        <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-            
-            <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="n">cached</span><span class="p">,</span>
-                                         <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-        <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
-
-<div class="viewcode-block" id="CMIknnMixed.run_test_raw"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.run_test_raw">[docs]</a>    <span class="k">def</span> <span class="nf">run_test_raw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">y_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">val_only</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test directly on input arrays x, y, z.</span>
-
-<span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
-<span class="sd">        classes must specify a function get_dependence_measure and either or</span>
-<span class="sd">        both functions get_analytic_significance and  get_shuffle_significance.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x, y, z : arrays</span>
-<span class="sd">            x,y,z are of the form (samples, dimension).</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as [x,y,z] which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-
-<span class="sd">            The test statistic value and the p-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;x,y must be arrays of shape (samples, dimension)&quot;</span>
-                             <span class="s2">&quot; where dimension can be 1.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;z must be array of shape (samples, dimension)&quot;</span>
-                             <span class="s2">&quot; where dimension can be 1.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">x_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">y_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;x_type and y_type must be set.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># Get the array to test on</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-            <span class="n">type_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-
-            <span class="c1"># xyz is the dimension indicator</span>
-            <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
-                           <span class="p">[</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Get the array to test on</span>
-            <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">z</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-            <span class="n">type_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">z_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-
-            <span class="c1"># xyz is the dimension indicator</span>
-            <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
-                           <span class="p">[</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
-                           <span class="p">[</span><span class="mi">2</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">z</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
-
-        <span class="c1"># Record the dimensions</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Ensure it is a valid array</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
-        <span class="c1"># Get the dependence measure</span>
-        <span class="n">val</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">val_only</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">val</span>
-        <span class="c1"># Get the p-value</span>
-        <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
-    
-<div class="viewcode-block" id="CMIknnMixed.get_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span>
-                         <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                         <span class="n">sig_override</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the p-value from whichever significance function is specified</span>
-<span class="sd">        for this test.  If an override is used, then it will call a different</span>
-<span class="sd">        function then specified by self.significance</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Test statistic value.</span>
-
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        T : int</span>
-<span class="sd">            Sample length</span>
-
-<span class="sd">        dim : int</span>
-<span class="sd">            Dimensionality, ie, number of features.</span>
-
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        sig_override : string</span>
-<span class="sd">            Must be in &#39;analytic&#39;, &#39;shuffle_test&#39;, &#39;fixed_thres&#39;</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float or numpy.nan</span>
-<span class="sd">            P-value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Defaults to the self.significance member value</span>
-        <span class="n">use_sig</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span>
-        <span class="k">if</span> <span class="n">sig_override</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">use_sig</span> <span class="o">=</span> <span class="n">sig_override</span>
-        <span class="c1"># Check if we are using the analytic significance</span>
-        <span class="k">if</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;analytic&#39;</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Analytic significance not defined for CMIknnMixed!&quot;</span><span class="p">)</span>
-        <span class="c1"># Check if we are using the shuffle significance</span>
-        <span class="k">elif</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;shuffle_test&#39;</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_shuffle_significance</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span>
-                                                 <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                 <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">,</span>
-                                                 <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="c1"># Check if we are using the fixed_thres significance</span>
-        <span class="k">elif</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_fixed_thres_significance</span><span class="p">(</span>
-                    <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">,</span>
-                    <span class="n">fixed_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> not known.&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span><span class="p">)</span>
-        <span class="c1"># Return the calculated value</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-    
-    
-    <span class="k">def</span> <span class="nf">_compute_discrete_entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">disc_values</span><span class="p">,</span> <span class="n">discrete_idxs</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">):</span>
-        <span class="n">current_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">discrete_idxs</span><span class="p">]</span> <span class="o">==</span> <span class="n">disc_values</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">discrete_idxs</span><span class="p">)]</span>
-
-        <span class="n">count</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">current_array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="n">count</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mf">0.</span>
-
-        <span class="n">prob</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">count</span><span class="p">)</span> <span class="o">/</span> <span class="n">num_samples</span>
-        <span class="c1"># print(prob)</span>
-        <span class="n">disc_entropy</span> <span class="o">=</span> <span class="n">prob</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span>
-        <span class="c1"># print(&#39;d&#39;, disc_entropy)</span>
-        <span class="k">return</span> <span class="n">disc_entropy</span>
-    
-    
-    <span class="k">def</span> <span class="nf">compute_discrete_entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">disc_values</span><span class="p">,</span> <span class="n">discrete_idxs</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">):</span>
-        <span class="n">current_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">discrete_idxs</span><span class="p">]</span> <span class="o">==</span> <span class="n">disc_values</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">discrete_idxs</span><span class="p">)]</span>
-
-        <span class="n">count</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">current_array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="n">count</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mf">0.</span>
-
-        <span class="n">prob</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">count</span><span class="p">)</span> <span class="o">/</span> <span class="n">num_samples</span>
-        <span class="n">disc_entropy</span> <span class="o">=</span> <span class="n">prob</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">disc_entropy</span>
-    
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">_get_nearest_neighbors_zeroinf_onehot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span>
-                                   <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns nearest neighbors according to Frenzel and Pompe (2007).</span>
-
-<span class="sd">        Retrieves the distances eps to the k-th nearest neighbors for every</span>
-<span class="sd">        sample in joint space XYZ and returns the numbers of nearest neighbors</span>
-<span class="sd">        within eps in subspaces Z, XZ, YZ. Accepts points as neighbors only </span>
-<span class="sd">        if the points are not at infinite distance. </span>
-<span class="sd">        Two points have infinite distance when the values for the discrete </span>
-<span class="sd">        dimensions of the points do not match.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        knn : int or float</span>
-<span class="sd">            Number of nearest-neighbors which determines the size of hyper-cubes</span>
-<span class="sd">            around each (high-dimensional) sample point. If smaller than 1, this</span>
-<span class="sd">            is computed as a fraction of T, hence knn=knn*T. For knn larger or</span>
-<span class="sd">            equal to 1, this is the absolute number.</span>
-<span class="sd">        </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        k_xz, k_yz, k_z : tuple of arrays of shape (T,)</span>
-<span class="sd">            Nearest neighbors in subspaces.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">xyz</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_mixed_data</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">)</span>
-            
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">type_mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">T</span>
-        
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">,</span> <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_to_one_hot_mixed</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> 
-                                                                                    <span class="n">xyz</span><span class="p">,</span> 
-                                                                                    <span class="n">type_mask</span><span class="p">,</span>
-                                                                                    <span class="n">zero_inf</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            
-        <span class="c1"># Subsample indices</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">xz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-        <span class="n">yz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-     
-        <span class="c1"># Fit trees</span>
-        <span class="n">tree_xyz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">knn</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                   <span class="n">distance_upper_bound</span><span class="o">=</span><span class="mi">9999999</span><span class="p">)</span>
-        
-        <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
-        
-        
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">knn</span><span class="p">]</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">:</span>
-                <span class="n">replacement_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-                <span class="n">r</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">replacement_idx</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">perc</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-                <span class="n">epsarray</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">r</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">epsarray</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">knn</span><span class="p">]</span>
-                
-        
-        <span class="n">neighbors_radius_xyz</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>
-        
-        <span class="n">k_tilde</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">))]</span>
-            
-        <span class="c1"># compute entropies</span>
-        <span class="n">xz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">]</span>
-        <span class="n">tree_xz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">xz</span><span class="p">)</span>
-        <span class="n">k_xz</span> <span class="o">=</span> <span class="n">tree_xz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">xz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        
-        <span class="n">yz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">]</span>
-        <span class="n">tree_yz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">yz</span><span class="p">)</span>
-        <span class="n">k_yz</span> <span class="o">=</span> <span class="n">tree_yz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">yz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">]</span>
-            <span class="n">tree_z</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">tree_z</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Number of neighbors is T when z is empty.</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float&#39;</span><span class="p">)</span>
-            
-        <span class="n">k_xz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_xz</span><span class="p">])</span>
-        <span class="n">k_yz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_yz</span><span class="p">])</span>
-        <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_z</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">k_tilde</span><span class="p">,</span> <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span>
-    
-<div class="viewcode-block" id="CMIknnMixed.get_dependence_measure_zeroinf"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_dependence_measure_zeroinf">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure_zeroinf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                                   <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns CMI estimate according to Frenzel and Pompe with an</span>
-<span class="sd">        altered distance metric: the 0-inf metric, which attributes </span>
-<span class="sd">        infinite distance to points where the values for the discrete dimensions</span>
-<span class="sd">        do not coincide.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-<span class="sd">        </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># compute knn</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-        
-
-        <span class="n">knn_tilde</span><span class="p">,</span> <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_nearest_neighbors_zeroinf_onehot</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span>
-                                                                                 <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                                                 <span class="n">knn</span><span class="o">=</span><span class="n">knn</span><span class="p">,</span>
-                                                                                 <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="n">non_zero</span> <span class="o">=</span> <span class="n">knn_tilde</span> <span class="o">-</span> <span class="n">k_xz</span> <span class="o">-</span> <span class="n">k_yz</span> <span class="o">+</span> <span class="n">k_z</span>
-        
-        <span class="n">non_zero_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">non_zero</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">non_zero</span><span class="p">)</span>
-        
-        <span class="n">val</span> <span class="o">=</span> <span class="p">(</span><span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">knn_tilde</span><span class="p">)</span> <span class="o">-</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_xz</span><span class="p">)</span> <span class="o">-</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_yz</span><span class="p">)</span> <span class="o">+</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_z</span><span class="p">))</span>
-        
-        <span class="n">val</span> <span class="o">=</span> <span class="n">val</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">val</span><span class="p">)]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> 
-
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">non_zero_count</span></div>
-    
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">_get_nearest_neighbors_MS_one_hot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                                          <span class="n">knn</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns nearest neighbors according to Messner and Shalizi (2021).</span>
-
-<span class="sd">        Retrieves the distances eps to the k-th nearest neighbors for every</span>
-<span class="sd">        sample in joint space XYZ and returns the numbers of nearest neighbors</span>
-<span class="sd">        within eps in subspaces Z, XZ, YZ. Uses a custom-defined metric for </span>
-<span class="sd">        discrete variables.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        knn : int or float</span>
-<span class="sd">            Number of nearest-neighbors which determines the size of hyper-cubes</span>
-<span class="sd">            around each (high-dimensional) sample point. If smaller than 1, this</span>
-<span class="sd">            is computed as a fraction of T, hence knn=knn*T. For knn larger or</span>
-<span class="sd">            equal to 1, this is the absolute number.</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        k_tilde, k_xz, k_yz, k_z : tuple of arrays of shape (T,)</span>
-<span class="sd">            Nearest neighbors in XYZ, XZ, YZ, and Z subspaces.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">xyz</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_mixed_data</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">)</span>
-
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">type_mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">T</span>
-        
-        <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">,</span> <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_to_one_hot_mixed</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> 
-                                                                                    <span class="n">xyz</span><span class="p">,</span> 
-                                                                                    <span class="n">type_mask</span><span class="p">)</span>
-        
-        <span class="c1"># Subsample indices</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">xz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-        <span class="n">yz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-            
-        <span class="c1"># Fit trees</span>
-        <span class="n">tree_xyz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">knn</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">)</span>
-        
-        
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        
-        <span class="n">neighbors_radius_xyz</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> 
-                                                         <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">)</span>
-        
-        <span class="c1"># search again for neighbors in the radius to find all of them</span>
-        <span class="c1"># in the discrete case k_tilde can be larger than the given knn</span>
-        <span class="n">k_tilde</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbors_radius_xyz</span><span class="p">))])</span>
-            
-        <span class="c1"># compute entropies</span>
-        <span class="n">xz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">]</span>
-        <span class="n">tree_xz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">xz</span><span class="p">)</span>
-        <span class="n">k_xz</span> <span class="o">=</span> <span class="n">tree_xz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">xz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                        <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        
-        <span class="n">yz</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">]</span>
-        <span class="n">tree_yz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">yz</span><span class="p">)</span>
-        <span class="n">k_yz</span> <span class="o">=</span> <span class="n">tree_yz</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">yz</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> 
-                                        <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">]</span>
-            <span class="n">tree_z</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">tree_z</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">epsarray</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                          <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span> <span class="n">return_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Number of neighbors is T when z is empty.</span>
-            <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float&#39;</span><span class="p">)</span>
-        
-        <span class="n">k_xz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_xz</span><span class="p">])</span>
-        <span class="n">k_yz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_yz</span><span class="p">])</span>
-        <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k_z</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">k_tilde</span><span class="p">,</span> <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span>
-    
-
-<div class="viewcode-block" id="CMIknnMixed.get_dependence_measure_MS"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_dependence_measure_MS">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure_MS</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                  <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns CMI estimate as described in Messner and Shalizi (2021).</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-<span class="sd">        </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># compute knn</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
-        
-        
-        <span class="n">knn_tilde</span><span class="p">,</span> <span class="n">k_xz</span><span class="p">,</span> <span class="n">k_yz</span><span class="p">,</span> <span class="n">k_z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_nearest_neighbors_MS_one_hot</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span>
-                                                                            <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                                            <span class="n">knn</span><span class="o">=</span><span class="n">knn</span><span class="p">,</span>
-                                                                            <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
-        
-        <span class="n">non_zero</span> <span class="o">=</span> <span class="n">knn_tilde</span> <span class="o">-</span> <span class="n">k_xz</span> <span class="o">-</span> <span class="n">k_yz</span> <span class="o">+</span> <span class="n">k_z</span>
-        
-        <span class="n">non_zero_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">non_zero</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">non_zero</span><span class="p">)</span>
-        
-        <span class="n">val</span> <span class="o">=</span> <span class="p">(</span><span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">knn_tilde</span><span class="p">)</span> <span class="o">-</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_xz</span><span class="p">)</span> <span class="o">-</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_yz</span><span class="p">)</span> <span class="o">+</span>
-                                           <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">k_z</span><span class="p">))</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">val</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">val</span><span class="p">)]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> 
-
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">non_zero_count</span></div>
-
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">_compute_continuous_entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">knn</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns entropy estimate as described by Kozachenko and Leonenko (1987).</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        knn : int</span>
-<span class="sd">            number of nearest-neighbors to use.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">T</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="k">if</span> <span class="n">T</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mf">0.</span>
-
-        <span class="k">if</span> <span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">rint</span><span class="p">(</span><span class="n">knn</span> <span class="o">*</span> <span class="n">T</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">tree</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="n">knn</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> 
-                              <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span>
-                              <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">)[</span><span class="mi">0</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        
-        <span class="n">epsarray</span> <span class="o">=</span> <span class="n">epsarray</span><span class="p">[</span><span class="n">epsarray</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
-        <span class="n">num_non_zero</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">epsarray</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">num_non_zero</span> <span class="o">==</span>  <span class="mi">0</span><span class="p">:</span> 
-            <span class="n">cmi_hat</span> <span class="o">=</span> <span class="mf">0.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">avg_dist</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">num_non_zero</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">epsarray</span><span class="p">))</span>
-            <span class="n">cmi_hat</span> <span class="o">=</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">num_non_zero</span><span class="p">)</span> <span class="o">-</span> <span class="n">special</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="n">knn</span><span class="p">)</span> <span class="o">+</span> <span class="n">avg_dist</span>
-
-        <span class="k">return</span> <span class="n">cmi_hat</span>
-    
-    <span class="k">def</span> <span class="nf">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> 
-                                              <span class="n">discrete_values</span><span class="p">,</span> 
-                                              <span class="n">discrete_idxs</span><span class="p">,</span> 
-                                              <span class="n">continuous_idxs</span><span class="p">,</span> 
-                                              <span class="n">total_num_samples</span><span class="p">,</span> 
-                                              <span class="n">knn</span><span class="p">,</span> 
-                                              <span class="n">use_local_knn</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns entropy estimates for a given array as described in ... add citation.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        discrete_values : tuple of dimension (len(discrete_idxs))</span>
-<span class="sd">            values of discrete variables for which the entropy is computed</span>
-<span class="sd">        </span>
-<span class="sd">        discrete_idxs : array of ints</span>
-<span class="sd">            indices of the dimensions with discrete data</span>
-<span class="sd">        </span>
-<span class="sd">        continuous_idxs : array of ints</span>
-<span class="sd">            indices of the dimensions with continuous data</span>
-<span class="sd">        </span>
-<span class="sd">        total_num_samples : int</span>
-<span class="sd">            total number of samples</span>
-<span class="sd">        </span>
-<span class="sd">        knn : int or float</span>
-<span class="sd">            if int, number of nearest-neighbors to use</span>
-<span class="sd">            if float, percentage of the number of samples </span>
-<span class="sd">            </span>
-<span class="sd">        use_local_knn : bool (default False)</span>
-<span class="sd">            if True, the knn is computed as a percentage of the number of samples</span>
-<span class="sd">            for one realization of the discrete values in each subspace, </span>
-<span class="sd">            otherwise the same knn is used for all subspaces.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val_continuous entropy, val_discrete_entropy : float, float</span>
-<span class="sd">            Tuple consisting of estimate for the entropy term for the continuous variables,</span>
-<span class="sd">            and the estimate for the entropy term for the discrete variables.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="c1"># select data for which the discrete values are the given ones</span>
-        <span class="n">current_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">discrete_idxs</span><span class="p">]</span> <span class="o">==</span> <span class="n">discrete_values</span><span class="p">,</span> 
-                                     <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">discrete_idxs</span><span class="p">)]</span>
-        <span class="c1"># if we do not have samples, we cannot estimate CMI</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">current_array</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span>
-
-        <span class="n">T</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">current_array</span><span class="o">.</span><span class="n">shape</span>
-        
-        <span class="c1"># if we have more samples than knns and samples are not purely discrete, we can</span>
-        <span class="c1"># compute CMI</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">continuous_idxs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">T</span> <span class="o">&gt;</span> <span class="n">knn</span><span class="p">:</span>
-            <span class="n">val_continuous_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">current_array</span><span class="p">[:,</span> <span class="n">continuous_idxs</span><span class="p">],</span> <span class="n">knn</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">val_continuous_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-        <span class="n">prob</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">total_num_samples</span>
-        
-        <span class="c1"># multiply by probabilities of occurence</span>
-        <span class="n">val_continuous_entropy</span> <span class="o">*=</span> <span class="n">prob</span>
-        <span class="c1"># compute entropy for that occurence</span>
-        <span class="n">val_discrete_entropy</span> <span class="o">=</span> <span class="n">prob</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">val_continuous_entropy</span><span class="p">,</span> <span class="n">val_discrete_entropy</span>
-
-<div class="viewcode-block" id="CMIknnMixed.get_dependence_measure_conditional"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_dependence_measure_conditional">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure_conditional</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                           <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns CMI estimate as described in ....</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># compute knn</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">knn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span>
-            
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">xyz</span> <span class="o">=</span> <span class="n">xyz</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-        
-        <span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_mixed_data</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">)</span>
-        
-        <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">type_mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">T</span>
-        
-        <span class="c1">#TODO</span>
-        
-        <span class="c1"># continue working with discrete idx list</span>
-        <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">type_mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Variables are continuous, cannot use CMIknnMixed conditional!&quot;</span><span class="p">)</span>
-            
-<span class="c1">#         if np.sum(discrete_idx_list) != np.sum(any_discrete_idx_list):</span>
-<span class="c1">#             raise ValueError(&quot;Variables contain mixtures, cannot use CMIknnMixed conditional!&quot;)</span>
-            
-        <span class="c1"># Subsample indices</span>
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">xz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-        <span class="n">yz_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">])</span>
-        
-        <span class="n">discrete_xz_indices</span> <span class="o">=</span> <span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">xz_indices</span><span class="p">]</span>
-        <span class="n">discrete_yz_indices</span> <span class="o">=</span> <span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">yz_indices</span><span class="p">]</span>
-        <span class="n">discrete_z_indices</span> <span class="o">=</span> <span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">z_indices</span><span class="p">]</span>
-
-        <span class="n">discrete_xyz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">discrete_xz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_xz_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">discrete_yz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_yz_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>    
-        <span class="n">discrete_z_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_z_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>  
-
-        <span class="n">continuous_xyz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">continuous_xz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_xz_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">continuous_yz_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_yz_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>    
-        <span class="n">continuous_z_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">discrete_z_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> 
-        
-        <span class="c1"># get the number of unique values for each category of the discrete variable</span>
-        <span class="c1"># add empty set for code not to break when accessing [0]</span>
-        <span class="n">num_xz_classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">][:,</span> <span class="n">index</span><span class="p">])</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_xz_indices</span><span class="p">))</span> <span class="k">if</span> <span class="p">(</span><span class="n">discrete_xz_indices</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)]</span>
-        <span class="n">num_yz_classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">][:,</span> <span class="n">index</span><span class="p">])</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_yz_indices</span><span class="p">))</span> <span class="k">if</span> <span class="p">(</span><span class="n">discrete_yz_indices</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)]</span>
-        <span class="n">num_z_classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">][:,</span> <span class="n">index</span><span class="p">])</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_z_indices</span><span class="p">))</span> <span class="k">if</span> <span class="p">(</span><span class="n">discrete_z_indices</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)]</span>
-        <span class="n">num_xyz_classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">index</span><span class="p">])</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">))</span> <span class="k">if</span> <span class="p">(</span><span class="n">discrete_idx_list</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)]</span>
-        
-        <span class="c1"># print(&#39;num classes&#39;, num_xyz_classes, num_xz_classes, num_yz_classes, num_z_classes)siz</span>
-
-        <span class="n">xyz_cartesian_product</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">xz_cartesian_product</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">yz_cartesian_product</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">z_cartesian_product</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_xyz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">xyz_cartesian_product</span> <span class="o">=</span> <span class="n">cartesian</span><span class="p">(</span><span class="n">num_xyz_classes</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_xyz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">xyz_cartesian_product</span> <span class="o">=</span> <span class="n">num_xyz_classes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_xz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">xz_cartesian_product</span> <span class="o">=</span> <span class="n">cartesian</span><span class="p">(</span><span class="n">num_xz_classes</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_xz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">xz_cartesian_product</span> <span class="o">=</span> <span class="n">num_xz_classes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_yz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">yz_cartesian_product</span> <span class="o">=</span> <span class="n">cartesian</span><span class="p">(</span><span class="n">num_yz_classes</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_yz_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">yz_cartesian_product</span> <span class="o">=</span> <span class="n">num_yz_classes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_z_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">z_cartesian_product</span> <span class="o">=</span> <span class="n">cartesian</span><span class="p">(</span><span class="n">num_z_classes</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">num_z_classes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z_cartesian_product</span> <span class="o">=</span> <span class="n">num_z_classes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    
-        <span class="c1"># print(&#39;cartesian&#39;, xyz_cartesian_product)</span>
-        <span class="c1"># , xz_cartesian_product, yz_cartesian_product, z_cartesian_product)</span>
-                
-        <span class="c1"># compute entropies in XYZ subspace </span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">xyz_cartesian_product</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">xyz_cmi</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="n">xyz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">entry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">xyz_cartesian_product</span><span class="p">):</span>
-                <span class="n">xyz_cont_entropy</span><span class="p">,</span> <span class="n">xyz_disc_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">entry</span><span class="p">,</span>
-                                                                       <span class="n">discrete_xyz_idx</span><span class="p">,</span> 
-                                                                       <span class="n">continuous_xyz_idx</span><span class="p">,</span> 
-                                                                       <span class="n">T</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span>
-                                                                       <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span><span class="p">)</span>
-                <span class="n">xyz_cmi</span> <span class="o">+=</span> <span class="n">xyz_cont_entropy</span>
-                <span class="n">xyz_entropy</span> <span class="o">-=</span> <span class="n">xyz_disc_entropy</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">xyz_cmi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">knn</span><span class="p">)</span>
-            <span class="n">xyz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-        <span class="c1"># print(xyz_cmi, xyz_entropy)</span>
-        
-        <span class="c1"># compute entropies in XZ subspace</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">xz_cartesian_product</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">xz_cmi</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="n">xz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">entry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">xz_cartesian_product</span><span class="p">):</span>
-                <span class="n">xz_cont_entropy</span><span class="p">,</span> <span class="n">xz_disc_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">],</span> <span class="n">entry</span><span class="p">,</span> 
-                                                                     <span class="n">discrete_xz_idx</span><span class="p">,</span> 
-                                                                     <span class="n">continuous_xz_idx</span><span class="p">,</span> 
-                                                                     <span class="n">T</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span> 
-                                                                     <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span><span class="p">)</span>
-                <span class="n">xz_cmi</span> <span class="o">+=</span> <span class="n">xz_cont_entropy</span>
-                <span class="n">xz_entropy</span> <span class="o">-=</span> <span class="n">xz_disc_entropy</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">xz_cmi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">xz_indices</span><span class="p">],</span> <span class="n">knn</span><span class="p">)</span>
-            <span class="n">xz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-        <span class="c1"># compute entropies in Xy subspace</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">yz_cartesian_product</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">yz_cmi</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="n">yz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">entry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">yz_cartesian_product</span><span class="p">):</span>
-                <span class="n">yz_cont_entropy</span><span class="p">,</span> <span class="n">yz_disc_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">],</span> <span class="n">entry</span><span class="p">,</span> 
-                                                                     <span class="n">discrete_yz_idx</span><span class="p">,</span> 
-                                                                     <span class="n">continuous_yz_idx</span><span class="p">,</span> 
-                                                                     <span class="n">T</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span> 
-                                                                     <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span><span class="p">)</span>
-                <span class="n">yz_cmi</span> <span class="o">+=</span> <span class="n">yz_cont_entropy</span>
-                <span class="n">yz_entropy</span> <span class="o">-=</span> <span class="n">yz_disc_entropy</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">yz_cmi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">yz_indices</span><span class="p">],</span> <span class="n">knn</span><span class="p">)</span>
-            <span class="n">yz_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-
-        <span class="c1"># compute entropies in Z subspace</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_cartesian_product</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">z_cmi</span> <span class="o">=</span> <span class="mf">0.</span>
-            <span class="n">z_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">entry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">z_cartesian_product</span><span class="p">):</span>
-                <span class="n">z_cont_entropy</span><span class="p">,</span> <span class="n">z_disc_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_entropies_for_discrete_entry</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">],</span> 
-                                                                   <span class="n">entry</span><span class="p">,</span> 
-                                                                   <span class="n">discrete_z_idx</span><span class="p">,</span> 
-                                                                   <span class="n">continuous_z_idx</span><span class="p">,</span> 
-                                                                   <span class="n">T</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span> 
-                                                                   <span class="bp">self</span><span class="o">.</span><span class="n">use_local_knn</span><span class="p">)</span>
-                <span class="n">z_cmi</span> <span class="o">+=</span> <span class="n">z_cont_entropy</span>
-                <span class="n">z_entropy</span> <span class="o">-=</span> <span class="n">z_disc_entropy</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">z_cmi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_continuous_entropy</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">],</span> <span class="n">knn</span><span class="p">)</span>
-            <span class="n">z_entropy</span> <span class="o">=</span> <span class="mf">0.</span>
-            
-        <span class="c1"># put it all together for the CMI estimation</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">xz_cmi</span> <span class="o">+</span> <span class="n">yz_cmi</span> <span class="o">-</span> <span class="n">xyz_cmi</span> <span class="o">-</span> <span class="n">z_cmi</span> <span class="o">+</span> <span class="n">xz_entropy</span> <span class="o">+</span> <span class="n">yz_entropy</span> <span class="o">-</span> <span class="n">xyz_entropy</span> <span class="o">-</span> <span class="n">z_entropy</span>
-        
-        <span class="n">entropies</span> <span class="o">=</span> <span class="p">(</span><span class="n">xz_cmi</span><span class="p">,</span> <span class="n">yz_cmi</span><span class="p">,</span> <span class="n">xyz_cmi</span><span class="p">,</span> <span class="n">z_cmi</span><span class="p">,</span> <span class="n">xz_entropy</span><span class="p">,</span> <span class="n">yz_entropy</span><span class="p">,</span> <span class="n">xyz_entropy</span><span class="p">,</span> <span class="n">z_entropy</span><span class="p">)</span>
-            
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">entropies</span></div>
-    
-<div class="viewcode-block" id="CMIknnMixed.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                               <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calls the appropriate function to estimate CMI.</span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,)</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Conditional mutual information estimate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># check that data is really mixed</span>
-        <span class="k">if</span> <span class="n">type_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type mask cannot be none for CMIknnMixed!&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type mask contains other values than 0 and 1!&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">==</span> <span class="s1">&#39;MS&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure_MS</span><span class="p">(</span><span class="n">array</span><span class="p">,</span>
-                                                  <span class="n">xyz</span><span class="p">,</span>
-                                                  <span class="n">type_mask</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">==</span> <span class="s1">&#39;cond&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure_conditional</span><span class="p">(</span><span class="n">array</span><span class="p">,</span>
-                                                           <span class="n">xyz</span><span class="p">,</span>
-                                                           <span class="n">type_mask</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">==</span> <span class="s1">&#39;FPinf&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure_zeroinf</span><span class="p">(</span><span class="n">array</span><span class="p">,</span>
-                                                   <span class="n">xyz</span><span class="p">,</span>
-                                                   <span class="n">type_mask</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No such estimator available!&#39;</span><span class="p">)</span></div>
-            
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">get_restricted_permutation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">shuffle_neighbors</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
-
-        <span class="n">restricted_permutation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-        <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">sample_index</span> <span class="ow">in</span> <span class="n">order</span><span class="p">:</span>
-            <span class="n">neighbors_to_use</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="n">sample_index</span><span class="p">]</span>
-            <span class="n">m</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">use</span> <span class="o">=</span> <span class="n">neighbors_to_use</span><span class="p">[</span><span class="n">m</span><span class="p">]</span>
-            <span class="k">while</span> <span class="p">((</span><span class="n">use</span> <span class="ow">in</span> <span class="n">used</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">m</span> <span class="o">&lt;</span> <span class="n">shuffle_neighbors</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)):</span>
-                <span class="n">m</span> <span class="o">+=</span> <span class="mi">1</span>
-                <span class="n">use</span> <span class="o">=</span> <span class="n">neighbors_to_use</span><span class="p">[</span><span class="n">m</span><span class="p">]</span>
-            <span class="n">restricted_permutation</span><span class="p">[</span><span class="n">sample_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">use</span>
-            <span class="n">used</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">used</span><span class="p">,</span> <span class="n">use</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">restricted_permutation</span>
-
-
-    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">_generate_random_permutation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">x_indices</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">):</span>
-
-        <span class="n">T</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="c1"># Generate random order in which to go through indices loop in</span>
-        <span class="c1"># next step</span>
-        <span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-                <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
-                <span class="n">n</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
-
-        <span class="c1"># Select a series of neighbor indices that contains as few as</span>
-        <span class="c1"># possible duplicates</span>
-        <span class="n">restricted_permutation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_restricted_permutation</span><span class="p">(</span>
-                <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span>
-                <span class="n">shuffle_neighbors</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">,</span>
-                <span class="n">neighbors</span><span class="o">=</span><span class="n">n</span><span class="p">,</span>
-                <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
-
-        <span class="n">array_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">type_mask_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x_indices</span><span class="p">:</span>
-            <span class="n">array_shuffled</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">restricted_permutation</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-            <span class="n">type_mask_shuffled</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">[</span><span class="n">restricted_permutation</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">array_shuffled</span><span class="p">,</span> <span class="n">type_mask_shuffled</span>
-
-<div class="viewcode-block" id="CMIknnMixed.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.cmiknnmixed.CMIknnMixed.get_shuffle_significance">[docs]</a>    <span class="nd">@jit</span><span class="p">(</span><span class="n">forceobj</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                 <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns p-value for nearest-neighbor shuffle significance test.</span>
-
-<span class="sd">        For non-empty Z, overwrites get_shuffle_significance from the parent</span>
-<span class="sd">        class  which is a block shuffle test, which does not preserve</span>
-<span class="sd">        dependencies of X and Y with Z. Here the parameter shuffle_neighbors is</span>
-<span class="sd">        used to permute only those values :math:`x_i` and :math:`x_j` for which</span>
-<span class="sd">        :math:`z_j` is among the nearest neighbors of :math:`z_i`. If Z is</span>
-<span class="sd">        empty, the block-shuffle test is used.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-<span class="sd">        </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            p-value</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-            
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span> <span class="o">&lt;</span> <span class="n">T</span><span class="p">:</span>
-            
-            <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">T</span>
-            <span class="n">type_mask</span> <span class="o">=</span> <span class="n">type_mask</span><span class="o">.</span><span class="n">T</span>
-
-            <span class="c1"># discrete_idx_list = np.where(np.all(type_mask == 1, axis=0), 1, 0)</span>
-
-            <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">,</span> <span class="n">discrete_idx_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_to_one_hot_mixed</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">,</span>
-                                                                                        <span class="n">zero_inf</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-            <span class="c1"># max_neighbors = max(1, int(max_neighbor_ratio*T))</span>
-            <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">z_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;            nearest-neighbor shuffle significance &quot;</span>
-                      <span class="s2">&quot;test with n = </span><span class="si">%d</span><span class="s2"> and </span><span class="si">%d</span><span class="s2"> surrogates&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                      <span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">))</span>
-            <span class="c1"># Get nearest neighbors around each sample point in Z</span>
-            <span class="n">z_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[:,</span> <span class="n">z_indices</span><span class="p">]</span>
-            <span class="n">tree_xyz</span> <span class="o">=</span> <span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">z_array</span><span class="p">)</span>
-            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tree_xyz</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">z_array</span><span class="p">,</span>
-                                       <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shuffle_neighbors</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
-                                       <span class="n">p</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
-                                       <span class="n">workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workers</span><span class="p">,</span>
-                                       <span class="n">distance_upper_bound</span><span class="o">=</span><span class="mi">9999999</span><span class="p">,</span>
-                                       <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
-            
-            <span class="c1"># remove all neighbors with distance infinite -&gt; from another class </span>
-            <span class="c1"># for those that are discrete </span>
-            <span class="n">valid_neighbors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-            <span class="c1"># fill valid neighbors with point -&gt; if infinite, the neighbor will </span>
-            <span class="c1"># be the point itself</span>
-            <span class="n">valid_neighbors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">valid_neighbors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">valid_neighbors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-            
-            <span class="n">valid_neighbors</span><span class="p">[</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">]</span>
-
-            <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">)</span>
-            
-            <span class="k">for</span> <span class="n">sam</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">):</span>
-                <span class="n">array_shuffled</span><span class="p">,</span> <span class="n">type_mask_shuffled</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generate_random_permutation</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> 
-                                                                                       <span class="n">valid_neighbors</span><span class="p">,</span> 
-                                                                                       <span class="n">x_indices</span><span class="p">,</span>
-                                                                                       <span class="n">type_mask</span><span class="p">)</span>
-                <span class="n">null_dist</span><span class="p">[</span><span class="n">sam</span><span class="p">],</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array_shuffled</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
-                                                                <span class="n">xyz</span><span class="p">,</span>
-                                                                <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask_shuffled</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">null_dist</span> <span class="o">=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_get_shuffle_dist</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                           <span class="n">sig_samples</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">,</span>
-                                           <span class="n">sig_blocklength</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span><span class="p">,</span>
-                                           <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">,</span>
-                                           <span class="n">verbosity</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
-
-        <span class="n">pval</span> <span class="o">=</span> <span class="p">(</span><span class="n">null_dist</span> <span class="o">&gt;=</span> <span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        
-        <span class="k">if</span> <span class="n">return_null_dist</span><span class="p">:</span>
-            <span class="c1"># Sort</span>
-            <span class="n">null_dist</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-            <span class="k">return</span> <span class="n">pval</span><span class="p">,</span> <span class="n">null_dist</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
-
-
-
-
-
-    
-    <span class="k">def</span> <span class="nf">_get_shuffle_dist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                          <span class="n">sig_samples</span><span class="p">,</span> <span class="n">sig_blocklength</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                          <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                          <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns shuffle distribution of test statistic.</span>
-
-<span class="sd">        The rows in array corresponding to the X-variable are shuffled using</span>
-<span class="sd">        a block-shuffle approach.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array : array-like</span>
-<span class="sd">            data array with X, Y, Z in rows and observations in columns</span>
-
-<span class="sd">        xyz : array of ints</span>
-<span class="sd">            XYZ identifier array of shape (dim,).</span>
-
-<span class="sd">        dependence_measure : object</span>
-<span class="sd">           Dependence measure function must be of form</span>
-<span class="sd">           dependence_measure(array, xyz) and return a numeric value</span>
-
-<span class="sd">        sig_samples : int, optional (default: 100)</span>
-<span class="sd">            Number of samples for shuffle significance test.</span>
-
-<span class="sd">        sig_blocklength : int, optional (default: None)</span>
-<span class="sd">            Block length for block-shuffle significance test. If None, the</span>
-<span class="sd">            block length is determined from the decay of the autocovariance as</span>
-<span class="sd">            explained in [1]_.</span>
-<span class="sd">            </span>
-<span class="sd">        type_mask : array-like</span>
-<span class="sd">            data array of same shape as array which describes whether variables</span>
-<span class="sd">            are continuous or discrete: 0s for continuous variables and </span>
-<span class="sd">            1s for discrete variables</span>
-
-<span class="sd">        verbosity : int, optional (default: 0)</span>
-<span class="sd">            Level of verbosity.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        null_dist : array of shape (sig_samples,)</span>
-<span class="sd">            Contains the sorted test statistic values estimated from the</span>
-<span class="sd">            shuffled arrays.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">x_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">dim_x</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_indices</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">sig_blocklength</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">sig_blocklength</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_block_length</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span>
-                                                     <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;significance&#39;</span><span class="p">)</span>
-            
-        <span class="n">n_blks</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">/</span><span class="n">sig_blocklength</span><span class="p">))</span>
-        
-        <span class="c1"># print &#39;n_blks &#39;, n_blks</span>
-        <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;            Significance test with block-length = </span><span class="si">%d</span><span class="s2"> &quot;</span>
-                  <span class="s2">&quot;...&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">sig_blocklength</span><span class="p">))</span>
-
-        <span class="n">array_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
-        <span class="n">type_mask_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">type_mask</span><span class="p">)</span>
-        <span class="c1"># block_starts = np.arange(0, T - sig_blocklength, sig_blocklength)</span>
-        <span class="n">block_starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_blks</span> <span class="o">*</span> <span class="n">sig_blocklength</span><span class="p">,</span> <span class="n">sig_blocklength</span><span class="p">)</span>
-        
-    
-        <span class="c1"># Dividing the array up into n_blks of length sig_blocklength may</span>
-        <span class="c1"># leave a tail. This tail is later randomly inserted</span>
-        <span class="n">tail</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">x_indices</span><span class="p">,</span> <span class="n">n_blks</span><span class="o">*</span><span class="n">sig_blocklength</span><span class="p">:]</span>
-        
-        <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">sig_samples</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">sam</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sig_samples</span><span class="p">):</span>
-
-            <span class="n">blk_starts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">block_starts</span><span class="p">)[:</span><span class="n">n_blks</span><span class="p">]</span>
-
-            <span class="n">x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim_x</span><span class="p">,</span> <span class="n">n_blks</span><span class="o">*</span><span class="n">sig_blocklength</span><span class="p">),</span>
-                                  <span class="n">dtype</span><span class="o">=</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-            <span class="n">type_x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim_x</span><span class="p">,</span> <span class="n">n_blks</span><span class="o">*</span><span class="n">sig_blocklength</span><span class="p">),</span>
-                                  <span class="n">dtype</span><span class="o">=</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">x_indices</span><span class="p">):</span>
-                <span class="k">for</span> <span class="n">blk</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sig_blocklength</span><span class="p">):</span>
-                    <span class="n">x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">blk</span><span class="p">::</span><span class="n">sig_blocklength</span><span class="p">]</span> <span class="o">=</span> \
-                            <span class="n">array</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="n">blk_starts</span> <span class="o">+</span> <span class="n">blk</span><span class="p">]</span>
-
-                    <span class="n">type_x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">blk</span><span class="p">::</span><span class="n">sig_blocklength</span><span class="p">]</span> <span class="o">=</span> \
-                            <span class="n">type_mask</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="n">blk_starts</span> <span class="o">+</span> <span class="n">blk</span><span class="p">]</span>
-
-            <span class="c1"># Insert tail randomly somewhere</span>
-            <span class="k">if</span> <span class="n">tail</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">insert_tail_at</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">block_starts</span><span class="p">)</span>
-                <span class="n">x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">x_shuffled</span><span class="p">,</span> <span class="n">insert_tail_at</span><span class="p">,</span>
-                                       <span class="n">tail</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-                <span class="n">type_x_shuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">type_x_shuffled</span><span class="p">,</span> <span class="n">insert_tail_at</span><span class="p">,</span>
-                                       <span class="n">tail</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-                
-    
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">x_indices</span><span class="p">):</span>
-                <span class="n">array_shuffled</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">type_mask_shuffled</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">type_x_shuffled</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                
-            <span class="n">null_dist</span><span class="p">[</span><span class="n">sam</span><span class="p">],</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">array_shuffled</span><span class="p">,</span>
-                                                         <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span>
-                                                         <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask_shuffled</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">null_dist</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    
-    <span class="kn">import</span> <span class="nn">tigramite</span>
-    <span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
-    <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-    <span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">CMIknn</span>
-    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-    <span class="n">random_state_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">cmi</span> <span class="o">=</span> <span class="n">CMIknnMixed</span><span class="p">(</span><span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                       <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;shuffle_test&#39;</span><span class="p">,</span>
-                       <span class="c1"># estimator=&#39;cond&#39;,</span>
-                       <span class="n">use_local_knn</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                       <span class="n">fixed_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                       <span class="n">sig_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-                       <span class="n">sig_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                       <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;scale&#39;</span><span class="p">,</span>
-                       <span class="n">knn</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                       <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-    <span class="c1"># cmiknn = CMIknn(mask_type=None,</span>
-    <span class="c1">#                    significance=&#39;shuffle_test&#39;,</span>
-    <span class="c1">#                    # estimator=&#39;FPinf&#39;,</span>
-    <span class="c1">#                    # use_local_knn=True,</span>
-    <span class="c1">#                    fixed_thres=None,</span>
-    <span class="c1">#                    sig_samples=500,</span>
-    <span class="c1">#                    sig_blocklength=1,</span>
-    <span class="c1">#                    transform=&#39;none&#39;,</span>
-    <span class="c1">#                    knn=0.1,</span>
-    <span class="c1">#                    verbosity=0)</span>
-
-
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    <span class="n">dimz</span> <span class="o">=</span> <span class="mi">1</span>
-
-    <span class="c1"># Discrete data</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">)</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
-        <span class="n">val</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-        <span class="n">prob</span> <span class="o">=</span> <span class="mf">0.2</span> <span class="o">+</span> <span class="n">val</span><span class="o">*</span><span class="mf">0.6</span>
-        <span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">])</span>
-        <span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">])</span>
-
-    <span class="c1"># Continuous data</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="n">z</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-    <span class="n">z2</span> <span class="o">=</span> <span class="n">random_state_</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">)</span>
-    <span class="n">zfull</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">z</span><span class="p">,</span> <span class="n">z2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;X _|_ Y&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">zfull</span><span class="p">,</span> 
-                    <span class="n">x_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">),</span>
-                    <span class="n">y_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">),</span>
-                    <span class="n">z_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> 
-                    <span class="c1"># val_only=True)</span>
-                    <span class="p">))</span>
-
-    <span class="c1"># print(cmiknn.run_test_raw(x, y, z=None))</span>
-        <span class="c1">#   </span>
-    <span class="c1"># print(&#39;X _|_ Y | Z&#39;)</span>
-    <span class="c1"># print(cmi.run_test_raw(x, y, z=z, </span>
-    <span class="c1">#                 x_type=np.zeros(T, dtype=&#39;bool&#39;), </span>
-    <span class="c1">#                 y_type=np.zeros(T, dtype=&#39;bool&#39;),</span>
-    <span class="c1">#                 z_type=np.zeros(T, dtype=&#39;bool&#39;)))</span>
-
-</pre></div>
-
-          </div>
-          
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-        <div class="sphinxsidebarwrapper">
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diff --git a/docs/_modules/tigramite/independence_tests/cmisymb.html b/docs/_modules/tigramite/independence_tests/cmisymb.html
index 04d7ed0c..07a3d4fc 100644
--- a/docs/_modules/tigramite/independence_tests/cmisymb.html
+++ b/docs/_modules/tigramite/independence_tests/cmisymb.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -40,8 +42,8 @@ <h1>Source code for tigramite.independence_tests.cmisymb</h1><div class="highlig
 <span class="kn">import</span> <span class="nn">warnings</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">scipy.stats.contingency</span> <span class="kn">import</span> <span class="n">crosstab</span>
-<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
-<span class="kn">import</span> <span class="nn">multiprocessing</span>
+<span class="c1"># from joblib import Parallel, delayed</span>
+<span class="c1"># import dask</span>
 <span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
 
 <span class="kn">from</span> <span class="nn">.independence_tests_base</span> <span class="kn">import</span> <span class="n">CondIndTest</span>
@@ -220,11 +222,16 @@ <h1>Source code for tigramite.independence_tests.cmisymb</h1><div class="highlig
                 <span class="n">neighbor_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">z_array</span> <span class="o">==</span> <span class="n">z_comb</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
                 <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">neighbor_indices</span><span class="p">)]</span> <span class="o">=</span> <span class="n">neighbor_indices</span>
 
-            <span class="n">num_cores</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
             <span class="n">random_seeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">integers</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">iinfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">)</span>
-            <span class="n">null_dist</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">num_cores</span><span class="p">)(</span>
-                <span class="n">delayed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parallelize_shuffles</span><span class="p">)(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">,</span> <span class="n">x_indices</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">z_comb</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span> <span class="k">for</span> <span class="n">seed</span> <span class="ow">in</span> <span class="n">random_seeds</span><span class="p">)</span>
-            <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">null_dist</span><span class="p">)</span>
+            <span class="c1"># null_dist = Parallel(n_jobs=-1)(</span>
+            <span class="c1">#     delayed(self.parallelize_shuffles)(array, xyz, z_indices, x_indices, T, z_comb, neighbors, seed=seed) for seed in random_seeds)</span>
+            <span class="c1"># dask_jobs = [dask.delayed(self.parallelize_shuffles)(array, xyz, z_indices, x_indices, T, z_comb, neighbors, seed=seed) for seed in random_seeds]</span>
+            <span class="c1"># null_dist = dask.compute(dask_jobs)</span>
+            <span class="c1"># null_dist = np.asarray(null_dist)</span>
+
+            <span class="n">null_dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">seed</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">random_seeds</span><span class="p">):</span>
+                <span class="n">null_dist</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parallelize_shuffles</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">,</span> <span class="n">x_indices</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">z_comb</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
 
         <span class="k">else</span><span class="p">:</span>
             <span class="n">null_dist</span> <span class="o">=</span> \
@@ -283,13 +290,15 @@ <h1>Source code for tigramite.independence_tests.cmisymb</h1><div class="highlig
     <span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
     <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
     <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="c1"># from dask.distributed import Client</span>
 
+    <span class="c1"># client = dask.distributed.Client(processes=True)</span>
     <span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
     <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">cmi</span> <span class="o">=</span> <span class="n">CMIsymb</span><span class="p">(</span><span class="n">sig_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+    <span class="n">cmi</span> <span class="o">=</span> <span class="n">CMIsymb</span><span class="p">(</span><span class="n">sig_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
 
     <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
-    <span class="n">dimz</span> <span class="o">=</span> <span class="mi">10</span>
+    <span class="n">dimz</span> <span class="o">=</span> <span class="mi">5</span>
     <span class="n">z</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">dimz</span><span class="p">)</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
     <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -300,8 +309,13 @@ <h1>Source code for tigramite.independence_tests.cmisymb</h1><div class="highlig
         <span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">prob</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">prob</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">])</span>
 
     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">))</span>
+    <span class="c1"># print(client.dashboard_link)</span>
+    <span class="c1"># print(cmi.run_test_raw(x, y, z=None))</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test_raw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">))</span>
+
+    <span class="c1"># client.close()</span>
+
+
 </pre></div>
 
           </div>
@@ -356,8 +370,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/gpdc.html b/docs/_modules/tigramite/independence_tests/gpdc.html
index 2904277a..7c24f23e 100644
--- a/docs/_modules/tigramite/independence_tests/gpdc.html
+++ b/docs/_modules/tigramite/independence_tests/gpdc.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -244,7 +246,7 @@ <h1>Source code for tigramite.independence_tests.gpdc</h1><div class="highlight"
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -576,12 +578,12 @@ <h1>Source code for tigramite.independence_tests.gpdc</h1><div class="highlight"
 
 
     <span class="k">def</span> <span class="nf">_get_dcorr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array_resid</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return distance correlation coefficient.</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Return distance correlation coefficient.</span>
 
 <span class="sd">        The variables are transformed to uniform marginals using the empirical</span>
 <span class="sd">        cumulative distribution function beforehand. Here the null distribution</span>
 <span class="sd">        is not analytically available, but can be precomputed with the function</span>
-<span class="sd">        generate_and_save_nulldists(...) which saves a \*.npz file containing</span>
+<span class="sd">        generate_and_save_nulldists(...) which saves a *.npz file containing</span>
 <span class="sd">        the null distribution for different sample sizes. This file can then be</span>
 <span class="sd">        supplied as null_dist_filename.</span>
 
@@ -743,8 +745,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/gpdc_torch.html b/docs/_modules/tigramite/independence_tests/gpdc_torch.html
index b2dd7f55..dbcd744c 100644
--- a/docs/_modules/tigramite/independence_tests/gpdc_torch.html
+++ b/docs/_modules/tigramite/independence_tests/gpdc_torch.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -251,7 +253,7 @@ <h1>Source code for tigramite.independence_tests.gpdc_torch</h1><div class="high
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).any():</span>
@@ -737,7 +739,7 @@ <h1>Source code for tigramite.independence_tests.gpdc_torch</h1><div class="high
 <span class="sd">        The variables are transformed to uniform marginals using the empirical</span>
 <span class="sd">        cumulative distribution function beforehand. Here the null distribution</span>
 <span class="sd">        is not analytically available, but can be precomputed with the function</span>
-<span class="sd">        generate_and_save_nulldists(...) which saves a \*.npz file containing</span>
+<span class="sd">        generate_and_save_nulldists(...) which saves a *.npz file containing</span>
 <span class="sd">        the null distribution for different sample sizes. This file can then be</span>
 <span class="sd">        supplied as null_dist_filename.</span>
 
@@ -899,8 +901,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/gsquared.html b/docs/_modules/tigramite/independence_tests/gsquared.html
index 3b702b6c..10f89b76 100644
--- a/docs/_modules/tigramite/independence_tests/gsquared.html
+++ b/docs/_modules/tigramite/independence_tests/gsquared.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -273,8 +275,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/independence_tests_base.html b/docs/_modules/tigramite/independence_tests/independence_tests_base.html
index 7626dc7c..4e1a0582 100644
--- a/docs/_modules/tigramite/independence_tests/independence_tests_base.html
+++ b/docs/_modules/tigramite/independence_tests/independence_tests_base.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -69,8 +71,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">        &#39;fixed_thres&#39; and &#39;shuffle_test&#39; are available.</span>
 
 <span class="sd">    fixed_thres : float, optional (default: 0.1)</span>
-<span class="sd">        If significance is &#39;fixed_thres&#39;, this specifies the threshold for the</span>
-<span class="sd">        absolute value of the dependence measure.</span>
+<span class="sd">        Deprecated.</span>
 
 <span class="sd">    sig_samples : int, optional (default: 500)</span>
 <span class="sd">        Number of samples for shuffle significance test.</span>
@@ -120,7 +121,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
                  <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
                  <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">significance</span><span class="o">=</span><span class="s1">&#39;analytic&#39;</span><span class="p">,</span>
-                 <span class="n">fixed_thres</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
+                 <span class="n">fixed_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">sig_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
                  <span class="n">sig_blocklength</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">confidence</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@@ -136,9 +137,11 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">=</span> <span class="n">significance</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span> <span class="o">=</span> <span class="n">sig_samples</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span> <span class="o">=</span> <span class="n">sig_blocklength</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span> <span class="o">=</span> <span class="n">fixed_thres</span>
+        <span class="k">if</span> <span class="n">fixed_thres</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;fixed_thres is replaced by providing alpha_or_thres in run_test&quot;</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ci_results</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="c1"># If we recycle residuals, then set up a residual cache</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span> <span class="o">=</span> <span class="n">recycle_residuals</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">recycle_residuals</span><span class="p">:</span>
@@ -190,9 +193,9 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;shuffle_test&#39;</span><span class="p">:</span>
             <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sig_samples = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_samples</span>
             <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sig_blocklength = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">sig_blocklength</span>
-        <span class="c1"># Check if we are using a fixed threshold</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
-            <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">fixed_thres = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span>
+        <span class="c1"># # Check if we are using a fixed threshold</span>
+        <span class="c1"># elif self.significance == &#39;fixed_thres&#39;:</span>
+        <span class="c1">#     info_str += &quot;\nfixed_thres = %s&quot; % self.fixed_thres</span>
         <span class="c1"># Check if we have a confidence type</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span><span class="p">:</span>
             <span class="n">info_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">confidence = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span>
@@ -251,7 +254,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
                                   <span class="s2">&quot; implemented for </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="CondIndTest.get_shuffle_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_shuffle_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_shuffle_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span>
-                                 <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                 <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                                  <span class="n">return_null_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Base class assumption that this is not implemented.  Concrete classes</span>
@@ -355,7 +358,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="k">return</span> <span class="n">combined_hash</span>
 
 
-<div class="viewcode-block" id="CondIndTest.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">):</span>
+<div class="viewcode-block" id="CondIndTest.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test.</span>
 
 <span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
@@ -369,11 +372,9 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">        X, Y, Z : list of tuples</span>
 <span class="sd">            X,Y,Z are of the form [(var, -tau)], where var specifies the</span>
 <span class="sd">            variable index and tau the time lag.</span>
-
 <span class="sd">        tau_max : int, optional (default: 0)</span>
 <span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
 <span class="sd">            different lags in X, Z, all have the same sample size.</span>
-
 <span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;}</span>
 <span class="sd">            How many samples to cutoff at the beginning. The default is</span>
 <span class="sd">            &#39;2xtau_max&#39;, which guarantees that MCI tests are all conducted on</span>
@@ -381,43 +382,72 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">            which uses the maximum of tau_max and the conditions, which is</span>
 <span class="sd">            useful to compare multiple models on the same sample.  Last,</span>
 <span class="sd">            &#39;max_lag&#39; uses as much samples as possible.</span>
+<span class="sd">        alpha_or_thres : float (optional)</span>
+<span class="sd">            Significance level (if significance=&#39;analytic&#39; or &#39;shuffle_test&#39;) or</span>
+<span class="sd">            threshold (if significance=&#39;fixed_thres&#39;). If given, run_test returns</span>
+<span class="sd">            the test decision dependent=True/False.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-<span class="sd">            The test statistic value and the p-value.</span>
+<span class="sd">        val, pval, [dependent] : Tuple of floats and bool</span>
+<span class="sd">            The test statistic value and the p-value. If alpha_or_thres is</span>
+<span class="sd">            given, run_test also returns the test decision dependent=True/False.      </span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
         <span class="c1"># Get the array to test on</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">cut_off</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
+        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">data_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">cut_off</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span><span class="p">)</span>
         <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">XYZ</span>
         
         <span class="c1"># Record the dimensions</span>
         <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
+
         <span class="c1"># Ensure it is a valid array</span>
         <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
 
         <span class="n">combined_hash</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array_hash</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">)</span>
 
+        <span class="c1"># Get test statistic value and p-value [cached if possible]</span>
         <span class="k">if</span> <span class="n">combined_hash</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
             <span class="n">cached</span> <span class="o">=</span> <span class="kc">True</span>
             <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">cached</span> <span class="o">=</span> <span class="kc">False</span>
             <span class="c1"># Get the dependence measure, reycling residuals if need be</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dependence_measure_recycle</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">)</span>
-            <span class="c1"># Get the p-value</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
+            <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dependence_measure_recycle</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">data_type</span><span class="p">)</span>
+            <span class="c1"># Get the p-value (None if significance = &#39;fixed_thres&#39;)</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_p_value</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cached_ci_results</span><span class="p">[</span><span class="n">combined_hash</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">)</span>
 
+        <span class="c1"># Make test decision</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;significance == &#39;fixed_thres&#39; requires setting alpha_or_thres&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">alpha_or_thres</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">val</span> <span class="o">&gt;=</span> <span class="n">alpha_or_thres</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span> <span class="k">if</span> <span class="n">dependent</span> <span class="k">else</span> <span class="mf">1.</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">pval</span> <span class="o">&lt;=</span> <span class="n">alpha_or_thres</span>
+                
+        <span class="bp">self</span><span class="o">.</span><span class="n">ci_results</span><span class="p">[(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">Y</span><span class="p">),</span><span class="nb">tuple</span><span class="p">(</span><span class="n">Z</span><span class="p">))]</span> <span class="o">=</span> <span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span><span class="p">)</span>
+
+        <span class="c1"># Return the calculated value(s)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="n">cached</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="n">cached</span><span class="p">,</span> <span class="n">dependent</span><span class="o">=</span><span class="n">dependent</span><span class="p">,</span>
                                          <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-        <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
 
-<div class="viewcode-block" id="CondIndTest.run_test_raw"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw">[docs]</a>    <span class="k">def</span> <span class="nf">run_test_raw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">y_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span>
+        <span class="k">else</span><span class="p">:</span>              
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span></div>
+
+
+<div class="viewcode-block" id="CondIndTest.run_test_raw"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw">[docs]</a>    <span class="k">def</span> <span class="nf">run_test_raw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">y_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform conditional independence test directly on input arrays x, y, z.</span>
 
 <span class="sd">        Calls the dependence measure and signficicance test functions. The child</span>
@@ -434,11 +464,16 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">            are continuous or discrete: 0s for continuous variables and</span>
 <span class="sd">            1s for discrete variables</span>
 
+<span class="sd">        alpha_or_thres : float (optional)</span>
+<span class="sd">            Significance level (if significance=&#39;analytic&#39; or &#39;shuffle_test&#39;) or</span>
+<span class="sd">            threshold (if significance=&#39;fixed_thres&#39;). If given, run_test returns</span>
+<span class="sd">            the test decision dependent=True/False.</span>
+<span class="sd">        </span>
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        val, pval : Tuple of floats</span>
-
-<span class="sd">            The test statistic value and the p-value.</span>
+<span class="sd">        val, pval, [dependent] : Tuple of floats and bool</span>
+<span class="sd">            The test statistic value and the p-value. If alpha_or_thres is</span>
+<span class="sd">            given, run_test also returns the test decision dependent=True/False.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
         <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
@@ -450,21 +485,21 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
                              <span class="s2">&quot; where dimension can be 1.&quot;</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="n">x_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">y_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">z_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">has_type_mask</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="n">has_data_type</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">has_type_mask</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="n">has_data_type</span> <span class="o">=</span> <span class="kc">False</span>
 
-        <span class="k">if</span> <span class="n">x_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_type_mask</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">x_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_data_type</span><span class="p">:</span>
             <span class="n">x_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int&#39;</span><span class="p">)</span>
 
-        <span class="k">if</span> <span class="n">y_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_type_mask</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">y_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_data_type</span><span class="p">:</span>
             <span class="n">y_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int&#39;</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="n">z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="c1"># Get the array to test on</span>
             <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">has_type_mask</span><span class="p">:</span>
-                <span class="n">type_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">has_data_type</span><span class="p">:</span>
+                <span class="n">data_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
 
             <span class="c1"># xyz is the dimension indicator</span>
             <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
@@ -473,11 +508,11 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="k">else</span><span class="p">:</span>
             <span class="c1"># Get the array to test on</span>
             <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">z</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">z_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_type_mask</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">z_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_data_type</span><span class="p">:</span>
                 <span class="n">z_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">z</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int&#39;</span><span class="p">)</span>
 
-            <span class="k">if</span> <span class="n">has_type_mask</span><span class="p">:</span>
-                <span class="n">type_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">z_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">has_data_type</span><span class="p">:</span>
+                <span class="n">data_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y_type</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">z_type</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
             <span class="c1"># xyz is the dimension indicator</span>
             <span class="n">xyz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
                            <span class="p">[</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">+</span>
@@ -489,22 +524,39 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
         <span class="c1"># Get the dependence measure</span>
-        <span class="k">if</span> <span class="n">has_type_mask</span><span class="p">:</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">has_data_type</span><span class="p">:</span>
+            <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">data_type</span><span class="o">=</span><span class="n">data_type</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">)</span>
 
         <span class="c1"># Get the p-value</span>
-        <span class="k">if</span> <span class="n">has_type_mask</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
-                    <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">has_data_type</span><span class="p">:</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_p_value</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
+                    <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">data_type</span><span class="o">=</span><span class="n">data_type</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_p_value</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
+                    <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span>    
+
+        <span class="c1"># Make test decision</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">two_sided</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">alpha_or_thres</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">val</span> <span class="o">&gt;=</span> <span class="n">alpha_or_thres</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span> <span class="k">if</span> <span class="n">dependent</span> <span class="k">else</span> <span class="mf">1.</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_significance</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="o">=</span><span class="n">xyz</span><span class="p">,</span> 
-                    <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span>            
+            <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dependent</span> <span class="o">=</span> <span class="n">pval</span> <span class="o">&lt;=</span> <span class="n">alpha_or_thres</span>
+
         <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
+        <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span>
+        <span class="k">else</span><span class="p">:</span>              
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span></div>
 
-    <span class="k">def</span> <span class="nf">_get_dependence_measure_recycle</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_get_dependence_measure_recycle</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the dependence_measure, optionally recycling residuals</span>
 
 <span class="sd">        If self.recycle_residuals is True, also _get_single_residuals must be</span>
@@ -522,7 +574,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">        array : array</span>
 <span class="sd">            Data array of shape (dim, T)</span>
 
-<span class="sd">       type_mask : array-like</span>
+<span class="sd">       data_type : array-like</span>
 <span class="sd">            Binary data array of same shape as array which describes whether </span>
 <span class="sd">            individual samples in a variable (or all samples) are continuous </span>
 <span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
@@ -545,9 +597,9 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array_resid</span><span class="p">,</span> <span class="n">xyz_resid</span><span class="p">)</span>
 
         <span class="c1"># If not, return the dependence measure on the array and xyz</span>
-        <span class="k">if</span> <span class="n">type_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">data_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> 
-                                        <span class="n">type_mask</span><span class="o">=</span><span class="n">type_mask</span><span class="p">)</span>
+                                        <span class="n">data_type</span><span class="o">=</span><span class="n">data_type</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dependence_measure</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">)</span>
 
@@ -587,12 +639,12 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="c1"># Return these residuals</span>
         <span class="k">return</span> <span class="n">x_resid</span>
 
-<div class="viewcode-block" id="CondIndTest.get_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span>
-                         <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="k">def</span> <span class="nf">_get_p_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span>
+                         <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                          <span class="n">sig_override</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the p-value from whichever significance function is specified</span>
-<span class="sd">        for this test.  If an override is used, then it will call a different</span>
+<span class="sd">        for this test. If an override is used, then it will call a different</span>
 <span class="sd">        function then specified by self.significance</span>
 
 <span class="sd">        Parameters</span>
@@ -612,7 +664,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">        dim : int</span>
 <span class="sd">            Dimensionality, ie, number of features.</span>
 <span class="sd">            </span>
-<span class="sd">       type_mask : array-like</span>
+<span class="sd">       data_type : array-like</span>
 <span class="sd">            Binary data array of same shape as array which describes whether </span>
 <span class="sd">            individual samples in a variable (or all samples) are continuous </span>
 <span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
@@ -639,16 +691,29 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
                                                  <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
         <span class="c1"># Check if we are using the fixed_thres significance</span>
         <span class="k">elif</span> <span class="n">use_sig</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_fixed_thres_significance</span><span class="p">(</span>
-                    <span class="n">value</span><span class="o">=</span><span class="n">val</span><span class="p">,</span>
-                    <span class="n">fixed_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fixed_thres</span><span class="p">)</span>
+            <span class="c1"># Determined outside then</span>
+            <span class="n">pval</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="c1"># if self.two_sided:</span>
+            <span class="c1">#     dependent = np.abs(val) &gt;= np.abs(alpha_or_thres)</span>
+            <span class="c1"># else:</span>
+            <span class="c1">#     dependent = val &gt;= alpha_or_thres</span>
+            <span class="c1"># pval = 0. if dependent else 1.</span>
+            <span class="c1"># # pval = self.get_fixed_thres_significance(</span>
+            <span class="c1"># #         value=val,</span>
+            <span class="c1"># #         fixed_thres=self.fixed_thres)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> not known.&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">significance</span><span class="p">)</span>
-        <span class="c1"># Return the calculated value</span>
-        <span class="k">return</span> <span class="n">pval</span></div>
+
+        <span class="c1"># # Return the calculated value(s)</span>
+        <span class="c1"># if alpha_or_thres is not None:</span>
+        <span class="c1">#     if use_sig != &#39;fixed_thres&#39;: </span>
+        <span class="c1">#         dependent = pval &lt;= alpha_or_thres </span>
+        <span class="c1">#     return pval, dependent </span>
+        <span class="c1"># else:</span>
+        <span class="k">return</span> <span class="n">pval</span>
 
 <div class="viewcode-block" id="CondIndTest.get_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
-                    <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+                    <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Estimate dependence measure.</span>
 
 <span class="sd">        Calls the dependence measure function. The child classes must specify</span>
@@ -664,7 +729,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
 <span class="sd">            different lags in X, Z, all have the same sample size.</span>
 <span class="sd">        </span>
-<span class="sd">       type_mask : array-like</span>
+<span class="sd">       data_type : array-like</span>
 <span class="sd">            Binary data array of same shape as array which describes whether </span>
 <span class="sd">            individual samples in a variable (or all samples) are continuous </span>
 <span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
@@ -686,7 +751,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dependence_measure_recycle</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="CondIndTest.get_confidence"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_confidence">[docs]</a>    <span class="k">def</span> <span class="nf">get_confidence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                       <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+                       <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform confidence interval estimation.</span>
 
 <span class="sd">        Calls the dependence measure and confidence test functions. The child</span>
@@ -704,7 +769,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">            Maximum time lag. This may be used to make sure that estimates for</span>
 <span class="sd">            different lags in X, Z, all have the same sample size.</span>
 <span class="sd">            </span>
-<span class="sd">       type_mask : array-like</span>
+<span class="sd">       data_type : array-like</span>
 <span class="sd">            Binary data array of same shape as array which describes whether </span>
 <span class="sd">            individual samples in a variable (or all samples) are continuous </span>
 <span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
@@ -727,7 +792,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span><span class="p">:</span>
             <span class="c1"># Make and check the array</span>
-            <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">data_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
             <span class="n">dim</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span>
             <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;nans in the array!&quot;</span><span class="p">)</span>
@@ -758,7 +823,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="c1"># Return the confidence interval</span>
         <span class="k">return</span> <span class="p">(</span><span class="n">conf_lower</span><span class="p">,</span> <span class="n">conf_upper</span><span class="p">)</span></div>
 
-    <span class="k">def</span> <span class="nf">_print_cond_ind_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_print_cond_ind_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dependent</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Print results from conditional independence test.</span>
 
 <span class="sd">        Parameters</span>
@@ -769,12 +834,17 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">        pval : float, optional (default: None)</span>
 <span class="sd">            p-value</span>
 
+<span class="sd">        dependent : bool</span>
+<span class="sd">            Test decision.</span>
+
 <span class="sd">        conf : tuple of floats, optional (default: None)</span>
 <span class="sd">            Confidence bounds.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">printstr</span> <span class="o">=</span> <span class="s2">&quot;        val = </span><span class="si">% .3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">val</span><span class="p">)</span>      
         <span class="k">if</span> <span class="n">pval</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | pval = </span><span class="si">%.5f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">pval</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">dependent</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | dependent = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">dependent</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">conf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">printstr</span> <span class="o">+=</span> <span class="s2">&quot; | conf bounds = (</span><span class="si">%.3f</span><span class="s2">, </span><span class="si">%.3f</span><span class="s2">)&quot;</span> <span class="o">%</span> <span class="p">(</span>
                 <span class="n">conf</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conf</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
@@ -786,7 +856,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <div class="viewcode-block" id="CondIndTest.get_bootstrap_confidence"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_bootstrap_confidence">[docs]</a>    <span class="k">def</span> <span class="nf">get_bootstrap_confidence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">dependence_measure</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                                  <span class="n">conf_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">conf_blocklength</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                                  <span class="n">conf_lev</span><span class="o">=</span><span class="mf">.95</span><span class="p">,</span> 
-                                 <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                 <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                                  <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform bootstrap confidence interval estimation.</span>
 
@@ -815,7 +885,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
 <span class="sd">            determined from the decay of the autocovariance as explained in</span>
 <span class="sd">            [1]_.</span>
 
-<span class="sd">       type_mask : array-like</span>
+<span class="sd">       data_type : array-like</span>
 <span class="sd">            Binary data array of same shape as array which describes whether </span>
 <span class="sd">            individual samples in a variable (or all samples) are continuous </span>
 <span class="sd">            or discrete: 0s for continuous variables and 1s for discrete variables.</span>
@@ -967,7 +1037,7 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
                     <span class="n">ydata</span><span class="o">=</span><span class="n">hilbert</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">phi</span> <span class="o">=</span> <span class="n">popt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="c1"># Formula of Peifer (2005) assuming non-overlapping blocks</span>
+                <span class="c1"># Formula assuming non-overlapping blocks</span>
                 <span class="n">l_opt</span> <span class="o">=</span> <span class="p">(</span><span class="mf">4.</span> <span class="o">*</span> <span class="n">T</span> <span class="o">*</span> <span class="p">(</span><span class="n">phi</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">)</span> <span class="o">+</span> <span class="n">phi</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
                          <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">phi</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">phi</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span>
                 <span class="n">block_len</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">block_len</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">l_opt</span><span class="p">))</span>
@@ -1067,31 +1137,15 @@ <h1>Source code for tigramite.independence_tests.independence_tests_base</h1><di
         <span class="k">return</span> <span class="n">null_dist</span>
 
 <div class="viewcode-block" id="CondIndTest.get_fixed_thres_significance"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance">[docs]</a>    <span class="k">def</span> <span class="nf">get_fixed_thres_significance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">fixed_thres</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns signficance for thresholding test.</span>
-
-<span class="sd">        Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1 else.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        value : number</span>
-<span class="sd">            Value of test statistic for unshuffled estimate.</span>
-
-<span class="sd">        fixed_thres : number</span>
-<span class="sd">            Fixed threshold, is made positive.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        pval : bool</span>
-<span class="sd">            Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1</span>
-<span class="sd">            else.</span>
-
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;DEPRECATED Returns signficance for thresholding test.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">fixed_thres</span><span class="p">):</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">1.</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;fixed_thres is replaced by alpha_or_thres in run_test.&quot;</span><span class="p">)</span></div>
+        <span class="c1"># if np.abs(value) &lt; np.abs(fixed_thres):</span>
+        <span class="c1">#     pval = 1.</span>
+        <span class="c1"># else:</span>
+        <span class="c1">#     pval = 0.</span>
 
-        <span class="k">return</span> <span class="n">pval</span></div>
+        <span class="c1"># return pval</span>
 
     <span class="k">def</span> <span class="nf">_trafo2uniform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Transforms input array to uniform marginals.</span>
@@ -1175,8 +1229,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/oracle_conditional_independence.html b/docs/_modules/tigramite/independence_tests/oracle_conditional_independence.html
index 703a759f..bb511c49 100644
--- a/docs/_modules/tigramite/independence_tests/oracle_conditional_independence.html
+++ b/docs/_modules/tigramite/independence_tests/oracle_conditional_independence.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -1081,7 +1083,7 @@ <h1>Source code for tigramite.independence_tests.oracle_conditional_independence
 
         <span class="k">return</span> <span class="n">any_path_observed</span></div>
 
-<div class="viewcode-block" id="OracleCI.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span>
+<div class="viewcode-block" id="OracleCI.run_test"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test">[docs]</a>    <span class="k">def</span> <span class="nf">run_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform oracle conditional independence test.</span>
 
@@ -1096,6 +1098,8 @@ <h1>Source code for tigramite.independence_tests.oracle_conditional_independence
 <span class="sd">            Not used here.</span>
 <span class="sd">        cut_off : {&#39;2xtau_max&#39;, &#39;max_lag&#39;, &#39;max_lag_or_tau_max&#39;}</span>
 <span class="sd">            Not used here.</span>
+<span class="sd">        alpha_or_thres : float</span>
+<span class="sd">            Not used here.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -1120,15 +1124,20 @@ <h1>Source code for tigramite.independence_tests.oracle_conditional_independence
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dsepsets</span><span class="p">[</span><span class="nb">str</span><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">))]:</span>
             <span class="n">val</span> <span class="o">=</span> <span class="mf">0.</span>
             <span class="n">pval</span> <span class="o">=</span> <span class="mf">1.</span>
+            <span class="n">dependent</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span>
             <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
+            <span class="n">dependent</span> <span class="o">=</span> <span class="kc">True</span>
 
         <span class="k">if</span> <span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_ind_results</span><span class="p">(</span><span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span> <span class="n">cached</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                          <span class="n">conf</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
         <span class="c1"># Return the value and the pvalue</span>
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span></div>
+        <span class="k">if</span> <span class="n">alpha_or_thres</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span></div>
 
 <div class="viewcode-block" id="OracleCI.get_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.oracle_conditional_independence.OracleCI.get_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns dependence measure.</span>
@@ -1650,8 +1659,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/parcorr.html b/docs/_modules/tigramite/independence_tests/parcorr.html
index 5ed4ec16..030bb8bc 100644
--- a/docs/_modules/tigramite/independence_tests/parcorr.html
+++ b/docs/_modules/tigramite/independence_tests/parcorr.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -128,7 +130,7 @@ <h1>Source code for tigramite.independence_tests.parcorr</h1><div class="highlig
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -340,6 +342,7 @@ <h1>Source code for tigramite.independence_tests.parcorr</h1><div class="highlig
             <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span> <span class="o">+</span> <span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">2.</span><span class="o">*</span><span class="n">p</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">T</span> <span class="o">-</span> <span class="n">p</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">score</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">rss</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">p</span>
+
         <span class="k">return</span> <span class="n">score</span></div></div>
 </pre></div>
 
@@ -395,8 +398,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/parcorr_mult.html b/docs/_modules/tigramite/independence_tests/parcorr_mult.html
index 390a8fcd..2206ba02 100644
--- a/docs/_modules/tigramite/independence_tests/parcorr_mult.html
+++ b/docs/_modules/tigramite/independence_tests/parcorr_mult.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -133,7 +135,7 @@ <h1>Source code for tigramite.independence_tests.parcorr_mult</h1><div class="hi
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -222,7 +224,7 @@ <h1>Source code for tigramite.independence_tests.parcorr_mult</h1><div class="hi
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -460,8 +462,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/parcorr_wls.html b/docs/_modules/tigramite/independence_tests/parcorr_wls.html
index e3656da9..bfeb64da 100644
--- a/docs/_modules/tigramite/independence_tests/parcorr_wls.html
+++ b/docs/_modules/tigramite/independence_tests/parcorr_wls.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -160,7 +162,7 @@ <h1>Source code for tigramite.independence_tests.parcorr_wls</h1><div class="hig
                                  <span class="bp">self</span><span class="o">.</span><span class="n">measure</span><span class="p">)</span>
 
         <span class="c1"># Call the wrapped function</span>
-        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">type_mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
+        <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">data_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
                                                             <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
                                                             <span class="n">mask_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_type</span><span class="p">,</span>
                                                             <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="n">return_cleaned_xyz</span><span class="p">,</span>
@@ -170,7 +172,7 @@ <h1>Source code for tigramite.independence_tests.parcorr_wls</h1><div class="hig
                                                             <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
         <span class="n">array_copy</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_get_stds</span><span class="p">(</span><span class="n">array_copy</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">cut_off</span><span class="p">,</span> <span class="n">verbosity</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">type_mask</span>
+        <span class="k">return</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">XYZ</span><span class="p">,</span> <span class="n">data_type</span>
 
     <span class="k">def</span> <span class="nf">_estimate_std_time</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">target_var</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -356,7 +358,7 @@ <h1>Source code for tigramite.independence_tests.parcorr_wls</h1><div class="hig
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="n">x_vals_sum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
             <span class="n">x_vals_has_nan</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">x_vals_sum</span><span class="p">)</span>
@@ -499,8 +501,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/regressionCI.html b/docs/_modules/tigramite/independence_tests/regressionCI.html
index b087a0de..228502c3 100644
--- a/docs/_modules/tigramite/independence_tests/regressionCI.html
+++ b/docs/_modules/tigramite/independence_tests/regressionCI.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -108,12 +110,12 @@ <h1>Source code for tigramite.independence_tests.regressionCI</h1><div class="hi
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;mask_type is not None, but no mask in dataframe.&quot;</span><span class="p">)</span>
             <span class="n">dataframe</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span>
         
-        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">type_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;type_mask cannot be None for RegressionCI.&quot;</span><span class="p">)</span>
-        <span class="n">dataframe</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">type_mask</span><span class="p">,</span> <span class="n">check_type_mask</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
+        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">data_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;data_type cannot be None for RegressionCI.&quot;</span><span class="p">)</span>
+        <span class="n">dataframe</span><span class="o">.</span><span class="n">_check_mask</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">data_type</span><span class="p">,</span> <span class="n">check_data_type</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
 
     <span class="c1"># @jit(forceobj=True)</span>
-<div class="viewcode-block" id="RegressionCI.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.regressionCI.RegressionCI.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="p">):</span>
+<div class="viewcode-block" id="RegressionCI.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.regressionCI.RegressionCI.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">data_type</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns test statistic.</span>
 
 <span class="sd">        Parameters</span>
@@ -124,7 +126,7 @@ <h1>Source code for tigramite.independence_tests.regressionCI</h1><div class="hi
 <span class="sd">        xyz : array of ints</span>
 <span class="sd">            XYZ identifier array of shape (dim,).</span>
 
-<span class="sd">        type_mask : array-like</span>
+<span class="sd">        data_type : array-like</span>
 <span class="sd">            array of same shape as array which describes whether samples</span>
 <span class="sd">            are continuous or discrete: 0s for continuous and</span>
 <span class="sd">            1s for discrete</span>
@@ -156,7 +158,7 @@ <h1>Source code for tigramite.independence_tests.regressionCI</h1><div class="hi
                 <span class="k">elif</span> <span class="n">var_type</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                     <span class="n">X_new</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">X_new</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="mi">1</span><span class="p">))))</span>
                 <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;type_mask only allows entries in {0, 1}&quot;</span><span class="p">)</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;data_type only allows entries in {0, 1}&quot;</span><span class="p">)</span>
             <span class="k">return</span> <span class="n">X_new</span>
 
         <span class="k">def</span> <span class="nf">calc_deviance_logistic</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">var_type</span><span class="p">):</span>
@@ -210,15 +212,15 @@ <h1>Source code for tigramite.independence_tests.regressionCI</h1><div class="hi
         <span class="n">x</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">x_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
         <span class="n">y</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">y_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
 
-        <span class="n">x_type</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">[</span><span class="n">x_indices</span><span class="p">]</span>
-        <span class="n">y_type</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">[</span><span class="n">y_indices</span><span class="p">]</span>
+        <span class="n">x_type</span> <span class="o">=</span> <span class="n">data_type</span><span class="p">[</span><span class="n">x_indices</span><span class="p">]</span>
+        <span class="n">y_type</span> <span class="o">=</span> <span class="n">data_type</span><span class="p">[</span><span class="n">y_indices</span><span class="p">]</span>
 
         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">z_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
             <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
             <span class="n">z_type</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">z</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">z_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
-            <span class="n">z_type</span> <span class="o">=</span> <span class="n">type_mask</span><span class="p">[</span><span class="n">z_indices</span><span class="p">]</span>
+            <span class="n">z_type</span> <span class="o">=</span> <span class="n">data_type</span><span class="p">[</span><span class="n">z_indices</span><span class="p">]</span>
             <span class="n">z_type</span> <span class="o">=</span> <span class="n">z_type</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 
         <span class="c1"># check, whether within X and within Y all datapoints have the same datatype</span>
@@ -384,13 +386,13 @@ <h1>Source code for tigramite.independence_tests.regressionCI</h1><div class="hi
         <span class="n">rate</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
 
         <span class="c1"># data = np.hstack((x, y, z))</span>
-        <span class="c1"># type_mask = np.zeros(data.shape)</span>
-        <span class="c1"># type_mask[:, 0] = x_example == &quot;discrete&quot;</span>
-        <span class="c1"># type_mask[:, 1] = y_example == &quot;discrete&quot;</span>
-        <span class="c1"># type_mask[:, 2] = z_example == &quot;discrete&quot;</span>
-        <span class="c1"># type_mask = type_mask.astype(&#39;int&#39;)</span>
-        <span class="c1"># # print(type_mask)</span>
-        <span class="c1"># dataframe = pp.DataFrame(data=data, type_mask=type_mask)</span>
+        <span class="c1"># data_type = np.zeros(data.shape)</span>
+        <span class="c1"># data_type[:, 0] = x_example == &quot;discrete&quot;</span>
+        <span class="c1"># data_type[:, 1] = y_example == &quot;discrete&quot;</span>
+        <span class="c1"># data_type[:, 2] = z_example == &quot;discrete&quot;</span>
+        <span class="c1"># data_type = data_type.astype(&#39;int&#39;)</span>
+        <span class="c1"># # print(data_type)</span>
+        <span class="c1"># dataframe = pp.DataFrame(data=data, data_type=data_type)</span>
         <span class="c1"># ci.set_dataframe(dataframe)</span>
         
         <span class="c1"># val, pval = ci.run_test(X=[(0, 0)], Y=[(1, 0)], Z=[(2, 0)])</span>
@@ -453,8 +455,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/independence_tests/robust_parcorr.html b/docs/_modules/tigramite/independence_tests/robust_parcorr.html
index e3251d81..432b3e3d 100644
--- a/docs/_modules/tigramite/independence_tests/robust_parcorr.html
+++ b/docs/_modules/tigramite/independence_tests/robust_parcorr.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -198,7 +200,7 @@ <h1>Source code for tigramite.independence_tests.robust_parcorr</h1><div class="
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
                     <span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">std</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Possibly constant array!&quot;</span><span class="p">)</span>
             <span class="c1"># array /= array.std(axis=1).reshape(dim, 1)</span>
             <span class="c1"># if np.isnan(array).sum() != 0:</span>
@@ -220,7 +222,7 @@ <h1>Source code for tigramite.independence_tests.robust_parcorr</h1><div class="
             <span class="k">return</span> <span class="p">(</span><span class="n">resid</span><span class="p">,</span> <span class="n">mean</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">resid</span>
 
-<div class="viewcode-block" id="RobustParCorr.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">type_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="RobustParCorr.get_dependence_measure"><a class="viewcode-back" href="../../../index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_dependence_measure">[docs]</a>    <span class="k">def</span> <span class="nf">get_dependence_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">data_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Return partial correlation.</span>
 
 <span class="sd">        Marginals are firstly transformed to standard normal scale. Dependence</span>
@@ -482,8 +484,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/lpcmci.html b/docs/_modules/tigramite/lpcmci.html
index 986199b3..f78d771c 100644
--- a/docs/_modules/tigramite/lpcmci.html
+++ b/docs/_modules/tigramite/lpcmci.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -39,9 +41,11 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="LPCMCI"><a class="viewcode-back" href="../../index.html#tigramite.lpcmci.LPCMCI">[docs]</a><span class="k">class</span> <span class="nc">LPCMCI</span><span class="p">(</span><span class="n">PCMCIbase</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot; LPCMCI is an algorithm for causal discovery in large-scale times series that allows for latent confounders and</span>
 <span class="sd">    learns lag-specific causal relationships. The algorithm is introduced and explained in:</span>
+
 <span class="sd">    [1] Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders.</span>
 <span class="sd">    Advances in Neural Information Processing Systems, 2020, 33.</span>
 <span class="sd">    https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</span>
+<span class="sd">    </span>
 <span class="sd">    NOTE: This method is still EXPERIMENTAL since the default settings of hyperparameters are still being fine-tuned.</span>
 <span class="sd">    We actually invite feedback on which work best in applications and numerical experiments.</span>
 <span class="sd">    The main function, which applies the algorithm, is &#39;run_lpcmci&#39;.</span>
@@ -371,6 +375,8 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">remember_only_parents</span> <span class="o">=</span> <span class="n">remember_only_parents</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">no_apr</span> <span class="o">=</span> <span class="n">no_apr</span>
 
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pc_alpha</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;pc_alpha must be single float in LPCMCI.&quot;</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">pc_alpha</span> <span class="o">&lt;</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="n">pc_alpha</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Choose 0 &lt;= pc_alpha &lt;= 1&quot;</span><span class="p">)</span>
             
@@ -858,7 +864,8 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_YX</span><span class="p">)</span>
 
                             <span class="c1"># Test conditional independence of X and Y given Z</span>
-                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                                <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ANC(Y):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -869,7 +876,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                             <span class="c1"># Check whether test result was significant</span>
-                            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                            <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1">#pval &gt; self.pc_alpha:</span>
 
                                 <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                                 <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -897,7 +904,8 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_XY</span><span class="p">)</span>
 
                             <span class="c1"># Test conditional independence of X and Y given Z</span>
-                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                                <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ANC(X):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -908,7 +916,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                             <span class="c1"># Check whether test result was significant</span>
-                            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                            <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                                 <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                                 <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -1184,7 +1192,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                         <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_YX</span><span class="p">)</span>
 
                         <span class="c1"># Test conditional independence of X and Y given Z</span>
-                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                        <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                            <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Non-ANC(Y):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -1195,7 +1205,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                         <span class="c1"># Check whether test result was significant</span>
-                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                             <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                             <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -1227,7 +1237,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">S_default_XY</span><span class="p">)</span>
 
                             <span class="c1"># Test conditional independence of X and Y given Z</span>
-                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                            <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                                <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Non-ANC(X):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  S_def = </span><span class="si">%s</span><span class="s2">, S_pc = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -1238,7 +1250,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                             <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                             <span class="c1"># Check whether test result was significant</span>
-                            <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                            <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                                 <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                                 <span class="n">to_remove</span><span class="p">[</span><span class="n">Y</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">X</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -1906,7 +1918,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="n">Z_A</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">Z</span> <span class="k">if</span> <span class="n">node</span> <span class="o">!=</span> <span class="n">A</span><span class="p">]</span>
 
                 <span class="c1"># Run the conditional independence test</span>
-                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">Z_A</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = Z_A, tau_max = self.tau_max)</span>
+                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">Z_A</span><span class="p">,</span> 
+                    <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MakeMin:    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_A = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -1917,7 +1931,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_A</span><span class="p">))</span>
 
                 <span class="c1"># Check whether the test result was significant</span>
-                <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                     <span class="n">new_sepsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z_A</span><span class="p">))</span>
                     <span class="n">val_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
 
@@ -2002,7 +2016,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
 
                 <span class="c1"># Test conditional independence of X and Y given Z</span>
-                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                    <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;BnotinSepSetAC(A):    </span><span class="si">%s</span><span class="s2"> _|_ </span><span class="si">%s</span><span class="s2">  |  Z_add = </span><span class="si">%s</span><span class="s2">, Z = </span><span class="si">%s</span><span class="s2">: val = </span><span class="si">%.2f</span><span class="s2"> / pval = </span><span class="si">% .4f</span><span class="s2">&quot;</span> <span class="o">%</span>
@@ -2013,7 +2029,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                 <span class="c1"># Check whether test result was significant</span>
-                <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                     <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
         <span class="c1"># Test for independence given all subsets of non-future adjacencies of C</span>
@@ -2031,7 +2047,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
 
                 <span class="c1"># Test conditional independence of X and Y given Z</span>
-                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                    <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                     <span class="c1"># print(&quot;BnotinSepSetAC(C):    %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f&quot; %</span>
@@ -2044,7 +2062,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                 <span class="c1"># Check whether test result was significant</span>
-                <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                     <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
         <span class="c1"># Append the already known sepset</span>
@@ -2128,8 +2146,10 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
+                    
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;BinSepSetAC(A):    %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f&quot; %</span>
                         <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z)]), val, pval))</span>
@@ -2141,7 +2161,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                         <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
             <span class="c1"># Test for independence given all subsets of non-future adjacencies of C</span>
@@ -2159,8 +2179,10 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">Z_add</span><span class="p">)</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
+                    
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;BinSepSetAC(C):     %s _|_ %s  |  Z = %s: val = %.2f / pval = % .4f&quot; %</span>
                         <span class="c1">#     (X, Y, &#39; &#39;.join([str(z) for z in list(Z)]), val, pval))</span>
@@ -2172,7 +2194,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                         <span class="n">all_sepsets</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 
             <span class="c1"># Append the already known sepset</span>
@@ -2794,7 +2816,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z_add2</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;ER00a(part1):    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f&quot; %</span>
@@ -2807,7 +2831,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_test</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                         <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                         <span class="n">remove_AB</span> <span class="o">=</span> <span class="kc">True</span>
@@ -2851,7 +2875,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z_add2</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;ER00a(part2):    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f&quot; %</span>
@@ -2864,7 +2890,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_test</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
                         
                         <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                         <span class="n">remove_CB</span> <span class="o">=</span> <span class="kc">True</span>
@@ -2959,7 +2985,9 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="n">Z_add2</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span> <span class="ow">in</span> <span class="n">Z_add</span><span class="o">.</span><span class="n">difference</span><span class="p">({</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">})</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lag</span> <span class="o">-</span> <span class="n">delta_lag</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">}</span>
 
                     <span class="c1"># Test conditional independence of X and Y given Z</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
+                    <span class="c1"># val, pval = self.cond_ind_test.run_test(X = [X], Y = [Y], Z = list(Z_test), tau_max = self.tau_max)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">Y</span><span class="p">],</span> <span class="n">Z</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Z_test</span><span class="p">),</span> 
+                        <span class="n">tau_max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
                         <span class="c1"># print(&quot;ER00b:    %s _|_ %s  |  Z_test = %s: val = %.2f / pval = % .4f&quot; %</span>
@@ -2972,7 +3000,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_update_pval_val_card_dicts</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">Z_test</span><span class="p">))</span>
 
                     <span class="c1"># Check whether test result was significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; self.pc_alpha:</span>
 
                         <span class="c1"># Mark the edge from X to Y for removal and save sepset</span>
                         <span class="n">remove_AB</span> <span class="o">=</span> <span class="kc">True</span>
@@ -3582,7 +3610,7 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
 
-    <span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span>
+    <span class="kn">from</span> <span class="nn">tigramite.independence_tests.parcorr</span> <span class="kn">import</span> <span class="n">ParCorr</span>
     <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
     <span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
     <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
@@ -3609,18 +3637,18 @@ <h1>Source code for tigramite.lpcmci</h1><div class="highlight"><pre>
     <span class="c1"># Data must be array of shape (time, variables)</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
     <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-    <span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
+    <span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">(</span><span class="n">significance</span><span class="o">=</span><span class="s1">&#39;fixed_thres&#39;</span><span class="p">)</span>
     <span class="n">lpcmci</span> <span class="o">=</span> <span class="n">LPCMCI</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">)</span>
-    <span class="c1"># results = pcmci.run_lpcmci(tau_max=2, pc_alpha=0.01)</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">lpcmci</span><span class="o">.</span><span class="n">run_lpcmci</span><span class="p">(</span><span class="n">tau_max</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
 
     <span class="c1"># # For a proper causal interpretation of the graph see the paper!</span>
     <span class="c1"># print(results[&#39;graph&#39;])</span>
     <span class="c1"># tp.plot_graph(graph=results[&#39;graph&#39;], val_matrix=results[&#39;val_matrix&#39;])</span>
     <span class="c1"># plt.show()</span>
 
-    <span class="n">results</span> <span class="o">=</span> <span class="n">lpcmci</span><span class="o">.</span><span class="n">run_sliding_window_of</span><span class="p">(</span>
-        <span class="n">window_step</span><span class="o">=</span><span class="mi">499</span><span class="p">,</span> <span class="n">window_length</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-        <span class="n">method</span><span class="o">=</span><span class="s1">&#39;run_lpcmci&#39;</span><span class="p">,</span> <span class="n">method_args</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;tau_max&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">})</span>
+    <span class="c1"># results = lpcmci.run_sliding_window_of(</span>
+    <span class="c1">#     window_step=499, window_length=500,</span>
+    <span class="c1">#     method=&#39;run_lpcmci&#39;, method_args={&#39;tau_max&#39;:1})</span>
 </pre></div>
 
           </div>
@@ -3675,8 +3703,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/models.html b/docs/_modules/tigramite/models.html
index 56b6ac46..63bc8646 100644
--- a/docs/_modules/tigramite/models.html
+++ b/docs/_modules/tigramite/models.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -30,7 +32,7 @@
           <div class="body" role="main">
             
   <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
+<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal inference for time series.&quot;&quot;&quot;</span>
 
 <span class="c1"># Author: Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
 <span class="c1">#</span>
@@ -111,6 +113,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
                 <span class="n">conditions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;max_lag_or_tau_max&#39;</span><span class="p">,</span>
+                <span class="n">empty_predictors_function</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span>
                 <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Fit time series model.</span>
 
@@ -133,6 +136,8 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
 <span class="sd">            sample. Other options are &#39;2xtau_max&#39;, which guarantees that MCI</span>
 <span class="sd">            tests are all conducted on the same samples. Last, &#39;max_lag&#39; uses</span>
 <span class="sd">            as much samples as possible.</span>
+<span class="sd">        empty_predictors_function : function</span>
+<span class="sd">            Function to apply to y if no predictors are given.</span>
 <span class="sd">        return_data : bool, optional (default: False)</span>
 <span class="sd">            Whether to save the data array.</span>
 
@@ -220,6 +225,15 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
             <span class="c1"># Target is only first entry of Y, ie [y]</span>
             <span class="n">target_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">xyz</span><span class="o">==</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="p">:]</span>
 
+            <span class="k">if</span> <span class="n">predictor_array</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="c1"># Just fit default (eg, mean)</span>
+                <span class="k">class</span> <span class="nc">EmptyPredictorModel</span><span class="p">:</span>
+                    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">result</span> <span class="o">=</span> <span class="n">empty_predictors_function</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+                    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
+                        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">result</span>
+                <span class="n">a_model</span> <span class="o">=</span> <span class="n">EmptyPredictorModel</span><span class="p">()</span>
+            
             <span class="n">a_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">predictor_array</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">target_array</span><span class="p">)</span>
             
             <span class="c1"># Cache the results</span>
@@ -378,6 +392,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
                 <span class="n">selected_variables</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;max_lag_or_tau_max&#39;</span><span class="p">,</span>
+                <span class="n">empty_predictors_function</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span>
                 <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Fit time series model.</span>
 
@@ -402,6 +417,8 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
 <span class="sd">            sample. Other options are &#39;2xtau_max&#39;, which guarantees that MCI</span>
 <span class="sd">            tests are all conducted on the same samples. Last, &#39;max_lag&#39; uses</span>
 <span class="sd">            as much samples as possible.</span>
+<span class="sd">        empty_predictors_function : function</span>
+<span class="sd">            Function to apply to y if no predictors are given.</span>
 <span class="sd">        return_data : bool, optional (default: False)</span>
 <span class="sd">            Whether to save the data array.</span>
 
@@ -460,13 +477,22 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
                 <span class="c1"># Cache the data if needed</span>
                 <span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;data&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">array</span>
                 <span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;used_indices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">use_indices_dataset_dict</span>
-            <span class="c1"># Fit the model if there are any parents for this variable to fit</span>
+            <span class="c1"># Copy and fit the model if there are any parents for this variable to fit</span>
+            <span class="n">a_model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">dim_z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="c1"># Copy and fit the model</span>
-                <span class="n">a_model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+                <span class="n">a_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">array</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">array</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># Just fit default (eg, mean)</span>
+                <span class="k">class</span> <span class="nc">EmptyPredictorModel</span><span class="p">:</span>
+                    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">result</span> <span class="o">=</span> <span class="n">empty_predictors_function</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+                    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
+                        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">result</span>
+                <span class="n">a_model</span> <span class="o">=</span> <span class="n">EmptyPredictorModel</span><span class="p">()</span>
+                <span class="c1"># a_model = empty_predictors_model(array[1])</span>
                 <span class="n">a_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">array</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">array</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
 
-                <span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">a_model</span>
+            <span class="n">fit_results</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">a_model</span>
 
         <span class="c1"># Cache and return the fit results</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fit_results</span> <span class="o">=</span> <span class="n">fit_results</span>
@@ -624,7 +650,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
                         <span class="n">verbosity</span><span class="o">=</span><span class="n">verbosity</span><span class="p">)</span>
 
 <div class="viewcode-block" id="LinearMediation.fit_model"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.fit_model">[docs]</a>    <span class="k">def</span> <span class="nf">fit_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">all_parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fit linear time series model.</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fit linear time series model.</span>
 
 <span class="sd">        Fits a sklearn.linear_model.LinearRegression model to the parents of</span>
 <span class="sd">        each variable and computes the coefficient matrices :math:`\Phi` and</span>
@@ -649,8 +675,8 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_psi</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">all_psi_k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_all_psi_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="p">)</span>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span> <span class="o">=</span> <span class="n">all_parents</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">tau_max</span></div>
+        <span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span> <span class="o">=</span> <span class="n">all_parents</span></div>
+        <span class="c1"># self.tau_max = tau_max</span>
 
 <div class="viewcode-block" id="LinearMediation.fit_model_bootstrap"><a class="viewcode-back" href="../../index.html#tigramite.models.LinearMediation.fit_model_bootstrap">[docs]</a>    <span class="k">def</span> <span class="nf">fit_model_bootstrap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
             <span class="n">boot_blocklength</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
@@ -697,7 +723,6 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
 
             <span class="n">dataframe_here</span><span class="o">.</span><span class="n">bootstrap</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;boot_blocklength&#39;</span><span class="p">:</span><span class="n">boot_blocklength</span><span class="p">,</span>
                                         <span class="s1">&#39;random_state&#39;</span><span class="p">:</span><span class="n">random_state</span><span class="p">}</span>
-
             <span class="n">model</span> <span class="o">=</span> <span class="n">Models</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe_here</span><span class="p">,</span>
                            <span class="n">model</span><span class="o">=</span><span class="n">sklearn</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">model_params</span><span class="p">),</span>
                            <span class="n">data_transform</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_transform</span><span class="p">,</span>
@@ -706,7 +731,6 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
 
             <span class="n">model</span><span class="o">.</span><span class="n">fit_full_model</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">all_parents</span><span class="p">,</span>
                            <span class="n">tau_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">)</span>
-
             <span class="c1"># Cache the results in the member variables</span>
             <span class="n">coeffs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_coefs</span><span class="p">()</span>
             <span class="n">phi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_phi</span><span class="p">(</span><span class="n">coeffs</span><span class="p">)</span>
@@ -847,9 +871,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
         <span class="n">psi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))</span>
 
         <span class="n">psi</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="o">-</span> <span class="n">phi</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
         <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="c1"># psi[tau] = np.matmul(psi[0], np.matmul(phi[tau], psi[0]))</span>
             <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
                 <span class="n">psi</span><span class="p">[</span><span class="n">tau</span><span class="p">]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">psi</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">phi</span><span class="p">[</span><span class="n">s</span><span class="p">],</span> <span class="n">psi</span><span class="p">[</span><span class="n">tau</span> <span class="o">-</span> <span class="n">s</span><span class="p">])</span> <span class="p">)</span> 
 
@@ -1579,6 +1601,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
             <span class="n">mask</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)}</span>
         <span class="c1"># Get the dataframe shape</span>
         <span class="n">T</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
         <span class="c1"># Have the default dataframe be the training data frame</span>
         <span class="n">train_mask</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
         <span class="n">train_mask</span><span class="p">[</span><span class="mi">0</span><span class="p">][[</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> <span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">train_indices</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">True</span>
@@ -1666,6 +1689,14 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
 <span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}</span>
 <span class="sd">            containing estimated predictors.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">selected_links</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">link_assumptions</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">selected_links</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">):</span><span class="s2">&quot;-?&gt;&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">+</span><span class="mi">1</span><span class="p">)}</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">link_assumptions</span> <span class="o">=</span> <span class="kc">None</span>
+
         <span class="c1"># Ensure an independence model is given</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No cond_ind_test given!&quot;</span><span class="p">)</span>
@@ -1673,7 +1704,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">selected_targets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">selected_variables</span> <span class="o">=</span> <span class="n">selected_targets</span>
-        <span class="n">predictors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">selected_links</span><span class="o">=</span><span class="n">selected_links</span><span class="p">,</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
                                         <span class="n">tau_min</span><span class="o">=</span><span class="n">steps_ahead</span><span class="p">,</span>
                                         <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
                                         <span class="n">save_iterations</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
@@ -1710,6 +1741,11 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
 <span class="sd">        self : instance of self</span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
+        <span class="k">if</span> <span class="n">selected_targets</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span> <span class="o">=</span> <span class="n">selected_targets</span>
+
         <span class="k">if</span> <span class="n">tau_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="c1"># Find the maximal parents lag</span>
             <span class="n">max_parents_lag</span> <span class="o">=</span> <span class="mi">0</span>
@@ -1722,17 +1758,13 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
 
         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">test_indices</span><span class="p">)</span> <span class="o">-</span> <span class="n">max_parents_lag</span><span class="p">)</span>
                 <span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_indices</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;test_indices - maxlag(predictors) [or tau_max] &quot;</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;test_indices - maxlag(predictors) [or tau_max] &quot;</span>
                 <span class="s2">&quot;overlaps with train_indices: Choose test_indices &quot;</span>
                 <span class="s2">&quot;such that there is a gap of max_lag to train_indices!&quot;</span><span class="p">)</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">target_predictors</span> <span class="o">=</span> <span class="n">target_predictors</span>
 
-        <span class="k">if</span> <span class="n">selected_targets</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span> <span class="o">=</span> <span class="n">selected_targets</span>
-
         <span class="k">for</span> <span class="n">target</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">target</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_predictors</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No predictors given for target </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">target</span><span class="p">)</span>
@@ -1789,7 +1821,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
                              <span class="s2">&quot;indicating the index of the variables to &quot;</span>
                              <span class="s2">&quot;predict.&quot;</span><span class="p">)</span>
 
-        <span class="k">if</span> <span class="n">target_list</span> <span class="o">==</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">target_list</span> <span class="o">==</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)):</span>
             <span class="n">return_type</span> <span class="o">=</span> <span class="s1">&#39;array&#39;</span>
         <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">target_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
             <span class="n">return_type</span> <span class="o">=</span> <span class="s1">&#39;series&#39;</span>
@@ -1797,6 +1829,7 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
             <span class="n">return_type</span> <span class="o">=</span> <span class="s1">&#39;list&#39;</span>
 
         <span class="n">pred_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">stored_test_array</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="k">for</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">target_list</span><span class="p">:</span>
             <span class="c1"># Print message</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
@@ -1811,11 +1844,11 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
             <span class="k">if</span> <span class="n">target</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selected_targets</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Target </span><span class="si">%s</span><span class="s2"> not yet fitted&quot;</span> <span class="o">%</span> <span class="n">target</span><span class="p">)</span>
             <span class="c1"># Construct the array form of the data</span>
-            <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">target</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+            <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="n">target</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>  <span class="c1"># dummy</span>
             <span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="n">target</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>  <span class="c1"># dummy</span>
             <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_predictors</span><span class="p">[</span><span class="n">target</span><span class="p">]</span>
+
             <span class="c1"># Check if we&#39;ve passed a new dataframe object</span>
-            <span class="n">test_array</span> <span class="o">=</span> <span class="kc">None</span>
             <span class="k">if</span> <span class="n">new_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="c1"># if new_data.mask is None:</span>
                 <span class="c1">#     # if no mask is supplied, use the same mask as for the fitted array</span>
@@ -1844,10 +1877,18 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
             <span class="k">if</span> <span class="n">a_transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">test_array</span> <span class="o">=</span> <span class="n">a_transform</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">test_array</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
             <span class="c1"># Cache the test array</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">test_array</span> <span class="o">=</span> <span class="n">test_array</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">stored_test_array</span><span class="p">[</span><span class="n">target</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_array</span>
             <span class="c1"># Run the predictor</span>
-            <span class="n">pred_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fitted_model</span><span class="p">[</span><span class="n">target</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-                <span class="n">X</span><span class="o">=</span><span class="n">test_array</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">pred_params</span><span class="p">))</span>
+            <span class="n">predicted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fitted_model</span><span class="p">[</span><span class="n">target</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
+                <span class="n">X</span><span class="o">=</span><span class="n">test_array</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">pred_params</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">test_array</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="c1"># If there are no predictors, return the value of </span>
+                <span class="c1"># empty_predictors_function, which is np.mean </span>
+                <span class="c1"># and expand to the test array length</span>
+                <span class="n">predicted</span> <span class="o">=</span> <span class="n">predicted</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">test_array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="n">pred_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">predicted</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="n">return_type</span> <span class="o">==</span> <span class="s1">&#39;series&#39;</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">pred_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
@@ -1857,21 +1898,23 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">pred_list</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span></div>
 
 <div class="viewcode-block" id="Prediction.get_train_array"><a class="viewcode-back" href="../../index.html#tigramite.models.Prediction.get_train_array">[docs]</a>    <span class="k">def</span> <span class="nf">get_train_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns training array.&quot;&quot;&quot;</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns training array for variable j.&quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fitted_model</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;data&#39;</span><span class="p">]</span></div>
 
-<div class="viewcode-block" id="Prediction.get_test_array"><a class="viewcode-back" href="../../index.html#tigramite.models.Prediction.get_test_array">[docs]</a>    <span class="k">def</span> <span class="nf">get_test_array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns test array.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_array</span></div></div>
+<div class="viewcode-block" id="Prediction.get_test_array"><a class="viewcode-back" href="../../index.html#tigramite.models.Prediction.get_test_array">[docs]</a>    <span class="k">def</span> <span class="nf">get_test_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns test array for variable j.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stored_test_array</span><span class="p">[</span><span class="n">j</span><span class="p">]</span></div></div>
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
    
     <span class="kn">import</span> <span class="nn">tigramite</span>
     <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
     <span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
-    <span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span>
+    <span class="kn">from</span> <span class="nn">tigramite.independence_tests.parcorr</span> <span class="kn">import</span> <span class="n">ParCorr</span>
     <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
 
+    <span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+
     <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
  
     <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
@@ -1879,40 +1922,46 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
     <span class="n">links</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
              <span class="mi">1</span><span class="p">:</span> <span class="p">[((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">-</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
              <span class="mi">2</span><span class="p">:</span> <span class="p">[((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span>  <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-             <span class="mi">3</span><span class="p">:</span> <span class="p">[((</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span> <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">),</span>  <span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">-</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)]</span>
+             <span class="c1"># 3: [((3, -1), 0.9, lin_f), ((1, 0), 0.8, lin_f),  ((2, 0), -0.9, lin_f)]</span>
              <span class="p">}</span>
     <span class="c1"># noises = [np.random.randn for j in links.keys()]</span>
     <span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
 
     <span class="n">missing_flag</span> <span class="o">=</span> <span class="mi">999</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">):</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">::</span><span class="mi">100</span><span class="p">]</span> <span class="o">=</span> <span class="n">missing_flag</span>
+    <span class="c1"># for i in range(0, 20):</span>
+    <span class="c1">#     data[i::100] = missing_flag</span>
 
     <span class="n">parents</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">_get_true_parent_neighbor_dict</span><span class="p">(</span><span class="n">links</span><span class="p">)</span>
     <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">missing_flag</span> <span class="o">=</span> <span class="n">missing_flag</span><span class="p">)</span>
 
+
+    <span class="c1"># model = LinearRegression()</span>
+    <span class="c1"># model.fit(X=np.random.randn(10,2), y=np.random.randn(10))</span>
+    <span class="c1"># model.predict(X=np.random.randn(10,2)[:,2:])</span>
+    <span class="c1"># sys.exit(0)</span>
+
     <span class="n">med</span> <span class="o">=</span> <span class="n">LinearMediation</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
         <span class="n">data_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-    <span class="n">med</span><span class="o">.</span><span class="n">fit_model</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+    <span class="n">med</span><span class="o">.</span><span class="n">fit_model</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
     <span class="n">med</span><span class="o">.</span><span class="n">fit_model_bootstrap</span><span class="p">(</span> 
                 <span class="n">boot_blocklength</span><span class="o">=</span><span class="s1">&#39;cube_root&#39;</span><span class="p">,</span>
                 <span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span><span class="p">,</span>
                 <span class="p">)</span>
 
-    <span class="c1"># print(med.get_val_matrix())</span>
+    <span class="c1"># # print(med.get_val_matrix())</span>
 
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_ce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_bootstrap_of</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="s1">&#39;get_ce&#39;</span><span class="p">,</span> 
-        <span class="n">function_args</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;i&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;tau&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span>   <span class="s1">&#39;j&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">},</span> <span class="n">conf_lev</span><span class="o">=</span><span class="mf">0.9</span><span class="p">))</span>
+    <span class="c1"># print (med.get_ce(i=0, tau=0,  j=3))</span>
+    <span class="c1"># print(med.get_bootstrap_of(function=&#39;get_ce&#39;, </span>
+    <span class="c1">#     function_args={&#39;i&#39;:0, &#39;tau&#39;:0,   &#39;j&#39;:3}, conf_lev=0.9))</span>
 
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_coeff</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">j</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+    <span class="c1"># print (med.get_coeff(i=0, tau=-2, j=1))</span>
 
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_ce_max</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">j</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_ce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_mce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_mce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">-</span> <span class="n">med</span><span class="o">.</span><span class="n">get_mce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_conditional_mce</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">notk</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">j</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
-    <span class="nb">print</span> <span class="p">(</span><span class="n">med</span><span class="o">.</span><span class="n">get_bootstrap_of</span><span class="p">(</span><span class="s1">&#39;get_conditional_mce&#39;</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;i&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;tau&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">:[</span><span class="mi">2</span><span class="p">],</span> <span class="s1">&#39;notk&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;j&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">}))</span>
+    <span class="c1"># print (med.get_ce_max(i=0, j=2))</span>
+    <span class="c1"># print (med.get_ce(i=0, tau=0, j=3))</span>
+    <span class="c1"># print (med.get_mce(i=0, tau=0, k=[2], j=3))</span>
+    <span class="c1"># print (med.get_mce(i=0, tau=0, k=[1,2], j=3) - med.get_mce(i=0, tau=0, k=[1], j=3))</span>
+    <span class="c1"># print (med.get_conditional_mce(i=0, tau=0, k=[2], notk=[1], j=3))</span>
+    <span class="c1"># print (med.get_bootstrap_of(&#39;get_conditional_mce&#39;, {&#39;i&#39;:0, &#39;tau&#39;:0, &#39;k&#39;:[2], &#39;notk&#39;:[1], &#39;j&#39;:3}))</span>
 
     <span class="c1"># print(med.get_joint_ce(i=0, j=2))</span>
     <span class="c1"># print(med.get_joint_mce(i=0, j=2, k=1))</span>
@@ -1969,13 +2018,13 @@ <h1>Source code for tigramite.models</h1><div class="highlight"><pre>
     <span class="c1"># #                        pc_alpha=0.2,</span>
     <span class="c1"># #                        max_conds_dim=None,</span>
     <span class="c1"># #                        max_combinations=1)</span>
-    <span class="c1"># predictors = {0: [(0, -1)],</span>
+    <span class="c1"># predictors = {0: [], # [(0, -1)],</span>
     <span class="c1">#              1: [(1, -1), (0, -1)],</span>
     <span class="c1">#              2: [(2, -1), (1, 0)]}</span>
     <span class="c1"># pred.fit(target_predictors=predictors,</span>
     <span class="c1">#         selected_targets=None, tau_max=None, return_data=False)</span>
 
-    <span class="c1"># res = pred.predict(target=2,</span>
+    <span class="c1"># res = pred.predict(target=0,</span>
     <span class="c1">#             new_data=None,</span>
     <span class="c1">#             pred_params=None,</span>
     <span class="c1">#             cut_off=&#39;max_lag_or_tau_max&#39;)</span>
@@ -2038,8 +2087,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/pcmci.html b/docs/_modules/tigramite/pcmci.html
index 4ec07f9b..6a562a71 100644
--- a/docs/_modules/tigramite/pcmci.html
+++ b/docs/_modules/tigramite/pcmci.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -446,12 +448,13 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                     <span class="k">if</span> <span class="n">link_assumptions_j</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">:</span>
                         <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span>
                         <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
+                        <span class="n">dependent</span> <span class="o">=</span> <span class="kc">True</span>
                     <span class="k">else</span><span class="p">:</span>
-                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[</span><span class="n">parent</span><span class="p">],</span>
+                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[</span><span class="n">parent</span><span class="p">],</span>
                                                     <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span>
                                                     <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
                                                     <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                    <span class="c1"># verbosity=self.verbosity</span>
+                                                    <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
                                                     <span class="p">)</span>
                     <span class="c1"># Print some information if needed</span>
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
@@ -473,7 +476,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                         <span class="n">a_iter</span><span class="p">[</span><span class="n">comb_index</span><span class="p">][</span><span class="s1">&#39;val&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
                         <span class="n">a_iter</span><span class="p">[</span><span class="n">comb_index</span><span class="p">][</span><span class="s1">&#39;pval&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
                     <span class="c1"># Delete link later and break while-loop if non-significant</span>
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1">#pval &gt; pc_alpha:</span>
                         <span class="n">nonsig_parents</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="n">parent</span><span class="p">))</span>
                         <span class="n">nonsig</span> <span class="o">=</span> <span class="kc">True</span>
                         <span class="k">break</span>
@@ -1108,15 +1111,14 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
             <span class="k">if</span> <span class="n">val_only</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
                 <span class="c1"># Run the independence tests and record the results</span>
-                <span class="k">if</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> 
-                     <span class="ow">and</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">)]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o-o&#39;</span><span class="p">]):</span>
+                <span class="k">if</span> <span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> 
+                     <span class="ow">and</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;--&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;o-o&#39;</span><span class="p">]):</span>
                     <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span> 
                     <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
                 <span class="k">else</span><span class="p">:</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span>
                                                         <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                                        <span class="c1"># verbosity=</span>
-                                                        <span class="c1"># self.verbosity</span>
+                                                        <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">,</span>
                                                         <span class="p">)</span>
                 <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">val</span>
                 <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">pval</span>
@@ -1139,13 +1141,21 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
         <span class="c1"># Correct the p_matrix if there is a fdr_method</span>
         <span class="k">if</span> <span class="n">fdr_method</span> <span class="o">!=</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;FDR-correction not compatible with significance == &#39;fixed_thres&#39;&quot;</span><span class="p">)</span>
             <span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_corrected_pvalues</span><span class="p">(</span><span class="n">p_matrix</span><span class="o">=</span><span class="n">p_matrix</span><span class="p">,</span> <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
                                                   <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
                                                   <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span>
                                                   <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
 
-        <span class="c1"># Threshold p_matrix to get graph</span>
-        <span class="n">final_graph</span> <span class="o">=</span> <span class="n">p_matrix</span> <span class="o">&lt;=</span> <span class="n">alpha_level</span>
+        <span class="c1"># Threshold p_matrix to get graph (or val_matrix for significance == &#39;fixed_thres&#39;)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">significance</span> <span class="o">==</span> <span class="s1">&#39;fixed_thres&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">two_sided</span><span class="p">:</span>
+                <span class="n">final_graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">val_matrix</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">alpha_level</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">final_graph</span> <span class="o">=</span> <span class="n">val_matrix</span> <span class="o">&gt;=</span> <span class="n">alpha_level</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">final_graph</span> <span class="o">=</span> <span class="n">p_matrix</span> <span class="o">&lt;=</span> <span class="n">alpha_level</span>
 
         <span class="c1"># Convert to string graph representation</span>
         <span class="n">graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_to_string_graph</span><span class="p">(</span><span class="n">final_graph</span><span class="p">)</span>
@@ -2006,8 +2016,8 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">        four-step procedure:</span>
 
 <span class="sd">        1.  Condition-selection (same as for PCMCI): For each variable</span>
-<span class="sd">        :math:`j`, estimate a *superset* of lagged parents :math:`\\widehat{</span>
-<span class="sd">        \\mathcal{B}}_t^-( X^j_t)` with the iterative PC1 algorithm,</span>
+<span class="sd">        :math:`j`, estimate a *superset* of lagged parents :math:`\widehat{</span>
+<span class="sd">        \mathcal{B}}_t^-( X^j_t)` with the iterative PC1 algorithm,</span>
 <span class="sd">        implemented as ``run_pc_stable``. The condition-selection step</span>
 <span class="sd">        reduces the dimensionality and avoids conditioning on irrelevant</span>
 <span class="sd">        variables.</span>
@@ -2092,49 +2102,6 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
 <span class="sd">        Further optional parameters are discussed in [5]_.</span>
 
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">        &gt;&gt;&gt; from tigramite.pcmci import PCMCI</span>
-<span class="sd">        &gt;&gt;&gt; from tigramite.independence_tests import ParCorr</span>
-<span class="sd">        &gt;&gt;&gt; import tigramite.data_processing as pp</span>
-<span class="sd">        &gt;&gt;&gt; from tigramite.toymodels import structural_causal_processes as toys</span>
-<span class="sd">        &gt;&gt;&gt; # Example process to play around with</span>
-<span class="sd">        &gt;&gt;&gt; # Each key refers to a variable and the incoming links are supplied</span>
-<span class="sd">        &gt;&gt;&gt; # as a list of format [((var, -lag), coeff, function), ...]</span>
-<span class="sd">        &gt;&gt;&gt; def lin_f(x): return x</span>
-<span class="sd">        &gt;&gt;&gt; links = {0: [((0, -1), 0.9, lin_f)],</span>
-<span class="sd">                     1: [((1, -1), 0.8, lin_f), ((0, -1), 0.8, lin_f)],</span>
-<span class="sd">                     2: [((2, -1), 0.7, lin_f), ((1, 0), 0.6, lin_f)],</span>
-<span class="sd">                     3: [((3, -1), 0.7, lin_f), ((2, 0), -0.5, lin_f)],</span>
-<span class="sd">                     }</span>
-<span class="sd">        &gt;&gt;&gt; data, nonstat = toys.structural_causal_process(links,</span>
-<span class="sd">                            T=1000, seed=7)</span>
-<span class="sd">        &gt;&gt;&gt; # Data must be array of shape (time, variables)</span>
-<span class="sd">        &gt;&gt;&gt; print (data.shape)</span>
-<span class="sd">        (1000, 4)</span>
-<span class="sd">        &gt;&gt;&gt; dataframe = pp.DataFrame(data)</span>
-<span class="sd">        &gt;&gt;&gt; cond_ind_test = ParCorr()</span>
-<span class="sd">        &gt;&gt;&gt; pcmci = PCMCI(dataframe=dataframe, cond_ind_test=cond_ind_test)</span>
-<span class="sd">        &gt;&gt;&gt; results = pcmci.run_pcmciplus(tau_min=0, tau_max=2, pc_alpha=0.01)</span>
-<span class="sd">        &gt;&gt;&gt; pcmci.print_results(results, alpha_level=0.01)</span>
-<span class="sd">            ## Significant links at alpha = 0.01:</span>
-
-<span class="sd">            Variable 0 has 1 link(s):</span>
-<span class="sd">                (0 -1): pval = 0.00000 | val =  0.676</span>
-
-<span class="sd">            Variable 1 has 2 link(s):</span>
-<span class="sd">                (1 -1): pval = 0.00000 | val =  0.602</span>
-<span class="sd">                (0 -1): pval = 0.00000 | val =  0.599</span>
-
-<span class="sd">            Variable 2 has 2 link(s):</span>
-<span class="sd">                (1  0): pval = 0.00000 | val =  0.486</span>
-<span class="sd">                (2 -1): pval = 0.00000 | val =  0.466</span>
-
-<span class="sd">            Variable 3 has 2 link(s):</span>
-<span class="sd">                (3 -1): pval = 0.00000 | val =  0.524</span>
-<span class="sd">                (2  0): pval = 0.00000 | val = -0.449 </span>
-
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        selected_links : dict or None</span>
@@ -2202,7 +2169,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
 <span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
 <span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        ambiguous_triples : list</span>
 <span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
@@ -2230,31 +2197,31 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                         <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
                         <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
 
-        <span class="c1"># else:</span>
-        <span class="c1">#     raise ValueError(&quot;pc_alpha=None not supported in PCMCIplus, choose&quot;</span>
-        <span class="c1">#                      &quot; 0 &lt; pc_alpha &lt; 1 (e.g., 0.01)&quot;)</span>
-
-        <span class="k">if</span> <span class="n">pc_alpha</span> <span class="o">&lt;</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="n">pc_alpha</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">elif</span> <span class="n">pc_alpha</span> <span class="o">&lt;</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="n">pc_alpha</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Choose 0 &lt;= pc_alpha &lt;= 1&quot;</span><span class="p">)</span>
 
         <span class="c1"># Check the limits on tau</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_check_tau_limits</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
-        <span class="c1"># Set the selected links</span>
-        <span class="c1"># _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)</span>
+        <span class="c1"># Set the link assumption</span>
         <span class="n">_int_link_assumptions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_link_assumptions</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
 
-        <span class="c1"># Step 1: Get a superset of lagged parents from run_pc_stable</span>
-        <span class="n">lagged_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
-                                            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
-                                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
-                                            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
-                                            <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
-                                            <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">)</span>
 
+        <span class="c1">#</span>
+        <span class="c1"># Phase 1: Get a superset of lagged parents from run_pc_stable</span>
+        <span class="c1">#</span>
+        <span class="n">lagged_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pc_stable</span><span class="p">(</span><span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
+                            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
+                            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
+                            <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
+                            <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">)</span>
+        <span class="c1"># Extract p- and val-matrix</span>
         <span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p_matrix</span>
         <span class="n">val_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_matrix</span>
 
-        <span class="c1"># Step 2+3+4: PC algorithm with contemp. conditions and MCI tests</span>
+        <span class="c1">#</span>
+        <span class="c1"># Phase 2: PC algorithm with contemp. conditions and MCI tests</span>
+        <span class="c1">#</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">##</span><span class="se">\n</span><span class="s2">## Step 2: PC algorithm with contemp. conditions &quot;</span>
                   <span class="s2">&quot;and MCI tests</span><span class="se">\n</span><span class="s2">##&quot;</span>
@@ -2275,6 +2242,180 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                   <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">fdr_method = </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">fdr_method</span>
                   <span class="p">)</span>
 
+        <span class="n">skeleton_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcmciplus_mci_skeleton_phase</span><span class="p">(</span>
+                            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span> 
+                            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span> 
+                            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
+                            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
+                            <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span> 
+                            <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">,</span> 
+                            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
+                            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span> 
+                            <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span> 
+                            <span class="n">reset_lagged_links</span><span class="o">=</span><span class="n">reset_lagged_links</span><span class="p">,</span> 
+                            <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">,</span>
+                            <span class="n">p_matrix</span><span class="o">=</span><span class="n">p_matrix</span><span class="p">,</span> 
+                            <span class="n">val_matrix</span><span class="o">=</span><span class="n">val_matrix</span><span class="p">,</span>
+                            <span class="p">)</span>
+
+        <span class="c1">#</span>
+        <span class="c1"># Phase 3: Collider orientations (with MCI tests for default majority collider rule)</span>
+        <span class="c1">#</span>
+        <span class="n">colliders_step_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcmciplus_collider_phase</span><span class="p">(</span>
+                            <span class="n">skeleton_graph</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">],</span> 
+                            <span class="n">sepsets</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">],</span> 
+                            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span> 
+                            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span> 
+                            <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
+                            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span> 
+                            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span> 
+                            <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
+                            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span> 
+                            <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">)</span>
+        
+        <span class="c1">#</span>
+        <span class="c1"># Phase 4: Meek rule orientations</span>
+        <span class="c1">#</span>
+        <span class="n">final_graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcmciplus_rule_orientation_phase</span><span class="p">(</span>
+                            <span class="n">collider_graph</span><span class="o">=</span><span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">],</span>
+                            <span class="n">ambiguous_triples</span><span class="o">=</span><span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">],</span> 
+                            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">)</span>
+
+        <span class="c1"># Store the parents in the pcmci member</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">all_lagged_parents</span> <span class="o">=</span> <span class="n">lagged_parents</span>
+
+        <span class="n">return_dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">final_graph</span><span class="p">,</span>
+            <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;sepsets&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">],</span>
+            <span class="p">}</span>
+
+        <span class="c1"># No confidence interval estimation here</span>
+        <span class="n">return_dict</span><span class="p">[</span><span class="s1">&#39;conf_matrix&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># Print the results</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">return_dict</span><span class="p">,</span> <span class="n">alpha_level</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">)</span>
+        
+        <span class="c1"># Return the dictionary</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="o">=</span> <span class="n">return_dict</span>
+        
+        <span class="k">return</span> <span class="n">return_dict</span></div>
+
+    
+        <span class="c1"># # Set the maximum condition dimension for Y and X</span>
+        <span class="c1"># max_conds_py = self._set_max_condition_dim(max_conds_py,</span>
+        <span class="c1">#                                            tau_min, tau_max)</span>
+        <span class="c1"># max_conds_px = self._set_max_condition_dim(max_conds_px,</span>
+        <span class="c1">#                                            tau_min, tau_max)</span>
+
+        <span class="c1"># if reset_lagged_links:</span>
+        <span class="c1">#     # Run PCalg on full graph, ignoring that some lagged links</span>
+        <span class="c1">#     # were determined as non-significant in PC1 step</span>
+        <span class="c1">#     links_for_pc = deepcopy(_int_link_assumptions)</span>
+        <span class="c1"># else:</span>
+        <span class="c1">#     # Run PCalg only on lagged parents found with PC1 </span>
+        <span class="c1">#     # plus all contemporaneous links</span>
+        <span class="c1">#     links_for_pc = {}  #deepcopy(lagged_parents)</span>
+        <span class="c1">#     for j in range(self.N):</span>
+        <span class="c1">#         links_for_pc[j] = {}</span>
+        <span class="c1">#         for parent in lagged_parents[j]:</span>
+        <span class="c1">#             if _int_link_assumptions[j][parent] in [&#39;-?&gt;&#39;, &#39;--&gt;&#39;]:</span>
+        <span class="c1">#                 links_for_pc[j][parent] = _int_link_assumptions[j][parent]</span>
+
+        <span class="c1">#         # Add contemporaneous links</span>
+        <span class="c1">#         for link in _int_link_assumptions[j]:</span>
+        <span class="c1">#             i, tau = link</span>
+        <span class="c1">#             link_type = _int_link_assumptions[j][link]</span>
+        <span class="c1">#             if abs(tau) == 0:</span>
+        <span class="c1">#                 links_for_pc[j][(i, 0)] = link_type</span>
+
+        <span class="c1"># results = self.run_pcalg(</span>
+        <span class="c1">#     link_assumptions=links_for_pc,</span>
+        <span class="c1">#     pc_alpha=pc_alpha,</span>
+        <span class="c1">#     tau_min=tau_min,</span>
+        <span class="c1">#     tau_max=tau_max,</span>
+        <span class="c1">#     max_conds_dim=max_conds_dim,</span>
+        <span class="c1">#     max_combinations=max_combinations,</span>
+        <span class="c1">#     lagged_parents=lagged_parents,</span>
+        <span class="c1">#     max_conds_py=max_conds_py,</span>
+        <span class="c1">#     max_conds_px=max_conds_px,</span>
+        <span class="c1">#     max_conds_px_lagged=max_conds_px_lagged,</span>
+        <span class="c1">#     mode=&#39;contemp_conds&#39;,</span>
+        <span class="c1">#     contemp_collider_rule=contemp_collider_rule,</span>
+        <span class="c1">#     conflict_resolution=conflict_resolution)</span>
+
+        <span class="c1"># graph = results[&#39;graph&#39;]</span>
+
+        <span class="c1"># # Update p_matrix and val_matrix with values from links_for_pc</span>
+        <span class="c1"># for j in range(self.N):</span>
+        <span class="c1">#     for link in links_for_pc[j]:</span>
+        <span class="c1">#         i, tau = link</span>
+        <span class="c1">#         if links_for_pc[j][link] not in [&#39;&lt;--&#39;, &#39;&lt;?-&#39;]:</span>
+        <span class="c1">#             p_matrix[i, j, abs(tau)] = results[&#39;p_matrix&#39;][i, j, abs(tau)]</span>
+        <span class="c1">#             val_matrix[i, j, abs(tau)] = results[&#39;val_matrix&#39;][i, j, </span>
+        <span class="c1">#                                                                abs(tau)]</span>
+
+        <span class="c1"># # Update p_matrix and val_matrix for indices of symmetrical links</span>
+        <span class="c1"># p_matrix[:, :, 0] = results[&#39;p_matrix&#39;][:, :, 0]</span>
+        <span class="c1"># val_matrix[:, :, 0] = results[&#39;val_matrix&#39;][:, :, 0]</span>
+
+        <span class="c1"># ambiguous = results[&#39;ambiguous_triples&#39;]</span>
+
+        <span class="c1"># conf_matrix = None</span>
+        <span class="c1"># TODO: implement confidence estimation, but how?</span>
+        <span class="c1"># if self.cond_ind_test.confidence is not False:</span>
+        <span class="c1">#     conf_matrix = results[&#39;conf_matrix&#39;]</span>
+
+        <span class="c1"># # Correct the p_matrix if there is a fdr_method</span>
+        <span class="c1"># if fdr_method != &#39;none&#39;:</span>
+        <span class="c1">#     p_matrix = self.get_corrected_pvalues(p_matrix=p_matrix, tau_min=tau_min, </span>
+        <span class="c1">#                                           tau_max=tau_max, </span>
+        <span class="c1">#                                           link_assumptions=_int_link_assumptions,</span>
+        <span class="c1">#                                           fdr_method=fdr_method)</span>
+
+        <span class="c1"># # Store the parents in the pcmci member</span>
+        <span class="c1"># self.all_lagged_parents = lagged_parents</span>
+
+        <span class="c1"># # p_matrix=results[&#39;p_matrix&#39;]</span>
+        <span class="c1"># # val_matrix=results[&#39;val_matrix&#39;]</span>
+
+        <span class="c1"># Cache the resulting values in the return dictionary</span>
+        <span class="c1"># return_dict = {&#39;graph&#39;: graph,</span>
+        <span class="c1">#                &#39;val_matrix&#39;: val_matrix,</span>
+        <span class="c1">#                &#39;p_matrix&#39;: p_matrix,</span>
+        <span class="c1">#                &#39;ambiguous_triples&#39;: ambiguous,</span>
+        <span class="c1">#                &#39;conf_matrix&#39;: conf_matrix}</span>
+
+        <span class="c1"># # Print the results</span>
+        <span class="c1"># if self.verbosity &gt; 0:</span>
+        <span class="c1">#     self.print_results(return_dict, alpha_level=pc_alpha)</span>
+        <span class="c1"># # Return the dictionary</span>
+        <span class="c1"># self.results = return_dict</span>
+        <span class="c1"># return return_dict</span>
+
+    <span class="k">def</span> <span class="nf">_pcmciplus_mci_skeleton_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+            <span class="n">lagged_parents</span><span class="p">,</span> 
+            <span class="n">link_assumptions</span><span class="p">,</span> 
+            <span class="n">pc_alpha</span><span class="p">,</span>
+            <span class="n">tau_min</span><span class="p">,</span> 
+            <span class="n">tau_max</span><span class="p">,</span> 
+            <span class="n">max_conds_dim</span><span class="p">,</span> 
+            <span class="n">max_combinations</span><span class="p">,</span> 
+            <span class="n">max_conds_py</span><span class="p">,</span> 
+            <span class="n">max_conds_px</span><span class="p">,</span> 
+            <span class="n">max_conds_px_lagged</span><span class="p">,</span> 
+            <span class="n">reset_lagged_links</span><span class="p">,</span>
+            <span class="n">fdr_method</span><span class="p">,</span>
+            <span class="n">p_matrix</span><span class="p">,</span> 
+            <span class="n">val_matrix</span><span class="p">,</span>
+            <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI Skeleton phase.&quot;&quot;&quot;</span>
+
         <span class="c1"># Set the maximum condition dimension for Y and X</span>
         <span class="n">max_conds_py</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_py</span><span class="p">,</span>
                                                    <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
@@ -2284,7 +2425,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">reset_lagged_links</span><span class="p">:</span>
             <span class="c1"># Run PCalg on full graph, ignoring that some lagged links</span>
             <span class="c1"># were determined as non-significant in PC1 step</span>
-            <span class="n">links_for_pc</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">_int_link_assumptions</span><span class="p">)</span>
+            <span class="n">links_for_pc</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="c1"># Run PCalg only on lagged parents found with PC1 </span>
             <span class="c1"># plus all contemporaneous links</span>
@@ -2292,75 +2433,120 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
             <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
                 <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
                 <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="n">lagged_parents</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
-                    <span class="k">if</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">]:</span>
-                        <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;--&gt;&#39;</span><span class="p">]:</span>
+                        <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">parent</span><span class="p">]</span>
 
                 <span class="c1"># Add contemporaneous links</span>
-                <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
+                <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
                     <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link</span>
-                    <span class="n">link_type</span> <span class="o">=</span> <span class="n">_int_link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">link</span><span class="p">]</span>
+                    <span class="n">link_type</span> <span class="o">=</span> <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">link</span><span class="p">]</span>
                     <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                         <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">=</span> <span class="n">link_type</span>
 
-        <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pcalg</span><span class="p">(</span>
-            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">links_for_pc</span><span class="p">,</span>
+
+        <span class="k">if</span> <span class="n">max_conds_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">max_conds_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span>
+
+        <span class="k">if</span> <span class="n">max_combinations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">max_combinations</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+
+        <span class="n">initial_graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dict_to_graph</span><span class="p">(</span><span class="n">links_for_pc</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">)</span>
+
+        <span class="n">skeleton_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_skeleton</span><span class="p">(</span>
+            <span class="n">initial_graph</span><span class="o">=</span><span class="n">initial_graph</span><span class="p">,</span>
+            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
+            <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;contemp_conds&#39;</span><span class="p">,</span>
             <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
             <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span>
             <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
             <span class="n">max_conds_dim</span><span class="o">=</span><span class="n">max_conds_dim</span><span class="p">,</span>
             <span class="n">max_combinations</span><span class="o">=</span><span class="n">max_combinations</span><span class="p">,</span>
-            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
             <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
             <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
             <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
-            <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;contemp_conds&#39;</span><span class="p">,</span>
-            <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">,</span>
-            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="c1"># Symmetrize p_matrix and val_matrix coming from skeleton</span>
+        <span class="n">symmetrized_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symmetrize_p_and_val_matrix</span><span class="p">(</span>
+                            <span class="n">p_matrix</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span> 
+                            <span class="n">val_matrix</span><span class="o">=</span><span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span> 
+                            <span class="n">link_assumptions</span><span class="o">=</span><span class="n">links_for_pc</span><span class="p">,</span>
+                            <span class="n">conf_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 
-        <span class="n">graph</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span>
+        <span class="c1"># Update p_matrix and val_matrix with values from skeleton phase</span>
+        <span class="c1"># Contemporaneous entries (not filled in run_pc_stable lagged phase)</span>
+        <span class="n">p_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">val_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
 
-        <span class="c1"># Update p_matrix and val_matrix with values from links_for_pc</span>
+        <span class="c1"># Update all entries computed in the MCI step </span>
+        <span class="c1"># (these are in links_for_pc); values for entries</span>
+        <span class="c1"># that were removed in the lagged-condition phase are kept from before</span>
         <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
             <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
                 <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">link</span>
                 <span class="k">if</span> <span class="n">links_for_pc</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">link</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;&lt;--&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;?-&#39;</span><span class="p">]:</span>
-                    <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
-                    <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> 
-                                                                       <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
+                    <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
+                    <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span> <span class="o">=</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> 
+                                                                 <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">)]</span>
 
-        <span class="c1"># Update p_matrix and val_matrix for indices of symmetrical links</span>
-        <span class="n">p_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
-        <span class="n">val_matrix</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">ambiguous</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span>
-
-        <span class="n">conf_matrix</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="c1"># TODO: implement confidence estimation, but how?</span>
-        <span class="c1"># if self.cond_ind_test.confidence is not False:</span>
-        <span class="c1">#     conf_matrix = results[&#39;conf_matrix&#39;]</span>
-
-        <span class="c1"># Correct the p_matrix if there is a fdr_method</span>
+        <span class="c1"># Optionally correct the p_matrix</span>
         <span class="k">if</span> <span class="n">fdr_method</span> <span class="o">!=</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
             <span class="n">p_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_corrected_pvalues</span><span class="p">(</span><span class="n">p_matrix</span><span class="o">=</span><span class="n">p_matrix</span><span class="p">,</span> <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> 
                                                   <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
-                                                  <span class="n">link_assumptions</span><span class="o">=</span><span class="n">_int_link_assumptions</span><span class="p">,</span>
+                                                  <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">,</span>
                                                   <span class="n">fdr_method</span><span class="o">=</span><span class="n">fdr_method</span><span class="p">)</span>
 
-        <span class="c1"># Store the parents in the pcmci member</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">all_lagged_parents</span> <span class="o">=</span> <span class="n">lagged_parents</span>
+        <span class="c1"># Update matrices</span>
+        <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">p_matrix</span>
+        <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_matrix</span>
+
+        <span class="k">return</span> <span class="n">skeleton_results</span>
+
+
+    <span class="k">def</span> <span class="nf">_pcmciplus_collider_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skeleton_graph</span><span class="p">,</span> <span class="n">sepsets</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span>
+        <span class="n">pc_alpha</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span> <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span>
+        <span class="n">conflict_resolution</span><span class="p">,</span> <span class="n">contemp_collider_rule</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI collider phase.&quot;&quot;&quot;</span>    
+
+        <span class="c1"># Set the maximum condition dimension for Y and X</span>
+        <span class="n">max_conds_py</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_py</span><span class="p">,</span>
+                                                   <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+        <span class="n">max_conds_px</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_max_condition_dim</span><span class="p">(</span><span class="n">max_conds_px</span><span class="p">,</span>
+                                                   <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+
+        <span class="c1"># Now change assumed links marks</span>
+        <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;o?o&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;o-o&#39;</span>
+        <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;--&gt;&#39;</span>
+        <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;&lt;?-&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;--&#39;</span>
+
+        <span class="n">colliders_step_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_colliders</span><span class="p">(</span>
+            <span class="n">graph</span><span class="o">=</span><span class="n">skeleton_graph</span><span class="p">,</span>
+            <span class="n">sepsets</span><span class="o">=</span><span class="n">sepsets</span><span class="p">,</span>
+            <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
+            <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;contemp_conds&#39;</span><span class="p">,</span>
+            <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
+            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
+            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
+            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span>
+            <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
+            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span>
+            <span class="n">contemp_collider_rule</span><span class="o">=</span><span class="n">contemp_collider_rule</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">colliders_step_results</span>
+
+    <span class="k">def</span> <span class="nf">_pcmciplus_rule_orientation_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">collider_graph</span><span class="p">,</span>
+         <span class="n">ambiguous_triples</span><span class="p">,</span> <span class="n">conflict_resolution</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI rule orientation phase.&quot;&quot;&quot;</span>  
+
+        <span class="n">final_graph</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_rules_timeseries</span><span class="p">(</span>
+            <span class="n">graph</span><span class="o">=</span><span class="n">collider_graph</span><span class="p">,</span>
+            <span class="n">ambiguous_triples</span><span class="o">=</span><span class="n">ambiguous_triples</span><span class="p">,</span>
+            <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">final_graph</span>
 
-        <span class="c1"># Cache the resulting values in the return dictionary</span>
-        <span class="n">return_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-                       <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">val_matrix</span><span class="p">,</span>
-                       <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">p_matrix</span><span class="p">,</span>
-                       <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">ambiguous</span><span class="p">,</span>
-                       <span class="s1">&#39;conf_matrix&#39;</span><span class="p">:</span> <span class="n">conf_matrix</span><span class="p">}</span>
-        <span class="c1"># Print the results</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">return_dict</span><span class="p">,</span> <span class="n">alpha_level</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">)</span>
-        <span class="c1"># Return the dictionary</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="o">=</span> <span class="n">return_dict</span>
-        <span class="k">return</span> <span class="n">return_dict</span></div>
 
 <div class="viewcode-block" id="PCMCI.run_pcalg"><a class="viewcode-back" href="../../index.html#tigramite.pcmci.PCMCI.run_pcalg">[docs]</a>    <span class="k">def</span> <span class="nf">run_pcalg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> 
                     <span class="n">selected_links</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
@@ -2448,7 +2634,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
 <span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
 <span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        ambiguous_triples : list</span>
 <span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
@@ -2501,16 +2687,16 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
         <span class="p">)</span>
 
         <span class="n">skeleton_graph</span> <span class="o">=</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span>
-        <span class="n">sepset</span> <span class="o">=</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">]</span>
+        <span class="n">sepsets</span> <span class="o">=</span> <span class="n">skeleton_results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">]</span>
 
-        <span class="c1"># Now change assumed links mark</span>
+        <span class="c1"># Now change assumed links marks</span>
         <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;o?o&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;o-o&#39;</span>
         <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;-?&gt;&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;--&gt;&#39;</span>
         <span class="n">skeleton_graph</span><span class="p">[</span><span class="n">skeleton_graph</span><span class="o">==</span><span class="s1">&#39;&lt;?-&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&lt;--&#39;</span>
 
         <span class="n">colliders_step_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pcalg_colliders</span><span class="p">(</span>
             <span class="n">graph</span><span class="o">=</span><span class="n">skeleton_graph</span><span class="p">,</span>
-            <span class="n">sepset</span><span class="o">=</span><span class="n">sepset</span><span class="p">,</span>
+            <span class="n">sepsets</span><span class="o">=</span><span class="n">sepsets</span><span class="p">,</span>
             <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span>
             <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span>
             <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
@@ -2545,7 +2731,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
             <span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph_str</span><span class="p">,</span>
             <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">],</span>
             <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">symmetrized_results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">],</span>
-            <span class="s1">&#39;sepset&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;sepsets&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">],</span>
             <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">colliders_step_results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">],</span>
         <span class="p">}</span>
 
@@ -2593,7 +2779,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
 <span class="sd">        p_matrix : array of shape [N, N, 1]</span>
 <span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        ambiguous_triples : list</span>
 <span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
@@ -2606,16 +2792,13 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                   <span class="n">conflict_resolution</span><span class="o">=</span><span class="n">conflict_resolution</span><span class="p">)</span>
 
         <span class="c1"># Remove tau-dimension</span>
-        <span class="c1"># results[&#39;graph&#39;] = results[&#39;graph&#39;].squeeze()</span>
-        <span class="c1"># results[&#39;val_matrix&#39;] = results[&#39;val_matrix&#39;].squeeze()</span>
-        <span class="c1"># results[&#39;p_matrix&#39;] = results[&#39;p_matrix&#39;].squeeze()</span>
-        <span class="n">old_sepsets</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">old_sepset</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">:</span>
-           <span class="n">new_sepset</span> <span class="o">=</span> <span class="p">(</span><span class="n">old_sepset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">old_sepset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-           <span class="n">conds</span> <span class="o">=</span> <span class="p">[</span><span class="n">cond</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">cond</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">[</span><span class="n">old_sepset</span><span class="p">]]</span>
+        <span class="n">old_sepsets</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">old_sepsets</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">:</span>
+           <span class="n">new_sepsets</span> <span class="o">=</span> <span class="p">(</span><span class="n">old_sepsets</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">old_sepsets</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+           <span class="n">conds</span> <span class="o">=</span> <span class="p">[</span><span class="n">cond</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">cond</span> <span class="ow">in</span> <span class="n">old_sepsets</span><span class="p">[</span><span class="n">old_sepsets</span><span class="p">]]</span>
 
-           <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepset&#39;</span><span class="p">][</span><span class="n">new_sepset</span><span class="p">]</span> <span class="o">=</span> <span class="n">conds</span>
+           <span class="n">results</span><span class="p">[</span><span class="s1">&#39;sepsets&#39;</span><span class="p">][</span><span class="n">new_sepsets</span><span class="p">]</span> <span class="o">=</span> <span class="n">conds</span>
 
         <span class="n">ambiguous_triples</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
         <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
@@ -2629,7 +2812,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
 
     <span class="k">def</span> <span class="nf">_run_pcalg_test</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span>
-                        <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
+                        <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;MCI conditional independence tests within PCMCIplus or PC algorithm.</span>
 
 <span class="sd">        Parameters</span>
@@ -2655,15 +2838,16 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            tests. If None is passed, this number is equal to max_conds_px.</span>
 <span class="sd">        tau_max : int</span>
 <span class="sd">            Maximum time lag.</span>
+<span class="sd">        alpha_or_thres : float</span>
+<span class="sd">            Significance level (if significance=&#39;analytic&#39; or &#39;shuffle_test&#39;) or</span>
+<span class="sd">            threshold (if significance=&#39;fixed_thres&#39;). If given, run_test returns</span>
+<span class="sd">            the test decision dependent=True/False.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        val : float</span>
-<span class="sd">            Test statistic value.</span>
-<span class="sd">        pval : float</span>
-<span class="sd">            Test statistic p-value.</span>
-<span class="sd">        Z : list</span>
-<span class="sd">            List of conditions.</span>
+<span class="sd">        val, pval, Z, [dependent] : Tuple of floats, list, and bool</span>
+<span class="sd">            The test statistic value and the p-value and list of conditions. If alpha_or_thres is</span>
+<span class="sd">            given, run_test also returns the test decision dependent=True/False.             </span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
         <span class="c1"># Perform independence test adding lagged parents</span>
@@ -2693,13 +2877,15 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">abstau</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span> <span class="ow">and</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">abstau</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;-&#39;</span><span class="p">:</span>
             <span class="n">val</span> <span class="o">=</span> <span class="mf">1.</span> 
             <span class="n">pval</span> <span class="o">=</span> <span class="mf">0.</span>
+            <span class="n">dependent</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span>
+            <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span>
                                                 <span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span>
+                                                <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">alpha_or_thres</span><span class="p">,</span>
                                                 <span class="c1"># verbosity=self.verbosity</span>
                                                 <span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span>
+        <span class="k">return</span> <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">dependent</span>
 
     <span class="k">def</span> <span class="nf">_print_triple_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">triple</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">n_triples</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Print info about the current triple being tested.</span>
@@ -2811,7 +2997,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">            Estimated matrix of test statistic values regarding adjacencies.</span>
 <span class="sd">        p_matrix : array of shape [N, N, tau_max+1]</span>
 <span class="sd">            Estimated matrix of p-values regarding adjacencies.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span>
@@ -2834,23 +3020,25 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
             <span class="n">adjt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_adj_time_series</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
 
         <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+        
         <span class="n">val_min</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
         <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
             <span class="n">val_min</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
                           <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">))}</span>
 
         <span class="c1"># Initialize p-values. Set to 1 if there&#39;s no link in the initial graph</span>
-        <span class="n">pvalues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">pvalues</span><span class="p">[</span><span class="n">graph</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+        <span class="n">p_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="n">p_matrix</span><span class="p">[</span><span class="n">graph</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+
         <span class="n">pval_max</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
         <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
             <span class="n">pval_max</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="mf">0.</span>
                            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">))}</span>
 
-        <span class="c1"># TODO: Remove sepset alltogether?</span>
+        <span class="c1"># TODO: Remove sepsets alltogether?</span>
         <span class="c1"># Intialize sepsets that store the conditions that make i and j</span>
         <span class="c1"># independent</span>
-        <span class="n">sepset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepset</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+        <span class="n">sepsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sepsets</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">--------------------------&quot;</span><span class="p">)</span>
@@ -2900,9 +3088,11 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                             <span class="k">break</span>
 
                         <span class="c1"># Run MCI test</span>
-                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                            <span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span>
-                            <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+                        <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span>
+                            <span class="n">i</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="o">=</span><span class="n">abstau</span><span class="p">,</span> <span class="n">j</span><span class="o">=</span><span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="o">=</span><span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span> 
+                            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
+                            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                         <span class="c1"># Store minimum test statistic value for sorting adjt</span>
                         <span class="c1"># (only internally used)</span>
@@ -2915,8 +3105,8 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                                             <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">)))</span>
 
                         <span class="c1"># Store max. p-value and corresponding value to return</span>
-                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;=</span> <span class="n">pvalues</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]:</span>
-                            <span class="n">pvalues</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
+                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;=</span> <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]:</span>
+                            <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="n">pval</span>
                             <span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
 
                         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
@@ -2924,16 +3114,18 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                                   <span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
 
                         <span class="c1"># If conditional independence is found, remove link</span>
-                        <span class="c1"># from graph and store sepset</span>
-                        <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pc_alpha</span><span class="p">:</span>
+                        <span class="c1"># from graph and store sepsets</span>
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1"># pval &gt; pc_alpha:</span>
                             <span class="n">nonsig</span> <span class="o">=</span> <span class="kc">True</span>
                             <span class="k">if</span> <span class="n">abstau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                                 <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">graph</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="n">sepset</span><span class="p">[</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="n">sepsets</span><span class="p">[</span>
                                     <span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
+                                <span class="c1"># Also store p-value in other contemp. entry</span>
+                                <span class="n">p_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">p_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
                             <span class="k">else</span><span class="p">:</span>
                                 <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">abstau</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">abstau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
                             <span class="k">break</span>
 
                     <span class="c1"># Print the results if needed</span>
@@ -2972,13 +3164,13 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                     <span class="s2">&quot; reached.&quot;</span> <span class="o">%</span> <span class="n">max_conds_dim</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-                <span class="s1">&#39;sepset&#39;</span><span class="p">:</span> <span class="n">sepset</span><span class="p">,</span>
-                <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">pvalues</span><span class="p">,</span>
+                <span class="s1">&#39;sepsets&#39;</span><span class="p">:</span> <span class="n">sepsets</span><span class="p">,</span>
+                <span class="s1">&#39;p_matrix&#39;</span><span class="p">:</span> <span class="n">p_matrix</span><span class="p">,</span>
                 <span class="s1">&#39;val_matrix&#39;</span><span class="p">:</span> <span class="n">val_matrix</span><span class="p">,</span>
                 <span class="p">}</span>
 
-    <span class="k">def</span> <span class="nf">_get_sepset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns initial sepset.</span>
+    <span class="k">def</span> <span class="nf">_get_sepsets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns initial sepsets.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
@@ -2989,15 +3181,15 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        sepset : dict</span>
-<span class="sd">            Initialized sepset.</span>
+<span class="sd">        sepsets : dict</span>
+<span class="sd">            Initialized sepsets.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">sepset</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">),</span> <span class="p">[])</span>
+        <span class="n">sepsets</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">),</span> <span class="p">[])</span>
                        <span class="k">for</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
                        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
                        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)])</span>
 
-        <span class="k">return</span> <span class="n">sepset</span>
+        <span class="k">return</span> <span class="n">sepsets</span>
 
     <span class="k">def</span> <span class="nf">_find_unshielded_triples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Find unshielded triples i_tau o-(&gt;) k_t o-o j_t with i_tau -/- j_t.</span>
@@ -3041,7 +3233,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
     <span class="k">def</span> <span class="nf">_pcalg_colliders</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                         <span class="n">graph</span><span class="p">,</span>
-                        <span class="n">sepset</span><span class="p">,</span>
+                        <span class="n">sepsets</span><span class="p">,</span>
                         <span class="n">lagged_parents</span><span class="p">,</span>
                         <span class="n">mode</span><span class="p">,</span>
                         <span class="n">pc_alpha</span><span class="p">,</span>
@@ -3059,7 +3251,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">        ----------</span>
 <span class="sd">        graph : array of shape (N, N, tau_max+1)</span>
 <span class="sd">            Current graph.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        lagged_parents : dictionary</span>
 <span class="sd">            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing</span>
@@ -3095,7 +3287,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 <span class="sd">        -------</span>
 <span class="sd">        graph : array of shape [N, N, tau_max+1]</span>
 <span class="sd">            Resulting causal graph, see description above for interpretation.</span>
-<span class="sd">        sepset : dictionary</span>
+<span class="sd">        sepsets : dictionary</span>
 <span class="sd">            Separating sets. See paper for details.</span>
 <span class="sd">        ambiguous_triples : list</span>
 <span class="sd">            List of ambiguous triples, only relevant for &#39;majority&#39; and</span>
@@ -3122,11 +3314,11 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
         <span class="k">if</span> <span class="n">contemp_collider_rule</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">contemp_collider_rule</span> <span class="o">==</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
             <span class="c1"># Standard collider orientation rule of PC algorithm</span>
-            <span class="c1"># If k_t not in sepset(i_tau, j_t), then orient</span>
+            <span class="c1"># If k_t not in sepsets(i_tau, j_t), then orient</span>
             <span class="c1"># as i_tau --&gt; k_t &lt;-- j_t</span>
             <span class="k">for</span> <span class="n">itaukj</span> <span class="ow">in</span> <span class="n">triples</span><span class="p">:</span>
                 <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">itaukj</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
                     <span class="n">v_structures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itaukj</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="c1"># Apply &#39;majority&#39; or &#39;conservative&#39; rule to orient colliders          </span>
@@ -3185,15 +3377,17 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                 <span class="c1"># Test which neighbor subsets separate i and j</span>
                 <span class="n">neighbor_sepsets</span> <span class="o">=</span> <span class="p">[]</span>
                 <span class="k">for</span> <span class="n">iss</span><span class="p">,</span> <span class="n">S</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">neighbor_subsets</span><span class="p">):</span>
-                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span>
-                        <span class="n">i</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="p">,</span> <span class="n">max_conds_py</span><span class="p">,</span>
-                        <span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+                    <span class="n">val</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">dependent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_run_pcalg_test</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span>
+                            <span class="n">i</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">abstau</span><span class="o">=</span><span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="o">=</span><span class="n">j</span><span class="p">,</span> <span class="n">S</span><span class="o">=</span><span class="n">S</span><span class="p">,</span> <span class="n">lagged_parents</span><span class="o">=</span><span class="n">lagged_parents</span><span class="p">,</span> 
+                            <span class="n">max_conds_py</span><span class="o">=</span><span class="n">max_conds_py</span><span class="p">,</span>
+                            <span class="n">max_conds_px</span><span class="o">=</span><span class="n">max_conds_px</span><span class="p">,</span> <span class="n">max_conds_px_lagged</span><span class="o">=</span><span class="n">max_conds_px_lagged</span><span class="p">,</span>
+                            <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">)</span>
 
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_print_cond_info</span><span class="p">(</span><span class="n">Z</span><span class="o">=</span><span class="n">S</span><span class="p">,</span> <span class="n">comb_index</span><span class="o">=</span><span class="n">iss</span><span class="p">,</span> <span class="n">pval</span><span class="o">=</span><span class="n">pval</span><span class="p">,</span>
                                               <span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
 
-                    <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">pc_alpha</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">dependent</span><span class="p">:</span> <span class="c1">#pval &gt; pc_alpha:</span>
                         <span class="n">neighbor_sepsets</span> <span class="o">+=</span> <span class="p">[</span><span class="n">S</span><span class="p">]</span>
 
                 <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbor_sepsets</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
@@ -3221,12 +3415,12 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                     <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
                                     <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is = 0 --&gt; collider &quot;</span>
                                     <span class="s2">&quot;found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also delete (k, 0) from sepset (if present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                            <span class="c1"># Also delete (k, 0) from sepsets (if present)</span>
+                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                             <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
+                                    <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                         <span class="k">elif</span> <span class="n">fraction</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                             <span class="c1"># If (k, 0) is in all of the neighbor_sepsets,</span>
                             <span class="c1"># leave unoriented</span>
@@ -3235,12 +3429,12 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                     <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
                                     <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is = 1 --&gt; &quot;</span>
                                     <span class="s2">&quot;non-collider found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also add (k, 0) to sepset (if not present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                            <span class="c1"># Also add (k, 0) to sepsets (if not present)</span>
+                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                             <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
+                                    <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                         <span class="k">else</span><span class="p">:</span>
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span>
@@ -3273,12 +3467,12 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                     <span class="s2">&quot;    Fraction of separating subsets &quot;</span>
                                     <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is &lt; 0.5 &quot;</span>
                                     <span class="s2">&quot;--&gt; collider found&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also delete (k, 0) from sepset (if present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                            <span class="c1"># Also delete (k, 0) from sepsets (if present)</span>
+                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                             <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
+                                    <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                         <span class="k">elif</span> <span class="n">fraction</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">:</span>
                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                                 <span class="nb">print</span><span class="p">(</span>
@@ -3286,12 +3480,12 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                                     <span class="s2">&quot;containing (</span><span class="si">%s</span><span class="s2"> 0) is &gt; 0.5 &quot;</span>
                                     <span class="s2">&quot;--&gt; non-collider found&quot;</span> <span class="o">%</span>
                                     <span class="bp">self</span><span class="o">.</span><span class="n">var_names</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-                            <span class="c1"># Also add (k, 0) to sepset (if not present)</span>
-                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
-                                <span class="n">sepset</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                            <span class="c1"># Also add (k, 0) to sepsets (if not present)</span>
+                            <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]:</span>
+                                <span class="n">sepsets</span><span class="p">[((</span><span class="n">i</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">j</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                             <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
-                                    <span class="n">sepset</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                                <span class="k">if</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]:</span>
+                                    <span class="n">sepsets</span><span class="p">[((</span><span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">),</span> <span class="n">i</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
 
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">v_structures</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Orienting links among colliders:&quot;</span><span class="p">)</span>
@@ -3362,7 +3556,7 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
             <span class="bp">self</span><span class="o">.</span><span class="n">_print_parents</span><span class="p">(</span><span class="n">all_parents</span><span class="o">=</span><span class="n">adjt</span><span class="p">,</span> <span class="n">val_min</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pval_max</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;graph&#39;</span><span class="p">:</span> <span class="n">graph</span><span class="p">,</span>
-                <span class="s1">&#39;sepset&#39;</span><span class="p">:</span> <span class="n">sepset</span><span class="p">,</span>
+                <span class="s1">&#39;sepsets&#39;</span><span class="p">:</span> <span class="n">sepsets</span><span class="p">,</span>
                 <span class="s1">&#39;ambiguous_triples&#39;</span><span class="p">:</span> <span class="n">ambiguous_triples</span><span class="p">,</span>
                 <span class="p">}</span>
 
@@ -3732,9 +3926,9 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
                 <span class="n">parents</span> <span class="o">=</span> <span class="p">[]</span>
                 <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tau</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">dag</span><span class="p">[:,</span><span class="n">j</span><span class="p">,:]</span> <span class="o">==</span> <span class="s2">&quot;--&gt;&quot;</span><span class="p">)):</span>
                     <span class="n">parents</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">tau</span><span class="p">))</span>
-                <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]</span> <span class="o">+=</span> \
-                    <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span>
+                <span class="n">score_j</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cond_ind_test</span><span class="o">.</span><span class="n">get_model_selection_criterion</span><span class="p">(</span>
                         <span class="n">j</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">)</span>
+                <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]</span> <span class="o">+=</span> <span class="n">score_j</span>
             <span class="n">score</span><span class="p">[</span><span class="n">iscore</span><span class="p">]</span> <span class="o">/=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
 
         <span class="c1"># Record the optimal alpha value</span>
@@ -3757,7 +3951,9 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
 
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span><span class="p">,</span> <span class="n">CMIknn</span><span class="p">,</span> <span class="n">ParCorrMult</span>
+    <span class="kn">from</span> <span class="nn">tigramite.independence_tests.parcorr</span> <span class="kn">import</span> <span class="n">ParCorr</span>
+    <span class="kn">from</span> <span class="nn">tigramite.independence_tests.cmiknn</span> <span class="kn">import</span> <span class="n">CMIknn</span>
+
     <span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
     <span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
     <span class="kn">import</span> <span class="nn">tigramite.plotting</span> <span class="k">as</span> <span class="nn">tp</span>
@@ -3770,53 +3966,62 @@ <h1>Source code for tigramite.pcmci</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
     <span class="k">def</span> <span class="nf">nonlin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mf">5.</span> <span class="o">*</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mf">20.</span><span class="p">))</span>
 
-    <span class="n">T</span> <span class="o">=</span> <span class="mi">2000</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standar_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">T</span> <span class="o">=</span> <span class="mi">1000</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
     <span class="c1"># Simple sun</span>
-    <span class="n">data</span><span class="p">[:,</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">*</span><span class="mi">20</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.1</span><span class="o">*</span><span class="n">random_state</span><span class="o">.</span><span class="n">standar_normal</span><span class="p">((</span><span class="n">T</span><span class="p">))</span>
+    <span class="n">data</span><span class="p">[:,</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">T</span><span class="p">))</span> <span class="c1"># np.sin(np.arange(T)*20/np.pi) + 0.1*random_state.standard_normal((T))</span>
     <span class="n">c</span> <span class="o">=</span> <span class="mf">0.8</span>
     <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span>
         <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.4</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.4</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.6</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.3</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
+        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
+        <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.6</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.3</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="c1">#+ c*data[t-1,3]</span>
     <span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">var_names</span><span class="o">=</span><span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$X^0$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$X^1$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$X^2$&#39;</span><span class="p">,</span> <span class="s1">&#39;Sun&#39;</span><span class="p">])</span>
     <span class="c1"># tp.plot_timeseries(dataframe); plt.show()</span>
 
-    <span class="n">parcorr</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
+    <span class="n">ci_test</span> <span class="o">=</span> <span class="n">CMIknn</span><span class="p">(</span><span class="n">significance</span><span class="o">=</span><span class="s2">&quot;fixed_thres&quot;</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>   <span class="c1">#</span>
+    <span class="c1"># ci_test = ParCorr() #significance=&quot;fixed_thres&quot;)   #</span>
     <span class="c1"># dataframe_nosun = pp.DataFrame(data[:,[0,1,2]], var_names=[r&#39;$X^0$&#39;, r&#39;$X^1$&#39;, r&#39;$X^2$&#39;])</span>
     <span class="c1"># pcmci_parcorr = PCMCI(</span>
     <span class="c1">#     dataframe=dataframe_nosun, </span>
     <span class="c1">#     cond_ind_test=parcorr,</span>
     <span class="c1">#     verbosity=0)</span>
-    <span class="n">tau_max</span> <span class="o">=</span> <span class="mi">2</span>
+    <span class="n">tau_max</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1">#2</span>
     <span class="c1"># results = pcmci_parcorr.run_pcmci(tau_max=tau_max, pc_alpha=0.2, alpha_level = 0.01)</span>
     <span class="c1"># Remove parents of variable 3</span>
     <span class="c1"># Only estimate parents of variables 0, 1, 2</span>
-    <span class="n">link_assumptions</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">j</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]:</span>
-            <span class="c1"># Directed lagged links</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{(</span><span class="n">var</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span><span class="p">):</span> <span class="s1">&#39;-?&gt;&#39;</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-                             <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)}</span>
-            <span class="c1"># Unoriented contemporaneous links</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span> <span class="s1">&#39;o?o&#39;</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">var</span> <span class="o">!=</span> <span class="n">j</span><span class="p">})</span>
-            <span class="c1"># Directed lagged and contemporaneous links from the sun (3)</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({(</span><span class="n">var</span><span class="p">,</span> <span class="o">-</span><span class="n">lag</span><span class="p">):</span> <span class="s1">&#39;-?&gt;&#39;</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-                             <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)})</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">link_assumptions</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="n">link_assumptions</span><span class="p">)</span>
+    <span class="n">link_assumptions</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1">#{}</span>
+    <span class="c1"># for j in range(4):</span>
+    <span class="c1">#     if j in [0, 1, 2]:</span>
+    <span class="c1">#         # Directed lagged links</span>
+    <span class="c1">#         link_assumptions[j] = {(var, -lag): &#39;-?&gt;&#39; for var in [0, 1, 2]</span>
+    <span class="c1">#                          for lag in range(1, tau_max + 1)}</span>
+    <span class="c1">#         # Unoriented contemporaneous links</span>
+    <span class="c1">#         link_assumptions[j].update({(var, 0): &#39;o?o&#39; for var in [0, 1, 2] if var != j})</span>
+    <span class="c1">#         # Directed lagged and contemporaneous links from the sun (3)</span>
+    <span class="c1">#         link_assumptions[j].update({(var, -lag): &#39;-?&gt;&#39; for var in [3]</span>
+    <span class="c1">#                          for lag in range(0, tau_max + 1)})</span>
+    <span class="c1">#     else:</span>
+    <span class="c1">#         link_assumptions[j] = {}</span>
+
+    <span class="c1"># for j in link_assumptions:</span>
+    <span class="c1">#     print(link_assumptions[j])</span>
     <span class="n">pcmci_parcorr</span> <span class="o">=</span> <span class="n">PCMCI</span><span class="p">(</span>
         <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> 
-        <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">parcorr</span><span class="p">,</span>
-        <span class="n">verbosity</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">results</span> <span class="o">=</span> <span class="n">pcmci_parcorr</span><span class="o">.</span><span class="n">run_pcmciplus</span><span class="p">(</span><span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> 
-                                      <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span><span class="p">)</span> <span class="c1">#, alpha_level = 0.01)</span>
+        <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">ci_test</span><span class="p">,</span>
+        <span class="n">verbosity</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">pcmci_parcorr</span><span class="o">.</span><span class="n">run_pcmciplus</span><span class="p">(</span><span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> 
+                    <span class="n">pc_alpha</span><span class="o">=</span><span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">],</span> 
+                    <span class="n">reset_lagged_links</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">link_assumptions</span><span class="o">=</span><span class="n">link_assumptions</span>
+                    <span class="p">)</span> <span class="c1">#, alpha_level = 0.01)</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">][:,</span><span class="mi">3</span><span class="p">,:])</span>
+    <span class="c1"># print(results[&#39;graph&#39;][:,3,:])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;p_matrix&#39;</span><span class="p">][:,:,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">][:,:,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">][:,:,</span><span class="mi">0</span><span class="p">])</span>
+
     <span class="c1"># Plot time series graph</span>
-    <span class="c1"># tp.plot_time_series_graph(</span>
+    <span class="c1"># tp.plot_graph(</span>
     <span class="c1">#     val_matrix=results[&#39;val_matrix&#39;],</span>
     <span class="c1">#     graph=results[&#39;graph&#39;],</span>
     <span class="c1">#     var_names=[r&#39;$X^0$&#39;, r&#39;$X^1$&#39;, r&#39;$X^2$&#39;, &#39;Sun&#39;],</span>
@@ -3922,8 +4127,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/plotting.html b/docs/_modules/tigramite/plotting.html
index 5e1b12f9..e7527d0a 100644
--- a/docs/_modules/tigramite/plotting.html
+++ b/docs/_modules/tigramite/plotting.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
     <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
    
@@ -1543,12 +1545,13 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
     <span class="c1"># link_edge_colorbar_label=&#39;link_edge&#39;,</span>
     <span class="n">inner_edge_curved</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
     <span class="n">inner_edge_style</span><span class="o">=</span><span class="s2">&quot;solid&quot;</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
+    <span class="c1"># network_lower_bound=0.2,</span>
     <span class="n">network_left_bound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
     <span class="n">show_colorbar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <span class="n">special_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
     <span class="n">autodep_sig_lags</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">show_autodependency_lags</span><span class="o">=</span><span class="kc">False</span>
+    <span class="n">show_autodependency_lags</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">transform</span><span class="o">=</span><span class="s1">&#39;data&#39;</span><span class="p">,</span>
 <span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to draw a network from networkx graph instance.</span>
 <span class="sd">    Various attributes are used to specify the graph&#39;s properties.</span>
@@ -1556,6 +1559,9 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
 <span class="sd">    customized.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
+    <span class="k">if</span> <span class="n">transform</span> <span class="o">==</span> <span class="s1">&#39;data&#39;</span><span class="p">:</span>
+        <span class="n">transform</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">transData</span>
+
     <span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">FancyArrowPatch</span><span class="p">,</span> <span class="n">Circle</span><span class="p">,</span> <span class="n">Ellipse</span>
 
     <span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
@@ -1715,6 +1721,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                 <span class="n">shrinkB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                 <span class="n">zorder</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
                 <span class="n">capstyle</span><span class="o">=</span><span class="s2">&quot;butt&quot;</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
             <span class="p">)</span>
             <span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">e_p</span><span class="p">)</span>
 
@@ -1736,6 +1743,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
               <span class="n">shrinkB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
               <span class="n">zorder</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
               <span class="n">capstyle</span><span class="o">=</span><span class="s2">&quot;butt&quot;</span><span class="p">,</span>
+              <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
             <span class="p">)</span>  
             <span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">e_p_back</span><span class="p">)</span>
 
@@ -1780,8 +1788,9 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                 <span class="n">patchB</span><span class="o">=</span><span class="n">n2</span><span class="p">,</span>
                 <span class="n">shrinkA</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                 <span class="n">shrinkB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">zorder</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                <span class="c1"># zorder=-1,</span>
                 <span class="n">capstyle</span><span class="o">=</span><span class="s2">&quot;butt&quot;</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
             <span class="p">)</span>
             <span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">e_p</span><span class="p">)</span>
 
@@ -1803,11 +1812,15 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                 <span class="n">shrinkB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                 <span class="n">zorder</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span>
                 <span class="n">capstyle</span><span class="o">=</span><span class="s2">&quot;butt&quot;</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
         <span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">e_p_marker</span><span class="p">)</span>
 
-        <span class="n">path</span> <span class="o">=</span> <span class="n">e_p_marker</span><span class="o">.</span><span class="n">get_path</span><span class="p">()</span>
-        <span class="n">vertices</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">vertices</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="c1"># marker_path = e_p_marker.get_path()</span>
+        <span class="n">vertices</span> <span class="o">=</span> <span class="n">e_p_marker</span><span class="o">.</span><span class="n">get_path</span><span class="p">()</span><span class="o">.</span><span class="n">vertices</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="c1"># vertices = e_p_marker.get_verts()</span>
+        <span class="c1"># vertices = e_p_marker.get_path().to_polygons(transform=None)[0]</span>
+        <span class="c1"># print(vertices.shape)</span>
         <span class="n">m</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="n">vertices</span><span class="o">.</span><span class="n">shape</span>
 
         <span class="c1"># print(vertices)</span>
@@ -1816,7 +1829,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
 
         <span class="c1"># This must be added to avoid rescaling of the plot, when no &#39;o&#39;</span>
         <span class="c1"># or &#39;x&#39; is added to the graph.</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">start</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">start</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,)</span>
 
         <span class="k">if</span> <span class="n">outer_edge</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;o-&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;o--&quot;</span><span class="p">]:</span>
@@ -1827,6 +1840,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-o&quot;</span><span class="p">:</span>
@@ -1837,6 +1851,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;--o&quot;</span><span class="p">:</span>
@@ -1847,6 +1862,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;x--&quot;</span><span class="p">,</span> <span class="s2">&quot;x-&gt;&quot;</span><span class="p">]:</span>
@@ -1857,6 +1873,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;+--&quot;</span><span class="p">,</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">]:</span>
@@ -1867,6 +1884,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-x&quot;</span><span class="p">:</span>
@@ -1877,6 +1895,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">:</span>
@@ -1887,6 +1906,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;--x&quot;</span><span class="p">:</span>
@@ -1897,6 +1917,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;o-o&quot;</span><span class="p">:</span>
@@ -1907,6 +1928,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
                 <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
@@ -1916,6 +1938,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;x-x&quot;</span><span class="p">:</span>
@@ -1926,6 +1949,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
                 <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
@@ -1935,6 +1959,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;o-x&quot;</span><span class="p">:</span>
@@ -1945,6 +1970,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
                 <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
@@ -1954,6 +1980,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;outer_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;x-o&quot;</span><span class="p">:</span>
@@ -1964,6 +1991,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
                 <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
@@ -1973,6 +2001,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
 
@@ -1985,6 +2014,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-o&quot;</span><span class="p">:</span>
@@ -1995,6 +2025,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;--o&quot;</span><span class="p">:</span>
@@ -2005,6 +2036,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;x--&quot;</span><span class="p">,</span> <span class="s2">&quot;x-&gt;&quot;</span><span class="p">]:</span>
@@ -2015,6 +2047,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;+--&quot;</span><span class="p">,</span> <span class="s2">&quot;+-&gt;&quot;</span><span class="p">]:</span>
@@ -2025,6 +2058,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-x&quot;</span><span class="p">:</span>
@@ -2035,6 +2069,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&lt;-+&quot;</span><span class="p">:</span>
@@ -2045,6 +2080,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;--x&quot;</span><span class="p">:</span>
@@ -2055,6 +2091,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;o-o&quot;</span><span class="p">:</span>
@@ -2065,6 +2102,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
                 <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
@@ -2074,6 +2112,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;x-x&quot;</span><span class="p">:</span>
@@ -2084,6 +2123,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
                 <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
@@ -2093,6 +2133,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;o-x&quot;</span><span class="p">:</span>
@@ -2103,6 +2144,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
                 <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
@@ -2112,6 +2154,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">d</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;inner_edge_type&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;x-o&quot;</span><span class="p">:</span>
@@ -2122,6 +2165,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_start</span><span class="p">)</span>
                 <span class="n">circle_marker_end</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
@@ -2131,16 +2175,42 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">edgecolor</span><span class="o">=</span><span class="n">facecolor</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">circle_marker_end</span><span class="p">)</span>
 
+
+
         <span class="k">if</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">outer_edge</span><span class="p">:</span>
+            <span class="k">def</span> <span class="nf">closest_node</span><span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span>
+                <span class="n">nodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span>
+                <span class="n">node</span> <span class="o">=</span> <span class="n">node</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+                <span class="n">dist_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">nodes</span> <span class="o">-</span> <span class="n">node</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">dist_2</span><span class="p">)</span>
+
             <span class="c1"># Attach labels of lags</span>
-            <span class="n">trans</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># patch.get_transform()</span>
-            <span class="n">path</span> <span class="o">=</span> <span class="n">e_p</span><span class="o">.</span><span class="n">get_path</span><span class="p">()</span>
-            <span class="n">verts</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">to_polygons</span><span class="p">(</span><span class="n">trans</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">verts</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="n">label_vert</span> <span class="o">=</span> <span class="n">verts</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span>
+            <span class="c1"># trans = None  # patch.get_transform()</span>
+            <span class="c1"># path = e_p.get_path()</span>
+            <span class="n">vertices</span> <span class="o">=</span> <span class="n">e_p_marker</span><span class="o">.</span><span class="n">get_path</span><span class="p">()</span><span class="o">.</span><span class="n">vertices</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">verts</span> <span class="o">=</span> <span class="n">e_p</span><span class="o">.</span><span class="n">get_path</span><span class="p">()</span><span class="o">.</span><span class="n">to_polygons</span><span class="p">(</span><span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="c1"># print(verts)</span>
+            <span class="c1"># print(verts.shape)</span>
+            <span class="c1"># print(vertices.shape)</span>
+            <span class="c1"># for num, vert in enumerate(verts):</span>
+            <span class="c1">#     ax.text(vert[0], vert[1], str(num), </span>
+            <span class="c1">#         transform=transform,)</span>
+            <span class="c1"># ax.scatter(verts[:,0], verts[:,1])</span>
+            <span class="c1"># mid_point = np.array([(start[0] + end[0])/2., (start[1] + end[1])/2.])</span>
+            <span class="c1"># print(start, end, mid_point)</span>
+            <span class="c1"># ax.scatter(mid_point[0], mid_point[1], marker=&#39;x&#39;, </span>
+            <span class="c1">#     s=100, zorder=10, transform=transform,)</span>
+            <span class="n">closest_node</span> <span class="o">=</span> <span class="n">closest_node</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span><span class="p">),:],</span> <span class="n">verts</span><span class="p">)</span>
+            <span class="c1"># print(closest_node, verts[closest_node])</span>
+            <span class="c1"># ax.scatter(verts[closest_node][0], verts[closest_node][1], marker=&#39;x&#39;)</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="c1"># label_vert = vertices[int(len(vertices)/2.),:] #verts[1, :]</span>
+                <span class="n">label_vert</span> <span class="o">=</span> <span class="n">verts</span><span class="p">[</span><span class="n">closest_node</span><span class="p">]</span> <span class="c1">#verts[1, :]</span>
                 <span class="n">l</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span>
                 <span class="n">string</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
                 <span class="n">txt</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
@@ -2152,6 +2222,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
                     <span class="n">color</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span>
                     <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="n">txt</span><span class="o">.</span><span class="n">set_path_effects</span><span class="p">(</span>
                     <span class="p">[</span><span class="n">PathEffects</span><span class="o">.</span><span class="n">withStroke</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">foreground</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)]</span>
@@ -2361,6 +2432,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                 <span class="n">facecolor</span><span class="o">=</span><span class="n">color_here</span><span class="p">,</span>
                 <span class="n">edgecolor</span><span class="o">=</span><span class="n">color_here</span><span class="p">,</span>
                 <span class="n">zorder</span><span class="o">=-</span><span class="n">ring</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
             <span class="p">)</span>
 
             <span class="c1"># else:</span>
@@ -2397,7 +2469,8 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                     <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
                     <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
                     <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-                    <span class="n">zorder</span><span class="o">=</span><span class="mf">5.</span>
+                    <span class="n">zorder</span><span class="o">=</span><span class="mf">5.</span><span class="p">,</span>
+                    <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                 <span class="p">)</span>
                 <span class="k">if</span> <span class="n">show_autodependency_lags</span><span class="p">:</span>
                     <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
@@ -2408,7 +2481,8 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
                         <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
                         <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
                         <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-                        <span class="n">zorder</span><span class="o">=</span><span class="mf">5.</span>
+                        <span class="n">zorder</span><span class="o">=</span><span class="mf">5.</span><span class="p">,</span>
+                        <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
                     <span class="p">)</span>
 
     <span class="c1"># Draw edges</span>
@@ -2461,7 +2535,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
     <span class="n">node_label_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
     <span class="n">link_label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
     <span class="n">lag_array</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
+    <span class="c1"># network_lower_bound=0.2,</span>
     <span class="n">show_colorbar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <span class="n">inner_edge_style</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span>
     <span class="n">link_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@@ -2506,7 +2580,9 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
 <span class="sd">    node_pos : dictionary, optional (default: None)</span>
 <span class="sd">        Dictionary of node positions in axis coordinates of form</span>
 <span class="sd">        node_pos = {&#39;x&#39;:array of shape (N,), &#39;y&#39;:array of shape(N)}. These</span>
-<span class="sd">        coordinates could have been transformed before for basemap plots.</span>
+<span class="sd">        coordinates could have been transformed before for basemap plots. You can</span>
+<span class="sd">        also add a key &#39;transform&#39;:ccrs.PlateCarree() in order to plot graphs on </span>
+<span class="sd">        a map using cartopy.</span>
 <span class="sd">    arrow_linewidth : float, optional (default: 30)</span>
 <span class="sd">        Linewidth.</span>
 <span class="sd">    vmin_edges : float, optional (default: -1)</span>
@@ -2545,8 +2621,6 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
 <span class="sd">        Fontsize of tick labels.</span>
 <span class="sd">    lag_array : array, optional (default: None)</span>
 <span class="sd">        Optional specification of lags overwriting np.arange(0, tau_max+1)</span>
-<span class="sd">    network_lower_bound : float, optional (default: 0.2)</span>
-<span class="sd">        Fraction of vertical space below graph plot.</span>
 <span class="sd">    show_colorbar : bool</span>
 <span class="sd">        Whether to show colorbars for links and nodes.</span>
 <span class="sd">    show_autodependency_lags : bool (default: False)</span>
@@ -2778,6 +2852,10 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
             <span class="n">pos</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">node_pos</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">node_pos</span><span class="p">[</span><span class="s2">&quot;y&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
 
+    <span class="k">if</span> <span class="n">node_pos</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="s1">&#39;transform&#39;</span> <span class="ow">in</span> <span class="n">node_pos</span><span class="p">:</span> 
+        <span class="n">transform</span> <span class="o">=</span> <span class="n">node_pos</span><span class="p">[</span><span class="s1">&#39;transform&#39;</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span> <span class="n">transform</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">transData</span>
+
     <span class="k">if</span> <span class="n">cmap_nodes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">node_color</span> <span class="o">=</span> <span class="kc">None</span>
 
@@ -2825,12 +2903,13 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="n">label_fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
         <span class="n">link_label_fontsize</span><span class="o">=</span><span class="n">link_label_fontsize</span><span class="p">,</span>
         <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span><span class="p">,</span>
+        <span class="c1"># network_lower_bound=network_lower_bound,</span>
         <span class="n">show_colorbar</span><span class="o">=</span><span class="n">show_colorbar</span><span class="p">,</span>
         <span class="c1"># label_fraction=label_fraction,</span>
         <span class="n">special_nodes</span><span class="o">=</span><span class="n">special_nodes</span><span class="p">,</span>
         <span class="n">autodep_sig_lags</span><span class="o">=</span><span class="n">autodep_sig_lags</span><span class="p">,</span>
-        <span class="n">show_autodependency_lags</span><span class="o">=</span><span class="n">show_autodependency_lags</span>
+        <span class="n">show_autodependency_lags</span><span class="o">=</span><span class="n">show_autodependency_lags</span><span class="p">,</span>
+        <span class="n">transform</span><span class="o">=</span><span class="n">transform</span>
     <span class="p">)</span>
 
     <span class="k">if</span> <span class="n">save_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
@@ -3021,9 +3100,6 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
     <span class="n">label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
     <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
     <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">label_space_left</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-    <span class="n">label_space_top</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
     <span class="n">inner_edge_style</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span>
     <span class="n">link_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
     <span class="n">special_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@@ -3088,12 +3164,6 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
 <span class="sd">        Fontsize of link labels.</span>
 <span class="sd">    tick_label_size : int, optional (default: 6)</span>
 <span class="sd">        Fontsize of tick labels.</span>
-<span class="sd">    label_space_left : float, optional (default: 0.1)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate left of plot for labels.</span>
-<span class="sd">    label_space_top : float, optional (default: 0.)</span>
-<span class="sd">        Fraction of vertical figure space to allocate top of plot for labels.</span>
-<span class="sd">    network_lower_bound : float, optional (default: 0.2)</span>
-<span class="sd">        Fraction of vertical space below graph plot.</span>
 <span class="sd">    inner_edge_style : string, optional (default: &#39;dashed&#39;)</span>
 <span class="sd">        Style of inner_edge contemporaneous links.</span>
 <span class="sd">    special_nodes : dict</span>
@@ -3338,8 +3408,8 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="n">label_fraction</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
         <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
         <span class="n">inner_edge_curved</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span><span class="p">,</span>
-        <span class="n">network_left_bound</span><span class="o">=</span><span class="n">label_space_left</span><span class="p">,</span>
+        <span class="c1"># network_lower_bound=network_lower_bound,</span>
+        <span class="c1"># network_left_bound=label_space_left,</span>
         <span class="n">inner_edge_style</span><span class="o">=</span><span class="n">inner_edge_style</span><span class="p">,</span>
         <span class="n">special_nodes</span><span class="o">=</span><span class="n">special_nodes</span><span class="p">,</span>
         <span class="n">show_colorbar</span><span class="o">=</span><span class="n">show_colorbar</span><span class="p">,</span>
@@ -3417,9 +3487,6 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
     <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
     <span class="n">node_label_size</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
     <span class="n">tick_label_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-    <span class="n">label_space_left</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-    <span class="n">label_space_top</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
     <span class="n">standard_color_links</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
     <span class="n">standard_color_nodes</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
 <span class="p">):</span>
@@ -3484,12 +3551,6 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
 <span class="sd">        Fontsize of node labels.</span>
 <span class="sd">    link_label_fontsize : int, optional (default: 6)</span>
 <span class="sd">        Fontsize of link labels.</span>
-<span class="sd">    label_space_left : float, optional (default: 0.1)</span>
-<span class="sd">        Fraction of horizontal figure space to allocate left of plot for labels.</span>
-<span class="sd">    label_space_top : float, optional (default: 0.)</span>
-<span class="sd">        Fraction of vertical figure space to allocate top of plot for labels.</span>
-<span class="sd">    network_lower_bound : float, optional (default: 0.2)</span>
-<span class="sd">        Fraction of vertical space below graph plot.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">path_node_array</span><span class="p">)</span>
     <span class="n">Nmaxlag</span> <span class="o">=</span> <span class="n">tsg_path_val_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
@@ -3658,7 +3719,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="n">label_fraction</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
         <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
         <span class="n">inner_edge_curved</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span>
+        <span class="c1"># network_lower_bound=network_lower_bound</span>
         <span class="c1"># inner_edge_style=inner_edge_style</span>
     <span class="p">)</span>
 
@@ -3666,7 +3727,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">transData</span><span class="p">)</span>
         <span class="c1"># trans = transforms.blended_transform_factory(fig.transFigure, ax.transData)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-            <span class="n">label_space_left</span><span class="p">,</span>
+            <span class="mf">0.</span><span class="p">,</span>
             <span class="n">pos</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
             <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]]),</span>
             <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
@@ -3681,7 +3742,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
             <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
                 <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label_space_top</span><span class="p">,</span>
+                <span class="mf">1.0</span><span class="p">,</span> <span class="c1"># - label_space_top,</span>
                 <span class="sa">r</span><span class="s2">&quot;$t$&quot;</span><span class="p">,</span>
                 <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
                 <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
@@ -3691,7 +3752,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
                 <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label_space_top</span><span class="p">,</span>
+                <span class="mf">1.0</span><span class="p">,</span> <span class="c1"># - label_space_top,</span>
                 <span class="sa">r</span><span class="s2">&quot;$t-</span><span class="si">%s</span><span class="s2">$&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
                 <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
                 <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
@@ -3737,7 +3798,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
     <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
     <span class="n">node_label_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
     <span class="n">link_label_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">network_lower_bound</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
+    <span class="c1"># network_lower_bound=0.2,</span>
     <span class="n">standard_color_links</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
     <span class="n">standard_color_nodes</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
 <span class="p">):</span>
@@ -3775,7 +3836,9 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
 <span class="sd">    node_pos : dictionary, optional (default: None)</span>
 <span class="sd">        Dictionary of node positions in axis coordinates of form</span>
 <span class="sd">        node_pos = {&#39;x&#39;:array of shape (N,), &#39;y&#39;:array of shape(N)}. These</span>
-<span class="sd">        coordinates could have been transformed before for basemap plots.</span>
+<span class="sd">        coordinates could have been transformed before for basemap plots. You can</span>
+<span class="sd">        also add a key &#39;transform&#39;:ccrs.PlateCarree() in order to plot graphs on </span>
+<span class="sd">        a map using cartopy.</span>
 <span class="sd">    arrow_linewidth : float, optional (default: 30)</span>
 <span class="sd">        Linewidth.</span>
 <span class="sd">    vmin_edges : float, optional (default: -1)</span>
@@ -3810,8 +3873,6 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
 <span class="sd">        Fontsize of node labels.</span>
 <span class="sd">    link_label_fontsize : int, optional (default: 6)</span>
 <span class="sd">        Fontsize of link labels.</span>
-<span class="sd">    network_lower_bound : float, optional (default: 0.2)</span>
-<span class="sd">        Fraction of vertical space below graph plot.</span>
 <span class="sd">    lag_array : array, optional (default: None)</span>
 <span class="sd">        Optional specification of lags overwriting np.arange(0, tau_max+1)</span>
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -3950,6 +4011,10 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
             <span class="n">pos</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">node_pos</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">node_pos</span><span class="p">[</span><span class="s2">&quot;y&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
 
+    <span class="k">if</span> <span class="n">node_pos</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="s1">&#39;transform&#39;</span> <span class="ow">in</span> <span class="n">node_pos</span><span class="p">:</span> 
+        <span class="n">transform</span> <span class="o">=</span> <span class="n">node_pos</span><span class="p">[</span><span class="s1">&#39;transform&#39;</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span> <span class="n">transform</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">transData</span>
+
     <span class="n">node_rings</span> <span class="o">=</span> <span class="p">{</span>
         <span class="mi">0</span><span class="p">:</span> <span class="p">{</span>
             <span class="s2">&quot;sizes&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
@@ -3993,9 +4058,10 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="n">label_fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
         <span class="n">link_label_fontsize</span><span class="o">=</span><span class="n">link_label_fontsize</span><span class="p">,</span>
         <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span><span class="p">,</span>
+        <span class="c1"># network_lower_bound=network_lower_bound,</span>
         <span class="c1"># label_fraction=label_fraction,</span>
         <span class="c1"># inner_edge_style=inner_edge_style</span>
+        <span class="n">transform</span><span class="o">=</span><span class="n">transform</span>
     <span class="p">)</span>
 
     <span class="c1"># fig.subplots_adjust(left=0.1, right=.9, bottom=.25, top=.95)</span>
@@ -4127,9 +4193,9 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
     <span class="n">label_fontsize</span> <span class="o">=</span> <span class="mi">10</span>
     <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span>
     <span class="n">node_label_size</span> <span class="o">=</span> <span class="mi">10</span>
-    <span class="n">label_space_left</span> <span class="o">=</span> <span class="mf">0.1</span>
-    <span class="n">label_space_top</span> <span class="o">=</span> <span class="mf">0.0</span>
-    <span class="n">network_lower_bound</span> <span class="o">=</span> <span class="mf">0.2</span>
+    <span class="c1"># label_space_left = 0.1</span>
+    <span class="c1"># label_space_top = 0.0</span>
+    <span class="c1"># network_lower_bound = 0.2</span>
     <span class="n">inner_edge_style</span> <span class="o">=</span> <span class="s2">&quot;dashed&quot;</span>
 
     <span class="n">node_color</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">)</span>  <span class="c1"># , dtype = &#39;object&#39;)</span>
@@ -4251,14 +4317,14 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="n">label_fraction</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
         <span class="n">link_colorbar_label</span><span class="o">=</span><span class="n">link_colorbar_label</span><span class="p">,</span>
         <span class="n">inner_edge_curved</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">network_lower_bound</span><span class="o">=</span><span class="n">network_lower_bound</span><span class="p">,</span>
+        <span class="c1"># network_lower_bound=network_lower_bound,</span>
         <span class="n">inner_edge_style</span><span class="o">=</span><span class="n">inner_edge_style</span><span class="p">,</span>
     <span class="p">)</span>
 
     <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
         <span class="n">trans</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">blended_transform_factory</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">transData</span><span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
-            <span class="n">label_space_left</span><span class="p">,</span>
+            <span class="mf">0.</span><span class="p">,</span>
             <span class="n">pos</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_lag</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
             <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">order</span><span class="p">[</span><span class="n">i</span><span class="p">]]),</span>
             <span class="n">fontsize</span><span class="o">=</span><span class="n">label_fontsize</span><span class="p">,</span>
@@ -4272,7 +4338,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">tau</span> <span class="o">==</span> <span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
             <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
                 <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label_space_top</span><span class="p">,</span>
+                <span class="mf">1.0</span><span class="p">,</span> <span class="c1">#- label_space_top,</span>
                 <span class="sa">r</span><span class="s2">&quot;$t$&quot;</span><span class="p">,</span>
                 <span class="n">fontsize</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">label_fontsize</span> <span class="o">*</span> <span class="mf">0.7</span><span class="p">),</span>
                 <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
@@ -4282,7 +4348,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
                 <span class="n">pos</span><span class="p">[</span><span class="n">tau</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label_space_top</span><span class="p">,</span>
+                <span class="mf">1.0</span><span class="p">,</span> <span class="c1"># - label_space_top,</span>
                 <span class="sa">r</span><span class="s2">&quot;$t-</span><span class="si">%s</span><span class="s2">$&quot;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">max_lag</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
                 <span class="n">fontsize</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">label_fontsize</span> <span class="o">*</span> <span class="mf">0.7</span><span class="p">),</span>
                 <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
@@ -4369,7 +4435,7 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">graph</span><span class="o">!=</span><span class="s1">&#39;&#39;</span><span class="p">)):</span>
             <span class="c1"># Only consider contemporaneous links once</span>
             <span class="k">if</span> <span class="n">tau</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&lt;=</span> <span class="n">j</span><span class="p">:</span>
-                <span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">tau</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">]]</span>
+                <span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="nb">str</span><span class="p">(</span><span class="n">var_names</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">tau</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">graph</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">]]</span>
                 <span class="k">if</span> <span class="n">val_matrix_exists</span><span class="p">:</span>
                     <span class="n">row</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">val_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">tau</span><span class="p">]</span><span class="si">:</span><span class="s2">.</span><span class="si">{</span><span class="n">digits</span><span class="si">}}</span><span class="s2">&quot;</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">link_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
@@ -4467,43 +4533,80 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
 
     <span class="c1"># Complete test case</span>
     <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
-    <span class="n">val_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">val_matrix</span> <span class="o">=</span> <span class="mf">0.</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
     <span class="n">val_matrix</span><span class="p">[:,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.2</span>
     <span class="n">graph</span><span class="p">[:]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-+&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;+-&gt;&quot;</span>
+    <span class="c1"># graph[0, 1, 0] = &quot;&lt;-+&quot;</span>
+    <span class="c1"># graph[1, 0, 0] = &quot;+-&gt;&quot;</span>
     <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
     <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;--&gt;&quot;</span>
 
     <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;+-&gt;&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;o-o&quot;</span>
+    <span class="c1"># graph[1, 0, 1] = &quot;o-o&quot;</span>
 
-    <span class="n">graph</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-&gt;&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&lt;-&gt;&quot;</span>
+    <span class="c1"># graph[1, 2, 0] = &quot;&lt;-&gt;&quot;</span>
+    <span class="c1"># graph[2, 1, 0] = &quot;&lt;-&gt;&quot;</span>
 
-    <span class="n">graph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;x-x&quot;</span>
-    <span class="n">graph</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;x-x&quot;</span>
+    <span class="c1"># graph[0, 2, 0] = &quot;x-x&quot;</span>
+    <span class="c1"># graph[2, 0, 0] = &quot;x-x&quot;</span>
     <span class="n">nolinks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
     <span class="c1"># nolinks[range(4), range(4), 1] = 1</span>
 
-    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="n">label_space_left</span> <span class="o">=</span> <span class="mf">0.2</span>
-    <span class="n">label_space_top</span> <span class="o">=</span> <span class="mf">0.</span>
-    <span class="n">network_lower_bound</span> <span class="o">=</span> <span class="mf">0.</span>
-    <span class="n">show_colorbar</span><span class="o">=</span><span class="kc">True</span>
-    <span class="n">plot_graph</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span>
-        <span class="c1"># fig_ax = (fig, axes),</span>
-        <span class="n">val_matrix</span><span class="o">=</span><span class="n">val_matrix</span><span class="p">,</span>
+    <span class="c1"># graph = graph[:2, :2, :]</span>
+
+    <span class="c1"># fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(6, 5))</span>
+
+
+    <span class="c1"># import cartopy.crs as ccrs</span>
+    <span class="n">graph</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U3&#39;</span><span class="p">)</span>
+    <span class="n">graph</span><span class="p">[:]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">graph</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;+-&gt;&#39;</span> 
+
+    <span class="c1"># fig = pyplot.figure(figsize=(8, 6))</span>
+    <span class="c1"># fig = pyplot.figure(figsize=(10, 5))</span>
+    <span class="c1"># ax = fig.add_subplot(1, 1, 1, projection=ccrs.Mollweide())</span>
+    <span class="c1"># make the map global rather than have it zoom in to</span>
+    <span class="c1"># the extents of any plotted data</span>
+    <span class="c1"># ax.set_global()</span>
+    <span class="c1"># ax.stock_img()</span>
+    <span class="c1"># ax.coastlines()</span>
+    <span class="c1"># # ymax = 1.</span>
+    <span class="c1"># node_pos = {&#39;x&#39;:np.linspace(0, ymax, graph.shape[0]), &#39;y&#39;:np.linspace(0, ymax, graph.shape[0]),}</span>
+    <span class="c1"># node_pos = {&#39;x&#39;:np.array([10,-20,80,-50,80]),</span>
+    <span class="c1">#             &#39;y&#39;:np.array([-10,70,60,-40,50]), </span>
+    <span class="c1">#         &#39;transform&#39;:ccrs.PlateCarree(), # t.PlateCarree()</span>
+    <span class="c1">#         }</span>
+
+    <span class="n">plot_time_series_graph</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span>
+        <span class="c1"># fig_ax = (fig, ax),</span>
+        <span class="c1"># val_matrix=val_matrix,</span>
         <span class="c1"># figsize=(5, 5),</span>
-        <span class="n">var_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Var </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">graph</span><span class="p">))],</span>
+        <span class="c1"># var_names = [&#39;Var %s&#39; %i for i in range(len(graph))],</span>
         <span class="c1"># arrow_linewidth=6,</span>
         <span class="c1"># label_space_left = label_space_left,</span>
         <span class="c1"># label_space_top = label_space_top,</span>
         <span class="c1"># # network_lower_bound=network_lower_bound,</span>
-        <span class="c1"># save_name=&quot;tsg_test.pdf&quot;</span>
+        <span class="n">save_name</span><span class="o">=</span><span class="s2">&quot;tsg_test.pdf&quot;</span>
         <span class="p">)</span>
+    <span class="n">pyplot</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
 
-    <span class="c1"># axes[0,0].scatter(np.random.randn(100), np.random.randn(100))</span>
+    <span class="c1"># network_lower_bound = 0.</span>
+    <span class="c1"># show_colorbar=True</span>
+    <span class="c1"># plot_graph(graph=graph,</span>
+    <span class="c1">#     fig_ax = (fig, ax),</span>
+    <span class="c1">#     node_pos = node_pos,</span>
+    <span class="c1">#     node_size = 20,</span>
+    <span class="c1">#     # val_matrix=val_matrix,</span>
+    <span class="c1">#     # figsize=(5, 5),</span>
+    <span class="c1">#     # var_names = [&#39;Var %s&#39; %i for i in range(len(graph))],</span>
+    <span class="c1">#     # arrow_linewidth=6,</span>
+    <span class="c1">#     # label_space_left = label_space_left,</span>
+    <span class="c1">#     # label_space_top = label_space_top,</span>
+    <span class="c1">#     # # network_lower_bound=network_lower_bound,</span>
+    <span class="c1">#     save_name=&quot;tsg_test.pdf&quot;</span>
+    <span class="c1">#     )</span>
+    <span class="c1"># pyplot.tight_layout()</span>
+    <span class="c1"># axes[0,0].scatter(np.random.rand(100), np.random.rand(100))</span>
 
     <span class="c1"># plot_graph(graph=graph,</span>
     <span class="c1">#     fig_ax = (fig, axes[0,0]),</span>
@@ -4511,9 +4614,8 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
     <span class="c1">#     # figsize=(5, 5),</span>
     <span class="c1">#     var_names = [&#39;Variable %s&#39; %i for i in range(len(graph))],</span>
     <span class="c1">#     arrow_linewidth=6,</span>
-    <span class="c1">#     label_space_left = label_space_left,</span>
-    <span class="c1">#     label_space_top = label_space_top,</span>
-    <span class="c1">#     network_lower_bound=network_lower_bound,</span>
+    <span class="c1">#     # label_space_left = label_space_left,</span>
+    <span class="c1">#     # label_space_top = label_space_top,</span>
     <span class="c1">#     # save_name=&quot;tsg_test.pdf&quot;</span>
     <span class="c1">#     )</span>
     <span class="c1"># plot_graph(graph=graph,</span>
@@ -4521,31 +4623,29 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
     <span class="c1">#     val_matrix=val_matrix,</span>
     <span class="c1">#     var_names = [&#39;Var %s&#39; %i for i in range(len(graph))],</span>
     <span class="c1">#     arrow_linewidth=6,</span>
-    <span class="c1">#     label_space_left = label_space_left,</span>
-    <span class="c1">#     label_space_top = label_space_top,</span>
-    <span class="c1">#     network_lower_bound=network_lower_bound,</span>
+    <span class="c1">#     # label_space_left = label_space_left,</span>
+    <span class="c1">#     # label_space_top = label_space_top,</span>
     <span class="c1">#     )</span>
     <span class="c1"># plot_graph(graph=graph,</span>
     <span class="c1">#     fig_ax = (fig, axes[1,0]),</span>
     <span class="c1">#     val_matrix=val_matrix,</span>
     <span class="c1">#     var_names = [&#39;Var %s&#39; %i for i in range(len(graph))],</span>
     <span class="c1">#     arrow_linewidth=6,</span>
-    <span class="c1">#     label_space_left = label_space_left,</span>
-    <span class="c1">#     label_space_top = label_space_top,</span>
-    <span class="c1">#     network_lower_bound=network_lower_bound,</span>
+    <span class="c1">#     # label_space_left = label_space_left,</span>
+    <span class="c1">#     # label_space_top = label_space_top,</span>
     <span class="c1">#     )</span>
     <span class="c1"># plot_graph(graph=graph,</span>
     <span class="c1">#     fig_ax = (fig, axes[1,1]),</span>
     <span class="c1">#     val_matrix=val_matrix,</span>
     <span class="c1">#     var_names = [&#39;Var %s&#39; %i for i in range(len(graph))],</span>
     <span class="c1">#     arrow_linewidth=6,</span>
-    <span class="c1">#     label_space_left = label_space_left,</span>
-    <span class="c1">#     label_space_top = label_space_top,</span>
-    <span class="c1">#     network_lower_bound=network_lower_bound,</span>
+    <span class="c1">#     n</span>
+    <span class="c1">#     # label_space_left = label_space_left,</span>
+    <span class="c1">#     # label_space_top = label_space_top,</span>
     <span class="c1">#     )</span>
-    <span class="c1"># pyplot.subplots_adjust(wspace=0.3, hspace=0.2)</span>
-    <span class="n">pyplot</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-    <span class="n">pyplot</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">&quot;test.pdf&quot;</span><span class="p">)</span>
+    <span class="c1"># # pyplot.subplots_adjust(wspace=0.3, hspace=0.2)</span>
+    <span class="c1"># pyplot.tight_layout()</span>
+    <span class="c1"># pyplot.savefig(&quot;test.pdf&quot;)</span>
 
     <span class="c1"># def lin_f(x): return x</span>
 
@@ -4560,10 +4660,10 @@ <h1>Source code for tigramite.plotting</h1><div class="highlight"><pre>
     <span class="c1"># val_matrix[:,:,0] = 0.</span>
     <span class="c1"># write_csv(graph=graph,</span>
     <span class="c1">#     val_matrix=val_matrix,</span>
-    <span class="c1">#     var_names=[&#39;s %d&#39; %i for i in range(graph.shape[0])],</span>
+    <span class="c1">#     var_names=[r&#39;$X^{%d}$&#39; %i for i in range(graph.shape[0])],</span>
     <span class="c1">#     link_width=np.ones(graph.shape),</span>
     <span class="c1">#     link_attribute = np.ones(graph.shape, dtype=&#39;&lt;U10&#39;),</span>
-    <span class="c1">#     save_name=&#39;test.cv&#39;)</span>
+    <span class="c1">#     save_name=&#39;test.csv&#39;)</span>
 
     <span class="c1"># # print(graph)</span>
     <span class="c1"># X = [(0,-1)]</span>
@@ -4627,8 +4727,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_modules/tigramite/rpcmci.html b/docs/_modules/tigramite/rpcmci.html
new file mode 100644
index 00000000..5c943291
--- /dev/null
+++ b/docs/_modules/tigramite/rpcmci.html
@@ -0,0 +1,565 @@
+
+<!DOCTYPE html>
+
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>tigramite.rpcmci &#8212; Tigramite 5.2 documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/alabaster.css" />
+    <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+    <script src="../../_static/jquery.js"></script>
+    <script src="../../_static/underscore.js"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
+    <script src="../../_static/doctools.js"></script>
+    <link rel="index" title="Index" href="../../genindex.html" />
+    <link rel="search" title="Search" href="../../search.html" />
+   
+  <link rel="stylesheet" href="../../_static/custom.css" type="text/css" />
+  
+  
+  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
+
+  </head><body>
+  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          
+
+          <div class="body" role="main">
+            
+  <h1>Source code for tigramite.rpcmci</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Tigramite causal discovery for time series.&quot;&quot;&quot;</span>
+
+<span class="c1"># Authors: Elena Saggioro, Sagar Simha, Matthias Bruhns, Jakob Runge &lt;jakob@jakob-runge.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: GNU General Public License v3.0</span>
+
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">sklearn</span>
+<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
+<span class="kn">from</span> <span class="nn">ortools.linear_solver</span> <span class="kn">import</span> <span class="n">pywraplp</span>
+<span class="kn">import</span> <span class="nn">traceback</span>
+
+<span class="kn">from</span> <span class="nn">tigramite.independence_tests.parcorr</span> <span class="kn">import</span> <span class="n">ParCorr</span>
+<span class="kn">from</span> <span class="nn">tigramite.data_processing</span> <span class="kn">import</span> <span class="n">DataFrame</span>
+<span class="kn">from</span> <span class="nn">tigramite.models</span> <span class="kn">import</span> <span class="n">Prediction</span>
+<span class="kn">from</span> <span class="nn">tigramite.pcmci</span> <span class="kn">import</span> <span class="n">PCMCI</span>
+
+<div class="viewcode-block" id="RPCMCI"><a class="viewcode-back" href="../../index.html#tigramite.rpcmci.RPCMCI">[docs]</a><span class="k">class</span> <span class="nc">RPCMCI</span><span class="p">(</span><span class="n">PCMCI</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;RPCMCI class for extracting causal regimes and the associated graphs from</span>
+<span class="sd">    time series data.</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    The Regime-PCMCI causal discovery method is described in: </span>
+
+<span class="sd">    Elena Saggioro, Jana de Wiljes, Marlene Kretschmer, Jakob Runge;</span>
+<span class="sd">    Reconstructing regime-dependent causal relationships from observational</span>
+<span class="sd">    time series. Chaos 1 November 2020; 30 (11): 113115.</span>
+<span class="sd">    https://doi.org/10.1063/5.0020538</span>
+
+<span class="sd">    The method iterates between two phases --a regime learning phase</span>
+<span class="sd">    (optimization-based) and a causal discovery phase (PCMCI)-- to identify</span>
+<span class="sd">    regime dependent causal relationships. A persistent discrete regime</span>
+<span class="sd">    variable is assumed that leads to a finite number of regimes within which</span>
+<span class="sd">    stationarity can be assumed.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    dataframe : data object</span>
+<span class="sd">        This is the Tigramite dataframe object. It has the attributes</span>
+<span class="sd">        dataframe.values yielding a numpy array of shape ( observations T,</span>
+<span class="sd">        variables N). For RPCMCI the mask will be ignored. You may use the</span>
+<span class="sd">        missing_flag to indicate missing values.</span>
+<span class="sd">    cond_ind_test : conditional independence test object</span>
+<span class="sd">        This can be ParCorr or other classes from</span>
+<span class="sd">        ``tigramite.independence_tests`` or an external test passed as a</span>
+<span class="sd">        callable. This test can be based on the class</span>
+<span class="sd">        tigramite.independence_tests.CondIndTest.</span>
+<span class="sd">    prediction_model : sklearn model object</span>
+<span class="sd">        For example, sklearn.linear_model.LinearRegression() for a linear</span>
+<span class="sd">        regression model. This should be consistent with cond_ind_test, ie, </span>
+<span class="sd">        use ParCorr() with a linear model and, eg, GPDC() with a </span>
+<span class="sd">        GaussianProcessRegressor model, or CMIknn with NearestNeighbors model.</span>
+<span class="sd">    seed : int</span>
+<span class="sd">        Random seed for annealing step.</span>
+<span class="sd">    verbosity : int, optional (default: -1)</span>
+<span class="sd">        Verbose levels -1, 0, 1, ...</span>
+<span class="sd">    </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">,</span> <span class="n">cond_ind_test</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
+                    <span class="n">prediction_model</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">=</span> <span class="n">verbosity</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">seed</span> <span class="o">=</span> <span class="n">seed</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">seed</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
+
+        <span class="c1"># Set prediction model to be used in optimization</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">prediction_model</span> <span class="o">=</span> <span class="n">prediction_model</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prediction_model</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">prediction_model</span> <span class="o">=</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">()</span>
+
+        <span class="c1"># Set conditional independence test</span>
+        <span class="k">if</span> <span class="n">cond_ind_test</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
+        <span class="n">cond_ind_test</span><span class="o">.</span><span class="n">set_mask_type</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">analysis_mode</span> <span class="o">!=</span> <span class="s1">&#39;single&#39;</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only single time series data allowed for RPCMCI.&quot;</span><span class="p">)</span>
+   
+        <span class="k">if</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">has_vector_data</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only scalar data allowed for RPCMCI.&quot;</span><span class="p">)</span>
+        
+               
+        <span class="c1"># Masking is not available in RPCMCI, but missing values can be specified</span>
+        <span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;bool&#39;</span><span class="p">)}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">missing_flag</span>
+
+        <span class="c1"># Init base class</span>
+        <span class="n">PCMCI</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span>
+                        <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">,</span>
+                        <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<div class="viewcode-block" id="RPCMCI.run_rpcmci"><a class="viewcode-back" href="../../index.html#tigramite.rpcmci.RPCMCI.run_rpcmci">[docs]</a>    <span class="k">def</span> <span class="nf">run_rpcmci</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                   <span class="n">num_regimes</span><span class="p">,</span>
+                   <span class="n">max_transitions</span><span class="p">,</span>
+                   <span class="n">switch_thres</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+                   <span class="n">num_iterations</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+                   <span class="n">max_anneal</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                   <span class="n">tau_min</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                   <span class="n">tau_max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                   <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
+                   <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
+                   <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                   <span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Run RPCMCI method for extracting causal regimes and the associated graphs from</span>
+<span class="sd">            time series data.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_regimes : int</span>
+<span class="sd">            Number of assumed regimes.</span>
+<span class="sd">        max_transitions : int</span>
+<span class="sd">            Maximum number of transitions within a single regime (persistency parameter).</span>
+<span class="sd">        switch_thres : float</span>
+<span class="sd">            Switch threshold.</span>
+<span class="sd">        num_iterations : int</span>
+<span class="sd">            Optimization iterations.</span>
+<span class="sd">        max_anneal : int</span>
+<span class="sd">            Maximum annealing runs.</span>
+<span class="sd">        tau_min : int, optional (default: 0)</span>
+<span class="sd">            Minimum time lag to test.</span>
+<span class="sd">        tau_max : int, optional (default: 1)</span>
+<span class="sd">            Maximum time lag. Must be larger or equal to tau_min.</span>
+<span class="sd">        pc_alpha : float, optional (default: 0.2)</span>
+<span class="sd">            Significance level in PCMCI.</span>
+<span class="sd">        alpha_level : float, optional (default: 0.05)</span>
+<span class="sd">            Significance level in PCMCI at which the p_matrix is thresholded to</span>
+<span class="sd">            get graph.</span>
+<span class="sd">        n_jobs : int, optional (default: -1)</span>
+<span class="sd">            Number of CPUs to use in joblib parallization. Default n_jobs=-1</span>
+<span class="sd">            uses all available.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        regimes : array of shape (n_regimes, T)</span>
+<span class="sd">            One-hot encoded regime variable.</span>
+<span class="sd">        causal_results: dictionary</span>
+<span class="sd">            Contains result of run_pcmci() after convergence.</span>
+<span class="sd">        diff_g_f : tuple</span>
+<span class="sd">            Difference between two consecutive optimizations for all annealings and</span>
+<span class="sd">            the optimal one with minimum objective value (see paper).</span>
+<span class="sd">        error_free_annealings : int</span>
+<span class="sd">            Number of annealings that converged without error.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">count_saved_ann</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1"># initialize residuals (objective value) of MIP optimize</span>
+        <span class="n">objmip_ann</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_anneal</span>
+        <span class="n">parents_ann</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_anneal</span>
+        <span class="n">causal_prediction</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_anneal</span>
+        <span class="n">links_ann</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_anneal</span>
+        <span class="n">gamma_ann</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_anneal</span>
+        <span class="n">diff_g_ann</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">max_anneal</span>
+        <span class="n">q_break_cycle</span> <span class="o">=</span> <span class="mi">5</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">def</span> <span class="nf">_pcmci</span><span class="p">(</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="p">,</span> <span class="n">alpha_level</span><span class="p">):</span>
+<span class="w">            </span><span class="sd">&quot;&quot;&quot;Wrapper around running PCMCI.&quot;&quot;&quot;</span>
+            <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pcmci</span><span class="p">(</span><span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="n">tau_max</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span> <span class="n">alpha_level</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">)</span>
+            <span class="n">graph</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;graph&#39;</span><span class="p">]</span> 
+            <span class="n">pcmci_parents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_parents_dict</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">graph</span><span class="p">,</span> <span class="n">val_matrix</span><span class="o">=</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;val_matrix&#39;</span><span class="p">])</span>
+            <span class="k">return</span> <span class="n">results</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">pcmci_parents</span>
+
+        <span class="k">def</span> <span class="nf">_optimize_gamma</span><span class="p">(</span><span class="n">resid_sq</span><span class="p">,</span> <span class="n">max_transitions</span><span class="p">):</span>
+<span class="w">            </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">            Solves the following optimization problem :</span>
+
+<span class="sd">                minimize       c * x</span>
+
+<span class="sd">            where c = resid_sq , flattened along num_regimes dimension</span>
+<span class="sd">                  x = Gamma , flattened along num_regimes dimension</span>
+
+<span class="sd">            with Constraints:</span>
+<span class="sd">                    (1) [\sum_{k=1,num_regimes}gamma^k(t) ]= 1</span>
+<span class="sd">                                            forall t    : uniqueness</span>
+<span class="sd">                    (2) [\sum_{t=1:T-1} | gamma^k(t+1) - gamma^k(t) | ] &lt;= max_transitions</span>
+<span class="sd">                                            forall k    : persistence</span>
+
+
+<span class="sd">            Inputs:</span>
+<span class="sd">             resid_sq ( np.shape = (num_regimes,T) )</span>
+<span class="sd">             max_transitions = max number of switchings allowed</span>
+
+<span class="sd">            Returns:</span>
+<span class="sd">            Gamma_updated ( np.shape = (num_regimes,T) ))</span>
+<span class="sd">            &quot;&quot;&quot;</span>
+
+            <span class="n">num_regimes</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">resid_sq</span><span class="o">.</span><span class="n">shape</span>
+
+            <span class="c1"># Create the linear solver with the GLOP backend.</span>
+            <span class="n">solver</span> <span class="o">=</span> <span class="n">pywraplp</span><span class="o">.</span><span class="n">Solver</span><span class="o">.</span><span class="n">CreateSolver</span><span class="p">(</span><span class="s2">&quot;GLOP&quot;</span><span class="p">)</span>
+            <span class="n">infinity</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">infinity</span><span class="p">()</span>
+
+            <span class="c1"># Define vector of integer variables in the interval [0,1].</span>
+            <span class="n">G</span> <span class="o">=</span> <span class="p">[</span><span class="n">solver</span><span class="o">.</span><span class="n">NumVar</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;x_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_regimes</span> <span class="o">*</span> <span class="n">T</span><span class="p">)]</span>
+
+            <span class="c1"># Define eta, auxiliary vars for constr. (2).</span>
+            <span class="n">E</span> <span class="o">=</span> <span class="p">[</span><span class="n">solver</span><span class="o">.</span><span class="n">NumVar</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">infinity</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;eta_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_regimes</span> <span class="o">*</span> <span class="n">T</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">G</span> <span class="o">+</span> <span class="n">E</span>
+            <span class="n">solver</span><span class="o">.</span><span class="n">Minimize</span><span class="p">(</span>
+                <span class="nb">sum</span><span class="p">([</span><span class="n">resid_sq</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">*</span> <span class="n">X</span><span class="p">[</span><span class="n">k</span> <span class="o">*</span> <span class="n">T</span> <span class="o">+</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_regimes</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">)])</span>
+            <span class="p">)</span>
+
+            <span class="n">con_lst</span> <span class="o">=</span> <span class="p">[</span><span class="nb">sum</span><span class="p">([</span><span class="n">X</span><span class="p">[</span><span class="n">k</span> <span class="o">*</span> <span class="n">T</span> <span class="o">+</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_regimes</span><span class="p">)])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">)]</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
+                <span class="n">solver</span><span class="o">.</span><span class="n">Add</span><span class="p">(</span><span class="n">con_lst</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_regimes</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+                    <span class="c1"># (2.1)</span>
+                    <span class="n">solver</span><span class="o">.</span><span class="n">Add</span><span class="p">(</span>
+                        <span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">k</span> <span class="o">*</span> <span class="n">T</span> <span class="o">+</span> <span class="n">t</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">X</span><span class="p">[</span><span class="n">k</span> <span class="o">*</span> <span class="n">T</span> <span class="o">+</span> <span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">X</span><span class="p">[</span><span class="n">k</span> <span class="o">*</span> <span class="n">T</span> <span class="o">+</span> <span class="n">t</span> <span class="o">+</span> <span class="n">num_regimes</span> <span class="o">*</span> <span class="n">T</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)</span>
+                    <span class="p">)</span>
+                    <span class="c1"># (2.2)</span>
+                    <span class="n">solver</span><span class="o">.</span><span class="n">Add</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="p">(</span>
+                                    <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">X</span><span class="p">[</span><span class="n">k</span> <span class="o">*</span> <span class="n">T</span> <span class="o">+</span> <span class="n">t</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+                                    <span class="o">+</span> <span class="n">X</span><span class="p">[</span><span class="n">k</span> <span class="o">*</span> <span class="n">T</span> <span class="o">+</span> <span class="n">t</span><span class="p">]</span>
+                                    <span class="o">-</span> <span class="n">X</span><span class="p">[</span><span class="n">k</span> <span class="o">*</span> <span class="n">T</span> <span class="o">+</span> <span class="n">t</span> <span class="o">+</span> <span class="n">num_regimes</span> <span class="o">*</span> <span class="n">T</span><span class="p">]</span>
+                                    <span class="o">&lt;=</span> <span class="mi">0</span>
+                            <span class="p">)</span>
+                        <span class="p">)</span>
+                    <span class="p">)</span>
+                    <span class="c1"># (2.3)</span>
+                    <span class="n">solver</span><span class="o">.</span><span class="n">Add</span><span class="p">(</span>
+                        <span class="p">((</span><span class="nb">sum</span><span class="p">([</span><span class="n">X</span><span class="p">[</span><span class="n">k</span> <span class="o">*</span> <span class="n">T</span> <span class="o">+</span> <span class="n">t</span> <span class="o">+</span> <span class="n">num_regimes</span> <span class="o">*</span> <span class="n">T</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span> <span class="o">&lt;=</span> <span class="n">max_transitions</span><span class="p">))</span>
+                    <span class="p">)</span>
+
+            <span class="n">status</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">Solve</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">status</span> <span class="o">==</span> <span class="n">pywraplp</span><span class="o">.</span><span class="n">Solver</span><span class="o">.</span><span class="n">OPTIMAL</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Optimal objective: reached.&quot;</span><span class="p">)</span>
+                <span class="n">gamma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">([</span><span class="n">g</span><span class="o">.</span><span class="n">solution_value</span><span class="p">()</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">G</span><span class="p">],</span> <span class="p">(</span><span class="n">num_regimes</span><span class="p">,</span> <span class="n">T</span><span class="p">))</span>
+                <span class="n">obj_value</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">Objective</span><span class="p">()</span><span class="o">.</span><span class="n">Value</span><span class="p">()</span>
+                <span class="k">return</span> <span class="n">gamma</span><span class="p">,</span> <span class="n">obj_value</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The problem does not have an optimal solution. Please change hyperparameters.&quot;</span><span class="p">)</span>
+                <span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">def</span> <span class="nf">one_annealing_step</span><span class="p">(</span><span class="n">a</span><span class="p">):</span>
+<span class="w">            </span><span class="sd">&quot;&quot;&quot;Executes one annealing step. The random seed is self.seed + a.&quot;&quot;&quot;</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">################# Annealing iteration a = </span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s2"> ####################</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+            <span class="n">T</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="c1"># Initialise gamma_0 as random matrix of 1s and 0s</span>
+            <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">seed</span> <span class="o">+</span> <span class="n">a</span><span class="p">)</span>
+            <span class="n">gamma_opt</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">num_regimes</span><span class="p">,</span> <span class="n">T</span><span class="p">))</span>  <span class="c1"># range is [0,1)!</span>
+
+            <span class="n">parents_opt</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># [None] * num_regimes</span>
+            <span class="n">results_opt</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># [None] * num_regimes</span>
+            <span class="n">links_opt</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># [None] * num_regimes</span>
+            <span class="n">objective_opt</span> <span class="o">=</span> <span class="mi">0</span>
+
+            <span class="c1"># Difference between two consecutive optimizations</span>
+            <span class="n">diff_g</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="c1">#</span>
+            <span class="c1"># Iteration over 1. causal discovery and 2. constrained optimization</span>
+            <span class="c1">#</span>
+            <span class="n">error_flag</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_iterations</span><span class="p">):</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">###### Optimization step q = </span><span class="si">{</span><span class="n">q</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+                <span class="c1"># Initialize to 0</span>
+                <span class="n">residuals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_regimes</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">))</span>
+
+                <span class="n">gamma_temp</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">gamma_opt</span><span class="p">)</span>
+
+                <span class="c1">#</span>
+                <span class="c1"># 1. Causal discovery and prediction</span>
+                <span class="c1"># </span>
+
+                <span class="c1"># Iterate over regimes</span>
+                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_regimes</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="mi">16</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="s1">&#39;#&#39;</span><span class="si">}</span><span class="s2"> Regime k = </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+                    <span class="c1"># Select sample according to gamma_opt, is a bool vector</span>
+                    <span class="n">selected_samples_k</span> <span class="o">=</span> <span class="p">(</span><span class="n">gamma_temp</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&gt;</span> <span class="n">switch_thres</span><span class="p">)</span>
+
+                    <span class="n">mask_of_k</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;bool&quot;</span><span class="p">)</span>
+                    <span class="n">mask_of_k</span><span class="p">[</span><span class="n">selected_samples_k</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+
+                    <span class="c1"># df_of_k = pp.DataFrame(data, mask=mask_of_k, missing_flag=self.missing_flag,</span>
+                    <span class="c1">#              var_names=self.var_names)</span>
+
+                    <span class="c1"># Change mask in dataframe for this step</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">mask</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">mask_of_k</span>
+
+                    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">((</span><span class="n">mask_of_k</span> <span class="o">==</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">5</span><span class="p">):</span>
+                        <span class="n">error_flag</span> <span class="o">=</span> <span class="kc">True</span>
+                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;*****Regime with too few samples in annealing a = </span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s2"> at iteration q = </span><span class="si">{</span><span class="n">q</span><span class="si">}</span><span class="s2">.*****</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;***** Break k-loop of regimes *****</span><span class="se">\n</span><span class="s2"> &quot;</span><span class="p">)</span>
+                        <span class="k">break</span>  <span class="c1"># from k-loop         </span>
+
+                    <span class="k">try</span><span class="p">:</span> 
+                        <span class="c1"># cond_ind_test = getattr(self, method)(**method_args)</span>
+                        <span class="c1"># pcmci = PCMCI(dataframe=df_of_k, </span>
+                        <span class="c1">#     cond_ind_test=self.cond_ind_test, </span>
+                        <span class="c1">#     verbosity=0)</span>
+                        <span class="n">results_temp</span><span class="p">,</span> <span class="n">link_temp</span><span class="p">,</span> <span class="n">parents_temp</span> <span class="o">=</span> <span class="n">_pcmci</span><span class="p">(</span>
+                                        <span class="c1"># pcmci,</span>
+                                                                        <span class="n">tau_max</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">tau_max</span><span class="p">),</span>
+                                                                        <span class="n">pc_alpha</span><span class="o">=</span><span class="n">pc_alpha</span><span class="p">,</span>
+                                                                        <span class="n">alpha_level</span><span class="o">=</span><span class="n">alpha_level</span><span class="p">,</span>
+                                                                        <span class="n">tau_min</span><span class="o">=</span><span class="n">tau_min</span><span class="p">,)</span>
+                    <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
+                        <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
+                        <span class="n">error_flag</span> <span class="o">=</span> <span class="kc">True</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;*****Value error in causal discovery for annealing a = </span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s2"> at iteration q = </span><span class="si">{</span><span class="n">q</span><span class="si">}</span><span class="s2">.*****</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;***** Break k-loop of regimes *****</span><span class="se">\n</span><span class="s2"> &quot;</span><span class="p">)</span>
+                        <span class="k">break</span>  <span class="c1"># from k-loop</span>
+
+                    <span class="n">parents_opt</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">parents_temp</span>
+                    <span class="n">results_opt</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">results_temp</span>
+                    <span class="n">links_opt</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">link_temp</span>
+
+                    <span class="k">try</span><span class="p">:</span> 
+                        <span class="c1"># Prediction with causal parents</span>
+                        <span class="n">pred</span> <span class="o">=</span> <span class="n">Prediction</span><span class="p">(</span>
+                            <span class="n">dataframe</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dataframe</span><span class="p">,</span>
+                            <span class="n">prediction_model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prediction_model</span><span class="p">,</span>
+                            <span class="n">data_transform</span><span class="o">=</span><span class="n">sklearn</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">StandardScaler</span><span class="p">(),</span>
+                            <span class="n">train_indices</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">),</span>
+                            <span class="n">test_indices</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">),</span>
+                            <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                        <span class="p">)</span>
+
+                        <span class="n">pred</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
+                            <span class="n">target_predictors</span><span class="o">=</span><span class="n">parents_temp</span><span class="p">,</span>
+                            <span class="n">selected_targets</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">),</span>
+                            <span class="n">tau_max</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">tau_max</span><span class="p">),</span>
+                        <span class="p">)</span> 
+                        <span class="c1"># print(parents_temp)</span>
+                        <span class="c1"># Compute the predicted residuals for each variable</span>
+                        <span class="n">predicted</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
+                            <span class="n">target</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)),</span> 
+                            <span class="n">new_data</span><span class="o">=</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">missing_flag</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">missing_flag</span><span class="p">)</span>
+                        <span class="p">)</span>
+
+                        <span class="n">original_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">predicted</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">target</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">):</span>
+                            <span class="c1"># print(data.shape, predicted.shape, original_data.shape, pred.get_test_array(target).shape, mask_of_k.sum(axis=0))</span>
+                            <span class="c1"># print(pred.get_test_array(target)[0].flatten().std())</span>
+                            <span class="n">original_data</span><span class="p">[:,</span> <span class="n">target</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">get_test_array</span><span class="p">(</span><span class="n">target</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+
+                    <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
+                        <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
+                        <span class="n">error_flag</span> <span class="o">=</span> <span class="kc">True</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;*****Value error in prediction for annealing a = </span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s2"> at iteration q = </span><span class="si">{</span><span class="n">q</span><span class="si">}</span><span class="s2">.*****</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;***** Break k-loop of regimes *****</span><span class="se">\n</span><span class="s2"> &quot;</span><span class="p">)</span>
+                        <span class="k">break</span>  <span class="c1"># from k-loop</span>
+
+
+                    <span class="c1"># Get residuals</span>
+                    <span class="n">residuals</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">tau_max</span><span class="p">):,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">original_data</span> <span class="o">-</span> <span class="n">predicted</span>
+                    <span class="c1"># print(np.abs(residuals[k, int(tau_max):, :]).mean(axis=0))</span>
+
+                <span class="k">if</span> <span class="n">error_flag</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;***** Break q-loop of optimization iterations for Annealing a = </span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s2"> at iteration q = </span><span class="si">{</span><span class="n">q</span><span class="si">}</span><span class="s2">.&quot;</span> 
+                            <span class="s2">&quot; Go to next annealing step. *****</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                    <span class="k">break</span>
+
+                <span class="c1">#</span>
+                <span class="c1"># 2. Regime optimization step with side constraints</span>
+                <span class="c1">#</span>
+
+                <span class="c1"># Comute the resid_sq</span>
+                <span class="n">res_sq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">residuals</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+                <span class="c1"># print(res_sq.shape)</span>
+
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="c1"># Optimization</span>
+                    <span class="n">gamma_opt</span><span class="p">,</span> <span class="n">objective_opt</span> <span class="o">=</span> <span class="n">_optimize_gamma</span><span class="p">(</span><span class="n">res_sq</span><span class="p">,</span> <span class="n">max_transitions</span><span class="p">)</span>
+
+                <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
+                    <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
+                    <span class="n">error_flag</span> <span class="o">=</span> <span class="kc">True</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;*****Value error in optimization for annealing a = </span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s2"> at iteration q = </span><span class="si">{</span><span class="n">q</span><span class="si">}</span><span class="s2">.*****</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                    <span class="k">break</span>  
+
+                <span class="n">diff_g</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">gamma_opt</span> <span class="o">-</span> <span class="n">gamma_temp</span><span class="p">)))</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Difference in abs value between the previous and current gamma &quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;(shape num_regimesxT) : </span><span class="si">{</span><span class="n">diff_g</span><span class="p">[</span><span class="n">q</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+                <span class="c1"># Break conditions</span>
+                <span class="k">if</span> <span class="n">diff_g</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Two consecutive gammas are equal: (local) minimum reached. &quot;</span>
+                            <span class="s2">&quot;Go to next annealing.</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                    <span class="k">break</span>
+
+                <span class="k">if</span> <span class="p">(</span><span class="n">q</span> <span class="o">&gt;=</span> <span class="n">q_break_cycle</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">diff_g</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">num_regimes</span> <span class="o">*</span> <span class="n">T</span> <span class="o">//</span> <span class="mi">100</span><span class="p">)):</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Iteration larger than </span><span class="si">{</span><span class="n">q_break_cycle</span><span class="si">}</span><span class="s2"> and two consecutive gammas are too similar. &quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;Go to next annealing.</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                    <span class="k">break</span>
+
+            <span class="k">if</span> <span class="n">error_flag</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbosity</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;*****Annealing a = </span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s2"> failed****</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+                <span class="k">return</span> <span class="kc">None</span>
+
+            <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">objective_opt</span><span class="p">,</span> <span class="n">parents_opt</span><span class="p">,</span> <span class="n">results_opt</span><span class="p">,</span> <span class="n">links_opt</span><span class="p">,</span> <span class="n">gamma_opt</span><span class="p">,</span> <span class="n">diff_g</span>
+
+        <span class="c1"># Parallelizing over annealing steps</span>
+        <span class="n">all_results</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
+            <span class="n">delayed</span><span class="p">(</span><span class="n">one_annealing_step</span><span class="p">)(</span><span class="n">a</span><span class="p">)</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_anneal</span><span class="p">))</span>
+
+        <span class="c1"># all_results = []</span>
+        <span class="c1"># for a in range(max_anneal):</span>
+        <span class="c1">#     all_results.append(one_annealing_step(a))</span>
+
+        <span class="n">error_free_annealings</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">result</span> <span class="ow">in</span> <span class="n">all_results</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">error_free_annealings</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">a</span><span class="p">,</span> <span class="n">objective_opt</span><span class="p">,</span> <span class="n">parents_opt</span><span class="p">,</span> <span class="n">results_opt</span><span class="p">,</span> <span class="n">links_opt</span><span class="p">,</span> <span class="n">gamma_opt</span><span class="p">,</span> <span class="n">diff_g</span> <span class="o">=</span> <span class="n">result</span>
+                
+                <span class="c1"># Save annealing results</span>
+                <span class="n">objmip_ann</span><span class="p">[</span><span class="n">a</span><span class="p">]</span> <span class="o">=</span> <span class="n">objective_opt</span>  
+                <span class="n">parents_ann</span><span class="p">[</span><span class="n">a</span><span class="p">]</span> <span class="o">=</span> <span class="n">parents_opt</span>  
+                <span class="n">causal_prediction</span><span class="p">[</span><span class="n">a</span><span class="p">]</span> <span class="o">=</span> <span class="n">results_opt</span>
+                <span class="n">links_ann</span><span class="p">[</span><span class="n">a</span><span class="p">]</span> <span class="o">=</span> <span class="n">links_opt</span>
+                <span class="n">gamma_ann</span><span class="p">[</span><span class="n">a</span><span class="p">]</span> <span class="o">=</span> <span class="n">gamma_opt</span> 
+                <span class="n">diff_g_ann</span><span class="p">[</span><span class="n">a</span><span class="p">]</span> <span class="o">=</span> <span class="n">diff_g</span> 
+
+        <span class="k">if</span> <span class="n">error_free_annealings</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No annealings have converged. Run failed.&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="kc">None</span>
+
+        <span class="c1"># If annealing values are larger than the default. </span>
+        <span class="c1"># Can happen for long time series and high dimensionality</span>
+        <span class="n">min_obj_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">([</span><span class="n">a</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">objmip_ann</span> <span class="k">if</span> <span class="n">a</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">])</span>
+        <span class="n">i_best</span> <span class="o">=</span> <span class="n">objmip_ann</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">min_obj_val</span><span class="p">)</span>
+
+        <span class="c1"># Final results based on best</span>
+        <span class="c1"># parents_f = parents_ann[i_best]</span>
+        <span class="n">results_f</span> <span class="o">=</span> <span class="n">causal_prediction</span><span class="p">[</span><span class="n">i_best</span><span class="p">]</span>
+        <span class="c1"># links_f = links_ann[i_best]</span>
+        <span class="n">gamma_f</span> <span class="o">=</span> <span class="n">gamma_ann</span><span class="p">[</span><span class="n">i_best</span><span class="p">]</span>
+        <span class="c1"># Convergence optimization</span>
+        <span class="n">diff_g_f</span> <span class="o">=</span> <span class="n">diff_g_ann</span><span class="p">,</span> <span class="n">diff_g_ann</span><span class="p">[</span><span class="n">i_best</span><span class="p">]</span>
+
+        <span class="n">final_results</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;regimes&#39;</span><span class="p">:</span> <span class="n">gamma_f</span><span class="p">,</span>
+                         <span class="s1">&#39;causal_results&#39;</span><span class="p">:</span><span class="n">results_f</span><span class="p">,</span>
+                         <span class="s1">&#39;diff_g_f&#39;</span><span class="p">:</span><span class="n">diff_g_f</span><span class="p">,</span>
+                         <span class="s1">&#39;error_free_annealings&#39;</span><span class="p">:</span><span class="n">error_free_annealings</span><span class="p">}</span>
+        
+        <span class="k">return</span> <span class="n">final_results</span></div></div>
+</pre></div>
+
+          </div>
+          
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+<h1 class="logo"><a href="../../index.html">Tigramite</a></h1>
+
+
+
+
+
+
+
+
+<h3>Navigation</h3>
+
+<div class="relations">
+<h3>Related Topics</h3>
+<ul>
+  <li><a href="../../index.html">Documentation overview</a><ul>
+  <li><a href="../index.html">Module code</a><ul>
+  </ul></li>
+  </ul></li>
+</ul>
+</div>
+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
+      <input type="submit" value="Go" />
+    </form>
+    </div>
+</div>
+<script>document.getElementById('searchbox').style.display = "block"</script>
+
+
+
+
+
+
+
+
+        </div>
+      </div>
+      <div class="clearer"></div>
+    </div>
+    <div class="footer">
+      &copy;2023, Jakob Runge.
+      
+      |
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
+      
+    </div>
+
+    
+
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/_modules/tigramite/toymodels/structural_causal_processes.html b/docs/_modules/tigramite/toymodels/structural_causal_processes.html
index 8ecaa4a2..76b23dc3 100644
--- a/docs/_modules/tigramite/toymodels/structural_causal_processes.html
+++ b/docs/_modules/tigramite/toymodels/structural_causal_processes.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
     <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
    
@@ -1233,8 +1235,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index 8227942e..6ed3fa3a 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -31,6 +31,7 @@ TIGRAMITE
 
 Tigramite is a causal time series analysis python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use these graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
 
+- Overview: Runge, J., Gerhardus, A., Varando, G. et al. Causal inference for time series. Nat Rev Earth Environ (2023). https://doi.org/10.1038/s43017-023-00431-y
 
 - PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019). https://advances.sciencemag.org/content/5/11/eaau4996
 
@@ -38,6 +39,8 @@ Tigramite is a causal time series analysis python package. It allows to efficien
 
 - LPCMCI: Gerhardus, A. & Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders Advances in Neural Information Processing Systems, 2020, 33. https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html
 
+- RPCMCI: Elena Saggioro, Jana de Wiljes, Marlene Kretschmer, Jakob Runge; Reconstructing regime-dependent causal relationships from observational time series. Chaos 1 November 2020; 30 (11): 113115. https://doi.org/10.1063/5.0020538
+
 - Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310. https://aip.scitation.org/doi/10.1063/1.5025050
 
 - Nature Communications Perspective paper: https://www.nature.com/articles/s41467-019-10105-3
@@ -60,6 +63,7 @@ Tigramite is a causal time series analysis python package. It allows to efficien
 
    tigramite.pcmci.PCMCI
    tigramite.lpcmci.LPCMCI
+   tigramite.rpcmci.RPCMCI
    tigramite.independence_tests.independence_tests_base.CondIndTest
    tigramite.independence_tests.parcorr.ParCorr
    tigramite.independence_tests.robust_parcorr.RobustParCorr
@@ -87,13 +91,17 @@ Tigramite is a causal time series analysis python package. It allows to efficien
 .. autoclass:: tigramite.pcmci.PCMCI
    :members:
 
-
 :mod:`tigramite.lpcmci`: LPCMCI
 ===========================================
 
 .. autoclass:: tigramite.lpcmci.LPCMCI
    :members:
 
+:mod:`tigramite.rpcmci`: RPCMCI
+===========================================
+
+.. autoclass:: tigramite.rpcmci.RPCMCI
+   :members:
 
 :mod:`tigramite.independence_tests`: Conditional independence tests
 =================================================================================
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diff --git a/docs/_static/alabaster.css b/docs/_static/alabaster.css
index 0eddaeb0..bc420a48 100644
--- a/docs/_static/alabaster.css
+++ b/docs/_static/alabaster.css
@@ -1,17 +1,33 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 @import url("basic.css");
 
 /* -- page layout ----------------------------------------------------------- */
 
 body {
-    font-family: Georgia, serif;
+    font-family: 'goudy old style', 'minion pro', 'bell mt', Georgia, 'Hiragino Mincho Pro', serif;
     font-size: 17px;
-    background-color: #fff;
+    background-color: white;
     color: #000;
     margin: 0;
     padding: 0;
 }
 
-
 div.document {
     width: 940px;
     margin: 30px auto 0 auto;
@@ -28,8 +44,6 @@ div.bodywrapper {
 
 div.sphinxsidebar {
     width: 220px;
-    font-size: 14px;
-    line-height: 1.5;
 }
 
 hr {
@@ -37,7 +51,7 @@ hr {
 }
 
 div.body {
-    background-color: #fff;
+    background-color: #ffffff;
     color: #3E4349;
     padding: 0 30px 0 30px;
 }
@@ -58,11 +72,6 @@ div.footer a {
     color: #888;
 }
 
-p.caption {
-    font-family: inherit;
-    font-size: inherit;
-}
-
 
 div.relations {
     display: none;
@@ -79,6 +88,11 @@ div.sphinxsidebar a:hover {
     border-bottom: 1px solid #999;
 }
 
+div.sphinxsidebar {
+    font-size: 14px;
+    line-height: 1.5;
+}
+
 div.sphinxsidebarwrapper {
     padding: 18px 10px;
 }
@@ -107,7 +121,7 @@ div.sphinxsidebarwrapper p.blurb {
 
 div.sphinxsidebar h3,
 div.sphinxsidebar h4 {
-    font-family: Georgia, serif;
+    font-family: 'Garamond', 'Georgia', serif;
     color: #444;
     font-size: 24px;
     font-weight: normal;
@@ -151,7 +165,7 @@ div.sphinxsidebar ul li.toctree-l2 > a {
 
 div.sphinxsidebar input {
     border: 1px solid #CCC;
-    font-family: Georgia, serif;
+    font-family: 'goudy old style', 'minion pro', 'bell mt', Georgia, 'Hiragino Mincho Pro', serif;
     font-size: 1em;
 }
 
@@ -166,19 +180,6 @@ div.sphinxsidebar hr {
     width: 50%;
 }
 
-div.sphinxsidebar .badge {
-    border-bottom: none;
-}
-
-div.sphinxsidebar .badge:hover {
-    border-bottom: none;
-}
-
-/* To address an issue with donation coming after search */
-div.sphinxsidebar h3.donation {
-    margin-top: 10px;
-}
-
 /* -- body styles ----------------------------------------------------------- */
 
 a {
@@ -197,7 +198,7 @@ div.body h3,
 div.body h4,
 div.body h5,
 div.body h6 {
-    font-family: Georgia, serif;
+    font-family: 'Garamond', 'Georgia', serif;
     font-weight: normal;
     margin: 30px 0px 10px 0px;
     padding: 0;
@@ -228,17 +229,21 @@ div.body p, div.body dd, div.body li {
 div.admonition {
     margin: 20px 0px;
     padding: 10px 30px;
-    background-color: #EEE;
-    border: 1px solid #CCC;
+    background-color: #FCC;
+    border: 1px solid #FAA;
 }
 
-div.admonition tt.xref, div.admonition code.xref, div.admonition a tt {
-    background-color: #FBFBFB;
+div.admonition tt.xref, div.admonition a tt {
     border-bottom: 1px solid #fafafa;
 }
 
+dd div.admonition {
+    margin-left: -60px;
+    padding-left: 60px;
+}
+
 div.admonition p.admonition-title {
-    font-family: Georgia, serif;
+    font-family: 'Garamond', 'Georgia', serif;
     font-weight: normal;
     font-size: 24px;
     margin: 0 0 10px 0;
@@ -251,71 +256,25 @@ div.admonition p.last {
 }
 
 div.highlight {
-    background-color: #fff;
+    background-color: white;
 }
 
 dt:target, .highlight {
     background: #FAF3E8;
 }
 
-div.warning {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-}
-
-div.danger {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-    -moz-box-shadow: 2px 2px 4px #D52C2C;
-    -webkit-box-shadow: 2px 2px 4px #D52C2C;
-    box-shadow: 2px 2px 4px #D52C2C;
-}
-
-div.error {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-    -moz-box-shadow: 2px 2px 4px #D52C2C;
-    -webkit-box-shadow: 2px 2px 4px #D52C2C;
-    box-shadow: 2px 2px 4px #D52C2C;
-}
-
-div.caution {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-}
-
-div.attention {
-    background-color: #FCC;
-    border: 1px solid #FAA;
-}
-
-div.important {
-    background-color: #EEE;
-    border: 1px solid #CCC;
-}
-
 div.note {
     background-color: #EEE;
     border: 1px solid #CCC;
 }
 
-div.tip {
-    background-color: #EEE;
-    border: 1px solid #CCC;
-}
-
-div.hint {
-    background-color: #EEE;
-    border: 1px solid #CCC;
-}
-
 div.seealso {
     background-color: #EEE;
     border: 1px solid #CCC;
 }
 
 div.topic {
-    background-color: #EEE;
+    background-color: #eee;
 }
 
 p.admonition-title {
@@ -327,7 +286,7 @@ p.admonition-title:after {
 }
 
 pre, tt, code {
-    font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+    font-family: 'Consolas', 'Menlo', 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
     font-size: 0.9em;
 }
 
@@ -350,16 +309,16 @@ tt.descname, code.descname {
 }
 
 img.screenshot {
-    -moz-box-shadow: 2px 2px 4px #EEE;
-    -webkit-box-shadow: 2px 2px 4px #EEE;
-    box-shadow: 2px 2px 4px #EEE;
+    -moz-box-shadow: 2px 2px 4px #eee;
+    -webkit-box-shadow: 2px 2px 4px #eee;
+    box-shadow: 2px 2px 4px #eee;
 }
 
 table.docutils {
     border: 1px solid #888;
-    -moz-box-shadow: 2px 2px 4px #EEE;
-    -webkit-box-shadow: 2px 2px 4px #EEE;
-    box-shadow: 2px 2px 4px #EEE;
+    -moz-box-shadow: 2px 2px 4px #eee;
+    -webkit-box-shadow: 2px 2px 4px #eee;
+    box-shadow: 2px 2px 4px #eee;
 }
 
 table.docutils td, table.docutils th {
@@ -399,18 +358,8 @@ table.field-list p {
     margin-bottom: 0.8em;
 }
 
-/* Cloned from
- * https://github.com/sphinx-doc/sphinx/commit/ef60dbfce09286b20b7385333d63a60321784e68
- */
-.field-name {
-    -moz-hyphens: manual;
-    -ms-hyphens: manual;
-    -webkit-hyphens: manual;
-    hyphens: manual;
-}
-
 table.footnote td.label {
-    width: .1px;
+    width: 0px;
     padding: 0.3em 0 0.3em 0.5em;
 }
 
@@ -433,7 +382,6 @@ blockquote {
 }
 
 ul, ol {
-    /* Matches the 30px from the narrow-screen "li > ul" selector below */
     margin: 10px 0 10px 30px;
     padding: 0;
 }
@@ -445,15 +393,16 @@ pre {
     line-height: 1.3em;
 }
 
-div.viewcode-block:target {
-    background: #ffd;
-}
-
 dl pre, blockquote pre, li pre {
     margin-left: 0;
     padding-left: 30px;
 }
 
+dl dl pre {
+    margin-left: -90px;
+    padding-left: 90px;
+}
+
 tt, code {
     background-color: #ecf0f3;
     color: #222;
@@ -462,7 +411,7 @@ tt, code {
 
 tt.xref, code.xref, a tt {
     background-color: #FBFBFB;
-    border-bottom: 1px solid #fff;
+    border-bottom: 1px solid white;
 }
 
 a.reference {
@@ -470,11 +419,6 @@ a.reference {
     border-bottom: 1px dotted #004B6B;
 }
 
-/* Don't put an underline on images */
-a.image-reference, a.image-reference:hover {
-    border-bottom: none;
-}
-
 a.reference:hover {
     border-bottom: 1px solid #6D4100;
 }
@@ -524,11 +468,6 @@ a:hover tt, a:hover code {
     	margin-left: 0;
     }
 
-	li > ul {
-        /* Matches the 30px from the "ul, ol" selector above */
-		margin-left: 30px;
-	}
-
     .document {
     	width: auto;
     }
@@ -564,7 +503,7 @@ a:hover tt, a:hover code {
 
     div.documentwrapper {
         float: none;
-        background: #fff;
+        background: white;
     }
 
     div.sphinxsidebar {
@@ -579,7 +518,7 @@ a:hover tt, a:hover code {
 
     div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p,
     div.sphinxsidebar h3 a {
-        color: #fff;
+        color: white;
     }
 
     div.sphinxsidebar a {
@@ -651,51 +590,4 @@ table.docutils.citation, table.docutils.citation td, table.docutils.citation th
   -moz-box-shadow: none;
   -webkit-box-shadow: none;
   box-shadow: none;
-}
-
-
-/* relbar */
-
-.related {
-    line-height: 30px;
-    width: 100%;
-    font-size: 0.9rem;
-}
-
-.related.top {
-    border-bottom: 1px solid #EEE;
-    margin-bottom: 20px;
-}
-
-.related.bottom {
-    border-top: 1px solid #EEE;
-}
-
-.related ul {
-    padding: 0;
-    margin: 0;
-    list-style: none;
-}
-
-.related li {
-    display: inline;
-}
-
-nav#rellinks {
-    float: right;
-}
-
-nav#rellinks li+li:before {
-    content: "|";
-}
-
-nav#breadcrumbs li+li:before {
-    content: "\00BB";
-}
-
-/* Hide certain items when printing */
-@media print {
-    div.related {
-        display: none;
-    }
 }
\ No newline at end of file
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index 08896771..dc88b5a2 100644
--- a/docs/_static/basic.css
+++ b/docs/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -15,12 +15,6 @@ div.clearer {
     clear: both;
 }
 
-div.section::after {
-    display: block;
-    content: '';
-    clear: left;
-}
-
 /* -- relbar ---------------------------------------------------------------- */
 
 div.related {
@@ -87,26 +81,10 @@ div.sphinxsidebar input {
     font-size: 1em;
 }
 
-div.sphinxsidebar #searchbox form.search {
-    overflow: hidden;
-}
-
 div.sphinxsidebar #searchbox input[type="text"] {
-    float: left;
-    width: 80%;
-    padding: 0.25em;
-    box-sizing: border-box;
+    width: 170px;
 }
 
-div.sphinxsidebar #searchbox input[type="submit"] {
-    float: left;
-    width: 20%;
-    border-left: none;
-    padding: 0.25em;
-    box-sizing: border-box;
-}
-
-
 img {
     border: 0;
     max-width: 100%;
@@ -130,7 +108,7 @@ ul.search li a {
     font-weight: bold;
 }
 
-ul.search li p.context {
+ul.search li div.context {
     color: #888;
     margin: 2px 0 0 30px;
     text-align: left;
@@ -221,11 +199,6 @@ table.modindextable td {
 
 /* -- general body styles --------------------------------------------------- */
 
-div.body {
-    min-width: 360px;
-    max-width: 800px;
-}
-
 div.body p, div.body dd, div.body li, div.body blockquote {
     -moz-hyphens: auto;
     -ms-hyphens: auto;
@@ -267,25 +240,19 @@ p.rubric {
     font-weight: bold;
 }
 
-img.align-left, figure.align-left, .figure.align-left, object.align-left {
+img.align-left, .figure.align-left, object.align-left {
     clear: left;
     float: left;
     margin-right: 1em;
 }
 
-img.align-right, figure.align-right, .figure.align-right, object.align-right {
+img.align-right, .figure.align-right, object.align-right {
     clear: right;
     float: right;
     margin-left: 1em;
 }
 
-img.align-center, figure.align-center, .figure.align-center, object.align-center {
-  display: block;
-  margin-left: auto;
-  margin-right: auto;
-}
-
-img.align-default, figure.align-default, .figure.align-default {
+img.align-center, .figure.align-center, object.align-center {
   display: block;
   margin-left: auto;
   margin-right: auto;
@@ -299,45 +266,30 @@ img.align-default, figure.align-default, .figure.align-default {
     text-align: center;
 }
 
-.align-default {
-    text-align: center;
-}
-
 .align-right {
     text-align: right;
 }
 
 /* -- sidebars -------------------------------------------------------------- */
 
-div.sidebar,
-aside.sidebar {
+div.sidebar {
     margin: 0 0 0.5em 1em;
     border: 1px solid #ddb;
-    padding: 7px;
+    padding: 7px 7px 0 7px;
     background-color: #ffe;
     width: 40%;
     float: right;
-    clear: right;
-    overflow-x: auto;
 }
 
 p.sidebar-title {
     font-weight: bold;
 }
-nav.contents,
-aside.topic,
-
-div.admonition, div.topic, blockquote {
-    clear: left;
-}
 
 /* -- topics ---------------------------------------------------------------- */
-nav.contents,
-aside.topic,
 
 div.topic {
     border: 1px solid #ccc;
-    padding: 7px;
+    padding: 7px 7px 0 7px;
     margin: 10px 0 10px 0;
 }
 
@@ -359,6 +311,10 @@ div.admonition dt {
     font-weight: bold;
 }
 
+div.admonition dl {
+    margin-bottom: 0;
+}
+
 p.admonition-title {
     margin: 0px 10px 5px 0px;
     font-weight: bold;
@@ -369,50 +325,13 @@ div.body p.centered {
     margin-top: 25px;
 }
 
-/* -- content of sidebars/topics/admonitions -------------------------------- */
-
-div.sidebar > :last-child,
-aside.sidebar > :last-child,
-nav.contents > :last-child,
-aside.topic > :last-child,
-
-div.topic > :last-child,
-div.admonition > :last-child {
-    margin-bottom: 0;
-}
-
-div.sidebar::after,
-aside.sidebar::after,
-nav.contents::after,
-aside.topic::after,
-
-div.topic::after,
-div.admonition::after,
-blockquote::after {
-    display: block;
-    content: '';
-    clear: both;
-}
-
 /* -- tables ---------------------------------------------------------------- */
 
 table.docutils {
-    margin-top: 10px;
-    margin-bottom: 10px;
     border: 0;
     border-collapse: collapse;
 }
 
-table.align-center {
-    margin-left: auto;
-    margin-right: auto;
-}
-
-table.align-default {
-    margin-left: auto;
-    margin-right: auto;
-}
-
 table caption span.caption-number {
     font-style: italic;
 }
@@ -428,6 +347,10 @@ table.docutils td, table.docutils th {
     border-bottom: 1px solid #aaa;
 }
 
+table.footnote td, table.footnote th {
+    border: 0 !important;
+}
+
 th {
     text-align: left;
     padding-right: 5px;
@@ -442,34 +365,22 @@ table.citation td {
     border-bottom: none;
 }
 
-th > :first-child,
-td > :first-child {
-    margin-top: 0px;
-}
-
-th > :last-child,
-td > :last-child {
-    margin-bottom: 0px;
-}
-
 /* -- figures --------------------------------------------------------------- */
 
-div.figure, figure {
+div.figure {
     margin: 0.5em;
     padding: 0.5em;
 }
 
-div.figure p.caption, figcaption {
+div.figure p.caption {
     padding: 0.3em;
 }
 
-div.figure p.caption span.caption-number,
-figcaption span.caption-number {
+div.figure p.caption span.caption-number {
     font-style: italic;
 }
 
-div.figure p.caption span.caption-text,
-figcaption span.caption-text {
+div.figure p.caption span.caption-text {
 }
 
 /* -- field list styles ----------------------------------------------------- */
@@ -487,81 +398,6 @@ table.field-list td, table.field-list th {
     margin: 0;
 }
 
-.field-name {
-    -moz-hyphens: manual;
-    -ms-hyphens: manual;
-    -webkit-hyphens: manual;
-    hyphens: manual;
-}
-
-/* -- hlist styles ---------------------------------------------------------- */
-
-table.hlist {
-    margin: 1em 0;
-}
-
-table.hlist td {
-    vertical-align: top;
-}
-
-/* -- object description styles --------------------------------------------- */
-
-.sig {
-	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
-}
-
-.sig-name, code.descname {
-    background-color: transparent;
-    font-weight: bold;
-}
-
-.sig-name {
-	font-size: 1.1em;
-}
-
-code.descname {
-    font-size: 1.2em;
-}
-
-.sig-prename, code.descclassname {
-    background-color: transparent;
-}
-
-.optional {
-    font-size: 1.3em;
-}
-
-.sig-paren {
-    font-size: larger;
-}
-
-.sig-param.n {
-	font-style: italic;
-}
-
-/* C++ specific styling */
-
-.sig-inline.c-texpr,
-.sig-inline.cpp-texpr {
-	font-family: unset;
-}
-
-.sig.c   .k, .sig.c   .kt,
-.sig.cpp .k, .sig.cpp .kt {
-	color: #0033B3;
-}
-
-.sig.c   .m,
-.sig.cpp .m {
-	color: #1750EB;
-}
-
-.sig.c   .s, .sig.c   .sc,
-.sig.cpp .s, .sig.cpp .sc {
-	color: #067D17;
-}
-
-
 /* -- other body styles ----------------------------------------------------- */
 
 ol.arabic {
@@ -584,106 +420,11 @@ ol.upperroman {
     list-style: upper-roman;
 }
 
-:not(li) > ol > li:first-child > :first-child,
-:not(li) > ul > li:first-child > :first-child {
-    margin-top: 0px;
-}
-
-:not(li) > ol > li:last-child > :last-child,
-:not(li) > ul > li:last-child > :last-child {
-    margin-bottom: 0px;
-}
-
-ol.simple ol p,
-ol.simple ul p,
-ul.simple ol p,
-ul.simple ul p {
-    margin-top: 0;
-}
-
-ol.simple > li:not(:first-child) > p,
-ul.simple > li:not(:first-child) > p {
-    margin-top: 0;
-}
-
-ol.simple p,
-ul.simple p {
-    margin-bottom: 0;
-}
-
-/* Docutils 0.17 and older (footnotes & citations) */
-dl.footnote > dt,
-dl.citation > dt {
-    float: left;
-    margin-right: 0.5em;
-}
-
-dl.footnote > dd,
-dl.citation > dd {
-    margin-bottom: 0em;
-}
-
-dl.footnote > dd:after,
-dl.citation > dd:after {
-    content: "";
-    clear: both;
-}
-
-/* Docutils 0.18+ (footnotes & citations) */
-aside.footnote > span,
-div.citation > span {
-    float: left;
-}
-aside.footnote > span:last-of-type,
-div.citation > span:last-of-type {
-  padding-right: 0.5em;
-}
-aside.footnote > p {
-  margin-left: 2em;
-}
-div.citation > p {
-  margin-left: 4em;
-}
-aside.footnote > p:last-of-type,
-div.citation > p:last-of-type {
-    margin-bottom: 0em;
-}
-aside.footnote > p:last-of-type:after,
-div.citation > p:last-of-type:after {
-    content: "";
-    clear: both;
-}
-
-/* Footnotes & citations ends */
-
-dl.field-list {
-    display: grid;
-    grid-template-columns: fit-content(30%) auto;
-}
-
-dl.field-list > dt {
-    font-weight: bold;
-    word-break: break-word;
-    padding-left: 0.5em;
-    padding-right: 5px;
-}
-
-dl.field-list > dt:after {
-    content: ":";
-}
-
-dl.field-list > dd {
-    padding-left: 0.5em;
-    margin-top: 0em;
-    margin-left: 0em;
-    margin-bottom: 0em;
-}
-
 dl {
     margin-bottom: 15px;
 }
 
-dd > :first-child {
+dd p {
     margin-top: 0px;
 }
 
@@ -697,24 +438,23 @@ dd {
     margin-left: 30px;
 }
 
-dl > dd:last-child,
-dl > dd:last-child > :last-child {
-    margin-bottom: 0;
-}
-
-dt:target, span.highlighted {
+dt:target, .highlighted {
     background-color: #fbe54e;
 }
 
-rect.highlighted {
-    fill: #fbe54e;
-}
-
 dl.glossary dt {
     font-weight: bold;
     font-size: 1.1em;
 }
 
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
 .versionmodified {
     font-style: italic;
 }
@@ -753,13 +493,6 @@ dl.glossary dt {
     font-style: oblique;
 }
 
-.classifier:before {
-    font-style: normal;
-    margin: 0 0.5em;
-    content: ":";
-    display: inline-block;
-}
-
 abbr, acronym {
     border-bottom: dotted 1px;
     cursor: help;
@@ -772,69 +505,29 @@ pre {
     overflow-y: hidden;  /* fixes display issues on Chrome browsers */
 }
 
-pre, div[class*="highlight-"] {
-    clear: both;
-}
-
 span.pre {
     -moz-hyphens: none;
     -ms-hyphens: none;
     -webkit-hyphens: none;
     hyphens: none;
-    white-space: nowrap;
-}
-
-div[class*="highlight-"] {
-    margin: 1em 0;
 }
 
 td.linenos pre {
+    padding: 5px 0px;
     border: 0;
     background-color: transparent;
     color: #aaa;
 }
 
 table.highlighttable {
-    display: block;
-}
-
-table.highlighttable tbody {
-    display: block;
-}
-
-table.highlighttable tr {
-    display: flex;
+    margin-left: 0.5em;
 }
 
 table.highlighttable td {
-    margin: 0;
-    padding: 0;
-}
-
-table.highlighttable td.linenos {
-    padding-right: 0.5em;
-}
-
-table.highlighttable td.code {
-    flex: 1;
-    overflow: hidden;
-}
-
-.highlight .hll {
-    display: block;
-}
-
-div.highlight pre,
-table.highlighttable pre {
-    margin: 0;
-}
-
-div.code-block-caption + div {
-    margin-top: 0;
+    padding: 0 0.5em 0 0.5em;
 }
 
 div.code-block-caption {
-    margin-top: 1em;
     padding: 2px 5px;
     font-size: small;
 }
@@ -843,14 +536,8 @@ div.code-block-caption code {
     background-color: transparent;
 }
 
-table.highlighttable td.linenos,
-span.linenos,
-div.highlight span.gp {  /* gp: Generic.Prompt */
-  user-select: none;
-  -webkit-user-select: text; /* Safari fallback only */
-  -webkit-user-select: none; /* Chrome/Safari */
-  -moz-user-select: none; /* Firefox */
-  -ms-user-select: none; /* IE10+ */
+div.code-block-caption + div > div.highlight > pre {
+    margin-top: 0;
 }
 
 div.code-block-caption span.caption-number {
@@ -862,7 +549,21 @@ div.code-block-caption span.caption-text {
 }
 
 div.literal-block-wrapper {
-    margin: 1em 0;
+    padding: 1em 1em 0;
+}
+
+div.literal-block-wrapper div.highlight {
+    margin: 0;
+}
+
+code.descname {
+    background-color: transparent;
+    font-weight: bold;
+    font-size: 1.2em;
+}
+
+code.descclassname {
+    background-color: transparent;
 }
 
 code.xref, a code {
@@ -903,7 +604,8 @@ span.eqno {
 }
 
 span.eqno a.headerlink {
-    position: absolute;
+    position: relative;
+    left: 0px;
     z-index: 1;
 }
 
diff --git a/docs/_static/comment-bright.png b/docs/_static/comment-bright.png
new file mode 100644
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diff --git a/docs/_static/comment.png b/docs/_static/comment.png
new file mode 100644
index 0000000000000000000000000000000000000000..dfbc0cbd512bdeefcb1984c99d8e577efb77f006
GIT binary patch
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diff --git a/docs/_static/contents.png b/docs/_static/contents.png
new file mode 100644
index 0000000000000000000000000000000000000000..6c59aa1f9c8c3b754b258b8ab4f6b95971c99109
GIT binary patch
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diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
index c3db08d1..56549772 100644
--- a/docs/_static/doctools.js
+++ b/docs/_static/doctools.js
@@ -2,263 +2,286 @@
  * doctools.js
  * ~~~~~~~~~~~
  *
- * Base JavaScript utilities for all Sphinx HTML documentation.
+ * Sphinx JavaScript utilities for all documentation.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
-"use strict";
 
-const _ready = (callback) => {
-  if (document.readyState !== "loading") {
-    callback();
-  } else {
-    document.addEventListener("DOMContentLoaded", callback);
-  }
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+/**
+ * make the code below compatible with browsers without
+ * an installed firebug like debugger
+if (!window.console || !console.firebug) {
+  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
+    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
+    "profile", "profileEnd"];
+  window.console = {};
+  for (var i = 0; i < names.length; ++i)
+    window.console[names[i]] = function() {};
+}
+ */
+
+/**
+ * small helper function to urldecode strings
+ */
+jQuery.urldecode = function(x) {
+  return decodeURIComponent(x).replace(/\+/g, ' ');
 };
 
 /**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
+ * small helper function to urlencode strings
  */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
+jQuery.urlencode = encodeURIComponent;
 
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+  if (typeof s == 'undefined')
+    s = document.location.search;
+  var parts = s.substr(s.indexOf('?') + 1).split('&');
+  var result = {};
+  for (var i = 0; i < parts.length; i++) {
+    var tmp = parts[i].split('=', 2);
+    var key = jQuery.urldecode(tmp[0]);
+    var value = jQuery.urldecode(tmp[1]);
+    if (key in result)
+      result[key].push(value);
+    else
+      result[key] = [value];
+  }
+  return result;
+};
 
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+  function highlight(node) {
+    if (node.nodeType == 3) {
+      var val = node.nodeValue;
+      var pos = val.toLowerCase().indexOf(text);
+      if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) {
+        var span = document.createElement("span");
+        span.className = className;
+        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
           document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
+          node.nextSibling));
+        node.nodeValue = val.substr(0, pos);
       }
     }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+    else if (!jQuery(node).is("button, select, textarea")) {
+      jQuery.each(node.childNodes, function() {
+        highlight(this);
+      });
+    }
   }
+  return this.each(function() {
+    highlight(this);
+  });
 };
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+  jQuery.uaMatch = function(ua) {
+    ua = ua.toLowerCase();
+
+    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+      /(msie) ([\w.]+)/.exec(ua) ||
+      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+      [];
+
+    return {
+      browser: match[ 1 ] || "",
+      version: match[ 2 ] || "0"
+    };
+  };
+  jQuery.browser = {};
+  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
 
 /**
  * Small JavaScript module for the documentation.
  */
-const Documentation = {
-  init: () => {
-    Documentation.highlightSearchWords();
-    Documentation.initDomainIndexTable();
-    Documentation.initOnKeyListeners();
+var Documentation = {
+
+  init : function() {
+    this.fixFirefoxAnchorBug();
+    this.highlightSearchWords();
+    this.initIndexTable();
+    
   },
 
   /**
    * i18n support
    */
-  TRANSLATIONS: {},
-  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
-  LOCALE: "unknown",
+  TRANSLATIONS : {},
+  PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; },
+  LOCALE : 'unknown',
 
   // gettext and ngettext don't access this so that the functions
   // can safely bound to a different name (_ = Documentation.gettext)
-  gettext: (string) => {
-    const translated = Documentation.TRANSLATIONS[string];
-    switch (typeof translated) {
-      case "undefined":
-        return string; // no translation
-      case "string":
-        return translated; // translation exists
-      default:
-        return translated[0]; // (singular, plural) translation tuple exists
-    }
+  gettext : function(string) {
+    var translated = Documentation.TRANSLATIONS[string];
+    if (typeof translated == 'undefined')
+      return string;
+    return (typeof translated == 'string') ? translated : translated[0];
   },
 
-  ngettext: (singular, plural, n) => {
-    const translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated !== "undefined")
-      return translated[Documentation.PLURAL_EXPR(n)];
-    return n === 1 ? singular : plural;
+  ngettext : function(singular, plural, n) {
+    var translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated == 'undefined')
+      return (n == 1) ? singular : plural;
+    return translated[Documentation.PLURALEXPR(n)];
   },
 
-  addTranslations: (catalog) => {
-    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
-    Documentation.PLURAL_EXPR = new Function(
-      "n",
-      `return (${catalog.plural_expr})`
-    );
-    Documentation.LOCALE = catalog.locale;
+  addTranslations : function(catalog) {
+    for (var key in catalog.messages)
+      this.TRANSLATIONS[key] = catalog.messages[key];
+    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
+    this.LOCALE = catalog.locale;
   },
 
   /**
-   * highlight the search words provided in the url in the text
+   * add context elements like header anchor links
    */
-  highlightSearchWords: () => {
-    const highlight =
-      new URLSearchParams(window.location.search).get("highlight") || "";
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:Documentation.hideSearchWords()">' +
-            Documentation.gettext("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
+  addContextElements : function() {
+    $('div[id] > :header:first').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this headline')).
+      appendTo(this);
+    });
+    $('dt[id]').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this definition')).
+      appendTo(this);
+    });
   },
 
   /**
-   * helper function to hide the search marks again
+   * workaround a firefox stupidity
+   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
    */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    const url = new URL(window.location);
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
+  fixFirefoxAnchorBug : function() {
+    if (document.location.hash)
+      window.setTimeout(function() {
+        document.location.href += '';
+      }, 10);
   },
 
   /**
-   * helper function to focus on search bar
+   * highlight the search words provided in the url in the text
    */
-  focusSearchBar: () => {
-    document.querySelectorAll("input[name=q]")[0]?.focus();
+  highlightSearchWords : function() {
+    var params = $.getQueryParameters();
+    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
+    if (terms.length) {
+      var body = $('div.body');
+      if (!body.length) {
+        body = $('body');
+      }
+      window.setTimeout(function() {
+        $.each(terms, function() {
+          body.highlightText(this.toLowerCase(), 'highlighted');
+        });
+      }, 10);
+      $('<p class="highlight-link"><a href="javascript:Documentation.' +
+        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
+          .appendTo($('#searchbox'));
+    }
   },
 
   /**
-   * Initialise the domain index toggle buttons
+   * init the domain index toggle buttons
    */
-  initDomainIndexTable: () => {
-    const toggler = (el) => {
-      const idNumber = el.id.substr(7);
-      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
-      if (el.src.substr(-9) === "minus.png") {
-        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
-        toggledRows.forEach((el) => (el.style.display = "none"));
-      } else {
-        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
-        toggledRows.forEach((el) => (el.style.display = ""));
-      }
-    };
-
-    const togglerElements = document.querySelectorAll("img.toggler");
-    togglerElements.forEach((el) =>
-      el.addEventListener("click", (event) => toggler(event.currentTarget))
-    );
-    togglerElements.forEach((el) => (el.style.display = ""));
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  initIndexTable : function() {
+    var togglers = $('img.toggler').click(function() {
+      var src = $(this).attr('src');
+      var idnum = $(this).attr('id').substr(7);
+      $('tr.cg-' + idnum).toggle();
+      if (src.substr(-9) == 'minus.png')
+        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
+      else
+        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
+    }).css('display', '');
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
+        togglers.click();
+    }
   },
 
-  initOnKeyListeners: () => {
-    // only install a listener if it is really needed
-    if (
-      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
-      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
-    )
-      return;
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords : function() {
+    $('#searchbox .highlight-link').fadeOut(300);
+    $('span.highlighted').removeClass('highlighted');
+  },
 
-    const blacklistedElements = new Set([
-      "TEXTAREA",
-      "INPUT",
-      "SELECT",
-      "BUTTON",
-    ]);
-    document.addEventListener("keydown", (event) => {
-      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
-      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
+  /**
+   * make the url absolute
+   */
+  makeURL : function(relativeURL) {
+    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
+  },
 
-      if (!event.shiftKey) {
-        switch (event.key) {
-          case "ArrowLeft":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+  /**
+   * get the current relative url
+   */
+  getCurrentURL : function() {
+    var path = document.location.pathname;
+    var parts = path.split(/\//);
+    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
+      if (this == '..')
+        parts.pop();
+    });
+    var url = parts.join('/');
+    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+  },
 
-            const prevLink = document.querySelector('link[rel="prev"]');
-            if (prevLink && prevLink.href) {
-              window.location.href = prevLink.href;
-              event.preventDefault();
+  initOnKeyListeners: function() {
+    $(document).keyup(function(event) {
+      var activeElementType = document.activeElement.tagName;
+      // don't navigate when in search box or textarea
+      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
+        switch (event.keyCode) {
+          case 37: // left
+            var prevHref = $('link[rel="prev"]').prop('href');
+            if (prevHref) {
+              window.location.href = prevHref;
+              return false;
             }
-            break;
-          case "ArrowRight":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const nextLink = document.querySelector('link[rel="next"]');
-            if (nextLink && nextLink.href) {
-              window.location.href = nextLink.href;
-              event.preventDefault();
+          case 39: // right
+            var nextHref = $('link[rel="next"]').prop('href');
+            if (nextHref) {
+              window.location.href = nextHref;
+              return false;
             }
-            break;
-          case "Escape":
-            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-            Documentation.hideSearchWords();
-            event.preventDefault();
         }
       }
-
-      // some keyboard layouts may need Shift to get /
-      switch (event.key) {
-        case "/":
-          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-          Documentation.focusSearchBar();
-          event.preventDefault();
-      }
     });
-  },
+  }
 };
 
 // quick alias for translations
-const _ = Documentation.gettext;
+_ = Documentation.gettext;
 
-_ready(Documentation.init);
+$(document).ready(function() {
+  Documentation.init();
+});
\ No newline at end of file
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index b5bf05a2..8a5f4b08 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -1,14 +1,9 @@
 var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '5.2',
-    LANGUAGE: 'en',
+    URL_ROOT: '',
+    VERSION: '4.0',
+    LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
-    BUILDER: 'html',
     FILE_SUFFIX: '.html',
-    LINK_SUFFIX: '.html',
     HAS_SOURCE: true,
-    SOURCELINK_SUFFIX: '.txt',
-    NAVIGATION_WITH_KEYS: false,
-    SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: false,
+    SOURCELINK_SUFFIX: '.txt'
 };
\ No newline at end of file
diff --git a/docs/_static/down-pressed.png b/docs/_static/down-pressed.png
new file mode 100644
index 0000000000000000000000000000000000000000..5756c8cad8854722893dc70b9eb4bb0400343a39
GIT binary patch
literal 222
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z%OS_REiHONoJ6{+Ks@6k3590|7k9F+ddB6!zw3#&!aw#S`x}3V3&=A(a#84O-&F7T
z^k3tZB;&iR9siw0|F|E|DAL<8r-F4!1H-;1{e*~yAKZN5f0|Ei6yUmR#Is)EM(Po_
zi`qJR6|P<~+)N+kSDgL7AjdIC_!O7Q?eGb+L+qOjm{~LLinM4NHn7U%HcK%uoMYO5
VJ~8zD2B3o(JYD@<);T3K0RV0%P>BEl

literal 0
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diff --git a/docs/_static/down.png b/docs/_static/down.png
new file mode 100644
index 0000000000000000000000000000000000000000..1b3bdad2ceffae91cee61b32f3295f9bbe646e48
GIT binary patch
literal 202
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zx!yQ<9v8bmJwa`TQk7YSw}WVQ()mRdQ;TC;*<T*?<?OIJF4)K~*`+^EdvW@{fRyJ(
z?weB&PpGe(dw+-5<oNH&JW|0z@kgId=L!~9O|3`?{k1Umt465Dp9i1pCaQ@{=WpF|
zklotk)=&P-c9F*_Q}bpRPq*XX{4Fk5IYl~|@#V*s^(ig4#DI=t@O1TaS?83{1OQnY
BO{f3>

literal 0
HcmV?d00001

diff --git a/docs/_static/jquery-3.1.0.js b/docs/_static/jquery-3.1.0.js
new file mode 100644
index 00000000..f2fc2747
--- /dev/null
+++ b/docs/_static/jquery-3.1.0.js
@@ -0,0 +1,10074 @@
+/*eslint-disable no-unused-vars*/
+/*!
+ * jQuery JavaScript Library v3.1.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright jQuery Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2016-07-07T21:44Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var document = window.document;
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var concat = arr.concat;
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+
+
+	function DOMEval( code, doc ) {
+		doc = doc || document;
+
+		var script = doc.createElement( "script" );
+
+		script.text = code;
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+/* global Symbol */
+// Defining this global in .eslintrc would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.1.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	},
+
+	// Support: Android <=4.0 only
+	// Make sure we trim BOM and NBSP
+	rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,
+
+	// Matches dashed string for camelizing
+	rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g,
+
+	// Used by jQuery.camelCase as callback to replace()
+	fcamelCase = function( all, letter ) {
+		return letter.toUpperCase();
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+		return num != null ?
+
+			// Return just the one element from the set
+			( num < 0 ? this[ num + this.length ] : this[ num ] ) :
+
+			// Return all the elements in a clean array
+			slice.call( this );
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !jQuery.isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				src = target[ name ];
+				copy = options[ name ];
+
+				// Prevent never-ending loop
+				if ( target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = jQuery.isArray( copy ) ) ) ) {
+
+					if ( copyIsArray ) {
+						copyIsArray = false;
+						clone = src && jQuery.isArray( src ) ? src : [];
+
+					} else {
+						clone = src && jQuery.isPlainObject( src ) ? src : {};
+					}
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isFunction: function( obj ) {
+		return jQuery.type( obj ) === "function";
+	},
+
+	isArray: Array.isArray,
+
+	isWindow: function( obj ) {
+		return obj != null && obj === obj.window;
+	},
+
+	isNumeric: function( obj ) {
+
+		// As of jQuery 3.0, isNumeric is limited to
+		// strings and numbers (primitives or objects)
+		// that can be coerced to finite numbers (gh-2662)
+		var type = jQuery.type( obj );
+		return ( type === "number" || type === "string" ) &&
+
+			// parseFloat NaNs numeric-cast false positives ("")
+			// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+			// subtraction forces infinities to NaN
+			!isNaN( obj - parseFloat( obj ) );
+	},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+
+		/* eslint-disable no-unused-vars */
+		// See https://github.com/eslint/eslint/issues/6125
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	type: function( obj ) {
+		if ( obj == null ) {
+			return obj + "";
+		}
+
+		// Support: Android <=2.3 only (functionish RegExp)
+		return typeof obj === "object" || typeof obj === "function" ?
+			class2type[ toString.call( obj ) ] || "object" :
+			typeof obj;
+	},
+
+	// Evaluates a script in a global context
+	globalEval: function( code ) {
+		DOMEval( code );
+	},
+
+	// Convert dashed to camelCase; used by the css and data modules
+	// Support: IE <=9 - 11, Edge 12 - 13
+	// Microsoft forgot to hump their vendor prefix (#9572)
+	camelCase: function( string ) {
+		return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+	},
+
+	nodeName: function( elem, name ) {
+		return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// Support: Android <=4.0 only
+	trim: function( text ) {
+		return text == null ?
+			"" :
+			( text + "" ).replace( rtrim, "" );
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return concat.apply( [], ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// Bind a function to a context, optionally partially applying any
+	// arguments.
+	proxy: function( fn, context ) {
+		var tmp, args, proxy;
+
+		if ( typeof context === "string" ) {
+			tmp = fn[ context ];
+			context = fn;
+			fn = tmp;
+		}
+
+		// Quick check to determine if target is callable, in the spec
+		// this throws a TypeError, but we will just return undefined.
+		if ( !jQuery.isFunction( fn ) ) {
+			return undefined;
+		}
+
+		// Simulated bind
+		args = slice.call( arguments, 2 );
+		proxy = function() {
+			return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+		};
+
+		// Set the guid of unique handler to the same of original handler, so it can be removed
+		proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+		return proxy;
+	},
+
+	now: Date.now,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = jQuery.type( obj );
+
+	if ( type === "function" || jQuery.isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.0
+ * https://sizzlejs.com/
+ *
+ * Copyright jQuery Foundation and other contributors
+ * Released under the MIT license
+ * http://jquery.org/license
+ *
+ * Date: 2016-01-04
+ */
+(function( window ) {
+
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ({}).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	push_native = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[i] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
+	identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+		// "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
+		"*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+
+	rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
+			"*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
+			"*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" +
+			whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
+	funescape = function( _, escaped, escapedWhitespace ) {
+		var high = "0x" + escaped - 0x10000;
+		// NaN means non-codepoint
+		// Support: Firefox<24
+		// Workaround erroneous numeric interpretation of +"0x"
+		return high !== high || escapedWhitespace ?
+			escaped :
+			high < 0 ?
+				// BMP codepoint
+				String.fromCharCode( high + 0x10000 ) :
+				// Supplemental Plane codepoint (surrogate pair)
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\x80-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	disabledAncestor = addCombinator(
+		function( elem ) {
+			return elem.disabled === true;
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		(arr = slice.call( preferredDoc.childNodes )),
+		preferredDoc.childNodes
+	);
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			push_native.apply( target, slice.call(els) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+			// Can't trust NodeList.length
+			while ( (target[j++] = els[i++]) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+
+		if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
+			setDocument( context );
+		}
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) {
+
+				// ID selector
+				if ( (m = match[1]) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( (elem = context.getElementById( m )) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && (elem = newContext.getElementById( m )) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[2] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( (m = match[3]) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!compilerCache[ selector + " " ] &&
+				(!rbuggyQSA || !rbuggyQSA.test( selector )) ) {
+
+				if ( nodeType !== 1 ) {
+					newContext = context;
+					newSelector = selector;
+
+				// qSA looks outside Element context, which is not what we want
+				// Thanks to Andrew Dupont for this workaround technique
+				// Support: IE <=8
+				// Exclude object elements
+				} else if ( context.nodeName.toLowerCase() !== "object" ) {
+
+					// Capture the context ID, setting it first if necessary
+					if ( (nid = context.getAttribute( "id" )) ) {
+						nid = nid.replace( rcssescape, fcssescape );
+					} else {
+						context.setAttribute( "id", (nid = expando) );
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[i] = "#" + nid + " " + toSelector( groups[i] );
+					}
+					newSelector = groups.join( "," );
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+				}
+
+				if ( newSelector ) {
+					try {
+						push.apply( results,
+							newContext.querySelectorAll( newSelector )
+						);
+						return results;
+					} catch ( qsaError ) {
+					} finally {
+						if ( nid === expando ) {
+							context.removeAttribute( "id" );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return (cache[ key + " " ] = value);
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement("fieldset");
+
+	try {
+		return !!fn( el );
+	} catch (e) {
+		return false;
+	} finally {
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split("|"),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[i] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( (cur = cur.nextSibling) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return (name === "input" || name === "button") && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+	// Known :disabled false positives:
+	// IE: *[disabled]:not(button, input, select, textarea, optgroup, option, menuitem, fieldset)
+	// not IE: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Check form elements and option elements for explicit disabling
+		return "label" in elem && elem.disabled === disabled ||
+			"form" in elem && elem.disabled === disabled ||
+
+			// Check non-disabled form elements for fieldset[disabled] ancestors
+			"form" in elem && elem.disabled === false && (
+				// Support: IE6-11+
+				// Ancestry is covered for us
+				elem.isDisabled === disabled ||
+
+				// Otherwise, assume any non-<option> under fieldset[disabled] is disabled
+				/* jshint -W018 */
+				elem.isDisabled !== !disabled &&
+					("label" in elem || !disabledAncestor( elem )) !== disabled
+			);
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction(function( argument ) {
+		argument = +argument;
+		return markFunction(function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ (j = matchIndexes[i]) ] ) {
+					seed[j] = !(matches[j] = seed[j]);
+				}
+			}
+		});
+	});
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	// documentElement is verified for cases where it doesn't yet exist
+	// (such as loading iframes in IE - #4833)
+	var documentElement = elem && (elem.ownerDocument || elem).documentElement;
+	return documentElement ? documentElement.nodeName !== "HTML" : false;
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9-11, Edge
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	if ( preferredDoc !== document &&
+		(subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert(function( el ) {
+		el.className = "i";
+		return !el.getAttribute("className");
+	});
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert(function( el ) {
+		el.appendChild( document.createComment("") );
+		return !el.getElementsByTagName("*").length;
+	});
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert(function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	});
+
+	// ID find and filter
+	if ( support.getById ) {
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var m = context.getElementById( id );
+				return m ? [ m ] : [];
+			}
+		};
+		Expr.filter["ID"] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute("id") === attrId;
+			};
+		};
+	} else {
+		// Support: IE6/7
+		// getElementById is not reliable as a find shortcut
+		delete Expr.find["ID"];
+
+		Expr.filter["ID"] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode("id");
+				return node && node.value === attrId;
+			};
+		};
+	}
+
+	// Tag
+	Expr.find["TAG"] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( (elem = results[i++]) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert(function( el ) {
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll("[selected]").length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push("~=");
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll(":checked").length ) {
+				rbuggyQSA.push(":checked");
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push(".#.+[+~]");
+			}
+		});
+
+		assert(function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement("input");
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll("[name=d]").length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll(":enabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll(":disabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll("*,:x");
+			rbuggyQSA.push(",.*:");
+		});
+	}
+
+	if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector) )) ) {
+
+		assert(function( el ) {
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		});
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			));
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( (b = b.parentNode) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			(!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+
+			// Choose the first element that is related to our preferred document
+			if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+				return -1;
+			}
+			if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+			return a === document ? -1 :
+				b === document ? 1 :
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( (cur = cur.parentNode) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( (cur = cur.parentNode) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[i] === bp[i] ) {
+			i++;
+		}
+
+		return i ?
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[i], bp[i] ) :
+
+			// Otherwise nodes in our document sort first
+			ap[i] === preferredDoc ? -1 :
+			bp[i] === preferredDoc ? 1 :
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	// Make sure that attribute selectors are quoted
+	expr = expr.replace( rattributeQuotes, "='$1']" );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!compilerCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+					// As well, disconnected nodes are said to be in a document
+					// fragment in IE 9
+					elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch (e) {}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+	// Set document vars if needed
+	if ( ( context.ownerDocument || context ) !== document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			(val = elem.getAttributeNode(name)) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return (sel + "").replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( (elem = results[i++]) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+		// If no nodeType, this is expected to be an array
+		while ( (node = elem[i++]) ) {
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[1] = match[1].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape );
+
+			if ( match[2] === "~=" ) {
+				match[3] = " " + match[3] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[1] = match[1].toLowerCase();
+
+			if ( match[1].slice( 0, 3 ) === "nth" ) {
+				// nth-* requires argument
+				if ( !match[3] ) {
+					Sizzle.error( match[0] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) );
+				match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" );
+
+			// other types prohibit arguments
+			} else if ( match[3] ) {
+				Sizzle.error( match[0] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[6] && match[2];
+
+			if ( matchExpr["CHILD"].test( match[0] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[3] ) {
+				match[2] = match[4] || match[5] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+				// Get excess from tokenize (recursively)
+				(excess = tokenize( unquoted, true )) &&
+				// advance to the next closing parenthesis
+				(excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+
+				// excess is a negative index
+				match[0] = match[0].slice( 0, excess );
+				match[2] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() { return true; } :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				(pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) &&
+				classCache( className, function( elem ) {
+					return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" );
+				});
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+			};
+		},
+
+		"CHILD": function( type, what, argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( (node = node[ dir ]) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || (node[ expando ] = {});
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								(outerCache[ node.uniqueID ] = {});
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( (node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								(diff = nodeIndex = 0) || start.pop()) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+							// Use previously-cached element index if available
+							if ( useCache ) {
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || (node[ expando ] = {});
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									(outerCache[ node.uniqueID ] = {});
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+								// Use the same loop as above to seek `elem` from the start
+								while ( (node = ++nodeIndex && node && node[ dir ] ||
+									(diff = nodeIndex = 0) || start.pop()) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] || (node[ expando ] = {});
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												(outerCache[ node.uniqueID ] = {});
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction(function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[i] );
+							seed[ idx ] = !( matches[ idx ] = matched[i] );
+						}
+					}) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+		// Potentially complex pseudos
+		"not": markFunction(function( selector ) {
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction(function( seed, matches, context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( (elem = unmatched[i]) ) {
+							seed[i] = !(matches[i] = elem);
+						}
+					}
+				}) :
+				function( elem, context, xml ) {
+					input[0] = elem;
+					matcher( input, null, xml, results );
+					// Don't keep the element (issue #299)
+					input[0] = null;
+					return !results.pop();
+				};
+		}),
+
+		"has": markFunction(function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		}),
+
+		"contains": markFunction(function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1;
+			};
+		}),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+			// lang value must be a valid identifier
+			if ( !ridentifier.test(lang || "") ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( (elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( (elem = elem.parentNode) && elem.nodeType === 1 );
+				return false;
+			};
+		}),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected);
+		},
+
+		"selected": function( elem ) {
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos["empty"]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo(function() {
+			return [ 0 ];
+		}),
+
+		"last": createPositionalPseudo(function( matchIndexes, length ) {
+			return [ length - 1 ];
+		}),
+
+		"eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		}),
+
+		"even": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"odd": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		})
+	}
+};
+
+Expr.pseudos["nth"] = Expr.pseudos["eq"];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || (match = rcomma.exec( soFar )) ) {
+			if ( match ) {
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[0].length ) || soFar;
+			}
+			groups.push( (tokens = []) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( (match = rcombinators.exec( soFar )) ) {
+			matched = match.shift();
+			tokens.push({
+				value: matched,
+				// Cast descendant combinators to space
+				type: match[0].replace( rtrim, " " )
+			});
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] ||
+				(match = preFilters[ type ]( match ))) ) {
+				matched = match.shift();
+				tokens.push({
+					value: matched,
+					type: type,
+					matches: match
+				});
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[i].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( (elem = elem[ dir ]) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || (elem[ expando ] = {});
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( (oldCache = uniqueCache[ key ]) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return (newCache[ 2 ] = oldCache[ 2 ]);
+						} else {
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[i]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[0];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[i], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( (elem = unmatched[i]) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction(function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( (elem = temp[i]) ) {
+					matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem);
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( (elem = matcherOut[i]) ) {
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( (matcherIn[i] = elem) );
+						}
+					}
+					postFinder( null, (matcherOut = []), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( (elem = matcherOut[i]) &&
+						(temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) {
+
+						seed[temp] = !(results[temp] = elem);
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	});
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[0].type ],
+		implicitRelative = leadingRelative || Expr.relative[" "],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				(checkContext = context).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( (matcher = Expr.relative[ tokens[i].type ]) ) {
+			matchers = [ addCombinator(elementMatcher( matchers ), matcher) ];
+		} else {
+			matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[j].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+						// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+						tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" })
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( (tokens = tokens.slice( j )) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1),
+				len = elems.length;
+
+			if ( outermost ) {
+				outermostContext = context === document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && (elem = elems[i]) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+					if ( !context && elem.ownerDocument !== document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( (matcher = elementMatchers[j++]) ) {
+						if ( matcher( elem, context || document, xml) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+					// They will have gone through all possible matchers
+					if ( (elem = !matcher && elem) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( (matcher = setMatchers[j++]) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !(unmatched[i] || setMatched[i]) ) {
+								setMatched[i] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[i] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( (selector = compiled.selector || selector) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[0] = match[0].slice( 0 );
+		if ( tokens.length > 2 && (token = tokens[0]).type === "ID" &&
+				support.getById && context.nodeType === 9 && documentIsHTML &&
+				Expr.relative[ tokens[1].type ] ) {
+
+			context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[i];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ (type = token.type) ] ) {
+				break;
+			}
+			if ( (find = Expr.find[ type ]) ) {
+				// Search, expanding context for leading sibling combinators
+				if ( (seed = find(
+					token.matches[0].replace( runescape, funescape ),
+					rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context
+				)) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert(function( el ) {
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
+});
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert(function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute("href") === "#" ;
+}) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	});
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert(function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+}) ) {
+	addHandle( "value", function( elem, name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	});
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert(function( el ) {
+	return el.getAttribute("disabled") == null;
+}) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+					(val = elem.getAttributeNode( name )) && val.specified ?
+					val.value :
+				null;
+		}
+	});
+}
+
+return Sizzle;
+
+})( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+var risSimple = /^.[^:#\[\.,]*$/;
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( jQuery.isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+
+	}
+
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+
+	}
+
+	if ( typeof qualifier === "string" ) {
+		if ( risSimple.test( qualifier ) ) {
+			return jQuery.filter( qualifier, elements, not );
+		}
+
+		qualifier = jQuery.filter( qualifier, elements );
+	}
+
+	return jQuery.grep( elements, function( elem ) {
+		return ( indexOf.call( qualifier, elem ) > -1 ) !== not && elem.nodeType === 1;
+	} );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	return elems.length === 1 && elem.nodeType === 1 ?
+		jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [] :
+		jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+			return elem.nodeType === 1;
+		} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( jQuery.isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( jQuery.isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		return elem.contentDocument || jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnotwhite = ( /\S+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnotwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( jQuery.isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && jQuery.type( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && jQuery.isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && jQuery.isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Support: Android 4.0 only
+			// Strict mode functions invoked without .call/.apply get global-object context
+			resolve.call( undefined, value );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.call( undefined, value );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = jQuery.isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && jQuery.isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( jQuery.isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				jQuery.isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Hold (or release) the ready event
+	holdReady: function( hold ) {
+		if ( hold ) {
+			jQuery.readyWait++;
+		} else {
+			jQuery.ready( true );
+		}
+	},
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( jQuery.type( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !jQuery.isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	return chainable ?
+		elems :
+
+		// Gets
+		bulk ?
+			fn.call( elems ) :
+			len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ jQuery.camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ jQuery.camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ jQuery.camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( jQuery.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( jQuery.camelCase );
+			} else {
+				key = jQuery.camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnotwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = data === "true" ? true :
+					data === "false" ? false :
+					data === "null" ? null :
+
+					// Only convert to a number if it doesn't change the string
+					+data + "" === data ? +data :
+					rbrace.test( data ) ? JSON.parse( data ) :
+					data;
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = jQuery.camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || jQuery.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			jQuery.contains( elem.ownerDocument, elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+var swap = function( elem, options, callback, args ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.apply( elem, args || [] );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted,
+		scale = 1,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		do {
+
+			// If previous iteration zeroed out, double until we get *something*.
+			// Use string for doubling so we don't accidentally see scale as unchanged below
+			scale = scale || ".5";
+
+			// Adjust and apply
+			initialInUnit = initialInUnit / scale;
+			jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Update scale, tolerating zero or NaN from tween.cur()
+		// Break the loop if scale is unchanged or perfect, or if we've just had enough.
+		} while (
+			scale !== ( scale = currentValue() / initial ) && scale !== 1 && --maxIterations
+		);
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) ),
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]+)/i );
+
+var rscriptType = ( /^$|\/(?:java|ecma)script/i );
+
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// Support: IE <=9 only
+	option: [ 1, "<select multiple='multiple'>", "</select>" ],
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+// Support: IE <=9 only
+wrapMap.optgroup = wrapMap.option;
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret = typeof context.getElementsByTagName !== "undefined" ?
+			context.getElementsByTagName( tag || "*" ) :
+			typeof context.querySelectorAll !== "undefined" ?
+				context.querySelectorAll( tag || "*" ) :
+			[];
+
+	return tag === undefined || tag && jQuery.nodeName( context, tag ) ?
+		jQuery.merge( [ context ], ret ) :
+		ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, contains, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( jQuery.type( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		contains = jQuery.contains( elem.ownerDocument, elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( contains ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+} )();
+var documentElement = document.documentElement;
+
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 only
+// See #13393 for more info
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Don't attach events to noData or text/comment nodes (but allow plain objects)
+		if ( !elemData ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = {};
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnotwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnotwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		// Make a writable jQuery.Event from the native event object
+		var event = jQuery.event.fix( nativeEvent );
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+			handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// Triggered event must either 1) have no namespace, or 2) have namespace(s)
+				// a subset or equal to those in the bound event (both can have no namespace).
+				if ( !event.rnamespace || event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, matches, sel, handleObj,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Support: IE <=9
+		// Find delegate handlers
+		// Black-hole SVG <use> instance trees (#13180)
+		//
+		// Support: Firefox <=42
+		// Avoid non-left-click in FF but don't block IE radio events (#3861, gh-2343)
+		if ( delegateCount && cur.nodeType &&
+			( event.type !== "click" || isNaN( event.button ) || event.button < 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && ( cur.disabled !== true || event.type !== "click" ) ) {
+					matches = [];
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matches[ sel ] === undefined ) {
+							matches[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matches[ sel ] ) {
+							matches.push( handleObj );
+						}
+					}
+					if ( matches.length ) {
+						handlerQueue.push( { elem: cur, handlers: matches } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: this, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: jQuery.isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		focus: {
+
+			// Fire native event if possible so blur/focus sequence is correct
+			trigger: function() {
+				if ( this !== safeActiveElement() && this.focus ) {
+					this.focus();
+					return false;
+				}
+			},
+			delegateType: "focusin"
+		},
+		blur: {
+			trigger: function() {
+				if ( this === safeActiveElement() && this.blur ) {
+					this.blur();
+					return false;
+				}
+			},
+			delegateType: "focusout"
+		},
+		click: {
+
+			// For checkbox, fire native event so checked state will be right
+			trigger: function() {
+				if ( this.type === "checkbox" && this.click && jQuery.nodeName( this, "input" ) ) {
+					this.click();
+					return false;
+				}
+			},
+
+			// For cross-browser consistency, don't fire native .click() on links
+			_default: function( event ) {
+				return jQuery.nodeName( event.target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || jQuery.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			return ( button & 1 ? 1 : ( button & 2 ? 3 : ( button & 4 ? 2 : 0 ) ) );
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	/* eslint-disable max-len */
+
+	// See https://github.com/eslint/eslint/issues/3229
+	rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi,
+
+	/* eslint-enable */
+
+	// Support: IE <=10 - 11, Edge 12 - 13
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rscriptTypeMasked = /^true\/(.*)/,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+function manipulationTarget( elem, content ) {
+	if ( jQuery.nodeName( elem, "table" ) &&
+		jQuery.nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return elem.getElementsByTagName( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	var match = rscriptTypeMasked.exec( elem.type );
+
+	if ( match ) {
+		elem.type = match[ 1 ];
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.access( src );
+		pdataCur = dataPriv.set( dest, pdataOld );
+		events = pdataOld.events;
+
+		if ( events ) {
+			delete pdataCur.handle;
+			pdataCur.events = {};
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = concat.apply( [], args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		isFunction = jQuery.isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( isFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( isFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl ) {
+								jQuery._evalUrl( node.src );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && jQuery.contains( node.ownerDocument, node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html.replace( rxhtmlTag, "<$1></$2>" );
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = jQuery.contains( elem.ownerDocument, elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rmargin = ( /^margin/ );
+
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		div.style.cssText =
+			"box-sizing:border-box;" +
+			"position:relative;display:block;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"top:1%;width:50%";
+		div.innerHTML = "";
+		documentElement.appendChild( container );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = divStyle.marginLeft === "2px";
+		boxSizingReliableVal = divStyle.width === "4px";
+
+		// Support: Android 4.0 - 4.3 only
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.marginRight = "50%";
+		pixelMarginRightVal = divStyle.marginRight === "4px";
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, pixelMarginRightVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	container.style.cssText = "border:0;width:8px;height:0;top:0;left:-9999px;" +
+		"padding:0;margin-top:1px;position:absolute";
+	container.appendChild( div );
+
+	jQuery.extend( support, {
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelMarginRight: function() {
+			computeStyleTests();
+			return pixelMarginRightVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// Support: IE <=9 only
+	// getPropertyValue is only needed for .css('filter') (#12537)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !jQuery.contains( elem.ownerDocument, elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelMarginRight() && rnumnonpx.test( ret ) && rmargin.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	},
+
+	cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style;
+
+// Return a css property mapped to a potentially vendor prefixed property
+function vendorPropName( name ) {
+
+	// Shortcut for names that are not vendor prefixed
+	if ( name in emptyStyle ) {
+		return name;
+	}
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+function setPositiveNumber( elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function augmentWidthOrHeight( elem, name, extra, isBorderBox, styles ) {
+	var i = extra === ( isBorderBox ? "border" : "content" ) ?
+
+		// If we already have the right measurement, avoid augmentation
+		4 :
+
+		// Otherwise initialize for horizontal or vertical properties
+		name === "width" ? 1 : 0,
+
+		val = 0;
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin, so add it if we want it
+		if ( extra === "margin" ) {
+			val += jQuery.css( elem, extra + cssExpand[ i ], true, styles );
+		}
+
+		if ( isBorderBox ) {
+
+			// border-box includes padding, so remove it if we want content
+			if ( extra === "content" ) {
+				val -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// At this point, extra isn't border nor margin, so remove border
+			if ( extra !== "margin" ) {
+				val -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		} else {
+
+			// At this point, extra isn't content, so add padding
+			val += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// At this point, extra isn't content nor padding, so add border
+			if ( extra !== "padding" ) {
+				val += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	return val;
+}
+
+function getWidthOrHeight( elem, name, extra ) {
+
+	// Start with offset property, which is equivalent to the border-box value
+	var val,
+		valueIsBorderBox = true,
+		styles = getStyles( elem ),
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+	// Support: IE <=11 only
+	// Running getBoundingClientRect on a disconnected node
+	// in IE throws an error.
+	if ( elem.getClientRects().length ) {
+		val = elem.getBoundingClientRect()[ name ];
+	}
+
+	// Some non-html elements return undefined for offsetWidth, so check for null/undefined
+	// svg - https://bugzilla.mozilla.org/show_bug.cgi?id=649285
+	// MathML - https://bugzilla.mozilla.org/show_bug.cgi?id=491668
+	if ( val <= 0 || val == null ) {
+
+		// Fall back to computed then uncomputed css if necessary
+		val = curCSS( elem, name, styles );
+		if ( val < 0 || val == null ) {
+			val = elem.style[ name ];
+		}
+
+		// Computed unit is not pixels. Stop here and return.
+		if ( rnumnonpx.test( val ) ) {
+			return val;
+		}
+
+		// Check for style in case a browser which returns unreliable values
+		// for getComputedStyle silently falls back to the reliable elem.style
+		valueIsBorderBox = isBorderBox &&
+			( support.boxSizingReliable() || val === elem.style[ name ] );
+
+		// Normalize "", auto, and prepare for extra
+		val = parseFloat( val ) || 0;
+	}
+
+	// Use the active box-sizing model to add/subtract irrelevant styles
+	return ( val +
+		augmentWidthOrHeight(
+			elem,
+			name,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {
+		"float": "cssFloat"
+	},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = jQuery.camelCase( name ),
+			style = elem.style;
+
+		name = jQuery.cssProps[ origName ] ||
+			( jQuery.cssProps[ origName ] = vendorPropName( origName ) || origName );
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			if ( type === "number" ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				style[ name ] = value;
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = jQuery.camelCase( name );
+
+		// Make sure that we're working with the right name
+		name = jQuery.cssProps[ origName ] ||
+			( jQuery.cssProps[ origName ] = vendorPropName( origName ) || origName );
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( i, name ) {
+	jQuery.cssHooks[ name ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, name, extra );
+						} ) :
+						getWidthOrHeight( elem, name, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = extra && getStyles( elem ),
+				subtract = extra && augmentWidthOrHeight(
+					elem,
+					name,
+					extra,
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+					styles
+				);
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ name ] = value;
+				value = jQuery.css( elem, name );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( !rmargin.test( prefix ) ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( jQuery.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 &&
+				( tween.elem.style[ jQuery.cssProps[ tween.prop ] ] != null ||
+					jQuery.cssHooks[ tween.prop ] ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, timerId,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function raf() {
+	if ( timerId ) {
+		window.requestAnimationFrame( raf );
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = jQuery.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 13
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = jQuery.camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( jQuery.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			if ( percent < 1 && length ) {
+				return remaining;
+			} else {
+				deferred.resolveWith( elem, [ animation ] );
+				return false;
+			}
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( jQuery.isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					jQuery.proxy( result.stop, result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( jQuery.isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	// attach callbacks from options
+	return animation.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( jQuery.isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnotwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			jQuery.isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !jQuery.isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off or if document is hidden
+	if ( jQuery.fx.off || document.hidden ) {
+		opt.duration = 0;
+
+	} else {
+		opt.duration = typeof opt.duration === "number" ?
+			opt.duration : opt.duration in jQuery.fx.speeds ?
+				jQuery.fx.speeds[ opt.duration ] : jQuery.fx.speeds._default;
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( jQuery.isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue && type !== false ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = jQuery.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Checks the timer has not already been removed
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	if ( timer() ) {
+		jQuery.fx.start();
+	} else {
+		jQuery.timers.pop();
+	}
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( !timerId ) {
+		timerId = window.requestAnimationFrame ?
+			window.requestAnimationFrame( raf ) :
+			window.setInterval( jQuery.fx.tick, jQuery.fx.interval );
+	}
+};
+
+jQuery.fx.stop = function() {
+	if ( window.cancelAnimationFrame ) {
+		window.cancelAnimationFrame( timerId );
+	} else {
+		window.clearInterval( timerId );
+	}
+
+	timerId = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					jQuery.nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+			attrNames = value && value.match( rnotwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				return tabindex ?
+					parseInt( tabindex, 10 ) :
+					rfocusable.test( elem.nodeName ) ||
+						rclickable.test( elem.nodeName ) && elem.href ?
+							0 :
+							-1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+var rclass = /[\t\r\n\f]/g;
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( typeof value === "string" && value ) {
+			classes = value.match( rnotwhite ) || [];
+
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 &&
+					( " " + curValue + " " ).replace( rclass, " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = jQuery.trim( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		if ( typeof value === "string" && value ) {
+			classes = value.match( rnotwhite ) || [];
+
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 &&
+					( " " + curValue + " " ).replace( rclass, " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = jQuery.trim( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value;
+
+		if ( typeof stateVal === "boolean" && type === "string" ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( type === "string" ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = value.match( rnotwhite ) || [];
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + getClass( elem ) + " " ).replace( rclass, " " )
+					.indexOf( className ) > -1
+			) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g,
+	rspaces = /[\x20\t\r\n\f]+/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, isFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				return typeof ret === "string" ?
+
+					// Handle most common string cases
+					ret.replace( rreturn, "" ) :
+
+					// Handle cases where value is null/undef or number
+					ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		isFunction = jQuery.isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( isFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( jQuery.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					jQuery.trim( jQuery.text( elem ) ).replace( rspaces, " " );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length,
+					i = index < 0 ?
+						max :
+						one ? index : 0;
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!jQuery.nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( jQuery.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/;
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && jQuery.isFunction( elem[ type ] ) && !jQuery.isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+					elem[ type ]();
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( i, name ) {
+
+	// Handle event binding
+	jQuery.fn[ name ] = function( data, fn ) {
+		return arguments.length > 0 ?
+			this.on( name, null, data, fn ) :
+			this.trigger( name );
+	};
+} );
+
+jQuery.fn.extend( {
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+
+
+
+support.focusin = "onfocusin" in window;
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = jQuery.now();
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( jQuery.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && jQuery.type( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = jQuery.isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( jQuery.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( i, elem ) {
+			var val = jQuery( this ).val();
+
+			return val == null ?
+				null :
+				jQuery.isArray( val ) ?
+					jQuery.map( val, function( val ) {
+						return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+					} ) :
+					{ name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rts = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnotwhite ) || [];
+
+		if ( jQuery.isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() ] = match[ 2 ];
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() ];
+					}
+					return match == null ? null : match;
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnotwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 13
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available, append data to url
+			if ( s.data ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add anti-cache in uncached url if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rts, "" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( jQuery.isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+
+jQuery._evalUrl = function( url ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+		"throws": true
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( jQuery.isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( jQuery.isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var isFunction = jQuery.isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( isFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain requests
+	if ( s.crossDomain ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" ).prop( {
+					charset: s.scriptCharset,
+					src: s.url
+				} ).on(
+					"load error",
+					callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					}
+				);
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = jQuery.isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && jQuery.isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = jQuery.trim( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( jQuery.isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+// Attach a bunch of functions for handling common AJAX events
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+/**
+ * Gets a window from an element
+ */
+function getWindow( elem ) {
+	return jQuery.isWindow( elem ) ? elem : elem.nodeType === 9 && elem.defaultView;
+}
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( jQuery.isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var docElem, win, rect, doc,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		rect = elem.getBoundingClientRect();
+
+		// Make sure element is not hidden (display: none)
+		if ( rect.width || rect.height ) {
+			doc = elem.ownerDocument;
+			win = getWindow( doc );
+			docElem = doc.documentElement;
+
+			return {
+				top: rect.top + win.pageYOffset - docElem.clientTop,
+				left: rect.left + win.pageXOffset - docElem.clientLeft
+			};
+		}
+
+		// Return zeros for disconnected and hidden elements (gh-2310)
+		return rect;
+	},
+
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// Fixed elements are offset from window (parentOffset = {top:0, left: 0},
+		// because it is its only offset parent
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume getBoundingClientRect is there when computed position is fixed
+			offset = elem.getBoundingClientRect();
+
+		} else {
+
+			// Get *real* offsetParent
+			offsetParent = this.offsetParent();
+
+			// Get correct offsets
+			offset = this.offset();
+			if ( !jQuery.nodeName( offsetParent[ 0 ], "html" ) ) {
+				parentOffset = offsetParent.offset();
+			}
+
+			// Add offsetParent borders
+			parentOffset = {
+				top: parentOffset.top + jQuery.css( offsetParent[ 0 ], "borderTopWidth", true ),
+				left: parentOffset.left + jQuery.css( offsetParent[ 0 ], "borderLeftWidth", true )
+			};
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+			var win = getWindow( elem );
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( jQuery.isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	}
+} );
+
+jQuery.parseJSON = JSON.parse;
+
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( !noGlobal ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+return jQuery;
+} );
diff --git a/docs/_static/jquery-3.2.1.js b/docs/_static/jquery-3.2.1.js
new file mode 100644
index 00000000..d2d8ca47
--- /dev/null
+++ b/docs/_static/jquery-3.2.1.js
@@ -0,0 +1,10253 @@
+/*!
+ * jQuery JavaScript Library v3.2.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2017-03-20T18:59Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var document = window.document;
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var concat = arr.concat;
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+
+
+	function DOMEval( code, doc ) {
+		doc = doc || document;
+
+		var script = doc.createElement( "script" );
+
+		script.text = code;
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.2.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	},
+
+	// Support: Android <=4.0 only
+	// Make sure we trim BOM and NBSP
+	rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,
+
+	// Matches dashed string for camelizing
+	rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g,
+
+	// Used by jQuery.camelCase as callback to replace()
+	fcamelCase = function( all, letter ) {
+		return letter.toUpperCase();
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !jQuery.isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				src = target[ name ];
+				copy = options[ name ];
+
+				// Prevent never-ending loop
+				if ( target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+
+					if ( copyIsArray ) {
+						copyIsArray = false;
+						clone = src && Array.isArray( src ) ? src : [];
+
+					} else {
+						clone = src && jQuery.isPlainObject( src ) ? src : {};
+					}
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isFunction: function( obj ) {
+		return jQuery.type( obj ) === "function";
+	},
+
+	isWindow: function( obj ) {
+		return obj != null && obj === obj.window;
+	},
+
+	isNumeric: function( obj ) {
+
+		// As of jQuery 3.0, isNumeric is limited to
+		// strings and numbers (primitives or objects)
+		// that can be coerced to finite numbers (gh-2662)
+		var type = jQuery.type( obj );
+		return ( type === "number" || type === "string" ) &&
+
+			// parseFloat NaNs numeric-cast false positives ("")
+			// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+			// subtraction forces infinities to NaN
+			!isNaN( obj - parseFloat( obj ) );
+	},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+
+		/* eslint-disable no-unused-vars */
+		// See https://github.com/eslint/eslint/issues/6125
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	type: function( obj ) {
+		if ( obj == null ) {
+			return obj + "";
+		}
+
+		// Support: Android <=2.3 only (functionish RegExp)
+		return typeof obj === "object" || typeof obj === "function" ?
+			class2type[ toString.call( obj ) ] || "object" :
+			typeof obj;
+	},
+
+	// Evaluates a script in a global context
+	globalEval: function( code ) {
+		DOMEval( code );
+	},
+
+	// Convert dashed to camelCase; used by the css and data modules
+	// Support: IE <=9 - 11, Edge 12 - 13
+	// Microsoft forgot to hump their vendor prefix (#9572)
+	camelCase: function( string ) {
+		return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// Support: Android <=4.0 only
+	trim: function( text ) {
+		return text == null ?
+			"" :
+			( text + "" ).replace( rtrim, "" );
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return concat.apply( [], ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// Bind a function to a context, optionally partially applying any
+	// arguments.
+	proxy: function( fn, context ) {
+		var tmp, args, proxy;
+
+		if ( typeof context === "string" ) {
+			tmp = fn[ context ];
+			context = fn;
+			fn = tmp;
+		}
+
+		// Quick check to determine if target is callable, in the spec
+		// this throws a TypeError, but we will just return undefined.
+		if ( !jQuery.isFunction( fn ) ) {
+			return undefined;
+		}
+
+		// Simulated bind
+		args = slice.call( arguments, 2 );
+		proxy = function() {
+			return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+		};
+
+		// Set the guid of unique handler to the same of original handler, so it can be removed
+		proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+		return proxy;
+	},
+
+	now: Date.now,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = jQuery.type( obj );
+
+	if ( type === "function" || jQuery.isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.3
+ * https://sizzlejs.com/
+ *
+ * Copyright jQuery Foundation and other contributors
+ * Released under the MIT license
+ * http://jquery.org/license
+ *
+ * Date: 2016-08-08
+ */
+(function( window ) {
+
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ({}).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	push_native = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[i] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
+	identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+		// "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
+		"*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+
+	rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
+			"*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
+			"*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" +
+			whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
+	funescape = function( _, escaped, escapedWhitespace ) {
+		var high = "0x" + escaped - 0x10000;
+		// NaN means non-codepoint
+		// Support: Firefox<24
+		// Workaround erroneous numeric interpretation of +"0x"
+		return high !== high || escapedWhitespace ?
+			escaped :
+			high < 0 ?
+				// BMP codepoint
+				String.fromCharCode( high + 0x10000 ) :
+				// Supplemental Plane codepoint (surrogate pair)
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	disabledAncestor = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && ("form" in elem || "label" in elem);
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		(arr = slice.call( preferredDoc.childNodes )),
+		preferredDoc.childNodes
+	);
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			push_native.apply( target, slice.call(els) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+			// Can't trust NodeList.length
+			while ( (target[j++] = els[i++]) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+
+		if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
+			setDocument( context );
+		}
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) {
+
+				// ID selector
+				if ( (m = match[1]) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( (elem = context.getElementById( m )) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && (elem = newContext.getElementById( m )) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[2] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( (m = match[3]) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!compilerCache[ selector + " " ] &&
+				(!rbuggyQSA || !rbuggyQSA.test( selector )) ) {
+
+				if ( nodeType !== 1 ) {
+					newContext = context;
+					newSelector = selector;
+
+				// qSA looks outside Element context, which is not what we want
+				// Thanks to Andrew Dupont for this workaround technique
+				// Support: IE <=8
+				// Exclude object elements
+				} else if ( context.nodeName.toLowerCase() !== "object" ) {
+
+					// Capture the context ID, setting it first if necessary
+					if ( (nid = context.getAttribute( "id" )) ) {
+						nid = nid.replace( rcssescape, fcssescape );
+					} else {
+						context.setAttribute( "id", (nid = expando) );
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[i] = "#" + nid + " " + toSelector( groups[i] );
+					}
+					newSelector = groups.join( "," );
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+				}
+
+				if ( newSelector ) {
+					try {
+						push.apply( results,
+							newContext.querySelectorAll( newSelector )
+						);
+						return results;
+					} catch ( qsaError ) {
+					} finally {
+						if ( nid === expando ) {
+							context.removeAttribute( "id" );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return (cache[ key + " " ] = value);
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement("fieldset");
+
+	try {
+		return !!fn( el );
+	} catch (e) {
+		return false;
+	} finally {
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split("|"),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[i] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( (cur = cur.nextSibling) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return (name === "input" || name === "button") && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+						disabledAncestor( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction(function( argument ) {
+		argument = +argument;
+		return markFunction(function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ (j = matchIndexes[i]) ] ) {
+					seed[j] = !(matches[j] = seed[j]);
+				}
+			}
+		});
+	});
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	// documentElement is verified for cases where it doesn't yet exist
+	// (such as loading iframes in IE - #4833)
+	var documentElement = elem && (elem.ownerDocument || elem).documentElement;
+	return documentElement ? documentElement.nodeName !== "HTML" : false;
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9-11, Edge
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	if ( preferredDoc !== document &&
+		(subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert(function( el ) {
+		el.className = "i";
+		return !el.getAttribute("className");
+	});
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert(function( el ) {
+		el.appendChild( document.createComment("") );
+		return !el.getElementsByTagName("*").length;
+	});
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert(function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	});
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter["ID"] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute("id") === attrId;
+			};
+		};
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter["ID"] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode("id");
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode("id");
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( (elem = elems[i++]) ) {
+						node = elem.getAttributeNode("id");
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find["TAG"] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( (elem = results[i++]) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert(function( el ) {
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll("[selected]").length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push("~=");
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll(":checked").length ) {
+				rbuggyQSA.push(":checked");
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push(".#.+[+~]");
+			}
+		});
+
+		assert(function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement("input");
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll("[name=d]").length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll(":enabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll(":disabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll("*,:x");
+			rbuggyQSA.push(",.*:");
+		});
+	}
+
+	if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector) )) ) {
+
+		assert(function( el ) {
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		});
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			));
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( (b = b.parentNode) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			(!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+
+			// Choose the first element that is related to our preferred document
+			if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+				return -1;
+			}
+			if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+			return a === document ? -1 :
+				b === document ? 1 :
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( (cur = cur.parentNode) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( (cur = cur.parentNode) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[i] === bp[i] ) {
+			i++;
+		}
+
+		return i ?
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[i], bp[i] ) :
+
+			// Otherwise nodes in our document sort first
+			ap[i] === preferredDoc ? -1 :
+			bp[i] === preferredDoc ? 1 :
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	// Make sure that attribute selectors are quoted
+	expr = expr.replace( rattributeQuotes, "='$1']" );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!compilerCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+					// As well, disconnected nodes are said to be in a document
+					// fragment in IE 9
+					elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch (e) {}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+	// Set document vars if needed
+	if ( ( context.ownerDocument || context ) !== document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			(val = elem.getAttributeNode(name)) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return (sel + "").replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( (elem = results[i++]) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+		// If no nodeType, this is expected to be an array
+		while ( (node = elem[i++]) ) {
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[1] = match[1].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape );
+
+			if ( match[2] === "~=" ) {
+				match[3] = " " + match[3] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[1] = match[1].toLowerCase();
+
+			if ( match[1].slice( 0, 3 ) === "nth" ) {
+				// nth-* requires argument
+				if ( !match[3] ) {
+					Sizzle.error( match[0] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) );
+				match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" );
+
+			// other types prohibit arguments
+			} else if ( match[3] ) {
+				Sizzle.error( match[0] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[6] && match[2];
+
+			if ( matchExpr["CHILD"].test( match[0] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[3] ) {
+				match[2] = match[4] || match[5] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+				// Get excess from tokenize (recursively)
+				(excess = tokenize( unquoted, true )) &&
+				// advance to the next closing parenthesis
+				(excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+
+				// excess is a negative index
+				match[0] = match[0].slice( 0, excess );
+				match[2] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() { return true; } :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				(pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) &&
+				classCache( className, function( elem ) {
+					return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" );
+				});
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+			};
+		},
+
+		"CHILD": function( type, what, argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( (node = node[ dir ]) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || (node[ expando ] = {});
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								(outerCache[ node.uniqueID ] = {});
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( (node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								(diff = nodeIndex = 0) || start.pop()) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+							// Use previously-cached element index if available
+							if ( useCache ) {
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || (node[ expando ] = {});
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									(outerCache[ node.uniqueID ] = {});
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+								// Use the same loop as above to seek `elem` from the start
+								while ( (node = ++nodeIndex && node && node[ dir ] ||
+									(diff = nodeIndex = 0) || start.pop()) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] || (node[ expando ] = {});
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												(outerCache[ node.uniqueID ] = {});
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction(function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[i] );
+							seed[ idx ] = !( matches[ idx ] = matched[i] );
+						}
+					}) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+		// Potentially complex pseudos
+		"not": markFunction(function( selector ) {
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction(function( seed, matches, context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( (elem = unmatched[i]) ) {
+							seed[i] = !(matches[i] = elem);
+						}
+					}
+				}) :
+				function( elem, context, xml ) {
+					input[0] = elem;
+					matcher( input, null, xml, results );
+					// Don't keep the element (issue #299)
+					input[0] = null;
+					return !results.pop();
+				};
+		}),
+
+		"has": markFunction(function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		}),
+
+		"contains": markFunction(function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1;
+			};
+		}),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+			// lang value must be a valid identifier
+			if ( !ridentifier.test(lang || "") ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( (elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( (elem = elem.parentNode) && elem.nodeType === 1 );
+				return false;
+			};
+		}),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected);
+		},
+
+		"selected": function( elem ) {
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos["empty"]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo(function() {
+			return [ 0 ];
+		}),
+
+		"last": createPositionalPseudo(function( matchIndexes, length ) {
+			return [ length - 1 ];
+		}),
+
+		"eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		}),
+
+		"even": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"odd": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		})
+	}
+};
+
+Expr.pseudos["nth"] = Expr.pseudos["eq"];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || (match = rcomma.exec( soFar )) ) {
+			if ( match ) {
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[0].length ) || soFar;
+			}
+			groups.push( (tokens = []) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( (match = rcombinators.exec( soFar )) ) {
+			matched = match.shift();
+			tokens.push({
+				value: matched,
+				// Cast descendant combinators to space
+				type: match[0].replace( rtrim, " " )
+			});
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] ||
+				(match = preFilters[ type ]( match ))) ) {
+				matched = match.shift();
+				tokens.push({
+					value: matched,
+					type: type,
+					matches: match
+				});
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[i].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( (elem = elem[ dir ]) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || (elem[ expando ] = {});
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( (oldCache = uniqueCache[ key ]) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return (newCache[ 2 ] = oldCache[ 2 ]);
+						} else {
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[i]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[0];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[i], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( (elem = unmatched[i]) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction(function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( (elem = temp[i]) ) {
+					matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem);
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( (elem = matcherOut[i]) ) {
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( (matcherIn[i] = elem) );
+						}
+					}
+					postFinder( null, (matcherOut = []), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( (elem = matcherOut[i]) &&
+						(temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) {
+
+						seed[temp] = !(results[temp] = elem);
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	});
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[0].type ],
+		implicitRelative = leadingRelative || Expr.relative[" "],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				(checkContext = context).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( (matcher = Expr.relative[ tokens[i].type ]) ) {
+			matchers = [ addCombinator(elementMatcher( matchers ), matcher) ];
+		} else {
+			matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[j].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+						// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+						tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" })
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( (tokens = tokens.slice( j )) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1),
+				len = elems.length;
+
+			if ( outermost ) {
+				outermostContext = context === document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && (elem = elems[i]) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+					if ( !context && elem.ownerDocument !== document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( (matcher = elementMatchers[j++]) ) {
+						if ( matcher( elem, context || document, xml) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+					// They will have gone through all possible matchers
+					if ( (elem = !matcher && elem) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( (matcher = setMatchers[j++]) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !(unmatched[i] || setMatched[i]) ) {
+								setMatched[i] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[i] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( (selector = compiled.selector || selector) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[0] = match[0].slice( 0 );
+		if ( tokens.length > 2 && (token = tokens[0]).type === "ID" &&
+				context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) {
+
+			context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[i];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ (type = token.type) ] ) {
+				break;
+			}
+			if ( (find = Expr.find[ type ]) ) {
+				// Search, expanding context for leading sibling combinators
+				if ( (seed = find(
+					token.matches[0].replace( runescape, funescape ),
+					rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context
+				)) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert(function( el ) {
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
+});
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert(function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute("href") === "#" ;
+}) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	});
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert(function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+}) ) {
+	addHandle( "value", function( elem, name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	});
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert(function( el ) {
+	return el.getAttribute("disabled") == null;
+}) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+					(val = elem.getAttributeNode( name )) && val.specified ?
+					val.value :
+				null;
+		}
+	});
+}
+
+return Sizzle;
+
+})( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+var risSimple = /^.[^:#\[\.,]*$/;
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( jQuery.isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Simple selector that can be filtered directly, removing non-Elements
+	if ( risSimple.test( qualifier ) ) {
+		return jQuery.filter( qualifier, elements, not );
+	}
+
+	// Complex selector, compare the two sets, removing non-Elements
+	qualifier = jQuery.filter( qualifier, elements );
+	return jQuery.grep( elements, function( elem ) {
+		return ( indexOf.call( qualifier, elem ) > -1 ) !== not && elem.nodeType === 1;
+	} );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( jQuery.isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( jQuery.isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+        if ( nodeName( elem, "iframe" ) ) {
+            return elem.contentDocument;
+        }
+
+        // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+        // Treat the template element as a regular one in browsers that
+        // don't support it.
+        if ( nodeName( elem, "template" ) ) {
+            elem = elem.content || elem;
+        }
+
+        return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( jQuery.isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && jQuery.type( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && jQuery.isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && jQuery.isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = jQuery.isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && jQuery.isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( jQuery.isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								jQuery.isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				jQuery.isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( jQuery.type( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !jQuery.isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ jQuery.camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ jQuery.camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ jQuery.camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( jQuery.camelCase );
+			} else {
+				key = jQuery.camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = jQuery.camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			jQuery.contains( elem.ownerDocument, elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+var swap = function( elem, options, callback, args ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.apply( elem, args || [] );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted,
+		scale = 1,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		do {
+
+			// If previous iteration zeroed out, double until we get *something*.
+			// Use string for doubling so we don't accidentally see scale as unchanged below
+			scale = scale || ".5";
+
+			// Adjust and apply
+			initialInUnit = initialInUnit / scale;
+			jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Update scale, tolerating zero or NaN from tween.cur()
+		// Break the loop if scale is unchanged or perfect, or if we've just had enough.
+		} while (
+			scale !== ( scale = currentValue() / initial ) && scale !== 1 && --maxIterations
+		);
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]+)/i );
+
+var rscriptType = ( /^$|\/(?:java|ecma)script/i );
+
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// Support: IE <=9 only
+	option: [ 1, "<select multiple='multiple'>", "</select>" ],
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+// Support: IE <=9 only
+wrapMap.optgroup = wrapMap.option;
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, contains, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( jQuery.type( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		contains = jQuery.contains( elem.ownerDocument, elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( contains ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+} )();
+var documentElement = document.documentElement;
+
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 only
+// See #13393 for more info
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Don't attach events to noData or text/comment nodes (but allow plain objects)
+		if ( !elemData ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = {};
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		// Make a writable jQuery.Event from the native event object
+		var event = jQuery.event.fix( nativeEvent );
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+			handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// Triggered event must either 1) have no namespace, or 2) have namespace(s)
+				// a subset or equal to those in the bound event (both can have no namespace).
+				if ( !event.rnamespace || event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: jQuery.isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		focus: {
+
+			// Fire native event if possible so blur/focus sequence is correct
+			trigger: function() {
+				if ( this !== safeActiveElement() && this.focus ) {
+					this.focus();
+					return false;
+				}
+			},
+			delegateType: "focusin"
+		},
+		blur: {
+			trigger: function() {
+				if ( this === safeActiveElement() && this.blur ) {
+					this.blur();
+					return false;
+				}
+			},
+			delegateType: "focusout"
+		},
+		click: {
+
+			// For checkbox, fire native event so checked state will be right
+			trigger: function() {
+				if ( this.type === "checkbox" && this.click && nodeName( this, "input" ) ) {
+					this.click();
+					return false;
+				}
+			},
+
+			// For cross-browser consistency, don't fire native .click() on links
+			_default: function( event ) {
+				return nodeName( event.target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || jQuery.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	/* eslint-disable max-len */
+
+	// See https://github.com/eslint/eslint/issues/3229
+	rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi,
+
+	/* eslint-enable */
+
+	// Support: IE <=10 - 11, Edge 12 - 13
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rscriptTypeMasked = /^true\/(.*)/,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( ">tbody", elem )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	var match = rscriptTypeMasked.exec( elem.type );
+
+	if ( match ) {
+		elem.type = match[ 1 ];
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.access( src );
+		pdataCur = dataPriv.set( dest, pdataOld );
+		events = pdataOld.events;
+
+		if ( events ) {
+			delete pdataCur.handle;
+			pdataCur.events = {};
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = concat.apply( [], args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		isFunction = jQuery.isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( isFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( isFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl ) {
+								jQuery._evalUrl( node.src );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && jQuery.contains( node.ownerDocument, node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html.replace( rxhtmlTag, "<$1></$2>" );
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = jQuery.contains( elem.ownerDocument, elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rmargin = ( /^margin/ );
+
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		div.style.cssText =
+			"box-sizing:border-box;" +
+			"position:relative;display:block;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"top:1%;width:50%";
+		div.innerHTML = "";
+		documentElement.appendChild( container );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = divStyle.marginLeft === "2px";
+		boxSizingReliableVal = divStyle.width === "4px";
+
+		// Support: Android 4.0 - 4.3 only
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.marginRight = "50%";
+		pixelMarginRightVal = divStyle.marginRight === "4px";
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, pixelMarginRightVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	container.style.cssText = "border:0;width:8px;height:0;top:0;left:-9999px;" +
+		"padding:0;margin-top:1px;position:absolute";
+	container.appendChild( div );
+
+	jQuery.extend( support, {
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelMarginRight: function() {
+			computeStyleTests();
+			return pixelMarginRightVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !jQuery.contains( elem.ownerDocument, elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelMarginRight() && rnumnonpx.test( ret ) && rmargin.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	},
+
+	cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style;
+
+// Return a css property mapped to a potentially vendor prefixed property
+function vendorPropName( name ) {
+
+	// Shortcut for names that are not vendor prefixed
+	if ( name in emptyStyle ) {
+		return name;
+	}
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a property mapped along what jQuery.cssProps suggests or to
+// a vendor prefixed property.
+function finalPropName( name ) {
+	var ret = jQuery.cssProps[ name ];
+	if ( !ret ) {
+		ret = jQuery.cssProps[ name ] = vendorPropName( name ) || name;
+	}
+	return ret;
+}
+
+function setPositiveNumber( elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function augmentWidthOrHeight( elem, name, extra, isBorderBox, styles ) {
+	var i,
+		val = 0;
+
+	// If we already have the right measurement, avoid augmentation
+	if ( extra === ( isBorderBox ? "border" : "content" ) ) {
+		i = 4;
+
+	// Otherwise initialize for horizontal or vertical properties
+	} else {
+		i = name === "width" ? 1 : 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin, so add it if we want it
+		if ( extra === "margin" ) {
+			val += jQuery.css( elem, extra + cssExpand[ i ], true, styles );
+		}
+
+		if ( isBorderBox ) {
+
+			// border-box includes padding, so remove it if we want content
+			if ( extra === "content" ) {
+				val -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// At this point, extra isn't border nor margin, so remove border
+			if ( extra !== "margin" ) {
+				val -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		} else {
+
+			// At this point, extra isn't content, so add padding
+			val += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// At this point, extra isn't content nor padding, so add border
+			if ( extra !== "padding" ) {
+				val += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	return val;
+}
+
+function getWidthOrHeight( elem, name, extra ) {
+
+	// Start with computed style
+	var valueIsBorderBox,
+		styles = getStyles( elem ),
+		val = curCSS( elem, name, styles ),
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+	// Computed unit is not pixels. Stop here and return.
+	if ( rnumnonpx.test( val ) ) {
+		return val;
+	}
+
+	// Check for style in case a browser which returns unreliable values
+	// for getComputedStyle silently falls back to the reliable elem.style
+	valueIsBorderBox = isBorderBox &&
+		( support.boxSizingReliable() || val === elem.style[ name ] );
+
+	// Fall back to offsetWidth/Height when value is "auto"
+	// This happens for inline elements with no explicit setting (gh-3571)
+	if ( val === "auto" ) {
+		val = elem[ "offset" + name[ 0 ].toUpperCase() + name.slice( 1 ) ];
+	}
+
+	// Normalize "", auto, and prepare for extra
+	val = parseFloat( val ) || 0;
+
+	// Use the active box-sizing model to add/subtract irrelevant styles
+	return ( val +
+		augmentWidthOrHeight(
+			elem,
+			name,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {
+		"float": "cssFloat"
+	},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = jQuery.camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			if ( type === "number" ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = jQuery.camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( i, name ) {
+	jQuery.cssHooks[ name ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, name, extra );
+						} ) :
+						getWidthOrHeight( elem, name, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = extra && getStyles( elem ),
+				subtract = extra && augmentWidthOrHeight(
+					elem,
+					name,
+					extra,
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+					styles
+				);
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ name ] = value;
+				value = jQuery.css( elem, name );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( !rmargin.test( prefix ) ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 &&
+				( tween.elem.style[ jQuery.cssProps[ tween.prop ] ] != null ||
+					jQuery.cssHooks[ tween.prop ] ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = jQuery.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 13
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = jQuery.camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( jQuery.isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					jQuery.proxy( result.stop, result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( jQuery.isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( jQuery.isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			jQuery.isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !jQuery.isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( jQuery.isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue && type !== false ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = jQuery.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://html.spec.whatwg.org/multipage/infrastructure.html#strip-and-collapse-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( typeof value === "string" && value ) {
+			classes = value.match( rnothtmlwhite ) || [];
+
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		if ( typeof value === "string" && value ) {
+			classes = value.match( rnothtmlwhite ) || [];
+
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value;
+
+		if ( typeof stateVal === "boolean" && type === "string" ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( jQuery.isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( type === "string" ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = value.match( rnothtmlwhite ) || [];
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, isFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		isFunction = jQuery.isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( isFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/;
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && jQuery.isFunction( elem[ type ] ) && !jQuery.isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+					elem[ type ]();
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( i, name ) {
+
+	// Handle event binding
+	jQuery.fn[ name ] = function( data, fn ) {
+		return arguments.length > 0 ?
+			this.on( name, null, data, fn ) :
+			this.trigger( name );
+	};
+} );
+
+jQuery.fn.extend( {
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+
+
+
+support.focusin = "onfocusin" in window;
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = jQuery.now();
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && jQuery.type( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = jQuery.isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( jQuery.isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() ] = match[ 2 ];
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() ];
+					}
+					return match == null ? null : match;
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 13
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available, append data to url
+			if ( s.data ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( jQuery.isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+
+jQuery._evalUrl = function( url ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+		"throws": true
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( jQuery.isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( jQuery.isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var isFunction = jQuery.isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( isFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain requests
+	if ( s.crossDomain ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" ).prop( {
+					charset: s.scriptCharset,
+					src: s.url
+				} ).on(
+					"load error",
+					callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					}
+				);
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = jQuery.isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && jQuery.isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( jQuery.isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+// Attach a bunch of functions for handling common AJAX events
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( jQuery.isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var doc, docElem, rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		rect = elem.getBoundingClientRect();
+
+		doc = elem.ownerDocument;
+		docElem = doc.documentElement;
+		win = doc.defaultView;
+
+		return {
+			top: rect.top + win.pageYOffset - docElem.clientTop,
+			left: rect.left + win.pageXOffset - docElem.clientLeft
+		};
+	},
+
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// Fixed elements are offset from window (parentOffset = {top:0, left: 0},
+		// because it is its only offset parent
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume getBoundingClientRect is there when computed position is fixed
+			offset = elem.getBoundingClientRect();
+
+		} else {
+
+			// Get *real* offsetParent
+			offsetParent = this.offsetParent();
+
+			// Get correct offsets
+			offset = this.offset();
+			if ( !nodeName( offsetParent[ 0 ], "html" ) ) {
+				parentOffset = offsetParent.offset();
+			}
+
+			// Add offsetParent borders
+			parentOffset = {
+				top: parentOffset.top + jQuery.css( offsetParent[ 0 ], "borderTopWidth", true ),
+				left: parentOffset.left + jQuery.css( offsetParent[ 0 ], "borderLeftWidth", true )
+			};
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( jQuery.isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( jQuery.isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	}
+} );
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( !noGlobal ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/_static/jquery-3.4.1.js b/docs/_static/jquery-3.4.1.js
new file mode 100644
index 00000000..773ad95c
--- /dev/null
+++ b/docs/_static/jquery-3.4.1.js
@@ -0,0 +1,10598 @@
+/*!
+ * jQuery JavaScript Library v3.4.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2019-05-01T21:04Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var document = window.document;
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var concat = arr.concat;
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.4.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	},
+
+	// Support: Android <=4.0 only
+	// Make sure we trim BOM and NBSP
+	rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a global context
+	globalEval: function( code, options ) {
+		DOMEval( code, { nonce: options && options.nonce } );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// Support: Android <=4.0 only
+	trim: function( text ) {
+		return text == null ?
+			"" :
+			( text + "" ).replace( rtrim, "" );
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return concat.apply( [], ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.4
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2019-04-08
+ */
+(function( window ) {
+
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ({}).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	push_native = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[i] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
+	identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+		// "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
+		"*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
+			"*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
+			"*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" +
+			whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
+	funescape = function( _, escaped, escapedWhitespace ) {
+		var high = "0x" + escaped - 0x10000;
+		// NaN means non-codepoint
+		// Support: Firefox<24
+		// Workaround erroneous numeric interpretation of +"0x"
+		return high !== high || escapedWhitespace ?
+			escaped :
+			high < 0 ?
+				// BMP codepoint
+				String.fromCharCode( high + 0x10000 ) :
+				// Supplemental Plane codepoint (surrogate pair)
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		(arr = slice.call( preferredDoc.childNodes )),
+		preferredDoc.childNodes
+	);
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			push_native.apply( target, slice.call(els) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+			// Can't trust NodeList.length
+			while ( (target[j++] = els[i++]) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+
+		if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
+			setDocument( context );
+		}
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) {
+
+				// ID selector
+				if ( (m = match[1]) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( (elem = context.getElementById( m )) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && (elem = newContext.getElementById( m )) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[2] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( (m = match[3]) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				(!rbuggyQSA || !rbuggyQSA.test( selector )) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				(nodeType !== 1 || context.nodeName.toLowerCase() !== "object") ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 && rdescend.test( selector ) ) {
+
+					// Capture the context ID, setting it first if necessary
+					if ( (nid = context.getAttribute( "id" )) ) {
+						nid = nid.replace( rcssescape, fcssescape );
+					} else {
+						context.setAttribute( "id", (nid = expando) );
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[i] = "#" + nid + " " + toSelector( groups[i] );
+					}
+					newSelector = groups.join( "," );
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return (cache[ key + " " ] = value);
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement("fieldset");
+
+	try {
+		return !!fn( el );
+	} catch (e) {
+		return false;
+	} finally {
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split("|"),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[i] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( (cur = cur.nextSibling) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return (name === "input" || name === "button") && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+						inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction(function( argument ) {
+		argument = +argument;
+		return markFunction(function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ (j = matchIndexes[i]) ] ) {
+					seed[j] = !(matches[j] = seed[j]);
+				}
+			}
+		});
+	});
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem.namespaceURI,
+		docElem = (elem.ownerDocument || elem).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9-11, Edge
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	if ( preferredDoc !== document &&
+		(subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert(function( el ) {
+		el.className = "i";
+		return !el.getAttribute("className");
+	});
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert(function( el ) {
+		el.appendChild( document.createComment("") );
+		return !el.getElementsByTagName("*").length;
+	});
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert(function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	});
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter["ID"] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute("id") === attrId;
+			};
+		};
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter["ID"] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode("id");
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find["ID"] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode("id");
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( (elem = elems[i++]) ) {
+						node = elem.getAttributeNode("id");
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find["TAG"] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( (elem = results[i++]) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert(function( el ) {
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll("[selected]").length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push("~=");
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll(":checked").length ) {
+				rbuggyQSA.push(":checked");
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push(".#.+[+~]");
+			}
+		});
+
+		assert(function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement("input");
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll("[name=d]").length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll(":enabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll(":disabled").length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll("*,:x");
+			rbuggyQSA.push(",.*:");
+		});
+	}
+
+	if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector) )) ) {
+
+		assert(function( el ) {
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		});
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			));
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( (b = b.parentNode) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			(!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+
+			// Choose the first element that is related to our preferred document
+			if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+				return -1;
+			}
+			if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+			return a === document ? -1 :
+				b === document ? 1 :
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( (cur = cur.parentNode) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( (cur = cur.parentNode) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[i] === bp[i] ) {
+			i++;
+		}
+
+		return i ?
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[i], bp[i] ) :
+
+			// Otherwise nodes in our document sort first
+			ap[i] === preferredDoc ? -1 :
+			bp[i] === preferredDoc ? 1 :
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+					// As well, disconnected nodes are said to be in a document
+					// fragment in IE 9
+					elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch (e) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+	// Set document vars if needed
+	if ( ( context.ownerDocument || context ) !== document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+	// Set document vars if needed
+	if ( ( elem.ownerDocument || elem ) !== document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			(val = elem.getAttributeNode(name)) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return (sel + "").replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( (elem = results[i++]) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+		// If no nodeType, this is expected to be an array
+		while ( (node = elem[i++]) ) {
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[1] = match[1].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape );
+
+			if ( match[2] === "~=" ) {
+				match[3] = " " + match[3] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[1] = match[1].toLowerCase();
+
+			if ( match[1].slice( 0, 3 ) === "nth" ) {
+				// nth-* requires argument
+				if ( !match[3] ) {
+					Sizzle.error( match[0] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) );
+				match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" );
+
+			// other types prohibit arguments
+			} else if ( match[3] ) {
+				Sizzle.error( match[0] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[6] && match[2];
+
+			if ( matchExpr["CHILD"].test( match[0] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[3] ) {
+				match[2] = match[4] || match[5] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+				// Get excess from tokenize (recursively)
+				(excess = tokenize( unquoted, true )) &&
+				// advance to the next closing parenthesis
+				(excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+
+				// excess is a negative index
+				match[0] = match[0].slice( 0, excess );
+				match[2] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() { return true; } :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				(pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) &&
+				classCache( className, function( elem ) {
+					return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" );
+				});
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+			};
+		},
+
+		"CHILD": function( type, what, argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( (node = node[ dir ]) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || (node[ expando ] = {});
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								(outerCache[ node.uniqueID ] = {});
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( (node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								(diff = nodeIndex = 0) || start.pop()) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+							// Use previously-cached element index if available
+							if ( useCache ) {
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || (node[ expando ] = {});
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									(outerCache[ node.uniqueID ] = {});
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+								// Use the same loop as above to seek `elem` from the start
+								while ( (node = ++nodeIndex && node && node[ dir ] ||
+									(diff = nodeIndex = 0) || start.pop()) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] || (node[ expando ] = {});
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												(outerCache[ node.uniqueID ] = {});
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction(function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[i] );
+							seed[ idx ] = !( matches[ idx ] = matched[i] );
+						}
+					}) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+		// Potentially complex pseudos
+		"not": markFunction(function( selector ) {
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction(function( seed, matches, context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( (elem = unmatched[i]) ) {
+							seed[i] = !(matches[i] = elem);
+						}
+					}
+				}) :
+				function( elem, context, xml ) {
+					input[0] = elem;
+					matcher( input, null, xml, results );
+					// Don't keep the element (issue #299)
+					input[0] = null;
+					return !results.pop();
+				};
+		}),
+
+		"has": markFunction(function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		}),
+
+		"contains": markFunction(function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		}),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+			// lang value must be a valid identifier
+			if ( !ridentifier.test(lang || "") ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( (elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( (elem = elem.parentNode) && elem.nodeType === 1 );
+				return false;
+			};
+		}),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected);
+		},
+
+		"selected": function( elem ) {
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos["empty"]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo(function() {
+			return [ 0 ];
+		}),
+
+		"last": createPositionalPseudo(function( matchIndexes, length ) {
+			return [ length - 1 ];
+		}),
+
+		"eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		}),
+
+		"even": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"odd": createPositionalPseudo(function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		}),
+
+		"gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		})
+	}
+};
+
+Expr.pseudos["nth"] = Expr.pseudos["eq"];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || (match = rcomma.exec( soFar )) ) {
+			if ( match ) {
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[0].length ) || soFar;
+			}
+			groups.push( (tokens = []) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( (match = rcombinators.exec( soFar )) ) {
+			matched = match.shift();
+			tokens.push({
+				value: matched,
+				// Cast descendant combinators to space
+				type: match[0].replace( rtrim, " " )
+			});
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] ||
+				(match = preFilters[ type ]( match ))) ) {
+				matched = match.shift();
+				tokens.push({
+					value: matched,
+					type: type,
+					matches: match
+				});
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[i].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( (elem = elem[ dir ]) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( (elem = elem[ dir ]) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || (elem[ expando ] = {});
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( (oldCache = uniqueCache[ key ]) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return (newCache[ 2 ] = oldCache[ 2 ]);
+						} else {
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[i]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[0];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[i], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( (elem = unmatched[i]) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction(function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( (elem = temp[i]) ) {
+					matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem);
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( (elem = matcherOut[i]) ) {
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( (matcherIn[i] = elem) );
+						}
+					}
+					postFinder( null, (matcherOut = []), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( (elem = matcherOut[i]) &&
+						(temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) {
+
+						seed[temp] = !(results[temp] = elem);
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	});
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[0].type ],
+		implicitRelative = leadingRelative || Expr.relative[" "],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				(checkContext = context).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( (matcher = Expr.relative[ tokens[i].type ]) ) {
+			matchers = [ addCombinator(elementMatcher( matchers ), matcher) ];
+		} else {
+			matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[j].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+						// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+						tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" })
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( (tokens = tokens.slice( j )) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1),
+				len = elems.length;
+
+			if ( outermost ) {
+				outermostContext = context === document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && (elem = elems[i]) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+					if ( !context && elem.ownerDocument !== document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( (matcher = elementMatchers[j++]) ) {
+						if ( matcher( elem, context || document, xml) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+					// They will have gone through all possible matchers
+					if ( (elem = !matcher && elem) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( (matcher = setMatchers[j++]) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !(unmatched[i] || setMatched[i]) ) {
+								setMatched[i] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[i] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( (selector = compiled.selector || selector) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[0] = match[0].slice( 0 );
+		if ( tokens.length > 2 && (token = tokens[0]).type === "ID" &&
+				context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) {
+
+			context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[i];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ (type = token.type) ] ) {
+				break;
+			}
+			if ( (find = Expr.find[ type ]) ) {
+				// Search, expanding context for leading sibling combinators
+				if ( (seed = find(
+					token.matches[0].replace( runescape, funescape ),
+					rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context
+				)) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert(function( el ) {
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
+});
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert(function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute("href") === "#" ;
+}) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	});
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert(function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+}) ) {
+	addHandle( "value", function( elem, name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	});
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert(function( el ) {
+	return el.getAttribute("disabled") == null;
+}) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+					(val = elem.getAttributeNode( name )) && val.specified ?
+					val.value :
+				null;
+		}
+	});
+}
+
+return Sizzle;
+
+})( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( typeof elem.contentDocument !== "undefined" ) {
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+var swap = function( elem, options, callback, args ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.apply( elem, args || [] );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// Support: IE <=9 only
+	option: [ 1, "<select multiple='multiple'>", "</select>" ],
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+// Support: IE <=9 only
+wrapMap.optgroup = wrapMap.option;
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+} )();
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Don't attach events to noData or text/comment nodes (but allow plain objects)
+		if ( !elemData ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = {};
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		// Make a writable jQuery.Event from the native event object
+		var event = jQuery.event.fix( nativeEvent );
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+			handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+						return result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	/* eslint-disable max-len */
+
+	// See https://github.com/eslint/eslint/issues/3229
+	rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi,
+
+	/* eslint-enable */
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.access( src );
+		pdataCur = dataPriv.set( dest, pdataOld );
+		events = pdataOld.events;
+
+		if ( events ) {
+			delete pdataCur.handle;
+			pdataCur.events = {};
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = concat.apply( [], args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								} );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html.replace( rxhtmlTag, "<$1></$2>" );
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Fall back to offsetWidth/offsetHeight when value is "auto"
+	// This happens for inline elements with no explicit setting (gh-3571)
+	// Support: Android <=4.1 - 4.3 only
+	// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+	// Support: IE 9-11 only
+	// Also use offsetWidth/offsetHeight for when box sizing is unreliable
+	// We use getClientRects() to check for hidden/disconnected.
+	// In those cases, the computed value can be trusted to be border-box
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+		val === "auto" ||
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+					jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue && type !== false ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = Date.now();
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+
+jQuery._evalUrl = function( url, options ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+// Attach a bunch of functions for handling common AJAX events
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( i, name ) {
+
+	// Handle event binding
+	jQuery.fn[ name ] = function( data, fn ) {
+		return arguments.length > 0 ?
+			this.on( name, null, data, fn ) :
+			this.trigger( name );
+	};
+} );
+
+jQuery.fn.extend( {
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	}
+} );
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( !noGlobal ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/_static/jquery-3.5.1.js b/docs/_static/jquery-3.5.1.js
new file mode 100644
index 00000000..50937333
--- /dev/null
+++ b/docs/_static/jquery-3.5.1.js
@@ -0,0 +1,10872 @@
+/*!
+ * jQuery JavaScript Library v3.5.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2020-05-04T22:49Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.5.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( _i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.5
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2020-03-14
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem.namespaceURI,
+		docElem = ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+					dataPriv.get( this, "events" ) || Object.create( null )
+				)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+						return result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px";
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+					jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = (
+					dataPriv.get( cur, "events" ) || Object.create( null )
+				)[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script
+			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			if ( typeof props.top === "number" ) {
+				props.top += "px";
+			}
+			if ( typeof props.left === "number" ) {
+				props.left += "px";
+			}
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	} );
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/_static/jquery.js b/docs/_static/jquery.js
index c4c6022f..f6a6a99e 100644
--- a/docs/_static/jquery.js
+++ b/docs/_static/jquery.js
@@ -1,2 +1,4 @@
-/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return 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diff --git a/docs/_static/language_data.js b/docs/_static/language_data.js
index 2e22b06a..5266fb19 100644
--- a/docs/_static/language_data.js
+++ b/docs/_static/language_data.js
@@ -5,16 +5,15 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
 
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
 
 
-/* Non-minified version is copied as a separate JS file, is available */
-
+/* Non-minified version JS is _stemmer.js if file is provided */ 
 /**
  * Porter Stemmer
  */
@@ -197,3 +196,102 @@ var Stemmer = function() {
   }
 }
 
+
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
+}
+
+
diff --git a/docs/_static/navigation.png b/docs/_static/navigation.png
new file mode 100644
index 0000000000000000000000000000000000000000..fda6cd29ede1be6168ce8b5e02d099ea9055ccfb
GIT binary patch
literal 120
zcmeAS@N?(olHy`uVBq!ia0vp^j6iI|!2~2VCcCc%QudxMjv*C{TfG{24>0g7{2zat
zIm@i@&@2&y-PR1xY$qjFM{J&XU;25XW1{_&{(Ldr+IaofPcAOcp2zd*+4*{gj8%&M
U)(><J1C3(vboFyt=akR{0L>{V3jhEB

literal 0
HcmV?d00001

diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css
index 87f8bd12..8213e90b 100644
--- a/docs/_static/pygments.css
+++ b/docs/_static/pygments.css
@@ -1,82 +1,65 @@
-pre { line-height: 125%; }
-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
-.highlight { background: #f8f8f8; }
-.highlight .c { color: #8f5902; font-style: italic } /* Comment */
-.highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
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-.highlight .k { color: #004461; font-weight: bold } /* Keyword */
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-.highlight .n { color: #000000 } /* Name */
-.highlight .o { color: #582800 } /* Operator */
-.highlight .x { color: #000000 } /* Other */
-.highlight .p { color: #000000; font-weight: bold } /* Punctuation */
-.highlight .ch { color: #8f5902; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #8f5902; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #8f5902 } /* Comment.Preproc */
-.highlight .cpf { color: #8f5902; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #8f5902; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #8f5902; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #a40000 } /* Generic.Deleted */
-.highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #ef2929 } /* Generic.Error */
+.highlight  { background: #eeffcc; }
+.highlight .c { color: #408090; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #007020; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #007020 } /* Comment.Preproc */
+.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
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 .highlight .gi { color: #00A000 } /* Generic.Inserted */
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+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
+.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
index ac4d5861..c8215738 100644
--- a/docs/_static/searchtools.js
+++ b/docs/_static/searchtools.js
@@ -1,497 +1,727 @@
 /*
- * searchtools.js
+ * searchtools.js_t
  * ~~~~~~~~~~~~~~~~
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
-"use strict";
 
+
+/* Non-minified version JS is _stemmer.js if file is provided */ 
 /**
- * Simple result scoring code.
+ * Porter Stemmer
  */
-if (typeof Scorer === "undefined") {
-  var Scorer = {
-    // Implement the following function to further tweak the score for each result
-    // The function takes a result array [docname, title, anchor, descr, score, filename]
-    // and returns the new score.
-    /*
-    score: result => {
-      const [docname, title, anchor, descr, score, filename] = result
-      return score
-    },
-    */
-
-    // query matches the full name of an object
-    objNameMatch: 11,
-    // or matches in the last dotted part of the object name
-    objPartialMatch: 6,
-    // Additive scores depending on the priority of the object
-    objPrio: {
-      0: 15, // used to be importantResults
-      1: 5, // used to be objectResults
-      2: -5, // used to be unimportantResults
-    },
-    //  Used when the priority is not in the mapping.
-    objPrioDefault: 0,
-
-    // query found in title
-    title: 15,
-    partialTitle: 7,
-    // query found in terms
-    term: 5,
-    partialTerm: 2,
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
   };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
 }
 
-const _removeChildren = (element) => {
-  while (element && element.lastChild) element.removeChild(element.lastChild);
-};
+
 
 /**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ * Simple result scoring code.
  */
-const _escapeRegExp = (string) =>
-  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, highlightTerms, searchTerms) => {
-  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
-  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-
-  const [docName, title, anchor, descr] = item;
-
-  let listItem = document.createElement("li");
-  let requestUrl;
-  let linkUrl;
-  if (docBuilder === "dirhtml") {
-    // dirhtml builder
-    let dirname = docName + "/";
-    if (dirname.match(/\/index\/$/))
-      dirname = dirname.substring(0, dirname.length - 6);
-    else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
-    linkUrl = requestUrl;
-  } else {
-    // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
-    linkUrl = docName + docLinkSuffix;
-  }
-  const params = new URLSearchParams();
-  params.set("highlight", [...highlightTerms].join(" "));
-  let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + "?" + params.toString() + anchor;
-  linkEl.innerHTML = title;
-  if (descr)
-    listItem.appendChild(document.createElement("span")).innerText =
-      " (" + descr + ")";
-  else if (showSearchSummary)
-    fetch(requestUrl)
-      .then((responseData) => responseData.text())
-      .then((data) => {
-        if (data)
-          listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, highlightTerms)
-          );
-      });
-  Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
-  Search.stopPulse();
-  Search.title.innerText = _("Search Results");
-  if (!resultCount)
-    Search.status.innerText = Documentation.gettext(
-      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
-    );
-  else
-    Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
-};
-const _displayNextItem = (
-  results,
-  resultCount,
-  highlightTerms,
-  searchTerms
-) => {
-  // results left, load the summary and display it
-  // this is intended to be dynamic (don't sub resultsCount)
-  if (results.length) {
-    _displayItem(results.pop(), highlightTerms, searchTerms);
-    setTimeout(
-      () => _displayNextItem(results, resultCount, highlightTerms, searchTerms),
-      5
-    );
-  }
-  // search finished, update title and status message
-  else _finishSearch(resultCount);
+var Scorer = {
+  // Implement the following function to further tweak the score for each result
+  // The function takes a result array [filename, title, anchor, descr, score]
+  // and returns the new score.
+  /*
+  score: function(result) {
+    return result[4];
+  },
+  */
+
+  // query matches the full name of an object
+  objNameMatch: 11,
+  // or matches in the last dotted part of the object name
+  objPartialMatch: 6,
+  // Additive scores depending on the priority of the object
+  objPrio: {0:  15,   // used to be importantResults
+            1:  5,   // used to be objectResults
+            2: -5},  // used to be unimportantResults
+  //  Used when the priority is not in the mapping.
+  objPrioDefault: 0,
+
+  // query found in title
+  title: 15,
+  // query found in terms
+  term: 5
 };
 
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
-  var splitQuery = (query) => query
-      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
-      .filter(term => term)  // remove remaining empty strings
+
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
 }
 
+
+
+
 /**
  * Search Module
  */
-const Search = {
-  _index: null,
-  _queued_query: null,
-  _pulse_status: -1,
-
-  htmlToText: (htmlString) => {
-    const htmlElement = document
-      .createRange()
-      .createContextualFragment(htmlString);
-    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
-    const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
-    console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
-    );
-    return "";
+var Search = {
+
+  _index : null,
+  _queued_query : null,
+  _pulse_status : -1,
+
+  init : function() {
+      var params = $.getQueryParameters();
+      if (params.q) {
+          var query = params.q[0];
+          $('input[name="q"]')[0].value = query;
+          this.performSearch(query);
+      }
   },
 
-  init: () => {
-    const query = new URLSearchParams(window.location.search).get("q");
-    document
-      .querySelectorAll('input[name="q"]')
-      .forEach((el) => (el.value = query));
-    if (query) Search.performSearch(query);
+  loadIndex : function(url) {
+    $.ajax({type: "GET", url: url, data: null,
+            dataType: "script", cache: true,
+            complete: function(jqxhr, textstatus) {
+              if (textstatus != "success") {
+                document.getElementById("searchindexloader").src = url;
+              }
+            }});
   },
 
-  loadIndex: (url) =>
-    (document.body.appendChild(document.createElement("script")).src = url),
-
-  setIndex: (index) => {
-    Search._index = index;
-    if (Search._queued_query !== null) {
-      const query = Search._queued_query;
-      Search._queued_query = null;
-      Search.query(query);
+  setIndex : function(index) {
+    var q;
+    this._index = index;
+    if ((q = this._queued_query) !== null) {
+      this._queued_query = null;
+      Search.query(q);
     }
   },
 
-  hasIndex: () => Search._index !== null,
-
-  deferQuery: (query) => (Search._queued_query = query),
+  hasIndex : function() {
+      return this._index !== null;
+  },
 
-  stopPulse: () => (Search._pulse_status = -1),
+  deferQuery : function(query) {
+      this._queued_query = query;
+  },
 
-  startPulse: () => {
-    if (Search._pulse_status >= 0) return;
+  stopPulse : function() {
+      this._pulse_status = 0;
+  },
 
-    const pulse = () => {
+  startPulse : function() {
+    if (this._pulse_status >= 0)
+        return;
+    function pulse() {
+      var i;
       Search._pulse_status = (Search._pulse_status + 1) % 4;
-      Search.dots.innerText = ".".repeat(Search._pulse_status);
-      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
-    };
+      var dotString = '';
+      for (i = 0; i < Search._pulse_status; i++)
+        dotString += '.';
+      Search.dots.text(dotString);
+      if (Search._pulse_status > -1)
+        window.setTimeout(pulse, 500);
+    }
     pulse();
   },
 
   /**
    * perform a search for something (or wait until index is loaded)
    */
-  performSearch: (query) => {
+  performSearch : function(query) {
     // create the required interface elements
-    const searchText = document.createElement("h2");
-    searchText.textContent = _("Searching");
-    const searchSummary = document.createElement("p");
-    searchSummary.classList.add("search-summary");
-    searchSummary.innerText = "";
-    const searchList = document.createElement("ul");
-    searchList.classList.add("search");
-
-    const out = document.getElementById("search-results");
-    Search.title = out.appendChild(searchText);
-    Search.dots = Search.title.appendChild(document.createElement("span"));
-    Search.status = out.appendChild(searchSummary);
-    Search.output = out.appendChild(searchList);
-
-    const searchProgress = document.getElementById("search-progress");
-    // Some themes don't use the search progress node
-    if (searchProgress) {
-      searchProgress.innerText = _("Preparing search...");
-    }
-    Search.startPulse();
+    this.out = $('#search-results');
+    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
+    this.dots = $('<span></span>').appendTo(this.title);
+    this.status = $('<p style="display: none"></p>').appendTo(this.out);
+    this.output = $('<ul class="search"/>').appendTo(this.out);
+
+    $('#search-progress').text(_('Preparing search...'));
+    this.startPulse();
 
     // index already loaded, the browser was quick!
-    if (Search.hasIndex()) Search.query(query);
-    else Search.deferQuery(query);
+    if (this.hasIndex())
+      this.query(query);
+    else
+      this.deferQuery(query);
   },
 
   /**
    * execute search (requires search index to be loaded)
    */
-  query: (query) => {
-    // stem the search terms and add them to the correct list
-    const stemmer = new Stemmer();
-    const searchTerms = new Set();
-    const excludedTerms = new Set();
-    const highlightTerms = new Set();
-    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
-    splitQuery(query.trim()).forEach((queryTerm) => {
-      const queryTermLower = queryTerm.toLowerCase();
-
-      // maybe skip this "word"
-      // stopwords array is from language_data.js
-      if (
-        stopwords.indexOf(queryTermLower) !== -1 ||
-        queryTerm.match(/^\d+$/)
-      )
-        return;
+  query : function(query) {
+    var i;
+    var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+
+    // stem the searchterms and add them to the correct list
+    var stemmer = new Stemmer();
+    var searchterms = [];
+    var excluded = [];
+    var hlterms = [];
+    var tmp = splitQuery(query);
+    var objectterms = [];
+    for (i = 0; i < tmp.length; i++) {
+      if (tmp[i] !== "") {
+          objectterms.push(tmp[i].toLowerCase());
+      }
 
+      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
+          tmp[i] === "") {
+        // skip this "word"
+        continue;
+      }
       // stem the word
-      let word = stemmer.stemWord(queryTermLower);
+      var word = stemmer.stemWord(tmp[i].toLowerCase());
+      // prevent stemmer from cutting word smaller than two chars
+      if(word.length < 3 && tmp[i].length >= 3) {
+        word = tmp[i];
+      }
+      var toAppend;
       // select the correct list
-      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      if (word[0] == '-') {
+        toAppend = excluded;
+        word = word.substr(1);
+      }
       else {
-        searchTerms.add(word);
-        highlightTerms.add(queryTermLower);
+        toAppend = searchterms;
+        hlterms.push(tmp[i].toLowerCase());
       }
-    });
+      // only add if not already in the list
+      if (!$u.contains(toAppend, word))
+        toAppend.push(word);
+    }
+    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
+
+    // console.debug('SEARCH: searching for:');
+    // console.info('required: ', searchterms);
+    // console.info('excluded: ', excluded);
 
-    // console.debug("SEARCH: searching for:");
-    // console.info("required: ", [...searchTerms]);
-    // console.info("excluded: ", [...excludedTerms]);
+    // prepare search
+    var terms = this._index.terms;
+    var titleterms = this._index.titleterms;
 
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
-    _removeChildren(document.getElementById("search-progress"));
+    // array of [filename, title, anchor, descr, score]
+    var results = [];
+    $('#search-progress').empty();
 
     // lookup as object
-    objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
-    );
+    for (i = 0; i < objectterms.length; i++) {
+      var others = [].concat(objectterms.slice(0, i),
+                             objectterms.slice(i+1, objectterms.length));
+      results = results.concat(this.performObjectSearch(objectterms[i], others));
+    }
 
     // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+    if (Scorer.score) {
+      for (i = 0; i < results.length; i++)
+        results[i][4] = Scorer.score(results[i]);
+    }
 
     // now sort the results by score (in opposite order of appearance, since the
     // display function below uses pop() to retrieve items) and then
     // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
+    results.sort(function(a, b) {
+      var left = a[4];
+      var right = b[4];
+      if (left > right) {
+        return 1;
+      } else if (left < right) {
+        return -1;
+      } else {
         // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+        left = a[1].toLowerCase();
+        right = b[1].toLowerCase();
+        return (left > right) ? -1 : ((left < right) ? 1 : 0);
       }
-      return leftScore > rightScore ? 1 : -1;
     });
 
-    // remove duplicate search results
-    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
-    let seen = new Set();
-    results = results.reverse().reduce((acc, result) => {
-      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
-      if (!seen.has(resultStr)) {
-        acc.push(result);
-        seen.add(resultStr);
-      }
-      return acc;
-    }, []);
-
-    results = results.reverse();
-
     // for debugging
     //Search.lastresults = results.slice();  // a copy
-    // console.info("search results:", Search.lastresults);
+    //console.info('search results:', Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, highlightTerms, searchTerms);
+    var resultCount = results.length;
+    function displayNextItem() {
+      // results left, load the summary and display it
+      if (results.length) {
+        var item = results.pop();
+        var listItem = $('<li style="display:none"></li>');
+        if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === '') {
+          // dirhtml builder
+          var dirname = item[0] + '/';
+          if (dirname.match(/\/index\/$/)) {
+            dirname = dirname.substring(0, dirname.length-6);
+          } else if (dirname == 'index/') {
+            dirname = '';
+          }
+          listItem.append($('<a/>').attr('href',
+            DOCUMENTATION_OPTIONS.URL_ROOT + dirname +
+            highlightstring + item[2]).html(item[1]));
+        } else {
+          // normal html builders
+          listItem.append($('<a/>').attr('href',
+            item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX +
+            highlightstring + item[2]).html(item[1]));
+        }
+        if (item[3]) {
+          listItem.append($('<span> (' + item[3] + ')</span>'));
+          Search.output.append(listItem);
+          listItem.slideDown(5, function() {
+            displayNextItem();
+          });
+        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
+          var suffix = DOCUMENTATION_OPTIONS.SOURCELINK_SUFFIX;
+          $.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[5] + (item[5].slice(-suffix.length) === suffix ? '' : suffix),
+                  dataType: "text",
+                  complete: function(jqxhr, textstatus) {
+                    var data = jqxhr.responseText;
+                    if (data !== '' && data !== undefined) {
+                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
+                    }
+                    Search.output.append(listItem);
+                    listItem.slideDown(5, function() {
+                      displayNextItem();
+                    });
+                  }});
+        } else {
+          // no source available, just display title
+          Search.output.append(listItem);
+          listItem.slideDown(5, function() {
+            displayNextItem();
+          });
+        }
+      }
+      // search finished, update title and status message
+      else {
+        Search.stopPulse();
+        Search.title.text(_('Search Results'));
+        if (!resultCount)
+          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
+        else
+            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
+        Search.status.fadeIn(500);
+      }
+    }
+    displayNextItem();
   },
 
   /**
    * search for object names
    */
-  performObjectSearch: (object, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const objects = Search._index.objects;
-    const objNames = Search._index.objnames;
-    const titles = Search._index.titles;
-
-    const results = [];
-
-    const objectSearchCallback = (prefix, match) => {
-      const name = match[4]
-      const fullname = (prefix ? prefix + "." : "") + name;
-      const fullnameLower = fullname.toLowerCase();
-      if (fullnameLower.indexOf(object) < 0) return;
-
-      let score = 0;
-      const parts = fullnameLower.split(".");
-
-      // check for different match types: exact matches of full name or
-      // "last name" (i.e. last dotted part)
-      if (fullnameLower === object || parts.slice(-1)[0] === object)
-        score += Scorer.objNameMatch;
-      else if (parts.slice(-1)[0].indexOf(object) > -1)
-        score += Scorer.objPartialMatch; // matches in last name
-
-      const objName = objNames[match[1]][2];
-      const title = titles[match[0]];
-
-      // If more than one term searched for, we require other words to be
-      // found in the name/title/description
-      const otherTerms = new Set(objectTerms);
-      otherTerms.delete(object);
-      if (otherTerms.size > 0) {
-        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
-        if (
-          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
-        )
-          return;
+  performObjectSearch : function(object, otherterms) {
+    var filenames = this._index.filenames;
+    var docnames = this._index.docnames;
+    var objects = this._index.objects;
+    var objnames = this._index.objnames;
+    var titles = this._index.titles;
+
+    var i;
+    var results = [];
+
+    for (var prefix in objects) {
+      for (var name in objects[prefix]) {
+        var fullname = (prefix ? prefix + '.' : '') + name;
+        if (fullname.toLowerCase().indexOf(object) > -1) {
+          var score = 0;
+          var parts = fullname.split('.');
+          // check for different match types: exact matches of full name or
+          // "last name" (i.e. last dotted part)
+          if (fullname == object || parts[parts.length - 1] == object) {
+            score += Scorer.objNameMatch;
+          // matches in last name
+          } else if (parts[parts.length - 1].indexOf(object) > -1) {
+            score += Scorer.objPartialMatch;
+          }
+          var match = objects[prefix][name];
+          var objname = objnames[match[1]][2];
+          var title = titles[match[0]];
+          // If more than one term searched for, we require other words to be
+          // found in the name/title/description
+          if (otherterms.length > 0) {
+            var haystack = (prefix + ' ' + name + ' ' +
+                            objname + ' ' + title).toLowerCase();
+            var allfound = true;
+            for (i = 0; i < otherterms.length; i++) {
+              if (haystack.indexOf(otherterms[i]) == -1) {
+                allfound = false;
+                break;
+              }
+            }
+            if (!allfound) {
+              continue;
+            }
+          }
+          var descr = objname + _(', in ') + title;
+
+          var anchor = match[3];
+          if (anchor === '')
+            anchor = fullname;
+          else if (anchor == '-')
+            anchor = objnames[match[1]][1] + '-' + fullname;
+          // add custom score for some objects according to scorer
+          if (Scorer.objPrio.hasOwnProperty(match[2])) {
+            score += Scorer.objPrio[match[2]];
+          } else {
+            score += Scorer.objPrioDefault;
+          }
+          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
+        }
       }
+    }
 
-      let anchor = match[3];
-      if (anchor === "") anchor = fullname;
-      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
-      const descr = objName + _(", in ") + title;
-
-      // add custom score for some objects according to scorer
-      if (Scorer.objPrio.hasOwnProperty(match[2]))
-        score += Scorer.objPrio[match[2]];
-      else score += Scorer.objPrioDefault;
-
-      results.push([
-        docNames[match[0]],
-        fullname,
-        "#" + anchor,
-        descr,
-        score,
-        filenames[match[0]],
-      ]);
-    };
-    Object.keys(objects).forEach((prefix) =>
-      objects[prefix].forEach((array) =>
-        objectSearchCallback(prefix, array)
-      )
-    );
     return results;
   },
 
   /**
    * search for full-text terms in the index
    */
-  performTermsSearch: (searchTerms, excludedTerms) => {
-    // prepare search
-    const terms = Search._index.terms;
-    const titleTerms = Search._index.titleterms;
-    const docNames = Search._index.docnames;
-    const filenames = Search._index.filenames;
-    const titles = Search._index.titles;
+  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
+    var docnames = this._index.docnames;
+    var filenames = this._index.filenames;
+    var titles = this._index.titles;
 
-    const scoreMap = new Map();
-    const fileMap = new Map();
+    var i, j, file;
+    var fileMap = {};
+    var scoreMap = {};
+    var results = [];
 
     // perform the search on the required terms
-    searchTerms.forEach((word) => {
-      const files = [];
-      const arr = [
-        { files: terms[word], score: Scorer.term },
-        { files: titleTerms[word], score: Scorer.title },
+    for (i = 0; i < searchterms.length; i++) {
+      var word = searchterms[i];
+      var files = [];
+      var _o = [
+        {files: terms[word], score: Scorer.term},
+        {files: titleterms[word], score: Scorer.title}
       ];
-      // add support for partial matches
-      if (word.length > 2) {
-        const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
-      }
 
       // no match but word was a required one
-      if (arr.every((record) => record.files === undefined)) return;
-
+      if ($u.every(_o, function(o){return o.files === undefined;})) {
+        break;
+      }
       // found search word in contents
-      arr.forEach((record) => {
-        if (record.files === undefined) return;
-
-        let recordFiles = record.files;
-        if (recordFiles.length === undefined) recordFiles = [recordFiles];
-        files.push(...recordFiles);
-
-        // set score for the word in each file
-        recordFiles.forEach((file) => {
-          if (!scoreMap.has(file)) scoreMap.set(file, {});
-          scoreMap.get(file)[word] = record.score;
-        });
+      $u.each(_o, function(o) {
+        var _files = o.files;
+        if (_files === undefined)
+          return
+
+        if (_files.length === undefined)
+          _files = [_files];
+        files = files.concat(_files);
+
+        // set score for the word in each file to Scorer.term
+        for (j = 0; j < _files.length; j++) {
+          file = _files[j];
+          if (!(file in scoreMap))
+            scoreMap[file] = {}
+          scoreMap[file][word] = o.score;
+        }
       });
 
       // create the mapping
-      files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
-      });
-    });
+      for (j = 0; j < files.length; j++) {
+        file = files[j];
+        if (file in fileMap)
+          fileMap[file].push(word);
+        else
+          fileMap[file] = [word];
+      }
+    }
 
     // now check if the files don't contain excluded terms
-    const results = [];
-    for (const [file, wordList] of fileMap) {
-      // check if all requirements are matched
+    for (file in fileMap) {
+      var valid = true;
 
-      // as search terms with length < 3 are discarded
-      const filteredTermCount = [...searchTerms].filter(
-        (term) => term.length > 2
-      ).length;
-      if (
-        wordList.length !== searchTerms.size &&
-        wordList.length !== filteredTermCount
-      )
-        continue;
+      // check if all requirements are matched
+      if (fileMap[file].length != searchterms.length)
+          continue;
 
       // ensure that none of the excluded terms is in the search result
-      if (
-        [...excludedTerms].some(
-          (term) =>
-            terms[term] === file ||
-            titleTerms[term] === file ||
-            (terms[term] || []).includes(file) ||
-            (titleTerms[term] || []).includes(file)
-        )
-      )
-        break;
+      for (i = 0; i < excluded.length; i++) {
+        if (terms[excluded[i]] == file ||
+            titleterms[excluded[i]] == file ||
+            $u.contains(terms[excluded[i]] || [], file) ||
+            $u.contains(titleterms[excluded[i]] || [], file)) {
+          valid = false;
+          break;
+        }
+      }
 
-      // select one (max) score for the file.
-      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
-      // add result to the result list
-      results.push([
-        docNames[file],
-        titles[file],
-        "",
-        null,
-        score,
-        filenames[file],
-      ]);
+      // if we have still a valid result we can add it to the result list
+      if (valid) {
+        // select one (max) score for the file.
+        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
+        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
+        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
+      }
     }
     return results;
   },
@@ -499,33 +729,30 @@ const Search = {
   /**
    * helper function to return a node containing the
    * search summary for a given text. keywords is a list
-   * of stemmed words, highlightWords is the list of normal, unstemmed
+   * of stemmed words, hlwords is the list of normal, unstemmed
    * words. the first one is used to find the occurrence, the
    * latter for highlighting it.
    */
-  makeSearchSummary: (htmlText, keywords, highlightWords) => {
-    const text = Search.htmlToText(htmlText).toLowerCase();
-    if (text === "") return null;
-
-    const actualStartPosition = [...keywords]
-      .map((k) => text.indexOf(k.toLowerCase()))
-      .filter((i) => i > -1)
-      .slice(-1)[0];
-    const startWithContext = Math.max(actualStartPosition - 120, 0);
-
-    const top = startWithContext === 0 ? "" : "...";
-    const tail = startWithContext + 240 < text.length ? "..." : "";
-
-    let summary = document.createElement("div");
-    summary.classList.add("context");
-    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
-
-    highlightWords.forEach((highlightWord) =>
-      _highlightText(summary, highlightWord, "highlighted")
-    );
-
-    return summary;
-  },
+  makeSearchSummary : function(text, keywords, hlwords) {
+    var textLower = text.toLowerCase();
+    var start = 0;
+    $.each(keywords, function() {
+      var i = textLower.indexOf(this.toLowerCase());
+      if (i > -1)
+        start = i;
+    });
+    start = Math.max(start - 120, 0);
+    var excerpt = ((start > 0) ? '...' : '') +
+      $.trim(text.substr(start, 240)) +
+      ((start + 240 - text.length) ? '...' : '');
+    var rv = $('<div class="context"></div>').text(excerpt);
+    $.each(hlwords, function() {
+      rv = rv.highlightText(this, 'highlighted');
+    });
+    return rv;
+  }
 };
 
-_ready(Search.init);
+$(document).ready(function() {
+  Search.init();
+});
\ No newline at end of file
diff --git a/docs/_static/sphinxdoc.css b/docs/_static/sphinxdoc.css
new file mode 100644
index 00000000..88767732
--- /dev/null
+++ b/docs/_static/sphinxdoc.css
@@ -0,0 +1,345 @@
+/*
+ * sphinxdoc.css_t
+ * ~~~~~~~~~~~~~~~
+ *
+ * Sphinx stylesheet -- sphinxdoc theme.  Originally created by
+ * Armin Ronacher for Werkzeug.
+ *
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+@import url("basic.css");
+
+/* -- page layout ----------------------------------------------------------- */
+
+body {
+    font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
+                 'Verdana', sans-serif;
+    font-size: 14px;
+    letter-spacing: -0.01em;
+    line-height: 150%;
+    text-align: center;
+    background-color: #BFD1D4;
+    color: black;
+    padding: 0;
+    border: 1px solid #aaa;
+
+    margin: 0px 80px 0px 80px;
+    min-width: 740px;
+}
+
+div.document {
+    background-color: white;
+    text-align: left;
+    background-image: url(contents.png);
+    background-repeat: repeat-x;
+}
+
+div.bodywrapper {
+    margin: 0 240px 0 0;
+    border-right: 1px solid #ccc;
+}
+
+div.body {
+    margin: 0;
+    padding: 0.5em 20px 20px 20px;
+}
+
+div.related {
+    font-size: 1em;
+}
+
+div.related ul {
+    background-image: url(navigation.png);
+    height: 2em;
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+}
+
+div.related ul li {
+    margin: 0;
+    padding: 0;
+    height: 2em;
+    float: left;
+}
+
+div.related ul li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+div.related ul li a {
+    margin: 0;
+    padding: 0 5px 0 5px;
+    line-height: 1.75em;
+    color: #EE9816;
+}
+
+div.related ul li a:hover {
+    color: #3CA8E7;
+}
+
+div.sphinxsidebarwrapper {
+    padding: 0;
+}
+
+div.sphinxsidebar {
+    margin: 0;
+    padding: 0.5em 15px 15px 0;
+    width: 210px;
+    float: right;
+    font-size: 1em;
+    text-align: left;
+}
+
+div.sphinxsidebar h3, div.sphinxsidebar h4 {
+    margin: 1em 0 0.5em 0;
+    font-size: 1em;
+    padding: 0.1em 0 0.1em 0.5em;
+    color: white;
+    border: 1px solid #86989B;
+    background-color: #AFC1C4;
+}
+
+div.sphinxsidebar h3 a {
+    color: white;
+}
+
+div.sphinxsidebar ul {
+    padding-left: 1.5em;
+    margin-top: 7px;
+    padding: 0;
+    line-height: 130%;
+}
+
+div.sphinxsidebar ul ul {
+    margin-left: 20px;
+}
+
+div.footer {
+    background-color: #E3EFF1;
+    color: #86989B;
+    padding: 3px 8px 3px 0;
+    clear: both;
+    font-size: 0.8em;
+    text-align: right;
+}
+
+div.footer a {
+    color: #86989B;
+    text-decoration: underline;
+}
+
+/* -- body styles ----------------------------------------------------------- */
+
+p {    
+    margin: 0.8em 0 0.5em 0;
+}
+
+a {
+    color: #CA7900;
+    text-decoration: none;
+}
+
+a:hover {
+    color: #2491CF;
+}
+
+div.body a {
+    text-decoration: underline;
+}
+
+h1 {
+    margin: 0;
+    padding: 0.7em 0 0.3em 0;
+    font-size: 1.5em;
+    color: #11557C;
+}
+
+h2 {
+    margin: 1.3em 0 0.2em 0;
+    font-size: 1.35em;
+    padding: 0;
+}
+
+h3 {
+    margin: 1em 0 -0.3em 0;
+    font-size: 1.2em;
+}
+
+div.body h1 a, div.body h2 a, div.body h3 a, div.body h4 a, div.body h5 a, div.body h6 a {
+    color: black!important;
+}
+
+h1 a.anchor, h2 a.anchor, h3 a.anchor, h4 a.anchor, h5 a.anchor, h6 a.anchor {
+    display: none;
+    margin: 0 0 0 0.3em;
+    padding: 0 0.2em 0 0.2em;
+    color: #aaa!important;
+}
+
+h1:hover a.anchor, h2:hover a.anchor, h3:hover a.anchor, h4:hover a.anchor,
+h5:hover a.anchor, h6:hover a.anchor {
+    display: inline;
+}
+
+h1 a.anchor:hover, h2 a.anchor:hover, h3 a.anchor:hover, h4 a.anchor:hover,
+h5 a.anchor:hover, h6 a.anchor:hover {
+    color: #777;
+    background-color: #eee;
+}
+
+a.headerlink {
+    color: #c60f0f!important;
+    font-size: 1em;
+    margin-left: 6px;
+    padding: 0 4px 0 4px;
+    text-decoration: none!important;
+}
+
+a.headerlink:hover {
+    background-color: #ccc;
+    color: white!important;
+}
+
+cite, code, code {
+    font-family: 'Consolas', 'Deja Vu Sans Mono',
+                 'Bitstream Vera Sans Mono', monospace;
+    font-size: 0.95em;
+    letter-spacing: 0.01em;
+}
+
+code {
+    background-color: #f2f2f2;
+    border-bottom: 1px solid #ddd;
+    color: #333;
+}
+
+code.descname, code.descclassname, code.xref {
+    border: 0;
+}
+
+hr {
+    border: 1px solid #abc;
+    margin: 2em;
+}
+
+a code {
+    border: 0;
+    color: #CA7900;
+}
+
+a code:hover {
+    color: #2491CF;
+}
+
+pre {
+    font-family: 'Consolas', 'Deja Vu Sans Mono',
+                 'Bitstream Vera Sans Mono', monospace;
+    font-size: 0.95em;
+    letter-spacing: 0.015em;
+    line-height: 120%;
+    padding: 0.5em;
+    border: 1px solid #ccc;
+    background-color: #f8f8f8;
+}
+
+pre a {
+    color: inherit;
+    text-decoration: underline;
+}
+
+td.linenos pre {
+    padding: 0.5em 0;
+}
+
+div.quotebar {
+    background-color: #f8f8f8;
+    max-width: 250px;
+    float: right;
+    padding: 2px 7px;
+    border: 1px solid #ccc;
+}
+
+div.topic {
+    background-color: #f8f8f8;
+}
+
+table {
+    border-collapse: collapse;
+    margin: 0 -0.5em 0 -0.5em;
+}
+
+table td, table th {
+    padding: 0.2em 0.5em 0.2em 0.5em;
+}
+
+div.admonition, div.warning {
+    font-size: 0.9em;
+    margin: 1em 0 1em 0;
+    border: 1px solid #86989B;
+    background-color: #f7f7f7;
+    padding: 0;
+}
+
+div.admonition p, div.warning p {
+    margin: 0.5em 1em 0.5em 1em;
+    padding: 0;
+}
+
+div.admonition pre, div.warning pre {
+    margin: 0.4em 1em 0.4em 1em;
+}
+
+div.admonition p.admonition-title,
+div.warning p.admonition-title {
+    margin: 0;
+    padding: 0.1em 0 0.1em 0.5em;
+    color: white;
+    border-bottom: 1px solid #86989B;
+    font-weight: bold;
+    background-color: #AFC1C4;
+}
+
+div.warning {
+    border: 1px solid #940000;
+}
+
+div.warning p.admonition-title {
+    background-color: #CF0000;
+    border-bottom-color: #940000;
+}
+
+div.admonition ul, div.admonition ol,
+div.warning ul, div.warning ol {
+    margin: 0.1em 0.5em 0.5em 3em;
+    padding: 0;
+}
+
+div.versioninfo {
+    margin: 1em 0 0 0;
+    border: 1px solid #ccc;
+    background-color: #DDEAF0;
+    padding: 8px;
+    line-height: 1.3em;
+    font-size: 0.9em;
+}
+
+.viewcode-back {
+    font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
+                 'Verdana', sans-serif;
+}
+
+div.viewcode-block:target {
+    background-color: #f4debf;
+    border-top: 1px solid #ac9;
+    border-bottom: 1px solid #ac9;
+}
+
+div.code-block-caption {
+    background-color: #ddd;
+    color: #222;
+    border: 1px solid #ccc;
+}
\ No newline at end of file
diff --git a/docs/_static/underscore-1.3.1.js b/docs/_static/underscore-1.3.1.js
new file mode 100644
index 00000000..208d4cd8
--- /dev/null
+++ b/docs/_static/underscore-1.3.1.js
@@ -0,0 +1,999 @@
+//     Underscore.js 1.3.1
+//     (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
+//     Underscore is freely distributable under the MIT license.
+//     Portions of Underscore are inspired or borrowed from Prototype,
+//     Oliver Steele's Functional, and John Resig's Micro-Templating.
+//     For all details and documentation:
+//     http://documentcloud.github.com/underscore
+
+(function() {
+
+  // Baseline setup
+  // --------------
+
+  // Establish the root object, `window` in the browser, or `global` on the server.
+  var root = this;
+
+  // Save the previous value of the `_` variable.
+  var previousUnderscore = root._;
+
+  // Establish the object that gets returned to break out of a loop iteration.
+  var breaker = {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var slice            = ArrayProto.slice,
+      unshift          = ArrayProto.unshift,
+      toString         = ObjProto.toString,
+      hasOwnProperty   = ObjProto.hasOwnProperty;
+
+  // All **ECMAScript 5** native function implementations that we hope to use
+  // are declared here.
+  var
+    nativeForEach      = ArrayProto.forEach,
+    nativeMap          = ArrayProto.map,
+    nativeReduce       = ArrayProto.reduce,
+    nativeReduceRight  = ArrayProto.reduceRight,
+    nativeFilter       = ArrayProto.filter,
+    nativeEvery        = ArrayProto.every,
+    nativeSome         = ArrayProto.some,
+    nativeIndexOf      = ArrayProto.indexOf,
+    nativeLastIndexOf  = ArrayProto.lastIndexOf,
+    nativeIsArray      = Array.isArray,
+    nativeKeys         = Object.keys,
+    nativeBind         = FuncProto.bind;
+
+  // Create a safe reference to the Underscore object for use below.
+  var _ = function(obj) { return new wrapper(obj); };
+
+  // Export the Underscore object for **Node.js**, with
+  // backwards-compatibility for the old `require()` API. If we're in
+  // the browser, add `_` as a global object via a string identifier,
+  // for Closure Compiler "advanced" mode.
+  if (typeof exports !== 'undefined') {
+    if (typeof module !== 'undefined' && module.exports) {
+      exports = module.exports = _;
+    }
+    exports._ = _;
+  } else {
+    root['_'] = _;
+  }
+
+  // Current version.
+  _.VERSION = '1.3.1';
+
+  // Collection Functions
+  // --------------------
+
+  // The cornerstone, an `each` implementation, aka `forEach`.
+  // Handles objects with the built-in `forEach`, arrays, and raw objects.
+  // Delegates to **ECMAScript 5**'s native `forEach` if available.
+  var each = _.each = _.forEach = function(obj, iterator, context) {
+    if (obj == null) return;
+    if (nativeForEach && obj.forEach === nativeForEach) {
+      obj.forEach(iterator, context);
+    } else if (obj.length === +obj.length) {
+      for (var i = 0, l = obj.length; i < l; i++) {
+        if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
+      }
+    } else {
+      for (var key in obj) {
+        if (_.has(obj, key)) {
+          if (iterator.call(context, obj[key], key, obj) === breaker) return;
+        }
+      }
+    }
+  };
+
+  // Return the results of applying the iterator to each element.
+  // Delegates to **ECMAScript 5**'s native `map` if available.
+  _.map = _.collect = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
+    each(obj, function(value, index, list) {
+      results[results.length] = iterator.call(context, value, index, list);
+    });
+    if (obj.length === +obj.length) results.length = obj.length;
+    return results;
+  };
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
+  _.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
+    var initial = arguments.length > 2;
+    if (obj == null) obj = [];
+    if (nativeReduce && obj.reduce === nativeReduce) {
+      if (context) iterator = _.bind(iterator, context);
+      return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
+    }
+    each(obj, function(value, index, list) {
+      if (!initial) {
+        memo = value;
+        initial = true;
+      } else {
+        memo = iterator.call(context, memo, value, index, list);
+      }
+    });
+    if (!initial) throw new TypeError('Reduce of empty array with no initial value');
+    return memo;
+  };
+
+  // The right-associative version of reduce, also known as `foldr`.
+  // Delegates to **ECMAScript 5**'s native `reduceRight` if available.
+  _.reduceRight = _.foldr = function(obj, iterator, memo, context) {
+    var initial = arguments.length > 2;
+    if (obj == null) obj = [];
+    if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
+      if (context) iterator = _.bind(iterator, context);
+      return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
+    }
+    var reversed = _.toArray(obj).reverse();
+    if (context && !initial) iterator = _.bind(iterator, context);
+    return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
+  };
+
+  // Return the first value which passes a truth test. Aliased as `detect`.
+  _.find = _.detect = function(obj, iterator, context) {
+    var result;
+    any(obj, function(value, index, list) {
+      if (iterator.call(context, value, index, list)) {
+        result = value;
+        return true;
+      }
+    });
+    return result;
+  };
+
+  // Return all the elements that pass a truth test.
+  // Delegates to **ECMAScript 5**'s native `filter` if available.
+  // Aliased as `select`.
+  _.filter = _.select = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
+    each(obj, function(value, index, list) {
+      if (iterator.call(context, value, index, list)) results[results.length] = value;
+    });
+    return results;
+  };
+
+  // Return all the elements for which a truth test fails.
+  _.reject = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    each(obj, function(value, index, list) {
+      if (!iterator.call(context, value, index, list)) results[results.length] = value;
+    });
+    return results;
+  };
+
+  // Determine whether all of the elements match a truth test.
+  // Delegates to **ECMAScript 5**'s native `every` if available.
+  // Aliased as `all`.
+  _.every = _.all = function(obj, iterator, context) {
+    var result = true;
+    if (obj == null) return result;
+    if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
+    each(obj, function(value, index, list) {
+      if (!(result = result && iterator.call(context, value, index, list))) return breaker;
+    });
+    return result;
+  };
+
+  // Determine if at least one element in the object matches a truth test.
+  // Delegates to **ECMAScript 5**'s native `some` if available.
+  // Aliased as `any`.
+  var any = _.some = _.any = function(obj, iterator, context) {
+    iterator || (iterator = _.identity);
+    var result = false;
+    if (obj == null) return result;
+    if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
+    each(obj, function(value, index, list) {
+      if (result || (result = iterator.call(context, value, index, list))) return breaker;
+    });
+    return !!result;
+  };
+
+  // Determine if a given value is included in the array or object using `===`.
+  // Aliased as `contains`.
+  _.include = _.contains = function(obj, target) {
+    var found = false;
+    if (obj == null) return found;
+    if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
+    found = any(obj, function(value) {
+      return value === target;
+    });
+    return found;
+  };
+
+  // Invoke a method (with arguments) on every item in a collection.
+  _.invoke = function(obj, method) {
+    var args = slice.call(arguments, 2);
+    return _.map(obj, function(value) {
+      return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
+    });
+  };
+
+  // Convenience version of a common use case of `map`: fetching a property.
+  _.pluck = function(obj, key) {
+    return _.map(obj, function(value){ return value[key]; });
+  };
+
+  // Return the maximum element or (element-based computation).
+  _.max = function(obj, iterator, context) {
+    if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
+    if (!iterator && _.isEmpty(obj)) return -Infinity;
+    var result = {computed : -Infinity};
+    each(obj, function(value, index, list) {
+      var computed = iterator ? iterator.call(context, value, index, list) : value;
+      computed >= result.computed && (result = {value : value, computed : computed});
+    });
+    return result.value;
+  };
+
+  // Return the minimum element (or element-based computation).
+  _.min = function(obj, iterator, context) {
+    if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
+    if (!iterator && _.isEmpty(obj)) return Infinity;
+    var result = {computed : Infinity};
+    each(obj, function(value, index, list) {
+      var computed = iterator ? iterator.call(context, value, index, list) : value;
+      computed < result.computed && (result = {value : value, computed : computed});
+    });
+    return result.value;
+  };
+
+  // Shuffle an array.
+  _.shuffle = function(obj) {
+    var shuffled = [], rand;
+    each(obj, function(value, index, list) {
+      if (index == 0) {
+        shuffled[0] = value;
+      } else {
+        rand = Math.floor(Math.random() * (index + 1));
+        shuffled[index] = shuffled[rand];
+        shuffled[rand] = value;
+      }
+    });
+    return shuffled;
+  };
+
+  // Sort the object's values by a criterion produced by an iterator.
+  _.sortBy = function(obj, iterator, context) {
+    return _.pluck(_.map(obj, function(value, index, list) {
+      return {
+        value : value,
+        criteria : iterator.call(context, value, index, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria, b = right.criteria;
+      return a < b ? -1 : a > b ? 1 : 0;
+    }), 'value');
+  };
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  _.groupBy = function(obj, val) {
+    var result = {};
+    var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
+    each(obj, function(value, index) {
+      var key = iterator(value, index);
+      (result[key] || (result[key] = [])).push(value);
+    });
+    return result;
+  };
+
+  // Use a comparator function to figure out at what index an object should
+  // be inserted so as to maintain order. Uses binary search.
+  _.sortedIndex = function(array, obj, iterator) {
+    iterator || (iterator = _.identity);
+    var low = 0, high = array.length;
+    while (low < high) {
+      var mid = (low + high) >> 1;
+      iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
+    }
+    return low;
+  };
+
+  // Safely convert anything iterable into a real, live array.
+  _.toArray = function(iterable) {
+    if (!iterable)                return [];
+    if (iterable.toArray)         return iterable.toArray();
+    if (_.isArray(iterable))      return slice.call(iterable);
+    if (_.isArguments(iterable))  return slice.call(iterable);
+    return _.values(iterable);
+  };
+
+  // Return the number of elements in an object.
+  _.size = function(obj) {
+    return _.toArray(obj).length;
+  };
+
+  // Array Functions
+  // ---------------
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. Aliased as `head`. The **guard** check allows it to work
+  // with `_.map`.
+  _.first = _.head = function(array, n, guard) {
+    return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
+  };
+
+  // Returns everything but the last entry of the array. Especcialy useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N. The **guard** check allows it to work with
+  // `_.map`.
+  _.initial = function(array, n, guard) {
+    return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
+  };
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  _.last = function(array, n, guard) {
+    if ((n != null) && !guard) {
+      return slice.call(array, Math.max(array.length - n, 0));
+    } else {
+      return array[array.length - 1];
+    }
+  };
+
+  // Returns everything but the first entry of the array. Aliased as `tail`.
+  // Especially useful on the arguments object. Passing an **index** will return
+  // the rest of the values in the array from that index onward. The **guard**
+  // check allows it to work with `_.map`.
+  _.rest = _.tail = function(array, index, guard) {
+    return slice.call(array, (index == null) || guard ? 1 : index);
+  };
+
+  // Trim out all falsy values from an array.
+  _.compact = function(array) {
+    return _.filter(array, function(value){ return !!value; });
+  };
+
+  // Return a completely flattened version of an array.
+  _.flatten = function(array, shallow) {
+    return _.reduce(array, function(memo, value) {
+      if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
+      memo[memo.length] = value;
+      return memo;
+    }, []);
+  };
+
+  // Return a version of the array that does not contain the specified value(s).
+  _.without = function(array) {
+    return _.difference(array, slice.call(arguments, 1));
+  };
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // Aliased as `unique`.
+  _.uniq = _.unique = function(array, isSorted, iterator) {
+    var initial = iterator ? _.map(array, iterator) : array;
+    var result = [];
+    _.reduce(initial, function(memo, el, i) {
+      if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
+        memo[memo.length] = el;
+        result[result.length] = array[i];
+      }
+      return memo;
+    }, []);
+    return result;
+  };
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  _.union = function() {
+    return _.uniq(_.flatten(arguments, true));
+  };
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays. (Aliased as "intersect" for back-compat.)
+  _.intersection = _.intersect = function(array) {
+    var rest = slice.call(arguments, 1);
+    return _.filter(_.uniq(array), function(item) {
+      return _.every(rest, function(other) {
+        return _.indexOf(other, item) >= 0;
+      });
+    });
+  };
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  _.difference = function(array) {
+    var rest = _.flatten(slice.call(arguments, 1));
+    return _.filter(array, function(value){ return !_.include(rest, value); });
+  };
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  _.zip = function() {
+    var args = slice.call(arguments);
+    var length = _.max(_.pluck(args, 'length'));
+    var results = new Array(length);
+    for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
+    return results;
+  };
+
+  // If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
+  // we need this function. Return the position of the first occurrence of an
+  // item in an array, or -1 if the item is not included in the array.
+  // Delegates to **ECMAScript 5**'s native `indexOf` if available.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  _.indexOf = function(array, item, isSorted) {
+    if (array == null) return -1;
+    var i, l;
+    if (isSorted) {
+      i = _.sortedIndex(array, item);
+      return array[i] === item ? i : -1;
+    }
+    if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
+    for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
+    return -1;
+  };
+
+  // Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
+  _.lastIndexOf = function(array, item) {
+    if (array == null) return -1;
+    if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
+    var i = array.length;
+    while (i--) if (i in array && array[i] === item) return i;
+    return -1;
+  };
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](http://docs.python.org/library/functions.html#range).
+  _.range = function(start, stop, step) {
+    if (arguments.length <= 1) {
+      stop = start || 0;
+      start = 0;
+    }
+    step = arguments[2] || 1;
+
+    var len = Math.max(Math.ceil((stop - start) / step), 0);
+    var idx = 0;
+    var range = new Array(len);
+
+    while(idx < len) {
+      range[idx++] = start;
+      start += step;
+    }
+
+    return range;
+  };
+
+  // Function (ahem) Functions
+  // ------------------
+
+  // Reusable constructor function for prototype setting.
+  var ctor = function(){};
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally). Binding with arguments is also known as `curry`.
+  // Delegates to **ECMAScript 5**'s native `Function.bind` if available.
+  // We check for `func.bind` first, to fail fast when `func` is undefined.
+  _.bind = function bind(func, context) {
+    var bound, args;
+    if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
+    if (!_.isFunction(func)) throw new TypeError;
+    args = slice.call(arguments, 2);
+    return bound = function() {
+      if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
+      ctor.prototype = func.prototype;
+      var self = new ctor;
+      var result = func.apply(self, args.concat(slice.call(arguments)));
+      if (Object(result) === result) return result;
+      return self;
+    };
+  };
+
+  // Bind all of an object's methods to that object. Useful for ensuring that
+  // all callbacks defined on an object belong to it.
+  _.bindAll = function(obj) {
+    var funcs = slice.call(arguments, 1);
+    if (funcs.length == 0) funcs = _.functions(obj);
+    each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
+    return obj;
+  };
+
+  // Memoize an expensive function by storing its results.
+  _.memoize = function(func, hasher) {
+    var memo = {};
+    hasher || (hasher = _.identity);
+    return function() {
+      var key = hasher.apply(this, arguments);
+      return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
+    };
+  };
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  _.delay = function(func, wait) {
+    var args = slice.call(arguments, 2);
+    return setTimeout(function(){ return func.apply(func, args); }, wait);
+  };
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  _.defer = function(func) {
+    return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
+  };
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time.
+  _.throttle = function(func, wait) {
+    var context, args, timeout, throttling, more;
+    var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
+    return function() {
+      context = this; args = arguments;
+      var later = function() {
+        timeout = null;
+        if (more) func.apply(context, args);
+        whenDone();
+      };
+      if (!timeout) timeout = setTimeout(later, wait);
+      if (throttling) {
+        more = true;
+      } else {
+        func.apply(context, args);
+      }
+      whenDone();
+      throttling = true;
+    };
+  };
+
+  // Returns a function, that, as long as it continues to be invoked, will not
+  // be triggered. The function will be called after it stops being called for
+  // N milliseconds.
+  _.debounce = function(func, wait) {
+    var timeout;
+    return function() {
+      var context = this, args = arguments;
+      var later = function() {
+        timeout = null;
+        func.apply(context, args);
+      };
+      clearTimeout(timeout);
+      timeout = setTimeout(later, wait);
+    };
+  };
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  _.once = function(func) {
+    var ran = false, memo;
+    return function() {
+      if (ran) return memo;
+      ran = true;
+      return memo = func.apply(this, arguments);
+    };
+  };
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  _.wrap = function(func, wrapper) {
+    return function() {
+      var args = [func].concat(slice.call(arguments, 0));
+      return wrapper.apply(this, args);
+    };
+  };
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  _.compose = function() {
+    var funcs = arguments;
+    return function() {
+      var args = arguments;
+      for (var i = funcs.length - 1; i >= 0; i--) {
+        args = [funcs[i].apply(this, args)];
+      }
+      return args[0];
+    };
+  };
+
+  // Returns a function that will only be executed after being called N times.
+  _.after = function(times, func) {
+    if (times <= 0) return func();
+    return function() {
+      if (--times < 1) { return func.apply(this, arguments); }
+    };
+  };
+
+  // Object Functions
+  // ----------------
+
+  // Retrieve the names of an object's properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`
+  _.keys = nativeKeys || function(obj) {
+    if (obj !== Object(obj)) throw new TypeError('Invalid object');
+    var keys = [];
+    for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
+    return keys;
+  };
+
+  // Retrieve the values of an object's properties.
+  _.values = function(obj) {
+    return _.map(obj, _.identity);
+  };
+
+  // Return a sorted list of the function names available on the object.
+  // Aliased as `methods`
+  _.functions = _.methods = function(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (_.isFunction(obj[key])) names.push(key);
+    }
+    return names.sort();
+  };
+
+  // Extend a given object with all the properties in passed-in object(s).
+  _.extend = function(obj) {
+    each(slice.call(arguments, 1), function(source) {
+      for (var prop in source) {
+        obj[prop] = source[prop];
+      }
+    });
+    return obj;
+  };
+
+  // Fill in a given object with default properties.
+  _.defaults = function(obj) {
+    each(slice.call(arguments, 1), function(source) {
+      for (var prop in source) {
+        if (obj[prop] == null) obj[prop] = source[prop];
+      }
+    });
+    return obj;
+  };
+
+  // Create a (shallow-cloned) duplicate of an object.
+  _.clone = function(obj) {
+    if (!_.isObject(obj)) return obj;
+    return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
+  };
+
+  // Invokes interceptor with the obj, and then returns obj.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  _.tap = function(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  };
+
+  // Internal recursive comparison function.
+  function eq(a, b, stack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
+    if (a === b) return a !== 0 || 1 / a == 1 / b;
+    // A strict comparison is necessary because `null == undefined`.
+    if (a == null || b == null) return a === b;
+    // Unwrap any wrapped objects.
+    if (a._chain) a = a._wrapped;
+    if (b._chain) b = b._wrapped;
+    // Invoke a custom `isEqual` method if one is provided.
+    if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
+    if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className != toString.call(b)) return false;
+    switch (className) {
+      // Strings, numbers, dates, and booleans are compared by value.
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return a == String(b);
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
+        // other numeric values.
+        return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a == +b;
+      // RegExps are compared by their source patterns and flags.
+      case '[object RegExp]':
+        return a.source == b.source &&
+               a.global == b.global &&
+               a.multiline == b.multiline &&
+               a.ignoreCase == b.ignoreCase;
+    }
+    if (typeof a != 'object' || typeof b != 'object') return false;
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+    var length = stack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (stack[length] == a) return true;
+    }
+    // Add the first object to the stack of traversed objects.
+    stack.push(a);
+    var size = 0, result = true;
+    // Recursively compare objects and arrays.
+    if (className == '[object Array]') {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      size = a.length;
+      result = size == b.length;
+      if (result) {
+        // Deep compare the contents, ignoring non-numeric properties.
+        while (size--) {
+          // Ensure commutative equality for sparse arrays.
+          if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
+        }
+      }
+    } else {
+      // Objects with different constructors are not equivalent.
+      if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
+      // Deep compare objects.
+      for (var key in a) {
+        if (_.has(a, key)) {
+          // Count the expected number of properties.
+          size++;
+          // Deep compare each member.
+          if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
+        }
+      }
+      // Ensure that both objects contain the same number of properties.
+      if (result) {
+        for (key in b) {
+          if (_.has(b, key) && !(size--)) break;
+        }
+        result = !size;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    stack.pop();
+    return result;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  _.isEqual = function(a, b) {
+    return eq(a, b, []);
+  };
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  _.isEmpty = function(obj) {
+    if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
+    for (var key in obj) if (_.has(obj, key)) return false;
+    return true;
+  };
+
+  // Is a given value a DOM element?
+  _.isElement = function(obj) {
+    return !!(obj && obj.nodeType == 1);
+  };
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native Array.isArray
+  _.isArray = nativeIsArray || function(obj) {
+    return toString.call(obj) == '[object Array]';
+  };
+
+  // Is a given variable an object?
+  _.isObject = function(obj) {
+    return obj === Object(obj);
+  };
+
+  // Is a given variable an arguments object?
+  _.isArguments = function(obj) {
+    return toString.call(obj) == '[object Arguments]';
+  };
+  if (!_.isArguments(arguments)) {
+    _.isArguments = function(obj) {
+      return !!(obj && _.has(obj, 'callee'));
+    };
+  }
+
+  // Is a given value a function?
+  _.isFunction = function(obj) {
+    return toString.call(obj) == '[object Function]';
+  };
+
+  // Is a given value a string?
+  _.isString = function(obj) {
+    return toString.call(obj) == '[object String]';
+  };
+
+  // Is a given value a number?
+  _.isNumber = function(obj) {
+    return toString.call(obj) == '[object Number]';
+  };
+
+  // Is the given value `NaN`?
+  _.isNaN = function(obj) {
+    // `NaN` is the only value for which `===` is not reflexive.
+    return obj !== obj;
+  };
+
+  // Is a given value a boolean?
+  _.isBoolean = function(obj) {
+    return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
+  };
+
+  // Is a given value a date?
+  _.isDate = function(obj) {
+    return toString.call(obj) == '[object Date]';
+  };
+
+  // Is the given value a regular expression?
+  _.isRegExp = function(obj) {
+    return toString.call(obj) == '[object RegExp]';
+  };
+
+  // Is a given value equal to null?
+  _.isNull = function(obj) {
+    return obj === null;
+  };
+
+  // Is a given variable undefined?
+  _.isUndefined = function(obj) {
+    return obj === void 0;
+  };
+
+  // Has own property?
+  _.has = function(obj, key) {
+    return hasOwnProperty.call(obj, key);
+  };
+
+  // Utility Functions
+  // -----------------
+
+  // Run Underscore.js in *noConflict* mode, returning the `_` variable to its
+  // previous owner. Returns a reference to the Underscore object.
+  _.noConflict = function() {
+    root._ = previousUnderscore;
+    return this;
+  };
+
+  // Keep the identity function around for default iterators.
+  _.identity = function(value) {
+    return value;
+  };
+
+  // Run a function **n** times.
+  _.times = function (n, iterator, context) {
+    for (var i = 0; i < n; i++) iterator.call(context, i);
+  };
+
+  // Escape a string for HTML interpolation.
+  _.escape = function(string) {
+    return (''+string).replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#x27;').replace(/\//g,'&#x2F;');
+  };
+
+  // Add your own custom functions to the Underscore object, ensuring that
+  // they're correctly added to the OOP wrapper as well.
+  _.mixin = function(obj) {
+    each(_.functions(obj), function(name){
+      addToWrapper(name, _[name] = obj[name]);
+    });
+  };
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  _.uniqueId = function(prefix) {
+    var id = idCounter++;
+    return prefix ? prefix + id : id;
+  };
+
+  // By default, Underscore uses ERB-style template delimiters, change the
+  // following template settings to use alternative delimiters.
+  _.templateSettings = {
+    evaluate    : /<%([\s\S]+?)%>/g,
+    interpolate : /<%=([\s\S]+?)%>/g,
+    escape      : /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /.^/;
+
+  // Within an interpolation, evaluation, or escaping, remove HTML escaping
+  // that had been previously added.
+  var unescape = function(code) {
+    return code.replace(/\\\\/g, '\\').replace(/\\'/g, "'");
+  };
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  _.template = function(str, data) {
+    var c  = _.templateSettings;
+    var tmpl = 'var __p=[],print=function(){__p.push.apply(__p,arguments);};' +
+      'with(obj||{}){__p.push(\'' +
+      str.replace(/\\/g, '\\\\')
+         .replace(/'/g, "\\'")
+         .replace(c.escape || noMatch, function(match, code) {
+           return "',_.escape(" + unescape(code) + "),'";
+         })
+         .replace(c.interpolate || noMatch, function(match, code) {
+           return "'," + unescape(code) + ",'";
+         })
+         .replace(c.evaluate || noMatch, function(match, code) {
+           return "');" + unescape(code).replace(/[\r\n\t]/g, ' ') + ";__p.push('";
+         })
+         .replace(/\r/g, '\\r')
+         .replace(/\n/g, '\\n')
+         .replace(/\t/g, '\\t')
+         + "');}return __p.join('');";
+    var func = new Function('obj', '_', tmpl);
+    if (data) return func(data, _);
+    return function(data) {
+      return func.call(this, data, _);
+    };
+  };
+
+  // Add a "chain" function, which will delegate to the wrapper.
+  _.chain = function(obj) {
+    return _(obj).chain();
+  };
+
+  // The OOP Wrapper
+  // ---------------
+
+  // If Underscore is called as a function, it returns a wrapped object that
+  // can be used OO-style. This wrapper holds altered versions of all the
+  // underscore functions. Wrapped objects may be chained.
+  var wrapper = function(obj) { this._wrapped = obj; };
+
+  // Expose `wrapper.prototype` as `_.prototype`
+  _.prototype = wrapper.prototype;
+
+  // Helper function to continue chaining intermediate results.
+  var result = function(obj, chain) {
+    return chain ? _(obj).chain() : obj;
+  };
+
+  // A method to easily add functions to the OOP wrapper.
+  var addToWrapper = function(name, func) {
+    wrapper.prototype[name] = function() {
+      var args = slice.call(arguments);
+      unshift.call(args, this._wrapped);
+      return result(func.apply(_, args), this._chain);
+    };
+  };
+
+  // Add all of the Underscore functions to the wrapper object.
+  _.mixin(_);
+
+  // Add all mutator Array functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    wrapper.prototype[name] = function() {
+      var wrapped = this._wrapped;
+      method.apply(wrapped, arguments);
+      var length = wrapped.length;
+      if ((name == 'shift' || name == 'splice') && length === 0) delete wrapped[0];
+      return result(wrapped, this._chain);
+    };
+  });
+
+  // Add all accessor Array functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    wrapper.prototype[name] = function() {
+      return result(method.apply(this._wrapped, arguments), this._chain);
+    };
+  });
+
+  // Start chaining a wrapped Underscore object.
+  wrapper.prototype.chain = function() {
+    this._chain = true;
+    return this;
+  };
+
+  // Extracts the result from a wrapped and chained object.
+  wrapper.prototype.value = function() {
+    return this._wrapped;
+  };
+
+}).call(this);
diff --git a/docs/_static/underscore.js b/docs/_static/underscore.js
index cf177d42..5b55f32b 100644
--- a/docs/_static/underscore.js
+++ b/docs/_static/underscore.js
@@ -1,6 +1,31 @@
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+function(){var a=i.call(arguments);H.call(a,this._wrapped);return v(c.apply(b,a),this._chain)}};b.mixin(b);j("pop,push,reverse,shift,sort,splice,unshift".split(","),function(a){var b=k[a];m.prototype[a]=function(){var d=this._wrapped;b.apply(d,arguments);var e=d.length;(a=="shift"||a=="splice")&&e===0&&delete d[0];return v(d,this._chain)}});j(["concat","join","slice"],function(a){var b=k[a];m.prototype[a]=function(){return v(b.apply(this._wrapped,arguments),this._chain)}});m.prototype.chain=function(){this._chain=
+true;return this};m.prototype.value=function(){return this._wrapped}}).call(this);
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diff --git a/docs/_static/websupport.js b/docs/_static/websupport.js
new file mode 100644
index 00000000..53f6a452
--- /dev/null
+++ b/docs/_static/websupport.js
@@ -0,0 +1,808 @@
+/*
+ * websupport.js
+ * ~~~~~~~~~~~~~
+ *
+ * sphinx.websupport utilities for all documentation.
+ *
+ * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+(function($) {
+  $.fn.autogrow = function() {
+    return this.each(function() {
+    var textarea = this;
+
+    $.fn.autogrow.resize(textarea);
+
+    $(textarea)
+      .focus(function() {
+        textarea.interval = setInterval(function() {
+          $.fn.autogrow.resize(textarea);
+        }, 500);
+      })
+      .blur(function() {
+        clearInterval(textarea.interval);
+      });
+    });
+  };
+
+  $.fn.autogrow.resize = function(textarea) {
+    var lineHeight = parseInt($(textarea).css('line-height'), 10);
+    var lines = textarea.value.split('\n');
+    var columns = textarea.cols;
+    var lineCount = 0;
+    $.each(lines, function() {
+      lineCount += Math.ceil(this.length / columns) || 1;
+    });
+    var height = lineHeight * (lineCount + 1);
+    $(textarea).css('height', height);
+  };
+})(jQuery);
+
+(function($) {
+  var comp, by;
+
+  function init() {
+    initEvents();
+    initComparator();
+  }
+
+  function initEvents() {
+    $(document).on("click", 'a.comment-close', function(event) {
+      event.preventDefault();
+      hide($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.vote', function(event) {
+      event.preventDefault();
+      handleVote($(this));
+    });
+    $(document).on("click", 'a.reply', function(event) {
+      event.preventDefault();
+      openReply($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.close-reply', function(event) {
+      event.preventDefault();
+      closeReply($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.sort-option', function(event) {
+      event.preventDefault();
+      handleReSort($(this));
+    });
+    $(document).on("click", 'a.show-proposal', function(event) {
+      event.preventDefault();
+      showProposal($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.hide-proposal', function(event) {
+      event.preventDefault();
+      hideProposal($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.show-propose-change', function(event) {
+      event.preventDefault();
+      showProposeChange($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.hide-propose-change', function(event) {
+      event.preventDefault();
+      hideProposeChange($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.accept-comment', function(event) {
+      event.preventDefault();
+      acceptComment($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.delete-comment', function(event) {
+      event.preventDefault();
+      deleteComment($(this).attr('id').substring(2));
+    });
+    $(document).on("click", 'a.comment-markup', function(event) {
+      event.preventDefault();
+      toggleCommentMarkupBox($(this).attr('id').substring(2));
+    });
+  }
+
+  /**
+   * Set comp, which is a comparator function used for sorting and
+   * inserting comments into the list.
+   */
+  function setComparator() {
+    // If the first three letters are "asc", sort in ascending order
+    // and remove the prefix.
+    if (by.substring(0,3) == 'asc') {
+      var i = by.substring(3);
+      comp = function(a, b) { return a[i] - b[i]; };
+    } else {
+      // Otherwise sort in descending order.
+      comp = function(a, b) { return b[by] - a[by]; };
+    }
+
+    // Reset link styles and format the selected sort option.
+    $('a.sel').attr('href', '#').removeClass('sel');
+    $('a.by' + by).removeAttr('href').addClass('sel');
+  }
+
+  /**
+   * Create a comp function. If the user has preferences stored in
+   * the sortBy cookie, use those, otherwise use the default.
+   */
+  function initComparator() {
+    by = 'rating'; // Default to sort by rating.
+    // If the sortBy cookie is set, use that instead.
+    if (document.cookie.length > 0) {
+      var start = document.cookie.indexOf('sortBy=');
+      if (start != -1) {
+        start = start + 7;
+        var end = document.cookie.indexOf(";", start);
+        if (end == -1) {
+          end = document.cookie.length;
+          by = unescape(document.cookie.substring(start, end));
+        }
+      }
+    }
+    setComparator();
+  }
+
+  /**
+   * Show a comment div.
+   */
+  function show(id) {
+    $('#ao' + id).hide();
+    $('#ah' + id).show();
+    var context = $.extend({id: id}, opts);
+    var popup = $(renderTemplate(popupTemplate, context)).hide();
+    popup.find('textarea[name="proposal"]').hide();
+    popup.find('a.by' + by).addClass('sel');
+    var form = popup.find('#cf' + id);
+    form.submit(function(event) {
+      event.preventDefault();
+      addComment(form);
+    });
+    $('#s' + id).after(popup);
+    popup.slideDown('fast', function() {
+      getComments(id);
+    });
+  }
+
+  /**
+   * Hide a comment div.
+   */
+  function hide(id) {
+    $('#ah' + id).hide();
+    $('#ao' + id).show();
+    var div = $('#sc' + id);
+    div.slideUp('fast', function() {
+      div.remove();
+    });
+  }
+
+  /**
+   * Perform an ajax request to get comments for a node
+   * and insert the comments into the comments tree.
+   */
+  function getComments(id) {
+    $.ajax({
+     type: 'GET',
+     url: opts.getCommentsURL,
+     data: {node: id},
+     success: function(data, textStatus, request) {
+       var ul = $('#cl' + id);
+       var speed = 100;
+       $('#cf' + id)
+         .find('textarea[name="proposal"]')
+         .data('source', data.source);
+
+       if (data.comments.length === 0) {
+         ul.html('<li>No comments yet.</li>');
+         ul.data('empty', true);
+       } else {
+         // If there are comments, sort them and put them in the list.
+         var comments = sortComments(data.comments);
+         speed = data.comments.length * 100;
+         appendComments(comments, ul);
+         ul.data('empty', false);
+       }
+       $('#cn' + id).slideUp(speed + 200);
+       ul.slideDown(speed);
+     },
+     error: function(request, textStatus, error) {
+       showError('Oops, there was a problem retrieving the comments.');
+     },
+     dataType: 'json'
+    });
+  }
+
+  /**
+   * Add a comment via ajax and insert the comment into the comment tree.
+   */
+  function addComment(form) {
+    var node_id = form.find('input[name="node"]').val();
+    var parent_id = form.find('input[name="parent"]').val();
+    var text = form.find('textarea[name="comment"]').val();
+    var proposal = form.find('textarea[name="proposal"]').val();
+
+    if (text == '') {
+      showError('Please enter a comment.');
+      return;
+    }
+
+    // Disable the form that is being submitted.
+    form.find('textarea,input').attr('disabled', 'disabled');
+
+    // Send the comment to the server.
+    $.ajax({
+      type: "POST",
+      url: opts.addCommentURL,
+      dataType: 'json',
+      data: {
+        node: node_id,
+        parent: parent_id,
+        text: text,
+        proposal: proposal
+      },
+      success: function(data, textStatus, error) {
+        // Reset the form.
+        if (node_id) {
+          hideProposeChange(node_id);
+        }
+        form.find('textarea')
+          .val('')
+          .add(form.find('input'))
+          .removeAttr('disabled');
+	var ul = $('#cl' + (node_id || parent_id));
+        if (ul.data('empty')) {
+          $(ul).empty();
+          ul.data('empty', false);
+        }
+        insertComment(data.comment);
+        var ao = $('#ao' + node_id);
+        ao.find('img').attr({'src': opts.commentBrightImage});
+        if (node_id) {
+          // if this was a "root" comment, remove the commenting box
+          // (the user can get it back by reopening the comment popup)
+          $('#ca' + node_id).slideUp();
+        }
+      },
+      error: function(request, textStatus, error) {
+        form.find('textarea,input').removeAttr('disabled');
+        showError('Oops, there was a problem adding the comment.');
+      }
+    });
+  }
+
+  /**
+   * Recursively append comments to the main comment list and children
+   * lists, creating the comment tree.
+   */
+  function appendComments(comments, ul) {
+    $.each(comments, function() {
+      var div = createCommentDiv(this);
+      ul.append($(document.createElement('li')).html(div));
+      appendComments(this.children, div.find('ul.comment-children'));
+      // To avoid stagnating data, don't store the comments children in data.
+      this.children = null;
+      div.data('comment', this);
+    });
+  }
+
+  /**
+   * After adding a new comment, it must be inserted in the correct
+   * location in the comment tree.
+   */
+  function insertComment(comment) {
+    var div = createCommentDiv(comment);
+
+    // To avoid stagnating data, don't store the comments children in data.
+    comment.children = null;
+    div.data('comment', comment);
+
+    var ul = $('#cl' + (comment.node || comment.parent));
+    var siblings = getChildren(ul);
+
+    var li = $(document.createElement('li'));
+    li.hide();
+
+    // Determine where in the parents children list to insert this comment.
+    for(i=0; i < siblings.length; i++) {
+      if (comp(comment, siblings[i]) <= 0) {
+        $('#cd' + siblings[i].id)
+          .parent()
+          .before(li.html(div));
+        li.slideDown('fast');
+        return;
+      }
+    }
+
+    // If we get here, this comment rates lower than all the others,
+    // or it is the only comment in the list.
+    ul.append(li.html(div));
+    li.slideDown('fast');
+  }
+
+  function acceptComment(id) {
+    $.ajax({
+      type: 'POST',
+      url: opts.acceptCommentURL,
+      data: {id: id},
+      success: function(data, textStatus, request) {
+        $('#cm' + id).fadeOut('fast');
+        $('#cd' + id).removeClass('moderate');
+      },
+      error: function(request, textStatus, error) {
+        showError('Oops, there was a problem accepting the comment.');
+      }
+    });
+  }
+
+  function deleteComment(id) {
+    $.ajax({
+      type: 'POST',
+      url: opts.deleteCommentURL,
+      data: {id: id},
+      success: function(data, textStatus, request) {
+        var div = $('#cd' + id);
+        if (data == 'delete') {
+          // Moderator mode: remove the comment and all children immediately
+          div.slideUp('fast', function() {
+            div.remove();
+          });
+          return;
+        }
+        // User mode: only mark the comment as deleted
+        div
+          .find('span.user-id:first')
+          .text('[deleted]').end()
+          .find('div.comment-text:first')
+          .text('[deleted]').end()
+          .find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id +
+                ', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id)
+          .remove();
+        var comment = div.data('comment');
+        comment.username = '[deleted]';
+        comment.text = '[deleted]';
+        div.data('comment', comment);
+      },
+      error: function(request, textStatus, error) {
+        showError('Oops, there was a problem deleting the comment.');
+      }
+    });
+  }
+
+  function showProposal(id) {
+    $('#sp' + id).hide();
+    $('#hp' + id).show();
+    $('#pr' + id).slideDown('fast');
+  }
+
+  function hideProposal(id) {
+    $('#hp' + id).hide();
+    $('#sp' + id).show();
+    $('#pr' + id).slideUp('fast');
+  }
+
+  function showProposeChange(id) {
+    $('#pc' + id).hide();
+    $('#hc' + id).show();
+    var textarea = $('#pt' + id);
+    textarea.val(textarea.data('source'));
+    $.fn.autogrow.resize(textarea[0]);
+    textarea.slideDown('fast');
+  }
+
+  function hideProposeChange(id) {
+    $('#hc' + id).hide();
+    $('#pc' + id).show();
+    var textarea = $('#pt' + id);
+    textarea.val('').removeAttr('disabled');
+    textarea.slideUp('fast');
+  }
+
+  function toggleCommentMarkupBox(id) {
+    $('#mb' + id).toggle();
+  }
+
+  /** Handle when the user clicks on a sort by link. */
+  function handleReSort(link) {
+    var classes = link.attr('class').split(/\s+/);
+    for (var i=0; i<classes.length; i++) {
+      if (classes[i] != 'sort-option') {
+	by = classes[i].substring(2);
+      }
+    }
+    setComparator();
+    // Save/update the sortBy cookie.
+    var expiration = new Date();
+    expiration.setDate(expiration.getDate() + 365);
+    document.cookie= 'sortBy=' + escape(by) +
+                     ';expires=' + expiration.toUTCString();
+    $('ul.comment-ul').each(function(index, ul) {
+      var comments = getChildren($(ul), true);
+      comments = sortComments(comments);
+      appendComments(comments, $(ul).empty());
+    });
+  }
+
+  /**
+   * Function to process a vote when a user clicks an arrow.
+   */
+  function handleVote(link) {
+    if (!opts.voting) {
+      showError("You'll need to login to vote.");
+      return;
+    }
+
+    var id = link.attr('id');
+    if (!id) {
+      // Didn't click on one of the voting arrows.
+      return;
+    }
+    // If it is an unvote, the new vote value is 0,
+    // Otherwise it's 1 for an upvote, or -1 for a downvote.
+    var value = 0;
+    if (id.charAt(1) != 'u') {
+      value = id.charAt(0) == 'u' ? 1 : -1;
+    }
+    // The data to be sent to the server.
+    var d = {
+      comment_id: id.substring(2),
+      value: value
+    };
+
+    // Swap the vote and unvote links.
+    link.hide();
+    $('#' + id.charAt(0) + (id.charAt(1) == 'u' ? 'v' : 'u') + d.comment_id)
+      .show();
+
+    // The div the comment is displayed in.
+    var div = $('div#cd' + d.comment_id);
+    var data = div.data('comment');
+
+    // If this is not an unvote, and the other vote arrow has
+    // already been pressed, unpress it.
+    if ((d.value !== 0) && (data.vote === d.value * -1)) {
+      $('#' + (d.value == 1 ? 'd' : 'u') + 'u' + d.comment_id).hide();
+      $('#' + (d.value == 1 ? 'd' : 'u') + 'v' + d.comment_id).show();
+    }
+
+    // Update the comments rating in the local data.
+    data.rating += (data.vote === 0) ? d.value : (d.value - data.vote);
+    data.vote = d.value;
+    div.data('comment', data);
+
+    // Change the rating text.
+    div.find('.rating:first')
+      .text(data.rating + ' point' + (data.rating == 1 ? '' : 's'));
+
+    // Send the vote information to the server.
+    $.ajax({
+      type: "POST",
+      url: opts.processVoteURL,
+      data: d,
+      error: function(request, textStatus, error) {
+        showError('Oops, there was a problem casting that vote.');
+      }
+    });
+  }
+
+  /**
+   * Open a reply form used to reply to an existing comment.
+   */
+  function openReply(id) {
+    // Swap out the reply link for the hide link
+    $('#rl' + id).hide();
+    $('#cr' + id).show();
+
+    // Add the reply li to the children ul.
+    var div = $(renderTemplate(replyTemplate, {id: id})).hide();
+    $('#cl' + id)
+      .prepend(div)
+      // Setup the submit handler for the reply form.
+      .find('#rf' + id)
+      .submit(function(event) {
+        event.preventDefault();
+        addComment($('#rf' + id));
+        closeReply(id);
+      })
+      .find('input[type=button]')
+      .click(function() {
+        closeReply(id);
+      });
+    div.slideDown('fast', function() {
+      $('#rf' + id).find('textarea').focus();
+    });
+  }
+
+  /**
+   * Close the reply form opened with openReply.
+   */
+  function closeReply(id) {
+    // Remove the reply div from the DOM.
+    $('#rd' + id).slideUp('fast', function() {
+      $(this).remove();
+    });
+
+    // Swap out the hide link for the reply link
+    $('#cr' + id).hide();
+    $('#rl' + id).show();
+  }
+
+  /**
+   * Recursively sort a tree of comments using the comp comparator.
+   */
+  function sortComments(comments) {
+    comments.sort(comp);
+    $.each(comments, function() {
+      this.children = sortComments(this.children);
+    });
+    return comments;
+  }
+
+  /**
+   * Get the children comments from a ul. If recursive is true,
+   * recursively include childrens' children.
+   */
+  function getChildren(ul, recursive) {
+    var children = [];
+    ul.children().children("[id^='cd']")
+      .each(function() {
+        var comment = $(this).data('comment');
+        if (recursive)
+          comment.children = getChildren($(this).find('#cl' + comment.id), true);
+        children.push(comment);
+      });
+    return children;
+  }
+
+  /** Create a div to display a comment in. */
+  function createCommentDiv(comment) {
+    if (!comment.displayed && !opts.moderator) {
+      return $('<div class="moderate">Thank you!  Your comment will show up '
+               + 'once it is has been approved by a moderator.</div>');
+    }
+    // Prettify the comment rating.
+    comment.pretty_rating = comment.rating + ' point' +
+      (comment.rating == 1 ? '' : 's');
+    // Make a class (for displaying not yet moderated comments differently)
+    comment.css_class = comment.displayed ? '' : ' moderate';
+    // Create a div for this comment.
+    var context = $.extend({}, opts, comment);
+    var div = $(renderTemplate(commentTemplate, context));
+
+    // If the user has voted on this comment, highlight the correct arrow.
+    if (comment.vote) {
+      var direction = (comment.vote == 1) ? 'u' : 'd';
+      div.find('#' + direction + 'v' + comment.id).hide();
+      div.find('#' + direction + 'u' + comment.id).show();
+    }
+
+    if (opts.moderator || comment.text != '[deleted]') {
+      div.find('a.reply').show();
+      if (comment.proposal_diff)
+        div.find('#sp' + comment.id).show();
+      if (opts.moderator && !comment.displayed)
+        div.find('#cm' + comment.id).show();
+      if (opts.moderator || (opts.username == comment.username))
+        div.find('#dc' + comment.id).show();
+    }
+    return div;
+  }
+
+  /**
+   * A simple template renderer. Placeholders such as <%id%> are replaced
+   * by context['id'] with items being escaped. Placeholders such as <#id#>
+   * are not escaped.
+   */
+  function renderTemplate(template, context) {
+    var esc = $(document.createElement('div'));
+
+    function handle(ph, escape) {
+      var cur = context;
+      $.each(ph.split('.'), function() {
+        cur = cur[this];
+      });
+      return escape ? esc.text(cur || "").html() : cur;
+    }
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+        <a href="#" class="sort-option byascage">newest</a>\
+        <a href="#" class="sort-option byage">oldest</a>\
+      </p>\
+      <div class="comment-header">Comments</div>\
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+        loading comments... <img src="<%loadingImage%>" alt="" /></div>\
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diff --git a/docs/conf.py b/docs/conf.py
index 58340441..a8e76602 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -12,7 +12,8 @@
 #
 # import os
 # import sys
-# sys.path.insert(0, os.path.abspath('.'))
+# # sys.path.insert(0, os.path.abspath('.'))
+# sys.path.insert(0, os.path.abspath('/home/jakobrunge/anaconda3/envs/py39/bin/python'))
 
 
 # -- Project information -----------------------------------------------------
diff --git a/docs/genindex.html b/docs/genindex.html
index 9f935672..76bb5291 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -9,8 +9,10 @@
     <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="_static/alabaster.css" />
     <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+    <script src="_static/jquery.js"></script>
+    <script src="_static/underscore.js"></script>
+    <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="_static/doctools.js"></script>
-    <script src="_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="#" />
     <link rel="search" title="Search" href="search.html" />
    
@@ -243,6 +245,8 @@ <h2 id="G">G</h2>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#tigramite.causal_effects.CausalEffects.get_dict_from_graph">get_dict_from_graph() (tigramite.causal_effects.CausalEffects static method)</a>
+</li>
       <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance">get_fixed_thres_significance() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
 </li>
       <li><a href="index.html#tigramite.models.Models.get_general_fitted_model">get_general_fitted_model() (tigramite.models.Models method)</a>
@@ -277,9 +281,11 @@ <h2 id="G">G</h2>
 </li>
       <li><a href="index.html#tigramite.causal_effects.CausalEffects.get_mediators">get_mediators() (tigramite.causal_effects.CausalEffects method)</a>
 </li>
-      <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC.get_model_selection_criterion">get_model_selection_criterion() (tigramite.independence_tests.gpdc.GPDC method)</a>
+      <li><a href="index.html#tigramite.independence_tests.cmiknn.CMIknn.get_model_selection_criterion">get_model_selection_criterion() (tigramite.independence_tests.cmiknn.CMIknn method)</a>
 
       <ul>
+        <li><a href="index.html#tigramite.independence_tests.gpdc.GPDC.get_model_selection_criterion">(tigramite.independence_tests.gpdc.GPDC method)</a>
+</li>
         <li><a href="index.html#tigramite.independence_tests.gpdc_torch.GPDCtorch.get_model_selection_criterion">(tigramite.independence_tests.gpdc_torch.GPDCtorch method)</a>
 </li>
         <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_model_selection_criterion">(tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
@@ -319,8 +325,6 @@ <h2 id="G">G</h2>
         <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_shuffle_significance">(tigramite.independence_tests.robust_parcorr.RobustParCorr method)</a>
 </li>
       </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance">get_significance() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
-</li>
       <li><a href="index.html#tigramite.models.Prediction.get_test_array">get_test_array() (tigramite.models.Prediction method)</a>
 </li>
       <li><a href="index.html#tigramite.models.Prediction.get_train_array">get_train_array() (tigramite.models.Prediction method)</a>
@@ -509,6 +513,8 @@ <h2 id="R">R</h2>
       <li><a href="index.html#tigramite.pcmci.PCMCI.return_significant_links">return_significant_links() (tigramite.pcmci.PCMCI method)</a>
 </li>
       <li><a href="index.html#tigramite.independence_tests.robust_parcorr.RobustParCorr">RobustParCorr (class in tigramite.independence_tests.robust_parcorr)</a>
+</li>
+      <li><a href="index.html#tigramite.rpcmci.RPCMCI">RPCMCI (class in tigramite.rpcmci)</a>
 </li>
       <li><a href="index.html#tigramite.pcmci.PCMCI.run_bivci">run_bivci() (tigramite.pcmci.PCMCI method)</a>
 </li>
@@ -529,6 +535,8 @@ <h2 id="R">R</h2>
       <li><a href="index.html#tigramite.pcmci.PCMCI.run_pcmci">run_pcmci() (tigramite.pcmci.PCMCI method)</a>
 </li>
       <li><a href="index.html#tigramite.pcmci.PCMCI.run_pcmciplus">run_pcmciplus() (tigramite.pcmci.PCMCI method)</a>
+</li>
+      <li><a href="index.html#tigramite.rpcmci.RPCMCI.run_rpcmci">run_rpcmci() (tigramite.rpcmci.RPCMCI method)</a>
 </li>
       <li><a href="index.html#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">run_test() (tigramite.independence_tests.independence_tests_base.CondIndTest method)</a>
 
@@ -696,8 +704,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
     </div>
 
diff --git a/docs/index.html b/docs/index.html
index a40a85e8..38987cf5 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -4,14 +4,16 @@
 <html lang="en">
   <head>
     <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
     <title>Welcome to Tigramite’s documentation! &#8212; Tigramite 5.2 documentation</title>
     <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
     <link rel="stylesheet" type="text/css" href="_static/alabaster.css" />
     <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+    <script src="_static/jquery.js"></script>
+    <script src="_static/underscore.js"></script>
+    <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="_static/doctools.js"></script>
-    <script src="_static/sphinx_highlight.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
    
@@ -48,9 +50,11 @@ <h1>TIGRAMITE<a class="headerlink" href="#tigramite" title="Permalink to this he
 <p><a class="reference external" href="https://github.com/jakobrunge/tigramite/">Github repo</a></p>
 <p>Tigramite is a causal time series analysis python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use these graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:</p>
 <ul class="simple">
+<li><p>Overview: Runge, J., Gerhardus, A., Varando, G. et al. Causal inference for time series. Nat Rev Earth Environ (2023). <a class="reference external" href="https://doi.org/10.1038/s43017-023-00431-y">https://doi.org/10.1038/s43017-023-00431-y</a></p></li>
 <li><p>PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019). <a class="reference external" href="https://advances.sciencemag.org/content/5/11/eaau4996">https://advances.sciencemag.org/content/5/11/eaau4996</a></p></li>
 <li><p>PCMCI+: J. Runge (2020): Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020,Toronto, Canada, 2019, AUAI Press, 2020. <a class="reference external" href="http://auai.org/uai2020/proceedings/579_main_paper.pdf">http://auai.org/uai2020/proceedings/579_main_paper.pdf</a></p></li>
 <li><p>LPCMCI: Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders Advances in Neural Information Processing Systems, 2020, 33. <a class="reference external" href="https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html">https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</a></p></li>
+<li><p>RPCMCI: Elena Saggioro, Jana de Wiljes, Marlene Kretschmer, Jakob Runge; Reconstructing regime-dependent causal relationships from observational time series. Chaos 1 November 2020; 30 (11): 113115. <a class="reference external" href="https://doi.org/10.1063/5.0020538">https://doi.org/10.1063/5.0020538</a></p></li>
 <li><p>Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310. <a class="reference external" href="https://aip.scitation.org/doi/10.1063/1.5025050">https://aip.scitation.org/doi/10.1063/1.5025050</a></p></li>
 <li><p>Nature Communications Perspective paper: <a class="reference external" href="https://www.nature.com/articles/s41467-019-10105-3">https://www.nature.com/articles/s41467-019-10105-3</a></p></li>
 <li><p>Causal effects: J. Runge, Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables, Advances in Neural Information Processing Systems, 2021, 34</p></li>
@@ -68,61 +72,64 @@ <h1>TIGRAMITE<a class="headerlink" href="#tigramite" title="Permalink to this he
 <tr class="row-even"><td><p><a class="reference internal" href="#tigramite.lpcmci.LPCMCI" title="tigramite.lpcmci.LPCMCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.lpcmci.LPCMCI</span></code></a>(dataframe, cond_ind_test)</p></td>
 <td><p>LPCMCI is an algorithm for causal discovery in large-scale times series that allows for latent confounders and learns lag-specific causal relationships.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.independence_tests_base.CondIndTest" title="tigramite.independence_tests.independence_tests_base.CondIndTest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.independence_tests_base.CondIndTest</span></code></a>([...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.rpcmci.RPCMCI" title="tigramite.rpcmci.RPCMCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.rpcmci.RPCMCI</span></code></a>(dataframe[, ...])</p></td>
+<td><p>RPCMCI class for extracting causal regimes and the associated graphs from time series data.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.independence_tests_base.CondIndTest" title="tigramite.independence_tests.independence_tests_base.CondIndTest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.independence_tests_base.CondIndTest</span></code></a>([...])</p></td>
 <td><p>Base class of conditional independence tests.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.parcorr.ParCorr" title="tigramite.independence_tests.parcorr.ParCorr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.parcorr.ParCorr</span></code></a>(...)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.parcorr.ParCorr" title="tigramite.independence_tests.parcorr.ParCorr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.parcorr.ParCorr</span></code></a>(...)</p></td>
 <td><p>Partial correlation test.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr" title="tigramite.independence_tests.robust_parcorr.RobustParCorr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.robust_parcorr.RobustParCorr</span></code></a>(...)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr" title="tigramite.independence_tests.robust_parcorr.RobustParCorr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.robust_parcorr.RobustParCorr</span></code></a>(...)</p></td>
 <td><p>Robust partial correlation test based on non-paranormal models.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.gpdc.GPDC" title="tigramite.independence_tests.gpdc.GPDC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.gpdc.GPDC</span></code></a>([...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.gpdc.GPDC" title="tigramite.independence_tests.gpdc.GPDC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.gpdc.GPDC</span></code></a>([...])</p></td>
 <td><p>GPDC conditional independence test based on Gaussian processes and distance correlation.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch" title="tigramite.independence_tests.gpdc_torch.GPDCtorch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.gpdc_torch.GPDCtorch</span></code></a>([...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.gpdc_torch.GPDCtorch" title="tigramite.independence_tests.gpdc_torch.GPDCtorch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.gpdc_torch.GPDCtorch</span></code></a>([...])</p></td>
 <td><p>GPDC conditional independence test based on Gaussian processes and distance correlation.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.cmiknn.CMIknn" title="tigramite.independence_tests.cmiknn.CMIknn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.cmiknn.CMIknn</span></code></a>([...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.cmiknn.CMIknn" title="tigramite.independence_tests.cmiknn.CMIknn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.cmiknn.CMIknn</span></code></a>([...])</p></td>
 <td><p>Conditional mutual information test based on nearest-neighbor estimator.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.cmisymb.CMIsymb" title="tigramite.independence_tests.cmisymb.CMIsymb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.cmisymb.CMIsymb</span></code></a>([...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.cmisymb.CMIsymb" title="tigramite.independence_tests.cmisymb.CMIsymb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.cmisymb.CMIsymb</span></code></a>([...])</p></td>
 <td><p>Conditional mutual information test for discrete/categorical data.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI" title="tigramite.independence_tests.oracle_conditional_independence.OracleCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.oracle_conditional_independence.OracleCI</span></code></a>([...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI" title="tigramite.independence_tests.oracle_conditional_independence.OracleCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.oracle_conditional_independence.OracleCI</span></code></a>([...])</p></td>
 <td><p>Oracle of conditional independence test X _|_ Y | Z given a graph.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult" title="tigramite.independence_tests.parcorr_mult.ParCorrMult"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.parcorr_mult.ParCorrMult</span></code></a>([...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.parcorr_mult.ParCorrMult" title="tigramite.independence_tests.parcorr_mult.ParCorrMult"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.parcorr_mult.ParCorrMult</span></code></a>([...])</p></td>
 <td><p>Partial correlation test for multivariate X and Y.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.gsquared.Gsquared" title="tigramite.independence_tests.gsquared.Gsquared"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.gsquared.Gsquared</span></code></a>([...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.gsquared.Gsquared" title="tigramite.independence_tests.gsquared.Gsquared"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.gsquared.Gsquared</span></code></a>([...])</p></td>
 <td><p>G-squared conditional independence test for categorical data.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.parcorr_wls.ParCorrWLS" title="tigramite.independence_tests.parcorr_wls.ParCorrWLS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.parcorr_wls.ParCorrWLS</span></code></a>([...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.parcorr_wls.ParCorrWLS" title="tigramite.independence_tests.parcorr_wls.ParCorrWLS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.parcorr_wls.ParCorrWLS</span></code></a>([...])</p></td>
 <td><p>Weighted partial correlation test.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.independence_tests.regressionCI.RegressionCI" title="tigramite.independence_tests.regressionCI.RegressionCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.regressionCI.RegressionCI</span></code></a>(...)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.independence_tests.regressionCI.RegressionCI" title="tigramite.independence_tests.regressionCI.RegressionCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.regressionCI.RegressionCI</span></code></a>(...)</p></td>
 <td><p>Flexible parametric conditional independence tests for continuous, categorical, or mixed data.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.causal_effects.CausalEffects" title="tigramite.causal_effects.CausalEffects"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.causal_effects.CausalEffects</span></code></a>(...)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.causal_effects.CausalEffects" title="tigramite.causal_effects.CausalEffects"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.causal_effects.CausalEffects</span></code></a>(...)</p></td>
 <td><p>Causal effect estimation.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.models.Models" title="tigramite.models.Models"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.Models</span></code></a>(dataframe, model[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.models.Models" title="tigramite.models.Models"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.Models</span></code></a>(dataframe, model[, ...])</p></td>
 <td><p>Base class for time series models.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.models.LinearMediation" title="tigramite.models.LinearMediation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.LinearMediation</span></code></a>(dataframe)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.models.LinearMediation" title="tigramite.models.LinearMediation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.LinearMediation</span></code></a>(dataframe)</p></td>
 <td><p>Linear mediation analysis for time series models.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tigramite.models.Prediction" title="tigramite.models.Prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.Prediction</span></code></a>(dataframe, ...)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tigramite.models.Prediction" title="tigramite.models.Prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.Prediction</span></code></a>(dataframe, ...)</p></td>
 <td><p>Prediction class for time series models.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#module-tigramite.data_processing" title="tigramite.data_processing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.data_processing</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#module-tigramite.data_processing" title="tigramite.data_processing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.data_processing</span></code></a></p></td>
 <td><p>Tigramite data processing functions.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#module-tigramite.toymodels.structural_causal_processes" title="tigramite.toymodels.structural_causal_processes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.toymodels.structural_causal_processes</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#module-tigramite.toymodels.structural_causal_processes" title="tigramite.toymodels.structural_causal_processes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.toymodels.structural_causal_processes</span></code></a></p></td>
 <td><p>Tigramite toymodels.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#module-tigramite.plotting" title="tigramite.plotting"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.plotting</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#module-tigramite.plotting" title="tigramite.plotting"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.plotting</span></code></a></p></td>
 <td><p>Tigramite plotting package.</p></td>
 </tr>
 </tbody>
@@ -172,10 +179,9 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 hypothetical interventions, you may better look at the causal effect
 estimation functionality of Tigramite.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id3" role="note">
 <span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
-<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id5">2</a>,<a role="doc-backlink" href="#id6">3</a>,<a role="doc-backlink" href="#id7">4</a>,<a role="doc-backlink" href="#id9">5</a>,<a role="doc-backlink" href="#id15">6</a>,<a role="doc-backlink" href="#id16">7</a>,<a role="doc-backlink" href="#id17">8</a>,<a role="doc-backlink" href="#id33">9</a>,<a role="doc-backlink" href="#id34">10</a>,<a role="doc-backlink" href="#id35">11</a>)</span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id5">2</a>,<a role="doc-backlink" href="#id6">3</a>,<a role="doc-backlink" href="#id7">4</a>,<a role="doc-backlink" href="#id9">5</a>,<a role="doc-backlink" href="#id15">6</a>,<a role="doc-backlink" href="#id16">7</a>,<a role="doc-backlink" href="#id17">8</a>,<a role="doc-backlink" href="#id33">9</a>,<a role="doc-backlink" href="#id34">10</a>)</span>
 <p>J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic,
 Detecting and quantifying causal associations in large nonlinear time
 series datasets. Sci. Adv. 5, eaau4996 (2019)
@@ -189,7 +195,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 autocorrelated nonlinear time series datasets
 <a class="reference external" href="http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf">http://www.auai.org/~w-auai/uai2020/proceedings/579_main_paper.pdf</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -282,7 +287,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>p_matrix</strong> (<em>array</em><em> of </em><em>shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
+<li><p><strong>p_matrix</strong> (<em>array of shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Estimated matrix of p-values, optionally adjusted if fdr_method is
 not ‘none’.</p></li>
 <li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level at which the p_matrix is thresholded to
 get graph.</p></li>
@@ -410,7 +415,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph, see description above for interpretation.</p></li>
+<li><p><strong>graph</strong> (<em>array of shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph, see description above for interpretation.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array-like</em>) – Matrix of test statistic values. Must be of shape (N, N, tau_max +
 1).</p></li>
 <li><p><strong>include_lagzero_parents</strong> (<em>bool</em><em> (</em><em>default: False</em><em>)</em>) – Whether the dictionary should also return parents at lag
@@ -610,7 +615,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Must be greater zero.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
 <li><p><strong>save_iterations</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether to save iteration step results such as conditions used.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list</em><em> of </em><em>floats</em><em>, </em><em>default:</em><em> [</em><em>0.05</em><em>, </em><em>0.1</em><em>, </em><em>0.2</em><em>, </em><em>...</em><em>, </em><em>0.5</em><em>]</em>) – Significance level in algorithm. If a list or None is passed, the
+<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default:</em><em> [</em><em>0.05</em><em>, </em><em>0.1</em><em>, </em><em>0.2</em><em>, </em><em>...</em><em>, </em><em>0.5</em><em>]</em>) – Significance level in algorithm. If a list or None is passed, the
 pc_alpha level is optimized for every variable across the given
 pc_alpha values using the score computed in
 cond_ind_test.get_model_selection_criterion().</p></li>
@@ -687,7 +692,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Resulting causal graph, see description above for interpretation.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values regarding adjacencies.</p></li>
 <li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values regarding adjacencies.</p></li>
-<li><p><strong>sepset</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
+<li><p><strong>sepsets</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
 <li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples, only relevant for ‘majority’ and
 ‘conservative’ rules, see paper for details.</p></li>
 </ul>
@@ -722,7 +727,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>graph</strong> (<em>array of shape [N, N, 1]</em>) – Resulting causal graph, see description above for interpretation.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array of shape [N, N, 1]</em>) – Estimated matrix of test statistic values regarding adjacencies.</p></li>
 <li><p><strong>p_matrix</strong> (<em>array of shape [N, N, 1]</em>) – Estimated matrix of p-values regarding adjacencies.</p></li>
-<li><p><strong>sepset</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
+<li><p><strong>sepsets</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
 <li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples, only relevant for ‘majority’ and
 ‘conservative’ rules, see paper for details.</p></li>
 </ul>
@@ -959,49 +964,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 which improves detection power for lagged links, but also leads to
 larger runtimes.</p>
 <p>Further optional parameters are discussed in <a class="footnote-reference brackets" href="#id4" id="id12" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.pcmci</span> <span class="kn">import</span> <span class="n">PCMCI</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="kn">import</span> <span class="n">ParCorr</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.toymodels</span> <span class="kn">import</span> <span class="n">structural_causal_processes</span> <span class="k">as</span> <span class="n">toys</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Example process to play around with</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Each key refers to a variable and the incoming links are supplied</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># as a list of format [((var, -lag), coeff, function), ...]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">lin_f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="n">x</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">links</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">lin_f</span><span class="p">)],</span>
-<span class="go">             1: [((1, -1), 0.8, lin_f), ((0, -1), 0.8, lin_f)],</span>
-<span class="go">             2: [((2, -1), 0.7, lin_f), ((1, 0), 0.6, lin_f)],</span>
-<span class="go">             3: [((3, -1), 0.7, lin_f), ((2, 0), -0.5, lin_f)],</span>
-<span class="go">             }</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">toys</span><span class="o">.</span><span class="n">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span>
-<span class="go">                    T=1000, seed=7)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Data must be array of shape (time, variables)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-<span class="go">(1000, 4)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">cond_ind_test</span> <span class="o">=</span> <span class="n">ParCorr</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pcmci</span> <span class="o">=</span> <span class="n">PCMCI</span><span class="p">(</span><span class="n">dataframe</span><span class="o">=</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">cond_ind_test</span><span class="o">=</span><span class="n">cond_ind_test</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">results</span> <span class="o">=</span> <span class="n">pcmci</span><span class="o">.</span><span class="n">run_pcmciplus</span><span class="p">(</span><span class="n">tau_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pc_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pcmci</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">alpha_level</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
-<span class="go">    ## Significant links at alpha = 0.01:</span>
-</pre></div>
-</div>
-<blockquote>
-<div><dl class="simple">
-<dt>Variable 0 has 1 link(s):</dt><dd><p>(0 -1): pval = 0.00000 | val =  0.676</p>
-</dd>
-<dt>Variable 1 has 2 link(s):</dt><dd><p>(1 -1): pval = 0.00000 | val =  0.602
-(0 -1): pval = 0.00000 | val =  0.599</p>
-</dd>
-<dt>Variable 2 has 2 link(s):</dt><dd><p>(1  0): pval = 0.00000 | val =  0.486
-(2 -1): pval = 0.00000 | val =  0.466</p>
-</dd>
-<dt>Variable 3 has 2 link(s):</dt><dd><p>(3 -1): pval = 0.00000 | val =  0.524
-(2  0): pval = 0.00000 | val = -0.449</p>
-</dd>
-</dl>
-</div></blockquote>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -1020,7 +982,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 or the links are assumed absent.</p></li>
 <li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Minimum time lag to test.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list</em><em> of </em><em>floats</em><em>, </em><em>default: 0.01</em>) – Significance level in algorithm. If a list or None is passed, the
+<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default: 0.01</em>) – Significance level in algorithm. If a list or None is passed, the
 pc_alpha level is optimized for every graph across the given
 pc_alpha values ([0.001, 0.005, 0.01, 0.025, 0.05] for None) using
 the score computed in cond_ind_test.get_model_selection_criterion().</p></li>
@@ -1054,7 +1016,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>graph</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Resulting causal graph, see description above for interpretation.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of test statistic values regarding adjacencies.</p></li>
 <li><p><strong>p_matrix</strong> (<em>array of shape [N, N, tau_max+1]</em>) – Estimated matrix of p-values regarding adjacencies.</p></li>
-<li><p><strong>sepset</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
+<li><p><strong>sepsets</strong> (<em>dictionary</em>) – Separating sets. See paper for details.</p></li>
 <li><p><strong>ambiguous_triples</strong> (<em>list</em>) – List of ambiguous triples, only relevant for ‘majority’ and
 ‘conservative’ rules, see paper for details.</p></li>
 </ul>
@@ -1072,11 +1034,11 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="sig sig-object py" id="tigramite.lpcmci.LPCMCI">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.lpcmci.</span></span><span class="sig-name descname"><span class="pre">LPCMCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cond_ind_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/lpcmci.html#LPCMCI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.lpcmci.LPCMCI" title="Permalink to this definition">¶</a></dt>
 <dd><p>LPCMCI is an algorithm for causal discovery in large-scale times series that allows for latent confounders and
-learns lag-specific causal relationships. The algorithm is introduced and explained in:
-[1] Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders.
+learns lag-specific causal relationships. The algorithm is introduced and explained in:</p>
+<p>[1] Gerhardus, A. &amp; Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders.
 Advances in Neural Information Processing Systems, 2020, 33.
-<a class="reference external" href="https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html">https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</a>
-NOTE: This method is still EXPERIMENTAL since the default settings of hyperparameters are still being fine-tuned.
+<a class="reference external" href="https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html">https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html</a></p>
+<p>NOTE: This method is still EXPERIMENTAL since the default settings of hyperparameters are still being fine-tuned.
 We actually invite feedback on which work best in applications and numerical experiments.
 The main function, which applies the algorithm, is ‘run_lpcmci’.</p>
 <p>Parameters passed to the constructor:
@@ -1293,13 +1255,91 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 </dd></dl>
 
+</section>
+<section id="tigramite-rpcmci-rpcmci">
+<h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.rpcmci</span></code>: RPCMCI<a class="headerlink" href="#tigramite-rpcmci-rpcmci" title="Permalink to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="tigramite.rpcmci.RPCMCI">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.rpcmci.</span></span><span class="sig-name descname"><span class="pre">RPCMCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cond_ind_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/rpcmci.html#RPCMCI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.rpcmci.RPCMCI" title="Permalink to this definition">¶</a></dt>
+<dd><p>RPCMCI class for extracting causal regimes and the associated graphs from
+time series data.</p>
+<p class="rubric">Notes</p>
+<p>The Regime-PCMCI causal discovery method is described in:</p>
+<p>Elena Saggioro, Jana de Wiljes, Marlene Kretschmer, Jakob Runge;
+Reconstructing regime-dependent causal relationships from observational
+time series. Chaos 1 November 2020; 30 (11): 113115.
+<a class="reference external" href="https://doi.org/10.1063/5.0020538">https://doi.org/10.1063/5.0020538</a></p>
+<p>The method iterates between two phases –a regime learning phase
+(optimization-based) and a causal discovery phase (PCMCI)– to identify
+regime dependent causal relationships. A persistent discrete regime
+variable is assumed that leads to a finite number of regimes within which
+stationarity can be assumed.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataframe</strong> (<em>data object</em>) – This is the Tigramite dataframe object. It has the attributes
+dataframe.values yielding a numpy array of shape ( observations T,
+variables N). For RPCMCI the mask will be ignored. You may use the
+missing_flag to indicate missing values.</p></li>
+<li><p><strong>cond_ind_test</strong> (<em>conditional independence test object</em>) – This can be ParCorr or other classes from
+<code class="docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code> or an external test passed as a
+callable. This test can be based on the class
+tigramite.independence_tests.CondIndTest.</p></li>
+<li><p><strong>prediction_model</strong> (<em>sklearn model object</em>) – For example, sklearn.linear_model.LinearRegression() for a linear
+regression model. This should be consistent with cond_ind_test, ie,
+use ParCorr() with a linear model and, eg, GPDC() with a
+GaussianProcessRegressor model, or CMIknn with NearestNeighbors model.</p></li>
+<li><p><strong>seed</strong> (<em>int</em>) – Random seed for annealing step.</p></li>
+<li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Verbose levels -1, 0, 1, …</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="tigramite.rpcmci.RPCMCI.run_rpcmci">
+<span class="sig-name descname"><span class="pre">run_rpcmci</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_regimes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_transitions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">switch_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_anneal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/rpcmci.html#RPCMCI.run_rpcmci"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.rpcmci.RPCMCI.run_rpcmci" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="simple">
+<dt>Run RPCMCI method for extracting causal regimes and the associated graphs from</dt><dd><p>time series data.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_regimes</strong> (<em>int</em>) – Number of assumed regimes.</p></li>
+<li><p><strong>max_transitions</strong> (<em>int</em>) – Maximum number of transitions within a single regime (persistency parameter).</p></li>
+<li><p><strong>switch_thres</strong> (<em>float</em>) – Switch threshold.</p></li>
+<li><p><strong>num_iterations</strong> (<em>int</em>) – Optimization iterations.</p></li>
+<li><p><strong>max_anneal</strong> (<em>int</em>) – Maximum annealing runs.</p></li>
+<li><p><strong>tau_min</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Minimum time lag to test.</p></li>
+<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
+<li><p><strong>pc_alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Significance level in PCMCI.</p></li>
+<li><p><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level in PCMCI at which the p_matrix is thresholded to
+get graph.</p></li>
+<li><p><strong>n_jobs</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Number of CPUs to use in joblib parallization. Default n_jobs=-1
+uses all available.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>regimes</strong> (<em>array of shape (n_regimes, T)</em>) – One-hot encoded regime variable.</p></li>
+<li><p><strong>causal_results</strong> (<em>dictionary</em>) – Contains result of run_pcmci() after convergence.</p></li>
+<li><p><strong>diff_g_f</strong> (<em>tuple</em>) – Difference between two consecutive optimizations for all annealings and
+the optimal one with minimum objective value (see paper).</p></li>
+<li><p><strong>error_free_annealings</strong> (<em>int</em>) – Number of annealings that converged without error.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </section>
 <section id="tigramite-independence-tests-conditional-independence-tests">
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code>: Conditional independence tests<a class="headerlink" href="#tigramite-independence-tests-conditional-independence-tests" title="Permalink to this heading">¶</a></h1>
 <p>Base class:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.independence_tests_base.</span></span><span class="sig-name descname"><span class="pre">CondIndTest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'analytic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">confidence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recycle_residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.independence_tests_base.</span></span><span class="sig-name descname"><span class="pre">CondIndTest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'analytic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">confidence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recycle_residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class of conditional independence tests.</p>
 <p>Provides useful general functions for different independence tests such as
 shuffle significance testing and bootstrap confidence estimation. Also
@@ -1314,8 +1354,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Explained in tutorial on masking and missing values.</p></li>
 <li><p><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'analytic'</em><em>)</em>) – Type of significance test to use. In this package ‘analytic’,
 ‘fixed_thres’ and ‘shuffle_test’ are available.</p></li>
-<li><p><strong>fixed_thres</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – If significance is ‘fixed_thres’, this specifies the threshold for the
-absolute value of the dependence measure.</p></li>
+<li><p><strong>fixed_thres</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Deprecated.</p></li>
 <li><p><strong>sig_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 500</em><em>)</em>) – Number of samples for shuffle significance test.</p></li>
 <li><p><strong>sig_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Block length for block-shuffle significance test. If None, the
 block length is determined from the decay of the autocovariance as
@@ -1349,7 +1388,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_bootstrap_confidence">
-<span class="sig-name descname"><span class="pre">get_bootstrap_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependence_measure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_bootstrap_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_bootstrap_confidence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_bootstrap_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependence_measure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_blocklength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conf_lev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_bootstrap_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_bootstrap_confidence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform bootstrap confidence interval estimation.</p>
 <blockquote>
 <div><p>With conf_blocklength &gt; 1 or None a block-bootstrap is performed.</p>
@@ -1372,7 +1411,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dl>
 </div></blockquote>
 <dl>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
+<dt>data_type<span class="classifier">array-like</span></dt><dd><blockquote>
 <div><p>Binary data array of same shape as array which describes whether
 individual samples in a variable (or all samples) are continuous
 or discrete: 0s for continuous variables and 1s for discrete variables.</p>
@@ -1391,7 +1430,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_confidence">
-<span class="sig-name descname"><span class="pre">get_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_confidence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_confidence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_confidence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_confidence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform confidence interval estimation.</p>
 <blockquote>
 <div><p>Calls the dependence measure and confidence test functions. The child
@@ -1408,7 +1447,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dl>
 </div></blockquote>
 <dl>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
+<dt>data_type<span class="classifier">array-like</span></dt><dd><blockquote>
 <div><p>Binary data array of same shape as array which describes whether
 individual samples in a variable (or all samples) are continuous
 or discrete: 0s for continuous variables and 1s for discrete variables.</p>
@@ -1430,28 +1469,12 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance">
 <span class="sig-name descname"><span class="pre">get_fixed_thres_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_thres</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_fixed_thres_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_fixed_thres_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns signficance for thresholding test.</p>
-<p>Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1 else.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
-<li><p><strong>fixed_thres</strong> (<em>number</em>) – Fixed threshold, is made positive.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>pval</strong> – Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1
-else.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>bool</p>
-</dd>
-</dl>
+<dd><p>DEPRECATED Returns signficance for thresholding test.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_measure">
-<span class="sig-name descname"><span class="pre">get_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_measure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_measure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Estimate dependence measure.</p>
 <blockquote>
 <div><p>Calls the dependence measure function. The child classes must specify
@@ -1466,7 +1489,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dl>
 </div></blockquote>
 <dl>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
+<dt>data_type<span class="classifier">array-like</span></dt><dd><blockquote>
 <div><p>Binary data array of same shape as array which describes whether
 individual samples in a variable (or all samples) are continuous
 or discrete: 0s for continuous variables and 1s for discrete variables.</p>
@@ -1488,49 +1511,11 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_shuffle_significance">
-<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class assumption that this is not implemented.  Concrete classes
 should override when possible.</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance">
-<span class="sig-name descname"><span class="pre">get_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sig_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.get_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.get_significance" title="Permalink to this definition">¶</a></dt>
-<dd><blockquote>
-<div><p>Returns the p-value from whichever significance function is specified
-for this test.  If an override is used, then it will call a different
-function then specified by self.significance</p>
-<dl class="simple">
-<dt>val<span class="classifier">float</span></dt><dd><p>Test statistic value.</p>
-</dd>
-<dt>array<span class="classifier">array-like</span></dt><dd><p>data array with X, Y, Z in rows and observations in columns</p>
-</dd>
-<dt>xyz<span class="classifier">array of ints</span></dt><dd><p>XYZ identifier array of shape (dim,).</p>
-</dd>
-<dt>T<span class="classifier">int</span></dt><dd><p>Sample length</p>
-</dd>
-<dt>dim<span class="classifier">int</span></dt><dd><p>Dimensionality, ie, number of features.</p>
-</dd>
-</dl>
-</div></blockquote>
-<dl>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
-<div><p>Binary data array of same shape as array which describes whether
-individual samples in a variable (or all samples) are continuous
-or discrete: 0s for continuous variables and 1s for discrete variables.</p>
-</div></blockquote>
-<dl class="simple">
-<dt>sig_override<span class="classifier">string</span></dt><dd><p>Must be in ‘analytic’, ‘shuffle_test’, ‘fixed_thres’</p>
-</dd>
-</dl>
-<dl class="simple">
-<dt>pval<span class="classifier">float or numpy.nan</span></dt><dd><p>P-value.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py property">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.measure">
 <em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">measure</span></span><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.measure" title="Permalink to this definition">¶</a></dt>
@@ -1545,7 +1530,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.run_test">
-<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_or_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform conditional independence test.</p>
 <p>Calls the dependence measure and signficicance test functions. The child
 classes must specify a function get_dependence_measure and either or
@@ -1555,11 +1540,11 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>X</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index and tau the time lag.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index and tau the time lag.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index and tau the time lag.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
 different lags in X, Z, all have the same sample size.</p></li>
@@ -1569,20 +1554,24 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 which uses the maximum of tau_max and the conditions, which is
 useful to compare multiple models on the same sample.  Last,
 ‘max_lag’ uses as much samples as possible.</p></li>
+<li><p><strong>alpha_or_thres</strong> (<em>float</em><em> (</em><em>optional</em><em>)</em>) – Significance level (if significance=’analytic’ or ‘shuffle_test’) or
+threshold (if significance=’fixed_thres’). If given, run_test returns
+the test decision dependent=True/False.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val, pval</strong> – The test statistic value and the p-value.</p>
+<dd class="field-even"><p><strong>val, pval, [dependent]</strong> – The test statistic value and the p-value. If alpha_or_thres is
+given, run_test also returns the test decision dependent=True/False.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of floats</p>
+<dd class="field-odd"><p>Tuple of floats and bool</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw">
-<span class="sig-name descname"><span class="pre">run_test_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test_raw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_test_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_or_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/independence_tests_base.html#CondIndTest.run_test_raw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.independence_tests_base.CondIndTest.run_test_raw" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform conditional independence test directly on input arrays x, y, z.</p>
 <p>Calls the dependence measure and signficicance test functions. The child
 classes must specify a function get_dependence_measure and either or
@@ -1602,13 +1591,17 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>z_type</strong> (<em>array-like</em>) – data arrays of same shape as x, y and z respectively, which describes whether variables
 are continuous or discrete: 0s for continuous variables and
 1s for discrete variables</p></li>
+<li><p><strong>alpha_or_thres</strong> (<em>float</em><em> (</em><em>optional</em><em>)</em>) – Significance level (if significance=’analytic’ or ‘shuffle_test’) or
+threshold (if significance=’fixed_thres’). If given, run_test returns
+the test decision dependent=True/False.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>val, pval</strong> – The test statistic value and the p-value.</p>
+<dd class="field-even"><p><strong>val, pval, [dependent]</strong> – The test statistic value and the p-value. If alpha_or_thres is
+given, run_test also returns the test decision dependent=True/False.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Tuple of floats</p>
+<dd class="field-odd"><p>Tuple of floats and bool</p>
 </dd>
 </dl>
 </dd></dl>
@@ -1704,7 +1697,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1726,7 +1719,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1769,7 +1762,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -1870,7 +1863,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1884,7 +1877,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.robust_parcorr.RobustParCorr.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/robust_parcorr.html#RobustParCorr.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/robust_parcorr.html#RobustParCorr.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.robust_parcorr.RobustParCorr.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return partial correlation.</p>
 <p>Marginals are firstly transformed to standard normal scale. Dependence
 Measure is then estimated as the Pearson correlation of the residuals
@@ -1893,7 +1886,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1942,7 +1935,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2019,7 +2012,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </div><p>The null distribution of the distance correlation should be pre-computed.
 Otherwise it is computed during runtime.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id20" role="note">
 <span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
 <span class="backrefs">(<a role="doc-backlink" href="#id18">1</a>,<a role="doc-backlink" href="#id19">2</a>,<a role="doc-backlink" href="#id21">3</a>,<a role="doc-backlink" href="#id22">4</a>)</span>
@@ -2027,7 +2019,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 testing dependence by correlation of distances,
 <a class="reference external" href="https://arxiv.org/abs/0803.4101">https://arxiv.org/abs/0803.4101</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -2098,7 +2089,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2120,7 +2111,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2162,7 +2153,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2279,7 +2270,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2301,7 +2292,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2343,7 +2334,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2366,7 +2357,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py class">
 <dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.cmiknn.</span></span><span class="sig-name descname"><span class="pre">CMIknn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">knn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'shuffle_test'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ranks'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.independence_tests.cmiknn.</span></span><span class="sig-name descname"><span class="pre">CMIknn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">knn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">significance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'shuffle_test'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ranks'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_selection_folds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conditional mutual information test based on nearest-neighbor estimator.</p>
 <p>Conditional mutual information is the most general dependency measure coming
 from an information-theoretic framework. It makes no assumptions about the
@@ -2398,7 +2389,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 negative quantity.</p>
 <p>This method requires the scipy.spatial.cKDTree package.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id24" role="note">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id23">3</a><span class="fn-bracket">]</span></span>
 <p>J. Runge (2018): Conditional Independence Testing Based on a
@@ -2407,7 +2397,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Intelligence and Statistics.
 <a class="reference external" href="http://proceedings.mlr.press/v84/runge18a.html">http://proceedings.mlr.press/v84/runge18a.html</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -2423,6 +2412,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 or transforming to uniform marginals.</p></li>
 <li><p><strong>workers</strong> (<em>int</em><em> (</em><em>optional</em><em>, </em><em>default = -1</em><em>)</em>) – Number of workers to use for parallel processing. If -1 is given
 all processors are used. Default: -1.</p></li>
+<li><p><strong>model_selection_folds</strong> (<em>int</em>) – Number of folds in cross-validation used in model selection.</p></li>
 <li><p><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'shuffle_test'</em><em>)</em>) – Type of significance test to use. For CMIknn only ‘fixed_thres’ and
 ‘shuffle_test’ are available.</p></li>
 <li><p><strong>**kwargs</strong> – Arguments passed on to parent class CondIndTest.</p></li>
@@ -2437,7 +2427,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,). Here only uses 0 for X and
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,). Here only uses 0 for X and
 1 for Y.</p></li>
 </ul>
 </dd>
@@ -2458,7 +2448,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2470,6 +2460,27 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn.get_model_selection_criterion">
+<span class="sig-name descname"><span class="pre">get_model_selection_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn.get_model_selection_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a cross-validation-based score for nearest-neighbor estimates.</p>
+<p>Fits a nearest-neighbor model of the parents to variable j and returns
+the score. The lower, the better the fit. Here used to determine
+optimal hyperparameters in PCMCI(pc_alpha or fixed thres).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>j</strong> (<em>int</em>) – Index of target variable in data array.</p></li>
+<li><p><strong>parents</strong> (<em>list</em>) – List of form [(0, -1), (3, -2), …] containing parents.</p></li>
+<li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
+different lags in X, Z, all have the same sample size.</p></li>
+<li><p><strong>Returns</strong> – </p></li>
+<li><p><strong>score</strong> (<em>float</em>) – Model score.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.cmiknn.CMIknn.get_shuffle_significance">
 <span class="sig-name descname"><span class="pre">get_shuffle_significance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_null_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/cmiknn.html#CMIknn.get_shuffle_significance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.cmiknn.CMIknn.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
@@ -2484,7 +2495,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2546,7 +2557,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2569,7 +2580,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for original (unshuffled) estimate.</p></li>
 </ul>
 </dd>
@@ -2601,7 +2612,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph.</p></li>
+<li><p><strong>graph</strong> (<em>array of shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em>) – Causal graph.</p></li>
 <li><p><strong>links</strong> (<em>dict</em>) – Dictionary of form {0:[(0, -1), …], 1:[…], …}.
 Alternatively can also digest {0: [((0, -1), coeff, func)], …}.</p></li>
 <li><p><strong>observed_vars</strong> (<em>None</em><em> or </em><em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Subset of keys in links definining which variables are
@@ -2635,9 +2646,9 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of variables chosen for testing paths.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of variables chosen for testing paths.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of variables chosen for testing paths.</p></li>
+<li><p><strong>X</strong> (<em>list of tuples</em>) – List of variables chosen for testing paths.</p></li>
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – List of variables chosen for testing paths.</p></li>
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – List of variables chosen for testing paths.</p></li>
 <li><p><strong>max_lag</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used here to constrain the has_path function to the graph
 truncated at max_lag instead of identifying the max_lag from
 ancestral search.</p></li>
@@ -2696,11 +2707,11 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>X</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
 <li><p><strong>[</strong> (<em>Y</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>Z</strong><strong>]</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Z</strong><strong>]</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
 different lags in X, Z, all have the same sample size.</p></li>
@@ -2730,20 +2741,21 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test">
-<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_or_thres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/oracle_conditional_independence.html#OracleCI.run_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.oracle_conditional_independence.OracleCI.run_test" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform oracle conditional independence test.</p>
 <p>Calls the d-separation function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>X</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index in the observed_vars and tau the time lag.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Not used here.</p></li>
 <li><p><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) – Not used here.</p></li>
+<li><p><strong>alpha_or_thres</strong> (<em>float</em>) – Not used here.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2805,7 +2817,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>value</strong> (<em>float</em>) – Test statistic value.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample length</p></li>
 <li><p><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2827,7 +2839,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2855,7 +2867,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -2882,7 +2894,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>standardize</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to standardize the array beforehand. Must be used for
 partial correlation.</p></li>
 </ul>
@@ -2915,13 +2927,11 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 \frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)}"/></p>
 </div><p>where <img class="math" src="_images/math/5a939c5280da7202ca4531f175a7780ad5e1f80a.png" alt="n"/> is the sample size. This is simply <img class="math" src="_images/math/88da5b28f5e08d9d8ed693d334e710f4adadbcfd.png" alt="2 n CMI(X;Y|Z)"/>.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id26" role="note">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id25">7</a><span class="fn-bracket">]</span></span>
 <p>Bishop, Y.M.M., Fienberg, S.E. and Holland, P.W. (1975) Discrete
 Multivariate Analysis: Theory and Practice. MIT Press, Cambridge.</p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -2946,7 +2956,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -3012,7 +3022,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -3055,7 +3065,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</p></li>
 </ul>
 </dd>
@@ -3101,14 +3111,14 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.independence_tests.regressionCI.RegressionCI.get_dependence_measure">
-<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/regressionCI.html#RegressionCI.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.regressionCI.RegressionCI.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_dependence_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests/regressionCI.html#RegressionCI.get_dependence_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.independence_tests.regressionCI.RegressionCI.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns test statistic.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns.</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
-<li><p><strong>type_mask</strong> (<em>array-like</em>) – array of same shape as array which describes whether samples
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>data_type</strong> (<em>array-like</em>) – array of same shape as array which describes whether samples
 are continuous or discrete: 0s for continuous and
 1s for discrete</p></li>
 </ul>
@@ -3162,7 +3172,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p>See the corresponding paper <a class="footnote-reference brackets" href="#id28" id="id27" role="doc-noteref"><span class="fn-bracket">[</span>6<span class="fn-bracket">]</span></a> and tigramite tutorial for an
 in-depth introduction.</p>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id28" role="note">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id27">6</a><span class="fn-bracket">]</span></span>
 <p>J. Runge, Necessary and sufficient graphical conditions for
@@ -3171,16 +3180,15 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Systems, 2021, 34
 <a class="reference external" href="https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html">https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>array</em><em> of </em><em>either shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>]</em><em>, </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em><em>, or </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>, </em><em>tau_max+1</em><em>]</em>) – Different graph types are supported, see tutorial.</p></li>
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of tuples [(i, -tau), …] containing cause variables.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of tuples [(j, 0), …] containing effect variables.</p></li>
-<li><p><strong>S</strong> (<em>list</em><em> of </em><em>tuples</em>) – List of tuples [(i, -tau), …] containing conditioned variables.</p></li>
+<li><p><strong>graph</strong> (<em>array of either shape</em><em> [</em><em>N</em><em>, </em><em>N</em><em>]</em><em>, </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>]</em><em>, or </em><em>[</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>, </em><em>tau_max+1</em><em>]</em>) – Different graph types are supported, see tutorial.</p></li>
+<li><p><strong>X</strong> (<em>list of tuples</em>) – List of tuples [(i, -tau), …] containing cause variables.</p></li>
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – List of tuples [(j, 0), …] containing effect variables.</p></li>
+<li><p><strong>S</strong> (<em>list of tuples</em>) – List of tuples [(i, -tau), …] containing conditioned variables.</p></li>
 <li><p><strong>graph_type</strong> (<em>str</em>) – Type of graph.</p></li>
-<li><p><strong>hidden_variables</strong> (<em>list</em><em> of </em><em>tuples</em>) – Hidden variables in format [(i, -tau), …]. The internal graph is
+<li><p><strong>hidden_variables</strong> (<em>list of tuples</em>) – Hidden variables in format [(i, -tau), …]. The internal graph is
 constructed by a latent projection.</p></li>
 <li><p><strong>check_SM_overlap</strong> (<em>bool</em>) – Whether to check whether S overlaps with M.</p></li>
 <li><p><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</p></li>
@@ -3253,7 +3261,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 optionally a mask of the same shape and a missing values flag.</p></li>
 <li><p><strong>estimator</strong> (<em>sklearn model object</em>) – For example, sklearn.linear_model.LinearRegression() for a linear
 regression model.</p></li>
-<li><p><strong>adjustment_set</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>tuples</em>) – If ‘optimal’ the Oset is used, if ‘minimized_optimal’ the minimized Oset,
+<li><p><strong>adjustment_set</strong> (<em>str</em><em> or </em><em>list of tuples</em>) – If ‘optimal’ the Oset is used, if ‘minimized_optimal’ the minimized Oset,
 and if ‘colliders_minimized_optimal’, the colliders-minimized Oset.
 If a list of tuples is passed, this set is used.</p></li>
 <li><p><strong>conditional_estimator</strong> (<em>sklearn model object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to fit conditional causal effects in nested regression.
@@ -3284,7 +3292,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
 yielding a numpy array of shape (observations T, variables N) and
 optionally a mask of the same shape and a missing values flag.</p></li>
-<li><p><strong>mediation</strong> (<em>None</em><em>, </em><em>'direct'</em><em>, or </em><em>list</em><em> of </em><em>tuples</em>) – If None, total effect is estimated, if ‘direct’ then only the direct effect is estimated,
+<li><p><strong>mediation</strong> (<em>None</em><em>, </em><em>'direct'</em><em>, or </em><em>list of tuples</em>) – If None, total effect is estimated, if ‘direct’ then only the direct effect is estimated,
 else only those causal paths are considerd that pass at least through one of these mediator nodes.</p></li>
 <li><p><strong>method</strong> (<em>{'parents'</em><em>, </em><em>'links_coeffs'</em><em>, </em><em>'optimal'}</em>) – Method to use for estimating Wright’s path coefficients. If ‘optimal’,
 the Oset is used, if ‘links_coeffs’, the coefficients in links_coeffs are used,
@@ -3302,6 +3310,26 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.get_dict_from_graph">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_dict_from_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.get_dict_from_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.get_dict_from_graph" title="Permalink to this definition">¶</a></dt>
+<dd><p>Helper function to convert graph to dictionary of links.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>graph</strong> (<em>array of shape</em><em> (</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>)</em>) – Matrix format of graph in string format.</p></li>
+<li><p><strong>parents_only</strong> (<em>bool</em>) – Whether to only return parents (’–&gt;’ in graph)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>links</strong> – Dictionary of form {0:{(0, -1): o-o, …}, 1:{…}, …}.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.causal_effects.CausalEffects.get_graph_from_dict">
 <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_graph_from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">links</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/causal_effects.html#CausalEffects.get_graph_from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.causal_effects.CausalEffects.get_graph_from_dict" title="Permalink to this definition">¶</a></dt>
@@ -3351,7 +3379,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>alternative_conditions</strong> (<em>set</em><em> of </em><em>tuples</em>) – Used only internally in optimality theorem. If None, self.S is used.</p></li>
+<li><p><strong>alternative_conditions</strong> (<em>set of tuples</em>) – Used only internally in optimality theorem. If None, self.S is used.</p></li>
 <li><p><strong>minimize</strong> (<em>{False</em><em>, </em><em>True</em><em>, </em><em>'colliders_only'}</em>) – Minimize optimal set. If True, minimize such that no subset
 can be removed without making it invalid. If ‘colliders_only’,
 only colliders are minimized.</p></li>
@@ -3473,7 +3501,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.models.Models.fit_full_model">
-<span class="sig-name descname"><span class="pre">fit_full_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">all_parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_variables</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max_lag_or_tau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.fit_full_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models.fit_full_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fit_full_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">all_parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_variables=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off='max_lag_or_tau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">empty_predictors_function=&lt;function</span> <span class="pre">mean&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_data=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.fit_full_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models.fit_full_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit time series model.</p>
 <p>For each variable in selected_variables, the sklearn model is fitted
 with <img class="math" src="_images/math/1b5e577d6216dca3af7d87aa122a0b9b360d6cb3.png" alt="y"/> given by the target variable, and <img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> given by its
@@ -3483,7 +3511,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>all_parents</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, 0), …], 1:[], …} containing
 the parents estimated with PCMCI.</p></li>
-<li><p><strong>selected_variables</strong> (<em>list</em><em> of </em><em>integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to estimate parents only for selected variables. If None is
+<li><p><strong>selected_variables</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to estimate parents only for selected variables. If None is
 passed, parents are estimated for all variables.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in all_parents is used.</p></li>
 <li><p><strong>cut_off</strong> (<em>{'max_lag_or_tau_max'</em><em>, </em><em>'2xtau_max'</em><em>, </em><em>'max_lag'}</em>) – How many samples to cutoff at the beginning. The default is
@@ -3492,6 +3520,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 sample. Other options are ‘2xtau_max’, which guarantees that MCI
 tests are all conducted on the same samples. Last, ‘max_lag’ uses
 as much samples as possible.</p></li>
+<li><p><strong>empty_predictors_function</strong> (<em>function</em>) – Function to apply to y if no predictors are given.</p></li>
 <li><p><strong>return_data</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to save the data array.</p></li>
 </ul>
 </dd>
@@ -3523,7 +3552,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.models.Models.get_general_fitted_model">
-<span class="sig-name descname"><span class="pre">get_general_fitted_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conditions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max_lag_or_tau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.get_general_fitted_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models.get_general_fitted_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_general_fitted_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conditions=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off='max_lag_or_tau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">empty_predictors_function=&lt;function</span> <span class="pre">mean&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_data=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.get_general_fitted_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Models.get_general_fitted_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit time series model.</p>
 <p>For each variable in selected_variables, the sklearn model is fitted
 with <img class="math" src="_images/math/1b5e577d6216dca3af7d87aa122a0b9b360d6cb3.png" alt="y"/> given by the target variable, and <img class="math" src="_images/math/ed38fa24f1c94891bd312012aab3f6673be3eb83.png" alt="X"/> given by its
@@ -3531,10 +3560,10 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>lists</em><em> of </em><em>tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
-<li><p><strong>Y</strong> (<em>lists</em><em> of </em><em>tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
-<li><p><strong>Z</strong> (<em>lists</em><em> of </em><em>tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
-<li><p><strong>conditions</strong> (<em>list</em><em> of </em><em>tuples.</em>) – Conditions for estimating conditional causal effects.</p></li>
+<li><p><strong>X</strong> (<em>lists of tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
+<li><p><strong>Y</strong> (<em>lists of tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
+<li><p><strong>Z</strong> (<em>lists of tuples</em>) – List of variables for estimating model Y = f(X,Z)</p></li>
+<li><p><strong>conditions</strong> (<em>list of tuples.</em>) – Conditions for estimating conditional causal effects.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in all_parents is used.</p></li>
 <li><p><strong>cut_off</strong> (<em>{'max_lag_or_tau_max'</em><em>, </em><em>'2xtau_max'</em><em>, </em><em>'max_lag'}</em>) – How many samples to cutoff at the beginning. The default is
 ‘max_lag_or_tau_max’, which uses the maximum of tau_max and the
@@ -3542,6 +3571,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 sample. Other options are ‘2xtau_max’, which guarantees that MCI
 tests are all conducted on the same samples. Last, ‘max_lag’ uses
 as much samples as possible.</p></li>
+<li><p><strong>empty_predictors_function</strong> (<em>function</em>) – Function to apply to y if no predictors are given.</p></li>
 <li><p><strong>return_data</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to save the data array.</p></li>
 </ul>
 </dd>
@@ -3650,7 +3680,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </pre></div>
 </div>
 <p class="rubric">References</p>
-<aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id31" role="note">
 <span class="label"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></span>
 <span class="backrefs">(<a role="doc-backlink" href="#id29">1</a>,<a role="doc-backlink" href="#id30">2</a>,<a role="doc-backlink" href="#id32">3</a>)</span>
@@ -3658,7 +3687,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 complex spatio-temporal systems.
 Nature Communications, 6, 8502. <a class="reference external" href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></p>
 </aside>
-</aside>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -3964,8 +3992,8 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
 <li><p><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</p></li>
 <li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables.</p></li>
-<li><p><strong>notk</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables to exclude.</p></li>
+<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list of ints</em>) – Indices of mediator variables.</p></li>
+<li><p><strong>notk</strong> (<em>int</em><em> or </em><em>list of ints</em>) – Indices of mediator variables to exclude.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -4036,7 +4064,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
 <li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables.</p></li>
+<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list of ints</em>) – Indices of mediator variables.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -4060,7 +4088,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>i</strong> (<em>int</em>) – Index of cause variable.</p></li>
 <li><p><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</p></li>
 <li><p><strong>j</strong> (<em>int</em>) – Index of effect variable.</p></li>
-<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – Indices of mediator variables.</p></li>
+<li><p><strong>k</strong> (<em>int</em><em> or </em><em>list of ints</em>) – Indices of mediator variables.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -4200,7 +4228,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>target_predictors</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …} containing
 the predictors estimated with PCMCI.</p></li>
-<li><p><strong>selected_targets</strong> (<em>list</em><em> of </em><em>integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to fit model only for selected targets. If None is
+<li><p><strong>selected_targets</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to fit model only for selected targets. If None is
 passed, models are estimated for all variables.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in target_predictors is
 used.</p></li>
@@ -4225,14 +4253,14 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>selected_targets</strong> (<em>list</em><em> of </em><em>ints</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variables to estimate predictors of. If None, predictors of
+<li><p><strong>selected_targets</strong> (<em>list of ints</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variables to estimate predictors of. If None, predictors of
 all variables are estimated.</p></li>
 <li><p><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
 specifying whether only selected links should be tested. If None is
 passed, all links are tested</p></li>
 <li><p><strong>steps_ahead</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Minimum time lag to test. Useful for multi-step ahead predictions.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</p></li>
-<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list</em><em> of </em><em>floats</em><em>, </em><em>default: 0.2</em>) – Significance level in algorithm. If a list or None is passed, the
+<li><p><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default: 0.2</em>) – Significance level in algorithm. If a list or None is passed, the
 pc_alpha level is optimized for every variable across the given
 pc_alpha values using the score computed in
 cond_ind_test.get_model_selection_criterion()</p></li>
@@ -4255,14 +4283,14 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.models.Prediction.get_test_array">
-<span class="sig-name descname"><span class="pre">get_test_array</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.get_test_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Prediction.get_test_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns test array.</p>
+<span class="sig-name descname"><span class="pre">get_test_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.get_test_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Prediction.get_test_array" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns test array for variable j.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.models.Prediction.get_train_array">
 <span class="sig-name descname"><span class="pre">get_train_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">j</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.get_train_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.models.Prediction.get_train_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns training array.</p>
+<dd><p>Returns training array for variable j.</p>
 </dd></dl>
 
 <dl class="py method">
@@ -4277,7 +4305,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>target</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>integers</em>) – Index or indices of target variable(s).</p></li>
+<li><p><strong>target</strong> (<em>int</em><em> or </em><em>list of integers</em>) – Index or indices of target variable(s).</p></li>
 <li><p><strong>new_data</strong> (<em>data object</em><em>, </em><em>optional</em>) – New Tigramite dataframe object with optional new mask. Note that
 the data will be cut off according to cut_off, see parameter
 <cite>cut_off</cite> below.</p></li>
@@ -4304,9 +4332,9 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p>Tigramite data processing functions.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="tigramite.data_processing.DataFrame">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">DataFrame</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_flag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vector_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datatime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analysis_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'single'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_missing_upto_maxlag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.data_processing.</span></span><span class="sig-name descname"><span class="pre">DataFrame</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_flag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vector_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datatime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analysis_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'single'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_missing_upto_maxlag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame" title="Permalink to this definition">¶</a></dt>
 <dd><dl>
-<dt>Data object containing single or multiple time series arrays and optional</dt><dd><p>mask.</p>
+<dt>Data object containing single or multiple time series arrays and optional</dt><dd><p>mask, as well as variable definitions.</p>
 <dl class="simple">
 <dt>data<span class="classifier">array-like</span></dt><dd><dl class="simple">
 <dt>if analysis_mode == ‘single’:</dt><dd><p>1) Numpy array of shape (observations T, variables N)
@@ -4328,7 +4356,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dd>
 </dl>
 </dd>
-<dt>type_mask<span class="classifier">array-like</span></dt><dd><blockquote>
+<dt>data_type<span class="classifier">array-like</span></dt><dd><blockquote>
 <div><p>Binary data array of same shape as array which describes whether
 individual samples in a variable (or all samples) are continuous
 or discrete: 0s for continuous variables and 1s for discrete variables.</p>
@@ -4339,7 +4367,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 remove_missing_upto_maxlag=True also flags samples for all lags up to
 2*tau_max (more precisely, this depends on the cut_off argument in
 self.construct_array(), see further below). This avoids biases, see
-section on masking in Supplement of <a class="footnote-reference brackets" href="#id3" id="id33" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
+section on masking in Supplement of Runge et al. SciAdv (2019).</p>
 </dd>
 <dt>vector_vars<span class="classifier">dict</span></dt><dd><p>Dictionary of vector variables of the form,
 Eg. {0: [(0, 0), (1, 0)], 1: [(2, 0)], 2: [(3, 0)], 3: [(4, 0)]}
@@ -4418,7 +4446,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt>self.mask<span class="classifier">dictionary</span></dt><dd><p>Mask internally mapped to a dictionary representation in the same way as
 data is mapped to self.values</p>
 </dd>
-<dt>self.type_mask<span class="classifier">array-like</span></dt><dd><p>Binary data array of same shape as array which describes whether
+<dt>self.data_type<span class="classifier">array-like</span></dt><dd><p>Binary data array of same shape as array which describes whether
 individual samples in a variable (or all samples) are continuous
 or discrete: 0s for continuous variables and 1s for discrete variables.</p>
 </dd>
@@ -4439,7 +4467,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt>If reference_points is not None:</dt><dd><p>1D numpy array holding all specified reference_points, less those
 smaller than 0 and larger than self.largest_time_step-1</p>
 </dd>
-<dt>If reference_points is None:</dt><dd><p>Is np.array(range(self.largest_time_step))</p>
+<dt>If reference_points is None:</dt><dd><p>Is np.array(self.largest_time_step)</p>
 </dd>
 </dl>
 </dd>
@@ -4469,7 +4497,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.data_processing.DataFrame.construct_array">
-<span class="sig-name descname"><span class="pre">construct_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extraZ</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_cleaned_xyz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_checks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_overlaps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame.construct_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame.construct_array" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">construct_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extraZ</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_cleaned_xyz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_checks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_overlaps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_off</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2xtau_max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbosity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame.construct_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame.construct_array" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs array from variables X, Y, Z from data.
 Data is of shape (T, N) if analysis_mode == ‘single’, where T is the
 time series length and N the number of variables, and of (n_ens, T, N)
@@ -4477,16 +4505,16 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
+<li><p><strong>X</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
 the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
 has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
 the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
 has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
 the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
 has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
-<li><p><strong>extraZ</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
+<li><p><strong>extraZ</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), X, Y, Z can be multivariate of
 the form [(var1, -lag), (var2, -lag), …]. At least one varlag in Y
 has to be at lag zero. extraZ is only used in CausalEffects class.</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em>) – Maximum time lag. This may be used to make sure that estimates for
@@ -4497,10 +4525,10 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>mask_type</strong> (<em>{None</em><em>, </em><em>'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
 measure I(X; Y | Z) the samples should be masked. If None, the mask
 is not used. Explained in tutorial on masking and missing values.</p></li>
-<li><p><strong>type_mask</strong> (<em>array-like</em>) – Binary data array of same shape as array which describes whether
+<li><p><strong>data_type</strong> (<em>array-like</em>) – Binary data array of same shape as array which describes whether
 individual samples in a variable (or all samples) are continuous
 or discrete: 0s for continuous variables and 1s for discrete variables.
-If it is set, then it overrides the self.type_mask assigned to the dataframe.</p></li>
+If it is set, then it overrides the self.data_type assigned to the dataframe.</p></li>
 <li><p><strong>return_cleaned_xyz</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to return cleaned X,Y,Z, where possible duplicates are
 removed.</p></li>
 <li><p><strong>do_checks</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to perform sanity checks on input X,Y,Z</p></li>
@@ -4569,9 +4597,9 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>array, xyz [,XYZ], type_mask</strong> – xyz identifier array of shape (dim,) identifying which row in array
+<dd class="field-even"><p><strong>array, xyz [,XYZ], data_type</strong> – xyz identifier array of shape (dim,) identifying which row in array
 corresponds to X, Y, and Z, and the type mask that indicates which samples
-are continuous or discrete. For example:: X = [(0, -1)],
+are continuous or discrete. For example: X = [(0, -1)],
 Y = [(1, 0)], Z = [(1, -1), (0, -2)] yields an array of shape
 (4, n_samples) and xyz is xyz = numpy.array([0,1,2,2]). If
 return_cleaned_xyz is True, also outputs the cleaned XYZ lists.</p>
@@ -4584,36 +4612,36 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="tigramite.data_processing.DataFrame.print_array_info">
-<span class="sig-name descname"><span class="pre">print_array_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_flag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extraZ</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame.print_array_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame.print_array_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_array_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_flag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extraZ</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame.print_array_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame.print_array_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Print info about the constructed array</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> (<em>Data array</em><em> of </em><em>shape</em><em> (</em><em>dim</em><em>, </em><em>T</em><em>)</em>) – Data array.</p></li>
-<li><p><strong>X</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+<li><p><strong>array</strong> (<em>Data array of shape</em><em> (</em><em>dim</em><em>, </em><em>T</em><em>)</em>) – Data array.</p></li>
+<li><p><strong>X</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
 where var specifies the variable index. X typically is of the form
 [(varX, -tau)] with tau denoting the time lag and Z can be
 multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>Y</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+<li><p><strong>Y</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
 where var specifies the variable index. X typically is of the form
 [(varX, -tau)] with tau denoting the time lag and Z can be
 multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>Z</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+<li><p><strong>Z</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
 where var specifies the variable index. X typically is of the form
 [(varX, -tau)] with tau denoting the time lag and Z can be
 multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
-<li><p><strong>extraZ</strong> (<em>list</em><em> of </em><em>tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+<li><p><strong>extraZ</strong> (<em>list of tuples</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
 where var specifies the variable index. X typically is of the form
 [(varX, -tau)] with tau denoting the time lag and Z can be
 multivariate [(var1, -lag), (var2, -lag), …] .</p></li>
 <li><p><strong>missing_flag</strong> (<em>number</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Flag for missing values. Dismisses all time slices of samples where
 missing values occur in any variable and also flags samples for all
 lags up to 2*tau_max. This avoids biases, see section on masking in
-Supplement of <a class="footnote-reference brackets" href="#id3" id="id34" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p></li>
+Supplement of <a class="footnote-reference brackets" href="#id3" id="id33" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p></li>
 <li><p><strong>mask_type</strong> (<em>{'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
 measure I(X; Y | Z) the samples should be masked. If None, the mask
 is not used. Explained in tutorial on masking and missing values.</p></li>
-<li><p><strong>type_mask</strong> (<em>array-like</em>) – Binary data array of same shape as array which describes whether
+<li><p><strong>data_type</strong> (<em>array-like</em>) – Binary data array of same shape as array which describes whether
 individual samples in a variable (or all samples) are continuous
 or discrete: 0s for continuous variables and 1s for discrete variables.</p></li>
 </ul>
@@ -4650,7 +4678,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd><p>Returns optimal block length for significance and confidence tests.</p>
 <p>Determine block length using approach in Mader (2013) [Eq. (6)] which
 improves the method of Pfeifer (2005) with non-overlapping blocks In
-case of multidimensional X, the max is used. Further details in <a class="footnote-reference brackets" href="#id3" id="id35" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
+case of multidimensional X, the max is used. Further details in <a class="footnote-reference brackets" href="#id3" id="id34" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
 Two modes are available. For mode=’significance’, only the indices
 corresponding to X are shuffled in array. For mode=’confidence’ all
 variables are jointly shuffled. If the autocorrelation curve fit fails,
@@ -4662,7 +4690,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</p></li>
-<li><p><strong>xyz</strong> (<em>array</em><em> of </em><em>ints</em>) – XYZ identifier array of shape (dim,).</p></li>
+<li><p><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</p></li>
 <li><p><strong>mode</strong> (<em>str</em>) – Which mode to use.</p></li>
 </ul>
 </dd>
@@ -4889,7 +4917,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd><p>Helper function to convert DAG graph to dictionary of parents.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>dag</strong> (<em>array</em><em> of </em><em>shape</em><em> (</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>)</em>) – Matrix format of graph in string format. Must be DAG.</p>
+<dd class="field-odd"><p><strong>dag</strong> (<em>array of shape</em><em> (</em><em>N</em><em>, </em><em>N</em><em>, </em><em>tau_max+1</em><em>)</em>) – Matrix format of graph in string format. Must be DAG.</p>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>parents</strong> – Dictionary of form {0:[(0, -1), …], 1:[…], …}.</p>
@@ -4902,7 +4930,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.toymodels.structural_causal_processes.generate_structural_causal_process">
-<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">generate_structural_causal_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">L</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_funcs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['linear']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[-0.5,</span> <span class="pre">0.5]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">0.7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contemp_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_dists</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['gaussian']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_means</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_sigmas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">2.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#generate_structural_causal_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.generate_structural_causal_process" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.toymodels.structural_causal_processes.</span></span><span class="sig-name descname"><span class="pre">generate_structural_causal_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">L</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_funcs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['linear']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependency_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[-</span> <span class="pre">0.5,</span> <span class="pre">0.5]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">0.7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contemp_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_dists</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['gaussian']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_means</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_sigmas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">2.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/toymodels/structural_causal_processes.html#generate_structural_causal_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.toymodels.structural_causal_processes.generate_structural_causal_process" title="Permalink to this definition">¶</a></dt>
 <dd><p>“Randomly generates a structural causal process based on input characteristics.</p>
 <p>The process has the form</p>
 <div class="math">
@@ -5005,7 +5033,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 number of variables N. coeff must be a float and func a python
 callable of one argument.</p></li>
 <li><p><strong>T</strong> (<em>int</em>) – Sample size.</p></li>
-<li><p><strong>noises</strong> (<em>list</em><em> of </em><em>callables</em><em> or </em><em>array</em><em>, </em><em>optional</em><em> (</em><em>default: 'np.random.randn'</em><em>)</em>) – Random distribution function that is called with noises[j](T). If an array,
+<li><p><strong>noises</strong> (<em>list of callables</em><em> or </em><em>array</em><em>, </em><em>optional</em><em> (</em><em>default: 'np.random.randn'</em><em>)</em>) – Random distribution function that is called with noises[j](T). If an array,
 it must be of shape ((transient_fraction + 1)*T, N).</p></li>
 <li><p><strong>intervention</strong> (<em>dict</em>) – Dictionary of format: {1:np.array, …} containing only keys of intervened
 variables with the value being the array of length T with interventional values.
@@ -5100,7 +5128,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.plotting.plot_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto-MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">network_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_autodependency_lags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto-MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_autodependency_lags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a network plot.</p>
 <p>This is still in beta. The network is defined from links in graph. Nodes
 denote variables, straight links contemporaneous dependencies and curved
@@ -5117,7 +5145,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 Must be of same shape as val_matrix.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
+<li><p><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
 <li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) – Test statistic label.</p></li>
@@ -5127,7 +5155,9 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>link_attribute</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – String array of val_matrix.shape specifying link attributes.</p></li>
 <li><p><strong>node_pos</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Dictionary of node positions in axis coordinates of form
 node_pos = {‘x’:array of shape (N,), ‘y’:array of shape(N)}. These
-coordinates could have been transformed before for basemap plots.</p></li>
+coordinates could have been transformed before for basemap plots. You can
+also add a key ‘transform’:ccrs.PlateCarree() in order to plot graphs on
+a map using cartopy.</p></li>
 <li><p><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</p></li>
 <li><p><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</p></li>
 <li><p><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</p></li>
@@ -5149,7 +5179,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</p></li>
 <li><p><strong>tick_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of tick labels.</p></li>
 <li><p><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional specification of lags overwriting np.arange(0, tau_max+1)</p></li>
-<li><p><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</p></li>
 <li><p><strong>show_colorbar</strong> (<em>bool</em>) – Whether to show colorbars for links and nodes.</p></li>
 <li><p><strong>show_autodependency_lags</strong> (<em>bool</em><em> (</em><em>default: False</em><em>)</em>) – Shows significant autodependencies for a node.</p></li>
 </ul>
@@ -5185,7 +5214,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.plotting.plot_mediation_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">network_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a network plot visualizing the pathways of a mediation analysis.
 This is still in beta. The network is defined from non-zero entries in
 <code class="docutils literal notranslate"><span class="pre">path_val_matrix</span></code>.  Nodes denote variables, straight links contemporaneous
@@ -5201,7 +5230,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>path_val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing link weight values.</p></li>
 <li><p><strong>path_node_array</strong> (<em>array_like</em>) – Array of shape (N,) containing node values.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
+<li><p><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
 <li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) – Link colorbar label.</p></li>
@@ -5210,7 +5239,9 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 given by arrow_linewidth. If None, all links have same width.</p></li>
 <li><p><strong>node_pos</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Dictionary of node positions in axis coordinates of form
 node_pos = {‘x’:array of shape (N,), ‘y’:array of shape(N)}. These
-coordinates could have been transformed before for basemap plots.</p></li>
+coordinates could have been transformed before for basemap plots. You can
+also add a key ‘transform’:ccrs.PlateCarree() in order to plot graphs on
+a map using cartopy.</p></li>
 <li><p><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</p></li>
 <li><p><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</p></li>
 <li><p><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</p></li>
@@ -5230,7 +5261,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</p></li>
 <li><p><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</p></li>
 <li><p><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</p></li>
-<li><p><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</p></li>
 <li><p><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional specification of lags overwriting np.arange(0, tau_max+1)</p></li>
 </ul>
 </dd>
@@ -5239,7 +5269,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.plotting.plot_mediation_time_series_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tsg_path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">network_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_time_series_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_mediation_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_node_array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tsg_path_val_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'link</span> <span class="pre">coeff.</span> <span class="pre">(edge</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCE</span> <span class="pre">(node</span> <span class="pre">color)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_time_series_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a mediation time series graph plot.
 This is still in beta. The time series graph’s links are colored by
 val_matrix.</p>
@@ -5249,7 +5279,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>tsg_path_val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N*tau_max, N*tau_max) containing link weight values.</p></li>
 <li><p><strong>path_node_array</strong> (<em>array_like</em>) – Array of shape (N,) containing node values.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
+<li><p><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
 <li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) – Link colorbar label.</p></li>
@@ -5276,9 +5306,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</p></li>
 <li><p><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</p></li>
 <li><p><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</p></li>
-<li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
-<li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
-<li><p><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</p></li>
 </ul>
 </dd>
 </dl>
@@ -5315,7 +5342,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="tigramite.plotting.plot_time_series_graph">
-<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">network_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_time_series_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">plot_time_series_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_colorbar_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'MCI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrow_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_ticks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap_edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'RdBu_r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_aspect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arrowhead_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">curved_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_edge_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dashed'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_links</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard_color_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightgrey'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_time_series_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.plot_time_series_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a time series graph.
 This is still in beta. The time series graph’s links are colored by
 val_matrix.</p>
@@ -5327,7 +5354,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 (N, N, tau_max+1, tau_max+1) describing auxADMG.</p></li>
 <li><p><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of same shape as graph containing test statistic values.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>fig_ax</strong> (<em>tuple</em><em> of </em><em>figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
+<li><p><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</p></li>
 <li><p><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</p></li>
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) – Test statistic label.</p></li>
@@ -5351,9 +5378,6 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</p></li>
 <li><p><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</p></li>
 <li><p><strong>tick_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of tick labels.</p></li>
-<li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
-<li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
-<li><p><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</p></li>
 <li><p><strong>inner_edge_style</strong> (<em>string</em><em>, </em><em>optional</em><em> (</em><em>default: 'dashed'</em><em>)</em>) – Style of inner_edge contemporaneous links.</p></li>
 <li><p><strong>special_nodes</strong> (<em>dict</em>) – Dictionary of format {(i, -tau): ‘blue’, …} to color special nodes.</p></li>
 </ul>
@@ -5374,9 +5398,9 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</p></li>
 <li><p><strong>fig_axes</strong> (<em>subplots instance</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created as
 fig, axes = pyplot.subplots(N,…)</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
+<li><p><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</p></li>
-<li><p><strong>var_units</strong> (<em>list</em><em> of </em><em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Units of variables.</p></li>
+<li><p><strong>var_units</strong> (<em>list of str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Units of variables.</p></li>
 <li><p><strong>time_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: ''</em><em>)</em>) – Label of time axis.</p></li>
 <li><p><strong>grey_masked_samples</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to mark masked samples by grey fills (‘fill’) or grey data
 (‘data’).</p></li>
@@ -5416,7 +5440,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>N</strong> (<em>int</em>) – Number of variables</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
+<li><p><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</p></li>
 <li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
 <li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
@@ -5468,7 +5492,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py class">
 <dt class="sig sig-object py" id="tigramite.plotting.setup_matrix">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">setup_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minimum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_gridlines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tigramite.plotting.</span></span><span class="sig-name descname"><span class="pre">setup_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minimum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_left</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_space_top</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tick_label_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_gridlines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create matrix of lag function panels.
 Class to setup figure object. The function add_lagfuncs(…) allows to plot
 the val_matrix of shape (N, N, tau_max+1). Multiple lagfunctions can be
@@ -5479,7 +5503,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <li><p><strong>N</strong> (<em>int</em>) – Number of variables</p></li>
 <li><p><strong>tau_max</strong> (<em>int</em>) – Maximum time lag.</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
+<li><p><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</p></li>
 <li><p><strong>minimum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Lower y-axis limit.</p></li>
 <li><p><strong>maximum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Upper y-axis limit.</p></li>
@@ -5544,7 +5568,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>N</strong> (<em>int</em>) – Number of variables</p></li>
 <li><p><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</p></li>
-<li><p><strong>figsize</strong> (<em>tuple</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
+<li><p><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</p></li>
 <li><p><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</p></li>
 <li><p><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</p></li>
@@ -5618,8 +5642,8 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </dd></dl>
 
 </section>
-<section id="id36">
-<h1>Indices and tables<a class="headerlink" href="#id36" title="Permalink to this heading">¶</a></h1>
+<section id="id35">
+<h1>Indices and tables<a class="headerlink" href="#id35" title="Permalink to this heading">¶</a></h1>
 <ul class="simple">
 <li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
 <li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
@@ -5678,8 +5702,8 @@ <h3 id="searchlabel">Quick search</h3>
       &copy;2023, Jakob Runge.
       
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 6.1.3</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
       
       |
       <a href="_sources/index.rst.txt"
diff --git a/docs/index.rst b/docs/index.rst
index af04214a..6ed3fa3a 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -31,6 +31,7 @@ TIGRAMITE
 
 Tigramite is a causal time series analysis python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use these graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
 
+- Overview: Runge, J., Gerhardus, A., Varando, G. et al. Causal inference for time series. Nat Rev Earth Environ (2023). https://doi.org/10.1038/s43017-023-00431-y
 
 - PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019). https://advances.sciencemag.org/content/5/11/eaau4996
 
@@ -40,7 +41,6 @@ Tigramite is a causal time series analysis python package. It allows to efficien
 
 - RPCMCI: Elena Saggioro, Jana de Wiljes, Marlene Kretschmer, Jakob Runge; Reconstructing regime-dependent causal relationships from observational time series. Chaos 1 November 2020; 30 (11): 113115. https://doi.org/10.1063/5.0020538
 
-
 - Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310. https://aip.scitation.org/doi/10.1063/1.5025050
 
 - Nature Communications Perspective paper: https://www.nature.com/articles/s41467-019-10105-3
diff --git a/docs/objects.inv b/docs/objects.inv
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\ No newline at end of file
diff --git a/setup.py b/setup.py
index b856e83c..b9f76811 100644
--- a/setup.py
+++ b/setup.py
@@ -62,7 +62,7 @@ def run(self):
 # Run the setup
 setup(
     name="tigramite",
-    version="5.2.1.23",
+    version="5.2.1.25",
     packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
     license="GNU General Public License v3.0",
     description="Tigramite causal inference for time series",
diff --git a/tigramite/data_processing.py b/tigramite/data_processing.py
index a726afce..b2e99b67 100644
--- a/tigramite/data_processing.py
+++ b/tigramite/data_processing.py
@@ -18,7 +18,7 @@
 
 class DataFrame():
     """Data object containing single or multiple time series arrays and optional 
-    mask.
+    mask, as well as variable definitions.
 
     Parameters
     ----------